diff --git a/404.html b/404.html index 5b76932cc..d82f2a584 100644 --- a/404.html +++ b/404.html @@ -20,11 +20,11 @@ - - + +
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r(--gradient-to-color,rgba(236,201,75,0))}.from-yellow-600{--gradient-from-color:#d69e2e;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(214,158,46,0))}.from-yellow-700{--gradient-from-color:#b7791f;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(183,121,31,0))}.from-yellow-800{--gradient-from-color:#975a16;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(151,90,22,0))}.from-yellow-900{--gradient-from-color:#744210;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(116,66,16,0))}.from-green-100{--gradient-from-color:#f0fff4;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(240,255,244,0))}.from-green-200{--gradient-from-color:#c6f6d5;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(198,246,213,0))}.from-green-300{--gradient-from-color:#9ae6b4;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(154,230,180,0))}.from-green-400{--gradient-from-color:#68d391;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(104,211,145,0))}.from-green-500{--gradient-from-color:#48bb78;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(72,187,120,0))}.from-green-600{--gradient-from-color:#38a169;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(56,161,105,0))}.from-green-700{--gradient-from-color:#2f855a;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(47,133,90,0))}.from-green-800{--gradient-from-color:#276749;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(39,103,73,0))}.from-green-900{--gradient-from-color:#22543d;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(34,84,61,0))}.from-teal-100{--gradient-from-color:#e6fffa;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(230,255,250,0))}.from-teal-200{--gradient-from-color:#b2f5ea;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(178,245,234,0))}.from-teal-300{--gradient-from-color:#81e6d9;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(129,230,217,0))}.from-teal-400{--gradient-from-color:#4fd1c5;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(79,209,197,0))}.from-teal-500{--gradient-from-color:#38b2ac;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(56,178,172,0))}.from-teal-600{--gradient-from-color:#319795;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(49,151,149,0))}.from-teal-700{--gradient-from-color:#2c7a7b;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(44,122,123,0))}.from-teal-800{--gradient-from-color:#285e61;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(40,94,97,0))}.from-teal-900{--gradient-from-color:#234e52;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(35,78,82,0))}.from-blue-100{--gradient-from-color:#ebf8ff;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(235,248,255,0))}.from-blue-200{--gradient-from-color:#bee3f8;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(190,227,248,0))}.from-blue-300{--gradient-from-color:#90cdf4;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(144,205,244,0))}.from-blue-400{--gradient-from-color:#63b3ed;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(99,179,237,0))}.from-blue-500{--gradient-from-color:#4299e1;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(66,153,225,0))}.from-blue-600{--gradient-from-color:#3182ce;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(49,130,206,0))}.from-blue-700{--gradient-from-color:#2b6cb0;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(43,108,176,0))}.from-blue-800{--gradient-from-color:#2c5282;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(44,82,130,0))}.from-blue-900{--gradient-from-color:#2a4365;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(42,67,101,0))}.from-indigo-100{--gradient-from-color:#ebf4ff;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(235,244,255,0))}.from-indigo-200{--gradient-from-color:#c3dafe;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(195,218,254,0))}.from-indigo-300{--gradient-from-color:#a3bffa;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(163,191,250,0))}.from-indigo-400{--gradient-from-color:#7f9cf5;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(127,156,245,0))}.from-indigo-500{--gradient-from-color:#667eea;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(102,126,234,0))}.from-indigo-600{--gradient-from-color:#5a67d8;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(90,103,216,0))}.from-indigo-700{--gradient-from-color:#4c51bf;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(76,81,191,0))}.from-indigo-800{--gradient-from-color:#434190;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(67,65,144,0))}.from-indigo-900{--gradient-from-color:#3c366b;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(60,54,107,0))}.from-purple-100{--gradient-from-color:#faf5ff;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(250,245,255,0))}.from-purple-200{--gradient-from-color:#e9d8fd;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(233,216,253,0))}.from-purple-300{--gradient-from-color:#d6bcfa;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(214,188,250,0))}.from-purple-400{--gradient-from-color:#b794f4;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(183,148,244,0))}.from-purple-500{--gradient-from-color:#9f7aea;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(159,122,234,0))}.from-purple-600{--gradient-from-color:#805ad5;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(128,90,213,0))}.from-purple-700{--gradient-from-color:#6b46c1;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(107,70,193,0))}.from-purple-800{--gradient-from-color:#553c9a;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(85,60,154,0))}.from-purple-900{--gradient-from-color:#44337a;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(68,51,122,0))}.from-pink-100{--gradient-from-color:#fff5f7;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(255,245,247,0))}.from-pink-200{--gradient-from-color:#fed7e2;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(254,215,226,0))}.from-pink-300{--gradient-from-color:#fbb6ce;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(251,182,206,0))}.from-pink-400{--gradient-from-color:#f687b3;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(246,135,179,0))}.from-pink-500{--gradient-from-color:#ed64a6;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(237,100,166,0))}.from-pink-600{--gradient-from-color:#d53f8c;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(213,63,140,0))}.from-pink-700{--gradient-from-color:#b83280;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(184,50,128,0))}.from-pink-800{--gradient-from-color:#97266d;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(151,38,109,0))}.from-pink-900{--gradient-from-color:#702459;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(112,36,89,0))}.from-primary{--gradient-from-color:#00d1ff;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(0,209,255,0))}.from-secondary{--gradient-from-color:#e077ff;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(224,119,255,0))}.from-greenish-blue{--gradient-from-color:#adffed;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(173,255,237,0))}.from-blue-gray{--gradient-from-color:#cfe1e6;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(207,225,230,0))}.via-transparent{--gradient-via-color:transparent;--gradient-color-stops:var(--gradient-from-color),var(--gradient-via-color),var(--gradient-to-color,transparent)}.via-current{--gradient-via-color:currentColor;--gradient-color-stops:var(--gradient-from-color),var(--gradient-via-color),var(--gradient-to-color,hsla(0,0%,100%,0))}.via-black{--gradient-via-color:#000;--gradient-color-stops:var(--gradient-from-color),var(--gradient-via-color),var(--gradient-to-color,transparent)}.via-white{--gradient-via-color:#fff;--gradient-color-stops:var(--gradient-from-color),var(--gradient-via-color),var(--gradient-to-color,hsla(0,0%,100%,0))}.via-gray-100{--gradient-via-color:#f7fafc;--gradient-color-stops:var(--gradient-from-color),var(--gradient-via-color),var(--gradient-to-color,rgba(247,250,252,0))}.via-gray-200{--gradient-via-color:#edf2f7;--gradient-color-stops:var(--gradient-from-color),var(--gradient-via-color),var(--gradient-to-color,rgba(237,242,247,0))}.via-gray-300{--gradient-via-color:#e2e8f0;--gradient-color-stops:var(--gradient-from-color),var(--gradient-via-color),var(--gradient-to-color,rgba(226,232,240,0))}.via-gray-400{--gradient-via-color:#cbd5e0;--gradient-color-stops:var(--gradient-from-color),var(--gradient-via-color),var(--gradient-to-color,rg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left,var(--gradient-color-stops))}.sm\:from-transparent{--gradient-from-color:transparent;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,transparent)}.sm\:from-current{--gradient-from-color:currentColor;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,hsla(0,0%,100%,0))}.sm\:from-black{--gradient-from-color:#000;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,transparent)}.sm\:from-white{--gradient-from-color:#fff;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,hsla(0,0%,100%,0))}.sm\:from-gray-100{--gradient-from-color:#f7fafc;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(247,250,252,0))}.sm\:from-gray-200{--gradient-from-color:#edf2f7;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(237,242,247,0))}.sm\:from-gray-300{--gradient-from-color:#e2e8f0;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(226,232,240,0))}.sm\:from-gray-400{--gradient-from-color:#cbd5e0;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(203,213,224,0))}.sm\:from-gray-500{--gradient-from-color:#a0aec0;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(160,174,192,0))}.sm\:from-gray-600{--gradient-from-color:#718096;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(113,128,150,0))}.sm\:from-gray-700{--gradient-from-color:#4a5568;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(74,85,104,0))}.sm\:from-gray-800{--gradient-from-color:#2d3748;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(45,55,72,0))}.sm\:from-gray-900{--gradient-from-color:#1a202c;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(26,32,44,0))}.sm\:from-red-100{--gradient-from-color:#fff5f5;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,hsla(0,100%,98%,0))}.sm\:from-red-200{--gradient-from-color:#fed7d7;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(254,215,215,0))}.sm\:from-red-300{--gradient-from-color:#feb2b2;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(254,178,178,0))}.sm\:from-red-400{--gradient-from-color:#fc8181;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(252,129,129,0))}.sm\:from-red-500{--gradient-from-color:#f56565;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(245,101,101,0))}.sm\:from-red-600{--gradient-from-color:#e53e3e;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(229,62,62,0))}.sm\:from-red-700{--gradient-from-color:#c53030;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(197,48,48,0))}.sm\:from-red-800{--gradient-from-color:#9b2c2c;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(155,44,44,0))}.sm\:from-red-900{--gradient-from-color:#742a2a;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(116,42,42,0))}.sm\:from-orange-100{--gradient-from-color:#fffaf0;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(255,250,240,0))}.sm\:from-orange-200{--gradient-from-color:#feebc8;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(254,235,200,0))}.sm\:from-orange-300{--gradient-from-color:#fbd38d;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(251,211,141,0))}.sm\:from-orange-400{--gradient-from-color:#f6ad55;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(246,173,85,0))}.sm\:from-orange-500{--gradient-from-color:#ed8936;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(237,137,54,0))}.sm\:from-orange-600{--gradient-from-color:#dd6b20;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(221,107,32,0))}.sm\:from-orange-700{--gradient-from-color:#c05621;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(192,86,33,0))}.sm\:from-orange-800{--gradient-from-color:#9c4221;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(156,66,33,0))}.sm\:from-orange-900{--gradient-from-color:#7b341e;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(123,52,30,0))}.sm\:from-yellow-100{--gradient-from-color:ivory;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(255,255,240,0))}.sm\:from-yellow-200{--gradient-from-color:#fefcbf;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(254,252,191,0))}.sm\:from-yellow-300{--gradient-from-color:#faf089;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(250,240,137,0))}.sm\:from-yellow-400{--gradient-from-color:#f6e05e;--gradient-color-stops:var(--gradient-from-color),var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left,var(--gradient-color-stops))}.md\:from-transparent{--gradient-from-color:transparent;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,transparent)}.md\:from-current{--gradient-from-color:currentColor;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,hsla(0,0%,100%,0))}.md\:from-black{--gradient-from-color:#000;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,transparent)}.md\:from-white{--gradient-from-color:#fff;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,hsla(0,0%,100%,0))}.md\:from-gray-100{--gradient-from-color:#f7fafc;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(247,250,252,0))}.md\:from-gray-200{--gradient-from-color:#edf2f7;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(237,242,247,0))}.md\:from-gray-300{--gradient-from-color:#e2e8f0;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(226,232,240,0))}.md\:from-gray-400{--gradient-from-color:#cbd5e0;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(203,213,224,0))}.md\:from-gray-500{--gradient-from-color:#a0aec0;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(160,174,192,0))}.md\:from-gray-600{--gradient-from-color:#718096;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(113,128,150,0))}.md\:from-gray-700{--gradient-from-color:#4a5568;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(74,85,104,0))}.md\:from-gray-800{--gradient-from-color:#2d3748;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(45,55,72,0))}.md\:from-gray-900{--gradient-from-color:#1a202c;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(26,32,44,0))}.md\:from-red-100{--gradient-from-color:#fff5f5;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,hsla(0,100%,98%,0))}.md\:from-red-200{--gradient-from-color:#fed7d7;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(254,215,215,0))}.md\:from-red-300{--gradient-from-color:#feb2b2;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(254,178,178,0))}.md\:from-red-400{--gradient-from-color:#fc8181;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(252,129,129,0))}.md\:from-red-500{--gradient-from-color:#f56565;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(245,101,101,0))}.md\:from-red-600{--gradient-from-color:#e53e3e;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(229,62,62,0))}.md\:from-red-700{--gradient-from-color:#c53030;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(197,48,48,0))}.md\:from-red-800{--gradient-from-color:#9b2c2c;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(155,44,44,0))}.md\:from-red-900{--gradient-from-color:#742a2a;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(116,42,42,0))}.md\:from-orange-100{--gradient-from-color:#fffaf0;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(255,250,240,0))}.md\:from-orange-200{--gradient-from-color:#feebc8;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(254,235,200,0))}.md\:from-orange-300{--gradient-from-color:#fbd38d;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(251,211,141,0))}.md\:from-orange-400{--gradient-from-color:#f6ad55;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(246,173,85,0))}.md\:from-orange-500{--gradient-from-color:#ed8936;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(237,137,54,0))}.md\:from-orange-600{--gradient-from-color:#dd6b20;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(221,107,32,0))}.md\:from-orange-700{--gradient-from-color:#c05621;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(192,86,33,0))}.md\:from-orange-800{--gradient-from-color:#9c4221;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(156,66,33,0))}.md\:from-orange-900{--gradient-from-color:#7b341e;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(123,52,30,0))}.md\:from-yellow-100{--gradient-from-color:ivory;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(255,255,240,0))}.md\:from-yellow-200{--gradient-from-color:#fefcbf;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(254,252,191,0))}.md\:from-yellow-300{--gradient-from-color:#faf089;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(250,240,137,0))}.md\:from-yellow-400{--gradient-from-color:#f6e05e;--gradient-color-stops:var(--gradient-from-color),var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left,var(--gradient-color-stops))}.lg\:from-transparent{--gradient-from-color:transparent;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,transparent)}.lg\:from-current{--gradient-from-color:currentColor;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,hsla(0,0%,100%,0))}.lg\:from-black{--gradient-from-color:#000;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,transparent)}.lg\:from-white{--gradient-from-color:#fff;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,hsla(0,0%,100%,0))}.lg\:from-gray-100{--gradient-from-color:#f7fafc;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(247,250,252,0))}.lg\:from-gray-200{--gradient-from-color:#edf2f7;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(237,242,247,0))}.lg\:from-gray-300{--gradient-from-color:#e2e8f0;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(226,232,240,0))}.lg\:from-gray-400{--gradient-from-color:#cbd5e0;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(203,213,224,0))}.lg\:from-gray-500{--gradient-from-color:#a0aec0;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(160,174,192,0))}.lg\:from-gray-600{--gradient-from-color:#718096;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(113,128,150,0))}.lg\:from-gray-700{--gradient-from-color:#4a5568;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(74,85,104,0))}.lg\:from-gray-800{--gradient-from-color:#2d3748;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(45,55,72,0))}.lg\:from-gray-900{--gradient-from-color:#1a202c;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(26,32,44,0))}.lg\:from-red-100{--gradient-from-color:#fff5f5;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,hsla(0,100%,98%,0))}.lg\:from-red-200{--gradient-from-color:#fed7d7;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(254,215,215,0))}.lg\:from-red-300{--gradient-from-color:#feb2b2;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(254,178,178,0))}.lg\:from-red-400{--gradient-from-color:#fc8181;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(252,129,129,0))}.lg\:from-red-500{--gradient-from-color:#f56565;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(245,101,101,0))}.lg\:from-red-600{--gradient-from-color:#e53e3e;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(229,62,62,0))}.lg\:from-red-700{--gradient-from-color:#c53030;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(197,48,48,0))}.lg\:from-red-800{--gradient-from-color:#9b2c2c;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(155,44,44,0))}.lg\:from-red-900{--gradient-from-color:#742a2a;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(116,42,42,0))}.lg\:from-orange-100{--gradient-from-color:#fffaf0;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(255,250,240,0))}.lg\:from-orange-200{--gradient-from-color:#feebc8;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(254,235,200,0))}.lg\:from-orange-300{--gradient-from-color:#fbd38d;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(251,211,141,0))}.lg\:from-orange-400{--gradient-from-color:#f6ad55;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(246,173,85,0))}.lg\:from-orange-500{--gradient-from-color:#ed8936;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(237,137,54,0))}.lg\:from-orange-600{--gradient-from-color:#dd6b20;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(221,107,32,0))}.lg\:from-orange-700{--gradient-from-color:#c05621;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(192,86,33,0))}.lg\:from-orange-800{--gradient-from-color:#9c4221;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(156,66,33,0))}.lg\:from-orange-900{--gradient-from-color:#7b341e;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(123,52,30,0))}.lg\:from-yellow-100{--gradient-from-color:ivory;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(255,255,240,0))}.lg\:from-yellow-200{--gradient-from-color:#fefcbf;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(254,252,191,0))}.lg\:from-yellow-300{--gradient-from-color:#faf089;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(250,240,137,0))}.lg\:from-yellow-400{--gradient-from-color:#f6e05e;--gradient-color-stops:var(--gradient-from-color),var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var(--gradient-from-color),var(--gradient-to-color,rgba(163,191,250,0))}.lg\:from-indigo-400{--gradient-from-color:#7f9cf5;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(127,156,245,0))}.lg\:from-indigo-500{--gradient-from-color:#667eea;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(102,126,234,0))}.lg\:from-indigo-600{--gradient-from-color:#5a67d8;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(90,103,216,0))}.lg\:from-indigo-700{--gradient-from-color:#4c51bf;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(76,81,191,0))}.lg\:from-indigo-800{--gradient-from-color:#434190;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(67,65,144,0))}.lg\:from-indigo-900{--gradient-from-color:#3c366b;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(60,54,107,0))}.lg\:from-purple-100{--gradient-from-color:#faf5ff;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(250,245,255,0))}.lg\:from-purple-200{--gradient-from-color:#e9d8fd;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(233,216,253,0))}.lg\:from-purple-300{--gradient-from-color:#d6bcfa;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(214,188,250,0))}.lg\:from-purple-400{--gradient-from-color:#b794f4;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(183,148,244,0))}.lg\:from-purple-500{--gradient-from-color:#9f7aea;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(159,122,234,0))}.lg\:from-purple-600{--gradient-from-color:#805ad5;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(128,90,213,0))}.lg\:from-purple-700{--gradient-from-color:#6b46c1;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(107,70,193,0))}.lg\:from-purple-800{--gradient-from-color:#553c9a;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(85,60,154,0))}.lg\:from-purple-900{--gradient-from-color:#44337a;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(68,51,122,0))}.lg\:from-pink-100{--gradient-from-color:#fff5f7;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(255,245,247,0))}.lg\:from-pink-200{--gradient-from-color:#fed7e2;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(254,215,226,0))}.lg\:from-pink-300{--gradient-from-color:#fbb6ce;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(251,182,206,0))}.lg\:from-pink-400{--gradient-from-color:#f687b3;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(246,135,179,0))}.lg\:from-pink-500{--gradient-from-color:#ed64a6;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(237,100,166,0))}.lg\:from-pink-600{--gradient-from-color:#d53f8c;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(213,63,140,0))}.lg\:from-pink-700{--gradient-from-color:#b83280;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(184,50,128,0))}.lg\:from-pink-800{--gradient-from-color:#97266d;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(151,38,109,0))}.lg\:from-pink-900{--gradient-from-color:#702459;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(112,36,89,0))}.lg\:from-primary{--gradient-from-color:#00d1ff;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(0,209,255,0))}.lg\:from-secondary{--gradient-from-color:#e077ff;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(224,119,255,0))}.lg\:from-greenish-blue{--gradient-from-color:#adffed;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(173,255,237,0))}.lg\:from-blue-gray{--gradient-from-color:#cfe1e6;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(207,225,230,0))}.lg\:via-transparent{--gradient-via-color:transparent;--gradient-color-stops:var(--gradient-from-color),var(--gradient-via-color),var(--gradient-to-color,transparent)}.lg\:via-current{--gradient-via-color:currentColor;--gradient-color-stops:var(--gradient-from-color),var(--gradient-via-color),var(--gradient-to-color,hsla(0,0%,100%,0))}.lg\:via-black{--gradient-via-color:#000;--gradient-color-stops:var(--gradient-from-color),var(--gradient-via-color),var(--gradient-to-color,transparent)}.lg\:via-white{--gradient-via-color:#fff;--gradient-color-stops:var(--gradient-from-color),var(--gradient-via-color),var(--gradient-to-color,hsla(0,0%,100%,0))}.lg\:via-gray-100{--gradient-via-color:#f7fafc;--gradient-color-stops:var(--gradient-from-color),var(--gradient-via-color),var(--gradient-to-color,rgba(247,250,252,0))}.lg\:via-gray-200{--gradient-via-color:#edf2f7;--gradient-col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top right,var(--gradient-color-stops))}.bg-gradient-to-r{background-image:linear-gradient(90deg,var(--gradient-color-stops))}.bg-gradient-to-br{background-image:linear-gradient(to bottom right,var(--gradient-color-stops))}.bg-gradient-to-b{background-image:linear-gradient(180deg,var(--gradient-color-stops))}.bg-gradient-to-bl{background-image:linear-gradient(to bottom left,var(--gradient-color-stops))}.bg-gradient-to-l{background-image:linear-gradient(270deg,var(--gradient-color-stops))}.bg-gradient-to-tl{background-image:linear-gradient(to top left,var(--gradient-color-stops))}.from-transparent{--gradient-from-color:transparent;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,transparent)}.from-current{--gradient-from-color:currentColor;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,hsla(0,0%,100%,0))}.from-black{--gradient-from-color:#000;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,transparent)}.from-white{--gradient-from-color:#fff;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,hsla(0,0%,100%,0))}.from-gray-100{--gradient-from-color:#f7fafc;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(247,250,252,0))}.from-gray-200{--gradient-from-color:#edf2f7;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(237,242,247,0))}.from-gray-300{--gradient-from-color:#e2e8f0;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(226,232,240,0))}.from-gray-400{--gradient-from-color:#cbd5e0;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(203,213,224,0))}.from-gray-500{--gradient-from-color:#a0aec0;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(160,174,192,0))}.from-gray-600{--gradient-from-color:#718096;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(113,128,150,0))}.from-gray-700{--gradient-from-color:#4a5568;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(74,85,104,0))}.from-gray-800{--gradient-from-color:#2d3748;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(45,55,72,0))}.from-gray-900{--gradient-from-color:#1a202c;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(26,32,44,0))}.from-red-100{--gradient-from-color:#fff5f5;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,hsla(0,100%,98%,0))}.from-red-200{--gradient-from-color:#fed7d7;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(254,215,215,0))}.from-red-300{--gradient-from-color:#feb2b2;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(254,178,178,0))}.from-red-400{--gradient-from-color:#fc8181;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(252,129,129,0))}.from-red-500{--gradient-from-color:#f56565;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(245,101,101,0))}.from-red-600{--gradient-from-color:#e53e3e;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(229,62,62,0))}.from-red-700{--gradient-from-color:#c53030;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(197,48,48,0))}.from-red-800{--gradient-from-color:#9b2c2c;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(155,44,44,0))}.from-red-900{--gradient-from-color:#742a2a;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(116,42,42,0))}.from-orange-100{--gradient-from-color:#fffaf0;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(255,250,240,0))}.from-orange-200{--gradient-from-color:#feebc8;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(254,235,200,0))}.from-orange-300{--gradient-from-color:#fbd38d;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(251,211,141,0))}.from-orange-400{--gradient-from-color:#f6ad55;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(246,173,85,0))}.from-orange-500{--gradient-from-color:#ed8936;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(237,137,54,0))}.from-orange-600{--gradient-from-color:#dd6b20;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(221,107,32,0))}.from-orange-700{--gradient-from-color:#c05621;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(192,86,33,0))}.from-orange-800{--gradient-from-color:#9c4221;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(156,66,33,0))}.from-orange-900{--gradient-from-color:#7b341e;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(123,52,30,0))}.from-yellow-100{--gradient-from-color:ivory;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(255,255,240,0))}.from-yellow-200{--gradient-from-color:#fefcbf;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(254,252,191,0))}.from-yellow-300{--gradient-from-color:#faf089;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(250,240,137,0))}.from-yellow-400{--gradient-from-color:#f6e05e;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(246,224,94,0))}.from-yellow-500{--gradient-from-color:#ecc94b;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(236,201,75,0))}.from-yellow-600{--gradient-from-color:#d69e2e;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(214,158,46,0))}.from-yellow-700{--gradient-from-color:#b7791f;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(183,121,31,0))}.from-yellow-800{--gradient-from-color:#975a16;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(151,90,22,0))}.from-yellow-900{--gradient-from-color:#744210;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(116,66,16,0))}.from-green-100{--gradient-from-color:#f0fff4;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(240,255,244,0))}.from-green-200{--gradient-from-color:#c6f6d5;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(198,246,213,0))}.from-green-300{--gradient-from-color:#9ae6b4;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(154,230,180,0))}.from-green-400{--gradient-from-color:#68d391;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(104,211,145,0))}.from-green-500{--gradient-from-color:#48bb78;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(72,187,120,0))}.from-green-600{--gradient-from-color:#38a169;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(56,161,105,0))}.from-green-700{--gradient-from-color:#2f855a;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(47,133,90,0))}.from-green-800{--gradient-from-color:#276749;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(39,103,73,0))}.from-green-900{--gradient-from-color:#22543d;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(34,84,61,0))}.from-teal-100{--gradient-from-color:#e6fffa;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(230,255,250,0))}.from-teal-200{--gradient-from-color:#b2f5ea;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(178,245,234,0))}.from-teal-300{--gradient-from-color:#81e6d9;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(129,230,217,0))}.from-teal-400{--gradient-from-color:#4fd1c5;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(79,209,197,0))}.from-teal-500{--gradient-from-color:#38b2ac;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(56,178,172,0))}.from-teal-600{--gradient-from-color:#319795;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(49,151,149,0))}.from-teal-700{--gradient-from-color:#2c7a7b;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(44,122,123,0))}.from-teal-800{--gradient-from-color:#285e61;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(40,94,97,0))}.from-teal-900{--gradient-from-color:#234e52;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(35,78,82,0))}.from-blue-100{--gradient-from-color:#ebf8ff;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(235,248,255,0))}.from-blue-200{--gradient-from-color:#bee3f8;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(190,227,248,0))}.from-blue-300{--gradient-from-color:#90cdf4;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(144,205,244,0))}.from-blue-400{--gradient-from-color:#63b3ed;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(99,179,237,0))}.from-blue-500{--gradient-from-color:#4299e1;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(66,153,225,0))}.from-blue-600{--gradient-from-color:#3182ce;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(49,130,206,0))}.from-blue-700{--gradient-from-color:#2b6cb0;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(43,108,176,0))}.from-blue-800{--gradient-from-color:#2c5282;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(44,82,130,0))}.from-blue-900{--gradient-from-color:#2a4365;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(42,67,101,0))}.from-indigo-100{--gradient-from-color:#ebf4ff;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(235,244,255,0))}.from-indigo-200{--gradient-from-color:#c3dafe;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(195,218,254,0))}.from-indigo-300{--gradient-from-color:#a3bffa;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(163,191,250,0))}.from-indigo-400{--gradient-from-color:#7f9cf5;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(127,156,245,0))}.from-indigo-500{--gradient-from-color:#667eea;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(102,126,234,0))}.from-indigo-600{--gradient-from-color:#5a67d8;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(90,103,216,0))}.from-indigo-700{--gradient-from-color:#4c51bf;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(76,81,191,0))}.from-indigo-800{--gradient-from-color:#434190;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(67,65,144,0))}.from-indigo-900{--gradient-from-color:#3c366b;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(60,54,107,0))}.from-purple-100{--gradient-from-color:#faf5ff;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(250,245,255,0))}.from-purple-200{--gradient-from-color:#e9d8fd;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(233,216,253,0))}.from-purple-300{--gradient-from-color:#d6bcfa;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(214,188,250,0))}.from-purple-400{--gradient-from-color:#b794f4;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(183,148,244,0))}.from-purple-500{--gradient-from-color:#9f7aea;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(159,122,234,0))}.from-purple-600{--gradient-from-color:#805ad5;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(128,90,213,0))}.from-purple-700{--gradient-from-color:#6b46c1;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(107,70,193,0))}.from-purple-800{--gradient-from-color:#553c9a;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(85,60,154,0))}.from-purple-900{--gradient-from-color:#44337a;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(68,51,122,0))}.from-pink-100{--gradient-from-color:#fff5f7;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(255,245,247,0))}.from-pink-200{--gradient-from-color:#fed7e2;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(254,215,226,0))}.from-pink-300{--gradient-from-color:#fbb6ce;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(251,182,206,0))}.from-pink-400{--gradient-from-color:#f687b3;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(246,135,179,0))}.from-pink-500{--gradient-from-color:#ed64a6;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(237,100,166,0))}.from-pink-600{--gradient-from-color:#d53f8c;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(213,63,140,0))}.from-pink-700{--gradient-from-color:#b83280;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(184,50,128,0))}.from-pink-800{--gradient-from-color:#97266d;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(151,38,109,0))}.from-pink-900{--gradient-from-color:#702459;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(112,36,89,0))}.from-primary{--gradient-from-color:#00d1ff;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(0,209,255,0))}.from-secondary{--gradient-from-color:#e077ff;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(224,119,255,0))}.from-greenish-blue{--gradient-from-color:#adffed;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(173,255,237,0))}.from-blue-gray{--gradient-from-color:#cfe1e6;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(207,225,230,0))}.via-transparent{--gradient-via-color:transparent;--gradient-color-stops:var(--gradient-from-color),var(--gradient-via-color),var(--gradient-to-color,transparent)}.via-current{--gradient-via-color:currentColor;--gradient-color-stops:var(--gradient-from-color),var(--gradient-via-color),var(--gradient-to-color,hsla(0,0%,100%,0))}.via-black{--gradient-via-color:#000;--gradient-color-stops:var(--gradient-from-color),var(--gradient-via-color),var(--gradient-to-color,transparent)}.via-white{--gradient-via-color:#fff;--gradient-color-stops:var(--gradient-from-color),var(--gradient-via-color),var(--gradient-to-color,hsla(0,0%,100%,0))}.via-gray-100{--gradient-via-color:#f7fafc;--gradient-color-stops:var(--gradient-from-color),var(--gradient-via-color),var(--gradient-to-color,rgba(247,250,252,0))}.via-gray-200{--gradient-via-color:#edf2f7;--gradient-color-stops:var(--gradient-from-color),var(--gradient-via-color),var(--gradient-to-color,rgba(237,242,247,0))}.via-gray-300{--gradient-via-color:#e2e8f0;--gradient-color-stops:var(--gradient-from-color),var(--gradient-via-color),var(--gradient-to-color,rgba(226,232,240,0))}.via-gray-400{--gradient-via-color:#cbd5e0;--gradient-color-stops:var(--gradient-from-color),var(--gradient-via-color),var(--gradient-to-color,rgba(203,213,224,0))}.via-gray-500{--gradient-via-color:#a0aec0;--gradient-color-stops:var(--gradient-from-color),var(--gradient-via-color),var(--gradient-to-color,rgba(160,174,192,0))}.via-gray-600{--gradient-via-color:#718096;--gradient-color-stops:var(--gradient-from-color),var(--gradient-via-color),var(--gradient-to-color,rgba(113,128,150,0))}.via-gray-700{--gradient-via-color:#4a5568;--gradient-color-stops:var(--gradient-from-color),var(--gradient-via-color),var(--gradient-to-color,rgba(74,85,104,0))}.via-gray-800{--gradient-via-color:#2d3748;--gradient-color-stops:var(--gradient-from-color),var(--gradient-via-color),var(--gradient-to-color,rgba(45,55,72,0))}.via-gray-900{--gradient-via-color:#1a202c;--gradient-color-stops:var(--gradient-from-color),var(--gradient-via-color),var(--gradient-to-color,rgba(26,32,44,0))}.via-red-100{--gradient-via-color:#fff5f5;--gradient-color-stops:var(--gradient-from-color),var(--gradient-via-color),var(--gradient-to-color,hsla(0,100%,98%,0))}.via-red-200{--gradient-via-color:#fed7d7;--gradient-color-stops:var(--gradient-from-color),var(--gradient-via-color),var(--gradient-to-color,rgba(254,215,215,0))}.via-red-300{--gradient-via-color:#feb2b2;--gradient-color-stops:var(--gradient-from-color),var(--gradient-via-color),var(--gradient-to-color,rgba(254,178,178,0))}.via-red-400{--gradient-via-color:#fc8181;--gradient-color-stops:var(--gradient-from-color),var(--gradient-via-color),var(--gradient-to-color,rgba(252,129,129,0))}.via-red-500{--gradient-via-color:#f56565;--gradient-color-stops:var(--gradient-from-color),var(--gradient-via-color),var(--gradient-to-color,rgba(245,101,101,0))}.via-red-600{--gradient-via-color:#e53e3e;--gradient-color-stops:var(--gradient-from-color),var(--gradient-via-color),var(--gradient-to-color,rgba(229,62,62,0))}.via-red-700{--gradient-via-color:#c53030;--gradient-color-stops:var(--gradient-from-color),var(--gradient-via-color),var(--gradient-to-color,rgba(197,48,48,0))}.via-red-800{--gradient-via-color:#9b2c2c;--gradient-color-stops:var(--gradient-from-color),var(--gradient-via-color),var(--gradient-to-color,rgba(155,44,44,0))}.via-red-900{--gradient-via-color:#742a2a;--gradient-color-stops:var(--gradient-from-color),var(--gradient-via-color),var(--gradient-to-color,rgba(116,42,42,0))}.via-orange-100{--gradient-via-color:#fffaf0;--gradient-color-stops:var(--gradient-from-color),var(--gradient-via-color),var(--gradient-to-color,rgba(255,250,240,0))}.via-orange-200{--gradient-via-color:#feebc8;--gradient-color-stops:var(--gradient-from-color),var(--gradient-via-color),var(--gradient-to-color,rgba(254,235,200,0))}.via-orange-300{--gradient-via-color:#fbd38d;--gradient-color-stops:var(--gradient-from-color),var(--gradient-via-color),var(--gradient-to-color,rgba(251,211,141,0))}.via-orange-400{--gradient-via-color:#f6ad55;--gradient-color-stops:var(--gradient-from-color),var(--gradient-via-color),var(--gradient-to-color,rgba(246,173,85,0))}.via-orange-500{--gradient-via-color:#ed8936;--gradient-color-stops:var(--gradient-from-color),var(--gradient-via-color),var(--gradient-to-color,rgba(237,137,54,0))}.via-orange-600{--gradient-via-color:#dd6b20;--gradient-color-stops:var(--gradient-from-color),var(--gradient-via-color),var(--gradient-to-color,rgba(221,107,32,0))}.via-orange-700{--gradient-via-color:#c05621;--gradient-color-stops:var(--gradient-from-color),var(--gradient-via-color),var(--gradient-to-color,rgba(192,86,33,0))}.via-orange-800{--gradient-via-color:#9c4221;--gradient-color-stops:var(--gradient-from-color),var(--gradient-via-color),var(--gradient-to-color,rgba(156,66,33,0))}.via-orange-900{--gradient-via-color:#7b341e;--gradient-color-stops:var(--gradient-from-color),var(--gradient-via-color),var(--gradient-to-color,rgba(123,52,30,0))}.via-yellow-100{--gradient-via-color:ivory;--gradient-color-stops:var(--gradient-from-color),var(--gradient-via-color),var(--gradient-to-color,rgba(255,255,240,0))}.via-yellow-200{--gradient-via-color:#fefcbf;--gradient-color-stops:var(--gradient-from-color),var(--gradient-via-color),var(--gradient-to-color,rgba(254,252,191,0))}.via-yellow-300{--gradient-via-color:#faf089;--gradient-color-stops:var(--gradient-from-color),var(--gradient-via-color),var(--gradient-to-color,rgba(250,240,137,0))}.via-yellow-400{--gradient-via-color:#f6e05e;--gradient-color-stops:var(--gradient-from-color),var(--gradient-via-color),var(--gradient-to-color,rgba(246,224,94,0))}.via-yellow-500{--gradient-via-color:#ecc94b;--gradient-color-stops:var(--gradient-from-color),var(--gradient-via-color),var(--gradient-to-color,rgba(236,201,75,0))}.via-yellow-600{--gradient-via-color:#d69e2e;--gradient-color-stops:var(--gradient-from-color),var(--gradient-via-color),var(--gradient-to-color,rgba(214,158,46,0))}.via-yellow-700{--gradient-via-color:#b7791f;--gradient-color-stops:var(--gradient-from-color),var(--gradient-via-color),var(--gradient-to-color,rgba(183,121,31,0))}.via-yellow-800{--gradient-via-color:#975a16;--gradient-color-stops:va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.via-indigo-300{--gradient-via-color:#a3bffa;--gradient-color-stops:var(--gradient-from-color),var(--gradient-via-color),var(--gradient-to-color,rgba(163,191,250,0))}.via-indigo-400{--gradient-via-color:#7f9cf5;--gradient-color-stops:var(--gradient-from-color),var(--gradient-via-color),var(--gradient-to-color,rgba(127,156,245,0))}.via-indigo-500{--gradient-via-color:#667eea;--gradient-color-stops:var(--gradient-from-color),var(--gradient-via-color),var(--gradient-to-color,rgba(102,126,234,0))}.via-indigo-600{--gradient-via-color:#5a67d8;--gradient-color-stops:var(--gradient-from-color),var(--gradient-via-color),var(--gradient-to-color,rgba(90,103,216,0))}.via-indigo-700{--gradient-via-color:#4c51bf;--gradient-color-stops:var(--gradient-from-color),var(--gradient-via-color),var(--gradient-to-color,rgba(76,81,191,0))}.via-indigo-800{--gradient-via-color:#434190;--gradient-color-stops:var(--gradient-from-color),var(--gradient-via-color),var(--gradient-to-color,rgba(67,65,144,0))}.via-indigo-900{--gradient-via-color:#3c366b;--gradient-color-stops:var(--gradient-from-color),var(--gradient-via-color),var(--gradient-to-color,rgba(60,54,107,0))}.via-purple-100{--gradient-via-color:#faf5ff;--gradient-color-stops:var(--gradient-from-color),var(--gradient-via-color),var(--gradient-to-color,rgba(250,245,255,0))}.via-purple-200{--gradient-via-color:#e9d8fd;--gradient-color-stops:var(--gradient-from-color),var(--gradient-via-color),var(--gradient-to-color,rgba(233,216,253,0))}.via-purple-300{--gradient-via-color:#d6bcfa;--gradient-color-stops:var(--gradient-from-color),var(--gradient-via-color),var(--gradient-to-color,rgba(214,188,250,0))}.via-purple-400{--gradient-via-color:#b794f4;--gradient-color-stops:var(--gradient-from-color),var(--gradient-via-color),var(--gradient-to-color,rgba(183,148,244,0))}.via-purple-500{--gradient-via-color:#9f7aea;--gradient-color-stops:var(--gradient-from-color),var(--gradient-via-color),var(--gradient-to-color,rgba(159,122,234,0))}.via-purple-600{--gradient-via-color:#805ad5;--gradient-color-stops:var(--gradient-from-color),var(--gradient-via-color),var(--gradient-to-color,rgba(128,90,213,0))}.via-purple-700{--gradient-via-color:#6b46c1;--gradient-color-stops:var(--gradient-from-color),var(--gradient-via-color),var(--gradient-to-color,rgba(107,70,193,0))}.via-purple-800{--gradient-via-color:#553c9a;--gradient-color-stops:var(--gradient-from-color),var(--gradient-via-color),var(--gradient-to-color,rgba(85,60,154,0))}.via-purple-900{--gradient-via-color:#44337a;--gradient-color-stops:var(--gradient-from-color),var(--gradient-via-color),var(--gradient-to-color,rgba(68,51,122,0))}.via-pink-100{--gradient-via-color:#fff5f7;--gradient-color-stops:var(--gradient-from-color),var(--gradient-via-color),var(--gradient-to-color,rgba(255,245,247,0))}.via-pink-200{--gradient-via-color:#fed7e2;--gradient-color-stops:var(--gradient-from-color),var(--gradient-via-color),var(--gradient-to-color,rgba(254,215,226,0))}.via-pink-300{--gradient-via-color:#fbb6ce;--gradient-color-stops:var(--gradient-from-color),var(--gradient-via-color),var(--gradient-to-color,rgba(251,182,206,0))}.via-pink-400{--gradient-via-color:#f687b3;--gradient-color-stops:var(--gradient-from-color),var(--gradient-via-color),var(--gradient-to-color,rgba(246,135,179,0))}.via-pink-500{--gradient-via-color:#ed64a6;--gradient-color-stops:var(--gradient-from-color),var(--gradient-via-color),var(--gradient-to-color,rgba(237,100,166,0))}.via-pink-600{--gradient-via-color:#d53f8c;--gradient-color-stops:var(--gradient-from-color),var(--gradient-via-color),var(--gradient-to-color,rgba(213,63,140,0))}.via-pink-700{--gradient-via-color:#b83280;--gradient-color-stops:var(--gradient-from-color),var(--gradient-via-color),var(--gradient-to-color,rgba(184,50,128,0))}.via-pink-800{--gradient-via-color:#97266d;--gradient-color-stops:var(--gradient-from-color),var(--gradient-via-color),var(--gradient-to-color,rgba(151,38,109,0))}.via-pink-900{--gradient-via-color:#702459;--gradient-color-stops:var(--gradient-from-color),var(--gradient-via-color),var(--gradient-to-color,rgba(112,36,89,0))}.via-primary{--gradient-via-color:#00d1ff;--gradient-color-stops:var(--gradient-from-color),var(--gradient-via-color),var(--gradient-to-color,rgba(0,209,255,0))}.via-secondary{--gradient-via-color:#e077ff;--gradient-color-stops:var(--gradient-from-color),var(--gradient-via-color),var(--gradient-to-color,rgba(224,119,255,0))}.via-greenish-blue{--gradient-via-color:#adffed;--gradient-color-stops:var(--gradient-from-color),var(--gradient-via-color),var(--gradient-to-color,rgba(173,255,237,0))}.via-blue-gray{--gradient-via-color:#cfe1e6;--gradient-color-stops:var(--gradient-from-color),var(--gradient-via-color),var(--gradient-to-color,rgba(207,225,230,0))}.to-transparent{--gradient-to-color:transparent}.to-current{--gradient-to-color:currentColor}.to-black{--gradient-to-color:#000}.to-white{--gradient-to-color:#fff}.to-gray-100{--gradient-to-color:#f7fafc}.to-gray-200{--gradien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left,var(--gradient-color-stops))}.sm\:from-transparent{--gradient-from-color:transparent;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,transparent)}.sm\:from-current{--gradient-from-color:currentColor;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,hsla(0,0%,100%,0))}.sm\:from-black{--gradient-from-color:#000;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,transparent)}.sm\:from-white{--gradient-from-color:#fff;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,hsla(0,0%,100%,0))}.sm\:from-gray-100{--gradient-from-color:#f7fafc;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(247,250,252,0))}.sm\:from-gray-200{--gradient-from-color:#edf2f7;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(237,242,247,0))}.sm\:from-gray-300{--gradient-from-color:#e2e8f0;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(226,232,240,0))}.sm\:from-gray-400{--gradient-from-color:#cbd5e0;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(203,213,224,0))}.sm\:from-gray-500{--gradient-from-color:#a0aec0;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(160,174,192,0))}.sm\:from-gray-600{--gradient-from-color:#718096;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(113,128,150,0))}.sm\:from-gray-700{--gradient-from-color:#4a5568;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(74,85,104,0))}.sm\:from-gray-800{--gradient-from-color:#2d3748;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(45,55,72,0))}.sm\:from-gray-900{--gradient-from-color:#1a202c;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(26,32,44,0))}.sm\:from-red-100{--gradient-from-color:#fff5f5;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,hsla(0,100%,98%,0))}.sm\:from-red-200{--gradient-from-color:#fed7d7;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(254,215,215,0))}.sm\:from-red-300{--gradient-from-color:#feb2b2;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(254,178,178,0))}.sm\:from-red-400{--gradient-from-color:#fc8181;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(252,129,129,0))}.sm\:from-red-500{--gradient-from-color:#f56565;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(245,101,101,0))}.sm\:from-red-600{--gradient-from-color:#e53e3e;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(229,62,62,0))}.sm\:from-red-700{--gradient-from-color:#c53030;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(197,48,48,0))}.sm\:from-red-800{--gradient-from-color:#9b2c2c;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(155,44,44,0))}.sm\:from-red-900{--gradient-from-color:#742a2a;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(116,42,42,0))}.sm\:from-orange-100{--gradient-from-color:#fffaf0;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(255,250,240,0))}.sm\:from-orange-200{--gradient-from-color:#feebc8;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(254,235,200,0))}.sm\:from-orange-300{--gradient-from-color:#fbd38d;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(251,211,141,0))}.sm\:from-orange-400{--gradient-from-color:#f6ad55;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(246,173,85,0))}.sm\:from-orange-500{--gradient-from-color:#ed8936;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(237,137,54,0))}.sm\:from-orange-600{--gradient-from-color:#dd6b20;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(221,107,32,0))}.sm\:from-orange-700{--gradient-from-color:#c05621;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(192,86,33,0))}.sm\:from-orange-800{--gradient-from-color:#9c4221;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(156,66,33,0))}.sm\:from-orange-900{--gradient-from-color:#7b341e;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(123,52,30,0))}.sm\:from-yellow-100{--gradient-from-color:ivory;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(255,255,240,0))}.sm\:from-yellow-200{--gradient-from-color:#fefcbf;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(254,252,191,0))}.sm\:from-yellow-300{--gradient-from-color:#faf089;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(250,240,137,0))}.sm\:from-yellow-400{--gradient-from-color:#f6e05e;--gradient-color-stops:var(--gradient-from-color),var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left,var(--gradient-color-stops))}.md\:from-transparent{--gradient-from-color:transparent;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,transparent)}.md\:from-current{--gradient-from-color:currentColor;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,hsla(0,0%,100%,0))}.md\:from-black{--gradient-from-color:#000;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,transparent)}.md\:from-white{--gradient-from-color:#fff;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,hsla(0,0%,100%,0))}.md\:from-gray-100{--gradient-from-color:#f7fafc;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(247,250,252,0))}.md\:from-gray-200{--gradient-from-color:#edf2f7;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(237,242,247,0))}.md\:from-gray-300{--gradient-from-color:#e2e8f0;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(226,232,240,0))}.md\:from-gray-400{--gradient-from-color:#cbd5e0;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(203,213,224,0))}.md\:from-gray-500{--gradient-from-color:#a0aec0;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(160,174,192,0))}.md\:from-gray-600{--gradient-from-color:#718096;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(113,128,150,0))}.md\:from-gray-700{--gradient-from-color:#4a5568;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(74,85,104,0))}.md\:from-gray-800{--gradient-from-color:#2d3748;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(45,55,72,0))}.md\:from-gray-900{--gradient-from-color:#1a202c;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(26,32,44,0))}.md\:from-red-100{--gradient-from-color:#fff5f5;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,hsla(0,100%,98%,0))}.md\:from-red-200{--gradient-from-color:#fed7d7;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(254,215,215,0))}.md\:from-red-300{--gradient-from-color:#feb2b2;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(254,178,178,0))}.md\:from-red-400{--gradient-from-color:#fc8181;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(252,129,129,0))}.md\:from-red-500{--gradient-from-color:#f56565;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(245,101,101,0))}.md\:from-red-600{--gradient-from-color:#e53e3e;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(229,62,62,0))}.md\:from-red-700{--gradient-from-color:#c53030;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(197,48,48,0))}.md\:from-red-800{--gradient-from-color:#9b2c2c;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(155,44,44,0))}.md\:from-red-900{--gradient-from-color:#742a2a;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(116,42,42,0))}.md\:from-orange-100{--gradient-from-color:#fffaf0;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(255,250,240,0))}.md\:from-orange-200{--gradient-from-color:#feebc8;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(254,235,200,0))}.md\:from-orange-300{--gradient-from-color:#fbd38d;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(251,211,141,0))}.md\:from-orange-400{--gradient-from-color:#f6ad55;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(246,173,85,0))}.md\:from-orange-500{--gradient-from-color:#ed8936;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(237,137,54,0))}.md\:from-orange-600{--gradient-from-color:#dd6b20;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(221,107,32,0))}.md\:from-orange-700{--gradient-from-color:#c05621;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(192,86,33,0))}.md\:from-orange-800{--gradient-from-color:#9c4221;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(156,66,33,0))}.md\:from-orange-900{--gradient-from-color:#7b341e;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(123,52,30,0))}.md\:from-yellow-100{--gradient-from-color:ivory;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(255,255,240,0))}.md\:from-yellow-200{--gradient-from-color:#fefcbf;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(254,252,191,0))}.md\:from-yellow-300{--gradient-from-color:#faf089;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(250,240,137,0))}.md\:from-yellow-400{--gradient-from-color:#f6e05e;--gradient-color-stops:var(--gradient-from-color),var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left,var(--gradient-color-stops))}.lg\:from-transparent{--gradient-from-color:transparent;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,transparent)}.lg\:from-current{--gradient-from-color:currentColor;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,hsla(0,0%,100%,0))}.lg\:from-black{--gradient-from-color:#000;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,transparent)}.lg\:from-white{--gradient-from-color:#fff;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,hsla(0,0%,100%,0))}.lg\:from-gray-100{--gradient-from-color:#f7fafc;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(247,250,252,0))}.lg\:from-gray-200{--gradient-from-color:#edf2f7;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(237,242,247,0))}.lg\:from-gray-300{--gradient-from-color:#e2e8f0;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(226,232,240,0))}.lg\:from-gray-400{--gradient-from-color:#cbd5e0;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(203,213,224,0))}.lg\:from-gray-500{--gradient-from-color:#a0aec0;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(160,174,192,0))}.lg\:from-gray-600{--gradient-from-color:#718096;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(113,128,150,0))}.lg\:from-gray-700{--gradient-from-color:#4a5568;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(74,85,104,0))}.lg\:from-gray-800{--gradient-from-color:#2d3748;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(45,55,72,0))}.lg\:from-gray-900{--gradient-from-color:#1a202c;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(26,32,44,0))}.lg\:from-red-100{--gradient-from-color:#fff5f5;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,hsla(0,100%,98%,0))}.lg\:from-red-200{--gradient-from-color:#fed7d7;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(254,215,215,0))}.lg\:from-red-300{--gradient-from-color:#feb2b2;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(254,178,178,0))}.lg\:from-red-400{--gradient-from-color:#fc8181;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(252,129,129,0))}.lg\:from-red-500{--gradient-from-color:#f56565;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(245,101,101,0))}.lg\:from-red-600{--gradient-from-color:#e53e3e;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(229,62,62,0))}.lg\:from-red-700{--gradient-from-color:#c53030;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(197,48,48,0))}.lg\:from-red-800{--gradient-from-color:#9b2c2c;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(155,44,44,0))}.lg\:from-red-900{--gradient-from-color:#742a2a;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(116,42,42,0))}.lg\:from-orange-100{--gradient-from-color:#fffaf0;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(255,250,240,0))}.lg\:from-orange-200{--gradient-from-color:#feebc8;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(254,235,200,0))}.lg\:from-orange-300{--gradient-from-color:#fbd38d;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(251,211,141,0))}.lg\:from-orange-400{--gradient-from-color:#f6ad55;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(246,173,85,0))}.lg\:from-orange-500{--gradient-from-color:#ed8936;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(237,137,54,0))}.lg\:from-orange-600{--gradient-from-color:#dd6b20;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(221,107,32,0))}.lg\:from-orange-700{--gradient-from-color:#c05621;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(192,86,33,0))}.lg\:from-orange-800{--gradient-from-color:#9c4221;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(156,66,33,0))}.lg\:from-orange-900{--gradient-from-color:#7b341e;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(123,52,30,0))}.lg\:from-yellow-100{--gradient-from-color:ivory;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(255,255,240,0))}.lg\:from-yellow-200{--gradient-from-color:#fefcbf;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(254,252,191,0))}.lg\:from-yellow-300{--gradient-from-color:#faf089;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(250,240,137,0))}.lg\:from-yellow-400{--gradient-from-color:#f6e05e;--gradient-color-stops:var(--gradient-from-color),var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var(--gradient-from-color),var(--gradient-to-color,rgba(163,191,250,0))}.lg\:from-indigo-400{--gradient-from-color:#7f9cf5;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(127,156,245,0))}.lg\:from-indigo-500{--gradient-from-color:#667eea;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(102,126,234,0))}.lg\:from-indigo-600{--gradient-from-color:#5a67d8;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(90,103,216,0))}.lg\:from-indigo-700{--gradient-from-color:#4c51bf;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(76,81,191,0))}.lg\:from-indigo-800{--gradient-from-color:#434190;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(67,65,144,0))}.lg\:from-indigo-900{--gradient-from-color:#3c366b;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(60,54,107,0))}.lg\:from-purple-100{--gradient-from-color:#faf5ff;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(250,245,255,0))}.lg\:from-purple-200{--gradient-from-color:#e9d8fd;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(233,216,253,0))}.lg\:from-purple-300{--gradient-from-color:#d6bcfa;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(214,188,250,0))}.lg\:from-purple-400{--gradient-from-color:#b794f4;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(183,148,244,0))}.lg\:from-purple-500{--gradient-from-color:#9f7aea;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(159,122,234,0))}.lg\:from-purple-600{--gradient-from-color:#805ad5;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(128,90,213,0))}.lg\:from-purple-700{--gradient-from-color:#6b46c1;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(107,70,193,0))}.lg\:from-purple-800{--gradient-from-color:#553c9a;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(85,60,154,0))}.lg\:from-purple-900{--gradient-from-color:#44337a;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(68,51,122,0))}.lg\:from-pink-100{--gradient-from-color:#fff5f7;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(255,245,247,0))}.lg\:from-pink-200{--gradient-from-color:#fed7e2;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(254,215,226,0))}.lg\:from-pink-300{--gradient-from-color:#fbb6ce;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(251,182,206,0))}.lg\:from-pink-400{--gradient-from-color:#f687b3;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(246,135,179,0))}.lg\:from-pink-500{--gradient-from-color:#ed64a6;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(237,100,166,0))}.lg\:from-pink-600{--gradient-from-color:#d53f8c;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(213,63,140,0))}.lg\:from-pink-700{--gradient-from-color:#b83280;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(184,50,128,0))}.lg\:from-pink-800{--gradient-from-color:#97266d;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(151,38,109,0))}.lg\:from-pink-900{--gradient-from-color:#702459;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(112,36,89,0))}.lg\:from-primary{--gradient-from-color:#00d1ff;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(0,209,255,0))}.lg\:from-secondary{--gradient-from-color:#e077ff;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(224,119,255,0))}.lg\:from-greenish-blue{--gradient-from-color:#adffed;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(173,255,237,0))}.lg\:from-blue-gray{--gradient-from-color:#cfe1e6;--gradient-color-stops:var(--gradient-from-color),var(--gradient-to-color,rgba(207,225,230,0))}.lg\:via-transparent{--gradient-via-color:transparent;--gradient-color-stops:var(--gradient-from-color),var(--gradient-via-color),var(--gradient-to-color,transparent)}.lg\:via-current{--gradient-via-color:currentColor;--gradient-color-stops:var(--gradient-from-color),var(--gradient-via-color),var(--gradient-to-color,hsla(0,0%,100%,0))}.lg\:via-black{--gradient-via-color:#000;--gradient-color-stops:var(--gradient-from-color),var(--gradient-via-color),var(--gradient-to-color,transparent)}.lg\:via-white{--gradient-via-color:#fff;--gradient-color-stops:var(--gradient-from-color),var(--gradient-via-color),var(--gradient-to-color,hsla(0,0%,100%,0))}.lg\:via-gray-100{--gradient-via-color:#f7fafc;--gradient-color-stops:var(--gradient-from-color),var(--gradient-via-color),var(--gradient-to-color,rgba(247,250,252,0))}.lg\:via-gray-200{--gradient-via-color:#edf2f7;--gradient-col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-(window.webpackJsonp=window.webpackJsonp||[]).push([[10],{419:function(e,t,a){e.exports=a.p+"assets/img/pescandodatos1.92f0c35b.png"},420:function(e,t,a){e.exports=a.p+"assets/img/pescandodatos2.d04a929b.png"},421:function(e,t,a){e.exports=a.p+"assets/img/pescandodatos3.41defd34.png"},422:function(e,t,a){e.exports=a.p+"assets/img/pescandodatos4.8d95dae7.png"},423:function(e,t,a){e.exports=a.p+"assets/img/pescandodatos5.aa82e072.png"},424:function(e,t,a){e.exports=a.p+"assets/img/pescandodatos6.9e89ebcb.png"},573:function(e,t,a){"use strict";a.r(t);var o=a(29),r=Object(o.a)({},(function(){var e=this,t=e.$createElement,o=e._self._c||t;return o("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[o("h2",{attrs:{id:"context"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#context"}},[e._v("#")]),e._v(" Context")]),e._v(" "),o("p",[e._v("Causa Natura is a non-profit organization based in Mexico. It supports public policies to allow management of natural resources respecting human rights, equity, efficiency and sustainability. This project, “Pescando Datos” seeks to advocate for improved public policies for more than just subsidies allocation, through the collection of, analysis, and visualization of data around subsidies available to fishing communities in Mexico.")]),e._v(" "),o("p",[e._v("After an extended period of analysis a web platform is being built in order to explore data and visualize it with launch due for later in 2017. Following a meeting at csv,conf after a presentation by Adrià Mercader on "),o("a",{attrs:{href:"https://www.youtube.com/watch?v=Gk2F4hncAgY&index=35&list=PLg5zZXwt2ZW5UIz13oI56vfZjF6mvpIXN",target:"_blank",rel:"noopener noreferrer"}},[e._v("‘Continuous Data Validation for Everybody’"),o("OutboundLink")],1),e._v(" we have piloted with Causa Natura to explore how our goodtables service can support the project. We spoke to Eduardo Rolón, Executive Director of Causa Natura and Gabriela Rodriguez who is working on the platform.")]),e._v(" "),o("h3",{attrs:{id:"problem-we-were-trying-to-solve"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#problem-we-were-trying-to-solve"}},[e._v("#")]),e._v(" Problem We Were Trying To Solve")]),e._v(" "),o("p",[e._v("Causa Natura are making a lot of freedom of Information requests in Mexico on information to do with fishers in order to understand how policies are impacting people. The data is needed to support a range of stakeholders from the many co-op fisher communities to advocacy organisations.")]),e._v(" "),o("blockquote",[o("p",[e._v("Eduardo Rolón: Advocacy organizations, either from CSOs or from the fisheries sector may be more interested in data that evaluates and supports policy recommendations. Fisher communities have more immediate needs, such as how to obtain better governmental services and support.")])]),e._v(" "),o("blockquote",[o("p",[e._v("Gabriela Rodriguez: The data is important to us because Campaigns and decisions will be made based on the analysis on the data Causa Natura collected. To be able to do the required analysis we need good data.")])]),e._v(" "),o("blockquote",[o("p",[e._v("Gabriela Rodriguez: Currently, there is a tedious process of cleaning to give us data that can be worked on. Much of the data Causa Natura was using came as PDFs and needed to be processed. We process a lot of PDFs and Excel files and there are a lot of problems getting the OCR to capture the information correctly to csv. 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This is how one of these reports looks like:")]),e._v(" "),o("p",[o("img",{attrs:{src:a(421),alt:"ADBio"}})]),e._v(" "),o("p",[e._v("Here, we see that there are 59 valid tables, but the report has identified source and structural errors in 41 of the other tables hosted on the repository, including:")]),e._v(" "),o("ul",[o("li",[e._v("duplicate rows")]),e._v(" "),o("li",[e._v("duplicate headers")]),e._v(" "),o("li",[e._v("blank rows")]),e._v(" "),o("li",[e._v("missing values")])]),e._v(" "),o("p",[e._v("The full list of checks exercised by "),o("a",{attrs:{href:"http://goodtables.io",target:"_blank",rel:"noopener noreferrer"}},[e._v("goodtables.io"),o("OutboundLink")],1),e._v(" can be found in the "),o("a",{attrs:{href:"https://github.com/frictionlessdata/data-quality-spec/blob/master/spec.json",target:"_blank",rel:"noopener noreferrer"}},[e._v("Data Quality Spec"),o("OutboundLink")],1),e._v(". And the full report can be found "),o("a",{attrs:{href:"http://goodtables.io/github/frictionlessdata/pilot-causanatura/jobs/7",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),o("OutboundLink")],1),e._v(".")]),e._v(" "),o("p",[e._v("After identifying errors we went back do a manual cleanup of the data. As we mentioned, there is no need to run "),o("a",{attrs:{href:"http://goodtables.io",target:"_blank",rel:"noopener noreferrer"}},[e._v("goodtables.io"),o("OutboundLink")],1),e._v(" validation manually - it happens on any GitHub push for all activated repositories:")]),e._v(" "),o("p",[o("img",{attrs:{src:a(422),alt:"ADBio"}})]),e._v(" "),o("p",[e._v("If we need to customize a validation process we can put a goodtables.yml configuration file on the repository root, allowing us to tweak settings like the actual checks to perform, limit of rows to check, etc:")]),e._v(" "),o("p",[o("img",{attrs:{src:a(423),alt:"ADBio"}})]),e._v(" "),o("p",[e._v("And instant feedback is available via GitHub commit statuses and a "),o("a",{attrs:{href:"http://goodtables.io",target:"_blank",rel:"noopener noreferrer"}},[e._v("goodtables.io"),o("OutboundLink")],1),e._v(" badge that can be included in the README file:")]),e._v(" "),o("p",[o("img",{attrs:{src:a(424),alt:"ADBio"}})]),e._v(" "),o("h2",{attrs:{id:"review"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#review"}},[e._v("#")]),e._v(" Review")]),e._v(" "),o("blockquote",[o("p",[e._v("Gabriela Rodriguez: Right now I have not been using it extensively yet but I have a lot of faith that it could get incorporated in the process of importing data into the Github repository. It should be easy to introduce into our workflow. I really like the process of hooks after git-push as I’m trying to get the organization to use Github for new data. I really like the validation part and that a report is generated each time data is pushed. This is very important and very useful. This makes it easier for the people who are doing the cleaning of data who may not have experience with GitHub.")])]),e._v(" "),o("blockquote",[o("p",[e._v("Gabriela Rodriguez: The web interface needs a lot of usability work. But the idea is awesome. There are problems and it is kind of hard to use at the moment as it takes a long time to sync repositories and the process is not clear, but i think it has a huge potential to make a difference to the work we are doing, mostly if people use Github to store data then it could make a difference.")])]),e._v(" "),o("h2",{attrs:{id:"next-steps"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#next-steps"}},[e._v("#")]),e._v(" Next Steps")]),e._v(" "),o("h3",{attrs:{id:"areas-for-further-work"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#areas-for-further-work"}},[e._v("#")]),e._v(" Areas for further work")]),e._v(" "),o("blockquote",[o("p",[e._v("Gabriela Rodriguez: With continuous integration it would be very helpful to be notified with messages about the problems in the data. Perhaps emails notifications would be a good way to go, or integrations with other programs - Slack for example - would be fantastic.")])]),e._v(" "),o("p",[e._v("One thing to note is that all the errors shown following the analysis refer to the structure of the data files (missing headers, duplicate rows, etc). Including schema validation against some of the files would be a very logical next step in testing whether the contents of the data are what is expected). We are now planning to work with Causa Natura to take the steps to identify a subset of the data and create a base schema/data package that will be easily expandable and extendable.")]),e._v(" "),o("h3",{attrs:{id:"find-out-more"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#find-out-more"}},[e._v("#")]),e._v(" Find Out More")]),e._v(" "),o("p",[e._v("To explore for the yourself and collaborate, see the Pescando Datos project on "),o("a",{attrs:{href:"https://github.com/pescandodatos/datos",target:"_blank",rel:"noopener noreferrer"}},[e._v("github"),o("OutboundLink")],1),e._v(" and our goodtables "),o("a",{attrs:{href:"http://goodtables.io/github/frictionlessdata/pilot-causanatura",target:"_blank",rel:"noopener noreferrer"}},[e._v("reports"),o("OutboundLink")],1),e._v(" from the project.")])])}),[],!1,null,null,null);t.default=r.exports}}]); \ No newline at end of file +(window.webpackJsonp=window.webpackJsonp||[]).push([[10],{515:function(e,t,a){e.exports=a.p+"assets/img/pescandodatos1.92f0c35b.png"},516:function(e,t,a){e.exports=a.p+"assets/img/pescandodatos2.d04a929b.png"},517:function(e,t,a){e.exports=a.p+"assets/img/pescandodatos3.41defd34.png"},518:function(e,t,a){e.exports=a.p+"assets/img/pescandodatos4.8d95dae7.png"},519:function(e,t,a){e.exports=a.p+"assets/img/pescandodatos5.aa82e072.png"},520:function(e,t,a){e.exports=a.p+"assets/img/pescandodatos6.9e89ebcb.png"},702:function(e,t,a){"use strict";a.r(t);var o=a(29),r=Object(o.a)({},(function(){var e=this,t=e.$createElement,o=e._self._c||t;return o("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[o("h2",{attrs:{id:"context"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#context"}},[e._v("#")]),e._v(" Context")]),e._v(" "),o("p",[e._v("Causa Natura is a non-profit organization based in Mexico. It supports public policies to allow management of natural resources respecting human rights, equity, efficiency and sustainability. This project, “Pescando Datos” seeks to advocate for improved public policies for more than just subsidies allocation, through the collection of, analysis, and visualization of data around subsidies available to fishing communities in Mexico.")]),e._v(" "),o("p",[e._v("After an extended period of analysis a web platform is being built in order to explore data and visualize it with launch due for later in 2017. Following a meeting at csv,conf after a presentation by Adrià Mercader on "),o("a",{attrs:{href:"https://www.youtube.com/watch?v=Gk2F4hncAgY&index=35&list=PLg5zZXwt2ZW5UIz13oI56vfZjF6mvpIXN",target:"_blank",rel:"noopener noreferrer"}},[e._v("‘Continuous Data Validation for Everybody’"),o("OutboundLink")],1),e._v(" we have piloted with Causa Natura to explore how our goodtables service can support the project. We spoke to Eduardo Rolón, Executive Director of Causa Natura and Gabriela Rodriguez who is working on the platform.")]),e._v(" "),o("h3",{attrs:{id:"problem-we-were-trying-to-solve"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#problem-we-were-trying-to-solve"}},[e._v("#")]),e._v(" Problem We Were Trying To Solve")]),e._v(" "),o("p",[e._v("Causa Natura are making a lot of freedom of Information requests in Mexico on information to do with fishers in order to understand how policies are impacting people. The data is needed to support a range of stakeholders from the many co-op fisher communities to advocacy organisations.")]),e._v(" "),o("blockquote",[o("p",[e._v("Eduardo Rolón: Advocacy organizations, either from CSOs or from the fisheries sector may be more interested in data that evaluates and supports policy recommendations. 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We introduced goodtables in a "),o("a",{attrs:{href:"http://okfnlabs.org/blog/2015/02/20/introducing-goodtables.html",target:"_blank",rel:"noopener noreferrer"}},[e._v("blog post"),o("OutboundLink")],1),e._v(" earlier this year.")]),e._v(" "),o("p",[e._v("On top of that, Open Knowledge International has developed "),o("a",{attrs:{href:"http://goodtables.io",target:"_blank",rel:"noopener noreferrer"}},[e._v("goodtables.io"),o("OutboundLink")],1),e._v(", a web service for a continuous data validation that connects to different data sources to generate structure and content reports.")]),e._v(" "),o("h3",{attrs:{id:"what-did-we-do"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#what-did-we-do"}},[e._v("#")]),e._v(" What Did We Do")]),e._v(" "),o("p",[e._v("Let’s see how "),o("a",{attrs:{href:"http://goodtables.io",target:"_blank",rel:"noopener noreferrer"}},[e._v("goodtables.io"),o("OutboundLink")],1),e._v(" has helped to identify source and structural errors in the Causa Natura pilot dataset:")]),e._v(" "),o("p",[o("img",{attrs:{src:a(515),alt:"ADBio"}})]),e._v(" "),o("p",[e._v("After we’ve signed in, we synchronize our GitHub repositories and activate the repository we want to validate ("),o("a",{attrs:{href:"https://github.com/frictionlessdata/pilot-causanatura",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://github.com/frictionlessdata/pilot-causanatura"),o("OutboundLink")],1),e._v("):")]),e._v(" "),o("p",[o("img",{attrs:{src:a(516),alt:"ADBio"}})]),e._v(" "),o("p",[e._v("Once the repository is activated, every time there is an update on the data hosted on GitHub, the service will generate a validation report. 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And the full report can be found "),o("a",{attrs:{href:"http://goodtables.io/github/frictionlessdata/pilot-causanatura/jobs/7",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),o("OutboundLink")],1),e._v(".")]),e._v(" "),o("p",[e._v("After identifying errors we went back do a manual cleanup of the data. As we mentioned, there is no need to run "),o("a",{attrs:{href:"http://goodtables.io",target:"_blank",rel:"noopener noreferrer"}},[e._v("goodtables.io"),o("OutboundLink")],1),e._v(" validation manually - it happens on any GitHub push for all activated repositories:")]),e._v(" "),o("p",[o("img",{attrs:{src:a(518),alt:"ADBio"}})]),e._v(" "),o("p",[e._v("If we need to customize a validation process we can put a goodtables.yml configuration file on the repository root, allowing us to tweak settings like the actual checks to perform, limit of rows to check, etc:")]),e._v(" "),o("p",[o("img",{attrs:{src:a(519),alt:"ADBio"}})]),e._v(" "),o("p",[e._v("And instant feedback is available via GitHub commit statuses and a "),o("a",{attrs:{href:"http://goodtables.io",target:"_blank",rel:"noopener noreferrer"}},[e._v("goodtables.io"),o("OutboundLink")],1),e._v(" badge that can be included in the README file:")]),e._v(" "),o("p",[o("img",{attrs:{src:a(520),alt:"ADBio"}})]),e._v(" "),o("h2",{attrs:{id:"review"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#review"}},[e._v("#")]),e._v(" Review")]),e._v(" "),o("blockquote",[o("p",[e._v("Gabriela Rodriguez: Right now I have not been using it extensively yet but I have a lot of faith that it could get incorporated in the process of importing data into the Github repository. 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There are problems and it is kind of hard to use at the moment as it takes a long time to sync repositories and the process is not clear, but i think it has a huge potential to make a difference to the work we are doing, mostly if people use Github to store data then it could make a difference.")])]),e._v(" "),o("h2",{attrs:{id:"next-steps"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#next-steps"}},[e._v("#")]),e._v(" Next Steps")]),e._v(" "),o("h3",{attrs:{id:"areas-for-further-work"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#areas-for-further-work"}},[e._v("#")]),e._v(" Areas for further work")]),e._v(" "),o("blockquote",[o("p",[e._v("Gabriela Rodriguez: With continuous integration it would be very helpful to be notified with messages about the problems in the data. 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Over the years we have had over a hundred different talks from a huge range of speakers, most of which you can still watch back on our "),n("a",{attrs:{href:"http://youtube.com/csvconf",target:"_blank",rel:"noopener noreferrer"}},[e._v("YouTube Channel"),n("OutboundLink")],1),e._v(".")]),e._v(" "),n("p",[e._v("COVID-19 threw a wrench in our plans for csv,conf,v5, and we ended up converting the conference to a virtual event. We were looking forward to our first conference in Washington DC, but unfortunately, like many other in-person events, this was not going to be possible in 2020. However, there were many positive outcomes of moving to a virtual conference. For instance, the number of attendees quadrupled (over 1000 people registered!) and people were able to attend from all over the world.")]),e._v(" "),n("p",[e._v("During the conference, there were several talks showcasing Frictionless Data. Two of the Frictionless Data Fellows, Monica Granados and Lily Zhao, presented a talk (“"),n("a",{attrs:{href:"https://youtu.be/tZmu5DGPRmA",target:"_blank",rel:"noopener noreferrer"}},[e._v("How Frictionless Data Can Help You Grease Your Data"),n("OutboundLink")],1),e._v("”) that had over 100 people watching live, which is many more than would have been at their talk in person. Other related projects gave talks that incorporated Frictionless Data, such as Christina Gosnell and Pablo Virgo from Catalyst Cooperative discussing “"),n("a",{attrs:{href:"https://youtu.be/ktLTC7SENHk",target:"_blank",rel:"noopener noreferrer"}},[e._v("Getting climate advocates the data they need."),n("OutboundLink")],1),e._v("” I also recommend watching “"),n("a",{attrs:{href:"https://youtu.be/3Ban-orpVtc",target:"_blank",rel:"noopener noreferrer"}},[e._v("Data and Code for Reproducible Research"),n("OutboundLink")],1),e._v("” by Lisa Federer and Maryam Zaringhalam, and “"),n("a",{attrs:{href:"https://youtu.be/XV_jxbB1cBY",target:"_blank",rel:"noopener noreferrer"}},[e._v("Low-Income Data Diaries - How “Low-Tech” Data Experiences Can Inspire Accessible Data Skills and Tool Design"),n("OutboundLink")],1),e._v("” by David Selassie Opoku. You can see the full list of talks, with links to slides and videos, on the csv,conf website: "),n("a",{attrs:{href:"https://csvconf.com/speakers/",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://csvconf.com/speakers/"),n("OutboundLink")],1),e._v(".")]),e._v(" "),n("p",[e._v("If you are planning on organizing a virtual event, you can read more about how csv,conf,v5 was planned here: "),n("a",{attrs:{href:"https://csvconf.com/going-online",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://csvconf.com/going-online"),n("OutboundLink")],1),e._v(".")]),e._v(" "),n("p",[e._v("We hope to see some of you next year for csv,conf,v6!")])])}),[],!1,null,null,null);t.default=a.exports}}]); \ No newline at end of file diff --git a/assets/js/101.7e1d6df4.js b/assets/js/101.4e8deb2e.js similarity index 98% rename from assets/js/101.7e1d6df4.js rename to assets/js/101.4e8deb2e.js index e2ae18b4b..e34cc5b96 100644 --- a/assets/js/101.7e1d6df4.js +++ b/assets/js/101.4e8deb2e.js @@ -1 +1 @@ -(window.webpackJsonp=window.webpackJsonp||[]).push([[101],{633:function(e,t,a){"use strict";a.r(t);var n=a(29),o=Object(n.a)({},(function(){var e=this,t=e.$createElement,a=e._self._c||t;return a("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[a("p",[a("em",[e._v("This grantee profile features Nikhil Vats for our series of Frictionless Data Tool Fund posts, written to shine a light on Frictionless Data’s Tool Fund grantees, their work and to let our technical community know how they can get involved.")])]),e._v(" "),a("h2",{attrs:{id:"meet-nikhil-vats"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#meet-nikhil-vats"}},[e._v("#")]),e._v(" Meet Nikhil Vats")]),e._v(" "),a("p",[e._v("I am an undergraduate student pursuing BE Computer Science and MSc Economics from BITS Pilani, India. My open-source journey started as a Google Summer of Code student with "),a("a",{attrs:{href:"https://www.open-bio.org/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Open Bioinformatics Foundation"),a("OutboundLink")],1),e._v(" in 2019 and currently, I am a mentor at "),a("a",{attrs:{href:"http://intermine.org/",target:"_blank",rel:"noopener noreferrer"}},[e._v("InterMine"),a("OutboundLink")],1),e._v(" for Outreachy. I’ve been working part-time as a full-stack web developer for the last two years. The latest project that I worked on was "),a("a",{attrs:{href:"https://daancorona.tech/",target:"_blank",rel:"noopener noreferrer"}},[e._v("DaanCorona"),a("OutboundLink")],1),e._v(" (daan is a Hindi word which means donation) - a non-profit initiative to help small businesses affected by Coronavirus in India. Through the Frictionless Data Tool Fund, I would like to give back to the open-source community by adding data package specifications to InterMine’s im-tables. Also, I love animals, music and cinema!")]),e._v(" "),a("h2",{attrs:{id:"how-did-you-first-hear-about-frictionless-data"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#how-did-you-first-hear-about-frictionless-data"}},[e._v("#")]),e._v(" How did you first hear about Frictionless Data?")]),e._v(" "),a("p",[e._v("I first heard about Frictionless Data from my mentor Yo Yehudi. She had sent an email to the InterMine community explaining the Frictionless Data initiative. The introductory video of Frictionless Data by Rufus Pollock inspired me deeply. I researched about Frictionless Data Specifications, Data Packages, and other tools and was amazed by how useful they can be while working with data. I wanted to contribute to Frictionless Data because I loved its design philosophy and the plethora of potential tools that can go a long way in changing how we produce, consume, and reuse data in research.")]),e._v(" "),a("h2",{attrs:{id:"what-specific-issues-are-you-looking-to-address-with-the-tool-fund"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#what-specific-issues-are-you-looking-to-address-with-the-tool-fund"}},[e._v("#")]),e._v(" What specific issues are you looking to address with the Tool Fund?")]),e._v(" "),a("p",[e._v("InterMine is an open-source biological data warehouse. Over thirty different InterMine instances exist and can be viewed using InterMine’s web interface "),a("a",{attrs:{href:"https://github.com/intermine/im-tables-3",target:"_blank",rel:"noopener noreferrer"}},[e._v("im-tables"),a("OutboundLink")],1),e._v(", a Javascript-based query results table data displayer. The export functionality of the im-tables supports common formats like CSV, TSV, and JSON. Whilst this is standardized across different instances of InterMine, exported data doesn’t conform to any specific standards, resulting in friction in data especially while integrating with other tools. Adding data package specifications and integrating with frictionless data specifications will ensure seamless integration, reusability, and sharing of data among individuals and apps, and will affect a broad number of InterMines based in research institutes around the world. In the long run, I would also like to develop and add a specification for InterMine’s data to the Frictionless Data registry.")]),e._v(" "),a("h2",{attrs:{id:"how-can-the-open-data-open-source-or-open-science-communities-engage-with-the-work-you-are-doing"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#how-can-the-open-data-open-source-or-open-science-communities-engage-with-the-work-you-are-doing"}},[e._v("#")]),e._v(" How can the open data, open source, or open science communities engage with the work you are doing?")]),e._v(" "),a("p",[e._v("I will be working on the "),a("a",{attrs:{href:"https://github.com/intermine/im-tables-3",target:"_blank",rel:"noopener noreferrer"}},[e._v("im-tables"),a("OutboundLink")],1),e._v(" and "),a("a",{attrs:{href:"https://github.com/intermine/intermine",target:"_blank",rel:"noopener noreferrer"}},[e._v("intermine"),a("OutboundLink")],1),e._v(" GitHub repository, writing blogs every month to share my progress. I also plan to write documentation, tutorials, and contributing guidelines to help new contributors get started easily. I want to encourage and welcome anyone who wants to contribute or get started with open-source to work on this project. I’ll be happy to help you get familiar with InterMine and this project. You can get in touch "),a("a",{attrs:{href:"http://chat.intermine.org/",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),a("OutboundLink")],1),e._v(" or "),a("a",{attrs:{href:"https://discord.com/invite/2UgfM2k",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),a("OutboundLink")],1),e._v(". Lastly, I welcome everyone to try out and use the features added during this project to make data frictionless, usable, and open!")])])}),[],!1,null,null,null);t.default=o.exports}}]); \ No newline at end of file +(window.webpackJsonp=window.webpackJsonp||[]).push([[101],{632:function(e,t,a){"use strict";a.r(t);var n=a(29),o=Object(n.a)({},(function(){var e=this,t=e.$createElement,a=e._self._c||t;return a("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[a("p",[a("em",[e._v("This grantee profile features Nikhil Vats for our series of Frictionless Data Tool Fund posts, written to shine a light on Frictionless Data’s Tool Fund grantees, their work and to let our technical community know how they can get involved.")])]),e._v(" "),a("h2",{attrs:{id:"meet-nikhil-vats"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#meet-nikhil-vats"}},[e._v("#")]),e._v(" Meet Nikhil Vats")]),e._v(" "),a("p",[e._v("I am an undergraduate student pursuing BE Computer Science and MSc Economics from BITS Pilani, India. My open-source journey started as a Google Summer of Code student with "),a("a",{attrs:{href:"https://www.open-bio.org/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Open Bioinformatics Foundation"),a("OutboundLink")],1),e._v(" in 2019 and currently, I am a mentor at "),a("a",{attrs:{href:"http://intermine.org/",target:"_blank",rel:"noopener noreferrer"}},[e._v("InterMine"),a("OutboundLink")],1),e._v(" for Outreachy. I’ve been working part-time as a full-stack web developer for the last two years. The latest project that I worked on was "),a("a",{attrs:{href:"https://daancorona.tech/",target:"_blank",rel:"noopener noreferrer"}},[e._v("DaanCorona"),a("OutboundLink")],1),e._v(" (daan is a Hindi word which means donation) - a non-profit initiative to help small businesses affected by Coronavirus in India. Through the Frictionless Data Tool Fund, I would like to give back to the open-source community by adding data package specifications to InterMine’s im-tables. Also, I love animals, music and cinema!")]),e._v(" "),a("h2",{attrs:{id:"how-did-you-first-hear-about-frictionless-data"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#how-did-you-first-hear-about-frictionless-data"}},[e._v("#")]),e._v(" How did you first hear about Frictionless Data?")]),e._v(" "),a("p",[e._v("I first heard about Frictionless Data from my mentor Yo Yehudi. She had sent an email to the InterMine community explaining the Frictionless Data initiative. The introductory video of Frictionless Data by Rufus Pollock inspired me deeply. I researched about Frictionless Data Specifications, Data Packages, and other tools and was amazed by how useful they can be while working with data. I wanted to contribute to Frictionless Data because I loved its design philosophy and the plethora of potential tools that can go a long way in changing how we produce, consume, and reuse data in research.")]),e._v(" "),a("h2",{attrs:{id:"what-specific-issues-are-you-looking-to-address-with-the-tool-fund"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#what-specific-issues-are-you-looking-to-address-with-the-tool-fund"}},[e._v("#")]),e._v(" What specific issues are you looking to address with the Tool Fund?")]),e._v(" "),a("p",[e._v("InterMine is an open-source biological data warehouse. Over thirty different InterMine instances exist and can be viewed using InterMine’s web interface "),a("a",{attrs:{href:"https://github.com/intermine/im-tables-3",target:"_blank",rel:"noopener noreferrer"}},[e._v("im-tables"),a("OutboundLink")],1),e._v(", a Javascript-based query results table data displayer. The export functionality of the im-tables supports common formats like CSV, TSV, and JSON. Whilst this is standardized across different instances of InterMine, exported data doesn’t conform to any specific standards, resulting in friction in data especially while integrating with other tools. Adding data package specifications and integrating with frictionless data specifications will ensure seamless integration, reusability, and sharing of data among individuals and apps, and will affect a broad number of InterMines based in research institutes around the world. In the long run, I would also like to develop and add a specification for InterMine’s data to the Frictionless Data registry.")]),e._v(" "),a("h2",{attrs:{id:"how-can-the-open-data-open-source-or-open-science-communities-engage-with-the-work-you-are-doing"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#how-can-the-open-data-open-source-or-open-science-communities-engage-with-the-work-you-are-doing"}},[e._v("#")]),e._v(" How can the open data, open source, or open science communities engage with the work you are doing?")]),e._v(" "),a("p",[e._v("I will be working on the "),a("a",{attrs:{href:"https://github.com/intermine/im-tables-3",target:"_blank",rel:"noopener noreferrer"}},[e._v("im-tables"),a("OutboundLink")],1),e._v(" and "),a("a",{attrs:{href:"https://github.com/intermine/intermine",target:"_blank",rel:"noopener noreferrer"}},[e._v("intermine"),a("OutboundLink")],1),e._v(" GitHub repository, writing blogs every month to share my progress. I also plan to write documentation, tutorials, and contributing guidelines to help new contributors get started easily. I want to encourage and welcome anyone who wants to contribute or get started with open-source to work on this project. I’ll be happy to help you get familiar with InterMine and this project. You can get in touch "),a("a",{attrs:{href:"http://chat.intermine.org/",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),a("OutboundLink")],1),e._v(" or "),a("a",{attrs:{href:"https://discord.com/invite/2UgfM2k",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),a("OutboundLink")],1),e._v(". Lastly, I welcome everyone to try out and use the features added during this project to make data frictionless, usable, and open!")])])}),[],!1,null,null,null);t.default=o.exports}}]); \ No newline at end of file diff --git a/assets/js/102.0e0446fa.js b/assets/js/102.869d7cbc.js similarity index 98% rename from assets/js/102.0e0446fa.js rename to assets/js/102.869d7cbc.js index 77d67c76a..cb2ec0b61 100644 --- a/assets/js/102.0e0446fa.js +++ b/assets/js/102.869d7cbc.js @@ -1 +1 @@ -(window.webpackJsonp=window.webpackJsonp||[]).push([[102],{634:function(e,t,a){"use strict";a.r(t);var o=a(29),r=Object(o.a)({},(function(){var e=this,t=e.$createElement,a=e._self._c||t;return a("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[a("p",[a("em",[e._v("This grantee profile features Carles Pina Estany for our series of Frictionless Data Tool Fund posts, written to shine a light on Frictionless Data’s Tool Fund grantees, their work and to let our technical community know how they can get involved.")])]),e._v(" "),a("h2",{attrs:{id:"meet-carles-pina-estany"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#meet-carles-pina-estany"}},[e._v("#")]),e._v(" Meet Carles Pina Estany")]),e._v(" "),a("p",[e._v("I’m Carles and I’m currently working part-time for the "),a("a",{attrs:{href:"https://swisspolar.ch/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Swiss Polar Institute"),a("OutboundLink")],1),e._v(" as a software engineer. I’m not a scientist but I like working with scientists, for science institutions, in research, education and with free/open source software. You can read more about me on my website: "),a("a",{attrs:{href:"https://carles.pina.cat/",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://carles.pina.cat/"),a("OutboundLink")],1),e._v(".")]),e._v(" "),a("p",[e._v("One of the tasks in the institute is to publish data and encourage researchers to provide detailed metadata. Often this metadata is written by researchers together with a data manager and without a tool in place to do this, it can become tricky to keep track of versions and progress. Frictionless Data schemas provide a model on which the metadata can be written to ensure it is machine-readable and standardised but completing the metadata in JSON files is not very user-friendly. My Tool Fund project, "),a("code",[e._v("schema-collaboration")]),e._v(", will help data managers and researchers collaborate easily to document data following the already-existing Frictionless Data schemas datapackage and tableschema.")]),e._v(" "),a("h2",{attrs:{id:"how-did-you-first-hear-about-frictionless-data"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#how-did-you-first-hear-about-frictionless-data"}},[e._v("#")]),e._v(" How did you first hear about Frictionless Data?")]),e._v(" "),a("p",[e._v("We have had Frictionless Data on our radar for about a year. "),a("a",{attrs:{href:"https://fosdem.org/2020/schedule/event/open_research_frictionless_data/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Lilly Winfree’s talk at FOSDEM 2020"),a("OutboundLink")],1),e._v(" gave us a good insight into how it could be used and we realised that it was a good fit. Recently we have been improving the way that we describe data for a collaborating organisation: Frictionless Data was a natural way to go and we started using it to describe all datasets. "),a("a",{attrs:{href:"https://create.frictionlessdata.io",target:"_blank",rel:"noopener noreferrer"}},[e._v("create.frictionlessdata.io"),a("OutboundLink")],1),e._v(" was a good start for creating a first draft of the tableschema and datapackage but we missed a tool to collaborate with the researchers when describing a data set.")]),e._v(" "),a("h2",{attrs:{id:"what-specific-issues-are-you-looking-to-address-with-the-tool-fund"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#what-specific-issues-are-you-looking-to-address-with-the-tool-fund"}},[e._v("#")]),e._v(" What specific issues are you looking to address with the Tool Fund?")]),e._v(" "),a("p",[e._v("Collaboration between data managers and researchers needs to be as easy as possible for both sides. Currently there is no tool to collaboratively document tabular data and data packages easily. Using this tool the researcher will be able to enter the information in a controlled manner and the data manager will be able to give feedback on what’s missing or what needs to be changed through a common platform.")]),e._v(" "),a("p",[e._v("Hopefully this will lead to more productive use of time for both sides and having the data described with machine-readable Frictionless Data schemas will make it easier to validate, reuse and have consistent documentation. The tool will be based on "),a("a",{attrs:{href:"https://github.com/frictionlessdata/datapackage-ui",target:"_blank",rel:"noopener noreferrer"}},[e._v("datapackage-ui"),a("OutboundLink")],1),e._v(" for the frontend, allowing all those involved to collaborate on the metadata through a user-friendly UI. Django will be used for the backend and Docker will be used for installation and deployments.")]),e._v(" "),a("h2",{attrs:{id:"how-can-the-open-data-open-source-or-open-science-communities-engage-with-the-work-you-are-doing"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#how-can-the-open-data-open-source-or-open-science-communities-engage-with-the-work-you-are-doing"}},[e._v("#")]),e._v(" How can the open data, open source, or open science communities engage with the work you are doing?")]),e._v(" "),a("p",[e._v("This project will be based on "),a("a",{attrs:{href:"https://github.com/frictionlessdata/datapackage-ui",target:"_blank",rel:"noopener noreferrer"}},[e._v("datapackage-ui"),a("OutboundLink")],1),e._v(" so using this tool and opening and fixing issues would be useful contributions to the project.")]),e._v(" "),a("p",[e._v("Feel free to submit issues, ideas and PR on the Github repository "),a("a",{attrs:{href:"https://github.com/frictionlessdata/schema-collaboration",target:"_blank",rel:"noopener noreferrer"}},[e._v("schema-collaboration"),a("OutboundLink")],1),e._v(" or "),a("a",{attrs:{href:"https://discord.com/invite/j9DNFNw",target:"_blank",rel:"noopener noreferrer"}},[e._v("Discord"),a("OutboundLink")],1),e._v(" and test the project on the staging deployment when available.")])])}),[],!1,null,null,null);t.default=r.exports}}]); \ No newline at end of file +(window.webpackJsonp=window.webpackJsonp||[]).push([[102],{633:function(e,t,a){"use strict";a.r(t);var o=a(29),r=Object(o.a)({},(function(){var e=this,t=e.$createElement,a=e._self._c||t;return a("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[a("p",[a("em",[e._v("This grantee profile features Carles Pina Estany for our series of Frictionless Data Tool Fund posts, written to shine a light on Frictionless Data’s Tool Fund grantees, their work and to let our technical community know how they can get involved.")])]),e._v(" "),a("h2",{attrs:{id:"meet-carles-pina-estany"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#meet-carles-pina-estany"}},[e._v("#")]),e._v(" Meet Carles Pina Estany")]),e._v(" "),a("p",[e._v("I’m Carles and I’m currently working part-time for the "),a("a",{attrs:{href:"https://swisspolar.ch/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Swiss Polar Institute"),a("OutboundLink")],1),e._v(" as a software engineer. I’m not a scientist but I like working with scientists, for science institutions, in research, education and with free/open source software. You can read more about me on my website: "),a("a",{attrs:{href:"https://carles.pina.cat/",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://carles.pina.cat/"),a("OutboundLink")],1),e._v(".")]),e._v(" "),a("p",[e._v("One of the tasks in the institute is to publish data and encourage researchers to provide detailed metadata. Often this metadata is written by researchers together with a data manager and without a tool in place to do this, it can become tricky to keep track of versions and progress. Frictionless Data schemas provide a model on which the metadata can be written to ensure it is machine-readable and standardised but completing the metadata in JSON files is not very user-friendly. My Tool Fund project, "),a("code",[e._v("schema-collaboration")]),e._v(", will help data managers and researchers collaborate easily to document data following the already-existing Frictionless Data schemas datapackage and tableschema.")]),e._v(" "),a("h2",{attrs:{id:"how-did-you-first-hear-about-frictionless-data"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#how-did-you-first-hear-about-frictionless-data"}},[e._v("#")]),e._v(" How did you first hear about Frictionless Data?")]),e._v(" "),a("p",[e._v("We have had Frictionless Data on our radar for about a year. "),a("a",{attrs:{href:"https://fosdem.org/2020/schedule/event/open_research_frictionless_data/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Lilly Winfree’s talk at FOSDEM 2020"),a("OutboundLink")],1),e._v(" gave us a good insight into how it could be used and we realised that it was a good fit. Recently we have been improving the way that we describe data for a collaborating organisation: Frictionless Data was a natural way to go and we started using it to describe all datasets. "),a("a",{attrs:{href:"https://create.frictionlessdata.io",target:"_blank",rel:"noopener noreferrer"}},[e._v("create.frictionlessdata.io"),a("OutboundLink")],1),e._v(" was a good start for creating a first draft of the tableschema and datapackage but we missed a tool to collaborate with the researchers when describing a data set.")]),e._v(" "),a("h2",{attrs:{id:"what-specific-issues-are-you-looking-to-address-with-the-tool-fund"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#what-specific-issues-are-you-looking-to-address-with-the-tool-fund"}},[e._v("#")]),e._v(" What specific issues are you looking to address with the Tool Fund?")]),e._v(" "),a("p",[e._v("Collaboration between data managers and researchers needs to be as easy as possible for both sides. Currently there is no tool to collaboratively document tabular data and data packages easily. Using this tool the researcher will be able to enter the information in a controlled manner and the data manager will be able to give feedback on what’s missing or what needs to be changed through a common platform.")]),e._v(" "),a("p",[e._v("Hopefully this will lead to more productive use of time for both sides and having the data described with machine-readable Frictionless Data schemas will make it easier to validate, reuse and have consistent documentation. The tool will be based on "),a("a",{attrs:{href:"https://github.com/frictionlessdata/datapackage-ui",target:"_blank",rel:"noopener noreferrer"}},[e._v("datapackage-ui"),a("OutboundLink")],1),e._v(" for the frontend, allowing all those involved to collaborate on the metadata through a user-friendly UI. Django will be used for the backend and Docker will be used for installation and deployments.")]),e._v(" "),a("h2",{attrs:{id:"how-can-the-open-data-open-source-or-open-science-communities-engage-with-the-work-you-are-doing"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#how-can-the-open-data-open-source-or-open-science-communities-engage-with-the-work-you-are-doing"}},[e._v("#")]),e._v(" How can the open data, open source, or open science communities engage with the work you are doing?")]),e._v(" "),a("p",[e._v("This project will be based on "),a("a",{attrs:{href:"https://github.com/frictionlessdata/datapackage-ui",target:"_blank",rel:"noopener noreferrer"}},[e._v("datapackage-ui"),a("OutboundLink")],1),e._v(" so using this tool and opening and fixing issues would be useful contributions to the project.")]),e._v(" "),a("p",[e._v("Feel free to submit issues, ideas and PR on the Github repository "),a("a",{attrs:{href:"https://github.com/frictionlessdata/schema-collaboration",target:"_blank",rel:"noopener noreferrer"}},[e._v("schema-collaboration"),a("OutboundLink")],1),e._v(" or "),a("a",{attrs:{href:"https://discord.com/invite/j9DNFNw",target:"_blank",rel:"noopener noreferrer"}},[e._v("Discord"),a("OutboundLink")],1),e._v(" and test the project on the staging deployment when available.")])])}),[],!1,null,null,null);t.default=r.exports}}]); \ No newline at end of file diff --git a/assets/js/104.a3c278c0.js b/assets/js/104.ac4a2b95.js similarity index 98% rename from assets/js/104.a3c278c0.js rename to assets/js/104.ac4a2b95.js index 69f41b0d3..a691f3b69 100644 --- a/assets/js/104.a3c278c0.js +++ b/assets/js/104.ac4a2b95.js @@ -1 +1 @@ -(window.webpackJsonp=window.webpackJsonp||[]).push([[104],{638:function(e,t,a){"use strict";a.r(t);var o=a(29),s=Object(o.a)({},(function(){var e=this,t=e.$createElement,a=e._self._c||t;return a("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[a("p",[a("em",[e._v("This grantee profile features Stephen Eglen for our series of Frictionless Data Tool Fund posts, written to shine a light on Frictionless Data’s Tool Fund grantees, their work and to let our technical community know how they can get involved.")])]),e._v(" "),a("h2",{attrs:{id:"meet-stephen-eglen"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#meet-stephen-eglen"}},[e._v("#")]),e._v(" Meet Stephen Eglen")]),e._v(" "),a("p",[e._v("I am a Reader in Computational Neuroscience at the University of Cambridge. A large part of my work involves analysing neuronal recordings taken from high-throughput recording devices, such as multi-electrode arrays. Despite these arrays having been in use for many years, there are still no standard formats for exchanging data, and so we spend lots of time simply reformatting data as we pass it around different groups, or use different analysis "),a("a",{attrs:{href:"https://doi.org/10.1186/2047-217X-3-3",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://doi.org/10.1186/2047-217X-3-3"),a("OutboundLink")],1),e._v(") used HDF5; the aim of our current project is to evaluate the use of Frictionless Data as a common format for the analysis of our spontaneous activity recordings, both past and present. The bulk of the work this summer will be done by a talented Natural Science undergraduate at Cambridge, Alexander Shtyrov.")]),e._v(" "),a("h2",{attrs:{id:"how-did-you-first-hear-about-frictionless-data"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#how-did-you-first-hear-about-frictionless-data"}},[e._v("#")]),e._v(" How did you first hear about Frictionless Data?")]),e._v(" "),a("p",[e._v("I had the good fortune to meet Dr Rufus Pollock in 2015 at a scientific meeting where I was presenting our work from 2014 and he was presenting an introduction to Frictionless Data. We then developed a case study (circa 2016) using a simpler data set (the spatial distribution of neurons in the retina). Skipping forward a few years, I saw the call for applications from Frictionless Data and decided it might be a good time to see how the project had developed. Rather than developing further tools, after discussions with the Frictionless Data team, we decided to make a case study for the application of these tools.")]),e._v(" "),a("h2",{attrs:{id:"what-specific-issues-are-you-looking-to-address-with-the-tool-fund"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#what-specific-issues-are-you-looking-to-address-with-the-tool-fund"}},[e._v("#")]),e._v(" What specific issues are you looking to address with the Tool Fund?")]),e._v(" "),a("p",[e._v("Our goals are:")]),e._v(" "),a("ol",[a("li",[e._v("Convert our existing datasets (Eglen et al 2014) into Frictionless Data containers.")]),e._v(" "),a("li",[e._v("Compare the relative merits of the containers vs HDF5 for storing “medium-sized” (megabytes, rather than gigabytes) data files. Aspects to consider will include portability, efficiency and ease of access.")]),e._v(" "),a("li",[e._v("Develop a case study for analysing spontaneous activity patterns with a generative approach to model the underlying neuronal networks. This code has been developed by colleagues at Cambridge in Matlab, but has yet to be tested on our spontaneous activity patterns.")]),e._v(" "),a("li",[e._v("Write up our findings for publication in a peer-reviewed journal.")])]),e._v(" "),a("h2",{attrs:{id:"how-can-the-open-data-open-source-or-open-science-communities-engage-with-the-work-you-are-doing"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#how-can-the-open-data-open-source-or-open-science-communities-engage-with-the-work-you-are-doing"}},[e._v("#")]),e._v(" How can the open data, open source, or open science communities engage with the work you are doing?")]),e._v(" "),a("p",[e._v("We have a GitHub repository, but it is currently private (shared also with Frictionless Data) as it contains some recent datasets relating to human patients that are not yet ready to be shared. We hope to release it as soon as we can, where it will be linked to from my home page: "),a("a",{attrs:{href:"https://sje30.github.io",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://sje30.github.io"),a("OutboundLink")],1),e._v(". We aim to share all our findings from this project for the benefit of the community.")])])}),[],!1,null,null,null);t.default=s.exports}}]); \ No newline at end of file +(window.webpackJsonp=window.webpackJsonp||[]).push([[104],{634:function(e,t,a){"use strict";a.r(t);var o=a(29),s=Object(o.a)({},(function(){var e=this,t=e.$createElement,a=e._self._c||t;return a("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[a("p",[a("em",[e._v("This grantee profile features Stephen Eglen for our series of Frictionless Data Tool Fund posts, written to shine a light on Frictionless Data’s Tool Fund grantees, their work and to let our technical community know how they can get involved.")])]),e._v(" "),a("h2",{attrs:{id:"meet-stephen-eglen"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#meet-stephen-eglen"}},[e._v("#")]),e._v(" Meet Stephen Eglen")]),e._v(" "),a("p",[e._v("I am a Reader in Computational Neuroscience at the University of Cambridge. A large part of my work involves analysing neuronal recordings taken from high-throughput recording devices, such as multi-electrode arrays. Despite these arrays having been in use for many years, there are still no standard formats for exchanging data, and so we spend lots of time simply reformatting data as we pass it around different groups, or use different analysis "),a("a",{attrs:{href:"https://doi.org/10.1186/2047-217X-3-3",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://doi.org/10.1186/2047-217X-3-3"),a("OutboundLink")],1),e._v(") used HDF5; the aim of our current project is to evaluate the use of Frictionless Data as a common format for the analysis of our spontaneous activity recordings, both past and present. The bulk of the work this summer will be done by a talented Natural Science undergraduate at Cambridge, Alexander Shtyrov.")]),e._v(" "),a("h2",{attrs:{id:"how-did-you-first-hear-about-frictionless-data"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#how-did-you-first-hear-about-frictionless-data"}},[e._v("#")]),e._v(" How did you first hear about Frictionless Data?")]),e._v(" "),a("p",[e._v("I had the good fortune to meet Dr Rufus Pollock in 2015 at a scientific meeting where I was presenting our work from 2014 and he was presenting an introduction to Frictionless Data. We then developed a case study (circa 2016) using a simpler data set (the spatial distribution of neurons in the retina). Skipping forward a few years, I saw the call for applications from Frictionless Data and decided it might be a good time to see how the project had developed. Rather than developing further tools, after discussions with the Frictionless Data team, we decided to make a case study for the application of these tools.")]),e._v(" "),a("h2",{attrs:{id:"what-specific-issues-are-you-looking-to-address-with-the-tool-fund"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#what-specific-issues-are-you-looking-to-address-with-the-tool-fund"}},[e._v("#")]),e._v(" What specific issues are you looking to address with the Tool Fund?")]),e._v(" "),a("p",[e._v("Our goals are:")]),e._v(" "),a("ol",[a("li",[e._v("Convert our existing datasets (Eglen et al 2014) into Frictionless Data containers.")]),e._v(" "),a("li",[e._v("Compare the relative merits of the containers vs HDF5 for storing “medium-sized” (megabytes, rather than gigabytes) data files. Aspects to consider will include portability, efficiency and ease of access.")]),e._v(" "),a("li",[e._v("Develop a case study for analysing spontaneous activity patterns with a generative approach to model the underlying neuronal networks. This code has been developed by colleagues at Cambridge in Matlab, but has yet to be tested on our spontaneous activity patterns.")]),e._v(" "),a("li",[e._v("Write up our findings for publication in a peer-reviewed journal.")])]),e._v(" "),a("h2",{attrs:{id:"how-can-the-open-data-open-source-or-open-science-communities-engage-with-the-work-you-are-doing"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#how-can-the-open-data-open-source-or-open-science-communities-engage-with-the-work-you-are-doing"}},[e._v("#")]),e._v(" How can the open data, open source, or open science communities engage with the work you are doing?")]),e._v(" "),a("p",[e._v("We have a GitHub repository, but it is currently private (shared also with Frictionless Data) as it contains some recent datasets relating to human patients that are not yet ready to be shared. We hope to release it as soon as we can, where it will be linked to from my home page: "),a("a",{attrs:{href:"https://sje30.github.io",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://sje30.github.io"),a("OutboundLink")],1),e._v(". We aim to share all our findings from this project for the benefit of the community.")])])}),[],!1,null,null,null);t.default=s.exports}}]); \ No newline at end of file diff --git a/assets/js/107.614f6bd7.js b/assets/js/107.1d7289ba.js similarity index 99% rename from assets/js/107.614f6bd7.js rename to assets/js/107.1d7289ba.js index d5a1417ce..8387eefb2 100644 --- a/assets/js/107.614f6bd7.js +++ b/assets/js/107.1d7289ba.js @@ -1 +1 @@ -(window.webpackJsonp=window.webpackJsonp||[]).push([[107],{639:function(e,t,a){"use strict";a.r(t);var r=a(29),n=Object(r.a)({},(function(){var e=this,t=e.$createElement,a=e._self._c||t;return a("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[a("p",[e._v("We are very excited to introduce the newest Fellows for Cohort 2 of the Frictionless Data "),a("a",{attrs:{href:"https://fellows.frictionlessdata.io/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Reproducible Research Fellows Programme"),a("OutboundLink")],1),e._v("! Over the next nine months, these eight early career researchers will be learning about open science, data management, and how to use Frictionless Data tooling in their work to make their data more open and their research more reusable. As an introduction, each Fellow has written a short blog about themselves and their goals. Read below to meet the Fellows and click on their individual blogs to learn more about them!")]),e._v(" "),a("hr"),e._v(" "),a("img",{attrs:{src:"/img/blog/katerina-fellow.jpg",alt:"Katerina picture",width:"100px",align:"right"}}),e._v(" "),a("p",[a("strong",[e._v("Hi everyone, my name is Katerina Drakoulaki")]),e._v(", I am from Greece and Cyprus, and I’m currently doing my PhD at the National and Kapodistrian University of Athens. My PhD combines all my interests: linguistics, language disorders, music cognition, and working with children! Research reproducibility is important in order to reliably identify and provide intervention to children with difficulties. Read more about "),a("a",{attrs:{href:"https://fellows.frictionlessdata.io/blog/hello-katerina/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Katerina here."),a("OutboundLink")],1)]),e._v(" "),a("hr"),e._v(" "),a("img",{attrs:{src:"/img/blog/evelyn-fellow.jpg",alt:"Evelyn picture",width:"100px",align:"right"}}),e._v(" "),a("p",[a("strong",[e._v("Hello everybody! I’m Evelyn Night")]),e._v(", an MSc student at the "),a("a",{attrs:{href:"https://www.uonbi.ac.ke/",target:"_blank",rel:"noopener noreferrer"}},[e._v("University of Nairobi"),a("OutboundLink")],1),e._v(" and a research fellow at the "),a("a",{attrs:{href:"http://www.icipe.org/",target:"_blank",rel:"noopener noreferrer"}},[e._v("International Center of Insect Physiology and Ecology"),a("OutboundLink")],1),e._v(". Growing up in a tiny village in Kano plains of Western Kenya, I always had a passion for learning. Fast forward through the years I find my way into academia pursuing a master’s degree and characterizing insect pollinator communities using morphometric and molecular tools for my thesis. My goal is to improve agricultural research capacity in the country and to also enhance formation of policies that would ensure increase in agricultural productivity. Read more about "),a("a",{attrs:{href:"https://fellows.frictionlessdata.io/blog/hello-evelyn/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Evelyn here."),a("OutboundLink")],1)]),e._v(" "),a("hr"),e._v(" "),a("img",{attrs:{src:"/img/blog/dani-fellow.png",alt:"Dani picture",width:"100px",align:"right"}}),e._v(" "),a("p",[a("strong",[e._v("Hi everyone! I’m Dani")]),e._v(", a cognitive neuroscientist and open science enthusiast. I live and work in San Sebastian, a beautiful city by the sea in northern Spain. We have a responsibility to overcome the current incentive system in the Academy to provide more honest, accessible, and quality research. I look forward to learning more about Frictionless Data tools and incorporating them into my work so that my research is open to everyone. Read more about "),a("a",{attrs:{href:"https://fellows.frictionlessdata.io/blog/hello-dani/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Dani here."),a("OutboundLink")],1)]),e._v(" "),a("hr"),e._v(" "),a("img",{attrs:{src:"/img/blog/kate-fellow.png",alt:"Kate picture",width:"100px",align:"right"}}),e._v(" "),a("p",[a("strong",[e._v("Hello hi! I’m Kate Bowie")]),e._v(", a 28-year-old midwesterner studying the human microbiome, or the collection of bacteria that live in and on the human body. As I dive deeper into the field of microbiome science, I am becoming an advocate for putting resources and time into improving research reproducibility. I wanted to become a Frictionless Fellow so that I could learn tools to help microbiome science data workflows become more reproducible and engage in the open science community. Read more about "),a("a",{attrs:{href:"https://fellows.frictionlessdata.io/blog/hello-kate/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Kate here."),a("OutboundLink")],1)]),e._v(" "),a("hr"),e._v(" "),a("img",{attrs:{src:"/img/blog/sam-fellow.jpeg",alt:"Sam picture",width:"100px",align:"right"}}),e._v(" "),a("p",[a("strong",[e._v("Hello! My name is Sam Wilairat.")]),e._v(" I am currently earning a Master of Library and Information Science degree (MLIS) and have an interest in data librarianship. As a fellow, I’m hoping to learn frictionless data principles and tools to ultimately promote them at my institution via education and outreach to researchers. I believe Open Science is the future and the more people embrace it, the more equitable and innovative research will be! Read more about "),a("a",{attrs:{href:"https://fellows.frictionlessdata.io/blog/hello-sam/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Sam here."),a("OutboundLink")],1)]),e._v(" "),a("hr"),e._v(" "),a("img",{attrs:{src:"/img/blog/anne-fellow.png",alt:"Anne picture",width:"100px",align:"right"}}),e._v(" "),a("p",[a("strong",[e._v("Hey everyone, I’m Anne!")]),e._v(" I’m a graduate student based in Geneva, Switzerland that was born and bred in a few places across the United States (including New York, Chicago, Houston, and Washington DC!). Here in Switzerland, I study international institutions with the eye of an anthropologist or sociologist, through long-term ethnographic research. I’m excited to learn how to apply the Frictionless Data tools in my work throughout these nine months, and to experiment with new forms of conveying social science research in the process.")]),e._v(" "),a("hr"),e._v(" "),a("img",{attrs:{src:"/img/blog/ritwik-fellow.png",alt:"Ritwik picture",height:"100px",align:"right"}}),e._v(" "),a("p",[a("strong",[e._v("Hi Ritwik here!")]),e._v(" I am based near Delhi, India and am doing my masters in Sustainable buildings, Energy conservation and Climate Change from International Institute of Information Technology Hyderabad. It is very important that the research which is carried in this domain is reproducible and available to all so we can use it to spread awareness among people. Read more about "),a("a",{attrs:{href:"https://fellows.frictionlessdata.io/blog/hello-ritwik/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Ritwik here."),a("OutboundLink")],1)]),e._v(" "),a("hr"),e._v(" "),a("img",{attrs:{src:"/img/blog/jacqueline-fellow.jpg",alt:"Jacqueline picture",width:"100px",align:"right"}}),e._v(" "),a("p",[a("strong",[e._v("Hi! My name is Jacqueline.")]),e._v(" I am a Master’s Candidate and Interdisciplinary Innovation Fellow in the Department of Computer and Information Science at the University of Pennsylvania. I applied to be a Reproducible Research Fellow to build space into my research process for actively exploring open science and reproducibility issues. As a scientist, I consider it an obligation to share my knowledge as widely and freely as possible and to ensure that my findings can be vetted through replication studies and other important checks. Read more about "),a("a",{attrs:{href:"https://fellows.frictionlessdata.io/blog/hello-jacqueline/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Jacqueline here."),a("OutboundLink")],1)])])}),[],!1,null,null,null);t.default=n.exports}}]); \ No newline at end of file +(window.webpackJsonp=window.webpackJsonp||[]).push([[107],{638:function(e,t,a){"use strict";a.r(t);var r=a(29),n=Object(r.a)({},(function(){var e=this,t=e.$createElement,a=e._self._c||t;return a("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[a("p",[e._v("We are very excited to introduce the newest Fellows for Cohort 2 of the Frictionless Data "),a("a",{attrs:{href:"https://fellows.frictionlessdata.io/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Reproducible Research Fellows Programme"),a("OutboundLink")],1),e._v("! Over the next nine months, these eight early career researchers will be learning about open science, data management, and how to use Frictionless Data tooling in their work to make their data more open and their research more reusable. As an introduction, each Fellow has written a short blog about themselves and their goals. Read below to meet the Fellows and click on their individual blogs to learn more about them!")]),e._v(" "),a("hr"),e._v(" "),a("img",{attrs:{src:"/img/blog/katerina-fellow.jpg",alt:"Katerina picture",width:"100px",align:"right"}}),e._v(" "),a("p",[a("strong",[e._v("Hi everyone, my name is Katerina Drakoulaki")]),e._v(", I am from Greece and Cyprus, and I’m currently doing my PhD at the National and Kapodistrian University of Athens. My PhD combines all my interests: linguistics, language disorders, music cognition, and working with children! Research reproducibility is important in order to reliably identify and provide intervention to children with difficulties. Read more about "),a("a",{attrs:{href:"https://fellows.frictionlessdata.io/blog/hello-katerina/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Katerina here."),a("OutboundLink")],1)]),e._v(" "),a("hr"),e._v(" "),a("img",{attrs:{src:"/img/blog/evelyn-fellow.jpg",alt:"Evelyn picture",width:"100px",align:"right"}}),e._v(" "),a("p",[a("strong",[e._v("Hello everybody! I’m Evelyn Night")]),e._v(", an MSc student at the "),a("a",{attrs:{href:"https://www.uonbi.ac.ke/",target:"_blank",rel:"noopener noreferrer"}},[e._v("University of Nairobi"),a("OutboundLink")],1),e._v(" and a research fellow at the "),a("a",{attrs:{href:"http://www.icipe.org/",target:"_blank",rel:"noopener noreferrer"}},[e._v("International Center of Insect Physiology and Ecology"),a("OutboundLink")],1),e._v(". Growing up in a tiny village in Kano plains of Western Kenya, I always had a passion for learning. 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We have a responsibility to overcome the current incentive system in the Academy to provide more honest, accessible, and quality research. I look forward to learning more about Frictionless Data tools and incorporating them into my work so that my research is open to everyone. Read more about "),a("a",{attrs:{href:"https://fellows.frictionlessdata.io/blog/hello-dani/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Dani here."),a("OutboundLink")],1)]),e._v(" "),a("hr"),e._v(" "),a("img",{attrs:{src:"/img/blog/kate-fellow.png",alt:"Kate picture",width:"100px",align:"right"}}),e._v(" "),a("p",[a("strong",[e._v("Hello hi! I’m Kate Bowie")]),e._v(", a 28-year-old midwesterner studying the human microbiome, or the collection of bacteria that live in and on the human body. As I dive deeper into the field of microbiome science, I am becoming an advocate for putting resources and time into improving research reproducibility. I wanted to become a Frictionless Fellow so that I could learn tools to help microbiome science data workflows become more reproducible and engage in the open science community. Read more about "),a("a",{attrs:{href:"https://fellows.frictionlessdata.io/blog/hello-kate/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Kate here."),a("OutboundLink")],1)]),e._v(" "),a("hr"),e._v(" "),a("img",{attrs:{src:"/img/blog/sam-fellow.jpeg",alt:"Sam picture",width:"100px",align:"right"}}),e._v(" "),a("p",[a("strong",[e._v("Hello! My name is Sam Wilairat.")]),e._v(" I am currently earning a Master of Library and Information Science degree (MLIS) and have an interest in data librarianship. As a fellow, I’m hoping to learn frictionless data principles and tools to ultimately promote them at my institution via education and outreach to researchers. I believe Open Science is the future and the more people embrace it, the more equitable and innovative research will be! Read more about "),a("a",{attrs:{href:"https://fellows.frictionlessdata.io/blog/hello-sam/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Sam here."),a("OutboundLink")],1)]),e._v(" "),a("hr"),e._v(" "),a("img",{attrs:{src:"/img/blog/anne-fellow.png",alt:"Anne picture",width:"100px",align:"right"}}),e._v(" "),a("p",[a("strong",[e._v("Hey everyone, I’m Anne!")]),e._v(" I’m a graduate student based in Geneva, Switzerland that was born and bred in a few places across the United States (including New York, Chicago, Houston, and Washington DC!). Here in Switzerland, I study international institutions with the eye of an anthropologist or sociologist, through long-term ethnographic research. I’m excited to learn how to apply the Frictionless Data tools in my work throughout these nine months, and to experiment with new forms of conveying social science research in the process.")]),e._v(" "),a("hr"),e._v(" "),a("img",{attrs:{src:"/img/blog/ritwik-fellow.png",alt:"Ritwik picture",height:"100px",align:"right"}}),e._v(" "),a("p",[a("strong",[e._v("Hi Ritwik here!")]),e._v(" I am based near Delhi, India and am doing my masters in Sustainable buildings, Energy conservation and Climate Change from International Institute of Information Technology Hyderabad. It is very important that the research which is carried in this domain is reproducible and available to all so we can use it to spread awareness among people. Read more about "),a("a",{attrs:{href:"https://fellows.frictionlessdata.io/blog/hello-ritwik/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Ritwik here."),a("OutboundLink")],1)]),e._v(" "),a("hr"),e._v(" "),a("img",{attrs:{src:"/img/blog/jacqueline-fellow.jpg",alt:"Jacqueline picture",width:"100px",align:"right"}}),e._v(" "),a("p",[a("strong",[e._v("Hi! My name is Jacqueline.")]),e._v(" I am a Master’s Candidate and Interdisciplinary Innovation Fellow in the Department of Computer and Information Science at the University of Pennsylvania. I applied to be a Reproducible Research Fellow to build space into my research process for actively exploring open science and reproducibility issues. As a scientist, I consider it an obligation to share my knowledge as widely and freely as possible and to ensure that my findings can be vetted through replication studies and other important checks. Read more about "),a("a",{attrs:{href:"https://fellows.frictionlessdata.io/blog/hello-jacqueline/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Jacqueline here."),a("OutboundLink")],1)])])}),[],!1,null,null,null);t.default=n.exports}}]); \ No newline at end of file diff --git a/assets/js/109.d3f9e9d9.js b/assets/js/109.fcabe4fe.js similarity index 99% rename from assets/js/109.d3f9e9d9.js rename to assets/js/109.fcabe4fe.js index d2aad15b7..21c5e2c2d 100644 --- a/assets/js/109.d3f9e9d9.js +++ b/assets/js/109.fcabe4fe.js @@ -1 +1 @@ -(window.webpackJsonp=window.webpackJsonp||[]).push([[109],{643:function(e,t,a){"use strict";a.r(t);var r=a(29),o=Object(r.a)({},(function(){var e=this,t=e.$createElement,a=e._self._c||t;return a("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[a("h2",{attrs:{id:"frictionless-framework"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#frictionless-framework"}},[e._v("#")]),e._v(" Frictionless Framework")]),e._v(" "),a("p",[e._v("We are excited to announce our new high-level Python framework, frictionless-py: "),a("a",{attrs:{href:"https://github.com/frictionlessdata/frictionless-py",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://github.com/frictionlessdata/frictionless-py"),a("OutboundLink")],1),e._v(". Frictionless-py was created to simplify overall user-experience for working with Frictionless Data in Python. It provides several high-level improvements in addition to many low-level fixes. Read more details below, or watch this intro video by Frictionless developer Evgeny: "),a("a",{attrs:{href:"https://youtu.be/VPnC8cc6ly0",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://youtu.be/VPnC8cc6ly0"),a("OutboundLink")],1)]),e._v(" "),a("h2",{attrs:{id:"why-did-we-write-new-python-code"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#why-did-we-write-new-python-code"}},[e._v("#")]),e._v(" Why did we write new Python code?")]),e._v(" "),a("p",[e._v("Frictionless Data has been in development for almost a decade, with global users and projects spanning domains from science to government to finance. However, our main Python libraries ("),a("code",[e._v("datapackage")]),e._v(","),a("code",[e._v("goodtables")]),e._v(", "),a("code",[e._v("tableschema")]),e._v(","),a("code",[e._v("tabulator")]),e._v(") were originally built with some inconsistencies that have confused users over the years. We had started redoing our documentation for our existing code, and realized we had a larger issue on our hands - mainly that the disparate Python libraries had overlapping functionalities and we were not able to clearly articulate how they all fit together to form a bigger picture. We realized that overall, the existing user experience was not where we wanted it to be. Evgeny, the Frictionless Data technical lead developer, had been thinking about ways to improve the Python code for a while, and the outcome of that work is "),a("code",[e._v("frictionless-py")]),e._v(".")]),e._v(" "),a("h2",{attrs:{id:"what-happens-to-the-old-python-code-datapackage-py-goodtables-py-tableschema-py-tabulator-py-how-does-this-affect-current-users"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#what-happens-to-the-old-python-code-datapackage-py-goodtables-py-tableschema-py-tabulator-py-how-does-this-affect-current-users"}},[e._v("#")]),e._v(" What happens to the old Python code ("),a("code",[e._v("datapackage-py")]),e._v(", "),a("code",[e._v("goodtables-py")]),e._v(", "),a("code",[e._v("tableschema-py")]),e._v(", "),a("code",[e._v("tabulator-py")]),e._v(")? How does this affect current users?")]),e._v(" "),a("p",[a("code",[e._v("Datapackage-py")]),e._v(" (see "),a("a",{attrs:{href:"https://github.com/frictionlessdata/datapackage-py#datapackage-py",target:"_blank",rel:"noopener noreferrer"}},[e._v("details"),a("OutboundLink")],1),e._v("), "),a("code",[e._v("tableschema-py")]),e._v(" (see "),a("a",{attrs:{href:"https://github.com/frictionlessdata/tableschema-py#tableschema-py",target:"_blank",rel:"noopener noreferrer"}},[e._v("details"),a("OutboundLink")],1),e._v("), "),a("code",[e._v("tabulator-py")]),e._v(" (see "),a("a",{attrs:{href:"https://github.com/frictionlessdata/tabulator-py#tabulator-py",target:"_blank",rel:"noopener noreferrer"}},[e._v("details"),a("OutboundLink")],1),e._v(") still exist, will not be altered, and will be maintained. If your project is using this code, these changes are not breaking and there is no action you need to take at this point. However, we will be focusing new development on "),a("code",[e._v("frictionless-py")]),e._v(", and encourage you to consider starting to experiment with or work with "),a("code",[e._v("frictionless-py")]),e._v(" during the last months of 2020 and migrate to it starting from 2021 "),a("a",{attrs:{href:"https://github.com/frictionlessdata/frictionless-py/blob/master/docs/target/migration-guide/README.md",target:"_blank",rel:"noopener noreferrer"}},[e._v("(here is our migration guide)"),a("OutboundLink")],1),e._v(". The one important thing to note is that "),a("code",[e._v("goodtables-py")]),e._v(" has been subsumed by "),a("code",[e._v("frictionless-py")]),e._v(" (since version 3 of Goodtables). 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We will release "),a("code",[e._v("frictionless@4.x")]),e._v(" by the end of 2020 to be the first SemVer/stable version.")]),e._v(" "),a("h2",{attrs:{id:"what-does-frictionless-py-do"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#what-does-frictionless-py-do"}},[e._v("#")]),e._v(" What does "),a("code",[e._v("frictionless-py")]),e._v(" do?")]),e._v(" "),a("p",[a("code",[e._v("Frictionless-py")]),e._v(" has four main functions for working with data: describe, extract, validate, and transform. These are inspired by typical data analysis and data management methods.")]),e._v(" "),a("p",[a("em",[e._v("Describe your data")]),e._v(": You can infer, edit and save metadata of your data tables. This is a first step for ensuring data quality and usability. Frictionless metadata includes general information about your data like textual description, as well as field types and other tabular data details.")]),e._v(" "),a("p",[a("em",[e._v("Extract your data")]),e._v(": You can read your data using a unified tabular interface. Data quality and consistency are guaranteed by a schema. Frictionless supports various file protocols like HTTP, FTP, and S3 and data formats like CSV, XLS, JSON, SQL, and others.")]),e._v(" "),a("p",[a("em",[e._v("Validate your data")]),e._v(": You can validate data tables, resources, and datasets. Frictionless generates a unified validation report, as well as supports a lot of options to customize the validation process.")]),e._v(" "),a("p",[a("em",[e._v("Transform your data")]),e._v(": You can clean, reshape, and transfer your data tables and datasets. Frictionless provides a pipeline capability and a lower-level interface to work with the data.")]),e._v(" "),a("p",[e._v("Additional features:")]),e._v(" "),a("ul",[a("li",[e._v("Powerful Python framework")]),e._v(" "),a("li",[e._v("Convenient command-line interface")]),e._v(" "),a("li",[e._v("Low memory consumption for data of any size")]),e._v(" "),a("li",[e._v("Reasonable performance on big data")]),e._v(" "),a("li",[e._v("Support for compressed files")]),e._v(" "),a("li",[e._v("Custom checks and formats")]),e._v(" "),a("li",[e._v("Fully pluggable architecture")]),e._v(" "),a("li",[e._v("The included API server")]),e._v(" "),a("li",[e._v("More than 1000+ tests")])]),e._v(" "),a("h2",{attrs:{id:"how-can-users-get-started"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#how-can-users-get-started"}},[e._v("#")]),e._v(" How can users get started?")]),e._v(" "),a("p",[e._v("We recommend that you begin by reading the "),a("a",{attrs:{href:"https://colab.research.google.com/drive/1VyDx6C3pxF3Vab8MxH_sI86OTSNmYuDJ",target:"_blank",rel:"noopener noreferrer"}},[e._v("Getting Started Guide"),a("OutboundLink")],1),e._v(" and the "),a("a",{attrs:{href:"https://colab.research.google.com/drive/1HGXJa7BWyEgoGZLkC6tKt2DMqgeHibEY",target:"_blank",rel:"noopener noreferrer"}},[e._v("Introduction Guide"),a("OutboundLink")],1),e._v(". We also have in depth documentation for "),a("a",{attrs:{href:"https://colab.research.google.com/drive/1eIq1ZTUntJplRxkGHxmqlxZ0zyXCm0wU",target:"_blank",rel:"noopener noreferrer"}},[e._v("Describing Data"),a("OutboundLink")],1),e._v(", "),a("a",{attrs:{href:"https://colab.research.google.com/drive/1is_PcpzFl42aWI2B2tHaBGj3jxsKZ_eZ",target:"_blank",rel:"noopener noreferrer"}},[e._v("Extracting Data"),a("OutboundLink")],1),e._v(", "),a("a",{attrs:{href:"https://colab.research.google.com/drive/1cJSZlG_v6OI3I2FtnXdKOSPjhwZNjMK1",target:"_blank",rel:"noopener noreferrer"}},[e._v("Validating Data"),a("OutboundLink")],1),e._v(", and "),a("a",{attrs:{href:"https://colab.research.google.com/drive/1C4dFWDExyxzGIwLUovrDQZghZK4JK2PD",target:"_blank",rel:"noopener noreferrer"}},[e._v("Transforming Data"),a("OutboundLink")],1),e._v(".")]),e._v(" "),a("h2",{attrs:{id:"how-can-you-give-us-feedback"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#how-can-you-give-us-feedback"}},[e._v("#")]),e._v(" How can you give us feedback?")]),e._v(" "),a("p",[e._v("What do you think? Let us know your thoughts, suggestions, or issues by joining us in our community chat on "),a("a",{attrs:{href:"https://discord.com/invite/j9DNFNw",target:"_blank",rel:"noopener noreferrer"}},[e._v("Discord"),a("OutboundLink")],1),e._v(" or by opening an issue in the "),a("code",[e._v("frictionless-py")]),e._v(" repo: "),a("a",{attrs:{href:"https://github.com/frictionlessdata/frictionless-py/issues",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://github.com/frictionlessdata/frictionless-py/issues"),a("OutboundLink")],1),e._v(".")]),e._v(" "),a("h2",{attrs:{id:"faq-s"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#faq-s"}},[e._v("#")]),e._v(" FAQ’s")]),e._v(" "),a("h3",{attrs:{id:"where-s-the-documentation"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#where-s-the-documentation"}},[e._v("#")]),e._v(" Where’s the documentation?")]),e._v(" "),a("p",[e._v("Are you a new user? Start here: "),a("a",{attrs:{href:"https://github.com/frictionlessdata/frictionless-py/blob/master/docs/target/getting-started/README.md",target:"_blank",rel:"noopener noreferrer"}},[e._v("Getting Started"),a("OutboundLink")],1),e._v(" & "),a("a",{attrs:{href:"https://github.com/frictionlessdata/frictionless-py/blob/master/docs/target/introduction-guide/README.md",target:"_blank",rel:"noopener noreferrer"}},[e._v("Introduction Guide"),a("OutboundLink")],1),a("br"),e._v("\nAre you an existing user? Start here: "),a("a",{attrs:{href:"https://github.com/frictionlessdata/frictionless-py/blob/master/docs/target/migration-guide/README.md",target:"_blank",rel:"noopener noreferrer"}},[e._v("Migration Guide"),a("OutboundLink")],1),a("br"),e._v("\nThe full list of documentation can be found here: "),a("a",{attrs:{href:"https://github.com/frictionlessdata/frictionless-py#documentation",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://github.com/frictionlessdata/frictionless-py#documentation"),a("OutboundLink")],1)]),e._v(" "),a("h3",{attrs:{id:"what-s-the-difference-between-datapackage-and-frictionless"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#what-s-the-difference-between-datapackage-and-frictionless"}},[e._v("#")]),e._v(" What’s the difference between "),a("code",[e._v("datapackage")]),e._v(" and "),a("code",[e._v("frictionless")]),e._v("?")]),e._v(" "),a("p",[e._v("In general, "),a("code",[e._v("frictionless")]),e._v(" is our new generation software while "),a("code",[e._v("tabulator")]),e._v("/"),a("code",[e._v("tableschema")]),e._v("/"),a("code",[e._v("datapackage")]),e._v("/"),a("code",[e._v("goodtables")]),e._v(" are our previous generation software. "),a("code",[e._v("Frictionless")]),e._v(" has a lot of improvements over them. Please see this issue for the full answer and a code example: "),a("a",{attrs:{href:"https://github.com/frictionlessdata/frictionless-py/issues/428",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://github.com/frictionlessdata/frictionless-py/issues/428"),a("OutboundLink")],1)]),e._v(" "),a("h3",{attrs:{id:"i-ve-spotted-a-bug-where-do-i-report-it"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#i-ve-spotted-a-bug-where-do-i-report-it"}},[e._v("#")]),e._v(" I’ve spotted a bug - where do I report it?")]),e._v(" "),a("p",[e._v("Let us know by opening an issue in the "),a("code",[e._v("frictionless-py")]),e._v(" repo: "),a("a",{attrs:{href:"https://github.com/frictionlessdata/frictionless-py/issues",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://github.com/frictionlessdata/frictionless-py/issues"),a("OutboundLink")],1),e._v(". For "),a("code",[e._v("tabulator")]),e._v("/"),a("code",[e._v("tableschema")]),e._v("/"),a("code",[e._v("datapackage")]),e._v(" issues, please use the corresponding issue tracker and we will triage it for you. Thanks!")]),e._v(" "),a("h3",{attrs:{id:"i-have-a-question-where-do-i-get-help"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#i-have-a-question-where-do-i-get-help"}},[e._v("#")]),e._v(" I have a question - where do I get help?")]),e._v(" "),a("p",[e._v("You can ask us questions in our Discord chat and someone from the main developer team or from the community will help you. Here is an invitation link: "),a("a",{attrs:{href:"https://discord.com/invite/j9DNFNw",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://discord.com/invite/j9DNFNw"),a("OutboundLink")],1),e._v(". We also have a Twitter account "),a("a",{attrs:{href:"https://twitter.com/frictionlessd8a",target:"_blank",rel:"noopener noreferrer"}},[e._v("(@frictionlessd8a)"),a("OutboundLink")],1),e._v(" and community calls where you can come meet the team and ask questions: "),a("a",{attrs:{href:"http://frictionlessdata.io/events/",target:"_blank",rel:"noopener noreferrer"}},[e._v("http://frictionlessdata.io/events/"),a("OutboundLink")],1),e._v(".")]),e._v(" "),a("h3",{attrs:{id:"i-want-to-help-how-do-i-contribute"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#i-want-to-help-how-do-i-contribute"}},[e._v("#")]),e._v(" I want to help - how do I contribute?")]),e._v(" "),a("p",[e._v("Amazing, thank you! We always welcome community contributions. Start here ("),a("a",{attrs:{href:"https://frictionlessdata.io/contribute/",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://frictionlessdata.io/contribute/"),a("OutboundLink")],1),e._v(") and here ("),a("a",{attrs:{href:"https://github.com/frictionlessdata/frictionless-py/blob/master/CONTRIBUTING.md",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://github.com/frictionlessdata/frictionless-py/blob/master/CONTRIBUTING.md"),a("OutboundLink")],1),e._v(") and you can also reach out to Evgeny (@roll) or Lilly (@lwinfree) on GitHub if you need help.")]),e._v(" "),a("h3",{attrs:{id:"additional-links-resources"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#additional-links-resources"}},[e._v("#")]),e._v(" Additional Links/Resources")]),e._v(" "),a("ul",[a("li",[e._v("Intro to "),a("code",[e._v("frictionless-py")]),e._v(" video: "),a("a",{attrs:{href:"https://youtu.be/VPnC8cc6ly0",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://youtu.be/VPnC8cc6ly0"),a("OutboundLink")],1)]),e._v(" "),a("li",[a("code",[e._v("frictionless-py")]),e._v(" repository: "),a("a",{attrs:{href:"https://github.com/frictionlessdata/frictionless-py",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://github.com/frictionlessdata/frictionless-py"),a("OutboundLink")],1)]),e._v(" "),a("li",[e._v("Frictionless Data website: "),a("a",{attrs:{href:"https://frictionlessdata.io/",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://frictionlessdata.io/"),a("OutboundLink")],1)])])])}),[],!1,null,null,null);t.default=o.exports}}]); \ No newline at end of file +(window.webpackJsonp=window.webpackJsonp||[]).push([[109],{640:function(e,t,a){"use strict";a.r(t);var r=a(29),o=Object(r.a)({},(function(){var e=this,t=e.$createElement,a=e._self._c||t;return a("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[a("h2",{attrs:{id:"frictionless-framework"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#frictionless-framework"}},[e._v("#")]),e._v(" Frictionless Framework")]),e._v(" "),a("p",[e._v("We are excited to announce our new high-level Python framework, frictionless-py: "),a("a",{attrs:{href:"https://github.com/frictionlessdata/frictionless-py",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://github.com/frictionlessdata/frictionless-py"),a("OutboundLink")],1),e._v(". Frictionless-py was created to simplify overall user-experience for working with Frictionless Data in Python. It provides several high-level improvements in addition to many low-level fixes. Read more details below, or watch this intro video by Frictionless developer Evgeny: "),a("a",{attrs:{href:"https://youtu.be/VPnC8cc6ly0",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://youtu.be/VPnC8cc6ly0"),a("OutboundLink")],1)]),e._v(" "),a("h2",{attrs:{id:"why-did-we-write-new-python-code"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#why-did-we-write-new-python-code"}},[e._v("#")]),e._v(" Why did we write new Python code?")]),e._v(" "),a("p",[e._v("Frictionless Data has been in development for almost a decade, with global users and projects spanning domains from science to government to finance. However, our main Python libraries ("),a("code",[e._v("datapackage")]),e._v(","),a("code",[e._v("goodtables")]),e._v(", "),a("code",[e._v("tableschema")]),e._v(","),a("code",[e._v("tabulator")]),e._v(") were originally built with some inconsistencies that have confused users over the years. We had started redoing our documentation for our existing code, and realized we had a larger issue on our hands - mainly that the disparate Python libraries had overlapping functionalities and we were not able to clearly articulate how they all fit together to form a bigger picture. We realized that overall, the existing user experience was not where we wanted it to be. Evgeny, the Frictionless Data technical lead developer, had been thinking about ways to improve the Python code for a while, and the outcome of that work is "),a("code",[e._v("frictionless-py")]),e._v(".")]),e._v(" "),a("h2",{attrs:{id:"what-happens-to-the-old-python-code-datapackage-py-goodtables-py-tableschema-py-tabulator-py-how-does-this-affect-current-users"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#what-happens-to-the-old-python-code-datapackage-py-goodtables-py-tableschema-py-tabulator-py-how-does-this-affect-current-users"}},[e._v("#")]),e._v(" What happens to the old Python code ("),a("code",[e._v("datapackage-py")]),e._v(", "),a("code",[e._v("goodtables-py")]),e._v(", "),a("code",[e._v("tableschema-py")]),e._v(", "),a("code",[e._v("tabulator-py")]),e._v(")? How does this affect current users?")]),e._v(" "),a("p",[a("code",[e._v("Datapackage-py")]),e._v(" (see "),a("a",{attrs:{href:"https://github.com/frictionlessdata/datapackage-py#datapackage-py",target:"_blank",rel:"noopener noreferrer"}},[e._v("details"),a("OutboundLink")],1),e._v("), "),a("code",[e._v("tableschema-py")]),e._v(" (see "),a("a",{attrs:{href:"https://github.com/frictionlessdata/tableschema-py#tableschema-py",target:"_blank",rel:"noopener noreferrer"}},[e._v("details"),a("OutboundLink")],1),e._v("), "),a("code",[e._v("tabulator-py")]),e._v(" (see "),a("a",{attrs:{href:"https://github.com/frictionlessdata/tabulator-py#tabulator-py",target:"_blank",rel:"noopener noreferrer"}},[e._v("details"),a("OutboundLink")],1),e._v(") still exist, will not be altered, and will be maintained. If your project is using this code, these changes are not breaking and there is no action you need to take at this point. However, we will be focusing new development on "),a("code",[e._v("frictionless-py")]),e._v(", and encourage you to consider starting to experiment with or work with "),a("code",[e._v("frictionless-py")]),e._v(" during the last months of 2020 and migrate to it starting from 2021 "),a("a",{attrs:{href:"https://github.com/frictionlessdata/frictionless-py/blob/master/docs/target/migration-guide/README.md",target:"_blank",rel:"noopener noreferrer"}},[e._v("(here is our migration guide)"),a("OutboundLink")],1),e._v(". The one important thing to note is that "),a("code",[e._v("goodtables-py")]),e._v(" has been subsumed by "),a("code",[e._v("frictionless-py")]),e._v(" (since version 3 of Goodtables). We will continue to bug-fix "),a("code",[e._v("goodtables@2.x")]),e._v(" in "),a("a",{attrs:{href:"https://github.com/frictionlessdata/goodtables-py/tree/goodtables",target:"_blank",rel:"noopener noreferrer"}},[e._v("this branch"),a("OutboundLink")],1),e._v(" and it is also still available on "),a("a",{attrs:{href:"https://pypi.org/project/goodtables/",target:"_blank",rel:"noopener noreferrer"}},[e._v("PyPi"),a("OutboundLink")],1),e._v(" as it was before. Please note that "),a("code",[e._v("frictionless@3.x")]),e._v(" version’s API is not stable as we are continuing to work on it at the moment. We will release "),a("code",[e._v("frictionless@4.x")]),e._v(" by the end of 2020 to be the first SemVer/stable version.")]),e._v(" "),a("h2",{attrs:{id:"what-does-frictionless-py-do"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#what-does-frictionless-py-do"}},[e._v("#")]),e._v(" What does "),a("code",[e._v("frictionless-py")]),e._v(" do?")]),e._v(" "),a("p",[a("code",[e._v("Frictionless-py")]),e._v(" has four main functions for working with data: describe, extract, validate, and transform. These are inspired by typical data analysis and data management methods.")]),e._v(" "),a("p",[a("em",[e._v("Describe your data")]),e._v(": You can infer, edit and save metadata of your data tables. This is a first step for ensuring data quality and usability. Frictionless metadata includes general information about your data like textual description, as well as field types and other tabular data details.")]),e._v(" "),a("p",[a("em",[e._v("Extract your data")]),e._v(": You can read your data using a unified tabular interface. Data quality and consistency are guaranteed by a schema. Frictionless supports various file protocols like HTTP, FTP, and S3 and data formats like CSV, XLS, JSON, SQL, and others.")]),e._v(" "),a("p",[a("em",[e._v("Validate your data")]),e._v(": You can validate data tables, resources, and datasets. Frictionless generates a unified validation report, as well as supports a lot of options to customize the validation process.")]),e._v(" "),a("p",[a("em",[e._v("Transform your data")]),e._v(": You can clean, reshape, and transfer your data tables and datasets. Frictionless provides a pipeline capability and a lower-level interface to work with the data.")]),e._v(" "),a("p",[e._v("Additional features:")]),e._v(" "),a("ul",[a("li",[e._v("Powerful Python framework")]),e._v(" "),a("li",[e._v("Convenient command-line interface")]),e._v(" "),a("li",[e._v("Low memory consumption for data of any size")]),e._v(" "),a("li",[e._v("Reasonable performance on big data")]),e._v(" "),a("li",[e._v("Support for compressed files")]),e._v(" "),a("li",[e._v("Custom checks and formats")]),e._v(" "),a("li",[e._v("Fully pluggable architecture")]),e._v(" "),a("li",[e._v("The included API server")]),e._v(" "),a("li",[e._v("More than 1000+ tests")])]),e._v(" "),a("h2",{attrs:{id:"how-can-users-get-started"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#how-can-users-get-started"}},[e._v("#")]),e._v(" How can users get started?")]),e._v(" "),a("p",[e._v("We recommend that you begin by reading the "),a("a",{attrs:{href:"https://colab.research.google.com/drive/1VyDx6C3pxF3Vab8MxH_sI86OTSNmYuDJ",target:"_blank",rel:"noopener noreferrer"}},[e._v("Getting Started Guide"),a("OutboundLink")],1),e._v(" and the "),a("a",{attrs:{href:"https://colab.research.google.com/drive/1HGXJa7BWyEgoGZLkC6tKt2DMqgeHibEY",target:"_blank",rel:"noopener noreferrer"}},[e._v("Introduction Guide"),a("OutboundLink")],1),e._v(". We also have in depth documentation for "),a("a",{attrs:{href:"https://colab.research.google.com/drive/1eIq1ZTUntJplRxkGHxmqlxZ0zyXCm0wU",target:"_blank",rel:"noopener noreferrer"}},[e._v("Describing Data"),a("OutboundLink")],1),e._v(", "),a("a",{attrs:{href:"https://colab.research.google.com/drive/1is_PcpzFl42aWI2B2tHaBGj3jxsKZ_eZ",target:"_blank",rel:"noopener noreferrer"}},[e._v("Extracting Data"),a("OutboundLink")],1),e._v(", "),a("a",{attrs:{href:"https://colab.research.google.com/drive/1cJSZlG_v6OI3I2FtnXdKOSPjhwZNjMK1",target:"_blank",rel:"noopener noreferrer"}},[e._v("Validating Data"),a("OutboundLink")],1),e._v(", and "),a("a",{attrs:{href:"https://colab.research.google.com/drive/1C4dFWDExyxzGIwLUovrDQZghZK4JK2PD",target:"_blank",rel:"noopener noreferrer"}},[e._v("Transforming Data"),a("OutboundLink")],1),e._v(".")]),e._v(" "),a("h2",{attrs:{id:"how-can-you-give-us-feedback"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#how-can-you-give-us-feedback"}},[e._v("#")]),e._v(" How can you give us feedback?")]),e._v(" "),a("p",[e._v("What do you think? Let us know your thoughts, suggestions, or issues by joining us in our community chat on "),a("a",{attrs:{href:"https://discord.com/invite/j9DNFNw",target:"_blank",rel:"noopener noreferrer"}},[e._v("Discord"),a("OutboundLink")],1),e._v(" or by opening an issue in the "),a("code",[e._v("frictionless-py")]),e._v(" repo: "),a("a",{attrs:{href:"https://github.com/frictionlessdata/frictionless-py/issues",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://github.com/frictionlessdata/frictionless-py/issues"),a("OutboundLink")],1),e._v(".")]),e._v(" "),a("h2",{attrs:{id:"faq-s"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#faq-s"}},[e._v("#")]),e._v(" FAQ’s")]),e._v(" "),a("h3",{attrs:{id:"where-s-the-documentation"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#where-s-the-documentation"}},[e._v("#")]),e._v(" Where’s the documentation?")]),e._v(" "),a("p",[e._v("Are you a new user? Start here: "),a("a",{attrs:{href:"https://github.com/frictionlessdata/frictionless-py/blob/master/docs/target/getting-started/README.md",target:"_blank",rel:"noopener noreferrer"}},[e._v("Getting Started"),a("OutboundLink")],1),e._v(" & "),a("a",{attrs:{href:"https://github.com/frictionlessdata/frictionless-py/blob/master/docs/target/introduction-guide/README.md",target:"_blank",rel:"noopener noreferrer"}},[e._v("Introduction Guide"),a("OutboundLink")],1),a("br"),e._v("\nAre you an existing user? Start here: "),a("a",{attrs:{href:"https://github.com/frictionlessdata/frictionless-py/blob/master/docs/target/migration-guide/README.md",target:"_blank",rel:"noopener noreferrer"}},[e._v("Migration Guide"),a("OutboundLink")],1),a("br"),e._v("\nThe full list of documentation can be found here: "),a("a",{attrs:{href:"https://github.com/frictionlessdata/frictionless-py#documentation",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://github.com/frictionlessdata/frictionless-py#documentation"),a("OutboundLink")],1)]),e._v(" "),a("h3",{attrs:{id:"what-s-the-difference-between-datapackage-and-frictionless"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#what-s-the-difference-between-datapackage-and-frictionless"}},[e._v("#")]),e._v(" What’s the difference between "),a("code",[e._v("datapackage")]),e._v(" and "),a("code",[e._v("frictionless")]),e._v("?")]),e._v(" "),a("p",[e._v("In general, "),a("code",[e._v("frictionless")]),e._v(" is our new generation software while "),a("code",[e._v("tabulator")]),e._v("/"),a("code",[e._v("tableschema")]),e._v("/"),a("code",[e._v("datapackage")]),e._v("/"),a("code",[e._v("goodtables")]),e._v(" are our previous generation software. "),a("code",[e._v("Frictionless")]),e._v(" has a lot of improvements over them. Please see this issue for the full answer and a code example: "),a("a",{attrs:{href:"https://github.com/frictionlessdata/frictionless-py/issues/428",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://github.com/frictionlessdata/frictionless-py/issues/428"),a("OutboundLink")],1)]),e._v(" "),a("h3",{attrs:{id:"i-ve-spotted-a-bug-where-do-i-report-it"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#i-ve-spotted-a-bug-where-do-i-report-it"}},[e._v("#")]),e._v(" I’ve spotted a bug - where do I report it?")]),e._v(" "),a("p",[e._v("Let us know by opening an issue in the "),a("code",[e._v("frictionless-py")]),e._v(" repo: "),a("a",{attrs:{href:"https://github.com/frictionlessdata/frictionless-py/issues",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://github.com/frictionlessdata/frictionless-py/issues"),a("OutboundLink")],1),e._v(". For "),a("code",[e._v("tabulator")]),e._v("/"),a("code",[e._v("tableschema")]),e._v("/"),a("code",[e._v("datapackage")]),e._v(" issues, please use the corresponding issue tracker and we will triage it for you. Thanks!")]),e._v(" "),a("h3",{attrs:{id:"i-have-a-question-where-do-i-get-help"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#i-have-a-question-where-do-i-get-help"}},[e._v("#")]),e._v(" I have a question - where do I get help?")]),e._v(" "),a("p",[e._v("You can ask us questions in our Discord chat and someone from the main developer team or from the community will help you. Here is an invitation link: "),a("a",{attrs:{href:"https://discord.com/invite/j9DNFNw",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://discord.com/invite/j9DNFNw"),a("OutboundLink")],1),e._v(". We also have a Twitter account "),a("a",{attrs:{href:"https://twitter.com/frictionlessd8a",target:"_blank",rel:"noopener noreferrer"}},[e._v("(@frictionlessd8a)"),a("OutboundLink")],1),e._v(" and community calls where you can come meet the team and ask questions: "),a("a",{attrs:{href:"http://frictionlessdata.io/events/",target:"_blank",rel:"noopener noreferrer"}},[e._v("http://frictionlessdata.io/events/"),a("OutboundLink")],1),e._v(".")]),e._v(" "),a("h3",{attrs:{id:"i-want-to-help-how-do-i-contribute"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#i-want-to-help-how-do-i-contribute"}},[e._v("#")]),e._v(" I want to help - how do I contribute?")]),e._v(" "),a("p",[e._v("Amazing, thank you! We always welcome community contributions. Start here ("),a("a",{attrs:{href:"https://frictionlessdata.io/contribute/",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://frictionlessdata.io/contribute/"),a("OutboundLink")],1),e._v(") and here ("),a("a",{attrs:{href:"https://github.com/frictionlessdata/frictionless-py/blob/master/CONTRIBUTING.md",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://github.com/frictionlessdata/frictionless-py/blob/master/CONTRIBUTING.md"),a("OutboundLink")],1),e._v(") and you can also reach out to Evgeny (@roll) or Lilly (@lwinfree) on GitHub if you need help.")]),e._v(" "),a("h3",{attrs:{id:"additional-links-resources"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#additional-links-resources"}},[e._v("#")]),e._v(" Additional Links/Resources")]),e._v(" "),a("ul",[a("li",[e._v("Intro to "),a("code",[e._v("frictionless-py")]),e._v(" video: "),a("a",{attrs:{href:"https://youtu.be/VPnC8cc6ly0",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://youtu.be/VPnC8cc6ly0"),a("OutboundLink")],1)]),e._v(" "),a("li",[a("code",[e._v("frictionless-py")]),e._v(" repository: "),a("a",{attrs:{href:"https://github.com/frictionlessdata/frictionless-py",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://github.com/frictionlessdata/frictionless-py"),a("OutboundLink")],1)]),e._v(" "),a("li",[e._v("Frictionless Data website: "),a("a",{attrs:{href:"https://frictionlessdata.io/",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://frictionlessdata.io/"),a("OutboundLink")],1)])])])}),[],!1,null,null,null);t.default=o.exports}}]); \ No newline at end of file diff --git a/assets/js/111.4283f401.js b/assets/js/111.1711918f.js similarity index 98% rename from assets/js/111.4283f401.js rename to assets/js/111.1711918f.js index e1bcbb217..583ee97b0 100644 --- a/assets/js/111.4283f401.js +++ b/assets/js/111.1711918f.js @@ -1 +1 @@ -(window.webpackJsonp=window.webpackJsonp||[]).push([[111],{644:function(e,t,r){"use strict";r.r(t);var o=r(29),a=Object(o.a)({},(function(){var e=this,t=e.$createElement,r=e._self._c||t;return r("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[r("h2",{attrs:{id:"did-you-miss-our-october-community-call"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#did-you-miss-our-october-community-call"}},[e._v("#")]),e._v(" Did you miss our October community call?")]),e._v(" "),r("p",[e._v("We had a great presentation by Keith Hughitt, who told us about his work on using Frictionless to create infrastructure for sharing biology and genomics data packages. You can watch his presentation here:"),r("br"),e._v(" "),r("iframe",{attrs:{width:"560",height:"315",src:"https://www.youtube.com/embed/GpIgfJE9UGw",frameborder:"0",allow:"accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture",allowfullscreen:""}})]),e._v(" "),r("h2",{attrs:{id:"other-agenda-items-of-note-included"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#other-agenda-items-of-note-included"}},[e._v("#")]),e._v(" Other agenda items of note included:")]),e._v(" "),r("ul",[r("li",[e._v("We are hiring a community manager! The full details are here: "),r("a",{attrs:{href:"https://okfn.org/about/jobs/",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://okfn.org/about/jobs/"),r("OutboundLink")],1)]),e._v(" "),r("li",[e._v("Help us prioritize adding new features to frictionless-py! You can vote on which features you want to see here: "),r("a",{attrs:{href:"https://github.com/frictionlessdata/frictionless-py/issues/486",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://github.com/frictionlessdata/frictionless-py/issues/486"),r("OutboundLink")],1)]),e._v(" "),r("li",[e._v("You can read more about frictionless-py here: "),r("a",{attrs:{href:"https://frictionlessdata.io/blog/2020/10/08/frictionless-framework/",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://frictionlessdata.io/blog/2020/10/08/frictionless-framework/"),r("OutboundLink")],1)])]),e._v(" "),r("h2",{attrs:{id:"join-us-next-month"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#join-us-next-month"}},[e._v("#")]),e._v(" Join us next month!")]),e._v(" "),r("p",[e._v("Our next meeting will be on 19th November. You can sign up here: "),r("a",{attrs:{href:"https://forms.gle/5HeMrt2MDCYSYWxT8",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://forms.gle/5HeMrt2MDCYSYWxT8"),r("OutboundLink")],1),e._v(". We’ll discuss new features of frictionless-py, and there will also be time for your updates too. Do you want to share something with the community? Let us know when you sign up!")]),e._v(" "),r("h2",{attrs:{id:"call-recording"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#call-recording"}},[e._v("#")]),e._v(" Call recording:")]),e._v(" "),r("p",[e._v("Here is the recording of the full call:"),r("br"),e._v(" "),r("iframe",{attrs:{width:"560",height:"315",src:"https://www.youtube.com/embed/kZ4vy5zP0M0",frameborder:"0",allow:"accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture",allowfullscreen:""}})]),e._v(" "),r("p",[e._v("As always, join us on "),r("a",{attrs:{href:"https://discord.com/invite/j9DNFNw",target:"_blank",rel:"noopener noreferrer"}},[e._v("Discord"),r("OutboundLink")],1),e._v(" or "),r("a",{attrs:{href:"https://twitter.com/frictionlessd8a",target:"_blank",rel:"noopener noreferrer"}},[e._v("Twitter"),r("OutboundLink")],1),e._v(" to say hi or ask any questions!")])])}),[],!1,null,null,null);t.default=a.exports}}]); \ No newline at end of file +(window.webpackJsonp=window.webpackJsonp||[]).push([[111],{643:function(e,t,r){"use strict";r.r(t);var o=r(29),a=Object(o.a)({},(function(){var e=this,t=e.$createElement,r=e._self._c||t;return r("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[r("h2",{attrs:{id:"did-you-miss-our-october-community-call"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#did-you-miss-our-october-community-call"}},[e._v("#")]),e._v(" Did you miss our October community call?")]),e._v(" "),r("p",[e._v("We had a great presentation by Keith Hughitt, who told us about his work on using Frictionless to create infrastructure for sharing biology and genomics data packages. You can watch his presentation here:"),r("br"),e._v(" "),r("iframe",{attrs:{width:"560",height:"315",src:"https://www.youtube.com/embed/GpIgfJE9UGw",frameborder:"0",allow:"accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture",allowfullscreen:""}})]),e._v(" "),r("h2",{attrs:{id:"other-agenda-items-of-note-included"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#other-agenda-items-of-note-included"}},[e._v("#")]),e._v(" Other agenda items of note included:")]),e._v(" "),r("ul",[r("li",[e._v("We are hiring a community manager! The full details are here: "),r("a",{attrs:{href:"https://okfn.org/about/jobs/",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://okfn.org/about/jobs/"),r("OutboundLink")],1)]),e._v(" "),r("li",[e._v("Help us prioritize adding new features to frictionless-py! You can vote on which features you want to see here: "),r("a",{attrs:{href:"https://github.com/frictionlessdata/frictionless-py/issues/486",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://github.com/frictionlessdata/frictionless-py/issues/486"),r("OutboundLink")],1)]),e._v(" "),r("li",[e._v("You can read more about frictionless-py here: "),r("a",{attrs:{href:"https://frictionlessdata.io/blog/2020/10/08/frictionless-framework/",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://frictionlessdata.io/blog/2020/10/08/frictionless-framework/"),r("OutboundLink")],1)])]),e._v(" "),r("h2",{attrs:{id:"join-us-next-month"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#join-us-next-month"}},[e._v("#")]),e._v(" Join us next month!")]),e._v(" "),r("p",[e._v("Our next meeting will be on 19th November. You can sign up here: "),r("a",{attrs:{href:"https://forms.gle/5HeMrt2MDCYSYWxT8",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://forms.gle/5HeMrt2MDCYSYWxT8"),r("OutboundLink")],1),e._v(". We’ll discuss new features of frictionless-py, and there will also be time for your updates too. Do you want to share something with the community? Let us know when you sign up!")]),e._v(" "),r("h2",{attrs:{id:"call-recording"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#call-recording"}},[e._v("#")]),e._v(" Call recording:")]),e._v(" "),r("p",[e._v("Here is the recording of the full call:"),r("br"),e._v(" "),r("iframe",{attrs:{width:"560",height:"315",src:"https://www.youtube.com/embed/kZ4vy5zP0M0",frameborder:"0",allow:"accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture",allowfullscreen:""}})]),e._v(" "),r("p",[e._v("As always, join us on "),r("a",{attrs:{href:"https://discord.com/invite/j9DNFNw",target:"_blank",rel:"noopener noreferrer"}},[e._v("Discord"),r("OutboundLink")],1),e._v(" or "),r("a",{attrs:{href:"https://twitter.com/frictionlessd8a",target:"_blank",rel:"noopener noreferrer"}},[e._v("Twitter"),r("OutboundLink")],1),e._v(" to say hi or ask any questions!")])])}),[],!1,null,null,null);t.default=a.exports}}]); \ No newline at end of file diff --git a/assets/js/114.7a312779.js b/assets/js/114.b028060f.js similarity index 99% rename from assets/js/114.7a312779.js rename to assets/js/114.b028060f.js index 378254045..f087d3b70 100644 --- a/assets/js/114.7a312779.js +++ b/assets/js/114.b028060f.js @@ -1 +1 @@ -(window.webpackJsonp=window.webpackJsonp||[]).push([[114],{647:function(a,e,t){"use strict";t.r(e);var o=t(29),r=Object(o.a)({},(function(){var a=this,e=a.$createElement,t=a._self._c||e;return t("ContentSlotsDistributor",{attrs:{"slot-key":a.$parent.slotKey}},[t("p",[a._v("Have you ever been looking at a dataset and had no idea what the data values mean? 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These past few months I’ve been learning about Frictionless Data and how to use Frictionless Data Tools to support reproducible research….To learn how to use the Frictionless Data Tools, I decided to pursue an independent project and am working on creating a comprehensive dataset of OER (open educational resources) health science materials that can be filtered by material type, media format, topic, and more.”")]),a._v(" "),t("hr"),a._v(" "),t("h3",{attrs:{id:"let-s-talk-data-packaging-by-evelyn-night-cohort-2"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#let-s-talk-data-packaging-by-evelyn-night-cohort-2"}},[a._v("#")]),a._v(" "),t("a",{attrs:{href:"https://fellows.frictionlessdata.io/blog/evelyn-datapackage-blog/",target:"_blank",rel:"noopener noreferrer"}},[a._v("Let’s Talk Data Packaging by Evelyn Night"),t("OutboundLink")],1),a._v(" (Cohort 2)")]),a._v(" "),t("p",[a._v("“A few weeks ago I met data packages for the first time and I was intrigued since I had spent too much time in the past wrangling missing and inconsistent values. Packaging data therefore taught me that arranging and preserving data does not have to be tedious anymore. Here, I show how I packaged a bit of my data (unpublished) into a neat json document using the Data Package creator . I am excited to show you just how much I have come from knowing nothing to being able to package and extract the json output.”")]),a._v(" "),t("hr"),a._v(" "),t("h3",{attrs:{id:"data-packaging-human-rights-with-the-universal-periodic-review-by-anne-lee-steele-cohort-2"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#data-packaging-human-rights-with-the-universal-periodic-review-by-anne-lee-steele-cohort-2"}},[a._v("#")]),a._v(" "),t("a",{attrs:{href:"https://fellows.frictionlessdata.io/blog/anne-datapackage-blog/",target:"_blank",rel:"noopener noreferrer"}},[a._v("[Data]packaging human rights with the Universal Periodic Review by Anne Lee Steele"),t("OutboundLink")],1),a._v(" (Cohort 2)")]),a._v(" "),t("p",[a._v("“All of the records for the Universal Periodic Review have been uploaded online, and are available for the public. However, it’s not likely that the everyday user would be able to make heads or tails of what it actually means….The way I think about it, the Data Package is a way of explaining the categories used within the data itself, in case someone besides an expert is using them. While sections like “Recommendation” and “Recommending State” may be somewhat self-explanatory, I can imagine that this will get way more complicated with purely numerical data.”")]),a._v(" "),t("hr"),a._v(" "),t("h3",{attrs:{id:"creating-a-datapackage-for-microbial-community-data-and-a-phyloseq-object-by-kate-bowie-cohort-2"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#creating-a-datapackage-for-microbial-community-data-and-a-phyloseq-object-by-kate-bowie-cohort-2"}},[a._v("#")]),a._v(" "),t("a",{attrs:{href:"https://fellows.frictionlessdata.io/blog/kate-datapackage-blog/",target:"_blank",rel:"noopener noreferrer"}},[a._v("Creating a datapackage for microbial community data (and a phyloseq object) by Kate Bowie"),t("OutboundLink")],1),a._v(" (Cohort 2)")]),a._v(" "),t("p",[a._v("“I study bacteria, and lucky for me, bacteria are everywhere….My lab often tries many different ways to handle the mock [bacteria] community, so it’s important that the analysis be documented and reproducible. To address this, I decided to generate a data package using a tool created by the Open Knowledge Foundation. Here is my experience creating a data package of our data, the metadata, and associated software.”")]),a._v(" "),t("hr"),a._v(" "),t("h3",{attrs:{id:"using-weather-and-rainfall-data-to-validate-by-ritwik-agarwal-cohort-2"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#using-weather-and-rainfall-data-to-validate-by-ritwik-agarwal-cohort-2"}},[a._v("#")]),a._v(" "),t("a",{attrs:{href:"https://fellows.frictionlessdata.io/blog/ritwik-datapackage-blog/",target:"_blank",rel:"noopener noreferrer"}},[a._v("Using Weather and Rainfall Data to Validate by Ritwik Agarwal"),t("OutboundLink")],1),a._v(" (Cohort 2)")]),a._v(" "),t("p",[a._v("“I am using a data resource from Telangana Open Data…it is an open source data repository commissioned by the state government here in India and basically it archives and stores Weather, Topological, Agriculture and Infrastructure data which then can be used by research students and stakeholders keen to study and make reports in it….CSV files are very versatile, but cannot handle the metadata with all the necessary context. We need to make sure that people can find our data and the information they need to understand our data. That’s where the Data Package comes in! ”")])])}),[],!1,null,null,null);e.default=r.exports}}]); \ No newline at end of file diff --git a/assets/js/115.6bee2741.js b/assets/js/115.24c7eaf7.js similarity index 98% rename from assets/js/115.6bee2741.js rename to assets/js/115.24c7eaf7.js index f41eb489d..fef48f9a8 100644 --- a/assets/js/115.6bee2741.js +++ b/assets/js/115.24c7eaf7.js @@ -1 +1 @@ -(window.webpackJsonp=window.webpackJsonp||[]).push([[115],{648:function(e,t,a){"use strict";a.r(t);var r=a(29),o=Object(r.a)({},(function(){var e=this,t=e.$createElement,a=e._self._c||t;return a("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[a("h2",{attrs:{id:"a-recap-from-our-december-community-call"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#a-recap-from-our-december-community-call"}},[e._v("#")]),e._v(" A recap from our December community call")]),e._v(" "),a("p",[e._v("We had a presentation about “using frictionless data package for web archive data package (WACZ format)”. More details in "),a("a",{attrs:{href:"https://github.com/frictionlessdata/forum/issues/69",target:"_blank",rel:"noopener noreferrer"}},[e._v("this GitHub issue"),a("OutboundLink")],1),e._v(".")]),e._v(" "),a("p",[e._v("If you would like to dive deeper and watch Ilya’s presentation, you can find it here:")]),e._v(" "),a("iframe",{attrs:{width:"560",height:"315",src:"https://www.youtube.com/embed/2aeRcEMmmSs",frameborder:"0",allow:"accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture",allowfullscreen:""}}),e._v(" "),a("h2",{attrs:{id:"other-agenda-items-from-our-hangout"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#other-agenda-items-from-our-hangout"}},[e._v("#")]),e._v(" Other agenda items from our hangout")]),e._v(" "),a("ul",[a("li",[e._v("People are interested in tools dealing with tools dealing with “small” data.")]),e._v(" "),a("li",[e._v("What are we up to for 2021? 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Its versatility is required for desktop apps, using dynamic frameworks like React, etc.\n"),a("ul",[a("li",[e._v("We will keep working on "),a("a",{attrs:{href:"https://github.com/frictionlessdata/frictionless-js",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://github.com/frictionlessdata/frictionless-js"),a("OutboundLink")],1)])])])])]),e._v(" "),a("li",[a("a",{attrs:{href:"https://github.com/datopian/data-cli",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://github.com/datopian/data-cli"),a("OutboundLink")],1),e._v(" Command line tool for working with data, Data Packages and the DataHub")]),e._v(" "),a("li",[a("a",{attrs:{href:"https://github.com/datopian/datapub",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://github.com/datopian/datapub"),a("OutboundLink")],1),e._v(" React-based framework for building data publishing workflows (esp for CKAN)")])]),e._v(" "),a("h2",{attrs:{id:"join-us-next-time"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#join-us-next-time"}},[e._v("#")]),e._v(" Join us next time!")]),e._v(" "),a("p",[e._v("Our next meeting will be announced in January 2021! You can "),a("a",{attrs:{href:"https://forms.gle/5HeMrt2MDCYSYWxT8",target:"_blank",rel:"noopener noreferrer"}},[e._v("sign up here"),a("OutboundLink")],1),e._v(" to be notified when the hangout will be scheduled. We’ll give some space to talk about geospatial data standards, coping with Covid and showing a member’s platform dedicated to open data hackathons!")]),e._v(" "),a("p",[e._v("As always, there will be time for your updates too. Do you want to share something with the community? Let us know when you sign up!")]),e._v(" "),a("h2",{attrs:{id:"call-recording"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#call-recording"}},[e._v("#")]),e._v(" Call recording")]),e._v(" "),a("p",[e._v("On a final note, here is the recording of the full call:")]),e._v(" "),a("iframe",{attrs:{width:"560",height:"315",src:"https://www.youtube.com/embed/lquzoKn9Flo",frameborder:"0",allow:"accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture",allowfullscreen:""}}),e._v(" "),a("p",[e._v("As usual, you can join us on "),a("a",{attrs:{href:"https://discord.com/invite/j9DNFNw",target:"_blank",rel:"noopener noreferrer"}},[e._v("Discord"),a("OutboundLink")],1),e._v(" or "),a("a",{attrs:{href:"https://twitter.com/frictionlessd8a",target:"_blank",rel:"noopener noreferrer"}},[e._v("Twitter"),a("OutboundLink")],1),e._v(" to say hi or ask any questions. See you there!")])])}),[],!1,null,null,null);t.default=o.exports}}]); \ No newline at end of file +(window.webpackJsonp=window.webpackJsonp||[]).push([[115],{647:function(e,t,a){"use strict";a.r(t);var r=a(29),o=Object(r.a)({},(function(){var e=this,t=e.$createElement,a=e._self._c||t;return a("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[a("h2",{attrs:{id:"a-recap-from-our-december-community-call"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#a-recap-from-our-december-community-call"}},[e._v("#")]),e._v(" A recap from our December community call")]),e._v(" "),a("p",[e._v("We had a presentation about “using frictionless data package for web archive data package (WACZ format)”. 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You can "),a("a",{attrs:{href:"https://forms.gle/5HeMrt2MDCYSYWxT8",target:"_blank",rel:"noopener noreferrer"}},[e._v("sign up here"),a("OutboundLink")],1),e._v(" to be notified when the hangout will be scheduled. We’ll give some space to talk about geospatial data standards, coping with Covid and showing a member’s platform dedicated to open data hackathons!")]),e._v(" "),a("p",[e._v("As always, there will be time for your updates too. Do you want to share something with the community? 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See you there!")])])}),[],!1,null,null,null);t.default=o.exports}}]); \ No newline at end of file diff --git a/assets/js/116.85bdea59.js b/assets/js/116.03f644d6.js similarity index 98% rename from assets/js/116.85bdea59.js rename to assets/js/116.03f644d6.js index 734d8222b..50d8f7363 100644 --- a/assets/js/116.85bdea59.js +++ b/assets/js/116.03f644d6.js @@ -1 +1 @@ -(window.webpackJsonp=window.webpackJsonp||[]).push([[116],{649:function(e,t,r){"use strict";r.r(t);var o=r(29),a=Object(o.a)({},(function(){var e=this,t=e.$createElement,r=e._self._c||t;return r("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[r("p",[e._v("Originally published: "),r("a",{attrs:{href:"https://blog.okfn.org/2021/01/12/partnering-with-odi-to-improve-frictionless-data/",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://blog.okfn.org/2021/01/12/partnering-with-odi-to-improve-frictionless-data/"),r("OutboundLink")],1)]),e._v(" "),r("p",[r("em",[e._v("In the framework of the Open Data Institute’s "),r("a",{attrs:{href:"https://theodi.org/article/call-for-proposals-funding-to-develop-open-source-tools-for-data-institutions/",target:"_blank",rel:"noopener noreferrer"}},[e._v("fund to develop open source tools for data institutions"),r("OutboundLink")],1),e._v(", the "),r("a",{attrs:{href:"okfn.org"}},[e._v("Open Knowledge Foundation (OKF)")]),e._v(" has been awarded funds to improve the quality and interoperability of Frictionless Data.")])]),e._v(" "),r("p",[e._v("In light of our effort to make data open and accessible, we are thrilled to announce we will be partnering with the "),r("a",{attrs:{href:"https://theodi.org/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Open Data Institute (ODI)"),r("OutboundLink")],1),e._v(" to improve our existing documentation and add new features on "),r("a",{attrs:{href:"https://frictionlessdata.io/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Frictionless Data"),r("OutboundLink")],1),e._v(" to create a better user experience for all."),r("br"),e._v("\nTo achieve this, we will be working with a cohort of users from our active and engaged community to create better documentation that fits their needs. 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By the beginning of April everything will be online, so stay tuned (and frictionless)!")]),e._v(" "),r("h2",{attrs:{id:"call-for-user-feedback"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#call-for-user-feedback"}},[e._v("#")]),e._v(" Call for user feedback")]),e._v(" "),r("p",[e._v("Feedback from our community is crucial to us, and part of this grant will be used to fund an evaluation of the existing documentation by our users in the format of user feedback sessions."),r("br"),e._v("\nAre you using our Frictionless Data tools or our Python data library? Then we want to hear from you!"),r("br"),e._v("\nWe are currently looking for novice and intermediate users to help us review our documentation, in order to make it more useful for you and all our future users."),r("br"),e._v("\nFor every user session you take part into, you will be given £50 for your time and feedback."),r("br"),e._v("\nAre you interested? Then fill in "),r("a",{attrs:{href:"https://docs.google.com/forms/d/e/1FAIpQLSezZVuKjqnFL9CHtuWVjDwDu8Cv1gQCAIs85TtDYQUv1t9hVw/viewform",target:"_blank",rel:"noopener noreferrer"}},[e._v("this form"),r("OutboundLink")],1),e._v(".")]),e._v(" "),r("h2",{attrs:{id:"more-about-frictionless-data"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#more-about-frictionless-data"}},[e._v("#")]),e._v(" More about Frictionless Data")]),e._v(" "),r("p",[e._v("Frictionless Data is a set of specifications for data and metadata interoperability, accompanied by a collection of software libraries that implement these specifications, and a range of best practices for data management. The project is funded by the Sloan Foundation.")])])}),[],!1,null,null,null);t.default=a.exports}}]); \ No newline at end of file +(window.webpackJsonp=window.webpackJsonp||[]).push([[116],{648:function(e,t,r){"use strict";r.r(t);var o=r(29),a=Object(o.a)({},(function(){var e=this,t=e.$createElement,r=e._self._c||t;return r("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[r("p",[e._v("Originally published: "),r("a",{attrs:{href:"https://blog.okfn.org/2021/01/12/partnering-with-odi-to-improve-frictionless-data/",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://blog.okfn.org/2021/01/12/partnering-with-odi-to-improve-frictionless-data/"),r("OutboundLink")],1)]),e._v(" "),r("p",[r("em",[e._v("In the framework of the Open Data Institute’s "),r("a",{attrs:{href:"https://theodi.org/article/call-for-proposals-funding-to-develop-open-source-tools-for-data-institutions/",target:"_blank",rel:"noopener noreferrer"}},[e._v("fund to develop open source tools for data institutions"),r("OutboundLink")],1),e._v(", the "),r("a",{attrs:{href:"okfn.org"}},[e._v("Open Knowledge Foundation (OKF)")]),e._v(" has been awarded funds to improve the quality and interoperability of Frictionless Data.")])]),e._v(" "),r("p",[e._v("In light of our effort to make data open and accessible, we are thrilled to announce we will be partnering with the "),r("a",{attrs:{href:"https://theodi.org/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Open Data Institute (ODI)"),r("OutboundLink")],1),e._v(" to improve our existing documentation and add new features on "),r("a",{attrs:{href:"https://frictionlessdata.io/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Frictionless Data"),r("OutboundLink")],1),e._v(" to create a better user experience for all."),r("br"),e._v("\nTo achieve this, we will be working with a cohort of users from our active and engaged community to create better documentation that fits their needs. Our main goal is to make it easier for current and future users to understand and make use of the Frictionless Data tools and data libraries to their fullest potential."),r("br"),e._v("\nWe know how frustrating it can be to try and use existing code (or learn new code) that has incomplete documentation and we don’t want that to be a barrier for our users anymore. This is why we are very grateful to the ODI for granting us the opportunity to improve upon our existing documentation.")]),e._v(" "),r("h2",{attrs:{id:"so-what-will-be-changing"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#so-what-will-be-changing"}},[e._v("#")]),e._v(" So, what will be changing?")]),e._v(" "),r("ul",[r("li",[e._v("We will have a new project overview section, to help our users understand how to use Frictionless Data for their specific needs.")]),e._v(" "),r("li",[e._v("We will improve the existing documentation, to make sure even brand new users can quickly understand everything.")]),e._v(" "),r("li",[e._v("We will have Tutorials, to showcase real users experience and have user-friendly examples.")]),e._v(" "),r("li",[e._v("We will add a FAQ session.")])]),e._v(" "),r("h2",{attrs:{id:"and-when-will-all-of-that-be-ready"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#and-when-will-all-of-that-be-ready"}},[e._v("#")]),e._v(" And when will all of that be ready?")]),e._v(" "),r("p",[e._v("Very soon! By the beginning of April everything will be online, so stay tuned (and frictionless)!")]),e._v(" "),r("h2",{attrs:{id:"call-for-user-feedback"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#call-for-user-feedback"}},[e._v("#")]),e._v(" Call for user feedback")]),e._v(" "),r("p",[e._v("Feedback from our community is crucial to us, and part of this grant will be used to fund an evaluation of the existing documentation by our users in the format of user feedback sessions."),r("br"),e._v("\nAre you using our Frictionless Data tools or our Python data library? Then we want to hear from you!"),r("br"),e._v("\nWe are currently looking for novice and intermediate users to help us review our documentation, in order to make it more useful for you and all our future users."),r("br"),e._v("\nFor every user session you take part into, you will be given £50 for your time and feedback."),r("br"),e._v("\nAre you interested? Then fill in "),r("a",{attrs:{href:"https://docs.google.com/forms/d/e/1FAIpQLSezZVuKjqnFL9CHtuWVjDwDu8Cv1gQCAIs85TtDYQUv1t9hVw/viewform",target:"_blank",rel:"noopener noreferrer"}},[e._v("this form"),r("OutboundLink")],1),e._v(".")]),e._v(" "),r("h2",{attrs:{id:"more-about-frictionless-data"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#more-about-frictionless-data"}},[e._v("#")]),e._v(" More about Frictionless Data")]),e._v(" "),r("p",[e._v("Frictionless Data is a set of specifications for data and metadata interoperability, accompanied by a collection of software libraries that implement these specifications, and a range of best practices for data management. The project is funded by the Sloan Foundation.")])])}),[],!1,null,null,null);t.default=a.exports}}]); \ No newline at end of file diff --git a/assets/js/117.85164eb1.js b/assets/js/117.72acabd5.js similarity index 99% rename from assets/js/117.85164eb1.js rename to assets/js/117.72acabd5.js index d642facb9..450384b5d 100644 --- a/assets/js/117.85164eb1.js +++ b/assets/js/117.72acabd5.js @@ -1 +1 @@ -(window.webpackJsonp=window.webpackJsonp||[]).push([[117],{650:function(e,a,t){"use strict";t.r(a);var o=t(29),r=Object(o.a)({},(function(){var e=this,a=e.$createElement,t=e._self._c||a;return t("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[t("p",[t("em",[e._v("This blog is part of a series showcasing projects developed during the 2020 Tool Fund. The Tool Fund provided five mini-grants of $5,000 to support individuals or organisations in developing an open tool for reproducible research built using the Frictionless Data specifications and software. This Fund is part of the Frictionless Data for Reproducible Research project, which is funded by the Sloan Foundation. This project applies our work in Frictionless Data to data-driven research disciplines, in order to facilitate reproducible data workflows in research contexts.")])]),e._v(" "),t("h2",{attrs:{id:"what-problem-does-schema-collaboration-solve"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#what-problem-does-schema-collaboration-solve"}},[e._v("#")]),e._v(" What problem does Schema-Collaboration solve?")]),e._v(" "),t("p",[e._v("As a software engineer, I’ve spent more than a decade developing software used by researchers or data managers using different technologies. I have been involved in free software communities and projects for more than 20 years.")]),e._v(" "),t("p",[e._v("Whilst working for a polar research institute, we saw the opportunity to take advantage of Frictionless data packages to describe datasets in a machine readable way ready for publication. But it was difficult for data managers and researchers to collaborate effectively on this, particularly when one or both groups were not familiar with Frictionless schemas. We needed a way for researchers submitting datasets to get feedback from the data managers to ensure that the dataset’s schema was correct.")]),e._v(" "),t("h2",{attrs:{id:"how-does-schema-collaboration-make-collaborating-easier"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#how-does-schema-collaboration-make-collaborating-easier"}},[e._v("#")]),e._v(" How does Schema-Collaboration make collaborating easier?")]),e._v(" "),t("p",[e._v("The Frictionless "),t("a",{attrs:{href:"https://create.frictionlessdata.io/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Data Package Creator"),t("OutboundLink")],1),e._v(" is a very good Web-based tool to create the schemas but it didn’t help out of the box on the collaboration part. The solution in this tool fund was to build a system that uses Data Package Creator to enable data managers and researchers to create and share dataset schemas, edit them, post messages and export the schemas in different formats (text, Markdown, PDF). To encourage collaboration within a project multiple researchers can work on the same schema. Being able to view the description in human-readable formats makes it easier to spot mistakes and to integrate with third-party data repositories.")]),e._v(" "),t("p",[e._v("From a data manager’s perspective the tool allows them to keep tabs on the datasets being managed and their progress. It prevents details getting lost in emails and hopefully provides a nicer interface to encourage better collaboration.")]),e._v(" "),t("p",[e._v("In other words: think of a very simplified “Google Docs” specialised for data packages.")]),e._v(" "),t("h2",{attrs:{id:"who-can-use-schema-collaboration"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#who-can-use-schema-collaboration"}},[e._v("#")]),e._v(" Who can use Schema-Collaboration?")]),e._v(" "),t("p",[e._v("The tool is designed to help data managers(*) and researchers document data packages. The documentation (which is based on Frictionless schemas) needs to be started by the data manager who then sends the URL to the researchers allowing them to edit the schema.")]),e._v(" "),t("p",[e._v("*: or anybody who wants to collaborate on creating a data package.")]),e._v(" "),t("p",[t("img",{attrs:{src:"https://user-images.githubusercontent.com/74717970/104922881-8e788c80-599b-11eb-9260-21b9a5747a8f.png",alt:"Data-packages"}}),t("br"),e._v(" "),t("em",[e._v("Data managers can view a list of datapackages within the Schema-Collaboration tool.")])]),e._v(" "),t("h2",{attrs:{id:"how-can-i-use-this-tool"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#how-can-i-use-this-tool"}},[e._v("#")]),e._v(" How can I use this tool?")]),e._v(" "),t("p",[e._v("To evaluate the tool it is possible to use the "),t("a",{attrs:{href:"https://carles.eu.pythonanywhere.com/",target:"_blank",rel:"noopener noreferrer"}},[e._v("public demo server"),t("OutboundLink")],1),e._v(" or to install it locally on a computer.")]),e._v(" "),t("p",[e._v("It was packaged in a Docker container to make it easier to install on servers. There is full "),t("a",{attrs:{href:"https://github.com/frictionlessdata/schema-collaboration/blob/master/docker/README.md",target:"_blank",rel:"noopener noreferrer"}},[e._v("documentation available"),t("OutboundLink")],1),e._v(".")]),e._v(" "),t("p",[e._v("Once the tool is installed it is used via a Web browser both by data managers and researchers.")]),e._v(" "),t("p",[t("img",{attrs:{src:"https://user-images.githubusercontent.com/74717970/104923256-19598700-599c-11eb-9cc4-19bb7637fdaa.png",alt:"datapackage-detail"}}),t("br"),e._v(" "),t("em",[e._v("You can view details about the datapackage, including comments from the data manager or other users, and also edit the datapackage.")])]),e._v(" "),t("h2",{attrs:{id:"future-plans-for-schema-collaboration"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#future-plans-for-schema-collaboration"}},[e._v("#")]),e._v(" Future plans for Schema-Collaboration")]),e._v(" "),t("p",[e._v("We plan to install the schema-collaboration at the Swiss Polar Institute to be used to describe polar data sets.")]),e._v(" "),t("p",[e._v("In the upcoming January Frictionless Data community call (sign up "),t("a",{attrs:{href:"https://docs.google.com/forms/d/e/1FAIpQLSeuNCopxXauMkrWvF6VHqOyHMcy54SfNDOseVXfWRQZWkvqjQ/viewform",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),t("OutboundLink")],1),e._v(" to join), I will do a demo and I would really appreciate feedback. Please feel free to use it and add issues (bugs or ideas) in the "),t("a",{attrs:{href:"https://github.com/frictionlessdata/schema-collaboration",target:"_blank",rel:"noopener noreferrer"}},[e._v("GitHub repository"),t("OutboundLink")],1),e._v(".")]),e._v(" "),t("h2",{attrs:{id:"tech-stack"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#tech-stack"}},[e._v("#")]),e._v(" Tech stack")]),e._v(" "),t("p",[e._v("For the curious: schema-collaboration is developed using Python and Django and uses the django-crispy-forms package to create the forms. It supports sqlite3 and MariaDB databases.")]),e._v(" "),t("h2",{attrs:{id:"thanks-to"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#thanks-to"}},[e._v("#")]),e._v(" Thanks to…")]),e._v(" "),t("p",[e._v("In order to integrate Data Package Creator with schema-collaboration some changes where needed in the Data Package Creator. Evgeny (@roll on GitHub/Discord) from Frictionlessdata project made the changes to Data Package Creator needed to achieve this and helped with the integration. Thank you very much!")]),e._v(" "),t("p",[t("strong",[e._v("Further reading:")])]),e._v(" "),t("p",[e._v("GitHub repository: "),t("a",{attrs:{href:"https://github.com/frictionlessdata/schema-collaboration",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://github.com/frictionlessdata/schema-collaboration"),t("OutboundLink")],1)]),e._v(" "),t("p",[e._v("Meet Carles Pina Estany: "),t("a",{attrs:{href:"https://frictionlessdata.io/blog/2020/07/16/tool-fund-polar-institute/#meet-carles-pina-estany",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://frictionlessdata.io/blog/2020/07/16/tool-fund-polar-institute/#meet-carles-pina-estany"),t("OutboundLink")],1)])])}),[],!1,null,null,null);a.default=r.exports}}]); \ No newline at end of file +(window.webpackJsonp=window.webpackJsonp||[]).push([[117],{649:function(e,a,t){"use strict";t.r(a);var o=t(29),r=Object(o.a)({},(function(){var e=this,a=e.$createElement,t=e._self._c||a;return t("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[t("p",[t("em",[e._v("This blog is part of a series showcasing projects developed during the 2020 Tool Fund. The Tool Fund provided five mini-grants of $5,000 to support individuals or organisations in developing an open tool for reproducible research built using the Frictionless Data specifications and software. This Fund is part of the Frictionless Data for Reproducible Research project, which is funded by the Sloan Foundation. This project applies our work in Frictionless Data to data-driven research disciplines, in order to facilitate reproducible data workflows in research contexts.")])]),e._v(" "),t("h2",{attrs:{id:"what-problem-does-schema-collaboration-solve"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#what-problem-does-schema-collaboration-solve"}},[e._v("#")]),e._v(" What problem does Schema-Collaboration solve?")]),e._v(" "),t("p",[e._v("As a software engineer, I’ve spent more than a decade developing software used by researchers or data managers using different technologies. I have been involved in free software communities and projects for more than 20 years.")]),e._v(" "),t("p",[e._v("Whilst working for a polar research institute, we saw the opportunity to take advantage of Frictionless data packages to describe datasets in a machine readable way ready for publication. But it was difficult for data managers and researchers to collaborate effectively on this, particularly when one or both groups were not familiar with Frictionless schemas. 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After a very happy decade in Brussels, I moved to Hamburg, Germany last year in February, just in time to live the global pandemic from a brand-new place. With social life put to a stop, I finally decided to start something I had wanted to do for quite some time: learn to code in order to do data visualisation. Right now I am learning Python slowly but surely and when I am not going bananas over cleaning data I draw comics or experiment fermentation with items in my kitchen (mostly vegetables). So if you are also a passionate breeder of lactobacillus bacteria, we should definitely get in touch!")]),e._v(" "),o("p",[e._v("Back in Brussels I worked with public libraries, developing projects with them, but also advocating for them to be on the EU agenda. Talking with some of the most innovative librarians, I became well aware of the importance of granting free access to information and knowledge to everyone in order to empower citizens and foster democracy. I started to take an interest in the whole open movement, monitoring projects and policy development, and quickly became passionate about it.")]),e._v(" "),o("p",[e._v("I think the real turning point for me was when I got to know an amazing project on opening air quality data developed by some public libraries in Colombia and tried to replicate it in Europe. At that point, I really understood the implications of the open movement: by opening data, citizens are able to gain ownership on compelling subjects for them and to advocate for policy improvement. Sadly more often than not, when data is made available, it is not directly reusable.")]),e._v(" "),o("p",[e._v("Frictionless Data provides tools that improve the quality of open data, making it more useful to society and reusable by a wide range of people. I think that this idea of serving society with a free and open service that people can use to empower themselves is what attracted me at first. Providing those kinds of services with no barriers to entry (and no friction, if you will) should be the purpose of any institution. This is why I am very excited to join the "),o("a",{attrs:{href:"https://okfn.org/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Open Knowledge Foundation"),o("OutboundLink")],1),e._v(" team working on this amazing project, doing what I think I can do best: interact with people and create links between them. My plan for the upcoming months is to build a proactive community around this project and engage with them. 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After a very happy decade in Brussels, I moved to Hamburg, Germany last year in February, just in time to live the global pandemic from a brand-new place. With social life put to a stop, I finally decided to start something I had wanted to do for quite some time: learn to code in order to do data visualisation. Right now I am learning Python slowly but surely and when I am not going bananas over cleaning data I draw comics or experiment fermentation with items in my kitchen (mostly vegetables). So if you are also a passionate breeder of lactobacillus bacteria, we should definitely get in touch!")]),e._v(" "),o("p",[e._v("Back in Brussels I worked with public libraries, developing projects with them, but also advocating for them to be on the EU agenda. Talking with some of the most innovative librarians, I became well aware of the importance of granting free access to information and knowledge to everyone in order to empower citizens and foster democracy. I started to take an interest in the whole open movement, monitoring projects and policy development, and quickly became passionate about it.")]),e._v(" "),o("p",[e._v("I think the real turning point for me was when I got to know an amazing project on opening air quality data developed by some public libraries in Colombia and tried to replicate it in Europe. At that point, I really understood the implications of the open movement: by opening data, citizens are able to gain ownership on compelling subjects for them and to advocate for policy improvement. Sadly more often than not, when data is made available, it is not directly reusable.")]),e._v(" "),o("p",[e._v("Frictionless Data provides tools that improve the quality of open data, making it more useful to society and reusable by a wide range of people. I think that this idea of serving society with a free and open service that people can use to empower themselves is what attracted me at first. 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However, it detected some of my variables as strings, when they are in fact integers. Of course, goodtables did not detect this, as my data were generally -in terms of formatting- valid. 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strict";e.r(a);var o=e(29),r=Object(o.a)({},(function(){var t=this,a=t.$createElement,o=t._self._c||a;return o("ContentSlotsDistributor",{attrs:{"slot-key":t.$parent.slotKey}},[o("p",[t._v("In this section, we will walk through the process of publishing, using a dataset of the periodic table of elements as an example. We will define its metadata by creating a data package, describe the structure of the CSV using a Table Schema, validate it on Goodtables, and finally publish to a public CKAN instance. Let’s start.")]),t._v(" "),o("p",[t._v("First, let’s look at the data. It is available as a CSV file on "),o("a",{attrs:{href:"https://raw.githubusercontent.com/frictionlessdata/example-data-packages/d2b96aaed6ab12db41d73022a2988eeb292116e9/periodic-table/data.csv",target:"_blank",rel:"noopener noreferrer"}},[t._v("this link"),o("OutboundLink")],1),t._v(". The first five rows look like:")]),t._v(" "),o("table",[o("thead",[o("tr",[o("th",[t._v("atomic number")]),t._v(" "),o("th",[t._v("symbol")]),t._v(" "),o("th",[t._v("name")]),t._v(" "),o("th",[t._v("atomic mass")]),t._v(" "),o("th",[t._v("metal or nonmetal?")])])]),t._v(" "),o("tbody",[o("tr",[o("td",[t._v("1")]),t._v(" "),o("td",[t._v("H")]),t._v(" "),o("td",[t._v("Hydrogen")]),t._v(" "),o("td",[t._v("1.00794")]),t._v(" "),o("td",[t._v("nonmetal")])]),t._v(" "),o("tr",[o("td",[t._v("2")]),t._v(" "),o("td",[t._v("He")]),t._v(" "),o("td",[t._v("Helium")]),t._v(" "),o("td",[t._v("4.002602")]),t._v(" "),o("td",[t._v("noble gas")])]),t._v(" "),o("tr",[o("td",[t._v("3")]),t._v(" "),o("td",[t._v("Li")]),t._v(" "),o("td",[t._v("Lithium")]),t._v(" "),o("td",[t._v("6.941")]),t._v(" "),o("td",[t._v("alkali metal")])]),t._v(" "),o("tr",[o("td",[t._v("4")]),t._v(" "),o("td",[t._v("Be")]),t._v(" "),o("td",[t._v("Beryllium")]),t._v(" "),o("td",[t._v("9.012182")]),t._v(" "),o("td",[t._v("alkaline earth metal")])]),t._v(" "),o("tr",[o("td",[t._v("5")]),t._v(" "),o("td",[t._v("B")]),t._v(" "),o("td",[t._v("Boron")]),t._v(" "),o("td",[t._v("10.811")]),t._v(" "),o("td",[t._v("metalloid")])])])]),t._v(" "),o("p",[t._v("As we can see, there are some numeric fields, both integers (atomic number) and floating point (atomic mass), and others are textual with strings. Our first objective is to describe the metadata by creating a Data Package, and its contents by creating a Table Schema.")]),t._v(" "),o("h2",{attrs:{id:"step-1-package-our-data-as-a-data-package"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#step-1-package-our-data-as-a-data-package"}},[t._v("#")]),t._v(" Step 1. Package our data as a Data Package")]),t._v(" "),o("p",[t._v("The easiest way to create a data package is using the "),o("a",{attrs:{href:"https://create.frictionlessdata.io/",title:"Data Package Creator",target:"_blank",rel:"noopener noreferrer"}},[t._v("Data Package Creator"),o("OutboundLink")],1),t._v(". It provides a graphical interface to describe the data package’s metadata, add resources, and define the schema for tabular resources. This is what you should see when you first open it:")]),t._v(" "),o("p",[o("img",{attrs:{src:e(452),alt:"Data Package Creator"}}),o("br"),t._v(" "),o("em",[t._v("Data Package Creator")])]),t._v(" "),o("p",[t._v("The left side bar contains the metadata for the Data Package as a whole, and the main part on the right contains the metadata for each specific resource.")]),t._v(" "),o("p",[t._v("Let’s add our CSV resource. On the main section of the page, fill the inputs with:")]),t._v(" "),o("ul",[o("li",[o("strong",[t._v("Name")]),t._v(": periodic-table")]),t._v(" "),o("li",[o("strong",[t._v("Path")]),t._v(": "),o("a",{attrs:{href:"https://raw.githubusercontent.com/frictionlessdata/example-data-packages/d2b96aaed6ab12db41d73022a2988eeb292116e9/periodic-table/data.csv",target:"_blank",rel:"noopener noreferrer"}},[t._v("https://raw.githubusercontent.com/frictionlessdata/example-data-packages/d2b96aaed6ab12db41d73022a2988eeb292116e9/periodic-table/data.csv"),o("OutboundLink")],1)])]),t._v(" "),o("p",[t._v("Resource and data package names must be unique, lowercase, can contain only letters, numbers, and the characters “.”, “-” and “_”.")]),t._v(" "),o("p",[t._v("And click on the "),o("em",[t._v("Load")]),t._v(" button. After a few seconds, a new box should appear with the text “Add all inferred fields (data has 5 extra column(s))”. Click on it, and the fields will be created, with their data types and formats inferred from the data. This saves us time, as we don’t need to start from scratch.")]),t._v(" "),o("p",[t._v("The Data Package Creator got almost all data types correctly, except the {atomic mass} column. It inferred the column as having integer values, but as we can see from the data, the numbers aren’t whole, they have floating points. Just change the type to "),o("strong",[t._v("number")]),t._v(", and that’s it.")]),t._v(" "),o("p",[t._v("You can view a sample of the data in each column by hovering the mouse below its name.")]),t._v(" "),o("p",[t._v("Usually, we would now add titles and descriptions to each field. However, as this is a simple dataset, the field names should be enough. In the end, the fields are:")]),t._v(" "),o("table",[o("thead",[o("tr",[o("th",[t._v("Name")]),t._v(" "),o("th",[t._v("Data type")]),t._v(" "),o("th",[t._v("Data format")])])]),t._v(" "),o("tbody",[o("tr",[o("td",[t._v("atomic number")]),t._v(" "),o("td",[t._v("integer")]),t._v(" "),o("td",[t._v("default")])]),t._v(" "),o("tr",[o("td",[t._v("symbol")]),t._v(" "),o("td",[t._v("string")]),t._v(" "),o("td",[t._v("default")])]),t._v(" "),o("tr",[o("td",[t._v("name")]),t._v(" "),o("td",[t._v("string")]),t._v(" "),o("td",[t._v("default")])]),t._v(" "),o("tr",[o("td",[t._v("atomic mass")]),t._v(" "),o("td",[t._v("number")]),t._v(" "),o("td",[t._v("default")])]),t._v(" "),o("tr",[o("td",[t._v("metal or nonmetal?")]),t._v(" "),o("td",[t._v("string")]),t._v(" "),o("td",[t._v("default")])])])]),t._v(" "),o("p",[t._v("We can add more information about this resource by clicking on the gear icon to the left of the “Load” button. Add the following information:")]),t._v(" "),o("ul",[o("li",[o("strong",[t._v("Title")]),t._v(": Periodic table")]),t._v(" "),o("li",[o("strong",[t._v("Profile")]),t._v(": Tabular Data Resource")]),t._v(" "),o("li",[o("strong",[t._v("Format")]),t._v(": csv")]),t._v(" "),o("li",[o("strong",[t._v("Encoding")]),t._v(": (blank)")]),t._v(" "),o("li",[o("strong",[t._v("Description")]),t._v(": (blank)")])]),t._v(" "),o("p",[t._v("After this, we’re only missing metadata for the data package as a whole, available on the left side bar. Add:")]),t._v(" "),o("ul",[o("li",[o("strong",[t._v("Name")]),t._v(": periodic-table")]),t._v(" "),o("li",[o("strong",[t._v("Title")]),t._v(": Periodic table")]),t._v(" "),o("li",[o("strong",[t._v("Profile")]),t._v(": Tabular Data Package")]),t._v(" "),o("li",[o("strong",[t._v("Description")]),t._v(": Periodic table of chemical elements")]),t._v(" "),o("li",[o("strong",[t._v("Home Page")]),t._v(": "),o("a",{attrs:{href:"https://github.com/frictionlessdata/example-data-packages/tree/master/periodic-table",target:"_blank",rel:"noopener noreferrer"}},[t._v("https://github.com/frictionlessdata/example-data-packages/tree/master/periodic-table"),o("OutboundLink")],1)]),t._v(" "),o("li",[o("strong",[t._v("Version")]),t._v(": 1.0.0")]),t._v(" "),o("li",[o("strong",[t._v("License")]),t._v(": CC0-1.0")]),t._v(" "),o("li",[o("strong",[t._v("Author")]),t._v(": (blank)")])]),t._v(" "),o("p",[o("img",{attrs:{src:e(453),alt:""}})]),t._v(" "),o("p",[t._v("Let’s validate the data package, to ensure we haven’t missed anything. Just click on the "),o("em",[t._v("Validate")]),t._v(" button on the bottom left, and you should see a green message “Data package is valid!”. This means that the data package is valid, but not necessarily its contents (we’ll check them in the next step).")]),t._v(" "),o("p",[t._v("Save the data package by clicking on the "),o("em",[t._v("Download")]),t._v(" button. This will download a “datapackage.json” file that contains everything we added here. Our next step is to use it to validate the data.")]),t._v(" "),o("h2",{attrs:{id:"step-2-validate-our-data-package-and-its-contents"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#step-2-validate-our-data-package-and-its-contents"}},[t._v("#")]),t._v(" Step 2. Validate our data package and its contents")]),t._v(" "),o("p",[t._v("We now have a data package with our CSV file, including with a table schema describing the contents and types of its columns. Our final step before publishing is validating the data, so we can avoid publishing data with errors. To do so, we’ll use goodtables.")]),t._v(" "),o("p",[o("a",{attrs:{href:"https://goodtables.io",target:"_blank",rel:"noopener noreferrer"}},[t._v("Goodtables"),o("OutboundLink")],1),t._v(" is a tabular data validator that allows you to automatically check for errors such as blank headers, duplicate rows, data with the wrong type (e.g. should be a number but is a date), and others. As it supports data packages, we can simply load the one we created in the previous step. Let’s do it.")]),t._v(" "),o("ol",[o("li",[t._v("Go to "),o("a",{attrs:{href:"https://try.goodtables.io",target:"_blank",rel:"noopener noreferrer"}},[t._v("https://try.goodtables.io"),o("OutboundLink")],1)]),t._v(" "),o("li",[t._v("On the "),o("em",[t._v("Source")]),t._v(" input, click on the "),o("em",[t._v("Upload File")]),t._v(" link.")]),t._v(" "),o("li",[t._v("Click on "),o("em",[t._v("Browse…")]),t._v(" and select the "),o("em",[t._v("datapackage.json")]),t._v(" file you downloaded in the previous step")]),t._v(" "),o("li",[t._v("Click on "),o("em",[t._v("Validate")])])]),t._v(" "),o("p",[t._v("After a few seconds, you should see:")]),t._v(" "),o("p",[o("img",{attrs:{src:e(454),alt:"try.goodtables"}}),o("br"),t._v(" "),o("em",[t._v("try.goodtables")])]),t._v(" "),o("p",[t._v("This means that:")]),t._v(" "),o("ul",[o("li",[t._v("The data package is valid")]),t._v(" "),o("li",[t._v("The CSV file is valid")]),t._v(" "),o("li",[t._v("There are no blank rows or headers, or duplicate rows")]),t._v(" "),o("li",[t._v("The data is valid according to the table schema we created (numbers are numbers, and so on)")])]),t._v(" "),o("p",[t._v("Although it can’t tell you if your data is correct, for example if the Aluminium"),o("br"),t._v("\natomic mass is 26.9815386, it does ensure you that all atomic mass values are"),o("br"),t._v("\nnumbers, among the other validations.")]),t._v(" "),o("p",[t._v("Now that we’ve created a data package, described our data with a table schema,"),o("br"),t._v("\nand validated it, we can finally publish it.")]),t._v(" "),o("h2",{attrs:{id:"step-3-publish-the-data"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#step-3-publish-the-data"}},[t._v("#")]),t._v(" Step 3. Publish the data")]),t._v(" "),o("p",[t._v("Our final step is to publish the dataset. The specifics instructions will vary depend on where you’re publishing to. In this example, we’ll see how to publish to a public "),o("a",{attrs:{href:"https://ckan.org",target:"_blank",rel:"noopener noreferrer"}},[t._v("CKAN"),o("OutboundLink")],1),t._v(" instance, the "),o("a",{attrs:{href:"https://old.datahub.io",target:"_blank",rel:"noopener noreferrer"}},[t._v("Datahub"),o("OutboundLink")],1),t._v(". If you want to use it and don’t have an account yet, you can request one via "),o("a",{attrs:{href:"https://discuss.okfn.org/c/open-knowledge-labs/datahub",target:"_blank",rel:"noopener noreferrer"}},[t._v("our community page"),o("OutboundLink")],1),t._v(". "),o("em",[t._v("(Note: this example is now out of date. See the "),o("a",{attrs:{href:"https://docs.ckan.org/en/2.9/",target:"_blank",rel:"noopener noreferrer"}},[t._v("CKAN docs"),o("OutboundLink")],1),t._v(" for more updated information")]),t._v("). Let’s start.")]),t._v(" "),o("p",[t._v("After you’re logged in, go to the "),o("a",{attrs:{href:"https://old.datahub.io/dataset",target:"_blank",rel:"noopener noreferrer"}},[t._v("datasets list page"),o("OutboundLink")],1),t._v(" and click on the "),o("code",[t._v("Import Data Package")]),t._v(" button. On this form, click on “Upload”, select the "),o("code",[t._v("datapackage.json")]),t._v(" file we created in the previous step, and choose your organisation. We’ll keep the visibility as private for now, so we can review the dataset before it’s made public.")]),t._v(" "),o("p",[o("img",{attrs:{src:e(455),alt:"Importing a data packate to the DataHub"}}),o("br"),t._v(" "),o("em",[t._v("Importing a data packate to the DataHub")])]),t._v(" "),o("p",[t._v("If you don’t see the “Import Data Package” button in your CKAN instance, install the "),o("a",{attrs:{href:"https://github.com/frictionlessdata/ckanext-datapackager",target:"_blank",rel:"noopener noreferrer"}},[t._v("ckanext-datapackager"),o("OutboundLink")],1),t._v(" extension to add support for importing and exporting your datasets as data packages.")]),t._v(" "),o("p",[t._v("You will be redirected to the newly created dataset on CKAN, with its metadata and resource extracted from the data package. Double check if everything seems fine, and when you’re finished, click on the “Manage” button and change the visibility to “Public”.")]),t._v(" "),o("p",[o("a",{attrs:{href:"https://datahub.ckan.io/dataset/period-table-9896953431",target:"_blank",rel:"noopener noreferrer"}},[o("img",{attrs:{src:e(456),alt:"Data package in CKAN"}}),o("OutboundLink")],1)]),t._v(" "),o("p",[t._v("That’s it! CKAN supports data packages via the "),o("a",{attrs:{href:"https://github.com/frictionlessdata/ckanext-datapackager",target:"_blank",rel:"noopener noreferrer"}},[t._v("ckanext-datapackager"),o("OutboundLink")],1),t._v(" extension, so importing (and exporting) data packages is trivial, as all the work on describing the dataset was done while creating the data package.")])])}),[],!1,null,null,null);a.default=r.exports}}]); \ No newline at end of file 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On the main section of the page, fill the inputs with:")]),t._v(" "),o("ul",[o("li",[o("strong",[t._v("Name")]),t._v(": periodic-table")]),t._v(" "),o("li",[o("strong",[t._v("Path")]),t._v(": "),o("a",{attrs:{href:"https://raw.githubusercontent.com/frictionlessdata/example-data-packages/d2b96aaed6ab12db41d73022a2988eeb292116e9/periodic-table/data.csv",target:"_blank",rel:"noopener noreferrer"}},[t._v("https://raw.githubusercontent.com/frictionlessdata/example-data-packages/d2b96aaed6ab12db41d73022a2988eeb292116e9/periodic-table/data.csv"),o("OutboundLink")],1)])]),t._v(" "),o("p",[t._v("Resource and data package names must be unique, lowercase, can contain only letters, numbers, and the characters “.”, “-” and “_”.")]),t._v(" "),o("p",[t._v("And click on the "),o("em",[t._v("Load")]),t._v(" button. After a few seconds, a new box should appear with the text “Add all inferred fields (data has 5 extra column(s))”. Click on it, and the fields will be created, with their data types and formats inferred from the data. This saves us time, as we don’t need to start from scratch.")]),t._v(" "),o("p",[t._v("The Data Package Creator got almost all data types correctly, except the {atomic mass} column. It inferred the column as having integer values, but as we can see from the data, the numbers aren’t whole, they have floating points. Just change the type to "),o("strong",[t._v("number")]),t._v(", and that’s it.")]),t._v(" "),o("p",[t._v("You can view a sample of the data in each column by hovering the mouse below its name.")]),t._v(" "),o("p",[t._v("Usually, we would now add titles and descriptions to each field. However, as this is a simple dataset, the field names should be enough. In the end, the fields are:")]),t._v(" "),o("table",[o("thead",[o("tr",[o("th",[t._v("Name")]),t._v(" "),o("th",[t._v("Data type")]),t._v(" "),o("th",[t._v("Data format")])])]),t._v(" "),o("tbody",[o("tr",[o("td",[t._v("atomic number")]),t._v(" "),o("td",[t._v("integer")]),t._v(" "),o("td",[t._v("default")])]),t._v(" "),o("tr",[o("td",[t._v("symbol")]),t._v(" "),o("td",[t._v("string")]),t._v(" "),o("td",[t._v("default")])]),t._v(" "),o("tr",[o("td",[t._v("name")]),t._v(" "),o("td",[t._v("string")]),t._v(" "),o("td",[t._v("default")])]),t._v(" "),o("tr",[o("td",[t._v("atomic mass")]),t._v(" "),o("td",[t._v("number")]),t._v(" "),o("td",[t._v("default")])]),t._v(" "),o("tr",[o("td",[t._v("metal or nonmetal?")]),t._v(" "),o("td",[t._v("string")]),t._v(" "),o("td",[t._v("default")])])])]),t._v(" "),o("p",[t._v("We can add more information about this resource by clicking on the gear icon to the left of the “Load” button. Add the following information:")]),t._v(" "),o("ul",[o("li",[o("strong",[t._v("Title")]),t._v(": Periodic table")]),t._v(" "),o("li",[o("strong",[t._v("Profile")]),t._v(": Tabular Data Resource")]),t._v(" "),o("li",[o("strong",[t._v("Format")]),t._v(": csv")]),t._v(" "),o("li",[o("strong",[t._v("Encoding")]),t._v(": (blank)")]),t._v(" "),o("li",[o("strong",[t._v("Description")]),t._v(": (blank)")])]),t._v(" "),o("p",[t._v("After this, we’re only missing metadata for the data package as a whole, available on the left side bar. Add:")]),t._v(" "),o("ul",[o("li",[o("strong",[t._v("Name")]),t._v(": periodic-table")]),t._v(" "),o("li",[o("strong",[t._v("Title")]),t._v(": Periodic table")]),t._v(" "),o("li",[o("strong",[t._v("Profile")]),t._v(": Tabular Data Package")]),t._v(" "),o("li",[o("strong",[t._v("Description")]),t._v(": Periodic table of chemical elements")]),t._v(" "),o("li",[o("strong",[t._v("Home Page")]),t._v(": "),o("a",{attrs:{href:"https://github.com/frictionlessdata/example-data-packages/tree/master/periodic-table",target:"_blank",rel:"noopener noreferrer"}},[t._v("https://github.com/frictionlessdata/example-data-packages/tree/master/periodic-table"),o("OutboundLink")],1)]),t._v(" "),o("li",[o("strong",[t._v("Version")]),t._v(": 1.0.0")]),t._v(" "),o("li",[o("strong",[t._v("License")]),t._v(": CC0-1.0")]),t._v(" "),o("li",[o("strong",[t._v("Author")]),t._v(": (blank)")])]),t._v(" "),o("p",[o("img",{attrs:{src:e(445),alt:""}})]),t._v(" "),o("p",[t._v("Let’s validate the data package, to ensure we haven’t missed anything. Just click on the "),o("em",[t._v("Validate")]),t._v(" button on the bottom left, and you should see a green message “Data package is valid!”. This means that the data package is valid, but not necessarily its contents (we’ll check them in the next step).")]),t._v(" "),o("p",[t._v("Save the data package by clicking on the "),o("em",[t._v("Download")]),t._v(" button. This will download a “datapackage.json” file that contains everything we added here. Our next step is to use it to validate the data.")]),t._v(" "),o("h2",{attrs:{id:"step-2-validate-our-data-package-and-its-contents"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#step-2-validate-our-data-package-and-its-contents"}},[t._v("#")]),t._v(" Step 2. Validate our data package and its contents")]),t._v(" "),o("p",[t._v("We now have a data package with our CSV file, including with a table schema describing the contents and types of its columns. Our final step before publishing is validating the data, so we can avoid publishing data with errors. To do so, we’ll use goodtables.")]),t._v(" "),o("p",[o("a",{attrs:{href:"https://goodtables.io",target:"_blank",rel:"noopener noreferrer"}},[t._v("Goodtables"),o("OutboundLink")],1),t._v(" is a tabular data validator that allows you to automatically check for errors such as blank headers, duplicate rows, data with the wrong type (e.g. should be a number but is a date), and others. As it supports data packages, we can simply load the one we created in the previous step. Let’s do it.")]),t._v(" "),o("ol",[o("li",[t._v("Go to "),o("a",{attrs:{href:"https://try.goodtables.io",target:"_blank",rel:"noopener noreferrer"}},[t._v("https://try.goodtables.io"),o("OutboundLink")],1)]),t._v(" "),o("li",[t._v("On the "),o("em",[t._v("Source")]),t._v(" input, click on the "),o("em",[t._v("Upload File")]),t._v(" link.")]),t._v(" "),o("li",[t._v("Click on "),o("em",[t._v("Browse…")]),t._v(" and select the "),o("em",[t._v("datapackage.json")]),t._v(" file you downloaded in the previous step")]),t._v(" "),o("li",[t._v("Click on "),o("em",[t._v("Validate")])])]),t._v(" "),o("p",[t._v("After a few seconds, you should see:")]),t._v(" "),o("p",[o("img",{attrs:{src:e(446),alt:"try.goodtables"}}),o("br"),t._v(" "),o("em",[t._v("try.goodtables")])]),t._v(" "),o("p",[t._v("This means that:")]),t._v(" "),o("ul",[o("li",[t._v("The data package is valid")]),t._v(" "),o("li",[t._v("The CSV file is valid")]),t._v(" "),o("li",[t._v("There are no blank rows or headers, or duplicate rows")]),t._v(" "),o("li",[t._v("The data is valid according to the table schema we created (numbers are numbers, and so on)")])]),t._v(" "),o("p",[t._v("Although it can’t tell you if your data is correct, for example if the Aluminium"),o("br"),t._v("\natomic mass is 26.9815386, it does ensure you that all atomic mass values are"),o("br"),t._v("\nnumbers, among the other validations.")]),t._v(" "),o("p",[t._v("Now that we’ve created a data package, described our data with a table schema,"),o("br"),t._v("\nand validated it, we can finally publish it.")]),t._v(" "),o("h2",{attrs:{id:"step-3-publish-the-data"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#step-3-publish-the-data"}},[t._v("#")]),t._v(" Step 3. Publish the data")]),t._v(" "),o("p",[t._v("Our final step is to publish the dataset. The specifics instructions will vary depend on where you’re publishing to. In this example, we’ll see how to publish to a public "),o("a",{attrs:{href:"https://ckan.org",target:"_blank",rel:"noopener noreferrer"}},[t._v("CKAN"),o("OutboundLink")],1),t._v(" instance, the "),o("a",{attrs:{href:"https://old.datahub.io",target:"_blank",rel:"noopener noreferrer"}},[t._v("Datahub"),o("OutboundLink")],1),t._v(". If you want to use it and don’t have an account yet, you can request one via "),o("a",{attrs:{href:"https://discuss.okfn.org/c/open-knowledge-labs/datahub",target:"_blank",rel:"noopener noreferrer"}},[t._v("our community page"),o("OutboundLink")],1),t._v(". "),o("em",[t._v("(Note: this example is now out of date. See the "),o("a",{attrs:{href:"https://docs.ckan.org/en/2.9/",target:"_blank",rel:"noopener noreferrer"}},[t._v("CKAN docs"),o("OutboundLink")],1),t._v(" for more updated information")]),t._v("). Let’s start.")]),t._v(" "),o("p",[t._v("After you’re logged in, go to the "),o("a",{attrs:{href:"https://old.datahub.io/dataset",target:"_blank",rel:"noopener noreferrer"}},[t._v("datasets list page"),o("OutboundLink")],1),t._v(" and click on the "),o("code",[t._v("Import Data Package")]),t._v(" button. On this form, click on “Upload”, select the "),o("code",[t._v("datapackage.json")]),t._v(" file we created in the previous step, and choose your organisation. We’ll keep the visibility as private for now, so we can review the dataset before it’s made public.")]),t._v(" "),o("p",[o("img",{attrs:{src:e(447),alt:"Importing a data packate to the DataHub"}}),o("br"),t._v(" "),o("em",[t._v("Importing a data packate to the DataHub")])]),t._v(" "),o("p",[t._v("If you don’t see the “Import Data Package” button in your CKAN instance, install the "),o("a",{attrs:{href:"https://github.com/frictionlessdata/ckanext-datapackager",target:"_blank",rel:"noopener noreferrer"}},[t._v("ckanext-datapackager"),o("OutboundLink")],1),t._v(" extension to add support for importing and exporting your datasets as data packages.")]),t._v(" "),o("p",[t._v("You will be redirected to the newly created dataset on CKAN, with its metadata and resource extracted from the data package. Double check if everything seems fine, and when you’re finished, click on the “Manage” button and change the visibility to “Public”.")]),t._v(" "),o("p",[o("a",{attrs:{href:"https://datahub.ckan.io/dataset/period-table-9896953431",target:"_blank",rel:"noopener noreferrer"}},[o("img",{attrs:{src:e(448),alt:"Data package in CKAN"}}),o("OutboundLink")],1)]),t._v(" "),o("p",[t._v("That’s it! CKAN supports data packages via the "),o("a",{attrs:{href:"https://github.com/frictionlessdata/ckanext-datapackager",target:"_blank",rel:"noopener noreferrer"}},[t._v("ckanext-datapackager"),o("OutboundLink")],1),t._v(" extension, so importing (and exporting) data packages is trivial, as all the work on describing the dataset was done while creating the data package.")])])}),[],!1,null,null,null);a.default=r.exports}}]); \ No newline at end of file diff --git a/assets/js/120.9ab4a56a.js b/assets/js/120.69138dce.js similarity index 98% rename from assets/js/120.9ab4a56a.js rename to assets/js/120.69138dce.js index 1188a4ea7..b4bfbffeb 100644 --- a/assets/js/120.9ab4a56a.js +++ b/assets/js/120.69138dce.js @@ -1 +1 @@ -(window.webpackJsonp=window.webpackJsonp||[]).push([[120],{652:function(e,t,r){"use strict";r.r(t);var a=r(29),o=Object(a.a)({},(function(){var e=this,t=e.$createElement,r=e._self._c||t;return r("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[r("h2",{attrs:{id:"a-recap-from-our-january-community-call"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#a-recap-from-our-january-community-call"}},[e._v("#")]),e._v(" A recap from our January community call")]),e._v(" "),r("p",[e._v("On January 28"),r("sup",[e._v("th")]),e._v(" we had our first Frictionless Data Community Call for 2021. It was great to see it was so well attended!")]),e._v(" "),r("p",[e._v("We heard a presentation by Carles Pina i Estany on schema-collaboration, a system that uses Data Package Creator to enable data managers and researchers to create and share dataset schemas, edit them, post messages and export the schemas in different formats (text, Markdown, PDF). Before this tool was developed, researchers communicated with a data manager via email for each datapackage they were publishing, which slowed down considerably the whole process, besides making it more difficult.")]),e._v(" "),r("p",[e._v("To discover more about schema-collaboration, have a look at it on "),r("a",{attrs:{href:"https://github.com/frictionlessdata/schema-collaboration/",target:"_blank",rel:"noopener noreferrer"}},[e._v("GitHub"),r("OutboundLink")],1),e._v(" or read "),r("a",{attrs:{href:"https://frictionlessdata.io/blog/2021/01/18/schema-collaboration/",target:"_blank",rel:"noopener noreferrer"}},[e._v("the blog"),r("OutboundLink")],1),e._v(" Carles wrote about the project. If you would like to dive deeper and watch Carles’ presentation, you can find it here:")]),e._v(" "),r("iframe",{attrs:{width:"560",height:"315",src:"https://www.youtube.com/embed/_0cs25Fj_yU",frameborder:"0",allow:"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture",allowfullscreen:""}}),e._v(" "),r("h2",{attrs:{id:"other-agenda-items-from-our-hangout"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#other-agenda-items-from-our-hangout"}},[e._v("#")]),e._v(" Other agenda items from our hangout")]),e._v(" "),r("ul",[r("li",[r("p",[r("a",{attrs:{href:"https://opendataday.org/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Open Data Day"),r("OutboundLink")],1),e._v(" is fast approaching! If you are organising something to celebrate open data on March 6"),r("sup",[e._v("th")]),e._v(", let us know! You still have a few days to apply for mini-grants for your community events.")])]),e._v(" "),r("li",[r("p",[r("a",{attrs:{href:"https://csvconf.com/",target:"_blank",rel:"noopener noreferrer"}},[e._v("csv,conf,v6"),r("OutboundLink")],1),e._v(" is happening on May 4-5. If you want to give a talk, make sure to submit a proposal by February 28"),r("sup",[e._v("th")]),e._v(". More info "),r("a",{attrs:{href:"https://csvconf.com/submit/",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),r("OutboundLink")],1),e._v(".")])])]),e._v(" "),r("h2",{attrs:{id:"news-from-the-community"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#news-from-the-community"}},[e._v("#")]),e._v(" News from the community")]),e._v(" "),r("p",[e._v("Giuseppe Peronato and "),r("a",{attrs:{href:"https://cividi.ch/",target:"_blank",rel:"noopener noreferrer"}},[e._v("cividi"),r("OutboundLink")],1),e._v(" started using Frictionless Data for data pipelines using (Geo-)Spatial datasets, e.g. raster data and GeoJSONs. You can have a look "),r("a",{attrs:{href:"https://github.com/datahq/dataflows/pull/153",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),r("OutboundLink")],1),e._v(". They have also been looking more closely at the Creator’s UI library in a "),r("a",{attrs:{href:"https://github.com/gperonato/archive-forger",target:"_blank",rel:"noopener noreferrer"}},[e._v("prototype"),r("OutboundLink")],1),e._v(" with researchers, and releasing a "),r("a",{attrs:{href:"https://blog.datalets.ch/073/",target:"_blank",rel:"noopener noreferrer"}},[e._v("QGIS plugin"),r("OutboundLink")],1),e._v(" for Frictionless Data.")]),e._v(" "),r("p",[e._v("Thorben started working on the official vaccination publication by the German Federal Health Authority, which was replaced daily with a Data Package Pipeline saved as a Data Package by a GitHub Action. If you are interested, have a look "),r("a",{attrs:{href:"https://github.com/n0rdlicht/rki-vaccination-scraper",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),r("OutboundLink")],1),e._v(".")]),e._v(" "),r("h2",{attrs:{id:"join-us-next-month"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#join-us-next-month"}},[e._v("#")]),e._v(" Join us next month!")]),e._v(" "),r("p",[e._v("Our next meeting will be on 25"),r("sup",[e._v("th")]),e._v(" February. Don’t miss the opportunity to get a code demonstration on "),r("a",{attrs:{href:"http://frictionless.py",target:"_blank",rel:"noopener noreferrer"}},[e._v("frictionless.py"),r("OutboundLink")],1),e._v(" by our very own Evgeny Karev (@roll). You can "),r("a",{attrs:{href:"https://docs.google.com/forms/d/e/1FAIpQLSeuNCopxXauMkrWvF6VHqOyHMcy54SfNDOseVXfWRQZWkvqjQ/viewform?usp=sf_link",target:"_blank",rel:"noopener noreferrer"}},[e._v("sign up here"),r("OutboundLink")],1),e._v(".")]),e._v(" "),r("p",[e._v("Do you want to share something with the community? Let us know when you sign up!")]),e._v(" "),r("h2",{attrs:{id:"call-recording"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#call-recording"}},[e._v("#")]),e._v(" Call recording:")]),e._v(" "),r("p",[e._v("On a final note, here is the recording of the full call:")]),e._v(" "),r("iframe",{attrs:{width:"560",height:"315",src:"https://www.youtube.com/embed/Z4-EM2RPKMA",frameborder:"0",allow:"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture",allowfullscreen:""}}),e._v(" "),r("p",[e._v("As usual, you can join us on "),r("a",{attrs:{href:"https://discord.com/invite/j9DNFNw",target:"_blank",rel:"noopener noreferrer"}},[e._v("Discord"),r("OutboundLink")],1),e._v(" or "),r("a",{attrs:{href:"https://twitter.com/frictionlessd8a",target:"_blank",rel:"noopener noreferrer"}},[e._v("Twitter"),r("OutboundLink")],1),e._v(" to say hi or ask any questions. See you there!")])])}),[],!1,null,null,null);t.default=o.exports}}]); \ No newline at end of file +(window.webpackJsonp=window.webpackJsonp||[]).push([[120],{651:function(e,t,r){"use strict";r.r(t);var a=r(29),o=Object(a.a)({},(function(){var e=this,t=e.$createElement,r=e._self._c||t;return r("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[r("h2",{attrs:{id:"a-recap-from-our-january-community-call"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#a-recap-from-our-january-community-call"}},[e._v("#")]),e._v(" A recap from our January community call")]),e._v(" "),r("p",[e._v("On January 28"),r("sup",[e._v("th")]),e._v(" we had our first Frictionless Data Community Call for 2021. It was great to see it was so well attended!")]),e._v(" "),r("p",[e._v("We heard a presentation by Carles Pina i Estany on schema-collaboration, a system that uses Data Package Creator to enable data managers and researchers to create and share dataset schemas, edit them, post messages and export the schemas in different formats (text, Markdown, PDF). Before this tool was developed, researchers communicated with a data manager via email for each datapackage they were publishing, which slowed down considerably the whole process, besides making it more difficult.")]),e._v(" "),r("p",[e._v("To discover more about schema-collaboration, have a look at it on "),r("a",{attrs:{href:"https://github.com/frictionlessdata/schema-collaboration/",target:"_blank",rel:"noopener noreferrer"}},[e._v("GitHub"),r("OutboundLink")],1),e._v(" or read "),r("a",{attrs:{href:"https://frictionlessdata.io/blog/2021/01/18/schema-collaboration/",target:"_blank",rel:"noopener noreferrer"}},[e._v("the blog"),r("OutboundLink")],1),e._v(" Carles wrote about the project. If you would like to dive deeper and watch Carles’ presentation, you can find it here:")]),e._v(" "),r("iframe",{attrs:{width:"560",height:"315",src:"https://www.youtube.com/embed/_0cs25Fj_yU",frameborder:"0",allow:"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture",allowfullscreen:""}}),e._v(" "),r("h2",{attrs:{id:"other-agenda-items-from-our-hangout"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#other-agenda-items-from-our-hangout"}},[e._v("#")]),e._v(" Other agenda items from our hangout")]),e._v(" "),r("ul",[r("li",[r("p",[r("a",{attrs:{href:"https://opendataday.org/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Open Data Day"),r("OutboundLink")],1),e._v(" is fast approaching! If you are organising something to celebrate open data on March 6"),r("sup",[e._v("th")]),e._v(", let us know! 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More info "),r("a",{attrs:{href:"https://csvconf.com/submit/",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),r("OutboundLink")],1),e._v(".")])])]),e._v(" "),r("h2",{attrs:{id:"news-from-the-community"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#news-from-the-community"}},[e._v("#")]),e._v(" News from the community")]),e._v(" "),r("p",[e._v("Giuseppe Peronato and "),r("a",{attrs:{href:"https://cividi.ch/",target:"_blank",rel:"noopener noreferrer"}},[e._v("cividi"),r("OutboundLink")],1),e._v(" started using Frictionless Data for data pipelines using (Geo-)Spatial datasets, e.g. raster data and GeoJSONs. You can have a look "),r("a",{attrs:{href:"https://github.com/datahq/dataflows/pull/153",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),r("OutboundLink")],1),e._v(". 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We had been looking very much forward to presenting the new framework to you all and we were very pleased that so many of you joined us. If you would like to know more about it, you can explore the new Frictionless Python framework through the "),t("a",{attrs:{href:"https://framework.frictionlessdata.io/",target:"_blank",rel:"noopener noreferrer"}},[e._v("documentation portal"),t("OutboundLink")],1),e._v(" or on "),t("a",{attrs:{href:"https://github.com/frictionlessdata/frictionless-py",target:"_blank",rel:"noopener noreferrer"}},[e._v("GitHub"),t("OutboundLink")],1),e._v(".")]),e._v(" "),t("p",[e._v("If you couldn’t make it to the call, or you are just curious and would like to go over the presentation again, here it is:")]),e._v(" "),t("iframe",{attrs:{width:"560",height:"315",src:"https://www.youtube.com/embed/WX4NbYmvu9M",frameborder:"0",allow:"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture",allowfullscreen:""}}),e._v(" "),t("h2",{attrs:{id:"other-agenda-items-from-our-hangout"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#other-agenda-items-from-our-hangout"}},[e._v("#")]),e._v(" Other agenda items from our hangout")]),e._v(" "),t("p",[t("a",{attrs:{href:"https://opendataday.org/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Open Data Day"),t("OutboundLink")],1),e._v(" is fast approaching with over 200 events organised online on March 6"),t("sup",[e._v("th")]),e._v(". Together with the "),t("a",{attrs:{href:"https://fellows.frictionlessdata.io/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Frictionless Data Fellows"),t("OutboundLink")],1),e._v(" we will be celebrating open research data. Join us online from 3pm UTC. "),t("a",{attrs:{href:"https://us02web.zoom.us/meeting/register/tZUvdeuspjMoGtK-rR8wV4IrnfEW_5-KdLkG",target:"_blank",rel:"noopener noreferrer"}},[e._v("RSVP here"),t("OutboundLink")],1),e._v(" for the link to join this virtual event. This event is open to everyone.")]),e._v(" "),t("h2",{attrs:{id:"join-us-next-month"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#join-us-next-month"}},[e._v("#")]),e._v(" Join us next month!")]),e._v(" "),t("p",[e._v("Our next meeting will be on 25"),t("sup",[e._v("th")]),e._v(" March. We will hear about Hackathons to facilitate the creation of web tools to create field-specific FAIR archive files from Oleg Lavrovsky and Giuseppe Peronato.")]),e._v(" "),t("p",[e._v("You can sign up "),t("a",{attrs:{href:"https://docs.google.com/forms/d/e/1FAIpQLSeuNCopxXauMkrWvF6VHqOyHMcy54SfNDOseVXfWRQZWkvqjQ/viewform?usp=sf_link",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),t("OutboundLink")],1),e._v(".")]),e._v(" "),t("p",[e._v("Do you want to share something with the community? Let us know when you sign up!")]),e._v(" "),t("h2",{attrs:{id:"call-recording"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#call-recording"}},[e._v("#")]),e._v(" Call recording:")]),e._v(" "),t("p",[e._v("On a final note, here is the recording of the full call:")]),e._v(" "),t("iframe",{attrs:{width:"560",height:"315",src:"https://www.youtube.com/embed/W0EHL6SSPcE",frameborder:"0",allow:"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture",allowfullscreen:""}}),e._v(" "),t("p",[e._v("As usual, you can join us on "),t("a",{attrs:{href:"https://discord.com/invite/j9DNFNw",target:"_blank",rel:"noopener noreferrer"}},[e._v("Discord"),t("OutboundLink")],1),e._v(" or "),t("a",{attrs:{href:"https://twitter.com/frictionlessd8a",target:"_blank",rel:"noopener noreferrer"}},[e._v("Twitter"),t("OutboundLink")],1),e._v(" to say hi or ask any questions. See you there!")])])}),[],!1,null,null,null);r.default=a.exports}}]); \ No newline at end of file diff --git a/assets/js/124.4132dc2c.js b/assets/js/124.6ad45b64.js similarity index 99% rename from assets/js/124.4132dc2c.js rename to assets/js/124.6ad45b64.js index 769dbe4bb..69d5709f2 100644 --- a/assets/js/124.4132dc2c.js +++ b/assets/js/124.6ad45b64.js @@ -1 +1 @@ -(window.webpackJsonp=window.webpackJsonp||[]).push([[124],{657:function(e,t,a){"use strict";a.r(t);var r=a(29),o=Object(r.a)({},(function(){var e=this,t=e.$createElement,a=e._self._c||t;return a("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[a("p",[a("em",[e._v("This blog is part of a series showcasing projects developed during the 2020-2021 Tool Fund. The Tool Fund provided five mini-grants of $5,000 to support individuals or organisations in developing an open tool for reproducible research built using the Frictionless Data specifications and software. This Fund is part of the Frictionless Data for Reproducible Research project, which is funded by the Sloan Foundation. This project applies our work in Frictionless Data to data-driven research disciplines, in order to facilitate reproducible data workflows in research contexts.")])]),e._v(" "),a("p",[e._v("We are Simon Tyrrell and Xingdong Bian, both research software engineers, in Robert Davey’s Data Infrastructure and Algorithms group at the Earlham Institute. We built the "),a("a",{attrs:{href:"https://grassroots.tools/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Grassroots Infrastructure project"),a("OutboundLink")],1),e._v(" which aims to create an easily-deployable suite of computing middleware tools to help users and developers gain access to scientific data. This is part of the "),a("a",{attrs:{href:"https://designingfuturewheat.org.uk/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Designing Future Wheat (DFW)"),a("OutboundLink")],1),e._v(" project. There are two separate parts of this project that we have added Frictionless Data support to and we’ll now describe each of these in turn.")]),e._v(" "),a("h2",{attrs:{id:"why-add-frictionless-to-the-designing-future-wheat-project"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#why-add-frictionless-to-the-designing-future-wheat-project"}},[e._v("#")]),e._v(" Why add Frictionless to the Designing Future Wheat project?")]),e._v(" "),a("p",[e._v("The first part of the Tool Fund project we added Frictionless Data to is the DFW data portal which delivers large scale wheat datasets that are also tied to semantically marked-up metadata. These datasets are heterogeneous and vary from field trial information, sequencing data, through to phenotyping images, etc. Given the different needs of users of this data, there is an increasing need to be able to manage this data and its associated metadata to allow for as easy dissemination as possible. So the issue that we had was how can we standardize the methods to access this data/metadata and label it using both well-defined ontologies and standards to deliver consistent data packages to users in an interoperable way. This is where Frictionless Data came in, allowing data scientists a consistent, well-defined standard to use when building programs or workflows to access the data stored on the portal.")]),e._v(" "),a("p",[e._v("The portal uses a combination of an "),a("a",{attrs:{href:"https://irods.org/",target:"_blank",rel:"noopener noreferrer"}},[e._v("iRODS"),a("OutboundLink")],1),e._v(" repository, to store the data and metadata, and "),a("a",{attrs:{href:"https://httpd.apache.org/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Apache"),a("OutboundLink")],1),e._v(" to host the files with our in-house developed Apache module, mod_eirods_dav, linking the two together. It was this module that we added the Frictionless Data support to and further details are available in the "),a("a",{attrs:{href:"https://github.com/billyfish/eirods-dav#frictionless-data-support",target:"_blank",rel:"noopener noreferrer"}},[e._v("documentation"),a("OutboundLink")],1),e._v(".")]),e._v(" "),a("h2",{attrs:{id:"how-does-the-new-frictionless-implementation-work"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#how-does-the-new-frictionless-implementation-work"}},[e._v("#")]),e._v(" How does the new Frictionless implementation work?")]),e._v(" "),a("p",[e._v("So what does it do? Well, it can generate a datapackage.json file automatically for any number of specified directories. These Data Packages can either be generated dynamically on each access or can optionally be written back to the iRODS repository and served like any other static file stored there. Since every iRODS repository can use different metadata keys for storing the information that the Data Packages require, the required key names are completely configurable by specifying the iRODS metadata keys to use in the mod_eirods_dav configuration file and you can do things like combining the values of multiple iRODS metadata keys with generic strings to produce the value that you want to use in the Data Package. Currently the Data Package’s name, title, description, authors, unique identifier and license details are all supported. For each entry within the Data Package’s resources array, the name, path checksum and size attributes are also stored.")]),e._v(" "),a("p",[e._v("As well as standard entries within the Data Package, we also added support for Tabular Data Packages. As with standard entries, all of the keys for the column names can be generated from setting the required directives within the module configuration file.")]),e._v(" "),a("p",[a("img",{attrs:{src:"https://user-images.githubusercontent.com/74717970/110128100-b5154a00-7dc6-11eb-8d8a-a915a49e6742.png",alt:"imgblog"}}),a("br"),e._v("\nFigure1: A Data Package generated automatically by mod_eirods_dav")]),e._v(" "),a("p",[a("img",{attrs:{src:"https://user-images.githubusercontent.com/74717970/110128509-25bc6680-7dc7-11eb-8c2e-ff966169f9c5.png",alt:"imgblog2"}}),a("br"),e._v("\nFigure2: Tabular Data Package generated automatically by mod_eirods_dav")]),e._v(" "),a("h2",{attrs:{id:"adding-ckan-support"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#adding-ckan-support"}},[e._v("#")]),e._v(" Adding CKAN support")]),e._v(" "),a("p",[e._v("The second of the tools that we have implemented Frictionless Data support for is the DFW CKAN website. Primarily we use this to store publications from the project output. We currently have over 300 entries in there and since its collection is getting larger and larger, we needed a more manageable way of having better data integration, especially when using other systems through the projects by our collaborators.")]),e._v(" "),a("p",[e._v("So we built a simple Python Django webapp to do this:")]),e._v(" "),a("p",[a("img",{attrs:{src:"https://user-images.githubusercontent.com/74717970/110128662-58fef580-7dc7-11eb-88c9-46e8e36b4def.png",alt:"imgblog3"}})]),e._v(" "),a("p",[e._v("By querying the REST API provided by CKAN and getting the datasets’ metadata as JSON output, followed by using the "),a("a",{attrs:{href:"https://github.com/frictionlessdata/frictionless-ckan-mapper",target:"_blank",rel:"noopener noreferrer"}},[e._v("Frictionless CKAN Mapper"),a("OutboundLink")],1),e._v(", the JSON is converted into datapackage.json, to conform with Frictionless Data standard. If any of the resources under a dataset is CSV, the headings will be extracted as the "),a("a",{attrs:{href:"https://specs.frictionlessdata.io/table-schema/",target:"_blank",rel:"noopener noreferrer"}},[e._v("tabular data package schema"),a("OutboundLink")],1),e._v(" and integrated into the datapackage.json file itself. As well as providing the datapackage.json file as a download through the Django web app, it is also possible to push the datapackage.json back to the CKAN as a resource file on the page. This requires the CKAN user key with the relevant permissions.")]),e._v(" "),a("p",[a("img",{attrs:{src:"https://user-images.githubusercontent.com/74717970/110128881-94012900-7dc7-11eb-9833-e46f351477be.png",alt:"imgblog4"}})]),e._v(" "),a("h2",{attrs:{id:"how-can-you-try-this-tool"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#how-can-you-try-this-tool"}},[e._v("#")]),e._v(" How can you try this tool?")]),e._v(" "),a("p",[e._v("The tool can be used by accessing its REST interface:")]),e._v(" "),a("ul",[a("li",[a("code",[e._v("/convert?q={ckan-dataset-id}")]),e._v(" - convert CKAN dataset json to datapackage json e.g. /convert?q=0c03fa08-2142-426b-b1ca-fa852f909aa6")]),e._v(" "),a("li",[a("code",[e._v("/convert_resources?q={ckan-dataset-id}")]),e._v(" - convert CKAN dataset json to datapackage json with resources, also if any of the resources files are CSV files, the tabular data package will be converted. e.g. /convert_resources?q=grassroots-frictionless-data-test")]),e._v(" "),a("li",[a("code",[e._v("/convert_push?q={ckan-dataset-id}&key={ckan-user-key}")]),e._v(" - push the generated datapackage.json to the CKAN entry."),a("br"),e._v("\nAn example REST query page:")])]),e._v(" "),a("p",[a("img",{attrs:{src:"https://user-images.githubusercontent.com/74717970/110129172-efcbb200-7dc7-11eb-9230-a70cbbd6d9cf.png",alt:"imgblog5"}})]),e._v(" "),a("p",[e._v("It is possible to have your own local deployment of the tool too by downloading the web app from its Github repository, installing the requirements, and running the server with")]),e._v(" "),a("p",[a("code",[e._v("$manage.py runserver 8000")])]),e._v(" "),a("p",[e._v("Our collaborators can utilise the datapackage.json and integrate the CKAN entries to their own tools or project with ease as it conforms to the Frictionless Data standard.")]),e._v(" "),a("h2",{attrs:{id:"next-steps-for-frictionlessly-designing-future-wheat"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#next-steps-for-frictionlessly-designing-future-wheat"}},[e._v("#")]),e._v(" Next Steps for Frictionlessly Designing Future Wheat")]),e._v(" "),a("p",[e._v("It has been a hugely positive step to implement support for Frictionless Data Packages and we’ve already used these packages ourselves after two of our servers decided to fall over within three days of each other! Our future plans are to add support for further metadata keys within the datapackage.json files and expose more datasets as Frictionless Data Packages. For the CKAN-side, there are a few improvements that can be made in future: firstly, make the base CKAN url configurable in a config file, so this can be used for any CKAN website. Secondly, create a docker file to include the whole Django app, so it is more portable and easier to be deployed. You can keep track of the project at the following links:")]),e._v(" "),a("ul",[a("li",[e._v("The Designing Future Wheat Data Portal: "),a("a",{attrs:{href:"https://opendata.earlham.ac.uk/wheat/under_license/toronto/",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://opendata.earlham.ac.uk/wheat/under_license/toronto/"),a("OutboundLink")],1)]),e._v(" "),a("li",[e._v("The Designing Future Wheat publications portal: "),a("a",{attrs:{href:"https://ckan.grassroots.tools",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://ckan.grassroots.tools"),a("OutboundLink")],1)]),e._v(" "),a("li",[e._v("mod_eirods_dav: "),a("a",{attrs:{href:"https://github.com/billyfish/eirods-dav",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://github.com/billyfish/eirods-dav"),a("OutboundLink")],1)]),e._v(" "),a("li",[e._v("CKAN Frictionless Data web application: "),a("a",{attrs:{href:"https://github.com/TGAC/ckan-frictionlessdata",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://github.com/TGAC/ckan-frictionlessdata"),a("OutboundLink")],1)])])])}),[],!1,null,null,null);t.default=o.exports}}]); \ No newline at end of file +(window.webpackJsonp=window.webpackJsonp||[]).push([[124],{656:function(e,t,a){"use strict";a.r(t);var r=a(29),o=Object(r.a)({},(function(){var e=this,t=e.$createElement,a=e._self._c||t;return a("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[a("p",[a("em",[e._v("This blog is part of a series showcasing projects developed during the 2020-2021 Tool Fund. The Tool Fund provided five mini-grants of $5,000 to support individuals or organisations in developing an open tool for reproducible research built using the Frictionless Data specifications and software. This Fund is part of the Frictionless Data for Reproducible Research project, which is funded by the Sloan Foundation. This project applies our work in Frictionless Data to data-driven research disciplines, in order to facilitate reproducible data workflows in research contexts.")])]),e._v(" "),a("p",[e._v("We are Simon Tyrrell and Xingdong Bian, both research software engineers, in Robert Davey’s Data Infrastructure and Algorithms group at the Earlham Institute. We built the "),a("a",{attrs:{href:"https://grassroots.tools/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Grassroots Infrastructure project"),a("OutboundLink")],1),e._v(" which aims to create an easily-deployable suite of computing middleware tools to help users and developers gain access to scientific data. This is part of the "),a("a",{attrs:{href:"https://designingfuturewheat.org.uk/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Designing Future Wheat (DFW)"),a("OutboundLink")],1),e._v(" project. There are two separate parts of this project that we have added Frictionless Data support to and we’ll now describe each of these in turn.")]),e._v(" "),a("h2",{attrs:{id:"why-add-frictionless-to-the-designing-future-wheat-project"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#why-add-frictionless-to-the-designing-future-wheat-project"}},[e._v("#")]),e._v(" Why add Frictionless to the Designing Future Wheat project?")]),e._v(" "),a("p",[e._v("The first part of the Tool Fund project we added Frictionless Data to is the DFW data portal which delivers large scale wheat datasets that are also tied to semantically marked-up metadata. These datasets are heterogeneous and vary from field trial information, sequencing data, through to phenotyping images, etc. Given the different needs of users of this data, there is an increasing need to be able to manage this data and its associated metadata to allow for as easy dissemination as possible. So the issue that we had was how can we standardize the methods to access this data/metadata and label it using both well-defined ontologies and standards to deliver consistent data packages to users in an interoperable way. This is where Frictionless Data came in, allowing data scientists a consistent, well-defined standard to use when building programs or workflows to access the data stored on the portal.")]),e._v(" "),a("p",[e._v("The portal uses a combination of an "),a("a",{attrs:{href:"https://irods.org/",target:"_blank",rel:"noopener noreferrer"}},[e._v("iRODS"),a("OutboundLink")],1),e._v(" repository, to store the data and metadata, and "),a("a",{attrs:{href:"https://httpd.apache.org/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Apache"),a("OutboundLink")],1),e._v(" to host the files with our in-house developed Apache module, mod_eirods_dav, linking the two together. It was this module that we added the Frictionless Data support to and further details are available in the "),a("a",{attrs:{href:"https://github.com/billyfish/eirods-dav#frictionless-data-support",target:"_blank",rel:"noopener noreferrer"}},[e._v("documentation"),a("OutboundLink")],1),e._v(".")]),e._v(" "),a("h2",{attrs:{id:"how-does-the-new-frictionless-implementation-work"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#how-does-the-new-frictionless-implementation-work"}},[e._v("#")]),e._v(" How does the new Frictionless implementation work?")]),e._v(" "),a("p",[e._v("So what does it do? Well, it can generate a datapackage.json file automatically for any number of specified directories. These Data Packages can either be generated dynamically on each access or can optionally be written back to the iRODS repository and served like any other static file stored there. Since every iRODS repository can use different metadata keys for storing the information that the Data Packages require, the required key names are completely configurable by specifying the iRODS metadata keys to use in the mod_eirods_dav configuration file and you can do things like combining the values of multiple iRODS metadata keys with generic strings to produce the value that you want to use in the Data Package. Currently the Data Package’s name, title, description, authors, unique identifier and license details are all supported. For each entry within the Data Package’s resources array, the name, path checksum and size attributes are also stored.")]),e._v(" "),a("p",[e._v("As well as standard entries within the Data Package, we also added support for Tabular Data Packages. As with standard entries, all of the keys for the column names can be generated from setting the required directives within the module configuration file.")]),e._v(" "),a("p",[a("img",{attrs:{src:"https://user-images.githubusercontent.com/74717970/110128100-b5154a00-7dc6-11eb-8d8a-a915a49e6742.png",alt:"imgblog"}}),a("br"),e._v("\nFigure1: A Data Package generated automatically by mod_eirods_dav")]),e._v(" "),a("p",[a("img",{attrs:{src:"https://user-images.githubusercontent.com/74717970/110128509-25bc6680-7dc7-11eb-8c2e-ff966169f9c5.png",alt:"imgblog2"}}),a("br"),e._v("\nFigure2: Tabular Data Package generated automatically by mod_eirods_dav")]),e._v(" "),a("h2",{attrs:{id:"adding-ckan-support"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#adding-ckan-support"}},[e._v("#")]),e._v(" Adding CKAN support")]),e._v(" "),a("p",[e._v("The second of the tools that we have implemented Frictionless Data support for is the DFW CKAN website. Primarily we use this to store publications from the project output. We currently have over 300 entries in there and since its collection is getting larger and larger, we needed a more manageable way of having better data integration, especially when using other systems through the projects by our collaborators.")]),e._v(" "),a("p",[e._v("So we built a simple Python Django webapp to do this:")]),e._v(" "),a("p",[a("img",{attrs:{src:"https://user-images.githubusercontent.com/74717970/110128662-58fef580-7dc7-11eb-88c9-46e8e36b4def.png",alt:"imgblog3"}})]),e._v(" "),a("p",[e._v("By querying the REST API provided by CKAN and getting the datasets’ metadata as JSON output, followed by using the "),a("a",{attrs:{href:"https://github.com/frictionlessdata/frictionless-ckan-mapper",target:"_blank",rel:"noopener noreferrer"}},[e._v("Frictionless CKAN Mapper"),a("OutboundLink")],1),e._v(", the JSON is converted into datapackage.json, to conform with Frictionless Data standard. If any of the resources under a dataset is CSV, the headings will be extracted as the "),a("a",{attrs:{href:"https://specs.frictionlessdata.io/table-schema/",target:"_blank",rel:"noopener noreferrer"}},[e._v("tabular data package schema"),a("OutboundLink")],1),e._v(" and integrated into the datapackage.json file itself. As well as providing the datapackage.json file as a download through the Django web app, it is also possible to push the datapackage.json back to the CKAN as a resource file on the page. This requires the CKAN user key with the relevant permissions.")]),e._v(" "),a("p",[a("img",{attrs:{src:"https://user-images.githubusercontent.com/74717970/110128881-94012900-7dc7-11eb-9833-e46f351477be.png",alt:"imgblog4"}})]),e._v(" "),a("h2",{attrs:{id:"how-can-you-try-this-tool"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#how-can-you-try-this-tool"}},[e._v("#")]),e._v(" How can you try this tool?")]),e._v(" "),a("p",[e._v("The tool can be used by accessing its REST interface:")]),e._v(" "),a("ul",[a("li",[a("code",[e._v("/convert?q={ckan-dataset-id}")]),e._v(" - convert CKAN dataset json to datapackage json e.g. /convert?q=0c03fa08-2142-426b-b1ca-fa852f909aa6")]),e._v(" "),a("li",[a("code",[e._v("/convert_resources?q={ckan-dataset-id}")]),e._v(" - convert CKAN dataset json to datapackage json with resources, also if any of the resources files are CSV files, the tabular data package will be converted. e.g. /convert_resources?q=grassroots-frictionless-data-test")]),e._v(" "),a("li",[a("code",[e._v("/convert_push?q={ckan-dataset-id}&key={ckan-user-key}")]),e._v(" - push the generated datapackage.json to the CKAN entry."),a("br"),e._v("\nAn example REST query page:")])]),e._v(" "),a("p",[a("img",{attrs:{src:"https://user-images.githubusercontent.com/74717970/110129172-efcbb200-7dc7-11eb-9230-a70cbbd6d9cf.png",alt:"imgblog5"}})]),e._v(" "),a("p",[e._v("It is possible to have your own local deployment of the tool too by downloading the web app from its Github repository, installing the requirements, and running the server with")]),e._v(" "),a("p",[a("code",[e._v("$manage.py runserver 8000")])]),e._v(" "),a("p",[e._v("Our collaborators can utilise the datapackage.json and integrate the CKAN entries to their own tools or project with ease as it conforms to the Frictionless Data standard.")]),e._v(" "),a("h2",{attrs:{id:"next-steps-for-frictionlessly-designing-future-wheat"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#next-steps-for-frictionlessly-designing-future-wheat"}},[e._v("#")]),e._v(" Next Steps for Frictionlessly Designing Future Wheat")]),e._v(" "),a("p",[e._v("It has been a hugely positive step to implement support for Frictionless Data Packages and we’ve already used these packages ourselves after two of our servers decided to fall over within three days of each other! Our future plans are to add support for further metadata keys within the datapackage.json files and expose more datasets as Frictionless Data Packages. For the CKAN-side, there are a few improvements that can be made in future: firstly, make the base CKAN url configurable in a config file, so this can be used for any CKAN website. Secondly, create a docker file to include the whole Django app, so it is more portable and easier to be deployed. You can keep track of the project at the following links:")]),e._v(" "),a("ul",[a("li",[e._v("The Designing Future Wheat Data Portal: "),a("a",{attrs:{href:"https://opendata.earlham.ac.uk/wheat/under_license/toronto/",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://opendata.earlham.ac.uk/wheat/under_license/toronto/"),a("OutboundLink")],1)]),e._v(" "),a("li",[e._v("The Designing Future Wheat publications portal: "),a("a",{attrs:{href:"https://ckan.grassroots.tools",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://ckan.grassroots.tools"),a("OutboundLink")],1)]),e._v(" "),a("li",[e._v("mod_eirods_dav: "),a("a",{attrs:{href:"https://github.com/billyfish/eirods-dav",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://github.com/billyfish/eirods-dav"),a("OutboundLink")],1)]),e._v(" "),a("li",[e._v("CKAN Frictionless Data web application: "),a("a",{attrs:{href:"https://github.com/TGAC/ckan-frictionlessdata",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://github.com/TGAC/ckan-frictionlessdata"),a("OutboundLink")],1)])])])}),[],!1,null,null,null);t.default=o.exports}}]); \ No newline at end of file diff --git a/assets/js/125.99f811c5.js b/assets/js/125.ac9d6293.js similarity index 99% rename from assets/js/125.99f811c5.js rename to assets/js/125.ac9d6293.js index 6c9cb2e17..d40ae0278 100644 --- a/assets/js/125.99f811c5.js +++ b/assets/js/125.ac9d6293.js @@ -1 +1 @@ -(window.webpackJsonp=window.webpackJsonp||[]).push([[125],{659:function(e,a,t){"use strict";t.r(a);var o=t(29),r=Object(o.a)({},(function(){var e=this,a=e.$createElement,t=e._self._c||a;return t("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[t("p",[e._v("The “reproducibility crisis” is a hot topic in scientific research these days. Can you reproduce published data from another laboratory? Can you follow the published scientific methods and get the same result? Unfortunately, the answer to these questions is often no.")]),e._v(" "),t("p",[e._v("One of the goals of Frictionless Data is to help researchers make their work more reproducible. To achieve this, we focus on making data more understandable (make sure to document your metadata!), of higher quality (via validation checks), and easier to reuse (by standardization and packaging).")]),e._v(" "),t("p",[e._v("As a test of these reproducibility measures, we tasked the Frictionless Fellows with reproducing each others’ data packages! This was a great learning experience for the Fellows and revealed some important lessons about how to make their data more (re)usable. Click on the blog links below to read more about their experiences!")]),e._v(" "),t("hr"),e._v(" "),t("h3",{attrs:{id:"reproduciendo-un-viaje-a-mo-rea-by-sele-yang-cohort-1"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#reproduciendo-un-viaje-a-mo-rea-by-sele-yang-cohort-1"}},[e._v("#")]),e._v(" "),t("a",{attrs:{href:"https://fellows.frictionlessdata.io/blog/sele-partner-blog/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Reproduciendo un viaje a Mo’rea by Sele Yang"),t("OutboundLink")],1),e._v(" (Cohort 1)")]),e._v(" "),t("p",[e._v("“Mi viaje a través de los datos de Lily, me llevó a Mo’rea, Polinesia Francesa, desde donde ella, a través de diferentes herramientas, recopiló un total de 175 entrevistas entre residentes y también investigadores/as de la región…Para reproducir los datos de Lily, utilicé inicialmente el DataPackage Creator tool para cargar su información en bruto y así empezar a revisar las especificaciones de su data type creados de manera automática por la herramienta.”")]),e._v(" "),t("hr"),e._v(" "),t("h3",{attrs:{id:"packaging-ouso-s-data-by-lily-zhao-cohort-1"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#packaging-ouso-s-data-by-lily-zhao-cohort-1"}},[e._v("#")]),e._v(" "),t("a",{attrs:{href:"https://fellows.frictionlessdata.io/blog/lily-partner-blog/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Packaging Ouso’s Data by Lily Zhao"),t("OutboundLink")],1),e._v(" (Cohort 1)")]),e._v(" "),t("p",[e._v("“This week I had the opportunity to work with my colleague’s data. 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Remember, FD seeks to facilitate and promote open and reproducible research, consequently promoting collaboration. By trying to reproduce Monica’s work I was able to capture an error, which I highlighted for her attention, thus improved the work. 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In my fields, many researchers tend to find this in ‘the art of the footnote’, but this type of informal knowledge or context is not operationalized in any real way.”")]),e._v(" "),t("hr"),e._v(" "),t("h3",{attrs:{id:"using-frictionless-tools-to-help-you-understand-open-data-by-dani-alcala-lopez-cohort-2"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#using-frictionless-tools-to-help-you-understand-open-data-by-dani-alcala-lopez-cohort-2"}},[e._v("#")]),e._v(" "),t("a",{attrs:{href:"https://fellows.frictionlessdata.io/blog/dani-partner-blog/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Using Frictionless tools to help you understand open data by Dani Alcalá-López"),t("OutboundLink")],1),e._v(" (Cohort 2)")]),e._v(" "),t("p",[e._v("“A few weeks ago, the fellows did an interesting exercise: We would try to replicate each others DataPackages in pairs. We had spent some time before creating and validating DataPacakges with our own data. 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Since the repository was in a completely different field, I really had to read through everything very carefully, and think about the terms they used….Validating the data (to the extent that it was possible after all) was easy using the goodtables tools.”")]),e._v(" "),t("hr"),e._v(" "),t("h3",{attrs:{id:"reproducing-jacqueline-s-datapackage-and-revalidating-her-data-by-sam-wilairat-cohort-2"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#reproducing-jacqueline-s-datapackage-and-revalidating-her-data-by-sam-wilairat-cohort-2"}},[e._v("#")]),e._v(" "),t("a",{attrs:{href:"https://fellows.frictionlessdata.io/blog/sam-reproduce-blog/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Reproducing Jacqueline’s Datapackage and Revalidating her Data! 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To achieve this, we focus on making data more understandable (make sure to document your metadata!), of higher quality (via validation checks), and easier to reuse (by standardization and packaging).")]),e._v(" "),t("p",[e._v("As a test of these reproducibility measures, we tasked the Frictionless Fellows with reproducing each others’ data packages! This was a great learning experience for the Fellows and revealed some important lessons about how to make their data more (re)usable. 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See you there!")])])}),[],!1,null,null,null);t.default=a.exports}}]); \ No newline at end of file +(window.webpackJsonp=window.webpackJsonp||[]).push([[126],{657:function(e,t,r){"use strict";r.r(t);var o=r(29),a=Object(o.a)({},(function(){var e=this,t=e.$createElement,r=e._self._c||t;return r("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[r("h2",{attrs:{id:"a-recap-from-our-march-community-call"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#a-recap-from-our-march-community-call"}},[e._v("#")]),e._v(" A recap from our March community call")]),e._v(" "),r("p",[e._v("On our last Frictionless Data community call on March 25"),r("sup",[e._v("th")]),e._v(", we dealt with a very current topic thanks to Thorben Westerhuys, who presented his project on Frictionless Vaccination data.")]),e._v(" "),r("p",[e._v("To compensate for the lack of time perspective in the government data, Thorben has developed a spatiotemporal tracker for state level covid vaccination data, which takes the data provided by the government, reformats it and makes it available to everyone in a structured, more machine readable form.")]),e._v(" "),r("p",[e._v("To discover more about this great project, have a look at it on "),r("a",{attrs:{href:"https://github.com/n0rdlicht/rki-vaccination-scraper",target:"_blank",rel:"noopener noreferrer"}},[e._v("GitHub"),r("OutboundLink")],1),e._v(". 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See you there!")])])}),[],!1,null,null,null);t.default=a.exports}}]); \ No newline at end of file diff --git a/assets/js/128.a714902d.js b/assets/js/128.7f2ef645.js similarity index 99% rename from assets/js/128.a714902d.js rename to assets/js/128.7f2ef645.js index b416a1268..d4acb233f 100644 --- a/assets/js/128.a714902d.js +++ b/assets/js/128.7f2ef645.js @@ -1 +1 @@ -(window.webpackJsonp=window.webpackJsonp||[]).push([[128],{663:function(e,t,o){"use strict";o.r(t);var a=o(29),n=Object(a.a)({},(function(){var e=this,t=e.$createElement,o=e._self._c||t;return o("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[o("p",[e._v("Originally published: "),o("a",{attrs:{href:"https://blog.okfn.org/2021/04/14/unveiling-the-new-frictionless-data-documentation-portal/",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://blog.okfn.org/2021/04/14/unveiling-the-new-frictionless-data-documentation-portal/"),o("OutboundLink")],1)]),e._v(" "),o("p",[e._v("Have you used Frictionless Data documentation in the past and been confused or wanted more examples? 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The project is funded by the Sloan Foundation.")])])}),[],!1,null,null,null);t.default=n.exports}}]); \ No newline at end of file +(window.webpackJsonp=window.webpackJsonp||[]).push([[128],{659:function(e,t,o){"use strict";o.r(t);var a=o(29),n=Object(a.a)({},(function(){var e=this,t=e.$createElement,o=e._self._c||t;return o("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[o("p",[e._v("Originally published: "),o("a",{attrs:{href:"https://blog.okfn.org/2021/04/14/unveiling-the-new-frictionless-data-documentation-portal/",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://blog.okfn.org/2021/04/14/unveiling-the-new-frictionless-data-documentation-portal/"),o("OutboundLink")],1)]),e._v(" "),o("p",[e._v("Have you used Frictionless Data documentation in the past and been confused or wanted more examples? 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Please reach out to us or open a "),o("a",{attrs:{href:"https://github.com/frictionlessdata/frictionless-py/pulls",target:"_blank",rel:"noopener noreferrer"}},[e._v("pull request"),o("OutboundLink")],1),e._v(" to contribute. Everyone is welcome to contribute! Learn how to do it "),o("a",{attrs:{href:"https://framework.frictionlessdata.io/docs/development/contributing",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),o("OutboundLink")],1),e._v(".")]),e._v(" "),o("h2",{attrs:{id:"thanks-thanks-thanks"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#thanks-thanks-thanks"}},[e._v("#")]),e._v(" Thanks, thanks, thanks!")]),e._v(" "),o("p",[e._v("Once again, we are very grateful to the Open Data Institute for giving us the chance to focus on this documentation in order to improve it. We cannot thank enough all our users who took part in the feedback sessions, your contributions were precious.")]),e._v(" "),o("h2",{attrs:{id:"more-about-frictionless-data"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#more-about-frictionless-data"}},[e._v("#")]),e._v(" More about Frictionless Data")]),e._v(" "),o("p",[e._v("Frictionless Data is a set of specifications for data and metadata interoperability, accompanied by a collection of software libraries that implement these specifications, and a range of best practices for data management. The project is funded by the Sloan Foundation.")])])}),[],!1,null,null,null);t.default=n.exports}}]); \ No newline at end of file diff --git a/assets/js/129.ec58e287.js b/assets/js/129.130b6b7a.js similarity index 99% rename from assets/js/129.ec58e287.js rename to assets/js/129.130b6b7a.js index a0261a129..499c74f1f 100644 --- a/assets/js/129.ec58e287.js +++ b/assets/js/129.130b6b7a.js @@ -1 +1 @@ -(window.webpackJsonp=window.webpackJsonp||[]).push([[129],{672:function(e,t,r){"use strict";r.r(t);var a=r(29),o=Object(a.a)({},(function(){var e=this,t=e.$createElement,r=e._self._c||t;return r("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[r("p",[e._v("On our last Frictionless Data community call on April 29"),r("sup",[e._v("th")]),e._v(" we had an interactive session with our great Frictionless Data Fellows: Daniel Alcalá López, Kate Bowie, Katerina Drakoulaki, Anne Lee, Jacqueline Maasch, Evelyn Night and Samantha Wilairat.")]),e._v(" "),r("p",[e._v("The Fellows are early career researchers recruited to become champions of the Frictionless Data tools and approaches in their field. 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See you there!")])])}),[],!1,null,null,null);t.default=o.exports}}]); \ No newline at end of file +(window.webpackJsonp=window.webpackJsonp||[]).push([[129],{663:function(e,t,r){"use strict";r.r(t);var a=r(29),o=Object(a.a)({},(function(){var e=this,t=e.$createElement,r=e._self._c||t;return r("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[r("p",[e._v("On our last Frictionless Data community call on April 29"),r("sup",[e._v("th")]),e._v(" we had an interactive session with our great Frictionless Data Fellows: Daniel Alcalá López, Kate Bowie, Katerina Drakoulaki, Anne Lee, Jacqueline Maasch, Evelyn Night and Samantha Wilairat.")]),e._v(" "),r("p",[e._v("The Fellows are early career researchers recruited to become champions of the Frictionless Data tools and approaches in their field. 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For this tutorial we will use a "),o("a",{attrs:{href:"https://raw.githubusercontent.com/frictionlessdata/goodtables-py/bc6470a970aacf65f20a3ddb7f71eb05a2a31c70/data/invalid-on-structure.csv",target:"_blank",rel:"noopener noreferrer"}},[t._v("sample CSV file with errors"),o("OutboundLink")],1),t._v(".")]),t._v(" "),o("p",[t._v("Copy and paste the file’s URL to the "),o("strong",[t._v("{Source}")]),t._v(" input. When you click on the "),o("strong",[t._v("{Validate}")]),t._v(" button, "),o("a",{attrs:{href:"https://try.goodtables.io/",target:"_blank",rel:"noopener noreferrer"}},[t._v("try.goodtables.io"),o("OutboundLink")],1),t._v(" presents an exhaustive list of structural errors in your dataset.")]),t._v(" "),o("p",[o("img",{attrs:{src:a(468),alt:"Add dataset link in the Source field, or select the Upload file option"}}),o("br"),t._v(" "),o("em",[t._v("Figure 1: Add dataset link in the Source field, or select the Upload file option.")])]),t._v(" "),o("p",[t._v("If needed, you can disable two types of validation checks:")]),t._v(" "),o("ul",[o("li",[o("p",[t._v("Ignore blank rows"),o("br"),t._v("\nUse this checkbox to indicate whether blank rows should be considered as errors, or simply ignored. Check this option if missing data is a known issue that cannot be fixed immediately i.e. if you are not the owner/publisher of the data.")])]),t._v(" "),o("li",[o("p",[t._v("Ignore duplicate rows"),o("br"),t._v("\nUse this checkbox to indicate whether duplicate rows should be considered as errors, or simply ignored.")])])]),t._v(" "),o("p",[t._v("We will leave all boxes unchecked for our example. On validate, we receive a list of 12 errors as we can see in figure 7 below.")]),t._v(" "),o("p",[o("img",{attrs:{src:a(469),alt:"dataset errors outlined on try.goodtables.io"}}),o("br"),t._v(" "),o("em",[t._v("Figure 2: dataset errors outlined on "),o("a",{attrs:{href:"http://try.goodtables.io",target:"_blank",rel:"noopener noreferrer"}},[t._v("try.goodtables.io"),o("OutboundLink")],1),t._v(".")])]),t._v(" "),o("p",[o("a",{attrs:{href:"https://try.goodtables.io",target:"_blank",rel:"noopener noreferrer"}},[t._v("try.goodtables.io"),o("OutboundLink")],1),t._v(" points us to specific cells containing errors so they can be fixed easily. We can use this list as a guide to fix all errors in our data manually, and run a second validation test to confirm that all issues are resolved. If there no validations could be found, the ensuing message will be as in figure 8 below:")]),t._v(" "),o("p",[o("img",{attrs:{src:a(470),alt:"valid data message on goodtables.io"}}),o("br"),t._v(" "),o("em",[t._v("Figure 3: valid data message on "),o("a",{attrs:{href:"http://goodtables.io",target:"_blank",rel:"noopener noreferrer"}},[t._v("goodtables.io"),o("OutboundLink")],1),t._v(".")])]),t._v(" "),o("p",[t._v("Improving data quality is an iterative process that should involve data publishers and maintainers. Tools such as "),o("a",{attrs:{href:"https://try.goodtables.io",target:"_blank",rel:"noopener noreferrer"}},[t._v("try.goodtables.io"),o("OutboundLink")],1),t._v(" allow you to focus on complex errors like if the presented data is correct, instead of wasting time with simple (but very common) errors like incorrect date formats.")]),t._v(" "),o("p",[o("strong",[t._v("Validating tabular data with a schema")])]),t._v(" "),o("p",[t._v("A data schema contains information on the structure of your tabular data. Providing a data schema as part of the validation process on "),o("a",{attrs:{href:"https://try.goodtables.io",target:"_blank",rel:"noopener noreferrer"}},[t._v("try.goodtables.io"),o("OutboundLink")],1),t._v(" makes it possible to check your dataset for content errors. For example, a schema contains information on fields and their assigned data types, making it possible to highlight misplaced data i.e. text in an amounts column where numeric data is expected. If you haven’t yet, learn how to create a data schema for your data collection before continuing with this section.")]),t._v(" "),o("p",[t._v("To test how this works, you can use:")]),t._v(" "),o("ul",[o("li",[t._v("any of the data packages from "),o("a",{attrs:{href:"https://github.com/frictionlessdata/example-data-packages",target:"_blank",rel:"noopener noreferrer"}},[t._v("this Data Package collection on GitHub"),o("OutboundLink")],1),t._v(", which comprises of example data packages curated by the Frictionless Data team or")]),t._v(" "),o("li",[o("a",{attrs:{href:"http://datahub.io/core/",target:"_blank",rel:"noopener noreferrer"}},[t._v("any of the Core Data Packages on DataHub"),o("OutboundLink")],1),t._v(". The Core Data project provides essential data for data wranglers and data science community. Read more about it "),o("a",{attrs:{href:"https://datahub.io/docs/core-data",target:"_blank",rel:"noopener noreferrer"}},[t._v("here"),o("OutboundLink")],1),t._v(".")])]),t._v(" "),o("p",[t._v("In any given Data Package, the "),o("em",[t._v("datapackage.json")]),t._v(" file contains the schema and the data folder contains tabular data to be validated against the schema.")]),t._v(" "),o("p",[t._v("Often, you will find that you may be working in workflows that involve many datasets, which are updated regularly. In cases such as this, one-time validation on "),o("a",{attrs:{href:"http://try.goodtables.io",target:"_blank",rel:"noopener noreferrer"}},[t._v("try.goodtables.io"),o("OutboundLink")],1),t._v(" is probably not the answer. But fear not! Goodtables has the ability to automate the validation process so that errors are checked for continually. Find out more in our continuous and "),o("a",{attrs:{href:"/blog/2018/03/12/automatically-validated-tabular-data"}},[t._v("automated data validation section")]),t._v(".")]),t._v(" "),o("h2",{attrs:{id:"one-time-data-validation-with-goodtables-command-line-tool"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#one-time-data-validation-with-goodtables-command-line-tool"}},[t._v("#")]),t._v(" One-time data validation with goodtables command line tool")]),t._v(" "),o("p",[t._v("The same validations that we’ve done on "),o("a",{attrs:{href:"http://try.goodtables.io",target:"_blank",rel:"noopener noreferrer"}},[t._v("try.goodtables.io"),o("OutboundLink")],1),t._v(", can also be done in your local machine using goodtables. This is especially useful for big datasets, or if your data is not publicly accessible online. However, this is a slightly technical task, which requires basic knowledge of the command line (CLI). If you don’t know how to use the CLI, or are a bit rusty, we recommend you to read the "),o("a",{attrs:{href:"https://tutorial.djangogirls.org/en/intro_to_command_line/",target:"_blank",rel:"noopener noreferrer"}},[t._v("Introduction to the command-line tutorial"),o("OutboundLink")],1),t._v(" before proceeding.")]),t._v(" "),o("p",[t._v("For this section, you will need:")]),t._v(" "),o("ul",[o("li",[t._v("Python, a programming language which the goodtables command-line tool is written in - ["),o("a",{attrs:{href:"https://tutorial.djangogirls.org/en/python_installation/",target:"_blank",rel:"noopener noreferrer"}},[t._v("installation instructions"),o("OutboundLink")],1),t._v("]")]),t._v(" "),o("li",[t._v("PIP, a tool that allows you to install packages written in Python. Installing Python automatically installs PIP, but in case not - [installation instructions]")]),t._v(" "),o("li",[t._v("Basic knowledge on how to use the command-line (see the "),o("a",{attrs:{href:"https://tutorial.djangogirls.org/en/intro_to_command_line/",target:"_blank",rel:"noopener noreferrer"}},[t._v("Introduction to the command-line"),o("OutboundLink")],1),t._v(" if you want to brush up your skills)")])]),t._v(" "),o("p",[t._v("Once Python is set up, open your "),o("strong",[t._v("Terminal")]),t._v(" and install goodtables using the package manager, PIP. The command "),o("code",[t._v("pip install goodtables")]),t._v(".")]),t._v(" "),o("p",[o("img",{attrs:{src:a(471),alt:"installing goodtables command-line tool with pip in Terminal"}}),o("br"),t._v(" "),o("em",[t._v("Figure 4: installing goodtables command-line tool with pip in Terminal.")])]),t._v(" "),o("p",[t._v("To validate a data file, type goodtables followed by the path to your file i.e. "),o("code",[t._v("goodtables path/to/file.csv")]),t._v(". You can pass multiple file paths one after the other, or even the path to a "),o("em",[t._v("datapackage.json")]),t._v(" file.")]),t._v(" "),o("p",[t._v("For our first example, we will download and check "),o("a",{attrs:{href:"https://github.com/frictionlessdata/datapackage-py/blob/master/data/data.csv",target:"_blank",rel:"noopener noreferrer"}},[t._v("this simple location CSV data file"),o("OutboundLink")],1),t._v(" for errors. In the second instance, we will validate this "),o("a",{attrs:{href:"https://raw.githubusercontent.com/frictionlessdata/goodtables-py/bc6470a970aacf65f20a3ddb7f71eb05a2a31c70/data/invalid-on-structure.csv",target:"_blank",rel:"noopener noreferrer"}},[t._v("Department of Data Expenses dataset, that contains errors"),o("OutboundLink")],1),t._v(".")]),t._v(" "),o("p",[o("img",{attrs:{src:a(472),alt:"Validating data files using goodtables in Terminal"}}),o("br"),t._v(" "),o("em",[t._v("Figure 5: Validating data files using goodtables in Terminal.")])]),t._v(" "),o("p",[t._v("You can see the list of options by running "),o("code",[t._v("goodtables --help")]),t._v(". The full documentation, including the list of validation checks that can be run, is available "),o("a",{attrs:{href:"https://github.com/frictionlessdata/goodtables-py",target:"_blank",rel:"noopener noreferrer"}},[t._v("on the goodtables-py repository on GitHub"),o("OutboundLink")],1),t._v(".")]),t._v(" "),o("p",[t._v("Congratulations, you now know how to validate your tabular data using the command-line!")]),t._v(" "),o("p",[t._v("If you regularly update your data or maintain many different datasets, running the validations manually can be time-consuming. The solution is to automate this process, so the data is validated every time it changes, ensuring the errors are caught as soon as possible. Find out how to do it in the “"),o("a",{attrs:{href:"/blog/2018/03/12/automatically-validated-tabular-data"}},[t._v("Automating the validation checks")]),t._v("” section.")])])}),[],!1,null,null,null);e.default=r.exports}}]); \ No newline at end of file +(window.webpackJsonp=window.webpackJsonp||[]).push([[13],{462:function(t,e,a){t.exports=a.p+"assets/img/figure-1.ab449321.png"},463:function(t,e,a){t.exports=a.p+"assets/img/figure-2.b334817a.png"},464:function(t,e,a){t.exports=a.p+"assets/img/figure-3.7144591c.png"},465:function(t,e,a){t.exports=a.p+"assets/img/figure-4.478e4974.gif"},466:function(t,e,a){t.exports=a.p+"assets/img/figure-5.e20a138b.gif"},604:function(t,e,a){"use strict";a.r(e);var o=a(29),r=Object(o.a)({},(function(){var t=this,e=t.$createElement,o=t._self._c||e;return o("ContentSlotsDistributor",{attrs:{"slot-key":t.$parent.slotKey}},[o("p",[t._v("Errors in data are not uncommon. They also often get in the way of quick and timely data analysis for many data users. 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All cells in the header row must have a value.")])]),t._v(" "),o("tr",[o("td",[t._v("duplicate-header")]),t._v(" "),o("td",[t._v("There are multiple columns with the same name. All column names must be unique.")])]),t._v(" "),o("tr",[o("td",[t._v("blank-row")]),t._v(" "),o("td",[t._v("Rows must have at least one non-blank cell.")])]),t._v(" "),o("tr",[o("td",[t._v("duplicate-row")]),t._v(" "),o("td",[t._v("Rows can’t be duplicated.")])]),t._v(" "),o("tr",[o("td",[t._v("extra-value")]),t._v(" "),o("td",[t._v("A row has more columns than the header.")])]),t._v(" "),o("tr",[o("td",[t._v("missing-value")]),t._v(" "),o("td",[t._v("A row has less columns than the header.")])]),t._v(" "),o("tr",[o("td",[o("strong",[t._v("Content Errors")])]),t._v(" "),o("td")]),t._v(" "),o("tr",[o("td",[t._v("schema-error")]),t._v(" "),o("td",[t._v("Schema is not valid.")])]),t._v(" "),o("tr",[o("td",[t._v("non-matching-header")]),t._v(" "),o("td",[t._v("The header’s name in the schema is different from what’s in the data.")])]),t._v(" "),o("tr",[o("td",[t._v("extra-header")]),t._v(" "),o("td",[t._v("The data contains a header not defined in the schema.")])]),t._v(" "),o("tr",[o("td",[t._v("missing-header")]),t._v(" "),o("td",[t._v("The data doesn’t contain a header defined in the schema.")])]),t._v(" "),o("tr",[o("td",[t._v("type-or-format-error")]),t._v(" "),o("td",[t._v("The value can’t be cast based on the schema type and format for this field.")])])])]),t._v(" "),o("p",[o("strong",[t._v("Load tabular data for one-time validation")])]),t._v(" "),o("p",[t._v("You can add a dataset for one-time validation on "),o("a",{attrs:{href:"https://try.goodtables.io",target:"_blank",rel:"noopener noreferrer"}},[t._v("try.goodtables.io"),o("OutboundLink")],1),t._v(" in two ways:")]),t._v(" "),o("ul",[o("li",[t._v("If your tabular data is publicly available online, obtain a link to the tabular data you would like to validate and paste it in the "),o("strong",[t._v("{Source}")]),t._v(" section.")]),t._v(" "),o("li",[t._v("Alternatively, Click on the Upload file prompt in the "),o("strong",[t._v("{Source}")]),t._v(" section to load a tabular dataset from your local machine")])]),t._v(" "),o("p",[o("strong",[t._v("Validating data without a schema")])]),t._v(" "),o("p",[t._v("In this section we will illustrate how to check tabular data for structural errors on "),o("a",{attrs:{href:"https://try.goodtables.io/",target:"_blank",rel:"noopener noreferrer"}},[t._v("try.goodtables.io"),o("OutboundLink")],1),t._v(" where a data schema is not available. 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Read more about it "),o("a",{attrs:{href:"https://datahub.io/docs/core-data",target:"_blank",rel:"noopener noreferrer"}},[t._v("here"),o("OutboundLink")],1),t._v(".")])]),t._v(" "),o("p",[t._v("In any given Data Package, the "),o("em",[t._v("datapackage.json")]),t._v(" file contains the schema and the data folder contains tabular data to be validated against the schema.")]),t._v(" "),o("p",[t._v("Often, you will find that you may be working in workflows that involve many datasets, which are updated regularly. In cases such as this, one-time validation on "),o("a",{attrs:{href:"http://try.goodtables.io",target:"_blank",rel:"noopener noreferrer"}},[t._v("try.goodtables.io"),o("OutboundLink")],1),t._v(" is probably not the answer. But fear not! Goodtables has the ability to automate the validation process so that errors are checked for continually. 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Find out how to do it in the “"),o("a",{attrs:{href:"/blog/2018/03/12/automatically-validated-tabular-data"}},[t._v("Automating the validation checks")]),t._v("” section.")])])}),[],!1,null,null,null);e.default=r.exports}}]); \ No newline at end of file diff --git a/assets/js/130.233f404d.js b/assets/js/130.45ab71bf.js similarity index 98% rename from assets/js/130.233f404d.js rename to assets/js/130.45ab71bf.js index 9994ad99e..d16e6c838 100644 --- a/assets/js/130.233f404d.js +++ b/assets/js/130.45ab71bf.js @@ -1 +1 @@ -(window.webpackJsonp=window.webpackJsonp||[]).push([[130],{664:function(e,t,r){"use strict";r.r(t);var o=r(29),a=Object(o.a)({},(function(){var e=this,t=e.$createElement,r=e._self._c||t;return r("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[r("p",[e._v("On our last Frictionless Data community call on May 29"),r("sup",[e._v("th")]),e._v(" we had Simon Tyrrell and Xingdong Bian from the Earlham Institute giving a presentation on Frictionless Data for Wheat. 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See you there!")])])}),[],!1,null,null,null);t.default=a.exports}}]); \ No newline at end of file +(window.webpackJsonp=window.webpackJsonp||[]).push([[130],{735:function(e,t,r){"use strict";r.r(t);var o=r(29),a=Object(o.a)({},(function(){var e=this,t=e.$createElement,r=e._self._c||t;return r("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[r("p",[e._v("On our last Frictionless Data community call on May 29"),r("sup",[e._v("th")]),e._v(" we had Simon Tyrrell and Xingdong Bian from the Earlham Institute giving a presentation on Frictionless Data for Wheat. The project was developed during the Frictionless Toolfund 2020-2021.")]),e._v(" "),r("p",[e._v("Simon and Xingdong are part of the Designing Future Wheat, a research group studying how to increment the amount of wheat that is produced in a field in order to meet the global demand by 2050. 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We invite you watch this great demo by developer Evgeny Karev:")]),e._v(" "),a("iframe",{attrs:{width:"560",height:"315",src:"https://www.youtube.com/embed/NMg-eCbO6L0",title:"YouTube video player",frameborder:"0",allow:"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture",allowfullscreen:""}}),e._v(" "),a("p",[e._v(" ")]),e._v(" "),a("p",[e._v("You can also have a look at the "),a("a",{attrs:{href:"https://frictionlessdata.github.io/livemark/",target:"_blank",rel:"noopener noreferrer"}},[e._v("documentation on GitHub"),a("OutboundLink")],1),e._v(".")]),e._v(" "),a("h2",{attrs:{id:"what-do-you-think"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#what-do-you-think"}},[e._v("#")]),e._v(" What do you think?")]),e._v(" "),a("p",[e._v("If you create a site using Livemark, please let us know! Frictionless Data is an open source project, therefore we encourage you to give us feedback. 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You can sign up "),a("a",{attrs:{href:"https://docs.google.com/forms/d/e/1FAIpQLSeuNCopxXauMkrWvF6VHqOyHMcy54SfNDOseVXfWRQZWkvqjQ/viewform?usp=sf_link",target:"_blank",rel:"noopener noreferrer"}},[e._v("here:"),a("OutboundLink")],1)]),e._v(" "),a("p",[e._v("Do you want to share something with the community? 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See you there!")])])}),[],!1,null,null,null);t.default=o.exports}}]); \ No newline at end of file +(window.webpackJsonp=window.webpackJsonp||[]).push([[134],{664:function(e,t,a){"use strict";a.r(t);var r=a(29),o=Object(r.a)({},(function(){var e=this,t=e.$createElement,a=e._self._c||t;return a("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[a("p",[e._v("At our last Frictionless Data community call on June 24"),a("sup",[e._v("th")]),e._v(" we had Nikhil Vats giving a presentation on Frictionless Package for InterMine. The project was developed during the Frictionless Toolfund 2020-2021.")]),e._v(" "),a("p",[e._v("InterMine is an open source biological data warehouse that creates databases of biological data accessed by sophisticated web query tools. 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See you there!")])])}),[],!1,null,null,null);t.default=o.exports}}]); \ No newline at end of file diff --git a/assets/js/135.89ac1b60.js b/assets/js/135.7f2aa5bc.js similarity index 98% rename from assets/js/135.89ac1b60.js rename to assets/js/135.7f2aa5bc.js index 96b59b279..be20c219f 100644 --- a/assets/js/135.89ac1b60.js +++ b/assets/js/135.7f2aa5bc.js @@ -1 +1 @@ -(window.webpackJsonp=window.webpackJsonp||[]).push([[135],{670:function(e,t,o){"use strict";o.r(t);var n=o(29),a=Object(n.a)({},(function(){var e=this,t=e.$createElement,o=e._self._c||t;return o("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[o("p",[e._v("To say that I am proud of the "),o("RouterLink",{attrs:{to:"/blog/2020/09/01/hello-fellows-cohort2/"}},[e._v("second cohort of Frictionless Fellows")]),e._v(" is an understatement. Their insight, discussions, and breakthroughs have been a true joy to witness, and I feel so lucky to have had the chance to work and learn with each of them. 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With the support of OKF, I’ve learned how to give presentations in public, and think about data in ways I never had before.”")])]),e._v(" "),o("li",[o("p",[o("a",{attrs:{href:"https://fellows.frictionlessdata.io/blog/evelyn-final-blog/",target:"_blank",rel:"noopener noreferrer"}},[e._v("A done deal - by Evelyn Night"),o("OutboundLink")],1),o("br"),e._v("\n“The fellowship was both exhilarating and educative. I got to engage in Open Science conversations, learned about and used frictionless tools like the Data Package Creator and Goodtables. I also navigated the open data landscape using CLI, Python, and git. I also got to engage in the Frictionless Community calls where software geniuses presented their work and also held Open science-centered conversations. These discussions enhanced my understanding of the Open Science movement and I felt a great honor to be involved in such meetings. I learned so much that the 9 months flew by.”")])]),e._v(" "),o("li",[o("p",[o("a",{attrs:{href:"https://fellows.frictionlessdata.io/blog/jacqueline-final-blog/",target:"_blank",rel:"noopener noreferrer"}},[e._v("A fellowship concludes - by Jacqueline Maasch"),o("OutboundLink")],1),o("br"),e._v("\n“It is hard to believe that my time as a Reproducible Research Fellow is over. I am most grateful for this program giving me a dedicated space in which to learn, a community with which to engage, and language with which to arm myself. I have been exposed to issues in open science that I had never encountered before, and have had the privilege of discussing these issues with people from across the world. I will miss the journal clubs the most!”")])]),e._v(" "),o("li",[o("p",[o("a",{attrs:{href:"https://fellows.frictionlessdata.io/blog/katerina-final-blog/",target:"_blank",rel:"noopener noreferrer"}},[e._v("My experience in the fellows program - a reflection - by Katerina Drakoulaki"),o("OutboundLink")],1),o("br"),e._v("\n“I got into the fellowship just with the hope of getting the opportunity to learn things I didn’t have the opportunity to learn on my own. That is, I did not have specific expectations, I was (and still am) grateful to be in. I feel that all the implicit expectations I might have had are all fulfilled. I got an amazing boost in my digital skills altogether and I know exactly why (no I did not gain a few IQ points). I was in a helpful community and I matured in a way that enabled me to have more of a growth mindset. I also saw other people ‘fail’, as in having their code not working and having to google the solution! I have to say all the readings, the discussions, the tutorials, the Frictionless tools have been amazing, but this shift in my mindset has been the greatest gift the fellowship has given me.”")])])]),e._v(" "),o("p",[e._v("Thank you Fellows! 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Their insight, discussions, and breakthroughs have been a true joy to witness, and I feel so lucky to have had the chance to work and learn with each of them. Over the last 9 months, they not only learned about Frictionless Data tooling, how to make their research more reproducible, and how to advocate for open science, they also gave many presentations (some for the first time in public!), published papers, wrote dissertations, and gained confidence in their coding skills. I know each of them will be a leader in the open space, so keep an eye on them!")],1),e._v(" "),o("p",[e._v("As a final assignment, the Fellows have written blogs reflecting upon their experiences and what they learned during the programme. I’ve copied blurbs from each below, but be sure to click on the links to read more from each Fellow!")]),e._v(" "),o("ul",[o("li",[o("p",[o("a",{attrs:{href:"https://fellows.frictionlessdata.io/blog/anne-final-blog/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Endings, Beginnings, and Reflections - by Anne Lee Steele"),o("OutboundLink")],1),o("br"),e._v("\n“What came out of this fellowship, as my colleagues have said time and time again, is much more than I ever could have imagined. Over the course of the past year, I’ve had fascinating debates with my cohort, and learned about how different disciplines unpack complex debates surrounding transparency, openness, and accessibility (as well as many other things). I’ve learned how to engage with the universe of open knowledge, and have even started working on my own related projects! With the support of OKF, I’ve learned how to give presentations in public, and think about data in ways I never had before.”")])]),e._v(" "),o("li",[o("p",[o("a",{attrs:{href:"https://fellows.frictionlessdata.io/blog/evelyn-final-blog/",target:"_blank",rel:"noopener noreferrer"}},[e._v("A done deal - by Evelyn Night"),o("OutboundLink")],1),o("br"),e._v("\n“The fellowship was both exhilarating and educative. I got to engage in Open Science conversations, learned about and used frictionless tools like the Data Package Creator and Goodtables. I also navigated the open data landscape using CLI, Python, and git. I also got to engage in the Frictionless Community calls where software geniuses presented their work and also held Open science-centered conversations. These discussions enhanced my understanding of the Open Science movement and I felt a great honor to be involved in such meetings. I learned so much that the 9 months flew by.”")])]),e._v(" "),o("li",[o("p",[o("a",{attrs:{href:"https://fellows.frictionlessdata.io/blog/jacqueline-final-blog/",target:"_blank",rel:"noopener noreferrer"}},[e._v("A fellowship concludes - by Jacqueline Maasch"),o("OutboundLink")],1),o("br"),e._v("\n“It is hard to believe that my time as a Reproducible Research Fellow is over. I am most grateful for this program giving me a dedicated space in which to learn, a community with which to engage, and language with which to arm myself. I have been exposed to issues in open science that I had never encountered before, and have had the privilege of discussing these issues with people from across the world. I will miss the journal clubs the most!”")])]),e._v(" "),o("li",[o("p",[o("a",{attrs:{href:"https://fellows.frictionlessdata.io/blog/katerina-final-blog/",target:"_blank",rel:"noopener noreferrer"}},[e._v("My experience in the fellows program - a reflection - by Katerina Drakoulaki"),o("OutboundLink")],1),o("br"),e._v("\n“I got into the fellowship just with the hope of getting the opportunity to learn things I didn’t have the opportunity to learn on my own. That is, I did not have specific expectations, I was (and still am) grateful to be in. I feel that all the implicit expectations I might have had are all fulfilled. I got an amazing boost in my digital skills altogether and I know exactly why (no I did not gain a few IQ points). I was in a helpful community and I matured in a way that enabled me to have more of a growth mindset. I also saw other people ‘fail’, as in having their code not working and having to google the solution! I have to say all the readings, the discussions, the tutorials, the Frictionless tools have been amazing, but this shift in my mindset has been the greatest gift the fellowship has given me.”")])])]),e._v(" "),o("p",[e._v("Thank you Fellows! As a bonus, here are the reflections from the first cohort of Fellows: "),o("a",{attrs:{href:"https://blog.okfn.org/2020/06/09/reflecting-on-the-first-cohort-of-frictionless-data-reproducible-research-fellows/",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://blog.okfn.org/2020/06/09/reflecting-on-the-first-cohort-of-frictionless-data-reproducible-research-fellows/"),o("OutboundLink")],1)])])}),[],!1,null,null,null);t.default=a.exports}}]); \ No newline at end of file diff --git a/assets/js/136.0fe7a54b.js b/assets/js/136.46fb5468.js similarity index 99% rename from assets/js/136.0fe7a54b.js rename to assets/js/136.46fb5468.js index 2d8768356..1aabd73dc 100644 --- a/assets/js/136.0fe7a54b.js +++ b/assets/js/136.46fb5468.js @@ -1 +1 @@ -(window.webpackJsonp=window.webpackJsonp||[]).push([[136],{666:function(e,a,t){"use strict";t.r(a);var n=t(29),r=Object(n.a)({},(function(){var e=this,a=e.$createElement,t=e._self._c||a;return t("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[t("p",[t("a",{attrs:{href:"https://www.opendatablend.io",target:"_blank",rel:"noopener noreferrer"}},[e._v("Open Data Blend"),t("OutboundLink")],1),e._v(" is a set of open data services that aim to make large and complex UK open data easier to analyse. We source the raw open data, transform it into "),t("a",{attrs:{href:"https://en.wikipedia.org/wiki/Dimensional_modeling",target:"_blank",rel:"noopener noreferrer"}},[e._v("dimensional models"),t("OutboundLink")],1),e._v(" (also referred to as ‘star schemas’), cleanse and enrich it, add metadata to support its reuse, and make this processed data openly available as compressed CSV, Apache ORC, and Apache Parquet data files. In summary, we provide analysis-ready open data with an emphasis on quality over quantity. We are excited to tell you more about Open Data Blend and how it uses Frictionless Data specifications to make this data easier to understand and use.")]),e._v(" "),t("p",[e._v("There are two core data services: Open Data Blend Datasets and Open Data Blend Analytics. Open Data Blend Datasets has a user interface (UI) called the "),t("a",{attrs:{href:"http://opendatablend.io/datasets",target:"_blank",rel:"noopener noreferrer"}},[e._v("Open Data Blend Dataset UI"),t("OutboundLink")],1),e._v(" and a bulk data API called the "),t("a",{attrs:{href:"https://packages.opendatablend.io/v1",target:"_blank",rel:"noopener noreferrer"}},[e._v("Open Data Blend Dataset API"),t("OutboundLink")],1),e._v(". "),t("a",{attrs:{href:"https://www.opendatablend.io/analytics",target:"_blank",rel:"noopener noreferrer"}},[e._v("Open Data blend Analytics"),t("OutboundLink")],1),e._v(" is an interactive analytical query service that can be used from popular BI tools like Excel, Power BI Desktop, and Tableau Desktop.")]),e._v(" "),t("p",[t("img",{attrs:{src:"https://user-images.githubusercontent.com/74717970/125306833-d4515480-e32f-11eb-8a6d-306ce25cc854.png",alt:"open-data-blend-home-page"}})]),e._v(" "),t("h2",{attrs:{id:"why-open-data-blend-was-created"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#why-open-data-blend-was-created"}},[e._v("#")]),e._v(" Why Open Data Blend Was Created")]),e._v(" "),t("p",[e._v("The idea behind Open Data Blend was born at "),t("a",{attrs:{href:"https://www.nimblelearn.com/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Nimble Learn"),t("OutboundLink")],1),e._v(" in 2014 after several pain points were experienced when working with large and complex UK open datasets. One of these pain points was that a significant effort, and access to large computational resources, was needed to prepare the data for analysis in a reasonable timeframe. Another pain point was that the lookups and data dictionaries would often be buried in unstructured sources like Word documents, PDF files, and web pages.")]),e._v(" "),t("h2",{attrs:{id:"our-frictionless-data-journey"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#our-frictionless-data-journey"}},[e._v("#")]),e._v(" Our Frictionless Data Journey")]),e._v(" "),t("p",[e._v("At Nimble Learn, we have over six years’ experience working with the Frictionless Data specifications. We have delivered two other Frictionless Data projects to date: Data Package M and Data Package Connector.")]),e._v(" "),t("p",[t("a",{attrs:{href:"https://github.com/nimblelearn/datapackage-m",target:"_blank",rel:"noopener noreferrer"}},[e._v("Data Package M"),t("OutboundLink")],1),e._v(" is a Power Query M library that simplifies the loading of Tabular Data Packages into Excel or Power BI.")]),e._v(" "),t("p",[t("img",{attrs:{src:"https://user-images.githubusercontent.com/74717970/125307259-314d0a80-e330-11eb-938b-c70cf3de7cc6.png",alt:"data-package-m"}})]),e._v(" "),t("p",[e._v("You can read the Frictionless Data case study for Data Package M "),t("a",{attrs:{href:"https://frictionlessdata.io/blog/2018/07/20/nimblelearn/",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),t("OutboundLink")],1),e._v(".")]),e._v(" "),t("p",[t("a",{attrs:{href:"https://github.com/nimblelearn/datapackage-connector",target:"_blank",rel:"noopener noreferrer"}},[e._v("Data Package Connector"),t("OutboundLink")],1),e._v(" is a "),t("a",{attrs:{href:"https://docs.microsoft.com/en-us/power-bi/connect-data/desktop-connector-extensibility#custom-connectors",target:"_blank",rel:"noopener noreferrer"}},[e._v("Power BI custom connector"),t("OutboundLink")],1),e._v(" that enables one or more tables from Data Packages, that implement the Table Schema specification, to be loaded directly into Power BI through the ‘Get Data’ experience.")]),e._v(" "),t("p",[t("img",{attrs:{src:"https://user-images.githubusercontent.com/74717970/125307384-4de94280-e330-11eb-9ef1-f66084ceca32.png",alt:"data-package-connector"}})]),e._v(" "),t("p",[e._v("The Frictionless Data case study for Data Package Connector can be read "),t("a",{attrs:{href:"https://frictionlessdata.io/blog/2019/07/22/nimblelearn-dpc/",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),t("OutboundLink")],1),e._v(".")]),e._v(" "),t("h2",{attrs:{id:"how-open-data-blend-uses-frictionless-data"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#how-open-data-blend-uses-frictionless-data"}},[e._v("#")]),e._v(" How Open Data Blend Uses Frictionless Data")]),e._v(" "),t("p",[e._v("During over six years of extensive research and development into open data publishing, we reviewed and evaluated several open standards that could be used as a base for our open data API. After carefully weighing the pros and cons of each, we chose to adopt the Frictionless Data specifications because they were lightweight, simple, robust, and highly scalable. We also wanted our users to benefit from the growing ecosystem of "),t("a",{attrs:{href:"https://libraries.frictionlessdata.io/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Frictionless Data tools"),t("OutboundLink")],1),e._v(" that make Frictionless Data even more accessible.")]),e._v(" "),t("p",[e._v("The Open Data Blend Dataset UI and the Open Data Blend Dataset API are both powered by Frictionless Data. When you visit the "),t("a",{attrs:{href:"https://www.opendatablend.io/datasets",target:"_blank",rel:"noopener noreferrer"}},[e._v("Open Data Blend Datasets"),t("OutboundLink")],1),e._v(" page, all of the information that you see nicely presented is coming from a data package that conforms to the "),t("a",{attrs:{href:"https://specs.frictionlessdata.io/patterns/#describing-data-package-catalogs-using-the-data-package-format",target:"_blank",rel:"noopener noreferrer"}},[e._v("Data Package Catalog pattern"),t("OutboundLink")],1),e._v(". Clicking on one of the datasets takes you to a dedicated dataset page that is driven by extended "),t("a",{attrs:{href:"https://specs.frictionlessdata.io/data-package/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Data Package metadata"),t("OutboundLink")],1),e._v(". The ‘Get metadata’ button at the top of each dataset page reveals the contents of the underlying datapackage.json file.")]),e._v(" "),t("p",[e._v("So far, we have implemented and extended the following Frictionless Data specifications and patterns:"),t("br"),e._v(" "),t("a",{attrs:{href:"https://specs.frictionlessdata.io/data-package/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Data Package"),t("OutboundLink")],1),t("br"),e._v(" "),t("a",{attrs:{href:"https://specs.frictionlessdata.io/table-schema/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Table Schema"),t("OutboundLink")],1),t("br"),e._v(" "),t("a",{attrs:{href:"https://specs.frictionlessdata.io/patterns/#describing-data-package-catalogs-using-the-data-package-format",target:"_blank",rel:"noopener noreferrer"}},[e._v("Data Catalogue pattern"),t("OutboundLink")],1),t("br"),e._v(" "),t("a",{attrs:{href:"https://specs.frictionlessdata.io/patterns/#compression-of-resources",target:"_blank",rel:"noopener noreferrer"}},[e._v("Compressed resources pattern"),t("OutboundLink")],1)]),e._v(" "),t("p",[e._v("You can see how deeply ingrained the Frictionless Data specifications are just by skimming through the Open Data Blend Dataset API "),t("a",{attrs:{href:"https://docs.opendatablend.io/open-data-blend-datasets/dataset-api",target:"_blank",rel:"noopener noreferrer"}},[e._v("reference documentation"),t("OutboundLink")],1),e._v(".")]),e._v(" "),t("h2",{attrs:{id:"how-open-data-blend-helps"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#how-open-data-blend-helps"}},[e._v("#")]),e._v(" How Open Data Blend Helps")]),e._v(" "),t("p",[e._v("Each Open Data Blend dataset is presented with helpful metadata. The data is modelled and enriched to enable effective data analysis. The columns that contain descriptive values are carefully combined into "),t("a",{attrs:{href:"https://en.wikipedia.org/wiki/Dimension_(data_warehouse)",target:"_blank",rel:"noopener noreferrer"}},[e._v("dimension tables"),t("OutboundLink")],1),e._v(" and those that contain measurable facts are grouped into "),t("a",{attrs:{href:"https://en.wikipedia.org/wiki/Fact_table",target:"_blank",rel:"noopener noreferrer"}},[e._v("fact tables"),t("OutboundLink")],1),e._v(". Modelling the data in this way makes it easier to understand and analyse. You can learn more about these dimensional modelling concepts "),t("a",{attrs:{href:"https://en.wikipedia.org/wiki/Dimensional_modeling",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),t("OutboundLink")],1),e._v(" and "),t("a",{attrs:{href:"https://en.wikipedia.org/wiki/Star_schema",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),t("OutboundLink")],1),e._v(".")]),e._v(" "),t("p",[t("img",{attrs:{src:"https://user-images.githubusercontent.com/74717970/125307186-21cdc180-e330-11eb-9650-68bbd75ea0dd.png",alt:"open-data-blend-datasets-page"}})]),e._v(" "),t("p",[e._v("In addition to CSVs, we make the data available as Apache ORC and Apache Parquet files because they are two of the most popular and efficient open file formats for analytical workloads. Libraries available for "),t("a",{attrs:{href:"https://arrow.apache.org/docs/python/parquet.html",target:"_blank",rel:"noopener noreferrer"}},[e._v("Python"),t("OutboundLink")],1),e._v(", "),t("a",{attrs:{href:"https://arrow.apache.org/docs/r/",target:"_blank",rel:"noopener noreferrer"}},[e._v("R"),t("OutboundLink")],1),e._v(", and other popular languages make it possible to query these files very quickly. If you are a data engineer, data analyst, or data scientist with access to data lake storage, such as Amazon S3 and Azure Data Lake Storage Gen2, the ORC or Parquet files can be ingested into your data lake. Once there, you can query them interactively using data lake engines like Apache Spark, Azure Synapse Analytics, Databricks, Dremio, and Trino.")]),e._v(" "),t("p",[e._v("To accelerate the data acquisition process when working with Open Data Blend datasets through code, we have developed a lightweight Python package called ‘opendatablend’. Once installed, this package allows you to effortlessly cache our data files locally with just a few lines of Python. Data engineers, data analysts, and data scientists can use the opendatablend package to get data and use it with whatever data tools they prefer. For example, a data scientist might start off doing some exploratory data analysis (EDA) in "),t("a",{attrs:{href:"https://pandas.pydata.org/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Pandas"),t("OutboundLink")],1),e._v(" or "),t("a",{attrs:{href:"https://koalas.readthedocs.io/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Koalas"),t("OutboundLink")],1),e._v(" using a "),t("a",{attrs:{href:"https://jupyter.org/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Jupyter notebook"),t("OutboundLink")],1),e._v(", transition to feature engineering, and then train and score machine learning models using "),t("a",{attrs:{href:"https://scikit-learn.org/",target:"_blank",rel:"noopener noreferrer"}},[e._v("scikit-learn"),t("OutboundLink")],1),e._v(" or "),t("a",{attrs:{href:"https://spark.apache.org/mllib/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Spark MLlib"),t("OutboundLink")],1),e._v(".")]),e._v(" "),t("p",[e._v("Below is a simple example that shows how easy the opendatablend for Python is to use:")]),e._v(" "),t("div",{staticClass:"language- extra-class"},[t("pre",{pre:!0,attrs:{class:"language-text"}},[t("code",[e._v("import opendatablend as odb\nimport pandas as pd\n\ndataset_path = 'https://packages.opendatablend.io/v1/open-data-blend-road-safety/datapackage.json'\n\n# Specify the resource name of the data file. In this example, the 'date' data file will be requested in .parquet format.\nresource_name = 'date-parquet'\n\n# Get the data and store the output object\noutput = odb.get_data(dataset_path, resource_name)\n\n# Print the file locations\nprint(output.data_file_name)\nprint(output.metadata_file_name)\n\n# Read a subset of the columns into a dataframe\ndf_date = pd.read_parquet(output.data_file_name, columns=['drv_date_key', 'drv_date', 'drv_month_name', 'drv_month_number', 'drv_quarter_name', 'drv_quarter_number', 'drv_year'])\n\n# Check the contents of the dataframe\ndf_date\n")])])]),t("p",[e._v("You can learn more about the opendatablend package "),t("a",{attrs:{href:"https://github.com/opendatablend/opendatablend-py",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),t("OutboundLink")],1),e._v(".")]),e._v(" "),t("p",[e._v("To further reduce the time to value and to make the open data insights more accessible, the "),t("a",{attrs:{href:"https://www.opendatablend.io/analytics",target:"_blank",rel:"noopener noreferrer"}},[e._v("Open Data Blend Analytics"),t("OutboundLink")],1),e._v(" service can be used with business intelligence (BI) tools like Excel, Power BI Desktop, and Tableau Desktop to directly analyse the data over a live connection. Depending on the use case, this can remove the need to work with the data files altogether.")]),e._v(" "),t("p",[t("img",{attrs:{src:"https://user-images.githubusercontent.com/74717970/125306996-f77c0400-e32f-11eb-99f4-1aa898678f9e.gif",alt:"open-data-blend-excel-experience"}})]),e._v(" "),t("h2",{attrs:{id:"want-to-learn-more-about-open-data-blend"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#want-to-learn-more-about-open-data-blend"}},[e._v("#")]),e._v(" Want to Learn More About Open Data Blend?")]),e._v(" "),t("p",[e._v("You can visit the Open Data Blend website "),t("a",{attrs:{href:"https://www.opendatablend.io",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),t("OutboundLink")],1),e._v(" to learn more about the services. We also have some comprehensive documentation available "),t("a",{attrs:{href:"https://docs.opendatablend.io/",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),t("OutboundLink")],1),e._v(", where Frictionless Data specific documentation can be found "),t("a",{attrs:{href:"https://docs.opendatablend.io/open-data-blend-datasets/frictionless-data",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),t("OutboundLink")],1),e._v(". If you would like to contribute to the project, you can find out how "),t("a",{attrs:{href:"https://www.opendatablend.io/get-involved",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),t("OutboundLink")],1),e._v(".")]),e._v(" "),t("p",[e._v("Follow us on Twitter "),t("a",{attrs:{href:"https://www.twitter.com/opendatablend",target:"_blank",rel:"noopener noreferrer"}},[e._v("@opendatablend"),t("OutboundLink")],1),e._v(" to get our latest news, feature highlights, thoughts, and tips.")])])}),[],!1,null,null,null);a.default=r.exports}}]); \ No newline at end of file +(window.webpackJsonp=window.webpackJsonp||[]).push([[136],{667:function(e,a,t){"use strict";t.r(a);var n=t(29),r=Object(n.a)({},(function(){var e=this,a=e.$createElement,t=e._self._c||a;return t("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[t("p",[t("a",{attrs:{href:"https://www.opendatablend.io",target:"_blank",rel:"noopener noreferrer"}},[e._v("Open Data Blend"),t("OutboundLink")],1),e._v(" is a set of open data services that aim to make large and complex UK open data easier to analyse. We source the raw open data, transform it into "),t("a",{attrs:{href:"https://en.wikipedia.org/wiki/Dimensional_modeling",target:"_blank",rel:"noopener noreferrer"}},[e._v("dimensional models"),t("OutboundLink")],1),e._v(" (also referred to as ‘star schemas’), cleanse and enrich it, add metadata to support its reuse, and make this processed data openly available as compressed CSV, Apache ORC, and Apache Parquet data files. In summary, we provide analysis-ready open data with an emphasis on quality over quantity. We are excited to tell you more about Open Data Blend and how it uses Frictionless Data specifications to make this data easier to understand and use.")]),e._v(" "),t("p",[e._v("There are two core data services: Open Data Blend Datasets and Open Data Blend Analytics. Open Data Blend Datasets has a user interface (UI) called the "),t("a",{attrs:{href:"http://opendatablend.io/datasets",target:"_blank",rel:"noopener noreferrer"}},[e._v("Open Data Blend Dataset UI"),t("OutboundLink")],1),e._v(" and a bulk data API called the "),t("a",{attrs:{href:"https://packages.opendatablend.io/v1",target:"_blank",rel:"noopener noreferrer"}},[e._v("Open Data Blend Dataset API"),t("OutboundLink")],1),e._v(". "),t("a",{attrs:{href:"https://www.opendatablend.io/analytics",target:"_blank",rel:"noopener noreferrer"}},[e._v("Open Data blend Analytics"),t("OutboundLink")],1),e._v(" is an interactive analytical query service that can be used from popular BI tools like Excel, Power BI Desktop, and Tableau Desktop.")]),e._v(" "),t("p",[t("img",{attrs:{src:"https://user-images.githubusercontent.com/74717970/125306833-d4515480-e32f-11eb-8a6d-306ce25cc854.png",alt:"open-data-blend-home-page"}})]),e._v(" "),t("h2",{attrs:{id:"why-open-data-blend-was-created"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#why-open-data-blend-was-created"}},[e._v("#")]),e._v(" Why Open Data Blend Was Created")]),e._v(" "),t("p",[e._v("The idea behind Open Data Blend was born at "),t("a",{attrs:{href:"https://www.nimblelearn.com/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Nimble Learn"),t("OutboundLink")],1),e._v(" in 2014 after several pain points were experienced when working with large and complex UK open datasets. One of these pain points was that a significant effort, and access to large computational resources, was needed to prepare the data for analysis in a reasonable timeframe. Another pain point was that the lookups and data dictionaries would often be buried in unstructured sources like Word documents, PDF files, and web pages.")]),e._v(" "),t("h2",{attrs:{id:"our-frictionless-data-journey"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#our-frictionless-data-journey"}},[e._v("#")]),e._v(" Our Frictionless Data Journey")]),e._v(" "),t("p",[e._v("At Nimble Learn, we have over six years’ experience working with the Frictionless Data specifications. We have delivered two other Frictionless Data projects to date: Data Package M and Data Package Connector.")]),e._v(" "),t("p",[t("a",{attrs:{href:"https://github.com/nimblelearn/datapackage-m",target:"_blank",rel:"noopener noreferrer"}},[e._v("Data Package M"),t("OutboundLink")],1),e._v(" is a Power Query M library that simplifies the loading of Tabular Data Packages into Excel or Power BI.")]),e._v(" "),t("p",[t("img",{attrs:{src:"https://user-images.githubusercontent.com/74717970/125307259-314d0a80-e330-11eb-938b-c70cf3de7cc6.png",alt:"data-package-m"}})]),e._v(" "),t("p",[e._v("You can read the Frictionless Data case study for Data Package M "),t("a",{attrs:{href:"https://frictionlessdata.io/blog/2018/07/20/nimblelearn/",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),t("OutboundLink")],1),e._v(".")]),e._v(" "),t("p",[t("a",{attrs:{href:"https://github.com/nimblelearn/datapackage-connector",target:"_blank",rel:"noopener noreferrer"}},[e._v("Data Package Connector"),t("OutboundLink")],1),e._v(" is a "),t("a",{attrs:{href:"https://docs.microsoft.com/en-us/power-bi/connect-data/desktop-connector-extensibility#custom-connectors",target:"_blank",rel:"noopener noreferrer"}},[e._v("Power BI custom connector"),t("OutboundLink")],1),e._v(" that enables one or more tables from Data Packages, that implement the Table Schema specification, to be loaded directly into Power BI through the ‘Get Data’ experience.")]),e._v(" "),t("p",[t("img",{attrs:{src:"https://user-images.githubusercontent.com/74717970/125307384-4de94280-e330-11eb-9ef1-f66084ceca32.png",alt:"data-package-connector"}})]),e._v(" "),t("p",[e._v("The Frictionless Data case study for Data Package Connector can be read "),t("a",{attrs:{href:"https://frictionlessdata.io/blog/2019/07/22/nimblelearn-dpc/",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),t("OutboundLink")],1),e._v(".")]),e._v(" "),t("h2",{attrs:{id:"how-open-data-blend-uses-frictionless-data"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#how-open-data-blend-uses-frictionless-data"}},[e._v("#")]),e._v(" How Open Data Blend Uses Frictionless Data")]),e._v(" "),t("p",[e._v("During over six years of extensive research and development into open data publishing, we reviewed and evaluated several open standards that could be used as a base for our open data API. After carefully weighing the pros and cons of each, we chose to adopt the Frictionless Data specifications because they were lightweight, simple, robust, and highly scalable. We also wanted our users to benefit from the growing ecosystem of "),t("a",{attrs:{href:"https://libraries.frictionlessdata.io/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Frictionless Data tools"),t("OutboundLink")],1),e._v(" that make Frictionless Data even more accessible.")]),e._v(" "),t("p",[e._v("The Open Data Blend Dataset UI and the Open Data Blend Dataset API are both powered by Frictionless Data. When you visit the "),t("a",{attrs:{href:"https://www.opendatablend.io/datasets",target:"_blank",rel:"noopener noreferrer"}},[e._v("Open Data Blend Datasets"),t("OutboundLink")],1),e._v(" page, all of the information that you see nicely presented is coming from a data package that conforms to the "),t("a",{attrs:{href:"https://specs.frictionlessdata.io/patterns/#describing-data-package-catalogs-using-the-data-package-format",target:"_blank",rel:"noopener noreferrer"}},[e._v("Data Package Catalog pattern"),t("OutboundLink")],1),e._v(". Clicking on one of the datasets takes you to a dedicated dataset page that is driven by extended "),t("a",{attrs:{href:"https://specs.frictionlessdata.io/data-package/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Data Package metadata"),t("OutboundLink")],1),e._v(". The ‘Get metadata’ button at the top of each dataset page reveals the contents of the underlying datapackage.json file.")]),e._v(" "),t("p",[e._v("So far, we have implemented and extended the following Frictionless Data specifications and patterns:"),t("br"),e._v(" "),t("a",{attrs:{href:"https://specs.frictionlessdata.io/data-package/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Data Package"),t("OutboundLink")],1),t("br"),e._v(" "),t("a",{attrs:{href:"https://specs.frictionlessdata.io/table-schema/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Table Schema"),t("OutboundLink")],1),t("br"),e._v(" "),t("a",{attrs:{href:"https://specs.frictionlessdata.io/patterns/#describing-data-package-catalogs-using-the-data-package-format",target:"_blank",rel:"noopener noreferrer"}},[e._v("Data Catalogue pattern"),t("OutboundLink")],1),t("br"),e._v(" "),t("a",{attrs:{href:"https://specs.frictionlessdata.io/patterns/#compression-of-resources",target:"_blank",rel:"noopener noreferrer"}},[e._v("Compressed resources pattern"),t("OutboundLink")],1)]),e._v(" "),t("p",[e._v("You can see how deeply ingrained the Frictionless Data specifications are just by skimming through the Open Data Blend Dataset API "),t("a",{attrs:{href:"https://docs.opendatablend.io/open-data-blend-datasets/dataset-api",target:"_blank",rel:"noopener noreferrer"}},[e._v("reference documentation"),t("OutboundLink")],1),e._v(".")]),e._v(" "),t("h2",{attrs:{id:"how-open-data-blend-helps"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#how-open-data-blend-helps"}},[e._v("#")]),e._v(" How Open Data Blend Helps")]),e._v(" "),t("p",[e._v("Each Open Data Blend dataset is presented with helpful metadata. The data is modelled and enriched to enable effective data analysis. The columns that contain descriptive values are carefully combined into "),t("a",{attrs:{href:"https://en.wikipedia.org/wiki/Dimension_(data_warehouse)",target:"_blank",rel:"noopener noreferrer"}},[e._v("dimension tables"),t("OutboundLink")],1),e._v(" and those that contain measurable facts are grouped into "),t("a",{attrs:{href:"https://en.wikipedia.org/wiki/Fact_table",target:"_blank",rel:"noopener noreferrer"}},[e._v("fact tables"),t("OutboundLink")],1),e._v(". Modelling the data in this way makes it easier to understand and analyse. You can learn more about these dimensional modelling concepts "),t("a",{attrs:{href:"https://en.wikipedia.org/wiki/Dimensional_modeling",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),t("OutboundLink")],1),e._v(" and "),t("a",{attrs:{href:"https://en.wikipedia.org/wiki/Star_schema",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),t("OutboundLink")],1),e._v(".")]),e._v(" "),t("p",[t("img",{attrs:{src:"https://user-images.githubusercontent.com/74717970/125307186-21cdc180-e330-11eb-9650-68bbd75ea0dd.png",alt:"open-data-blend-datasets-page"}})]),e._v(" "),t("p",[e._v("In addition to CSVs, we make the data available as Apache ORC and Apache Parquet files because they are two of the most popular and efficient open file formats for analytical workloads. Libraries available for "),t("a",{attrs:{href:"https://arrow.apache.org/docs/python/parquet.html",target:"_blank",rel:"noopener noreferrer"}},[e._v("Python"),t("OutboundLink")],1),e._v(", "),t("a",{attrs:{href:"https://arrow.apache.org/docs/r/",target:"_blank",rel:"noopener noreferrer"}},[e._v("R"),t("OutboundLink")],1),e._v(", and other popular languages make it possible to query these files very quickly. If you are a data engineer, data analyst, or data scientist with access to data lake storage, such as Amazon S3 and Azure Data Lake Storage Gen2, the ORC or Parquet files can be ingested into your data lake. Once there, you can query them interactively using data lake engines like Apache Spark, Azure Synapse Analytics, Databricks, Dremio, and Trino.")]),e._v(" "),t("p",[e._v("To accelerate the data acquisition process when working with Open Data Blend datasets through code, we have developed a lightweight Python package called ‘opendatablend’. Once installed, this package allows you to effortlessly cache our data files locally with just a few lines of Python. Data engineers, data analysts, and data scientists can use the opendatablend package to get data and use it with whatever data tools they prefer. For example, a data scientist might start off doing some exploratory data analysis (EDA) in "),t("a",{attrs:{href:"https://pandas.pydata.org/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Pandas"),t("OutboundLink")],1),e._v(" or "),t("a",{attrs:{href:"https://koalas.readthedocs.io/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Koalas"),t("OutboundLink")],1),e._v(" using a "),t("a",{attrs:{href:"https://jupyter.org/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Jupyter notebook"),t("OutboundLink")],1),e._v(", transition to feature engineering, and then train and score machine learning models using "),t("a",{attrs:{href:"https://scikit-learn.org/",target:"_blank",rel:"noopener noreferrer"}},[e._v("scikit-learn"),t("OutboundLink")],1),e._v(" or "),t("a",{attrs:{href:"https://spark.apache.org/mllib/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Spark MLlib"),t("OutboundLink")],1),e._v(".")]),e._v(" "),t("p",[e._v("Below is a simple example that shows how easy the opendatablend for Python is to use:")]),e._v(" "),t("div",{staticClass:"language- extra-class"},[t("pre",{pre:!0,attrs:{class:"language-text"}},[t("code",[e._v("import opendatablend as odb\nimport pandas as pd\n\ndataset_path = 'https://packages.opendatablend.io/v1/open-data-blend-road-safety/datapackage.json'\n\n# Specify the resource name of the data file. In this example, the 'date' data file will be requested in .parquet format.\nresource_name = 'date-parquet'\n\n# Get the data and store the output object\noutput = odb.get_data(dataset_path, resource_name)\n\n# Print the file locations\nprint(output.data_file_name)\nprint(output.metadata_file_name)\n\n# Read a subset of the columns into a dataframe\ndf_date = pd.read_parquet(output.data_file_name, columns=['drv_date_key', 'drv_date', 'drv_month_name', 'drv_month_number', 'drv_quarter_name', 'drv_quarter_number', 'drv_year'])\n\n# Check the contents of the dataframe\ndf_date\n")])])]),t("p",[e._v("You can learn more about the opendatablend package "),t("a",{attrs:{href:"https://github.com/opendatablend/opendatablend-py",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),t("OutboundLink")],1),e._v(".")]),e._v(" "),t("p",[e._v("To further reduce the time to value and to make the open data insights more accessible, the "),t("a",{attrs:{href:"https://www.opendatablend.io/analytics",target:"_blank",rel:"noopener noreferrer"}},[e._v("Open Data Blend Analytics"),t("OutboundLink")],1),e._v(" service can be used with business intelligence (BI) tools like Excel, Power BI Desktop, and Tableau Desktop to directly analyse the data over a live connection. Depending on the use case, this can remove the need to work with the data files altogether.")]),e._v(" "),t("p",[t("img",{attrs:{src:"https://user-images.githubusercontent.com/74717970/125306996-f77c0400-e32f-11eb-99f4-1aa898678f9e.gif",alt:"open-data-blend-excel-experience"}})]),e._v(" "),t("h2",{attrs:{id:"want-to-learn-more-about-open-data-blend"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#want-to-learn-more-about-open-data-blend"}},[e._v("#")]),e._v(" Want to Learn More About Open Data Blend?")]),e._v(" "),t("p",[e._v("You can visit the Open Data Blend website "),t("a",{attrs:{href:"https://www.opendatablend.io",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),t("OutboundLink")],1),e._v(" to learn more about the services. We also have some comprehensive documentation available "),t("a",{attrs:{href:"https://docs.opendatablend.io/",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),t("OutboundLink")],1),e._v(", where Frictionless Data specific documentation can be found "),t("a",{attrs:{href:"https://docs.opendatablend.io/open-data-blend-datasets/frictionless-data",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),t("OutboundLink")],1),e._v(". If you would like to contribute to the project, you can find out how "),t("a",{attrs:{href:"https://www.opendatablend.io/get-involved",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),t("OutboundLink")],1),e._v(".")]),e._v(" "),t("p",[e._v("Follow us on Twitter "),t("a",{attrs:{href:"https://www.twitter.com/opendatablend",target:"_blank",rel:"noopener noreferrer"}},[e._v("@opendatablend"),t("OutboundLink")],1),e._v(" to get our latest news, feature highlights, thoughts, and tips.")])])}),[],!1,null,null,null);a.default=r.exports}}]); \ No newline at end of file diff --git a/assets/js/14.b66cad77.js b/assets/js/14.6550d494.js similarity index 92% rename from assets/js/14.b66cad77.js rename to assets/js/14.6550d494.js index 74ae15d8d..93f377944 100644 --- a/assets/js/14.b66cad77.js +++ b/assets/js/14.6550d494.js @@ -1 +1 @@ -(window.webpackJsonp=window.webpackJsonp||[]).push([[14],{431:function(e,t,a){e.exports=a.p+"assets/img/openml-dashboard-intro.4d6e718b.png"},432:function(e,t,a){e.exports=a.p+"assets/img/openml-upload-data.79f2aa75.png"},433:function(e,t,a){e.exports=a.p+"assets/img/openml-dataset-list.9aa35113.png"},434:function(e,t,a){e.exports=a.p+"assets/img/openml-dataset-overview.14685b05.png"},582:function(e,t,a){"use strict";a.r(t);var o=a(29),r=Object(o.a)({},(function(){var e=this,t=e.$createElement,o=e._self._c||t;return o("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[o("p",[o("a",{attrs:{href:"http://openml.org",target:"_blank",rel:"noopener noreferrer"}},[e._v("OpenML"),o("OutboundLink")],1),e._v(" is an online platform and service for machine learning, whose goal is to make machine learning and data analysis simple, accessible, collaborative and open with an optimal division of labour between computers and humans. 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One of the OpenML core members is also involved in School of Data and used data packages in one of the open data workshops from School of Data Switzerland. In the coming months, we are looking to adopt "),o("a",{attrs:{href:"https://specs.frictionlessdata.io/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Frictionless Data specifications"),o("OutboundLink")],1),e._v(" to improve user friendliness on OpenML. We hope to make it possible for users to upload and connect datasets in "),o("a",{attrs:{href:"https://specs.frictionlessdata.io/data-package/",target:"_blank",rel:"noopener noreferrer"}},[e._v("data packages format"),o("OutboundLink")],1),e._v(". This will be a great shift because it would enable people to easily build and share machine learning models trained on any dataset in the frictionless data ecosystem.")]),e._v(" "),o("p",[e._v("OpenML currently works with tabular data in Attribute Relation File Format ("),o("a",{attrs:{href:"https://weka.wikispaces.com/ARFF+%28stable+version%29",target:"_blank",rel:"noopener noreferrer"}},[e._v("ARFF"),o("OutboundLink")],1),e._v(") accompanied by metadata in an XML or JSON file. It is actually very similar to Frictionless Data’s "),o("a",{attrs:{href:"https://specs.frictionlessdata.io/tabular-data-package/",target:"_blank",rel:"noopener noreferrer"}},[e._v("tabular data package"),o("OutboundLink")],1),e._v(" specification, but with ARFF instead of csv.")]),e._v(" "),o("p",[o("img",{attrs:{src:a(433),alt:""}}),o("br"),e._v(" "),o("em",[e._v("Image of dataset list on OpenML")])]),e._v(" "),o("p",[e._v("ARFF (Attribute-Relation File Format) is a CSV file with a header that lists the names of the attributes (columns) and their data types. Especially the latter is very important to do data analysis. For instance, say that you have a column with values 1,2,3. It is very important to know whether that is just a number (1,2,3 ice creams), a rank (1st, 2nd, 3rd place), or a category (item 1, item 2, item 3). This is missing from CSV data. ARFF also allows to connect multiple tables together, although we don’t really use this right now.")]),e._v(" "),o("p",[o("img",{attrs:{src:a(434),alt:""}}),o("br"),e._v(" "),o("em",[e._v("Image of a dataset overview on openML")])]),e._v(" "),o("p",[e._v("The metadata is free-form information about the dataset. It is mostly key-value data, although some values are more structured. It is stored in our database and exported to simple JSON or XML. "),o("a",{attrs:{href:"https://www.openml.org/d/2/json",target:"_blank",rel:"noopener noreferrer"}},[e._v("Here’s an example"),o("OutboundLink")],1),e._v(". It covers basic information (textual description of the dataset, owner, format, license, et al) as well as statistics (number of instances, number of features, number of missing values, details about the data distribution, and results of simple machine learning algorithms run on the data), and summary statistics (mainly used for the quick overview plots).")]),e._v(" "),o("p",[e._v("We firmly believe that if data packages become the go-to specification for sharing data in scientific communities, accessibility to data that’s currently ‘hidden’ in data platforms and university libraries will improve vastly, and are keen to adopt and use the specification on OpenML in the coming months.")]),e._v(" "),o("p",[e._v("Interested in contributing to our quest to adopt the "),o("a",{attrs:{href:"https://specs.frictionlessdata.io/data-package/",target:"_blank",rel:"noopener noreferrer"}},[e._v("data package specification"),o("OutboundLink")],1),e._v(" as an import and export option for data on the OpenML platform? 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People can upload and share data sets and questions (prediction tasks) on OpenML that they then collaboratively solve using machine learning algorithms.")]),e._v(" "),o("p",[o("a",{attrs:{href:"https://www.youtube.com/embed/1N3qATxXrpE",target:"_blank",rel:"noopener noreferrer"}},[o("img",{attrs:{src:a(425),alt:""}}),o("OutboundLink")],1),o("br"),e._v(" "),o("em",[e._v("A brief introduction to openML")])]),e._v(" "),o("p",[e._v("We offer "),o("a",{attrs:{href:"https://www.openml.org/guide/api",target:"_blank",rel:"noopener noreferrer"}},[e._v("open source tools"),o("OutboundLink")],1),e._v(" to download data into your "),o("a",{attrs:{href:"https://www.openml.org/guide/integrations",target:"_blank",rel:"noopener noreferrer"}},[e._v("favorite machine learning environments"),o("OutboundLink")],1),e._v(" and work with it. You can then upload your results back onto the platform so that others can learn from you. If you have data, you can use OpenML to get insights on what machine learning method works well to answer your question. Machine Learners can use OpenML to find interesting data sets and questions that are relevant for others and also for machine learning research (e.g. learning how algorithms behave on different types of data sets).")]),e._v(" "),o("p",[e._v("Users typically store their data in all kinds of formats, which makes it hard to simplify the data upload process on OpenML. Currently we only allow data in ARFF format. We are looking to make it as easy as possible for users to upload data, download and work with data from OpenML while keeping the datasets in machine readable formats and availing metadata in easy to read formats for our users. We also like to avail datasets from other services on OpenML. Most of these external sources currently contain data in varied formats, but some i.e. "),o("a",{attrs:{href:"https://data.world/",target:"_blank",rel:"noopener noreferrer"}},[e._v("data.world"),o("OutboundLink")],1),e._v(" have started adopting and using "),o("a",{attrs:{href:"https://specs.frictionlessdata.io/data-package/",target:"_blank",rel:"noopener noreferrer"}},[e._v("data packages"),o("OutboundLink")],1),e._v(". You can read more about data.world’s adoption and use of data packages "),o("RouterLink",{attrs:{to:"/blog/2017/04/11/dataworld/"}},[e._v("here")]),e._v(" and "),o("a",{attrs:{href:"https://meta.data.world/try-this-frictionless-data-world-ad36b6422ceb",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),o("OutboundLink")],1),e._v(".")],1),e._v(" "),o("p",[o("a",{attrs:{href:"https://biteable.com/watch/upload-data-to-openml-1575659/4500a42627a119f548c7cb0ec3ec4a25ee8a576f",target:"_blank",rel:"noopener noreferrer"}},[o("img",{attrs:{src:a(426),alt:""}}),o("OutboundLink")],1),o("br"),e._v(" "),o("em",[e._v("Learn how to upload data on OpenML in 1 minute")])]),e._v(" "),o("p",[e._v("We first heard about the Frictionless Data project through "),o("a",{attrs:{href:"https://schoolofdata.org",target:"_blank",rel:"noopener noreferrer"}},[e._v("School of Data"),o("OutboundLink")],1),e._v(". One of the OpenML core members is also involved in School of Data and used data packages in one of the open data workshops from School of Data Switzerland. In the coming months, we are looking to adopt "),o("a",{attrs:{href:"https://specs.frictionlessdata.io/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Frictionless Data specifications"),o("OutboundLink")],1),e._v(" to improve user friendliness on OpenML. We hope to make it possible for users to upload and connect datasets in "),o("a",{attrs:{href:"https://specs.frictionlessdata.io/data-package/",target:"_blank",rel:"noopener noreferrer"}},[e._v("data packages format"),o("OutboundLink")],1),e._v(". This will be a great shift because it would enable people to easily build and share machine learning models trained on any dataset in the frictionless data ecosystem.")]),e._v(" "),o("p",[e._v("OpenML currently works with tabular data in Attribute Relation File Format ("),o("a",{attrs:{href:"https://weka.wikispaces.com/ARFF+%28stable+version%29",target:"_blank",rel:"noopener noreferrer"}},[e._v("ARFF"),o("OutboundLink")],1),e._v(") accompanied by metadata in an XML or JSON file. It is actually very similar to Frictionless Data’s "),o("a",{attrs:{href:"https://specs.frictionlessdata.io/tabular-data-package/",target:"_blank",rel:"noopener noreferrer"}},[e._v("tabular data package"),o("OutboundLink")],1),e._v(" specification, but with ARFF instead of csv.")]),e._v(" "),o("p",[o("img",{attrs:{src:a(427),alt:""}}),o("br"),e._v(" "),o("em",[e._v("Image of dataset list on OpenML")])]),e._v(" "),o("p",[e._v("ARFF (Attribute-Relation File Format) is a CSV file with a header that lists the names of the attributes (columns) and their data types. Especially the latter is very important to do data analysis. For instance, say that you have a column with values 1,2,3. It is very important to know whether that is just a number (1,2,3 ice creams), a rank (1st, 2nd, 3rd place), or a category (item 1, item 2, item 3). This is missing from CSV data. ARFF also allows to connect multiple tables together, although we don’t really use this right now.")]),e._v(" "),o("p",[o("img",{attrs:{src:a(428),alt:""}}),o("br"),e._v(" "),o("em",[e._v("Image of a dataset overview on openML")])]),e._v(" "),o("p",[e._v("The metadata is free-form information about the dataset. It is mostly key-value data, although some values are more structured. It is stored in our database and exported to simple JSON or XML. "),o("a",{attrs:{href:"https://www.openml.org/d/2/json",target:"_blank",rel:"noopener noreferrer"}},[e._v("Here’s an example"),o("OutboundLink")],1),e._v(". It covers basic information (textual description of the dataset, owner, format, license, et al) as well as statistics (number of instances, number of features, number of missing values, details about the data distribution, and results of simple machine learning algorithms run on the data), and summary statistics (mainly used for the quick overview plots).")]),e._v(" "),o("p",[e._v("We firmly believe that if data packages become the go-to specification for sharing data in scientific communities, accessibility to data that’s currently ‘hidden’ in data platforms and university libraries will improve vastly, and are keen to adopt and use the specification on OpenML in the coming months.")]),e._v(" "),o("p",[e._v("Interested in contributing to our quest to adopt the "),o("a",{attrs:{href:"https://specs.frictionlessdata.io/data-package/",target:"_blank",rel:"noopener noreferrer"}},[e._v("data package specification"),o("OutboundLink")],1),e._v(" as an import and export option for data on the OpenML platform? "),o("a",{attrs:{href:"https://github.com/openml/OpenML/issues/482",target:"_blank",rel:"noopener noreferrer"}},[e._v("Start here"),o("OutboundLink")],1),e._v(".")])])}),[],!1,null,null,null);t.default=r.exports}}]); \ No newline at end of file diff --git a/assets/js/140.7170293a.js b/assets/js/140.3843790f.js similarity index 98% rename from assets/js/140.7170293a.js rename to assets/js/140.3843790f.js index 5bc0987d1..6f4c4e57e 100644 --- a/assets/js/140.7170293a.js +++ b/assets/js/140.3843790f.js @@ -1 +1 @@ -(window.webpackJsonp=window.webpackJsonp||[]).push([[140],{675:function(e,a,t){"use strict";t.r(a);var r=t(29),o=Object(r.a)({},(function(){var e=this,a=e.$createElement,t=e._self._c||a;return t("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[t("p",[e._v("What happens to scientific data after it is generated? The answer is complicated - sometimes that data is shared with other researchers, sometimes it is hidden away on a private hard drive. Sharing research data is a key part of open science, the movement to make research more accessible and usable by everyone to drive faster advances in science. A great way to share research data is to upload it to a repository, but simply uploading data is not the final step here. Ideally, the uploaded data will be of high quality - that is, it won’t have errors or missing data, and it will have enough descriptive information that other researchers can also use it! Over the last 6 months, we collaborated with the data repository Dryad to make it easier for researchers to upload their high quality data for sharing.")]),e._v(" "),t("p",[t("a",{attrs:{href:"https://datadryad.org/stash",target:"_blank",rel:"noopener noreferrer"}},[e._v("Dryad"),t("OutboundLink")],1),e._v(" is a community-led data repository that allows researchers to submit data from any field, which not only promotes open science, but also helps researchers comply with open data policies from funders and journals. Because Dryad accepts all kinds of data, they need to curate that data for quality and ensure that the data does not present risk, and have comprehensive metadata to reuse the data. We quickly realized our shared goals, and formed a Pilot collaboration to add Frictionless validation functionality to the Dryad data upload page. Both teams agreed how important it is to give researchers immediate feedback about their data as they are submitting it so they can make edits in that moment, and learn about data best practices.")]),e._v(" "),t("p",[e._v("The outcome of this collaboration is a revamped upload page for the Dryad application. Researchers uploading tabular data (CSV, XLS, XLSX) under 25MB will have the files automatically validated using the Frictionless tool. These checks are based on the built-in validation of Frictionless Framework (read the validation guide "),t("a",{attrs:{href:"https://framework.frictionlessdata.io/docs/guides/validation-guide",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),t("OutboundLink")],1),e._v("), and include checking for data errors such as blank cells, missing headers, or incorrectly formatted data. The Frictionless report will help guide researchers on which issues should be resolved, allowing researchers to edit and re-upload files before submitting their dataset for curation and publication.")]),e._v(" "),t("p",[t("img",{attrs:{src:"https://user-images.githubusercontent.com/74717970/128690898-2095f1c7-060d-4398-ac92-33f65c068c4c.png",alt:"Screen Shot 2021-08-06 at 8 10 41 AM"}}),t("br"),e._v(" "),t("em",[e._v("When a data file is uploaded, researchers can see if the data passed the Tabular Data Checks or if there are any issues. Clicking to “View 1 Issues” shows more details describing the error.")])]),e._v(" "),t("p",[t("img",{attrs:{src:"https://user-images.githubusercontent.com/74717970/128690994-16be9845-59ec-4f3b-9b76-28a163dfa1e3.png",alt:"Screen Shot 2021-08-06 at 8 12 01 AM"}}),t("br"),e._v(" "),t("em",[e._v("This uploaded data file has a blank header. With this information, the researcher can fix the error and re-upload the data.")])]),e._v(" "),t("p",[e._v("This work was funded by the Sloan Foundation as part of the Frictionless Data for Reproducible Research project. This project was truly collaboratory - most of the technical work was completed by contractor Cassiano Reinert Novais dos Santos with supervision and support from the Dryad team: Daniella Lowenberg, Scott Fisher, Ryan Scherle, and the CDL UX team (Rachael Hu and John Kratz); as well as support from the Frictionless team, Evgeny Karev, Lilly Winfree, and Sara Petti. If you have any feedback on the Dryad upload page, please let us know!")])])}),[],!1,null,null,null);a.default=o.exports}}]); \ No newline at end of file +(window.webpackJsonp=window.webpackJsonp||[]).push([[140],{670:function(e,a,t){"use strict";t.r(a);var r=t(29),o=Object(r.a)({},(function(){var e=this,a=e.$createElement,t=e._self._c||a;return t("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[t("p",[e._v("What happens to scientific data after it is generated? The answer is complicated - sometimes that data is shared with other researchers, sometimes it is hidden away on a private hard drive. Sharing research data is a key part of open science, the movement to make research more accessible and usable by everyone to drive faster advances in science. A great way to share research data is to upload it to a repository, but simply uploading data is not the final step here. Ideally, the uploaded data will be of high quality - that is, it won’t have errors or missing data, and it will have enough descriptive information that other researchers can also use it! Over the last 6 months, we collaborated with the data repository Dryad to make it easier for researchers to upload their high quality data for sharing.")]),e._v(" "),t("p",[t("a",{attrs:{href:"https://datadryad.org/stash",target:"_blank",rel:"noopener noreferrer"}},[e._v("Dryad"),t("OutboundLink")],1),e._v(" is a community-led data repository that allows researchers to submit data from any field, which not only promotes open science, but also helps researchers comply with open data policies from funders and journals. Because Dryad accepts all kinds of data, they need to curate that data for quality and ensure that the data does not present risk, and have comprehensive metadata to reuse the data. We quickly realized our shared goals, and formed a Pilot collaboration to add Frictionless validation functionality to the Dryad data upload page. Both teams agreed how important it is to give researchers immediate feedback about their data as they are submitting it so they can make edits in that moment, and learn about data best practices.")]),e._v(" "),t("p",[e._v("The outcome of this collaboration is a revamped upload page for the Dryad application. Researchers uploading tabular data (CSV, XLS, XLSX) under 25MB will have the files automatically validated using the Frictionless tool. These checks are based on the built-in validation of Frictionless Framework (read the validation guide "),t("a",{attrs:{href:"https://framework.frictionlessdata.io/docs/guides/validation-guide",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),t("OutboundLink")],1),e._v("), and include checking for data errors such as blank cells, missing headers, or incorrectly formatted data. The Frictionless report will help guide researchers on which issues should be resolved, allowing researchers to edit and re-upload files before submitting their dataset for curation and publication.")]),e._v(" "),t("p",[t("img",{attrs:{src:"https://user-images.githubusercontent.com/74717970/128690898-2095f1c7-060d-4398-ac92-33f65c068c4c.png",alt:"Screen Shot 2021-08-06 at 8 10 41 AM"}}),t("br"),e._v(" "),t("em",[e._v("When a data file is uploaded, researchers can see if the data passed the Tabular Data Checks or if there are any issues. Clicking to “View 1 Issues” shows more details describing the error.")])]),e._v(" "),t("p",[t("img",{attrs:{src:"https://user-images.githubusercontent.com/74717970/128690994-16be9845-59ec-4f3b-9b76-28a163dfa1e3.png",alt:"Screen Shot 2021-08-06 at 8 12 01 AM"}}),t("br"),e._v(" "),t("em",[e._v("This uploaded data file has a blank header. With this information, the researcher can fix the error and re-upload the data.")])]),e._v(" "),t("p",[e._v("This work was funded by the Sloan Foundation as part of the Frictionless Data for Reproducible Research project. This project was truly collaboratory - most of the technical work was completed by contractor Cassiano Reinert Novais dos Santos with supervision and support from the Dryad team: Daniella Lowenberg, Scott Fisher, Ryan Scherle, and the CDL UX team (Rachael Hu and John Kratz); as well as support from the Frictionless team, Evgeny Karev, Lilly Winfree, and Sara Petti. If you have any feedback on the Dryad upload page, please let us know!")])])}),[],!1,null,null,null);a.default=o.exports}}]); \ No newline at end of file diff --git a/assets/js/141.c3c7f0da.js b/assets/js/141.4f0a46cd.js similarity index 98% rename from assets/js/141.c3c7f0da.js rename to assets/js/141.4f0a46cd.js index 4a7d79cb8..7af851d21 100644 --- a/assets/js/141.c3c7f0da.js +++ b/assets/js/141.4f0a46cd.js @@ -1 +1 @@ -(window.webpackJsonp=window.webpackJsonp||[]).push([[141],{674:function(e,t,r){"use strict";r.r(t);var o=r(29),a=Object(o.a)({},(function(){var e=this,t=e.$createElement,r=e._self._c||t;return r("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[r("p",[e._v("On our last Frictionless Data community call on August 12"),r("sup",[e._v("th")]),e._v(" we had Dave Rowe (aka Libraries Hacked) giving a presentation on Frictionless Data standards and tooling for public libraries’ data.")]),e._v(" "),r("p",[e._v("Libraries Hacked is a project promoting open data in libraries and creating digital prototypes from that data. Public libraries hold a lot of data, but this data is often not shared and it is lacking common standards for data sharing. With the introduction of data schemas, Dave developed a series of tools to show libraries what they could do with their data. For example, Dave demonstrated membership mapping, libraries maps and a "),r("a",{attrs:{href:"https://www.mobilelibraries.org/map",target:"_blank",rel:"noopener noreferrer"}},[e._v("mobile libraries dashboard"),r("OutboundLink")],1),e._v(" that displays mobile libraries vans, estimates their location and automatically generates paper timelines.")]),e._v(" "),r("p",[e._v("You can learn more about the Libraries Hacked project "),r("a",{attrs:{href:"https://www.librarieshacked.org/",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),r("OutboundLink")],1),e._v(". If you would like to dive deeper and discover all about what you can do with Frictionless library data, , you can watch Dave Rowe’ presentation here::")]),e._v(" "),r("iframe",{attrs:{width:"560",height:"315",src:"https://www.youtube.com/embed/R0U9Iwd8J00",title:"YouTube video player",frameborder:"0",allow:"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture",allowfullscreen:""}}),e._v(" "),r("h2",{attrs:{id:"other-agenda-items-from-our-hangout"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#other-agenda-items-from-our-hangout"}},[e._v("#")]),e._v(" Other agenda items from our hangout")]),e._v(" "),r("h3",{attrs:{id:"frictionless-hackathon-in-october"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#frictionless-hackathon-in-october"}},[e._v("#")]),e._v(" Frictionless Hackathon in October!")]),e._v(" "),r("p",[e._v("Join the Frictionless Data community for a two-day virtual event to create new project prototypes based on existing Frictionless open source code. It’s going to be fun!"),r("br"),e._v("\nWe need to decide on a date to hold this event, and are currently considering Thursday and Fridays in October. You can vote on Discord."),r("br"),e._v("\nKeep an eye on the website for more info: "),r("a",{attrs:{href:"https://frictionlessdata.io/hackathon/#what-s-a-hackathon",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://frictionlessdata.io/hackathon/#what-s-a-hackathon"),r("OutboundLink")],1)]),e._v(" "),r("h3",{attrs:{id:"recruiting-the-3rd-cohort-of-frictionless-fellows"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#recruiting-the-3rd-cohort-of-frictionless-fellows"}},[e._v("#")]),e._v(" Recruiting the 3rd cohort of Frictionless Fellows")]),e._v(" "),r("p",[e._v("Are you an early career researcher interested in Open Science? We are recruiting the 3rd cohort of Frictionless Fellows! During their 9-month Fellowship, Fellows will lead training workshops, host events at universities and in labs, and write blogs and other communications content. You will be mentored by Frictionless Data product manager Lilly Winfree, PhD and we will help you learn Frictionless Data tooling and software. Applications are open until August 31"),r("sup",[e._v("st")]),e._v("."),r("br"),e._v("\nMore info "),r("a",{attrs:{href:"https://frictionlessdata.io/blog/2021/08/02/apply-fellows/",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),r("OutboundLink")],1),r("br"),e._v("\nYou can apply via this "),r("a",{attrs:{href:"https://docs.google.com/forms/d/e/1FAIpQLSdR1Qz5GL5A1BrqgFxDBOXScvNoS5AeyCWixNwtcApXUttT8Q/viewform",target:"_blank",rel:"noopener noreferrer"}},[e._v("form"),r("OutboundLink")],1),e._v(".")]),e._v(" "),r("h1",{attrs:{id:"join-us-in-2-weeks"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#join-us-in-2-weeks"}},[e._v("#")]),e._v(" Join us in 2 weeks!")]),e._v(" "),r("p",[e._v("Yes, that’s right, August is our lucky month, we don’t have one, but two community calls! Our next meeting will be in just 2 weeks, on August 26"),r("sup",[e._v("th")]),e._v(". We will hear a presentation from"),r("br"),e._v("\nAmber York and Adam Shepherd from BCO-DMO on Frictionless Data Pipelines. You can sign up "),r("a",{attrs:{href:"https://docs.google.com/forms/d/e/1FAIpQLSeuNCopxXauMkrWvF6VHqOyHMcy54SfNDOseVXfWRQZWkvqjQ/viewform?usp=sf_link",target:"_blank",rel:"noopener noreferrer"}},[e._v("here:"),r("OutboundLink")],1)]),e._v(" "),r("p",[e._v("Do you want to share something with the community? Let us know when you sign up!")]),e._v(" "),r("h1",{attrs:{id:"call-recording"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#call-recording"}},[e._v("#")]),e._v(" Call recording:")]),e._v(" "),r("p",[e._v("On a final note, here is the recording of the full call:")]),e._v(" "),r("iframe",{attrs:{width:"560",height:"315",src:"https://www.youtube.com/embed/IGhcP2dDNIg",title:"YouTube video player",frameborder:"0",allow:"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture",allowfullscreen:""}}),e._v(" "),r("p",[e._v("As usual, you can join us on "),r("a",{attrs:{href:"https://discord.com/invite/j9DNFNw",target:"_blank",rel:"noopener noreferrer"}},[e._v("Discord"),r("OutboundLink")],1),e._v(" or "),r("a",{attrs:{href:"https://twitter.com/frictionlessd8a",target:"_blank",rel:"noopener noreferrer"}},[e._v("Twitter"),r("OutboundLink")],1),e._v(" to say hi or ask any questions. See you there!")])])}),[],!1,null,null,null);t.default=a.exports}}]); \ No newline at end of file +(window.webpackJsonp=window.webpackJsonp||[]).push([[141],{672:function(e,t,r){"use strict";r.r(t);var o=r(29),a=Object(o.a)({},(function(){var e=this,t=e.$createElement,r=e._self._c||t;return r("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[r("p",[e._v("On our last Frictionless Data community call on August 12"),r("sup",[e._v("th")]),e._v(" we had Dave Rowe (aka Libraries Hacked) giving a presentation on Frictionless Data standards and tooling for public libraries’ data.")]),e._v(" "),r("p",[e._v("Libraries Hacked is a project promoting open data in libraries and creating digital prototypes from that data. Public libraries hold a lot of data, but this data is often not shared and it is lacking common standards for data sharing. With the introduction of data schemas, Dave developed a series of tools to show libraries what they could do with their data. 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If you would like to dive deeper and discover all about what you can do with Frictionless library data, , you can watch Dave Rowe’ presentation here::")]),e._v(" "),r("iframe",{attrs:{width:"560",height:"315",src:"https://www.youtube.com/embed/R0U9Iwd8J00",title:"YouTube video player",frameborder:"0",allow:"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture",allowfullscreen:""}}),e._v(" "),r("h2",{attrs:{id:"other-agenda-items-from-our-hangout"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#other-agenda-items-from-our-hangout"}},[e._v("#")]),e._v(" Other agenda items from our hangout")]),e._v(" "),r("h3",{attrs:{id:"frictionless-hackathon-in-october"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#frictionless-hackathon-in-october"}},[e._v("#")]),e._v(" Frictionless Hackathon in October!")]),e._v(" "),r("p",[e._v("Join the Frictionless Data community for a two-day virtual event to create new project prototypes based on existing Frictionless open source code. It’s going to be fun!"),r("br"),e._v("\nWe need to decide on a date to hold this event, and are currently considering Thursday and Fridays in October. You can vote on Discord."),r("br"),e._v("\nKeep an eye on the website for more info: "),r("a",{attrs:{href:"https://frictionlessdata.io/hackathon/#what-s-a-hackathon",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://frictionlessdata.io/hackathon/#what-s-a-hackathon"),r("OutboundLink")],1)]),e._v(" "),r("h3",{attrs:{id:"recruiting-the-3rd-cohort-of-frictionless-fellows"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#recruiting-the-3rd-cohort-of-frictionless-fellows"}},[e._v("#")]),e._v(" Recruiting the 3rd cohort of Frictionless Fellows")]),e._v(" "),r("p",[e._v("Are you an early career researcher interested in Open Science? We are recruiting the 3rd cohort of Frictionless Fellows! During their 9-month Fellowship, Fellows will lead training workshops, host events at universities and in labs, and write blogs and other communications content. You will be mentored by Frictionless Data product manager Lilly Winfree, PhD and we will help you learn Frictionless Data tooling and software. Applications are open until August 31"),r("sup",[e._v("st")]),e._v("."),r("br"),e._v("\nMore info "),r("a",{attrs:{href:"https://frictionlessdata.io/blog/2021/08/02/apply-fellows/",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),r("OutboundLink")],1),r("br"),e._v("\nYou can apply via this "),r("a",{attrs:{href:"https://docs.google.com/forms/d/e/1FAIpQLSdR1Qz5GL5A1BrqgFxDBOXScvNoS5AeyCWixNwtcApXUttT8Q/viewform",target:"_blank",rel:"noopener noreferrer"}},[e._v("form"),r("OutboundLink")],1),e._v(".")]),e._v(" "),r("h1",{attrs:{id:"join-us-in-2-weeks"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#join-us-in-2-weeks"}},[e._v("#")]),e._v(" Join us in 2 weeks!")]),e._v(" "),r("p",[e._v("Yes, that’s right, August is our lucky month, we don’t have one, but two community calls! Our next meeting will be in just 2 weeks, on August 26"),r("sup",[e._v("th")]),e._v(". We will hear a presentation from"),r("br"),e._v("\nAmber York and Adam Shepherd from BCO-DMO on Frictionless Data Pipelines. You can sign up "),r("a",{attrs:{href:"https://docs.google.com/forms/d/e/1FAIpQLSeuNCopxXauMkrWvF6VHqOyHMcy54SfNDOseVXfWRQZWkvqjQ/viewform?usp=sf_link",target:"_blank",rel:"noopener noreferrer"}},[e._v("here:"),r("OutboundLink")],1)]),e._v(" "),r("p",[e._v("Do you want to share something with the community? Let us know when you sign up!")]),e._v(" "),r("h1",{attrs:{id:"call-recording"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#call-recording"}},[e._v("#")]),e._v(" Call recording:")]),e._v(" "),r("p",[e._v("On a final note, here is the recording of the full call:")]),e._v(" "),r("iframe",{attrs:{width:"560",height:"315",src:"https://www.youtube.com/embed/IGhcP2dDNIg",title:"YouTube video player",frameborder:"0",allow:"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture",allowfullscreen:""}}),e._v(" "),r("p",[e._v("As usual, you can join us on "),r("a",{attrs:{href:"https://discord.com/invite/j9DNFNw",target:"_blank",rel:"noopener noreferrer"}},[e._v("Discord"),r("OutboundLink")],1),e._v(" or "),r("a",{attrs:{href:"https://twitter.com/frictionlessd8a",target:"_blank",rel:"noopener noreferrer"}},[e._v("Twitter"),r("OutboundLink")],1),e._v(" to say hi or ask any questions. See you there!")])])}),[],!1,null,null,null);t.default=a.exports}}]); \ No newline at end of file diff --git a/assets/js/142.0d7bc7fa.js b/assets/js/142.3e6ea283.js similarity index 98% rename from assets/js/142.0d7bc7fa.js rename to assets/js/142.3e6ea283.js index 9d6fa47e1..535e709f6 100644 --- a/assets/js/142.0d7bc7fa.js +++ b/assets/js/142.3e6ea283.js @@ -1 +1 @@ -(window.webpackJsonp=window.webpackJsonp||[]).push([[142],{673:function(e,t,o){"use strict";o.r(t);var a=o(29),r=Object(a.a)({},(function(){var e=this,t=e.$createElement,o=e._self._c||t;return o("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[o("p",[e._v("On our last Frictionless Data community call on August 26"),o("sup",[e._v("th")]),e._v(" we had Amber York and Adam Shepherd from BCO-DMO giving a presentation on Frictionless Data Pipelines for Ocean Science.")]),e._v(" "),o("p",[e._v("BCO-DMO is a biological and chemical oceanography data management office, working with scientists to make sure that their data is publicly available and archived for everyone else to use.")]),e._v(" "),o("p",[e._v("BCO-DMO processes around 500 datasets a year, with all sorts of variability. In the beginning the staff was writing ad hoc scripts and software to process that data, but that quickly became a challenge, as the catalogue continued to grow in both size and the variety of data types it curates.")]),e._v(" "),o("p",[e._v("Having worked for several years with Frictionless Data, BCO-DMO identified the Data Package Pipelines (DPP) project in the Frictionless toolkit as key to overcoming those challenges and achieving its data curation goals."),o("br"),e._v("\nTogether with the Frictionless Data team at Open Knowledge Foundation, BCO-DMO developed Laminar, a web application to create Frictionless Data Package Pipelines. Laminar helps data managers process data efficiently while recording the provenance of their activities to support reproducibility of results")]),e._v(" "),o("p",[e._v("You can learn more on the project "),o("a",{attrs:{href:"https://frictionlessdata.io/blog/2020/02/10/frictionless-data-pipelines-for-open-ocean/",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),o("OutboundLink")],1),e._v(". If you would like to dive deeper and discover all about Frictionless Data Pipelines, you can watch Amber York’s and Adam Shepherd’s presentation:")]),e._v(" "),o("iframe",{attrs:{width:"560",height:"315",src:"https://www.youtube.com/embed/R0U9Iwd8J00",title:"YouTube video player",frameborder:"0",allow:"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture",allowfullscreen:""}}),e._v(" "),o("h2",{attrs:{id:"other-agenda-items-from-our-hangout"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#other-agenda-items-from-our-hangout"}},[e._v("#")]),e._v(" Other agenda items from our hangout")]),e._v(" "),o("h3",{attrs:{id:"frictionless-hackathon-on-7-8-october"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#frictionless-hackathon-on-7-8-october"}},[e._v("#")]),e._v(" Frictionless Hackathon on 7-8 October!")]),e._v(" "),o("p",[e._v("Join the Frictionless Data community for a two-day virtual event to create new project prototypes based on existing Frictionless open source code. It’s going to be fun!"),o("br"),e._v("\nWe are currently accepting project submissions, so if you have a cool project in mind, using based on existing Frictionless open source code, this could be an excellent opportunity to prototype it, together with other Frictionless users from all around the world. You can pitch anything - your idea doesn’t need to be complete/fully planned. We can also help you formulate a project if you have an idea but aren’t sure about it. You can also submit ideas for existing projects you need help with!")]),e._v(" "),o("p",[e._v("Use "),o("a",{attrs:{href:"https://docs.google.com/forms/d/e/1FAIpQLSdd41pbfWaCYQHkQNTaf49kht1cUg7_Tg-NzqdP11pHWrD7yA/viewform",target:"_blank",rel:"noopener noreferrer"}},[e._v("this form"),o("OutboundLink")],1),e._v(" to submit your project."),o("br"),e._v("\nKeep an eye "),o("a",{attrs:{href:"https://frictionlessdata.io/hackathon/",target:"_blank",rel:"noopener noreferrer"}},[e._v("on the website"),o("OutboundLink")],1),e._v(" for more info.")]),e._v(" "),o("h1",{attrs:{id:"join-us-next-month"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#join-us-next-month"}},[e._v("#")]),e._v(" Join us next month!")]),e._v(" "),o("p",[e._v("Our next meeting will be on September 30"),o("sup",[e._v("th")]),e._v(", exceptionally one hour later than usual. We will hear a presentation from Daniella Lowenberg and Cassiano Reinert Novais dos Santos on the Frictionless Data validation implemented for the Dryad application.")]),e._v(" "),o("p",[e._v("You can sign up "),o("a",{attrs:{href:"https://docs.google.com/forms/d/e/1FAIpQLSeuNCopxXauMkrWvF6VHqOyHMcy54SfNDOseVXfWRQZWkvqjQ/viewform?usp=sf_link",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),o("OutboundLink")],1),e._v(".")]),e._v(" "),o("p",[e._v("Do you want to share something with the community? Let us know when you sign up!")]),e._v(" "),o("h1",{attrs:{id:"call-recording"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#call-recording"}},[e._v("#")]),e._v(" Call recording:")]),e._v(" "),o("p",[e._v("On a final note, here is the recording of the full call:")]),e._v(" "),o("iframe",{attrs:{width:"560",height:"315",src:"https://www.youtube.com/embed/-y6njoJPMbE",title:"YouTube video player",frameborder:"0",allow:"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture",allowfullscreen:""}}),e._v(" "),o("p",[e._v("As usual, you can join us on "),o("a",{attrs:{href:"https://discord.com/invite/j9DNFNw",target:"_blank",rel:"noopener noreferrer"}},[e._v("Discord"),o("OutboundLink")],1),e._v(" or "),o("a",{attrs:{href:"https://twitter.com/frictionlessd8a",target:"_blank",rel:"noopener noreferrer"}},[e._v("Twitter"),o("OutboundLink")],1),e._v(" to say hi or ask any questions. See you there!")])])}),[],!1,null,null,null);t.default=r.exports}}]); \ No newline at end of file +(window.webpackJsonp=window.webpackJsonp||[]).push([[142],{674:function(e,t,o){"use strict";o.r(t);var a=o(29),r=Object(a.a)({},(function(){var e=this,t=e.$createElement,o=e._self._c||t;return o("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[o("p",[e._v("On our last Frictionless Data community call on August 26"),o("sup",[e._v("th")]),e._v(" we had Amber York and Adam Shepherd from BCO-DMO giving a presentation on Frictionless Data Pipelines for Ocean Science.")]),e._v(" "),o("p",[e._v("BCO-DMO is a biological and chemical oceanography data management office, working with scientists to make sure that their data is publicly available and archived for everyone else to use.")]),e._v(" "),o("p",[e._v("BCO-DMO processes around 500 datasets a year, with all sorts of variability. In the beginning the staff was writing ad hoc scripts and software to process that data, but that quickly became a challenge, as the catalogue continued to grow in both size and the variety of data types it curates.")]),e._v(" "),o("p",[e._v("Having worked for several years with Frictionless Data, BCO-DMO identified the Data Package Pipelines (DPP) project in the Frictionless toolkit as key to overcoming those challenges and achieving its data curation goals."),o("br"),e._v("\nTogether with the Frictionless Data team at Open Knowledge Foundation, BCO-DMO developed Laminar, a web application to create Frictionless Data Package Pipelines. Laminar helps data managers process data efficiently while recording the provenance of their activities to support reproducibility of results")]),e._v(" "),o("p",[e._v("You can learn more on the project "),o("a",{attrs:{href:"https://frictionlessdata.io/blog/2020/02/10/frictionless-data-pipelines-for-open-ocean/",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),o("OutboundLink")],1),e._v(". If you would like to dive deeper and discover all about Frictionless Data Pipelines, you can watch Amber York’s and Adam Shepherd’s presentation:")]),e._v(" "),o("iframe",{attrs:{width:"560",height:"315",src:"https://www.youtube.com/embed/R0U9Iwd8J00",title:"YouTube video player",frameborder:"0",allow:"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture",allowfullscreen:""}}),e._v(" "),o("h2",{attrs:{id:"other-agenda-items-from-our-hangout"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#other-agenda-items-from-our-hangout"}},[e._v("#")]),e._v(" Other agenda items from our hangout")]),e._v(" "),o("h3",{attrs:{id:"frictionless-hackathon-on-7-8-october"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#frictionless-hackathon-on-7-8-october"}},[e._v("#")]),e._v(" Frictionless Hackathon on 7-8 October!")]),e._v(" "),o("p",[e._v("Join the Frictionless Data community for a two-day virtual event to create new project prototypes based on existing Frictionless open source code. It’s going to be fun!"),o("br"),e._v("\nWe are currently accepting project submissions, so if you have a cool project in mind, using based on existing Frictionless open source code, this could be an excellent opportunity to prototype it, together with other Frictionless users from all around the world. You can pitch anything - your idea doesn’t need to be complete/fully planned. We can also help you formulate a project if you have an idea but aren’t sure about it. You can also submit ideas for existing projects you need help with!")]),e._v(" "),o("p",[e._v("Use "),o("a",{attrs:{href:"https://docs.google.com/forms/d/e/1FAIpQLSdd41pbfWaCYQHkQNTaf49kht1cUg7_Tg-NzqdP11pHWrD7yA/viewform",target:"_blank",rel:"noopener noreferrer"}},[e._v("this form"),o("OutboundLink")],1),e._v(" to submit your project."),o("br"),e._v("\nKeep an eye "),o("a",{attrs:{href:"https://frictionlessdata.io/hackathon/",target:"_blank",rel:"noopener noreferrer"}},[e._v("on the website"),o("OutboundLink")],1),e._v(" for more info.")]),e._v(" "),o("h1",{attrs:{id:"join-us-next-month"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#join-us-next-month"}},[e._v("#")]),e._v(" Join us next month!")]),e._v(" "),o("p",[e._v("Our next meeting will be on September 30"),o("sup",[e._v("th")]),e._v(", exceptionally one hour later than usual. We will hear a presentation from Daniella Lowenberg and Cassiano Reinert Novais dos Santos on the Frictionless Data validation implemented for the Dryad application.")]),e._v(" "),o("p",[e._v("You can sign up "),o("a",{attrs:{href:"https://docs.google.com/forms/d/e/1FAIpQLSeuNCopxXauMkrWvF6VHqOyHMcy54SfNDOseVXfWRQZWkvqjQ/viewform?usp=sf_link",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),o("OutboundLink")],1),e._v(".")]),e._v(" "),o("p",[e._v("Do you want to share something with the community? Let us know when you sign up!")]),e._v(" "),o("h1",{attrs:{id:"call-recording"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#call-recording"}},[e._v("#")]),e._v(" Call recording:")]),e._v(" "),o("p",[e._v("On a final note, here is the recording of the full call:")]),e._v(" "),o("iframe",{attrs:{width:"560",height:"315",src:"https://www.youtube.com/embed/-y6njoJPMbE",title:"YouTube video player",frameborder:"0",allow:"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture",allowfullscreen:""}}),e._v(" "),o("p",[e._v("As usual, you can join us on "),o("a",{attrs:{href:"https://discord.com/invite/j9DNFNw",target:"_blank",rel:"noopener noreferrer"}},[e._v("Discord"),o("OutboundLink")],1),e._v(" or "),o("a",{attrs:{href:"https://twitter.com/frictionlessd8a",target:"_blank",rel:"noopener noreferrer"}},[e._v("Twitter"),o("OutboundLink")],1),e._v(" to say hi or ask any questions. See you there!")])])}),[],!1,null,null,null);t.default=r.exports}}]); \ No newline at end of file diff --git a/assets/js/143.6495e26e.js b/assets/js/143.fec8edd4.js similarity index 98% rename from assets/js/143.6495e26e.js rename to assets/js/143.fec8edd4.js index 846ba6e55..c62cb74db 100644 --- a/assets/js/143.6495e26e.js +++ b/assets/js/143.fec8edd4.js @@ -1 +1 @@ -(window.webpackJsonp=window.webpackJsonp||[]).push([[143],{676:function(e,t,o){"use strict";o.r(t);var r=o(29),n=Object(r.a)({},(function(){var e=this,t=e.$createElement,o=e._self._c||t;return o("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[o("p",[e._v("The Frictionless Data Online Hackathon is fast approaching and we just can’t wait for it to start!")]),e._v(" "),o("p",[e._v("If you are not sure yet whether to participate or not, bear in mind that it will be a great opportunity to test some of the newest Frictionless tools, like Livemark, Repository, play around with Frictionless-py and other new Frictionless code. It will also be a great chance for you to meet other Frictionless users and contributors from all around the world and build a project prototype together.")]),e._v(" "),o("p",[o("strong",[e._v("Not convinced yet? Go and explore the proposed projects on the "),o("a",{attrs:{href:"https://frictionless-hackathon.herokuapp.com/event/1#top",target:"_blank",rel:"noopener noreferrer"}},[e._v("Dashboard"),o("OutboundLink")],1),e._v("! You will see, there is a project for every taste, so surely there must be one that sounds right for you!")])]),e._v(" "),o("p",[e._v("Are you a big fan of geodata? In that case you will probably want to join the "),o("a",{attrs:{href:"https://frictionless-hackathon.herokuapp.com/project/9",target:"_blank",rel:"noopener noreferrer"}},[e._v("frictionless-geojson team"),o("OutboundLink")],1),e._v(", who is planning to create a frictionless-py plugin to add support for reading, writing and inlining geojson. If you are a devoted CKAN user who would like to see more Frictionless functionalities in it, you may decide to join the "),o("a",{attrs:{href:"https://frictionless-hackathon.herokuapp.com/project/8",target:"_blank",rel:"noopener noreferrer"}},[e._v("Data package manager for CKAN project"),o("OutboundLink")],1),e._v(".")]),e._v(" "),o("p",[e._v("In case you read our "),o("a",{attrs:{href:"https://frictionlessdata.io/blog/2021/06/22/livemark/",target:"_blank",rel:"noopener noreferrer"}},[e._v("blog about Livemark"),o("OutboundLink")],1),e._v(" and have been intrigued by this new Frictionless tool ever since, your moment has come! You can finally try it out by joining the "),o("a",{attrs:{href:"https://frictionless-hackathon.herokuapp.com/project/11",target:"_blank",rel:"noopener noreferrer"}},[e._v("Citation Context Reports"),o("OutboundLink")],1),e._v(", the "),o("a",{attrs:{href:"https://frictionless-hackathon.herokuapp.com/project/3",target:"_blank",rel:"noopener noreferrer"}},[e._v("Dataset List"),o("OutboundLink")],1),e._v(" project, or the "),o("a",{attrs:{href:"http://frictionless-hackathon.herokuapp.com/project/12",target:"_blank",rel:"noopener noreferrer"}},[e._v("Frictionless Community Insights"),o("OutboundLink")],1),e._v(" project. If you are interested in datasets discoverability and linkage, you may want to join the "),o("a",{attrs:{href:"https://frictionless-hackathon.herokuapp.com/project/10",target:"_blank",rel:"noopener noreferrer"}},[e._v("Things not Datasets"),o("OutboundLink")],1),e._v(" team.")]),e._v(" "),o("p",[e._v("Oh, and please let us know in advance if you are a big bugs smasher! You will be a coveted participant for all projects and we need to make sure everybody gets a fair share of your skills, including us in our effort to improve the "),o("a",{attrs:{href:"https://frictionless-hackathon.herokuapp.com/project/4",target:"_blank",rel:"noopener noreferrer"}},[e._v("Frictionless Python Framework"),o("OutboundLink")],1),e._v(".")]),e._v(" "),o("p",[e._v("But enough of describing the projects, instead hear about them directly from the people who proposed them:")]),e._v(" "),o("iframe",{attrs:{width:"560",height:"315",src:"https://www.youtube.com/embed/kO0YflqQQzI",title:"YouTube video player",frameborder:"0",allow:"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture",allowfullscreen:""}}),e._v(" "),o("p",[e._v("Hurry up to register for the hackathon if you haven’t done so yet, you can do it only until the end of this week via "),o("a",{attrs:{href:"https://forms.gle/Xr4gcnQnhShMJrWeA",target:"_blank",rel:"noopener noreferrer"}},[e._v("this form"),o("OutboundLink")],1)]),e._v(" "),o("p",[e._v("More information on the Frictionless Data Hackathon is available on the "),o("a",{attrs:{href:"https://frictionlessdata.io/hackathon/",target:"_blank",rel:"noopener noreferrer"}},[e._v("dedicated webpage"),o("OutboundLink")],1),e._v(". You can also follow news on the day itself through "),o("a",{attrs:{href:"https://twitter.com/frictionlessd8a/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Twitter"),o("OutboundLink")],1),e._v(": #FrictionlessHackathon and #FrictionlessHack2021.")])])}),[],!1,null,null,null);t.default=n.exports}}]); \ No newline at end of file +(window.webpackJsonp=window.webpackJsonp||[]).push([[143],{673:function(e,t,o){"use strict";o.r(t);var r=o(29),n=Object(r.a)({},(function(){var e=this,t=e.$createElement,o=e._self._c||t;return o("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[o("p",[e._v("The Frictionless Data Online Hackathon is fast approaching and we just can’t wait for it to start!")]),e._v(" "),o("p",[e._v("If you are not sure yet whether to participate or not, bear in mind that it will be a great opportunity to test some of the newest Frictionless tools, like Livemark, Repository, play around with Frictionless-py and other new Frictionless code. 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You can finally try it out by joining the "),o("a",{attrs:{href:"https://frictionless-hackathon.herokuapp.com/project/11",target:"_blank",rel:"noopener noreferrer"}},[e._v("Citation Context Reports"),o("OutboundLink")],1),e._v(", the "),o("a",{attrs:{href:"https://frictionless-hackathon.herokuapp.com/project/3",target:"_blank",rel:"noopener noreferrer"}},[e._v("Dataset List"),o("OutboundLink")],1),e._v(" project, or the "),o("a",{attrs:{href:"http://frictionless-hackathon.herokuapp.com/project/12",target:"_blank",rel:"noopener noreferrer"}},[e._v("Frictionless Community Insights"),o("OutboundLink")],1),e._v(" project. If you are interested in datasets discoverability and linkage, you may want to join the "),o("a",{attrs:{href:"https://frictionless-hackathon.herokuapp.com/project/10",target:"_blank",rel:"noopener noreferrer"}},[e._v("Things not Datasets"),o("OutboundLink")],1),e._v(" team.")]),e._v(" "),o("p",[e._v("Oh, and please let us know in advance if you are a big bugs smasher! You will be a coveted participant for all projects and we need to make sure everybody gets a fair share of your skills, including us in our effort to improve the "),o("a",{attrs:{href:"https://frictionless-hackathon.herokuapp.com/project/4",target:"_blank",rel:"noopener noreferrer"}},[e._v("Frictionless Python Framework"),o("OutboundLink")],1),e._v(".")]),e._v(" "),o("p",[e._v("But enough of describing the projects, instead hear about them directly from the people who proposed them:")]),e._v(" "),o("iframe",{attrs:{width:"560",height:"315",src:"https://www.youtube.com/embed/kO0YflqQQzI",title:"YouTube video player",frameborder:"0",allow:"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture",allowfullscreen:""}}),e._v(" "),o("p",[e._v("Hurry up to register for the hackathon if you haven’t done so yet, you can do it only until the end of this week via "),o("a",{attrs:{href:"https://forms.gle/Xr4gcnQnhShMJrWeA",target:"_blank",rel:"noopener noreferrer"}},[e._v("this form"),o("OutboundLink")],1)]),e._v(" "),o("p",[e._v("More information on the Frictionless Data Hackathon is available on the "),o("a",{attrs:{href:"https://frictionlessdata.io/hackathon/",target:"_blank",rel:"noopener noreferrer"}},[e._v("dedicated webpage"),o("OutboundLink")],1),e._v(". You can also follow news on the day itself through "),o("a",{attrs:{href:"https://twitter.com/frictionlessd8a/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Twitter"),o("OutboundLink")],1),e._v(": #FrictionlessHackathon and #FrictionlessHack2021.")])])}),[],!1,null,null,null);t.default=n.exports}}]); \ No newline at end of file diff --git a/assets/js/144.4c70106f.js b/assets/js/144.26d71996.js similarity index 98% rename from assets/js/144.4c70106f.js rename to assets/js/144.26d71996.js index 5fa790b8c..9d1917876 100644 --- a/assets/js/144.4c70106f.js +++ b/assets/js/144.26d71996.js @@ -1 +1 @@ -(window.webpackJsonp=window.webpackJsonp||[]).push([[144],{678:function(e,t,a){"use strict";a.r(t);var o=a(29),r=Object(o.a)({},(function(){var e=this,t=e.$createElement,a=e._self._c||t;return a("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[a("p",[e._v("On our last Frictionless Data community call on September 30"),a("sup",[e._v("th")]),e._v(" we had Daniella Lowenberg from Dryad and developer Cassiano Reinert Novais dos Santos giving a presentation on the Frictionless Data integration into Dryad.")]),e._v(" "),a("p",[e._v("Dryad is a community-led repository that makes research data discoverable, freely reusable, and citable. To ensure the quality of the submitted data, Dryad needs to curate it. It therefore made total sense to integrate the Frictionless Data validation functionality to its uploading page.")]),e._v(" "),a("p",[e._v("A pilot was started at the beginning of 2021 to add an automatic tabular data validation check to all uploaded files under 25MB, and it went live in June 2021. Since then, more than 11000 research data files have been validated, and around 1000 failed the validation test. 98,4% of the researchers whose files failed, managed to fix their errors easily and resubmit their data.")]),e._v(" "),a("p",[e._v("All the code of the Frictionless Data integration is open source and lives in the "),a("a",{attrs:{href:"https://github.com/orgs/CDL-Dryad/repositories",target:"_blank",rel:"noopener noreferrer"}},[e._v("Dryad GitHub repository"),a("OutboundLink")],1),e._v(", so go and have a look if you want and please let us know if you have any feedback.")]),e._v(" "),a("p",[e._v("You can learn more on the project "),a("a",{attrs:{href:"https://frictionlessdata.io/blog/2021/08/09/dryad-pilot/",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),a("OutboundLink")],1),e._v(". If you would like to dive deeper and discover all about the Frictionless Data validation functionality integrated into Dryad, you can watch Daniella Lowenberg’s and Cassiano Reinert Novais dos Santos’ presentation here:")]),e._v(" "),a("iframe",{attrs:{width:"560",height:"315",src:"https://www.youtube.com/embed/IHVUjWGh2oY",title:"YouTube video player",frameborder:"0",allow:"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture",allowfullscreen:""}}),e._v(" "),a("h2",{attrs:{id:"other-agenda-items-from-our-hangout"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#other-agenda-items-from-our-hangout"}},[e._v("#")]),e._v(" Other agenda items from our hangout")]),e._v(" "),a("h3",{attrs:{id:"frictionless-hackathon-on-7-8-october"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#frictionless-hackathon-on-7-8-october"}},[e._v("#")]),e._v(" Frictionless Hackathon on 7-8 October!")]),e._v(" "),a("p",[e._v("Join the Frictionless Data community for a two-day virtual event to create new project prototypes based on existing Frictionless open source code. It’s going to be fun!"),a("br"),e._v("\nGo and explore the dashboard to know more about all the projects we plan to work on."),a("br"),e._v("\nFor general information, just go to the "),a("a",{attrs:{href:"https://frictionlessdata.io/hackathon/",target:"_blank",rel:"noopener noreferrer"}},[e._v("dedicated page"),a("OutboundLink")],1),e._v("."),a("br"),e._v("\nWe are accepting last minute registrations "),a("a",{attrs:{href:"https://forms.gle/ZhrVfSBrNy2UPRZc9",target:"_blank",rel:"noopener noreferrer"}},[e._v("via this form"),a("OutboundLink")],1),e._v(", so hurry up if you want to be on board!")]),e._v(" "),a("h1",{attrs:{id:"join-us-next-month"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#join-us-next-month"}},[e._v("#")]),e._v(" Join us next month!")]),e._v(" "),a("p",[e._v("Our next meeting will be on October 28"),a("sup",[e._v("th")]),e._v(". We will hear a presentation from Michael Amadi on Open Data Blend datasets powered by Frictionless Data.")]),e._v(" "),a("p",[e._v("Ahead of our next call, you can learn more about Open Data Blend "),a("a",{attrs:{href:"https://frictionlessdata.io/blog/2021/07/12/open-data-blend/",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),a("OutboundLink")],1)]),e._v(" "),a("p",[e._v("You can sign up "),a("a",{attrs:{href:"https://docs.google.com/forms/d/e/1FAIpQLSeuNCopxXauMkrWvF6VHqOyHMcy54SfNDOseVXfWRQZWkvqjQ/viewform?usp=sf_link",target:"_blank",rel:"noopener noreferrer"}},[e._v("here:"),a("OutboundLink")],1)]),e._v(" "),a("p",[e._v("Do you want to share something with the community? Let us know when you sign up!")]),e._v(" "),a("h1",{attrs:{id:"call-recording"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#call-recording"}},[e._v("#")]),e._v(" Call recording:")]),e._v(" "),a("p",[e._v("On a final note, here is the recording of the full call:")]),e._v(" "),a("iframe",{attrs:{width:"560",height:"315",src:"https://www.youtube.com/embed/V3SJcq_XYIA",title:"YouTube video player",frameborder:"0",allow:"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture",allowfullscreen:""}}),e._v(" "),a("p",[e._v("As usual, you can join us on "),a("a",{attrs:{href:"https://discord.com/invite/j9DNFNw",target:"_blank",rel:"noopener noreferrer"}},[e._v("Discord"),a("OutboundLink")],1),e._v(" or "),a("a",{attrs:{href:"https://twitter.com/frictionlessd8a",target:"_blank",rel:"noopener noreferrer"}},[e._v("Twitter"),a("OutboundLink")],1),e._v(" to say hi or ask any questions. See you there!")])])}),[],!1,null,null,null);t.default=r.exports}}]); \ No newline at end of file +(window.webpackJsonp=window.webpackJsonp||[]).push([[144],{675:function(e,t,a){"use strict";a.r(t);var o=a(29),r=Object(o.a)({},(function(){var e=this,t=e.$createElement,a=e._self._c||t;return a("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[a("p",[e._v("On our last Frictionless Data community call on September 30"),a("sup",[e._v("th")]),e._v(" we had Daniella Lowenberg from Dryad and developer Cassiano Reinert Novais dos Santos giving a presentation on the Frictionless Data integration into Dryad.")]),e._v(" "),a("p",[e._v("Dryad is a community-led repository that makes research data discoverable, freely reusable, and citable. To ensure the quality of the submitted data, Dryad needs to curate it. 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Since then, more than 11000 research data files have been validated, and around 1000 failed the validation test. 98,4% of the researchers whose files failed, managed to fix their errors easily and resubmit their data.")]),e._v(" "),a("p",[e._v("All the code of the Frictionless Data integration is open source and lives in the "),a("a",{attrs:{href:"https://github.com/orgs/CDL-Dryad/repositories",target:"_blank",rel:"noopener noreferrer"}},[e._v("Dryad GitHub repository"),a("OutboundLink")],1),e._v(", so go and have a look if you want and please let us know if you have any feedback.")]),e._v(" "),a("p",[e._v("You can learn more on the project "),a("a",{attrs:{href:"https://frictionlessdata.io/blog/2021/08/09/dryad-pilot/",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),a("OutboundLink")],1),e._v(". If you would like to dive deeper and discover all about the Frictionless Data validation functionality integrated into Dryad, you can watch Daniella Lowenberg’s and Cassiano Reinert Novais dos Santos’ presentation here:")]),e._v(" "),a("iframe",{attrs:{width:"560",height:"315",src:"https://www.youtube.com/embed/IHVUjWGh2oY",title:"YouTube video player",frameborder:"0",allow:"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture",allowfullscreen:""}}),e._v(" "),a("h2",{attrs:{id:"other-agenda-items-from-our-hangout"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#other-agenda-items-from-our-hangout"}},[e._v("#")]),e._v(" Other agenda items from our hangout")]),e._v(" "),a("h3",{attrs:{id:"frictionless-hackathon-on-7-8-october"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#frictionless-hackathon-on-7-8-october"}},[e._v("#")]),e._v(" Frictionless Hackathon on 7-8 October!")]),e._v(" "),a("p",[e._v("Join the Frictionless Data community for a two-day virtual event to create new project prototypes based on existing Frictionless open source code. 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We will hear a presentation from Michael Amadi on Open Data Blend datasets powered by Frictionless Data.")]),e._v(" "),a("p",[e._v("Ahead of our next call, you can learn more about Open Data Blend "),a("a",{attrs:{href:"https://frictionlessdata.io/blog/2021/07/12/open-data-blend/",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),a("OutboundLink")],1)]),e._v(" "),a("p",[e._v("You can sign up "),a("a",{attrs:{href:"https://docs.google.com/forms/d/e/1FAIpQLSeuNCopxXauMkrWvF6VHqOyHMcy54SfNDOseVXfWRQZWkvqjQ/viewform?usp=sf_link",target:"_blank",rel:"noopener noreferrer"}},[e._v("here:"),a("OutboundLink")],1)]),e._v(" "),a("p",[e._v("Do you want to share something with the community? 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See you there!")])])}),[],!1,null,null,null);t.default=r.exports}}]); \ No newline at end of file diff --git a/assets/js/146.6e7c9a8b.js b/assets/js/146.64d30d40.js similarity index 98% rename from assets/js/146.6e7c9a8b.js rename to assets/js/146.64d30d40.js index 0b7bd52d8..31f1e49d3 100644 --- a/assets/js/146.6e7c9a8b.js +++ b/assets/js/146.64d30d40.js @@ -1 +1 @@ -(window.webpackJsonp=window.webpackJsonp||[]).push([[146],{679:function(e,t,a){"use strict";a.r(t);var o=a(29),r=Object(o.a)({},(function(){var e=this,t=e.$createElement,a=e._self._c||t;return a("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[a("p",[e._v("On our last Frictionless Data community call on October 28"),a("sup",[e._v("th")]),e._v(" we had Michael Amadi from Nimble Learn giving a presentation on Open Data Blend and their Frictionless Data journey.")]),e._v(" "),a("p",[e._v("Open Data Blend is a set of open data services that aim to make large and complex UK open data easier to analyse. The Open Data Blend datasets have two interfaces: a UI and an API, both powered by Frictionless Data. 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If you would like to dive deeper and discover all about how Open Data Blend uses the Frictionless Data toolkit, you can watch Michael Amadi’s presentation here:")]),e._v(" "),a("iframe",{attrs:{width:"560",height:"315",src:"https://www.youtube.com/embed/avAXe3SUEKI",title:"YouTube video player",frameborder:"0",allow:"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture",allowfullscreen:""}}),e._v(" "),a("h1",{attrs:{id:"other-agenda-items-from-our-hangout"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#other-agenda-items-from-our-hangout"}},[e._v("#")]),e._v(" Other agenda items from our hangout")]),e._v(" "),a("ul",[a("li",[e._v("Senior developer Evgeny Karev presented Livemark at PyData on October 29"),a("sup",[e._v("th")]),e._v(". If you missed it and want to have a look, check out the recording "),a("a",{attrs:{href:"https://zoom.us/rec/play/yyFTEAW3_v4cPGUNbiHS95-vlgICgNYeVdK_N9VHOdHxLDoKbTE9EZvbVpZMjIV8-WAr3qmZ9vZPoVsU.QXvKRI1hOrCwv8Lg?startTime=1635487241000&_x_zm_rtaid=iuuaYWHFSEec21FRLG7Cig.1635861744121.d2b5a7e329a988e4ea49b64e3d6e66b6&_x_zm_rhtaid=460",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),a("OutboundLink")],1),e._v(" (for Livemark jump at 1:03:03).")]),e._v(" "),a("li",[e._v("The third cohort of Frictionless Fellows has officially kicked off mid-October. You will get to meet them next year during one of our community calls. Meanwhile, stay tuned to know more about them!")]),e._v(" "),a("li",[e._v("We don’t have any presentation planned for the December community call yet. Would you like to present something? Drop us a line to let us know!")])]),e._v(" "),a("h1",{attrs:{id:"join-us-next-month"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#join-us-next-month"}},[e._v("#")]),e._v(" Join us next month!")]),e._v(" "),a("p",[e._v("Next community call is one week earlier than usual (to avoid conflict with American Thanksgiving), on November 18"),a("sup",[e._v("th")]),e._v(". We will hear a presentation from Peter Desmet on Frictionless Data exchange format for camera trapping data.")]),e._v(" "),a("p",[e._v("You can sign up "),a("a",{attrs:{href:"https://docs.google.com/forms/d/e/1FAIpQLSeuNCopxXauMkrWvF6VHqOyHMcy54SfNDOseVXfWRQZWkvqjQ/viewform?usp=sf_link",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),a("OutboundLink")],1),e._v(".")]),e._v(" "),a("p",[e._v("Do you want to share something with the community? 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See you there!")])])}),[],!1,null,null,null);t.default=r.exports}}]); \ No newline at end of file +(window.webpackJsonp=window.webpackJsonp||[]).push([[146],{676:function(e,t,a){"use strict";a.r(t);var o=a(29),r=Object(o.a)({},(function(){var e=this,t=e.$createElement,a=e._self._c||t;return a("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[a("p",[e._v("On our last Frictionless Data community call on October 28"),a("sup",[e._v("th")]),e._v(" we had Michael Amadi from Nimble Learn giving a presentation on Open Data Blend and their Frictionless Data journey.")]),e._v(" "),a("p",[e._v("Open Data Blend is a set of open data services that aim to make large and complex UK open data easier to analyse. The Open Data Blend datasets have two interfaces: a UI and an API, both powered by Frictionless Data. The datasets themselves are built on top of three Frictionless Data specifications: data package, data resource and table schema; and they incorporate some Frictionless Data patterns.")]),e._v(" "),a("p",[e._v("The project addresses some of the main open data challenges:")]),e._v(" "),a("ul",[a("li",[e._v("Large data volumes that are difficult to manage due to their size")]),e._v(" "),a("li",[e._v("Overwhelming complexity in data analysis")]),e._v(" "),a("li",[e._v("Open data shared in sub-optimal file formats for data analysis (e.g. PDFs)")]),e._v(" "),a("li",[e._v("When companies and organisation aggregate data, refine it and add value to it, they often don’t openly share the cleaned data")])]),e._v(" "),a("p",[e._v("You can learn more on the project "),a("a",{attrs:{href:"https://frictionlessdata.io/blog/2021/07/12/open-data-blend/",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),a("OutboundLink")],1),e._v(". If you would like to dive deeper and discover all about how Open Data Blend uses the Frictionless Data toolkit, you can watch Michael Amadi’s presentation here:")]),e._v(" "),a("iframe",{attrs:{width:"560",height:"315",src:"https://www.youtube.com/embed/avAXe3SUEKI",title:"YouTube video player",frameborder:"0",allow:"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture",allowfullscreen:""}}),e._v(" "),a("h1",{attrs:{id:"other-agenda-items-from-our-hangout"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#other-agenda-items-from-our-hangout"}},[e._v("#")]),e._v(" Other agenda items from our hangout")]),e._v(" "),a("ul",[a("li",[e._v("Senior developer Evgeny Karev presented Livemark at PyData on October 29"),a("sup",[e._v("th")]),e._v(". If you missed it and want to have a look, check out the recording "),a("a",{attrs:{href:"https://zoom.us/rec/play/yyFTEAW3_v4cPGUNbiHS95-vlgICgNYeVdK_N9VHOdHxLDoKbTE9EZvbVpZMjIV8-WAr3qmZ9vZPoVsU.QXvKRI1hOrCwv8Lg?startTime=1635487241000&_x_zm_rtaid=iuuaYWHFSEec21FRLG7Cig.1635861744121.d2b5a7e329a988e4ea49b64e3d6e66b6&_x_zm_rhtaid=460",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),a("OutboundLink")],1),e._v(" (for Livemark jump at 1:03:03).")]),e._v(" "),a("li",[e._v("The third cohort of Frictionless Fellows has officially kicked off mid-October. You will get to meet them next year during one of our community calls. Meanwhile, stay tuned to know more about them!")]),e._v(" "),a("li",[e._v("We don’t have any presentation planned for the December community call yet. Would you like to present something? Drop us a line to let us know!")])]),e._v(" "),a("h1",{attrs:{id:"join-us-next-month"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#join-us-next-month"}},[e._v("#")]),e._v(" Join us next month!")]),e._v(" "),a("p",[e._v("Next community call is one week earlier than usual (to avoid conflict with American Thanksgiving), on November 18"),a("sup",[e._v("th")]),e._v(". We will hear a presentation from Peter Desmet on Frictionless Data exchange format for camera trapping data.")]),e._v(" "),a("p",[e._v("You can sign up "),a("a",{attrs:{href:"https://docs.google.com/forms/d/e/1FAIpQLSeuNCopxXauMkrWvF6VHqOyHMcy54SfNDOseVXfWRQZWkvqjQ/viewform?usp=sf_link",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),a("OutboundLink")],1),e._v(".")]),e._v(" "),a("p",[e._v("Do you want to share something with the community? Let us know when you sign up!")]),e._v(" "),a("h1",{attrs:{id:"call-recording"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#call-recording"}},[e._v("#")]),e._v(" Call recording:")]),e._v(" "),a("p",[e._v("On a final note, here is the recording of the full call:")]),e._v(" "),a("iframe",{attrs:{width:"560",height:"315",src:"https://www.youtube.com/embed/MFffZRM8qjs",title:"YouTube video player",frameborder:"0",allow:"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture",allowfullscreen:""}}),e._v(" "),a("p",[e._v("As usual, you can join us on "),a("a",{attrs:{href:"https://discord.com/invite/j9DNFNw",target:"_blank",rel:"noopener noreferrer"}},[e._v("Discord"),a("OutboundLink")],1),e._v(" or "),a("a",{attrs:{href:"https://twitter.com/frictionlessd8a",target:"_blank",rel:"noopener noreferrer"}},[e._v("Twitter"),a("OutboundLink")],1),e._v(" to say hi or ask any questions. See you there!")])])}),[],!1,null,null,null);t.default=r.exports}}]); \ No newline at end of file diff --git a/assets/js/147.a316a5ee.js b/assets/js/147.8f111805.js similarity index 98% rename from assets/js/147.a316a5ee.js rename to assets/js/147.8f111805.js index 68504e8af..270ca2118 100644 --- a/assets/js/147.a316a5ee.js +++ b/assets/js/147.8f111805.js @@ -1 +1 @@ -(window.webpackJsonp=window.webpackJsonp||[]).push([[147],{682:function(e,t,a){"use strict";a.r(t);var r=a(29),o=Object(r.a)({},(function(){var e=this,t=e.$createElement,a=e._self._c||t;return a("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[a("p",[e._v("On our last Frictionless Data community call on November 18"),a("sup",[e._v("th")]),e._v(" we had Peter Desmet from the Research Institute for Nature and Forest (INBO) giving a presentation on Frictionless Data exchange format for camera trapping data.")]),e._v(" "),a("p",[e._v("Camera trapping is a non-invasive wildlife monitoring technique generating more and more data in the last few years. Darwin Core, a well established standard in the biodiversity field, does not capture the full scope of camera trapping data (e.g. it does not express your camera setup) and it is therefore not ideal. To tackle this problem, the camera trapped data package was developed, using Frictionless Data standards. The camera trapped data package is both a "),a("strong",[e._v("model")]),e._v(" and a "),a("strong",[e._v("format")]),e._v(" to exchange camera trapping data, and it is designed to capture all the essential data and metadata of camera trap studies.")]),e._v(" "),a("p",[e._v("The camera trap data package model includes:")]),e._v(" "),a("ul",[a("li",[e._v("Metadata about the project")]),e._v(" "),a("li",[e._v("Deployments info about the location, the camera and the time")]),e._v(" "),a("li",[e._v("Media including the file url, the timestamp and if it is a sequence")]),e._v(" "),a("li",[e._v("Observation about the file (Is it blank? What kind of animal can we see? etc…)")])]),e._v(" "),a("p",[e._v("The format is similar to a Frictionless Data data package. It includes: "),a("strong",[e._v("metadata")]),e._v(" about the project and the data package structure, "),a("strong",[e._v("csv files")]),e._v(" for the deployments, the media captured in the deployments, and the observations in those media.")]),e._v(" "),a("p",[e._v("If you would like to dive deeper and discover all about the Frictionless Data exchange format for camera trapping data, you can watch Peter Desmet’s presentation here:")]),e._v(" "),a("iframe",{attrs:{width:"560",height:"315",src:"https://www.youtube.com/embed/Pi_kbQ_KYiM",title:"YouTube video player",frameborder:"0",allow:"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture",allowfullscreen:""}}),e._v(" "),a("p",[e._v("You can also find Peter’s presentation deck "),a("a",{attrs:{href:"https://speakerdeck.com/peterdesmet/camtrap-dp-using-frictionless-standards-for-a-camera-trapping-data-exchange-format",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),a("OutboundLink")],1),e._v(".")]),e._v(" "),a("h2",{attrs:{id:"other-agenda-items-from-our-hangout"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#other-agenda-items-from-our-hangout"}},[e._v("#")]),e._v(" Other agenda items from our hangout")]),e._v(" "),a("p",[e._v("We are part of the organisation of the FOSDEM DevRoom Open Research Tools & Technologies this year too. We would love to have someone from the Frictionless community giving a talk. If you are interested please let us know! We are very happy to help you structure your idea, if needed. Calls for participation will be issued soon. Keep an eye on "),a("a",{attrs:{href:"https://fosdem.org/2022/news/2021-11-02-devroom-cfp/",target:"_blank",rel:"noopener noreferrer"}},[e._v("this page"),a("OutboundLink")],1),e._v(".")]),e._v(" "),a("h1",{attrs:{id:"join-us-next-month"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#join-us-next-month"}},[e._v("#")]),e._v(" Join us next month!")]),e._v(" "),a("p",[e._v("Next community call is one week earlier than usual, on December 16"),a("sup",[e._v("th")]),e._v(", because of the Winter holidays. Keith Hughitt is going to present some ideas around representing data processing flows as a DAG inside of a datapackage.json, and tools for interacting with and visualizing such DAGs.")]),e._v(" "),a("p",[e._v("You can sign up "),a("a",{attrs:{href:"https://docs.google.com/forms/d/e/1FAIpQLSeuNCopxXauMkrWvF6VHqOyHMcy54SfNDOseVXfWRQZWkvqjQ/viewform?usp=sf_link",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),a("OutboundLink")],1),e._v(".")]),e._v(" "),a("p",[e._v("Do you want to share something with the community? Let us know when you sign up!")]),e._v(" "),a("h1",{attrs:{id:"call-recording"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#call-recording"}},[e._v("#")]),e._v(" Call recording:")]),e._v(" "),a("p",[e._v("On a final note, here is the recording of the full call:")]),e._v(" "),a("iframe",{attrs:{width:"560",height:"315",src:"https://www.youtube.com/embed/DQ4hpARBVSE",title:"YouTube video player",frameborder:"0",allow:"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture",allowfullscreen:""}}),e._v(" "),a("p",[e._v("As usual, you can join us on "),a("a",{attrs:{href:"https://discord.com/invite/j9DNFNw",target:"_blank",rel:"noopener noreferrer"}},[e._v("Discord"),a("OutboundLink")],1),e._v(" or "),a("a",{attrs:{href:"https://twitter.com/frictionlessd8a",target:"_blank",rel:"noopener noreferrer"}},[e._v("Twitter"),a("OutboundLink")],1),e._v(" to say hi or ask any questions. See you there!")])])}),[],!1,null,null,null);t.default=o.exports}}]); \ No newline at end of file +(window.webpackJsonp=window.webpackJsonp||[]).push([[147],{678:function(e,t,a){"use strict";a.r(t);var r=a(29),o=Object(r.a)({},(function(){var e=this,t=e.$createElement,a=e._self._c||t;return a("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[a("p",[e._v("On our last Frictionless Data community call on November 18"),a("sup",[e._v("th")]),e._v(" we had Peter Desmet from the Research Institute for Nature and Forest (INBO) giving a presentation on Frictionless Data exchange format for camera trapping data.")]),e._v(" "),a("p",[e._v("Camera trapping is a non-invasive wildlife monitoring technique generating more and more data in the last few years. Darwin Core, a well established standard in the biodiversity field, does not capture the full scope of camera trapping data (e.g. it does not express your camera setup) and it is therefore not ideal. To tackle this problem, the camera trapped data package was developed, using Frictionless Data standards. The camera trapped data package is both a "),a("strong",[e._v("model")]),e._v(" and a "),a("strong",[e._v("format")]),e._v(" to exchange camera trapping data, and it is designed to capture all the essential data and metadata of camera trap studies.")]),e._v(" "),a("p",[e._v("The camera trap data package model includes:")]),e._v(" "),a("ul",[a("li",[e._v("Metadata about the project")]),e._v(" "),a("li",[e._v("Deployments info about the location, the camera and the time")]),e._v(" "),a("li",[e._v("Media including the file url, the timestamp and if it is a sequence")]),e._v(" "),a("li",[e._v("Observation about the file (Is it blank? What kind of animal can we see? etc…)")])]),e._v(" "),a("p",[e._v("The format is similar to a Frictionless Data data package. It includes: "),a("strong",[e._v("metadata")]),e._v(" about the project and the data package structure, "),a("strong",[e._v("csv files")]),e._v(" for the deployments, the media captured in the deployments, and the observations in those media.")]),e._v(" "),a("p",[e._v("If you would like to dive deeper and discover all about the Frictionless Data exchange format for camera trapping data, you can watch Peter Desmet’s presentation here:")]),e._v(" "),a("iframe",{attrs:{width:"560",height:"315",src:"https://www.youtube.com/embed/Pi_kbQ_KYiM",title:"YouTube video player",frameborder:"0",allow:"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture",allowfullscreen:""}}),e._v(" "),a("p",[e._v("You can also find Peter’s presentation deck "),a("a",{attrs:{href:"https://speakerdeck.com/peterdesmet/camtrap-dp-using-frictionless-standards-for-a-camera-trapping-data-exchange-format",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),a("OutboundLink")],1),e._v(".")]),e._v(" "),a("h2",{attrs:{id:"other-agenda-items-from-our-hangout"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#other-agenda-items-from-our-hangout"}},[e._v("#")]),e._v(" Other agenda items from our hangout")]),e._v(" "),a("p",[e._v("We are part of the organisation of the FOSDEM DevRoom Open Research Tools & Technologies this year too. We would love to have someone from the Frictionless community giving a talk. If you are interested please let us know! We are very happy to help you structure your idea, if needed. Calls for participation will be issued soon. Keep an eye on "),a("a",{attrs:{href:"https://fosdem.org/2022/news/2021-11-02-devroom-cfp/",target:"_blank",rel:"noopener noreferrer"}},[e._v("this page"),a("OutboundLink")],1),e._v(".")]),e._v(" "),a("h1",{attrs:{id:"join-us-next-month"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#join-us-next-month"}},[e._v("#")]),e._v(" Join us next month!")]),e._v(" "),a("p",[e._v("Next community call is one week earlier than usual, on December 16"),a("sup",[e._v("th")]),e._v(", because of the Winter holidays. Keith Hughitt is going to present some ideas around representing data processing flows as a DAG inside of a datapackage.json, and tools for interacting with and visualizing such DAGs.")]),e._v(" "),a("p",[e._v("You can sign up "),a("a",{attrs:{href:"https://docs.google.com/forms/d/e/1FAIpQLSeuNCopxXauMkrWvF6VHqOyHMcy54SfNDOseVXfWRQZWkvqjQ/viewform?usp=sf_link",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),a("OutboundLink")],1),e._v(".")]),e._v(" "),a("p",[e._v("Do you want to share something with the community? Let us know when you sign up!")]),e._v(" "),a("h1",{attrs:{id:"call-recording"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#call-recording"}},[e._v("#")]),e._v(" Call recording:")]),e._v(" "),a("p",[e._v("On a final note, here is the recording of the full call:")]),e._v(" "),a("iframe",{attrs:{width:"560",height:"315",src:"https://www.youtube.com/embed/DQ4hpARBVSE",title:"YouTube video player",frameborder:"0",allow:"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture",allowfullscreen:""}}),e._v(" "),a("p",[e._v("As usual, you can join us on "),a("a",{attrs:{href:"https://discord.com/invite/j9DNFNw",target:"_blank",rel:"noopener noreferrer"}},[e._v("Discord"),a("OutboundLink")],1),e._v(" or "),a("a",{attrs:{href:"https://twitter.com/frictionlessd8a",target:"_blank",rel:"noopener noreferrer"}},[e._v("Twitter"),a("OutboundLink")],1),e._v(" to say hi or ask any questions. See you there!")])])}),[],!1,null,null,null);t.default=o.exports}}]); \ No newline at end of file diff --git a/assets/js/148.3d70d053.js b/assets/js/148.f186d227.js similarity index 99% rename from assets/js/148.3d70d053.js rename to assets/js/148.f186d227.js index d29ccff53..528746cb4 100644 --- a/assets/js/148.3d70d053.js +++ b/assets/js/148.f186d227.js @@ -1 +1 @@ -(window.webpackJsonp=window.webpackJsonp||[]).push([[148],{680:function(e,a,t){"use strict";t.r(a);var i=t(29),r=Object(i.a)({},(function(){var e=this,a=e.$createElement,t=e._self._c||a;return t("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[t("p",[e._v("We are very excited to introduce you to the 3rd cohort of "),t("a",{attrs:{href:"https://fellows.frictionlessdata.io/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Frictionless Data Reproducible Research Fellows"),t("OutboundLink")],1),e._v("! Over the coming months, this group of six early career researchers will be learning about open science, data management, and how to use Frictionless Data tooling in their work to make their data more open and their research more reusable. Keep an eye on them, as they are on their way to becoming champions of reproducibility! For now, go and read the introductory blogs they wrote about themselves to know more about them and their goals for this fellowship.")]),e._v(" "),t("img",{staticStyle:{margin:"10px",border:"5px solid black"},attrs:{src:"/img/blog/GQ.jpeg",width:"200px",align:"left"}}),e._v(" "),t("p",[t("strong",[e._v("Hi, everyone! My name is Guo-Qiang Zhang")]),e._v(", and I am from China. Right after I finished my residency training in Pediatrics, I joined Prof Bright I. Nwaru’s group and started my doctoral studies at Krefting Research Centre in University of Gothenburg (Sweden). My doctoral project is to look at the effects of sex hormones on women’s health (especially asthma), utilizing epidemiological methods as well as evidence synthesis tools (e.g., systematic review, umbrella review).")]),e._v(" "),t("p",[e._v("In my first year of doctoral studies, I had the opportunity to participate in the course “Reproducibility in Medical Research” led by Prof Nwaru. It was my first time to hear about Open Science and research reproducibility. As a “fresh” full-time doctoral student full of passion for medical research, I felt overwhelmed by waves of frustration when I came to know the reproducibility crisis. After spending some time with my frustration, I came to realize that in fact I can do something. In my first project, my colleagues and I conducted an umbrella review on a highly controversial topic on the impact of menopausal hormone therapy on women’s health. We put extensive efforts into making the review process as transparent as possible: we developed beforehand protocols for data extraction and statistical analysis, documented key steps of the review process, verified data in the published literature, and made all datasets and R scripts publicly available."),t("br"),e._v("\nTo keep on reading about Guo-Qiang click "),t("a",{attrs:{href:"https://fellows.frictionlessdata.io/blog/hello-guo-qiang/",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),t("OutboundLink")],1),e._v(".")]),e._v(" "),t("img",{staticStyle:{margin:"10px",border:"5px solid black"},attrs:{src:"/img/blog/Victoria.jpeg",width:"140px",align:"left"}}),e._v(" "),t("p",[t("strong",[e._v("Hi all! My name is Victoria")]),e._v(". I’m a physics graduate student and recovering engineer living in Berlin. I grew up mainly in my family’s native country of Singapore, but consider myself an American, and am still workshopping a straightforward answer to “where are you from.”")]),e._v(" "),t("p",[e._v("In my past life I worked in materials QA testing; currently, I’m at the German Aerospace Centre designing laser systems in the THz range - a type of non-visible light that hangs out on the electromagnetic spectrum between infrared and microwave.")]),e._v(" "),t("p",[e._v("My Open Science journey has just begun and I’m stoked! I started to get interested in topics around data transparency and accessibility after a series of escalating frustrations with information dynamics in medical technology, beginning in my own field of gas sensing, then discovering similar disparities in tangential fields."),t("br"),e._v("\nRead more about Victoria "),t("a",{attrs:{href:"https://fellows.frictionlessdata.io/blog/hello-victoria/",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),t("OutboundLink")],1),e._v(".")]),e._v(" "),t("img",{staticStyle:{margin:"10px",border:"5px solid black"},attrs:{src:"/img/blog/Zarena.jpeg",width:"140px",align:"left"}}),e._v(" "),t("p",[t("strong",[e._v("Hello everybody! My name is Zarena")]),e._v(". I grew up in the Kyrgyz Republic, yet spent half of my life studying and working abroad. Currently, I am a Research Assistant for the project Creating Culturally Appropriate Research Ethics in Central Asia (CARE) at Nazarbayev University in Kazakhstan. I am also a Mad activist and an interdisciplinary human rights researcher. I like to consider my research activities going beyond academia to encompass and make an effect on broader socio-political structures.")]),e._v(" "),t("p",[e._v("Although I believe that life would not progress without frictions, when it comes to science and research, I feel, ‘frictions’ - manifested in a form of paywalls, bureaucratic and corporate management, or other structural barriers - should be deconstructed. So, I am joining the Frictionless Data Fellowship Programme with the purpose to learn more about open and FAIR research."),t("br"),e._v("\nLearn more about Zarena "),t("a",{attrs:{href:"https://fellows.frictionlessdata.io/blog/hello-zarena/",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),t("OutboundLink")],1),e._v(".")]),e._v(" "),t("img",{staticStyle:{margin:"10px",border:"5px solid black"},attrs:{src:"/img/blog/Melvin.jpeg",width:"120px",align:"left"}}),e._v(" "),t("p",[t("strong",[e._v("Hi everyone, my name is Melvin Ochieng")]),e._v(", and I’m a pathologist and up-coming soil scientist. I was born in Kenya in a town called Eldoret that is famous for producing Kenyan marathon champions. I was raised in Kenya in my early childhood days and in Tanzania afterwards. I like to consider myself both Kenyan and Tanzanian at heart because the two countries took part in molding the person I am today. I am currently a masters student at University of Mohammed VI polytechnic in Morocco, studying fertilizer science and technology. Over the past two years, my research focused on potato cyst nematode (PCN) which is a quarantine pest that had been reported in Kenya in 2015.")]),e._v(" "),t("p",[e._v("I’m excited to start this journey as a Frictionless Data fellow with my fellows for this cohort. I just recently found out about open science and I couldn’t be more excited to learn more about this concept and how it will influence me as a researcher. Advancement in technology has opened up the world in so many ways and made possible extensive networks for collaborations globally. Notably, the problems the world is facing today require a global/collaborative approach to solve. Therefore, reproducible research is of key importance in promoting this collaboration."),t("br"),e._v("\nTo know more about Melvin click "),t("a",{attrs:{href:"https://fellows.frictionlessdata.io/blog/hello-melvin/",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),t("OutboundLink")],1),e._v(".")]),e._v(" "),t("img",{staticStyle:{margin:"10px",border:"5px solid black"},attrs:{src:"/img/blog/Kevin-Photo.jpeg",width:"140px",align:"left"}}),e._v(" "),t("p",[t("strong",[e._v("Hello! My name is Kevin Kidambasi")]),e._v("(KK). I was born and raised in Vihiga County of western Kenya. Currently, I live in Nairobi, the capital city of Kenya. I am a master’s student in Jomo Kenyatta University of Agriculture and Technology (JKUAT) registered at the department of Biochemistry. My MSc research at the International Centre of Insect Physiology and Ecology (icipe) focuses on the role of haematophagous camel-specific biting keds (Hippobosca camelina) in disease transmission in Laisamis, Marsabit County of northern Kenya. My broad research interest focuses on studying host-pathogen interactions to understand infection mechanisms of diseases in order to discover novel control and treatment targets.")]),e._v(" "),t("p",[e._v("I am interested in improving research reproducibility because it allows other researchers to confirm the accuracy of my data and correct any bias as well as validate the relevance of the conclusions drawn from the results. This also allows data to be analyzed in different ways and thus, give new insights and lead the research in new directions. In addition, improving research reproducibility would allow the scientific community to understand how the conclusions of a study were made and pinpoint out any mistakes in data analyses. In general, research reproducibility enhances openness, research collaboration, and data accessibility which in turn increase public trust in science and hence permits their participation and support for research. This enables public understanding of how research is conducted and its importance."),t("br"),e._v("\nRead more about Kevin "),t("a",{attrs:{href:"https://fellows.frictionlessdata.io/blog/hello-kevin/",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),t("OutboundLink")],1),e._v(".")]),e._v(" "),t("img",{staticStyle:{margin:"10px",border:"5px solid black"},attrs:{src:"/img/blog/Lindsay.jpeg",width:"120px",align:"left"}}),e._v(" "),t("p",[t("strong",[e._v("Greetings! My name is Lindsay Gypin")]),e._v(", she/her. I grew up in Denver, Colorado and began my career as a K-12 educator. I taught high school English and worked as a school librarian before becoming disillusioned with the politicization of public education and determining my skills were better suited for work in public libraries. Attending library school after having worked in libraries for so many years, I found myself drawn to courses in the research data management track of librarianship, and in qualitative research methods.I recently became a Data Services Librarian at the University of North Carolina Greensboro, where I hope to assist scholars in making their research data more open and accessible.")]),e._v(" "),t("p",[e._v("For some time, I have wanted to build a reproducible workflow to uncover systemic bias in library catalogs. I’m hoping the Fellows Programme will help me build the foundation to do so."),t("br"),e._v("\nTo learn more about Lindsay click "),t("a",{attrs:{href:"https://fellows.frictionlessdata.io/blog/hello-lindsay/",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),t("OutboundLink")],1),e._v(".")])])}),[],!1,null,null,null);a.default=r.exports}}]); \ No newline at end of file +(window.webpackJsonp=window.webpackJsonp||[]).push([[148],{679:function(e,a,t){"use strict";t.r(a);var i=t(29),r=Object(i.a)({},(function(){var e=this,a=e.$createElement,t=e._self._c||a;return t("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[t("p",[e._v("We are very excited to introduce you to the 3rd cohort of "),t("a",{attrs:{href:"https://fellows.frictionlessdata.io/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Frictionless Data Reproducible Research Fellows"),t("OutboundLink")],1),e._v("! Over the coming months, this group of six early career researchers will be learning about open science, data management, and how to use Frictionless Data tooling in their work to make their data more open and their research more reusable. Keep an eye on them, as they are on their way to becoming champions of reproducibility! For now, go and read the introductory blogs they wrote about themselves to know more about them and their goals for this fellowship.")]),e._v(" "),t("img",{staticStyle:{margin:"10px",border:"5px solid black"},attrs:{src:"/img/blog/GQ.jpeg",width:"200px",align:"left"}}),e._v(" "),t("p",[t("strong",[e._v("Hi, everyone! My name is Guo-Qiang Zhang")]),e._v(", and I am from China. Right after I finished my residency training in Pediatrics, I joined Prof Bright I. Nwaru’s group and started my doctoral studies at Krefting Research Centre in University of Gothenburg (Sweden). My doctoral project is to look at the effects of sex hormones on women’s health (especially asthma), utilizing epidemiological methods as well as evidence synthesis tools (e.g., systematic review, umbrella review).")]),e._v(" "),t("p",[e._v("In my first year of doctoral studies, I had the opportunity to participate in the course “Reproducibility in Medical Research” led by Prof Nwaru. It was my first time to hear about Open Science and research reproducibility. As a “fresh” full-time doctoral student full of passion for medical research, I felt overwhelmed by waves of frustration when I came to know the reproducibility crisis. After spending some time with my frustration, I came to realize that in fact I can do something. In my first project, my colleagues and I conducted an umbrella review on a highly controversial topic on the impact of menopausal hormone therapy on women’s health. We put extensive efforts into making the review process as transparent as possible: we developed beforehand protocols for data extraction and statistical analysis, documented key steps of the review process, verified data in the published literature, and made all datasets and R scripts publicly available."),t("br"),e._v("\nTo keep on reading about Guo-Qiang click "),t("a",{attrs:{href:"https://fellows.frictionlessdata.io/blog/hello-guo-qiang/",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),t("OutboundLink")],1),e._v(".")]),e._v(" "),t("img",{staticStyle:{margin:"10px",border:"5px solid black"},attrs:{src:"/img/blog/Victoria.jpeg",width:"140px",align:"left"}}),e._v(" "),t("p",[t("strong",[e._v("Hi all! My name is Victoria")]),e._v(". I’m a physics graduate student and recovering engineer living in Berlin. I grew up mainly in my family’s native country of Singapore, but consider myself an American, and am still workshopping a straightforward answer to “where are you from.”")]),e._v(" "),t("p",[e._v("In my past life I worked in materials QA testing; currently, I’m at the German Aerospace Centre designing laser systems in the THz range - a type of non-visible light that hangs out on the electromagnetic spectrum between infrared and microwave.")]),e._v(" "),t("p",[e._v("My Open Science journey has just begun and I’m stoked! I started to get interested in topics around data transparency and accessibility after a series of escalating frustrations with information dynamics in medical technology, beginning in my own field of gas sensing, then discovering similar disparities in tangential fields."),t("br"),e._v("\nRead more about Victoria "),t("a",{attrs:{href:"https://fellows.frictionlessdata.io/blog/hello-victoria/",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),t("OutboundLink")],1),e._v(".")]),e._v(" "),t("img",{staticStyle:{margin:"10px",border:"5px solid black"},attrs:{src:"/img/blog/Zarena.jpeg",width:"140px",align:"left"}}),e._v(" "),t("p",[t("strong",[e._v("Hello everybody! My name is Zarena")]),e._v(". I grew up in the Kyrgyz Republic, yet spent half of my life studying and working abroad. Currently, I am a Research Assistant for the project Creating Culturally Appropriate Research Ethics in Central Asia (CARE) at Nazarbayev University in Kazakhstan. I am also a Mad activist and an interdisciplinary human rights researcher. I like to consider my research activities going beyond academia to encompass and make an effect on broader socio-political structures.")]),e._v(" "),t("p",[e._v("Although I believe that life would not progress without frictions, when it comes to science and research, I feel, ‘frictions’ - manifested in a form of paywalls, bureaucratic and corporate management, or other structural barriers - should be deconstructed. So, I am joining the Frictionless Data Fellowship Programme with the purpose to learn more about open and FAIR research."),t("br"),e._v("\nLearn more about Zarena "),t("a",{attrs:{href:"https://fellows.frictionlessdata.io/blog/hello-zarena/",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),t("OutboundLink")],1),e._v(".")]),e._v(" "),t("img",{staticStyle:{margin:"10px",border:"5px solid black"},attrs:{src:"/img/blog/Melvin.jpeg",width:"120px",align:"left"}}),e._v(" "),t("p",[t("strong",[e._v("Hi everyone, my name is Melvin Ochieng")]),e._v(", and I’m a pathologist and up-coming soil scientist. I was born in Kenya in a town called Eldoret that is famous for producing Kenyan marathon champions. I was raised in Kenya in my early childhood days and in Tanzania afterwards. I like to consider myself both Kenyan and Tanzanian at heart because the two countries took part in molding the person I am today. I am currently a masters student at University of Mohammed VI polytechnic in Morocco, studying fertilizer science and technology. Over the past two years, my research focused on potato cyst nematode (PCN) which is a quarantine pest that had been reported in Kenya in 2015.")]),e._v(" "),t("p",[e._v("I’m excited to start this journey as a Frictionless Data fellow with my fellows for this cohort. I just recently found out about open science and I couldn’t be more excited to learn more about this concept and how it will influence me as a researcher. Advancement in technology has opened up the world in so many ways and made possible extensive networks for collaborations globally. Notably, the problems the world is facing today require a global/collaborative approach to solve. Therefore, reproducible research is of key importance in promoting this collaboration."),t("br"),e._v("\nTo know more about Melvin click "),t("a",{attrs:{href:"https://fellows.frictionlessdata.io/blog/hello-melvin/",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),t("OutboundLink")],1),e._v(".")]),e._v(" "),t("img",{staticStyle:{margin:"10px",border:"5px solid black"},attrs:{src:"/img/blog/Kevin-Photo.jpeg",width:"140px",align:"left"}}),e._v(" "),t("p",[t("strong",[e._v("Hello! My name is Kevin Kidambasi")]),e._v("(KK). I was born and raised in Vihiga County of western Kenya. Currently, I live in Nairobi, the capital city of Kenya. I am a master’s student in Jomo Kenyatta University of Agriculture and Technology (JKUAT) registered at the department of Biochemistry. My MSc research at the International Centre of Insect Physiology and Ecology (icipe) focuses on the role of haematophagous camel-specific biting keds (Hippobosca camelina) in disease transmission in Laisamis, Marsabit County of northern Kenya. My broad research interest focuses on studying host-pathogen interactions to understand infection mechanisms of diseases in order to discover novel control and treatment targets.")]),e._v(" "),t("p",[e._v("I am interested in improving research reproducibility because it allows other researchers to confirm the accuracy of my data and correct any bias as well as validate the relevance of the conclusions drawn from the results. This also allows data to be analyzed in different ways and thus, give new insights and lead the research in new directions. In addition, improving research reproducibility would allow the scientific community to understand how the conclusions of a study were made and pinpoint out any mistakes in data analyses. In general, research reproducibility enhances openness, research collaboration, and data accessibility which in turn increase public trust in science and hence permits their participation and support for research. This enables public understanding of how research is conducted and its importance."),t("br"),e._v("\nRead more about Kevin "),t("a",{attrs:{href:"https://fellows.frictionlessdata.io/blog/hello-kevin/",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),t("OutboundLink")],1),e._v(".")]),e._v(" "),t("img",{staticStyle:{margin:"10px",border:"5px solid black"},attrs:{src:"/img/blog/Lindsay.jpeg",width:"120px",align:"left"}}),e._v(" "),t("p",[t("strong",[e._v("Greetings! My name is Lindsay Gypin")]),e._v(", she/her. I grew up in Denver, Colorado and began my career as a K-12 educator. I taught high school English and worked as a school librarian before becoming disillusioned with the politicization of public education and determining my skills were better suited for work in public libraries. Attending library school after having worked in libraries for so many years, I found myself drawn to courses in the research data management track of librarianship, and in qualitative research methods.I recently became a Data Services Librarian at the University of North Carolina Greensboro, where I hope to assist scholars in making their research data more open and accessible.")]),e._v(" "),t("p",[e._v("For some time, I have wanted to build a reproducible workflow to uncover systemic bias in library catalogs. I’m hoping the Fellows Programme will help me build the foundation to do so."),t("br"),e._v("\nTo learn more about Lindsay click "),t("a",{attrs:{href:"https://fellows.frictionlessdata.io/blog/hello-lindsay/",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),t("OutboundLink")],1),e._v(".")])])}),[],!1,null,null,null);a.default=r.exports}}]); \ No newline at end of file diff --git a/assets/js/149.9c67d050.js b/assets/js/149.4bf2a3a2.js similarity index 98% rename from assets/js/149.9c67d050.js rename to assets/js/149.4bf2a3a2.js index e1ef5c7fc..891779805 100644 --- a/assets/js/149.9c67d050.js +++ b/assets/js/149.4bf2a3a2.js @@ -1 +1 @@ -(window.webpackJsonp=window.webpackJsonp||[]).push([[149],{681:function(e,t,a){"use strict";a.r(t);var o=a(29),r=Object(o.a)({},(function(){var e=this,t=e.$createElement,a=e._self._c||t;return a("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[a("p",[e._v("On the last Frictionless Data community call of the year, on December 16"),a("sup",[e._v("th")]),e._v(", we had Keith Hughitt from the National Cancer Institute (NCI) sharing (and demoing) his ideas around representing data processing flows as a DAG (Directed Acyclic Graph) inside of a datapackage.json, and tools for interacting with and visualizing such DAGs.")]),e._v(" "),a("p",[e._v("Keith started thinking about this when he realised that cleaning and processing data are not obvious processes, on the contrary, there is a lot of bias in them. The decisions made to clean the raw data are not generally included in the publications and are not made available in any transparent way. To allow collaboration and reproducibility, Keith thought of embedding and annotated data provenance DAG in a datapackage.json using the Frictionless specs.")]),e._v(" "),a("p",[e._v("The basic process Keith has in mind to solve this problem is:")]),e._v(" "),a("ul",[a("li",[e._v("The data provenance is encoded as a DAG in the metadata")]),e._v(" "),a("li",[e._v("For each step in processing the workflow, the previous DAG is copied and extended")]),e._v(" "),a("li",[e._v("Each node of the DAG represents a dataset at a particular stage of processing, and it can be associated with annotations, views")]),e._v(" "),a("li",[e._v("Datapackages would be generated and associated with each node")]),e._v(" "),a("li",[e._v("Have a web UI that reads the metadata and renders the DAG.")])]),e._v(" "),a("p",[e._v("If you would like to dive deeper and discover all about representing data processing flows as DAG inside of a Data Package, you can watch Keith Hughitt’s presentation here:")]),e._v(" "),a("iframe",{attrs:{width:"560",height:"315",src:"https://www.youtube.com/embed/pDpAuyTCvF0",title:"YouTube video player",frameborder:"0",allow:"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture",allowfullscreen:""}}),e._v(" "),a("p",[e._v("If you find this idea interesting, come and talk to Keith on "),a("a",{attrs:{href:"https://discord.com/invite/j9DNFNw",target:"_blank",rel:"noopener noreferrer"}},[e._v("Discord"),a("OutboundLink")],1),e._v("! He would love to hear what you think and if you have other ideas in mind.")]),e._v(" "),a("h2",{attrs:{id:"other-agenda-items-from-our-hangout"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#other-agenda-items-from-our-hangout"}},[e._v("#")]),e._v(" Other agenda items from our hangout")]),e._v(" "),a("p",[e._v("We are part of the organisation of the "),a("a",{attrs:{href:"https://fosdem.org/",target:"_blank",rel:"noopener noreferrer"}},[e._v("FOSDEM"),a("OutboundLink")],1),e._v(" Thematic Track "),a("em",[e._v("Open Research Tools & Technologies")]),e._v(" this year too. We would love to have someone from the Frictionless community giving a talk. The deadline has been extended and you have time until December 23"),a("sup",[e._v("rd")]),e._v(" to submit a talk proposal! More info at "),a("a",{attrs:{href:"https://fosdem.org/2022/news/2021-11-02-devroom-cfp/",target:"_blank",rel:"noopener noreferrer"}},[e._v("this page"),a("OutboundLink")],1),e._v(".")]),e._v(" "),a("h1",{attrs:{id:"join-us-next-month"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#join-us-next-month"}},[e._v("#")]),e._v(" Join us next month!")]),e._v(" "),a("p",[e._v("Next community call is next year, on January 21"),a("sup",[e._v("st")]),e._v(". Francisco Alves, from the DPCKAN team who won the Frictionless Data hackathon back in October, is going to present their prototype and how it evolved.")]),e._v(" "),a("p",[e._v("You can sign up "),a("a",{attrs:{href:"https://docs.google.com/forms/d/e/1FAIpQLSeuNCopxXauMkrWvF6VHqOyHMcy54SfNDOseVXfWRQZWkvqjQ/viewform?usp=sf_link",target:"_blank",rel:"noopener noreferrer"}},[e._v("here:"),a("OutboundLink")],1)]),e._v(" "),a("p",[e._v("Do you want to share something with the community? Let us know when you sign up!")]),e._v(" "),a("h1",{attrs:{id:"call-recording"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#call-recording"}},[e._v("#")]),e._v(" Call recording:")]),e._v(" "),a("p",[e._v("On a final note, here is the recording of the full call:")]),e._v(" "),a("iframe",{attrs:{width:"560",height:"315",src:"https://www.youtube.com/embed/FaWixB29SUA",title:"YouTube video player",frameborder:"0",allow:"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture",allowfullscreen:""}}),e._v(" "),a("p",[e._v("As usual, you can join us on "),a("a",{attrs:{href:"https://discord.com/invite/j9DNFNw",target:"_blank",rel:"noopener noreferrer"}},[e._v("Discord"),a("OutboundLink")],1),e._v(" or "),a("a",{attrs:{href:"https://twitter.com/frictionlessd8a",target:"_blank",rel:"noopener noreferrer"}},[e._v("Twitter"),a("OutboundLink")],1),e._v(" to say hi or ask any questions. See you there!")])])}),[],!1,null,null,null);t.default=r.exports}}]); \ No newline at end of file +(window.webpackJsonp=window.webpackJsonp||[]).push([[149],{680:function(e,t,a){"use strict";a.r(t);var o=a(29),r=Object(o.a)({},(function(){var e=this,t=e.$createElement,a=e._self._c||t;return a("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[a("p",[e._v("On the last Frictionless Data community call of the year, on December 16"),a("sup",[e._v("th")]),e._v(", we had Keith Hughitt from the National Cancer Institute (NCI) sharing (and demoing) his ideas around representing data processing flows as a DAG (Directed Acyclic Graph) inside of a datapackage.json, and tools for interacting with and visualizing such DAGs.")]),e._v(" "),a("p",[e._v("Keith started thinking about this when he realised that cleaning and processing data are not obvious processes, on the contrary, there is a lot of bias in them. The decisions made to clean the raw data are not generally included in the publications and are not made available in any transparent way. To allow collaboration and reproducibility, Keith thought of embedding and annotated data provenance DAG in a datapackage.json using the Frictionless specs.")]),e._v(" "),a("p",[e._v("The basic process Keith has in mind to solve this problem is:")]),e._v(" "),a("ul",[a("li",[e._v("The data provenance is encoded as a DAG in the metadata")]),e._v(" "),a("li",[e._v("For each step in processing the workflow, the previous DAG is copied and extended")]),e._v(" "),a("li",[e._v("Each node of the DAG represents a dataset at a particular stage of processing, and it can be associated with annotations, views")]),e._v(" "),a("li",[e._v("Datapackages would be generated and associated with each node")]),e._v(" "),a("li",[e._v("Have a web UI that reads the metadata and renders the DAG.")])]),e._v(" "),a("p",[e._v("If you would like to dive deeper and discover all about representing data processing flows as DAG inside of a Data Package, you can watch Keith Hughitt’s presentation here:")]),e._v(" "),a("iframe",{attrs:{width:"560",height:"315",src:"https://www.youtube.com/embed/pDpAuyTCvF0",title:"YouTube video player",frameborder:"0",allow:"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture",allowfullscreen:""}}),e._v(" "),a("p",[e._v("If you find this idea interesting, come and talk to Keith on "),a("a",{attrs:{href:"https://discord.com/invite/j9DNFNw",target:"_blank",rel:"noopener noreferrer"}},[e._v("Discord"),a("OutboundLink")],1),e._v("! He would love to hear what you think and if you have other ideas in mind.")]),e._v(" "),a("h2",{attrs:{id:"other-agenda-items-from-our-hangout"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#other-agenda-items-from-our-hangout"}},[e._v("#")]),e._v(" Other agenda items from our hangout")]),e._v(" "),a("p",[e._v("We are part of the organisation of the "),a("a",{attrs:{href:"https://fosdem.org/",target:"_blank",rel:"noopener noreferrer"}},[e._v("FOSDEM"),a("OutboundLink")],1),e._v(" Thematic Track "),a("em",[e._v("Open Research Tools & Technologies")]),e._v(" this year too. We would love to have someone from the Frictionless community giving a talk. The deadline has been extended and you have time until December 23"),a("sup",[e._v("rd")]),e._v(" to submit a talk proposal! More info at "),a("a",{attrs:{href:"https://fosdem.org/2022/news/2021-11-02-devroom-cfp/",target:"_blank",rel:"noopener noreferrer"}},[e._v("this page"),a("OutboundLink")],1),e._v(".")]),e._v(" "),a("h1",{attrs:{id:"join-us-next-month"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#join-us-next-month"}},[e._v("#")]),e._v(" Join us next month!")]),e._v(" "),a("p",[e._v("Next community call is next year, on January 21"),a("sup",[e._v("st")]),e._v(". Francisco Alves, from the DPCKAN team who won the Frictionless Data hackathon back in October, is going to present their prototype and how it evolved.")]),e._v(" "),a("p",[e._v("You can sign up "),a("a",{attrs:{href:"https://docs.google.com/forms/d/e/1FAIpQLSeuNCopxXauMkrWvF6VHqOyHMcy54SfNDOseVXfWRQZWkvqjQ/viewform?usp=sf_link",target:"_blank",rel:"noopener noreferrer"}},[e._v("here:"),a("OutboundLink")],1)]),e._v(" "),a("p",[e._v("Do you want to share something with the community? Let us know when you sign up!")]),e._v(" "),a("h1",{attrs:{id:"call-recording"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#call-recording"}},[e._v("#")]),e._v(" Call recording:")]),e._v(" "),a("p",[e._v("On a final note, here is the recording of the full call:")]),e._v(" "),a("iframe",{attrs:{width:"560",height:"315",src:"https://www.youtube.com/embed/FaWixB29SUA",title:"YouTube video player",frameborder:"0",allow:"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture",allowfullscreen:""}}),e._v(" "),a("p",[e._v("As usual, you can join us on "),a("a",{attrs:{href:"https://discord.com/invite/j9DNFNw",target:"_blank",rel:"noopener noreferrer"}},[e._v("Discord"),a("OutboundLink")],1),e._v(" or "),a("a",{attrs:{href:"https://twitter.com/frictionlessd8a",target:"_blank",rel:"noopener noreferrer"}},[e._v("Twitter"),a("OutboundLink")],1),e._v(" to say hi or ask any questions. See you there!")])])}),[],!1,null,null,null);t.default=r.exports}}]); \ No newline at end of file diff --git a/assets/js/15.10b9d46b.js b/assets/js/15.a9f93b29.js similarity index 95% rename from assets/js/15.10b9d46b.js rename to assets/js/15.a9f93b29.js index 31f3d4055..3910cf513 100644 --- a/assets/js/15.10b9d46b.js +++ b/assets/js/15.a9f93b29.js @@ -1 +1 @@ -(window.webpackJsonp=window.webpackJsonp||[]).push([[15],{517:function(e,t,a){e.exports=a.p+"assets/img/deploy-solutions-img-1.e03bb95a.png"},518:function(e,t,a){e.exports=a.p+"assets/img/Deploy-solutions-img-2.50645805.png"},519:function(e,t,a){e.exports=a.p+"assets/img/deploy-solutions-img-3.690aace9.png"},520:function(e,t,a){e.exports=a.p+"assets/img/OASIS-Prototype-Features-Import-Data.e63330ef.gif"},702:function(e,t,a){"use strict";a.r(t);var o=a(29),s=Object(o.a)({},(function(){var e=this,t=e.$createElement,o=e._self._c||t;return o("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[o("h1",{attrs:{id:"our-climate-change-research-and-development-program"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#our-climate-change-research-and-development-program"}},[e._v("#")]),e._v(" Our Climate Change Research and Development Program")]),e._v(" "),o("p",[e._v("We’re Canadian software developers passionate about space and the Earth."),o("br"),e._v("\nSince 2019, we have been working with hundreds of students, citizens, NGOs, scientists, and government officials to identify top communication problems getting citizens to act on climate change risks. We are exploring ways software can help authorities and ordinary citizens communicate better and prepare for, respond to, and recover from climate change impacts and natural disasters."),o("br"),e._v("\nWe have gathered unprecedented insights into how software can help and are now building a prototype to put these insights into practice and help individuals and communities before, during, and after a climate change impact or natural disaster.")]),e._v(" "),o("h1",{attrs:{id:"our-climate-change-software-prototype"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#our-climate-change-software-prototype"}},[e._v("#")]),e._v(" Our Climate Change Software Prototype")]),e._v(" "),o("p",[e._v("The prototype is based on historic floods that occurred in Ottawa-Gatineau in 2017 and 2019, and is intended to demonstrate ways that software can help emergency responders and local government officials communicate better with the general public."),o("br"),e._v("\nIt isn’t intended to replace any emergency alert or other government systems. Instead, its focus is on communicating, collaborating with, and supporting individuals and communities as they prepare, respond, and recover from disasters and impacts."),o("br"),e._v("\nThe prototype is modular and organized into different sections, grouped by the intended main audience:")]),e._v(" "),o("h2",{attrs:{id:"trusted-authorities-web-portal"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#trusted-authorities-web-portal"}},[e._v("#")]),e._v(" “Trusted Authorities” web portal")]),e._v(" "),o("p",[e._v("This audience includes emergency responders, government officials at the municipal, provincial, and federal levels, officials and staff at hydro-electric stations and municipal infrastructure, and other groups who would have authority over the planning, response, and recovery efforts. They produce and consume official data sources, and can share some of that information with the general public or citizen scientist audiences. The web portal contains sensitive, non-public information and therefore requires a secure login for verified users."),o("br"),e._v('\nThis is an example of the Trusted Authority Dashboard (showing other prototype"modules" in addition to the flooding):')]),e._v(" "),o("p",[o("img",{attrs:{src:a(517),alt:"deploy-solutions-img-1"}})]),e._v(" "),o("h2",{attrs:{id:"general-public-anonymous-website"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#general-public-anonymous-website"}},[e._v("#")]),e._v(" General public (anonymous website)")]),e._v(" "),o("p",[e._v("The audience for this section of the prototype is the members of the public who are impacted by the flood, or likely to be. They consume official data sources but do not produce any of their own (in our current prototype). This section is a public, read-only website which does not require any login."),o("br"),e._v("\nThe information the public will see includes a variety of data sources including: checklists for disaster preparation, response, and recovery; curated news and social media information on the disaster; useful resources including emergency and insurance information; and more.")]),e._v(" "),o("p",[o("img",{attrs:{src:a(518),alt:"deploy-solutions-img-2"}})]),e._v(" "),o("h1",{attrs:{id:"what-data-does-the-prototype-need"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#what-data-does-the-prototype-need"}},[e._v("#")]),e._v(" What Data Does the Prototype Need?")]),e._v(" "),o("p",[e._v("As you can see from the screens above, the prototype needs to manage a wide variety of flood disaster-related data sets, from all kinds of providers and with varying degrees of reliability and quality."),o("br"),e._v('\nSome of this data we consider to be"official" and “static” (meaning it exists before a disaster occurs). This kind of data include official government-sourced open data, such as a list of hospitals, police stations, and municipal infrastructure.'),o("br"),e._v("\nObviously these lists wouldn’t change in the middle of a disaster, and would be prepared ahead of time."),o("br"),e._v("\nHowever, there would also be a set of ad-hoc lists created to suit the particular needs of the disaster response and community involvement. This is data that is highly subjective to the particular circumstances of the disaster, cannot be known ahead of time, and is likely to evolve over the course of the disaster."),o("br"),e._v("\nExamples of such “dynamic data” include locations where sandbags are prepared, locations of emergency shelters, lists of emergency supplies and so on.")]),e._v(" "),o("p",[o("img",{attrs:{src:a(519),alt:"Deploy-solutions-img-3"}})]),e._v(" "),o("p",[e._v("Our software prototype allows users to upload CSV files as part of a data import process to make it easy and quick to populate the system with static or dynamic data."),o("br"),e._v("\nEnsuring the quality of ad-hoc data sets is of particular concern, since there are likely to be fewer quality checks for data generated rapidly in response to urgent need.")]),e._v(" "),o("h1",{attrs:{id:"why-do-we-need-frictionless-data"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#why-do-we-need-frictionless-data"}},[e._v("#")]),e._v(" Why Do We Need Frictionless Data?")]),e._v(" "),o("p",[e._v("Therefore, the prototype needs to ensure the users can only upload data that is considered valid according to the Frictionless schema. So before completing the import we have to check every file a user uploads. If it isn’t valid, we need to display a detailed error message to the user so they can fix any errors and retry."),o("br"),e._v("\nThis prevents a user from accidentally entering invalid information and potentially causing corrupt or incomplete records."),o("br"),e._v("\nIt may appear that adding a validation check slows things down. However, this is actually a significantly faster and more user-friendly data import process. From a technical perspective, we can make an asynchronous RESTful call to the Frictionless Data validation service and the response is quick. From a process perspective, without Frictionless Data validation a user might not find out about issues until after the import, and would then have to waste valuable time and effort cleaning up data integrity errors."),o("br"),e._v("\nSince the prototype is intended to help with a flood disaster response, it is critical to avoid any such delays or data integrity risks!")]),e._v(" "),o("h1",{attrs:{id:"how-we-use-frictionless-data"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#how-we-use-frictionless-data"}},[e._v("#")]),e._v(" How We Use Frictionless Data")]),e._v(" "),o("p",[e._v("To achieve the benefits above, we use the "),o("a",{attrs:{href:"https://framework.frictionlessdata.io/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Python Frictionless SDK"),o("OutboundLink")],1),e._v(" hosted in the Amazon Web Services (AWS) cloud."),o("br"),e._v("\nThe code is written in Python programming language. The main Frictionless elements we use currently are the "),o("code",[e._v("Resource")]),e._v(" schema and the "),o("code",[e._v("validate()")]),e._v(" feature. The Python code is deployed as a Lambda layer behind an AWS API Gateway service."),o("br"),e._v("\nThis technical architecture allows us to easily call the Frictionless Data validation feature from our software solutions via simple REST methods calls. The feature can be deployed, secured, and scaled independently of the other prototype components."),o("br"),e._v("\nOf course the code needs to know what schemas to validate against. We create and store Deploy Solutions frictionless schemas for the data sets we described above. There is one schema created for each type of data that we allow a user to import. We store these in a single AmazonS3 bucket, that can only be accessed by the Lambda layer."),o("br"),e._v("\nWhen a user uploads a CSV file into our prototype, they have to choose which type of data they are saving so we know which schema to validate against (depending on which page they’re on, we can usually make this choice for them)."),o("br"),e._v("\nFor example, if a user was uploading a list of Sandbag assembly locations, the software would know to validate against "),o("em",[e._v("the dss-sandbaggingregion_schema.json")]),e._v(" Frictionless schema."),o("br"),e._v("\nWe then create a Frictionless Resource file which consists of the uploaded CSV and the matching schema. Using this Resource file, our software calls the Frictionless "),o("code",[e._v("validate()")]),e._v(" method and receives the validation report back."),o("br"),e._v("\nIf errors are found, these need to be returned back to the user. In our front-end web client (we use Angular), we format the raw report into table display. The report tells the user precisely which row, column and cell requires fixing, saving them valuable time and eliminating more errors!"),o("br"),e._v("\nYou can see the result in the animated GIF below.")]),e._v(" "),o("p",[o("img",{attrs:{src:a(520),alt:"OASIS Prototype Features Import Data - Invalid Data is Blocked-high"}})]),e._v(" "),o("p",[e._v("Obviously the technical complexities mentioned above are hidden away and the user just sees a very simple and intuitive set of validation messages - or hopefully just a success message!"),o("br"),e._v("\nYou can see a full video of this process on our video showcase page: "),o("a",{attrs:{href:"https://showcase.oasis.climatechange.ca",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://showcase.oasis.climatechange.ca"),o("OutboundLink")],1)]),e._v(" "),o("h1",{attrs:{id:"want-to-learn-more-about-our-software-prototype-and-climate-change-software-r-d"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#want-to-learn-more-about-our-software-prototype-and-climate-change-software-r-d"}},[e._v("#")]),e._v(" Want to Learn More About Our Software Prototype and Climate Change Software R&D?")]),e._v(" "),o("p",[e._v("We’ve created a new service to share our climate change software development knowledge. It’s called “OASIS: Software Solutions for Climate Change Problems”."),o("br"),e._v("\nThe OASIS service includes a free weekly newsletter and premium subscription service. It shares unique insights, content, resources, and guidance on how to use software and data from Earth and space to build solutions for climate change problems."),o("br"),e._v("\nLearn more, and subscribe to the free newsletter, at "),o("a",{attrs:{href:"https://oasis.climatechange.ca",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://oasis.climatechange.ca"),o("OutboundLink")],1),e._v(".")])])}),[],!1,null,null,null);t.default=s.exports}}]); \ No newline at end of file +(window.webpackJsonp=window.webpackJsonp||[]).push([[15],{511:function(e,t,a){e.exports=a.p+"assets/img/deploy-solutions-img-1.e03bb95a.png"},512:function(e,t,a){e.exports=a.p+"assets/img/Deploy-solutions-img-2.50645805.png"},513:function(e,t,a){e.exports=a.p+"assets/img/deploy-solutions-img-3.690aace9.png"},514:function(e,t,a){e.exports=a.p+"assets/img/OASIS-Prototype-Features-Import-Data.e63330ef.gif"},701:function(e,t,a){"use strict";a.r(t);var o=a(29),s=Object(o.a)({},(function(){var e=this,t=e.$createElement,o=e._self._c||t;return o("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[o("h1",{attrs:{id:"our-climate-change-research-and-development-program"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#our-climate-change-research-and-development-program"}},[e._v("#")]),e._v(" Our Climate Change Research and Development Program")]),e._v(" "),o("p",[e._v("We’re Canadian software developers passionate about space and the Earth."),o("br"),e._v("\nSince 2019, we have been working with hundreds of students, citizens, NGOs, scientists, and government officials to identify top communication problems getting citizens to act on climate change risks. We are exploring ways software can help authorities and ordinary citizens communicate better and prepare for, respond to, and recover from climate change impacts and natural disasters."),o("br"),e._v("\nWe have gathered unprecedented insights into how software can help and are now building a prototype to put these insights into practice and help individuals and communities before, during, and after a climate change impact or natural disaster.")]),e._v(" "),o("h1",{attrs:{id:"our-climate-change-software-prototype"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#our-climate-change-software-prototype"}},[e._v("#")]),e._v(" Our Climate Change Software Prototype")]),e._v(" "),o("p",[e._v("The prototype is based on historic floods that occurred in Ottawa-Gatineau in 2017 and 2019, and is intended to demonstrate ways that software can help emergency responders and local government officials communicate better with the general public."),o("br"),e._v("\nIt isn’t intended to replace any emergency alert or other government systems. Instead, its focus is on communicating, collaborating with, and supporting individuals and communities as they prepare, respond, and recover from disasters and impacts."),o("br"),e._v("\nThe prototype is modular and organized into different sections, grouped by the intended main audience:")]),e._v(" "),o("h2",{attrs:{id:"trusted-authorities-web-portal"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#trusted-authorities-web-portal"}},[e._v("#")]),e._v(" “Trusted Authorities” web portal")]),e._v(" "),o("p",[e._v("This audience includes emergency responders, government officials at the municipal, provincial, and federal levels, officials and staff at hydro-electric stations and municipal infrastructure, and other groups who would have authority over the planning, response, and recovery efforts. They produce and consume official data sources, and can share some of that information with the general public or citizen scientist audiences. The web portal contains sensitive, non-public information and therefore requires a secure login for verified users."),o("br"),e._v('\nThis is an example of the Trusted Authority Dashboard (showing other prototype"modules" in addition to the flooding):')]),e._v(" "),o("p",[o("img",{attrs:{src:a(511),alt:"deploy-solutions-img-1"}})]),e._v(" "),o("h2",{attrs:{id:"general-public-anonymous-website"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#general-public-anonymous-website"}},[e._v("#")]),e._v(" General public (anonymous website)")]),e._v(" "),o("p",[e._v("The audience for this section of the prototype is the members of the public who are impacted by the flood, or likely to be. They consume official data sources but do not produce any of their own (in our current prototype). This section is a public, read-only website which does not require any login."),o("br"),e._v("\nThe information the public will see includes a variety of data sources including: checklists for disaster preparation, response, and recovery; curated news and social media information on the disaster; useful resources including emergency and insurance information; and more.")]),e._v(" "),o("p",[o("img",{attrs:{src:a(512),alt:"deploy-solutions-img-2"}})]),e._v(" "),o("h1",{attrs:{id:"what-data-does-the-prototype-need"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#what-data-does-the-prototype-need"}},[e._v("#")]),e._v(" What Data Does the Prototype Need?")]),e._v(" "),o("p",[e._v("As you can see from the screens above, the prototype needs to manage a wide variety of flood disaster-related data sets, from all kinds of providers and with varying degrees of reliability and quality."),o("br"),e._v('\nSome of this data we consider to be"official" and “static” (meaning it exists before a disaster occurs). This kind of data include official government-sourced open data, such as a list of hospitals, police stations, and municipal infrastructure.'),o("br"),e._v("\nObviously these lists wouldn’t change in the middle of a disaster, and would be prepared ahead of time."),o("br"),e._v("\nHowever, there would also be a set of ad-hoc lists created to suit the particular needs of the disaster response and community involvement. This is data that is highly subjective to the particular circumstances of the disaster, cannot be known ahead of time, and is likely to evolve over the course of the disaster."),o("br"),e._v("\nExamples of such “dynamic data” include locations where sandbags are prepared, locations of emergency shelters, lists of emergency supplies and so on.")]),e._v(" "),o("p",[o("img",{attrs:{src:a(513),alt:"Deploy-solutions-img-3"}})]),e._v(" "),o("p",[e._v("Our software prototype allows users to upload CSV files as part of a data import process to make it easy and quick to populate the system with static or dynamic data."),o("br"),e._v("\nEnsuring the quality of ad-hoc data sets is of particular concern, since there are likely to be fewer quality checks for data generated rapidly in response to urgent need.")]),e._v(" "),o("h1",{attrs:{id:"why-do-we-need-frictionless-data"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#why-do-we-need-frictionless-data"}},[e._v("#")]),e._v(" Why Do We Need Frictionless Data?")]),e._v(" "),o("p",[e._v("Therefore, the prototype needs to ensure the users can only upload data that is considered valid according to the Frictionless schema. So before completing the import we have to check every file a user uploads. If it isn’t valid, we need to display a detailed error message to the user so they can fix any errors and retry."),o("br"),e._v("\nThis prevents a user from accidentally entering invalid information and potentially causing corrupt or incomplete records."),o("br"),e._v("\nIt may appear that adding a validation check slows things down. However, this is actually a significantly faster and more user-friendly data import process. From a technical perspective, we can make an asynchronous RESTful call to the Frictionless Data validation service and the response is quick. From a process perspective, without Frictionless Data validation a user might not find out about issues until after the import, and would then have to waste valuable time and effort cleaning up data integrity errors."),o("br"),e._v("\nSince the prototype is intended to help with a flood disaster response, it is critical to avoid any such delays or data integrity risks!")]),e._v(" "),o("h1",{attrs:{id:"how-we-use-frictionless-data"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#how-we-use-frictionless-data"}},[e._v("#")]),e._v(" How We Use Frictionless Data")]),e._v(" "),o("p",[e._v("To achieve the benefits above, we use the "),o("a",{attrs:{href:"https://framework.frictionlessdata.io/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Python Frictionless SDK"),o("OutboundLink")],1),e._v(" hosted in the Amazon Web Services (AWS) cloud."),o("br"),e._v("\nThe code is written in Python programming language. The main Frictionless elements we use currently are the "),o("code",[e._v("Resource")]),e._v(" schema and the "),o("code",[e._v("validate()")]),e._v(" feature. The Python code is deployed as a Lambda layer behind an AWS API Gateway service."),o("br"),e._v("\nThis technical architecture allows us to easily call the Frictionless Data validation feature from our software solutions via simple REST methods calls. The feature can be deployed, secured, and scaled independently of the other prototype components."),o("br"),e._v("\nOf course the code needs to know what schemas to validate against. We create and store Deploy Solutions frictionless schemas for the data sets we described above. There is one schema created for each type of data that we allow a user to import. We store these in a single AmazonS3 bucket, that can only be accessed by the Lambda layer."),o("br"),e._v("\nWhen a user uploads a CSV file into our prototype, they have to choose which type of data they are saving so we know which schema to validate against (depending on which page they’re on, we can usually make this choice for them)."),o("br"),e._v("\nFor example, if a user was uploading a list of Sandbag assembly locations, the software would know to validate against "),o("em",[e._v("the dss-sandbaggingregion_schema.json")]),e._v(" Frictionless schema."),o("br"),e._v("\nWe then create a Frictionless Resource file which consists of the uploaded CSV and the matching schema. Using this Resource file, our software calls the Frictionless "),o("code",[e._v("validate()")]),e._v(" method and receives the validation report back."),o("br"),e._v("\nIf errors are found, these need to be returned back to the user. In our front-end web client (we use Angular), we format the raw report into table display. The report tells the user precisely which row, column and cell requires fixing, saving them valuable time and eliminating more errors!"),o("br"),e._v("\nYou can see the result in the animated GIF below.")]),e._v(" "),o("p",[o("img",{attrs:{src:a(514),alt:"OASIS Prototype Features Import Data - Invalid Data is Blocked-high"}})]),e._v(" "),o("p",[e._v("Obviously the technical complexities mentioned above are hidden away and the user just sees a very simple and intuitive set of validation messages - or hopefully just a success message!"),o("br"),e._v("\nYou can see a full video of this process on our video showcase page: "),o("a",{attrs:{href:"https://showcase.oasis.climatechange.ca",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://showcase.oasis.climatechange.ca"),o("OutboundLink")],1)]),e._v(" "),o("h1",{attrs:{id:"want-to-learn-more-about-our-software-prototype-and-climate-change-software-r-d"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#want-to-learn-more-about-our-software-prototype-and-climate-change-software-r-d"}},[e._v("#")]),e._v(" Want to Learn More About Our Software Prototype and Climate Change Software R&D?")]),e._v(" "),o("p",[e._v("We’ve created a new service to share our climate change software development knowledge. It’s called “OASIS: Software Solutions for Climate Change Problems”."),o("br"),e._v("\nThe OASIS service includes a free weekly newsletter and premium subscription service. It shares unique insights, content, resources, and guidance on how to use software and data from Earth and space to build solutions for climate change problems."),o("br"),e._v("\nLearn more, and subscribe to the free newsletter, at "),o("a",{attrs:{href:"https://oasis.climatechange.ca",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://oasis.climatechange.ca"),o("OutboundLink")],1),e._v(".")])])}),[],!1,null,null,null);t.default=s.exports}}]); \ No newline at end of file diff --git a/assets/js/150.10801377.js b/assets/js/150.fdc13174.js similarity index 98% rename from assets/js/150.10801377.js rename to assets/js/150.fdc13174.js index 9e11a878c..c6a7bc920 100644 --- a/assets/js/150.10801377.js +++ b/assets/js/150.fdc13174.js @@ -1 +1 @@ -(window.webpackJsonp=window.webpackJsonp||[]).push([[150],{685:function(e,t,a){"use strict";a.r(t);var n=a(29),o=Object(n.a)({},(function(){var e=this,t=e.$createElement,a=e._self._c||t;return a("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[a("p",[e._v("Originally published: "),a("a",{attrs:{href:"https://blog.okfn.org/2022/01/10/frictionless-planet-save-the-date/",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://blog.okfn.org/2022/01/10/frictionless-planet-save-the-date/"),a("OutboundLink")],1)]),e._v(" "),a("p",[e._v("We believe that an ecosystem of organisations combining tools, techniques and strategies to transform datasets relevant to the climate crisis into applied knowledge and actionable campaigns can get us closer to the Paris agreement goals. Today, scientists, academics and activists are working against the clock to save us from the greatest catastrophe of our times. But they are doing so under-resourced, siloed and disconnected. Sometimes even facing physical threats or achieving very local, isolated impact. We want to reverse that by activating a cross-sectoral sharing process of tools, techniques and technologies to open the data and unleash the power of knowledge to fight against climate change. We already started with the Frictionless Data process – collaborating with researcher groups to "),a("a",{attrs:{href:"https://frictionlessdata.io/blog/2020/09/16/goodtables-bcodmo/",target:"_blank",rel:"noopener noreferrer"}},[e._v("better manage ocean research data"),a("OutboundLink")],1),e._v(" and "),a("a",{attrs:{href:"https://frictionlessdata.io/blog/2020/03/18/frictionless-data-pilot-study/",target:"_blank",rel:"noopener noreferrer"}},[e._v("openly publish cleaned, integrated energy data"),a("OutboundLink")],1),e._v(" – and we want to expand an action-oriented alliance leading to cross regional, cross sectoral, sustainable collaboration. We need to use the best tools and the best minds of our times to fight the problems of our times.")]),e._v(" "),a("p",[e._v("We consider you-your organisation- as leading thinkers-doers-communicators leveraging technology and creativity in a unique way, with the potential to lead to meaningful change and we would love to invite you to an initial brainstorming session as we think of common efforts, a sustainability path and a road of action to work the next three years and beyond.")]),e._v(" "),a("p",[e._v("What will we do together during this brainstorming session? Our overarching goal is to make open climate data more useful. To that end, during this initial session, we will conceptualise ways of cleaning and standardising open climate data, creating more reproducible and efficient methods of consuming and analysing that data, and focus on ways to put this data into the hands of those that can truly drive change.")]),e._v(" "),a("h1",{attrs:{id:"what-to-bring"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#what-to-bring"}},[e._v("#")]),e._v(" WHAT TO BRING?")]),e._v(" "),a("ul",[a("li",[e._v("An effort-idea that is effective and you feel proud of at the intersection of digital and climate change.")]),e._v(" "),a("li",[e._v("A data problem you are struggling with.")]),e._v(" "),a("li",[e._v("Your best post-holidays smile.")])]),e._v(" "),a("h1",{attrs:{id:"when"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#when"}},[e._v("#")]),e._v(" When?")]),e._v(" "),a("p",[e._v("13:30 GMT – 20 January – Registration open "),a("a",{attrs:{href:"https://www.eventbrite.co.uk/e/frictionless-planet-tickets-242708286017",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),a("OutboundLink")],1),e._v(". "),a("strong",[e._v("SOLD OUT")])]),e._v(" "),a("p",[e._v("20:30 GMT – 21 January – Registration open "),a("a",{attrs:{href:"https://www.eventbrite.co.uk/e/frictionless-planet-tickets-242807803677",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),a("OutboundLink")],1),e._v(".")]),e._v(" "),a("p",[e._v("Limited slots, 25 attendees per session.")])])}),[],!1,null,null,null);t.default=o.exports}}]); \ No newline at end of file +(window.webpackJsonp=window.webpackJsonp||[]).push([[150],{682:function(e,t,a){"use strict";a.r(t);var n=a(29),o=Object(n.a)({},(function(){var e=this,t=e.$createElement,a=e._self._c||t;return a("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[a("p",[e._v("Originally published: "),a("a",{attrs:{href:"https://blog.okfn.org/2022/01/10/frictionless-planet-save-the-date/",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://blog.okfn.org/2022/01/10/frictionless-planet-save-the-date/"),a("OutboundLink")],1)]),e._v(" "),a("p",[e._v("We believe that an ecosystem of organisations combining tools, techniques and strategies to transform datasets relevant to the climate crisis into applied knowledge and actionable campaigns can get us closer to the Paris agreement goals. Today, scientists, academics and activists are working against the clock to save us from the greatest catastrophe of our times. But they are doing so under-resourced, siloed and disconnected. Sometimes even facing physical threats or achieving very local, isolated impact. We want to reverse that by activating a cross-sectoral sharing process of tools, techniques and technologies to open the data and unleash the power of knowledge to fight against climate change. We already started with the Frictionless Data process – collaborating with researcher groups to "),a("a",{attrs:{href:"https://frictionlessdata.io/blog/2020/09/16/goodtables-bcodmo/",target:"_blank",rel:"noopener noreferrer"}},[e._v("better manage ocean research data"),a("OutboundLink")],1),e._v(" and "),a("a",{attrs:{href:"https://frictionlessdata.io/blog/2020/03/18/frictionless-data-pilot-study/",target:"_blank",rel:"noopener noreferrer"}},[e._v("openly publish cleaned, integrated energy data"),a("OutboundLink")],1),e._v(" – and we want to expand an action-oriented alliance leading to cross regional, cross sectoral, sustainable collaboration. We need to use the best tools and the best minds of our times to fight the problems of our times.")]),e._v(" "),a("p",[e._v("We consider you-your organisation- as leading thinkers-doers-communicators leveraging technology and creativity in a unique way, with the potential to lead to meaningful change and we would love to invite you to an initial brainstorming session as we think of common efforts, a sustainability path and a road of action to work the next three years and beyond.")]),e._v(" "),a("p",[e._v("What will we do together during this brainstorming session? Our overarching goal is to make open climate data more useful. To that end, during this initial session, we will conceptualise ways of cleaning and standardising open climate data, creating more reproducible and efficient methods of consuming and analysing that data, and focus on ways to put this data into the hands of those that can truly drive change.")]),e._v(" "),a("h1",{attrs:{id:"what-to-bring"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#what-to-bring"}},[e._v("#")]),e._v(" WHAT TO BRING?")]),e._v(" "),a("ul",[a("li",[e._v("An effort-idea that is effective and you feel proud of at the intersection of digital and climate change.")]),e._v(" "),a("li",[e._v("A data problem you are struggling with.")]),e._v(" "),a("li",[e._v("Your best post-holidays smile.")])]),e._v(" "),a("h1",{attrs:{id:"when"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#when"}},[e._v("#")]),e._v(" When?")]),e._v(" "),a("p",[e._v("13:30 GMT – 20 January – Registration open "),a("a",{attrs:{href:"https://www.eventbrite.co.uk/e/frictionless-planet-tickets-242708286017",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),a("OutboundLink")],1),e._v(". "),a("strong",[e._v("SOLD OUT")])]),e._v(" "),a("p",[e._v("20:30 GMT – 21 January – Registration open "),a("a",{attrs:{href:"https://www.eventbrite.co.uk/e/frictionless-planet-tickets-242807803677",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),a("OutboundLink")],1),e._v(".")]),e._v(" "),a("p",[e._v("Limited slots, 25 attendees per session.")])])}),[],!1,null,null,null);t.default=o.exports}}]); \ No newline at end of file diff --git a/assets/js/152.f2ecd916.js b/assets/js/152.a7c792b3.js similarity index 99% rename from assets/js/152.f2ecd916.js rename to assets/js/152.a7c792b3.js index 1752685f3..08cfc36cf 100644 --- a/assets/js/152.f2ecd916.js +++ b/assets/js/152.a7c792b3.js @@ -1 +1 @@ -(window.webpackJsonp=window.webpackJsonp||[]).push([[152],{686:function(e,a,t){"use strict";t.r(a);var r=t(29),i=Object(r.a)({},(function(){var e=this,a=e.$createElement,t=e._self._c||a;return t("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[t("p",[e._v("I started the "),t("a",{attrs:{href:"https://www.librarieshacked.org/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Libraries Hacked"),t("OutboundLink")],1),e._v(" project in 2014. Inspired by ‘tech for good’ open data groups and hackathons, I wanted to explore how libraries could leverage data for innovation and service improvement. I had already been involved in the work of the group "),t("a",{attrs:{href:"https://www.bathhacked.org/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Bath Hacked"),t("OutboundLink")],1),e._v(", and worked at the local Council in Bath, releasing large amounts of open data that was well used by the community. That included data such as live car park occupancy, traffic surveys, and air quality monitoring.")]),e._v(" "),t("p",[e._v("Getting involved in civic data publishing led me to explore data software, tools, and standards. I’ve used the Frictionless standards of Table Schema and CSV Dialect, as well as the code libraries that can be utilised to implement these. Data standards are an essential tool for data publishers in order to make data easily usable and reproducible across different organisations.")]),e._v(" "),t("p",[e._v("Public library services in England are managed by 150 local government organisations. The central government department for Digital, Culture, Media, and Sport (DCMS) hold responsibility for superintending those services. In September 2019 they convened a meeting about public library data.")]),e._v(" "),t("p",[e._v("Library data, of many kinds, is not well utilised in England.")]),e._v(" "),t("ul",[t("li",[t("strong",[e._v("Lack of public data")]),e._v(". There are relatively few library services sharing data about themselves for public use.")]),e._v(" "),t("li",[t("strong",[e._v("Low expectations")]),e._v(". There is no guidance on what data to share. Some services will publish certain datasets, but these will likely be different to the ones other publish.")]),e._v(" "),t("li",[t("strong",[e._v("Few standards")]),e._v(". The structure of any published data will be unique to each library service. For example, there are published lists of library branches from "),t("a",{attrs:{href:"https://www.opendatanottingham.org.uk/dataset.aspx?id=1",target:"_blank",rel:"noopener noreferrer"}},[e._v("Nottinghamshire County Council"),t("OutboundLink")],1),e._v(" and "),t("a",{attrs:{href:"https://data.gov.uk/dataset/9342032d-ab88-462f-b31c-4fb07fd4da6f/libraries",target:"_blank",rel:"noopener noreferrer"}},[e._v("North Somerset Council"),t("OutboundLink")],1),e._v(". Both are out of date, and have different fields, field names, field types, and file formats.")])]),e._v(" "),t("p",[e._v("The meeting discussed these issues, amongst others. The problems are understood, but difficult to tackle, as no organisation has direct responsibility for library data. There are also difficult underlying causes - low skills and funding being two major ones.")]),e._v(" "),t("p",[e._v("Large scale culture change will take many years. But to begin some sector-led collaborative work, a group of the attendees agreed to define the fields for a core selection of library datasets. The project would involve data practitioners from across English library services.")]),e._v(" "),t("p",[e._v("The datasets would cover:")]),e._v(" "),t("ul",[t("li",[t("strong",[e._v("Events")]),e._v(": the events that happen in libraries, their attendance, and outcomes")]),e._v(" "),t("li",[t("strong",[e._v("Library branches")]),e._v(": physical building locations, opening hours, and contact details")]),e._v(" "),t("li",[t("strong",[e._v("Loans")]),e._v(": the items lent from libraries, with counts, time periods, and categories")]),e._v(" "),t("li",[t("strong",[e._v("Stock")]),e._v(": the number of items held in libraries, with categories")]),e._v(" "),t("li",[t("strong",[e._v("Mobile library stops")]),e._v(": locations of mobile library stops, and their timetabled frequency")]),e._v(" "),t("li",[t("strong",[e._v("Physical visits")]),e._v(": how many people visit library premises")]),e._v(" "),t("li",[t("strong",[e._v("Membership")]),e._v(": counts of people who are library members, at small-area geographies.")])]),e._v(" "),t("p",[e._v("These can be split into 3 categories:")]),e._v(" "),t("ul",[t("li",[t("strong",[e._v("Registers")]),e._v(". Data that should be updated when it changes. A list of library branches is a permanent register, to be updated when there are changes to those branches.")]),e._v(" "),t("li",[t("strong",[e._v("Snapshot")]),e._v(". Data that is released as a point in time representation. Library membership will be continually changing, but a snapshot of membership counts should be released at regular intervals.")]),e._v(" "),t("li",[t("strong",[e._v("Time-series")]),e._v(". Data that is new every time it is published. Loans data should be published at regular intervals, each published file being an addition to the existing set.")])]),e._v(" "),t("p",[e._v("To work on these, we held an in-person workshop at the DCMS offices. This featured an exciting interruption by a fire drill, and we had to relocate to a nearby café (difficult for a meeting with many people held in in London!). We also formed an online group using Slack to trial and discuss the data.")]),e._v(" "),t("h2",{attrs:{id:"schemas-and-frictionless-data"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#schemas-and-frictionless-data"}},[e._v("#")]),e._v(" Schemas and Frictionless Data")]),e._v(" "),t("p",[e._v("The majority of our discussions were practical rather than technical, such as what data would be most useful, whether or not it was currently used locally by services, and common problems.")]),e._v(" "),t("p",[e._v("However, to formalise how data should be structured, it became clear that it would be necessary to create technical 'data schemas’.")]),e._v(" "),t("p",[e._v("It can be easy to decide on the data you want, but fail to describe it properly. For example, we could provide people with a spreadsheet that included a column title such as ‘Closed date’. I’d expect people to enter a date in that column, but we’d end up with all kinds of formats.")]),e._v(" "),t("p",[e._v("The "),t("a",{attrs:{href:"https://specs.frictionlessdata.io/table-schema/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Table Schema"),t("OutboundLink")],1),e._v(" specification for defining data, from Frictionless Data, provided a good option for tackling this problem. Not only would it allow us to create a detailed description for the data fields, but we could use other frictionless tools such as "),t("a",{attrs:{href:"https://goodtables.io/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Good Tables"),t("OutboundLink")],1),e._v(". This would allow library services to validate their data before publishing. Things like mismatching date formats would be picked up by the validator, and it would give instructions for how to fix the issue. We would additionally also provide ‘human-readable’ guidance on the datasets.")]),e._v(" "),t("p",[e._v("Frictionless Data is an "),t("a",{attrs:{href:"https://okfn.org/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Open Knowledge Foundation"),t("OutboundLink")],1),e._v(" project, and using tools from an internationally renowned body was also a good practice. The schemas are UK-centric but could be adapted and reused by international library services.")]),e._v(" "),t("p",[e._v("The schemas are all documented at "),t("a",{attrs:{href:"https://schema.librarydata.uk/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Public Library Open Data"),t("OutboundLink")],1),e._v(", including guidance, links to sample data, and the technical definition files.")]),e._v(" "),t("h2",{attrs:{id:"lessons-learned"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#lessons-learned"}},[e._v("#")]),e._v(" Lessons learned")]),e._v(" "),t("p",[e._v("The initial datasets are not comprehensive. They are designed to be a starting point, allowing more to be developed from service requirements.")]),e._v(" "),t("p",[e._v("They are overly focussed towards ‘physical’ library services. It wasn’t long after these meetings that public libraries adjusted to provide all-digital services due to lockdowns. There is nothing here to cover valuable usage datasets like the video views that library services receive on YouTube and Facebook.")]),e._v(" "),t("p",[e._v("There are some that have become even more important. The physical visits schema describes how to structure library footfall data, allowing for differences in collection methods and intervals. This kind of data is now in high demand, to analyse how library service visits recover.")]),e._v(" "),t("p",[e._v("Some of the discussions we had were fascinating. It was important to involve the people who work with this data on a daily basis. They will know how easy it is to extract and manipulate, and many of the pitfalls that come with interpreting it.")]),e._v(" "),t("h3",{attrs:{id:"complexity"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#complexity"}},[e._v("#")]),e._v(" Complexity")]),e._v(" "),t("p",[e._v("There was often a battle between complexity and simplicity. Complex data is good, it often means it is more robust, such as using external identifiers. But simplicity is also good, for data publishers and consumers.")]),e._v(" "),t("p",[e._v("Public library services will primarily employ data workers who are not formally trained in using data. Where there are complex concepts (e.g. Table Schema itself), they are used because they make data publishing easier and more consistent.")]),e._v(" "),t("p",[e._v("Public data should also be made as accessible as possible for the public, while being detailed enough to be useful. In this way the data schemas tend towards simplicity.")]),e._v(" "),t("h3",{attrs:{id:"standards-not-standardisation"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#standards-not-standardisation"}},[e._v("#")]),e._v(" Standards not standardisation")]),e._v(" "),t("p",[e._v("There is a difference between a standard format for data, and standardised data. The schemas are primarily aimed at getting data from multiple services into the same format, to share analysis techniques between library services, and to have usable data when merged with other services.")]),e._v(" "),t("p",[e._v("There were some cases where we decided against standardising the actual data within data fields. For example, there is a column in the loans and the stock datasets called ‘Item type’. This is a category description of the library item, such as ‘Adult fiction’. In some other previous examples of data collection this data is standardised into a uniform set of categories, in order to make it easily comparable.")]),e._v(" "),t("p",[e._v("That kind of exercise defies reality though. Library services may have their own set of categories, many of them interesting and unique. To use a standard set would mean that library services would have to convert their underlying data. As well as extra work, it would be a loss of data. It would also mean that library services would be unlikely to use the converted data themselves. Why use such data if it doesn’t reflect what you actually hold?")]),e._v(" "),t("p",[e._v("The downside is that anyone analysing combined data would have to decide themselves how to compare data in those fields. However, that would be at least a clear task for the data analyst - and would most likely be an easier exercise to do in bulk.")]),e._v(" "),t("h3",{attrs:{id:"detail"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#detail"}},[e._v("#")]),e._v(" Detail")]),e._v(" "),t("p",[e._v("In my ideal world, data would be as detailed as possible. Instead of knowing how many items a library lent every month, I want that data for every hour. In fact I want to have every lending record! But feasibly that would make the data unwieldy and difficult to work with, and wouldn’t be in-line with the statistics libraries are used to.")]),e._v(" "),t("p",[e._v("We primarily made decisions based upon what library services already do. In a lot of cases this was data aggregated into monthly counts, with fields such as library branch and item type used to break down that data.")]),e._v(" "),t("h2",{attrs:{id:"the-future"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#the-future"}},[e._v("#")]),e._v(" The future")]),e._v(" "),t("p",[e._v("The initial meetings were held over two years ago, and it seems longer than that! A lot has happened in the meantime. We are still in a global pandemic that from library perspectives has de-prioritised anything other than core services.")]),e._v(" "),t("p",[e._v("However, there are good examples of the data in action. Barnet libraries "),t("a",{attrs:{href:"https://open.barnet.gov.uk/dataset/e14dj/library-data",target:"_blank",rel:"noopener noreferrer"}},[e._v("publish 5 out of the 7 data schemas"),t("OutboundLink")],1),e._v(" on a regular basis.")]),e._v(" "),t("p",[e._v("I have also been creating tools that highlight how the data can be used such as "),t("a",{attrs:{href:"https://www.librarymap.co.uk",target:"_blank",rel:"noopener noreferrer"}},[e._v("Library map"),t("OutboundLink")],1),e._v(" and "),t("a",{attrs:{href:"https://www.mobilelibraries.org",target:"_blank",rel:"noopener noreferrer"}},[e._v("Mobile libraries"),t("OutboundLink")],1),e._v(".")]),e._v(" "),t("p",[e._v("There is national work underway that can make use of these schemas. The British Library is working on a "),t("a",{attrs:{href:"https://www.artscouncil.org.uk/blog/single-digital-presence-libraries",target:"_blank",rel:"noopener noreferrer"}},[e._v("Single Digital Presence"),t("OutboundLink")],1),e._v(" project that will require data from library services in a standard form.")]),e._v(" "),t("p",[e._v("Internationally there are calls for more public library open data. The International Federation of Library Associations and Institutions (IFLA) has "),t("a",{attrs:{href:"https://www.ifla.org/news/ifla-releases-statement-on-open-library-data/",target:"_blank",rel:"noopener noreferrer"}},[e._v("released a statement on Open Library Data"),t("OutboundLink")],1),e._v(" calling for “governments to ensure, either directly or through supporting others, the collection and open publication of data about libraries and their use”. It would be great to work with organisations like IFLA to promote schemas that could be reused Internationally as well as for local services. There could also be the opportunity to use other Frictionless Data tools to aid in publishing data, such as "),t("a",{attrs:{href:"https://datahub.io/",target:"_blank",rel:"noopener noreferrer"}},[e._v("DataHub"),t("OutboundLink")],1),e._v(".")]),e._v(" "),t("p",[e._v("Hopefully in the future there can be workshops, training events, and conferences that allow these data schemas to be discussed and further developed.")])])}),[],!1,null,null,null);a.default=i.exports}}]); \ No newline at end of file +(window.webpackJsonp=window.webpackJsonp||[]).push([[152],{684:function(e,a,t){"use strict";t.r(a);var r=t(29),i=Object(r.a)({},(function(){var e=this,a=e.$createElement,t=e._self._c||a;return t("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[t("p",[e._v("I started the "),t("a",{attrs:{href:"https://www.librarieshacked.org/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Libraries Hacked"),t("OutboundLink")],1),e._v(" project in 2014. Inspired by ‘tech for good’ open data groups and hackathons, I wanted to explore how libraries could leverage data for innovation and service improvement. I had already been involved in the work of the group "),t("a",{attrs:{href:"https://www.bathhacked.org/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Bath Hacked"),t("OutboundLink")],1),e._v(", and worked at the local Council in Bath, releasing large amounts of open data that was well used by the community. That included data such as live car park occupancy, traffic surveys, and air quality monitoring.")]),e._v(" "),t("p",[e._v("Getting involved in civic data publishing led me to explore data software, tools, and standards. I’ve used the Frictionless standards of Table Schema and CSV Dialect, as well as the code libraries that can be utilised to implement these. Data standards are an essential tool for data publishers in order to make data easily usable and reproducible across different organisations.")]),e._v(" "),t("p",[e._v("Public library services in England are managed by 150 local government organisations. The central government department for Digital, Culture, Media, and Sport (DCMS) hold responsibility for superintending those services. In September 2019 they convened a meeting about public library data.")]),e._v(" "),t("p",[e._v("Library data, of many kinds, is not well utilised in England.")]),e._v(" "),t("ul",[t("li",[t("strong",[e._v("Lack of public data")]),e._v(". There are relatively few library services sharing data about themselves for public use.")]),e._v(" "),t("li",[t("strong",[e._v("Low expectations")]),e._v(". There is no guidance on what data to share. Some services will publish certain datasets, but these will likely be different to the ones other publish.")]),e._v(" "),t("li",[t("strong",[e._v("Few standards")]),e._v(". The structure of any published data will be unique to each library service. For example, there are published lists of library branches from "),t("a",{attrs:{href:"https://www.opendatanottingham.org.uk/dataset.aspx?id=1",target:"_blank",rel:"noopener noreferrer"}},[e._v("Nottinghamshire County Council"),t("OutboundLink")],1),e._v(" and "),t("a",{attrs:{href:"https://data.gov.uk/dataset/9342032d-ab88-462f-b31c-4fb07fd4da6f/libraries",target:"_blank",rel:"noopener noreferrer"}},[e._v("North Somerset Council"),t("OutboundLink")],1),e._v(". Both are out of date, and have different fields, field names, field types, and file formats.")])]),e._v(" "),t("p",[e._v("The meeting discussed these issues, amongst others. The problems are understood, but difficult to tackle, as no organisation has direct responsibility for library data. There are also difficult underlying causes - low skills and funding being two major ones.")]),e._v(" "),t("p",[e._v("Large scale culture change will take many years. But to begin some sector-led collaborative work, a group of the attendees agreed to define the fields for a core selection of library datasets. The project would involve data practitioners from across English library services.")]),e._v(" "),t("p",[e._v("The datasets would cover:")]),e._v(" "),t("ul",[t("li",[t("strong",[e._v("Events")]),e._v(": the events that happen in libraries, their attendance, and outcomes")]),e._v(" "),t("li",[t("strong",[e._v("Library branches")]),e._v(": physical building locations, opening hours, and contact details")]),e._v(" "),t("li",[t("strong",[e._v("Loans")]),e._v(": the items lent from libraries, with counts, time periods, and categories")]),e._v(" "),t("li",[t("strong",[e._v("Stock")]),e._v(": the number of items held in libraries, with categories")]),e._v(" "),t("li",[t("strong",[e._v("Mobile library stops")]),e._v(": locations of mobile library stops, and their timetabled frequency")]),e._v(" "),t("li",[t("strong",[e._v("Physical visits")]),e._v(": how many people visit library premises")]),e._v(" "),t("li",[t("strong",[e._v("Membership")]),e._v(": counts of people who are library members, at small-area geographies.")])]),e._v(" "),t("p",[e._v("These can be split into 3 categories:")]),e._v(" "),t("ul",[t("li",[t("strong",[e._v("Registers")]),e._v(". Data that should be updated when it changes. A list of library branches is a permanent register, to be updated when there are changes to those branches.")]),e._v(" "),t("li",[t("strong",[e._v("Snapshot")]),e._v(". Data that is released as a point in time representation. Library membership will be continually changing, but a snapshot of membership counts should be released at regular intervals.")]),e._v(" "),t("li",[t("strong",[e._v("Time-series")]),e._v(". Data that is new every time it is published. Loans data should be published at regular intervals, each published file being an addition to the existing set.")])]),e._v(" "),t("p",[e._v("To work on these, we held an in-person workshop at the DCMS offices. This featured an exciting interruption by a fire drill, and we had to relocate to a nearby café (difficult for a meeting with many people held in in London!). We also formed an online group using Slack to trial and discuss the data.")]),e._v(" "),t("h2",{attrs:{id:"schemas-and-frictionless-data"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#schemas-and-frictionless-data"}},[e._v("#")]),e._v(" Schemas and Frictionless Data")]),e._v(" "),t("p",[e._v("The majority of our discussions were practical rather than technical, such as what data would be most useful, whether or not it was currently used locally by services, and common problems.")]),e._v(" "),t("p",[e._v("However, to formalise how data should be structured, it became clear that it would be necessary to create technical 'data schemas’.")]),e._v(" "),t("p",[e._v("It can be easy to decide on the data you want, but fail to describe it properly. For example, we could provide people with a spreadsheet that included a column title such as ‘Closed date’. I’d expect people to enter a date in that column, but we’d end up with all kinds of formats.")]),e._v(" "),t("p",[e._v("The "),t("a",{attrs:{href:"https://specs.frictionlessdata.io/table-schema/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Table Schema"),t("OutboundLink")],1),e._v(" specification for defining data, from Frictionless Data, provided a good option for tackling this problem. Not only would it allow us to create a detailed description for the data fields, but we could use other frictionless tools such as "),t("a",{attrs:{href:"https://goodtables.io/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Good Tables"),t("OutboundLink")],1),e._v(". This would allow library services to validate their data before publishing. Things like mismatching date formats would be picked up by the validator, and it would give instructions for how to fix the issue. We would additionally also provide ‘human-readable’ guidance on the datasets.")]),e._v(" "),t("p",[e._v("Frictionless Data is an "),t("a",{attrs:{href:"https://okfn.org/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Open Knowledge Foundation"),t("OutboundLink")],1),e._v(" project, and using tools from an internationally renowned body was also a good practice. The schemas are UK-centric but could be adapted and reused by international library services.")]),e._v(" "),t("p",[e._v("The schemas are all documented at "),t("a",{attrs:{href:"https://schema.librarydata.uk/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Public Library Open Data"),t("OutboundLink")],1),e._v(", including guidance, links to sample data, and the technical definition files.")]),e._v(" "),t("h2",{attrs:{id:"lessons-learned"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#lessons-learned"}},[e._v("#")]),e._v(" Lessons learned")]),e._v(" "),t("p",[e._v("The initial datasets are not comprehensive. They are designed to be a starting point, allowing more to be developed from service requirements.")]),e._v(" "),t("p",[e._v("They are overly focussed towards ‘physical’ library services. It wasn’t long after these meetings that public libraries adjusted to provide all-digital services due to lockdowns. There is nothing here to cover valuable usage datasets like the video views that library services receive on YouTube and Facebook.")]),e._v(" "),t("p",[e._v("There are some that have become even more important. The physical visits schema describes how to structure library footfall data, allowing for differences in collection methods and intervals. This kind of data is now in high demand, to analyse how library service visits recover.")]),e._v(" "),t("p",[e._v("Some of the discussions we had were fascinating. It was important to involve the people who work with this data on a daily basis. They will know how easy it is to extract and manipulate, and many of the pitfalls that come with interpreting it.")]),e._v(" "),t("h3",{attrs:{id:"complexity"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#complexity"}},[e._v("#")]),e._v(" Complexity")]),e._v(" "),t("p",[e._v("There was often a battle between complexity and simplicity. Complex data is good, it often means it is more robust, such as using external identifiers. But simplicity is also good, for data publishers and consumers.")]),e._v(" "),t("p",[e._v("Public library services will primarily employ data workers who are not formally trained in using data. Where there are complex concepts (e.g. Table Schema itself), they are used because they make data publishing easier and more consistent.")]),e._v(" "),t("p",[e._v("Public data should also be made as accessible as possible for the public, while being detailed enough to be useful. In this way the data schemas tend towards simplicity.")]),e._v(" "),t("h3",{attrs:{id:"standards-not-standardisation"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#standards-not-standardisation"}},[e._v("#")]),e._v(" Standards not standardisation")]),e._v(" "),t("p",[e._v("There is a difference between a standard format for data, and standardised data. The schemas are primarily aimed at getting data from multiple services into the same format, to share analysis techniques between library services, and to have usable data when merged with other services.")]),e._v(" "),t("p",[e._v("There were some cases where we decided against standardising the actual data within data fields. For example, there is a column in the loans and the stock datasets called ‘Item type’. This is a category description of the library item, such as ‘Adult fiction’. In some other previous examples of data collection this data is standardised into a uniform set of categories, in order to make it easily comparable.")]),e._v(" "),t("p",[e._v("That kind of exercise defies reality though. Library services may have their own set of categories, many of them interesting and unique. To use a standard set would mean that library services would have to convert their underlying data. As well as extra work, it would be a loss of data. It would also mean that library services would be unlikely to use the converted data themselves. Why use such data if it doesn’t reflect what you actually hold?")]),e._v(" "),t("p",[e._v("The downside is that anyone analysing combined data would have to decide themselves how to compare data in those fields. However, that would be at least a clear task for the data analyst - and would most likely be an easier exercise to do in bulk.")]),e._v(" "),t("h3",{attrs:{id:"detail"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#detail"}},[e._v("#")]),e._v(" Detail")]),e._v(" "),t("p",[e._v("In my ideal world, data would be as detailed as possible. Instead of knowing how many items a library lent every month, I want that data for every hour. In fact I want to have every lending record! But feasibly that would make the data unwieldy and difficult to work with, and wouldn’t be in-line with the statistics libraries are used to.")]),e._v(" "),t("p",[e._v("We primarily made decisions based upon what library services already do. In a lot of cases this was data aggregated into monthly counts, with fields such as library branch and item type used to break down that data.")]),e._v(" "),t("h2",{attrs:{id:"the-future"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#the-future"}},[e._v("#")]),e._v(" The future")]),e._v(" "),t("p",[e._v("The initial meetings were held over two years ago, and it seems longer than that! A lot has happened in the meantime. We are still in a global pandemic that from library perspectives has de-prioritised anything other than core services.")]),e._v(" "),t("p",[e._v("However, there are good examples of the data in action. Barnet libraries "),t("a",{attrs:{href:"https://open.barnet.gov.uk/dataset/e14dj/library-data",target:"_blank",rel:"noopener noreferrer"}},[e._v("publish 5 out of the 7 data schemas"),t("OutboundLink")],1),e._v(" on a regular basis.")]),e._v(" "),t("p",[e._v("I have also been creating tools that highlight how the data can be used such as "),t("a",{attrs:{href:"https://www.librarymap.co.uk",target:"_blank",rel:"noopener noreferrer"}},[e._v("Library map"),t("OutboundLink")],1),e._v(" and "),t("a",{attrs:{href:"https://www.mobilelibraries.org",target:"_blank",rel:"noopener noreferrer"}},[e._v("Mobile libraries"),t("OutboundLink")],1),e._v(".")]),e._v(" "),t("p",[e._v("There is national work underway that can make use of these schemas. The British Library is working on a "),t("a",{attrs:{href:"https://www.artscouncil.org.uk/blog/single-digital-presence-libraries",target:"_blank",rel:"noopener noreferrer"}},[e._v("Single Digital Presence"),t("OutboundLink")],1),e._v(" project that will require data from library services in a standard form.")]),e._v(" "),t("p",[e._v("Internationally there are calls for more public library open data. The International Federation of Library Associations and Institutions (IFLA) has "),t("a",{attrs:{href:"https://www.ifla.org/news/ifla-releases-statement-on-open-library-data/",target:"_blank",rel:"noopener noreferrer"}},[e._v("released a statement on Open Library Data"),t("OutboundLink")],1),e._v(" calling for “governments to ensure, either directly or through supporting others, the collection and open publication of data about libraries and their use”. It would be great to work with organisations like IFLA to promote schemas that could be reused Internationally as well as for local services. There could also be the opportunity to use other Frictionless Data tools to aid in publishing data, such as "),t("a",{attrs:{href:"https://datahub.io/",target:"_blank",rel:"noopener noreferrer"}},[e._v("DataHub"),t("OutboundLink")],1),e._v(".")]),e._v(" "),t("p",[e._v("Hopefully in the future there can be workshops, training events, and conferences that allow these data schemas to be discussed and further developed.")])])}),[],!1,null,null,null);a.default=i.exports}}]); \ No newline at end of file diff --git a/assets/js/153.53c6673a.js b/assets/js/153.0af4c05d.js similarity index 98% rename from assets/js/153.53c6673a.js rename to assets/js/153.0af4c05d.js index 4f8a83ed2..43ca641d9 100644 --- a/assets/js/153.53c6673a.js +++ b/assets/js/153.0af4c05d.js @@ -1 +1 @@ -(window.webpackJsonp=window.webpackJsonp||[]).push([[153],{687:function(e,t,a){"use strict";a.r(t);var r=a(29),s=Object(r.a)({},(function(){var e=this,t=e.$createElement,a=e._self._c||t;return a("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[a("p",[e._v("We are very excited to announce that we responded to a "),a("a",{attrs:{href:"https://sam.gov/opp/869f4051df38475591fa48fce5b0868d/view",target:"_blank",rel:"noopener noreferrer"}},[e._v("request for information"),a("OutboundLink")],1),e._v(" that was recently published by NASA for its "),a("a",{attrs:{href:"https://science.nasa.gov/earth-science/earth-system-observatory",target:"_blank",rel:"noopener noreferrer"}},[e._v("Earth System Observatory (ESO)"),a("OutboundLink")],1),e._v(".")]),e._v(" "),a("p",[e._v("What is ESO? It is a set of (mainly satellite) missions providing information on planet Earth, which can guide efforts related to climate change, natural hazard mitigation, fighting forest fires, and improving real-time agricultural processes.")]),e._v(" "),a("p",[e._v("With this request for information, ESO wants to gather expert advice on ways to find a more integrated approach to enhance data architecture efficiency and promote the open science principles.")]),e._v(" "),a("p",[a("strong",[e._v("We believe Frictionless Data would benefit the mission science data processing in several ways.")]),e._v(" Here’s how:")]),e._v(" "),a("p",[e._v("First, Frictionless automatically infers metadata and schemas from a data file, and allows users to edit that information. Creating good metadata is vital for downstream data users – if you can’t understand the data, you can’t use it (or can’t "),a("em",[e._v("easily")]),e._v(" use it). Similarly, having a data schema is useful for interoperability, promoting the usefulness of datasets.")]),e._v(" "),a("p",[e._v("The second Frictionless function we think will be helpful is data validation. Frictionless validates both the structure and content of a dataset, using built-in and custom checks. For instance, Frictionless will check for missing values, incorrect data types, or other constraints (e.g. temperature data points that exceed a certain threshold). If any errors are detected, Frictionless will generate a report for the user detailing the error so the user can fix the data during processing.")]),e._v(" "),a("p",[e._v("Finally, users can write reproducible data transformation pipelines with Frictionless. Writing declarative transform pipelines allows humans and machines to understand the data cleaning steps and repeat those processes if needed in the future. Collectively, these functions create well documented, high quality, clean data that can then be used in further downstream analysis.")]),e._v(" "),a("p",[e._v("We provided them with two examples of relevant collaboration:")]),e._v(" "),a("h3",{attrs:{id:"use-case-1"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#use-case-1"}},[e._v("#")]),e._v(" Use Case 1")]),e._v(" "),a("p",[e._v("The "),a("a",{attrs:{href:"https://www.bco-dmo.org/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Biological and Chemical Oceanography Data Management Office (BCO-DMO)"),a("OutboundLink")],1),e._v(" cleans and hosts a wide variety of open oceanography data sets for use by researchers. A main problem for them was data being submitted to them was messy and not standardized, and it was time consuming and difficult for their data managers to clean in a reproducible, documented way. They implemented Frictionless code to create a new data transformation pipeline that ingests the messy data, performs defined cleaning/transforming steps, documents those steps, and produces a cleaned, standardized dataset. It also produces a (human and machine-readable) document detailing all the transformation steps so that downstream users could understand what happened to the data and undo/repeat if necessary. This process not only helps data managers clean data faster and more efficiently, it also drives open science by making the hosted data more understandable and usable while preserving provenance.")]),e._v(" "),a("p",[e._v("More info on this use case "),a("a",{attrs:{href:"https://frictionlessdata.io/blog/2020/02/10/frictionless-data-pipelines-for-open-ocean/",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),a("OutboundLink")],1),e._v(".")]),e._v(" "),a("h3",{attrs:{id:"use-case-2"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#use-case-2"}},[e._v("#")]),e._v(" Use Case 2")]),e._v(" "),a("p",[a("a",{attrs:{href:"https://datadryad.org/stash",target:"_blank",rel:"noopener noreferrer"}},[e._v("Dryad"),a("OutboundLink")],1),e._v(" is a biological data repository with a large user base. In our collaboration, their main issue was that they do not have the people-power to curate all the submitted datasets, so they implemented Frictionless tooling to help data submitters curate their data as they submit it. When data is submitted on the Dryad platform, Frictionless performs validation checks, and generates a report if any errors are found. The data submitter can then fix that error (e.g. there are no headers in row 1) and resubmit. Creating easy-to-understand error reports helps submitters understand how to create more useable, standardized data, and also frees up valuable time for the Dryad data management team. Ultimately, now the Dryad data repository hosts higher quality open science data.")]),e._v(" "),a("p",[e._v("More info on this use case "),a("a",{attrs:{href:"https://frictionlessdata.io/blog/2021/08/09/dryad-pilot/",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),a("OutboundLink")],1),e._v(".")]),e._v(" "),a("hr"),e._v(" "),a("p",[e._v("Are there other ways you think Frictionless Data could help the ESO project? Let us know!")]),e._v(" "),a("p",[a("em",[e._v("Image used: Antarctica Eclipsed. NASA image courtesy of the DSCOVR EPIC team. NASA Earth Observatory images by Joshua Stevens, using Landsat data from the U.S. Geological Survey. Story by Sara E. Pratt.")])])])}),[],!1,null,null,null);t.default=s.exports}}]); \ No newline at end of file +(window.webpackJsonp=window.webpackJsonp||[]).push([[153],{685:function(e,t,a){"use strict";a.r(t);var r=a(29),s=Object(r.a)({},(function(){var e=this,t=e.$createElement,a=e._self._c||t;return a("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[a("p",[e._v("We are very excited to announce that we responded to a "),a("a",{attrs:{href:"https://sam.gov/opp/869f4051df38475591fa48fce5b0868d/view",target:"_blank",rel:"noopener noreferrer"}},[e._v("request for information"),a("OutboundLink")],1),e._v(" that was recently published by NASA for its "),a("a",{attrs:{href:"https://science.nasa.gov/earth-science/earth-system-observatory",target:"_blank",rel:"noopener noreferrer"}},[e._v("Earth System Observatory (ESO)"),a("OutboundLink")],1),e._v(".")]),e._v(" "),a("p",[e._v("What is ESO? It is a set of (mainly satellite) missions providing information on planet Earth, which can guide efforts related to climate change, natural hazard mitigation, fighting forest fires, and improving real-time agricultural processes.")]),e._v(" "),a("p",[e._v("With this request for information, ESO wants to gather expert advice on ways to find a more integrated approach to enhance data architecture efficiency and promote the open science principles.")]),e._v(" "),a("p",[a("strong",[e._v("We believe Frictionless Data would benefit the mission science data processing in several ways.")]),e._v(" Here’s how:")]),e._v(" "),a("p",[e._v("First, Frictionless automatically infers metadata and schemas from a data file, and allows users to edit that information. Creating good metadata is vital for downstream data users – if you can’t understand the data, you can’t use it (or can’t "),a("em",[e._v("easily")]),e._v(" use it). Similarly, having a data schema is useful for interoperability, promoting the usefulness of datasets.")]),e._v(" "),a("p",[e._v("The second Frictionless function we think will be helpful is data validation. Frictionless validates both the structure and content of a dataset, using built-in and custom checks. For instance, Frictionless will check for missing values, incorrect data types, or other constraints (e.g. temperature data points that exceed a certain threshold). If any errors are detected, Frictionless will generate a report for the user detailing the error so the user can fix the data during processing.")]),e._v(" "),a("p",[e._v("Finally, users can write reproducible data transformation pipelines with Frictionless. Writing declarative transform pipelines allows humans and machines to understand the data cleaning steps and repeat those processes if needed in the future. Collectively, these functions create well documented, high quality, clean data that can then be used in further downstream analysis.")]),e._v(" "),a("p",[e._v("We provided them with two examples of relevant collaboration:")]),e._v(" "),a("h3",{attrs:{id:"use-case-1"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#use-case-1"}},[e._v("#")]),e._v(" Use Case 1")]),e._v(" "),a("p",[e._v("The "),a("a",{attrs:{href:"https://www.bco-dmo.org/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Biological and Chemical Oceanography Data Management Office (BCO-DMO)"),a("OutboundLink")],1),e._v(" cleans and hosts a wide variety of open oceanography data sets for use by researchers. A main problem for them was data being submitted to them was messy and not standardized, and it was time consuming and difficult for their data managers to clean in a reproducible, documented way. They implemented Frictionless code to create a new data transformation pipeline that ingests the messy data, performs defined cleaning/transforming steps, documents those steps, and produces a cleaned, standardized dataset. It also produces a (human and machine-readable) document detailing all the transformation steps so that downstream users could understand what happened to the data and undo/repeat if necessary. This process not only helps data managers clean data faster and more efficiently, it also drives open science by making the hosted data more understandable and usable while preserving provenance.")]),e._v(" "),a("p",[e._v("More info on this use case "),a("a",{attrs:{href:"https://frictionlessdata.io/blog/2020/02/10/frictionless-data-pipelines-for-open-ocean/",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),a("OutboundLink")],1),e._v(".")]),e._v(" "),a("h3",{attrs:{id:"use-case-2"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#use-case-2"}},[e._v("#")]),e._v(" Use Case 2")]),e._v(" "),a("p",[a("a",{attrs:{href:"https://datadryad.org/stash",target:"_blank",rel:"noopener noreferrer"}},[e._v("Dryad"),a("OutboundLink")],1),e._v(" is a biological data repository with a large user base. In our collaboration, their main issue was that they do not have the people-power to curate all the submitted datasets, so they implemented Frictionless tooling to help data submitters curate their data as they submit it. When data is submitted on the Dryad platform, Frictionless performs validation checks, and generates a report if any errors are found. The data submitter can then fix that error (e.g. there are no headers in row 1) and resubmit. Creating easy-to-understand error reports helps submitters understand how to create more useable, standardized data, and also frees up valuable time for the Dryad data management team. Ultimately, now the Dryad data repository hosts higher quality open science data.")]),e._v(" "),a("p",[e._v("More info on this use case "),a("a",{attrs:{href:"https://frictionlessdata.io/blog/2021/08/09/dryad-pilot/",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),a("OutboundLink")],1),e._v(".")]),e._v(" "),a("hr"),e._v(" "),a("p",[e._v("Are there other ways you think Frictionless Data could help the ESO project? Let us know!")]),e._v(" "),a("p",[a("em",[e._v("Image used: Antarctica Eclipsed. NASA image courtesy of the DSCOVR EPIC team. NASA Earth Observatory images by Joshua Stevens, using Landsat data from the U.S. Geological Survey. Story by Sara E. Pratt.")])])])}),[],!1,null,null,null);t.default=s.exports}}]); \ No newline at end of file diff --git a/assets/js/154.010ebfe2.js b/assets/js/154.4f5652f9.js similarity index 98% rename from assets/js/154.010ebfe2.js rename to assets/js/154.4f5652f9.js index e5cba128a..d548a6254 100644 --- a/assets/js/154.010ebfe2.js +++ b/assets/js/154.4f5652f9.js @@ -1 +1 @@ -(window.webpackJsonp=window.webpackJsonp||[]).push([[154],{692:function(e,t,r){"use strict";r.r(t);var a=r(29),o=Object(a.a)({},(function(){var e=this,t=e.$createElement,r=e._self._c||t;return r("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[r("p",[e._v("On our second community call of the year, on February 24"),r("sup",[e._v("th")]),e._v(", we had Ilya Kreymer and Ed Summers from "),r("a",{attrs:{href:"https://webrecorder.net/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Webrecorder"),r("OutboundLink")],1),e._v(" updating us on their effort in standardising the WAZC format (which they discussed with us already when it was still at an early development stage, in the community call of December 2020 (you can read the blog "),r("a",{attrs:{href:"https://frictionlessdata.io/blog/2020/12/17/december-virtual-hangout/#a-recap-from-our-december-community-call",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),r("OutboundLink")],1),e._v(").")]),e._v(" "),r("p",[e._v("Webrecorder is a suite of open source tools and packages to capture interactive websites and replay them at a later time as accurately as possible. They created the WACZ format to have a portable format for archived web content that can be distributed and contain additional useful metadata about the web archives, using the Frictionless Data Package standard.")]),e._v(" "),r("p",[e._v("Ed & Ilya also hoped to discuss with the community the possibility of signing these Data Packages, in order to provide an optional mechanism to make web archives bundled in WACZ more trusted, because a cryptographic proof of who the author of a Data Package is might be interesting for other projects as well. Unfortunately the call was rather empty. Maybe it was because of the change of time, but in case there are other reasons why you did not come, please let us know (dropping an email at "),r("a",{attrs:{href:"mailto:sara.petti@okfn.org"}},[e._v("sara.petti@okfn.org")]),e._v(" or with a direct message on Discord/Matrix).")]),e._v(" "),r("p",[e._v("We did record the call though, so in case anyone is interested in having that discussion, we could always try to have it asynchronously on "),r("a",{attrs:{href:"https://discord.com/invite/Sewv6av",target:"_blank",rel:"noopener noreferrer"}},[e._v("Discord"),r("OutboundLink")],1),e._v(" or "),r("a",{attrs:{href:"https://matrix.to/#/#frictionless-data:matrix.org",target:"_blank",rel:"noopener noreferrer"}},[e._v("Matrix"),r("OutboundLink")],1),e._v(".")]),e._v(" "),r("iframe",{attrs:{width:"560",height:"315",src:"https://www.youtube.com/embed/TIyOTEyAu7k",title:"YouTube video player",frameborder:"0",allow:"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture",allowfullscreen:""}}),e._v(" "),r("p",[e._v("Their current proposal to create signed WACZ packages is summarised in "),r("a",{attrs:{href:"https://github.com/webrecorder/wacz-auth-spec/blob/main/spec.md",target:"_blank",rel:"noopener noreferrer"}},[e._v("on GitHub"),r("OutboundLink")],1),e._v(", so you can always reach out to them there as well.")]),e._v(" "),r("h1",{attrs:{id:"join-us-next-month"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#join-us-next-month"}},[e._v("#")]),e._v(" Join us next month!")]),e._v(" "),r("p",[e._v("Next community call is on March 31"),r("sup",[e._v("st")]),e._v(". We are going to hear from Johan Richer from Multi, who is going to present the latest prototype of Etalab and his theory of "),r("a",{attrs:{href:"https://jailbreak.gitlab.io/investigation-catalogue/synthese.html#/3",target:"_blank",rel:"noopener noreferrer"}},[e._v("portal vs catalogue"),r("OutboundLink")],1)]),e._v(" "),r("p",[e._v("You can sign up for the call already "),r("a",{attrs:{href:"https://docs.google.com/forms/d/e/1FAIpQLSeuNCopxXauMkrWvF6VHqOyHMcy54SfNDOseVXfWRQZWkvqjQ/viewform?usp=sf_link",target:"_blank",rel:"noopener noreferrer"}},[e._v("here:"),r("OutboundLink")],1)]),e._v(" "),r("p",[e._v("Do you want to share something with the community? Let us know when you sign up!")]),e._v(" "),r("h1",{attrs:{id:"call-recording"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#call-recording"}},[e._v("#")]),e._v(" Call recording:")]),e._v(" "),r("p",[e._v("On a final note, here is the recording of the full call:")]),e._v(" "),r("iframe",{attrs:{width:"560",height:"315",src:"https://www.youtube.com/embed/ukxLQCdyndc",title:"YouTube video player",frameborder:"0",allow:"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture",allowfullscreen:""}}),e._v(" "),r("p",[e._v("As usual, you can join us on "),r("a",{attrs:{href:"https://discord.com/invite/j9DNFNw",target:"_blank",rel:"noopener noreferrer"}},[e._v("Discord"),r("OutboundLink")],1),e._v(", "),r("a",{attrs:{href:"https://matrix.to/#/#frictionless-data:matrix.org",target:"_blank",rel:"noopener noreferrer"}},[e._v("Matrix"),r("OutboundLink")],1),e._v(" or "),r("a",{attrs:{href:"https://twitter.com/frictionlessd8a",target:"_blank",rel:"noopener noreferrer"}},[e._v("Twitter"),r("OutboundLink")],1),e._v(" to say hi or ask any questions. See you there!")])])}),[],!1,null,null,null);t.default=o.exports}}]); \ No newline at end of file +(window.webpackJsonp=window.webpackJsonp||[]).push([[154],{686:function(e,t,r){"use strict";r.r(t);var a=r(29),o=Object(a.a)({},(function(){var e=this,t=e.$createElement,r=e._self._c||t;return r("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[r("p",[e._v("On our second community call of the year, on February 24"),r("sup",[e._v("th")]),e._v(", we had Ilya Kreymer and Ed Summers from "),r("a",{attrs:{href:"https://webrecorder.net/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Webrecorder"),r("OutboundLink")],1),e._v(" updating us on their effort in standardising the WAZC format (which they discussed with us already when it was still at an early development stage, in the community call of December 2020 (you can read the blog "),r("a",{attrs:{href:"https://frictionlessdata.io/blog/2020/12/17/december-virtual-hangout/#a-recap-from-our-december-community-call",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),r("OutboundLink")],1),e._v(").")]),e._v(" "),r("p",[e._v("Webrecorder is a suite of open source tools and packages to capture interactive websites and replay them at a later time as accurately as possible. They created the WACZ format to have a portable format for archived web content that can be distributed and contain additional useful metadata about the web archives, using the Frictionless Data Package standard.")]),e._v(" "),r("p",[e._v("Ed & Ilya also hoped to discuss with the community the possibility of signing these Data Packages, in order to provide an optional mechanism to make web archives bundled in WACZ more trusted, because a cryptographic proof of who the author of a Data Package is might be interesting for other projects as well. Unfortunately the call was rather empty. Maybe it was because of the change of time, but in case there are other reasons why you did not come, please let us know (dropping an email at "),r("a",{attrs:{href:"mailto:sara.petti@okfn.org"}},[e._v("sara.petti@okfn.org")]),e._v(" or with a direct message on Discord/Matrix).")]),e._v(" "),r("p",[e._v("We did record the call though, so in case anyone is interested in having that discussion, we could always try to have it asynchronously on "),r("a",{attrs:{href:"https://discord.com/invite/Sewv6av",target:"_blank",rel:"noopener noreferrer"}},[e._v("Discord"),r("OutboundLink")],1),e._v(" or "),r("a",{attrs:{href:"https://matrix.to/#/#frictionless-data:matrix.org",target:"_blank",rel:"noopener noreferrer"}},[e._v("Matrix"),r("OutboundLink")],1),e._v(".")]),e._v(" "),r("iframe",{attrs:{width:"560",height:"315",src:"https://www.youtube.com/embed/TIyOTEyAu7k",title:"YouTube video player",frameborder:"0",allow:"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture",allowfullscreen:""}}),e._v(" "),r("p",[e._v("Their current proposal to create signed WACZ packages is summarised in "),r("a",{attrs:{href:"https://github.com/webrecorder/wacz-auth-spec/blob/main/spec.md",target:"_blank",rel:"noopener noreferrer"}},[e._v("on GitHub"),r("OutboundLink")],1),e._v(", so you can always reach out to them there as well.")]),e._v(" "),r("h1",{attrs:{id:"join-us-next-month"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#join-us-next-month"}},[e._v("#")]),e._v(" Join us next month!")]),e._v(" "),r("p",[e._v("Next community call is on March 31"),r("sup",[e._v("st")]),e._v(". We are going to hear from Johan Richer from Multi, who is going to present the latest prototype of Etalab and his theory of "),r("a",{attrs:{href:"https://jailbreak.gitlab.io/investigation-catalogue/synthese.html#/3",target:"_blank",rel:"noopener noreferrer"}},[e._v("portal vs catalogue"),r("OutboundLink")],1)]),e._v(" "),r("p",[e._v("You can sign up for the call already "),r("a",{attrs:{href:"https://docs.google.com/forms/d/e/1FAIpQLSeuNCopxXauMkrWvF6VHqOyHMcy54SfNDOseVXfWRQZWkvqjQ/viewform?usp=sf_link",target:"_blank",rel:"noopener noreferrer"}},[e._v("here:"),r("OutboundLink")],1)]),e._v(" "),r("p",[e._v("Do you want to share something with the community? Let us know when you sign up!")]),e._v(" "),r("h1",{attrs:{id:"call-recording"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#call-recording"}},[e._v("#")]),e._v(" Call recording:")]),e._v(" "),r("p",[e._v("On a final note, here is the recording of the full call:")]),e._v(" "),r("iframe",{attrs:{width:"560",height:"315",src:"https://www.youtube.com/embed/ukxLQCdyndc",title:"YouTube video player",frameborder:"0",allow:"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture",allowfullscreen:""}}),e._v(" "),r("p",[e._v("As usual, you can join us on "),r("a",{attrs:{href:"https://discord.com/invite/j9DNFNw",target:"_blank",rel:"noopener noreferrer"}},[e._v("Discord"),r("OutboundLink")],1),e._v(", "),r("a",{attrs:{href:"https://matrix.to/#/#frictionless-data:matrix.org",target:"_blank",rel:"noopener noreferrer"}},[e._v("Matrix"),r("OutboundLink")],1),e._v(" or "),r("a",{attrs:{href:"https://twitter.com/frictionlessd8a",target:"_blank",rel:"noopener noreferrer"}},[e._v("Twitter"),r("OutboundLink")],1),e._v(" to say hi or ask any questions. See you there!")])])}),[],!1,null,null,null);t.default=o.exports}}]); \ No newline at end of file diff --git a/assets/js/155.50f8aa63.js b/assets/js/155.bfba67f5.js similarity index 99% rename from assets/js/155.50f8aa63.js rename to assets/js/155.bfba67f5.js index b5477bb29..84c89cd7d 100644 --- a/assets/js/155.50f8aa63.js +++ b/assets/js/155.bfba67f5.js @@ -1 +1 @@ -(window.webpackJsonp=window.webpackJsonp||[]).push([[155],{689:function(e,t,a){"use strict";a.r(t);var n=a(29),r=Object(n.a)({},(function(){var e=this,t=e.$createElement,a=e._self._c||t;return a("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[a("p",[e._v("During these past tumultuous years, it has been striking to witness the role that information has played in furthering suffering: misinformation, lack of data transparency, and closed technology have worsened the pandemic, increased political strife, and hurt climate policy. Building on these observations, the team at Open Knowledge Foundation are refocusing our energies on how we can come together to empower people, communities, and organisations to create and use open knowledge to solve the most urgent issues of our time, including climate change, inequality, and access to knowledge . Undaunted by these substantial challenges, we entered 2022 with enthusiasm for finding ways to work together, starting with climate data.")]),e._v(" "),a("p",[e._v("To start this year fresh and inspired, we convened two gatherings of climate researchers, activists, and organisations to brainstorm ways to collaborate to make open climate data more usable, accessible, and impactful. Over 30 experts attended the two sessions, from organisations around the world, and we identified and discussed many problems in the climate data space. We confirmed our initial theory that many of us are working siloed and that combining skills, knowledge and networks can result in a powerful alliance across tech communities, data experts and climate crisis activists.")]),e._v(" "),a("p",[e._v("Now, we want to share with you some common themes from these sessions and ask: how can we work together to solve these pressing climate issues?")]),e._v(" "),a("p",[e._v("A primary concern of attendees was "),a("strong",[e._v("the disconnect between how (and why) data is produced and how data can (and should) be used")]),e._v(". This disconnect shows up as frictions for data use: we know that much existing “open” data isn’t actually usable. During the call, many participants mentioned they frequently can’t find open data, and even when they can find it, they can’t easily access it. Even when they can access the data, they often can’t easily use it.")]),e._v(" "),a("p",[e._v("So why is it so hard to find, access, and use climate data? First, climate data is not particularly well standardised or curated, and data creators need better training in data management best practices. Another issue is that many climate data users don’t have technical training or knowledge required to clean messy data, greatly slowing down their research or policy work.")]),e._v(" "),a("h3",{attrs:{id:"how-will-the-open-knowledge-foundation-fix-the-identified-problems-skills-standards-and-community"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#how-will-the-open-knowledge-foundation-fix-the-identified-problems-skills-standards-and-community"}},[e._v("#")]),e._v(" How will the Open Knowledge Foundation fix the identified problems? Skills, standards and community.")]),e._v(" "),a("p",[e._v("An aim for this work will be to bridge the gaps between data creators and users. We plan to host several workshops in the future to work with both these groups, focusing on identifying both skills gaps and data gaps, then working towards capacity building.")]),e._v(" "),a("p",[e._v("Our goal with capacity building will be to give a data platform to those most affected by climate change. How do we make it easier for less technical or newer data users to effectively use climate data? Our future workshops will focus on training data creators and users with the "),a("a",{attrs:{href:"https://frictionlessdata.io/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Open Knowledge Frictionless Data tooling"),a("OutboundLink")],1),e._v(" to better manage data, create higher quality data, and share data in impactful ways that will empower trained researchers and activists alike. For instance, the Frictionless toolbox can help data creators generate clean data that is easy to understand, share, and use, and the new Frictionless tool Livemark can help data consumers easily share climate data with impactful visualisations and narratives.")]),e._v(" "),a("p",[e._v("Another theme that emerged from the brainstorm sessions was the role data plays in generating knowledge versus the role knowledge plays in generating data, and how this interplay can be maximised to create change. For instance, "),a("strong",[e._v("we need to take a hard look at how “open” replicates cycles of inequalities")]),e._v(". Several people brought up the great work citizen scientists are doing for climate research, but how these efforts are rarely recognised by governments or other official research channels. So much vital data on local impacts of climate change are being lost as they aren’t being incorporated into official datasets. How do we make data more equitable, ensuring that those being most affected by climate change can use data to tell their stories?")]),e._v(" "),a("p",[e._v("We call on data organisations, climate researchers, and activists to join us in these efforts. How can we best work together to solve pressing climate change issues? Would you like to partner with us for workshops, or do you have other ideas for collaborations? Let us know! We would like to give our utmost thanks to the organisations that joined our brainstorming sessions for paving the way in this important work. To continue planning this work, we are creating a space to talk in our Frictionless Data community chat, and we invite all interested parties to join us. We are currently migrating our community from Discord to Slack. We encourage you to join the Slack channel, which will soon be populated with all Frictionless community members: "),a("a",{attrs:{href:"https://join.slack.com/t/frictionlessdata/shared_invite/zt-14x9bxnkm-2y~uQcmmrqarSP2kV39_Kg",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://join.slack.com/t/frictionlessdata/shared_invite/zt-14x9bxnkm-2y~uQcmmrqarSP2kV39_Kg"),a("OutboundLink")],1),a("br"),e._v("\n(We also have a Matrix mirror if you prefer Matrix: "),a("a",{attrs:{href:"https://matrix.to/#/#frictionless-data:matrix.org",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://matrix.to/#/#frictionless-data:matrix.org"),a("OutboundLink")],1),e._v(")")]),e._v(" "),a("p",[e._v("Finally, we’d like to share this list of resources that attendees shared during the calls:")]),e._v(" "),a("ul",[a("li",[e._v("Patrick J McGovern Data for Climate 2022 Accelerator: "),a("a",{attrs:{href:"https://www.mcgovern.org/foundation-awards-4-5m-including-new-accelerator-grants-to-advance-data-driven-climate-solutions/",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://www.mcgovern.org/foundation-awards-4-5m-including-new-accelerator-grants-to-advance-data-driven-climate-solutions/"),a("OutboundLink")],1)]),e._v(" "),a("li",[e._v("Open Climate: "),a("a",{attrs:{href:"https://www.appropedia.org/OpenClimate",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://www.appropedia.org/OpenClimate"),a("OutboundLink")],1)]),e._v(" "),a("li",[e._v("Environmental Data and Governance Initiative: "),a("a",{attrs:{href:"https://envirodatagov.org/",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://envirodatagov.org/"),a("OutboundLink")],1)]),e._v(" "),a("li",[e._v("Earth Science Information Partners: "),a("a",{attrs:{href:"https://www.esipfed.org/about",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://www.esipfed.org/about"),a("OutboundLink")],1),a("br"),e._v("\nCourse on environmental data journalism by School of Data Brazil: "),a("a",{attrs:{href:"https://escoladedados.org/courses/jornalismo-de-dados-ambientais/",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://escoladedados.org/courses/jornalismo-de-dados-ambientais/"),a("OutboundLink")],1)]),e._v(" "),a("li",[e._v("Catalogue of environmental databases in Brazil by School of Data Brazil: "),a("a",{attrs:{href:"https://bit.ly/dados-ambientais",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://bit.ly/dados-ambientais"),a("OutboundLink")],1)]),e._v(" "),a("li",[e._v("A monthly meetup for small companies to share best practices (and data): "),a("a",{attrs:{href:"https://climatiq.io/blog/climate-action-net-zero-ambition-best-practices-for-sme",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://climatiq.io/blog/climate-action-net-zero-ambition-best-practices-for-sme"),a("OutboundLink")],1)]),e._v(" "),a("li",[e._v("Reddit Datasets: "),a("a",{attrs:{href:"https://www.reddit.com/r/datasets/",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://www.reddit.com/r/datasets/"),a("OutboundLink")],1)]),e._v(" "),a("li",[e._v("Hardware information standard: "),a("a",{attrs:{href:"https://barbal.co/the-open-know-how-manifest-specification-version-1-0/",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://barbal.co/the-open-know-how-manifest-specification-version-1-0/"),a("OutboundLink")],1)]),e._v(" "),a("li",[e._v("Catalyst Cooperative: "),a("a",{attrs:{href:"https://github.com/catalyst-cooperative/pudl",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://github.com/catalyst-cooperative/pudl"),a("OutboundLink")],1),e._v(" and "),a("a",{attrs:{href:"https://zenodo.org/communities/catalyst-cooperative/",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://zenodo.org/communities/catalyst-cooperative/"),a("OutboundLink")],1)]),e._v(" "),a("li",[e._v("Research Data Alliance Agriculture: "),a("a",{attrs:{href:"https://www.rd-alliance.org/rda-disciplines/rda-and-agriculture",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://www.rd-alliance.org/rda-disciplines/rda-and-agriculture"),a("OutboundLink")],1)]),e._v(" "),a("li",[e._v("Open Climate Now!: "),a("a",{attrs:{href:"https://branch.climateaction.tech/issues/issue-2/open-climate-now/",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://branch.climateaction.tech/issues/issue-2/open-climate-now/"),a("OutboundLink")],1)]),e._v(" "),a("li",[e._v("Metadata Game Changers: "),a("a",{attrs:{href:"https://metadatagamechangers.com",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://metadatagamechangers.com"),a("OutboundLink")],1)]),e._v(" "),a("li",[e._v("Excellent lecture by J McGlade bridging attitudes etc. to the data story and behaviour change effects: "),a("a",{attrs:{href:"https://www.youtube.com/watch?v=eIRlLlrnmBM&t=1561s",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://www.youtube.com/watch?v=eIRlLlrnmBM&t=1561s"),a("OutboundLink")],1)]),e._v(" "),a("li",[e._v("The Integrated-Assessment Modeling Community (IAMC) is developing a Python package “pyam” for scenario analysis & data visualization: "),a("a",{attrs:{href:"https://pyam-iamc.readthedocs.io",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://pyam-iamc.readthedocs.io"),a("OutboundLink")],1)]),e._v(" "),a("li",[e._v("IIASA is hosting numerous scenario ensemble databases, see "),a("a",{attrs:{href:"https://data.ece.iiasa.ac.at",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://data.ece.iiasa.ac.at"),a("OutboundLink")],1),e._v(", most importantly the scenario ensemble supporting the quantitative assessment in the IPCC 1.5°C Special Report (2018), and a similar database will be released in two months together with IPCC AR6 WG3")]),e._v(" "),a("li",[e._v("Letter to IEA by the openmod community, "),a("a",{attrs:{href:"https://forum.openmod.org/t/open-letter-to-iea-and-member-countries-requesting-open-data/2949",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://forum.openmod.org/t/open-letter-to-iea-and-member-countries-requesting-open-data/2949"),a("OutboundLink")],1)])])])}),[],!1,null,null,null);t.default=r.exports}}]); \ No newline at end of file +(window.webpackJsonp=window.webpackJsonp||[]).push([[155],{687:function(e,t,a){"use strict";a.r(t);var n=a(29),r=Object(n.a)({},(function(){var e=this,t=e.$createElement,a=e._self._c||t;return a("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[a("p",[e._v("During these past tumultuous years, it has been striking to witness the role that information has played in furthering suffering: misinformation, lack of data transparency, and closed technology have worsened the pandemic, increased political strife, and hurt climate policy. Building on these observations, the team at Open Knowledge Foundation are refocusing our energies on how we can come together to empower people, communities, and organisations to create and use open knowledge to solve the most urgent issues of our time, including climate change, inequality, and access to knowledge . Undaunted by these substantial challenges, we entered 2022 with enthusiasm for finding ways to work together, starting with climate data.")]),e._v(" "),a("p",[e._v("To start this year fresh and inspired, we convened two gatherings of climate researchers, activists, and organisations to brainstorm ways to collaborate to make open climate data more usable, accessible, and impactful. Over 30 experts attended the two sessions, from organisations around the world, and we identified and discussed many problems in the climate data space. We confirmed our initial theory that many of us are working siloed and that combining skills, knowledge and networks can result in a powerful alliance across tech communities, data experts and climate crisis activists.")]),e._v(" "),a("p",[e._v("Now, we want to share with you some common themes from these sessions and ask: how can we work together to solve these pressing climate issues?")]),e._v(" "),a("p",[e._v("A primary concern of attendees was "),a("strong",[e._v("the disconnect between how (and why) data is produced and how data can (and should) be used")]),e._v(". This disconnect shows up as frictions for data use: we know that much existing “open” data isn’t actually usable. During the call, many participants mentioned they frequently can’t find open data, and even when they can find it, they can’t easily access it. Even when they can access the data, they often can’t easily use it.")]),e._v(" "),a("p",[e._v("So why is it so hard to find, access, and use climate data? First, climate data is not particularly well standardised or curated, and data creators need better training in data management best practices. Another issue is that many climate data users don’t have technical training or knowledge required to clean messy data, greatly slowing down their research or policy work.")]),e._v(" "),a("h3",{attrs:{id:"how-will-the-open-knowledge-foundation-fix-the-identified-problems-skills-standards-and-community"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#how-will-the-open-knowledge-foundation-fix-the-identified-problems-skills-standards-and-community"}},[e._v("#")]),e._v(" How will the Open Knowledge Foundation fix the identified problems? Skills, standards and community.")]),e._v(" "),a("p",[e._v("An aim for this work will be to bridge the gaps between data creators and users. We plan to host several workshops in the future to work with both these groups, focusing on identifying both skills gaps and data gaps, then working towards capacity building.")]),e._v(" "),a("p",[e._v("Our goal with capacity building will be to give a data platform to those most affected by climate change. How do we make it easier for less technical or newer data users to effectively use climate data? Our future workshops will focus on training data creators and users with the "),a("a",{attrs:{href:"https://frictionlessdata.io/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Open Knowledge Frictionless Data tooling"),a("OutboundLink")],1),e._v(" to better manage data, create higher quality data, and share data in impactful ways that will empower trained researchers and activists alike. For instance, the Frictionless toolbox can help data creators generate clean data that is easy to understand, share, and use, and the new Frictionless tool Livemark can help data consumers easily share climate data with impactful visualisations and narratives.")]),e._v(" "),a("p",[e._v("Another theme that emerged from the brainstorm sessions was the role data plays in generating knowledge versus the role knowledge plays in generating data, and how this interplay can be maximised to create change. For instance, "),a("strong",[e._v("we need to take a hard look at how “open” replicates cycles of inequalities")]),e._v(". Several people brought up the great work citizen scientists are doing for climate research, but how these efforts are rarely recognised by governments or other official research channels. So much vital data on local impacts of climate change are being lost as they aren’t being incorporated into official datasets. How do we make data more equitable, ensuring that those being most affected by climate change can use data to tell their stories?")]),e._v(" "),a("p",[e._v("We call on data organisations, climate researchers, and activists to join us in these efforts. How can we best work together to solve pressing climate change issues? Would you like to partner with us for workshops, or do you have other ideas for collaborations? Let us know! We would like to give our utmost thanks to the organisations that joined our brainstorming sessions for paving the way in this important work. To continue planning this work, we are creating a space to talk in our Frictionless Data community chat, and we invite all interested parties to join us. We are currently migrating our community from Discord to Slack. We encourage you to join the Slack channel, which will soon be populated with all Frictionless community members: "),a("a",{attrs:{href:"https://join.slack.com/t/frictionlessdata/shared_invite/zt-14x9bxnkm-2y~uQcmmrqarSP2kV39_Kg",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://join.slack.com/t/frictionlessdata/shared_invite/zt-14x9bxnkm-2y~uQcmmrqarSP2kV39_Kg"),a("OutboundLink")],1),a("br"),e._v("\n(We also have a Matrix mirror if you prefer Matrix: "),a("a",{attrs:{href:"https://matrix.to/#/#frictionless-data:matrix.org",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://matrix.to/#/#frictionless-data:matrix.org"),a("OutboundLink")],1),e._v(")")]),e._v(" "),a("p",[e._v("Finally, we’d like to share this list of resources that attendees shared during the calls:")]),e._v(" "),a("ul",[a("li",[e._v("Patrick J McGovern Data for Climate 2022 Accelerator: "),a("a",{attrs:{href:"https://www.mcgovern.org/foundation-awards-4-5m-including-new-accelerator-grants-to-advance-data-driven-climate-solutions/",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://www.mcgovern.org/foundation-awards-4-5m-including-new-accelerator-grants-to-advance-data-driven-climate-solutions/"),a("OutboundLink")],1)]),e._v(" "),a("li",[e._v("Open Climate: "),a("a",{attrs:{href:"https://www.appropedia.org/OpenClimate",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://www.appropedia.org/OpenClimate"),a("OutboundLink")],1)]),e._v(" "),a("li",[e._v("Environmental Data and Governance Initiative: "),a("a",{attrs:{href:"https://envirodatagov.org/",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://envirodatagov.org/"),a("OutboundLink")],1)]),e._v(" "),a("li",[e._v("Earth Science Information Partners: "),a("a",{attrs:{href:"https://www.esipfed.org/about",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://www.esipfed.org/about"),a("OutboundLink")],1),a("br"),e._v("\nCourse on environmental data journalism by School of Data Brazil: "),a("a",{attrs:{href:"https://escoladedados.org/courses/jornalismo-de-dados-ambientais/",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://escoladedados.org/courses/jornalismo-de-dados-ambientais/"),a("OutboundLink")],1)]),e._v(" "),a("li",[e._v("Catalogue of environmental databases in Brazil by School of Data Brazil: "),a("a",{attrs:{href:"https://bit.ly/dados-ambientais",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://bit.ly/dados-ambientais"),a("OutboundLink")],1)]),e._v(" "),a("li",[e._v("A monthly meetup for small companies to share best practices (and data): "),a("a",{attrs:{href:"https://climatiq.io/blog/climate-action-net-zero-ambition-best-practices-for-sme",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://climatiq.io/blog/climate-action-net-zero-ambition-best-practices-for-sme"),a("OutboundLink")],1)]),e._v(" "),a("li",[e._v("Reddit Datasets: "),a("a",{attrs:{href:"https://www.reddit.com/r/datasets/",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://www.reddit.com/r/datasets/"),a("OutboundLink")],1)]),e._v(" "),a("li",[e._v("Hardware information standard: "),a("a",{attrs:{href:"https://barbal.co/the-open-know-how-manifest-specification-version-1-0/",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://barbal.co/the-open-know-how-manifest-specification-version-1-0/"),a("OutboundLink")],1)]),e._v(" "),a("li",[e._v("Catalyst Cooperative: "),a("a",{attrs:{href:"https://github.com/catalyst-cooperative/pudl",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://github.com/catalyst-cooperative/pudl"),a("OutboundLink")],1),e._v(" and "),a("a",{attrs:{href:"https://zenodo.org/communities/catalyst-cooperative/",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://zenodo.org/communities/catalyst-cooperative/"),a("OutboundLink")],1)]),e._v(" "),a("li",[e._v("Research Data Alliance Agriculture: "),a("a",{attrs:{href:"https://www.rd-alliance.org/rda-disciplines/rda-and-agriculture",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://www.rd-alliance.org/rda-disciplines/rda-and-agriculture"),a("OutboundLink")],1)]),e._v(" "),a("li",[e._v("Open Climate Now!: "),a("a",{attrs:{href:"https://branch.climateaction.tech/issues/issue-2/open-climate-now/",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://branch.climateaction.tech/issues/issue-2/open-climate-now/"),a("OutboundLink")],1)]),e._v(" "),a("li",[e._v("Metadata Game Changers: "),a("a",{attrs:{href:"https://metadatagamechangers.com",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://metadatagamechangers.com"),a("OutboundLink")],1)]),e._v(" "),a("li",[e._v("Excellent lecture by J McGlade bridging attitudes etc. to the data story and behaviour change effects: "),a("a",{attrs:{href:"https://www.youtube.com/watch?v=eIRlLlrnmBM&t=1561s",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://www.youtube.com/watch?v=eIRlLlrnmBM&t=1561s"),a("OutboundLink")],1)]),e._v(" "),a("li",[e._v("The Integrated-Assessment Modeling Community (IAMC) is developing a Python package “pyam” for scenario analysis & data visualization: "),a("a",{attrs:{href:"https://pyam-iamc.readthedocs.io",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://pyam-iamc.readthedocs.io"),a("OutboundLink")],1)]),e._v(" "),a("li",[e._v("IIASA is hosting numerous scenario ensemble databases, see "),a("a",{attrs:{href:"https://data.ece.iiasa.ac.at",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://data.ece.iiasa.ac.at"),a("OutboundLink")],1),e._v(", most importantly the scenario ensemble supporting the quantitative assessment in the IPCC 1.5°C Special Report (2018), and a similar database will be released in two months together with IPCC AR6 WG3")]),e._v(" "),a("li",[e._v("Letter to IEA by the openmod community, "),a("a",{attrs:{href:"https://forum.openmod.org/t/open-letter-to-iea-and-member-countries-requesting-open-data/2949",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://forum.openmod.org/t/open-letter-to-iea-and-member-countries-requesting-open-data/2949"),a("OutboundLink")],1)])])])}),[],!1,null,null,null);t.default=r.exports}}]); \ No newline at end of file diff --git a/assets/js/157.a603a66c.js b/assets/js/157.7202e235.js similarity index 98% rename from assets/js/157.a603a66c.js rename to assets/js/157.7202e235.js index 6c93c0bc9..8d4dd288f 100644 --- a/assets/js/157.a603a66c.js +++ b/assets/js/157.7202e235.js @@ -1 +1 @@ -(window.webpackJsonp=window.webpackJsonp||[]).push([[157],{693:function(e,t,o){"use strict";o.r(t);var r=o(29),a=Object(r.a)({},(function(){var e=this,t=e.$createElement,o=e._self._c||t;return o("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[o("p",[e._v("At our last community call on April 28"),o("sup",[e._v("th")]),e._v(", we had a discussion around open science best practices and the Turing Way with Anne Lee Steele, who - you might remember, was part of the "),o("a",{attrs:{href:"https://frictionlessdata.io/blog/2020/09/01/hello-fellows-cohort2/",target:"_blank",rel:"noopener noreferrer"}},[e._v("second cohort of Frictionless Fellows"),o("OutboundLink")],1),e._v(".")]),e._v(" "),o("p",[e._v("The Turing Way is an open source and community-led handbook for reproducible, ethical and collaborative research. It is composed of more than 240 pages created by ~300 researchers over the course of 3 years, written collaboratively via GitHub PRs - contrasting to the notion of single/small-authorship papers.")]),e._v(" "),o("p",[e._v("There is currently an effort to make the Turing way develop meta-practices that can be applied to other areas as well, one example is documentation.")]),e._v(" "),o("p",[e._v("A great outcome of the call was the proposal to have a closer cooperation between the Frictionless Data community and the Turing Way’s one, possibly developing a chapter for Open Infrastructures for research to contribute upstream. This chapter would set the context and provide a vision for how to evaluate tools and platforms with a Turing Way perspective on reproducibility, ethical alternatives and collaboration in practice. For more info about this proposal, check "),o("a",{attrs:{href:"https://github.com/alan-turing-institute/the-turing-way/issues/2337",target:"_blank",rel:"noopener noreferrer"}},[e._v("this issue"),o("OutboundLink")],1),e._v(".")]),e._v(" "),o("p",[e._v("If you want to know more about the Turing Way, have a look at the "),o("a",{attrs:{href:"https://the-turing-way.netlify.app/welcome.html",target:"_blank",rel:"noopener noreferrer"}},[e._v("project website"),o("OutboundLink")],1),e._v(". You can also check out the full recording of the call:")]),e._v(" "),o("iframe",{attrs:{width:"560",height:"315",src:"https://www.youtube.com/embed/-RyRFcMAGCE",title:"YouTube video player",frameborder:"0",allow:"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture",allowfullscreen:""}}),e._v(" "),o("h1",{attrs:{id:"news-from-the-community"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#news-from-the-community"}},[e._v("#")]),e._v(" News from the community")]),e._v(" "),o("ul",[o("li",[o("p",[e._v("You’re all invited to join the Frictionless Fellows for a free virtual workshop on Open Science best practices on May 25 at 2pm UTC!"),o("br"),e._v("\nIn this beginner-friendly workshop, Fellows will demonstrate how to use the Frictionless tools to make research data more understandable, usable, and open. You will learn how to use the Frictionless non-coding tools to manipulate metadata and schemas (and why that is important!) and how to validate data in a hands-on format. Learn more & sign up on the Fellows website: "),o("a",{attrs:{href:"https://fellows.frictionlessdata.io/",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://fellows.frictionlessdata.io/"),o("OutboundLink")],1),e._v(".")])]),e._v(" "),o("li",[o("p",[e._v("Reminder that our community chat has moved to Slack. Join us "),o("a",{attrs:{href:"https://join.slack.com/t/frictionlessdata/shared_invite/zt-17kpbffnm-tRfDW_wJgOw8tJVLvZTrBg",target:"_blank",rel:"noopener noreferrer"}},[e._v("there"),o("OutboundLink")],1),e._v(". We now also have a fully operating "),o("a",{attrs:{href:"https://matrix.to/#/#frictionlessdata:matrix.okfn.org",target:"_blank",rel:"noopener noreferrer"}},[e._v("Matrix bridge"),o("OutboundLink")],1),e._v(", so if you prefer you can join us from there as well.")])])]),e._v(" "),o("h1",{attrs:{id:"join-us-next-month"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#join-us-next-month"}},[e._v("#")]),e._v(" Join us next month!")]),e._v(" "),o("p",[e._v("Next community call is on May 26"),o("sup",[e._v("th")]),e._v(". We are going to hear Nick Kellett from Deploy Solutions explain to us how to build citizen science and climate change solutions, using Frictionless.")]),e._v(" "),o("p",[e._v("You can sign up for the call already "),o("a",{attrs:{href:"https://docs.google.com/forms/d/e/1FAIpQLSeuNCopxXauMkrWvF6VHqOyHMcy54SfNDOseVXfWRQZWkvqjQ/viewform?usp=sf_link",target:"_blank",rel:"noopener noreferrer"}},[e._v("here:"),o("OutboundLink")],1)]),e._v(" "),o("p",[e._v("Do you want to share something with the community? Let us know when you sign up.")]),e._v(" "),o("p",[e._v("Join us on "),o("a",{attrs:{href:"https://join.slack.com/t/frictionlessdata/shared_invite/zt-17kpbffnm-tRfDW_wJgOw8tJVLvZTrBg",target:"_blank",rel:"noopener noreferrer"}},[e._v("Slack"),o("OutboundLink")],1),e._v(" (also via "),o("a",{attrs:{href:"https://matrix.to/#/#frictionlessdata:matrix.okfn.org",target:"_blank",rel:"noopener noreferrer"}},[e._v("Matrix"),o("OutboundLink")],1),e._v(") or "),o("a",{attrs:{href:"https://twitter.com/frictionlessd8a",target:"_blank",rel:"noopener noreferrer"}},[e._v("Twitter"),o("OutboundLink")],1),e._v(" to say hi or ask any questions. See you there!")])])}),[],!1,null,null,null);t.default=a.exports}}]); \ No newline at end of file +(window.webpackJsonp=window.webpackJsonp||[]).push([[157],{689:function(e,t,o){"use strict";o.r(t);var r=o(29),a=Object(r.a)({},(function(){var e=this,t=e.$createElement,o=e._self._c||t;return o("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[o("p",[e._v("At our last community call on April 28"),o("sup",[e._v("th")]),e._v(", we had a discussion around open science best practices and the Turing Way with Anne Lee Steele, who - you might remember, was part of the "),o("a",{attrs:{href:"https://frictionlessdata.io/blog/2020/09/01/hello-fellows-cohort2/",target:"_blank",rel:"noopener noreferrer"}},[e._v("second cohort of Frictionless Fellows"),o("OutboundLink")],1),e._v(".")]),e._v(" "),o("p",[e._v("The Turing Way is an open source and community-led handbook for reproducible, ethical and collaborative research. It is composed of more than 240 pages created by ~300 researchers over the course of 3 years, written collaboratively via GitHub PRs - contrasting to the notion of single/small-authorship papers.")]),e._v(" "),o("p",[e._v("There is currently an effort to make the Turing way develop meta-practices that can be applied to other areas as well, one example is documentation.")]),e._v(" "),o("p",[e._v("A great outcome of the call was the proposal to have a closer cooperation between the Frictionless Data community and the Turing Way’s one, possibly developing a chapter for Open Infrastructures for research to contribute upstream. This chapter would set the context and provide a vision for how to evaluate tools and platforms with a Turing Way perspective on reproducibility, ethical alternatives and collaboration in practice. For more info about this proposal, check "),o("a",{attrs:{href:"https://github.com/alan-turing-institute/the-turing-way/issues/2337",target:"_blank",rel:"noopener noreferrer"}},[e._v("this issue"),o("OutboundLink")],1),e._v(".")]),e._v(" "),o("p",[e._v("If you want to know more about the Turing Way, have a look at the "),o("a",{attrs:{href:"https://the-turing-way.netlify.app/welcome.html",target:"_blank",rel:"noopener noreferrer"}},[e._v("project website"),o("OutboundLink")],1),e._v(". You can also check out the full recording of the call:")]),e._v(" "),o("iframe",{attrs:{width:"560",height:"315",src:"https://www.youtube.com/embed/-RyRFcMAGCE",title:"YouTube video player",frameborder:"0",allow:"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture",allowfullscreen:""}}),e._v(" "),o("h1",{attrs:{id:"news-from-the-community"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#news-from-the-community"}},[e._v("#")]),e._v(" News from the community")]),e._v(" "),o("ul",[o("li",[o("p",[e._v("You’re all invited to join the Frictionless Fellows for a free virtual workshop on Open Science best practices on May 25 at 2pm UTC!"),o("br"),e._v("\nIn this beginner-friendly workshop, Fellows will demonstrate how to use the Frictionless tools to make research data more understandable, usable, and open. You will learn how to use the Frictionless non-coding tools to manipulate metadata and schemas (and why that is important!) and how to validate data in a hands-on format. Learn more & sign up on the Fellows website: "),o("a",{attrs:{href:"https://fellows.frictionlessdata.io/",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://fellows.frictionlessdata.io/"),o("OutboundLink")],1),e._v(".")])]),e._v(" "),o("li",[o("p",[e._v("Reminder that our community chat has moved to Slack. Join us "),o("a",{attrs:{href:"https://join.slack.com/t/frictionlessdata/shared_invite/zt-17kpbffnm-tRfDW_wJgOw8tJVLvZTrBg",target:"_blank",rel:"noopener noreferrer"}},[e._v("there"),o("OutboundLink")],1),e._v(". We now also have a fully operating "),o("a",{attrs:{href:"https://matrix.to/#/#frictionlessdata:matrix.okfn.org",target:"_blank",rel:"noopener noreferrer"}},[e._v("Matrix bridge"),o("OutboundLink")],1),e._v(", so if you prefer you can join us from there as well.")])])]),e._v(" "),o("h1",{attrs:{id:"join-us-next-month"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#join-us-next-month"}},[e._v("#")]),e._v(" Join us next month!")]),e._v(" "),o("p",[e._v("Next community call is on May 26"),o("sup",[e._v("th")]),e._v(". We are going to hear Nick Kellett from Deploy Solutions explain to us how to build citizen science and climate change solutions, using Frictionless.")]),e._v(" "),o("p",[e._v("You can sign up for the call already "),o("a",{attrs:{href:"https://docs.google.com/forms/d/e/1FAIpQLSeuNCopxXauMkrWvF6VHqOyHMcy54SfNDOseVXfWRQZWkvqjQ/viewform?usp=sf_link",target:"_blank",rel:"noopener noreferrer"}},[e._v("here:"),o("OutboundLink")],1)]),e._v(" "),o("p",[e._v("Do you want to share something with the community? Let us know when you sign up.")]),e._v(" "),o("p",[e._v("Join us on "),o("a",{attrs:{href:"https://join.slack.com/t/frictionlessdata/shared_invite/zt-17kpbffnm-tRfDW_wJgOw8tJVLvZTrBg",target:"_blank",rel:"noopener noreferrer"}},[e._v("Slack"),o("OutboundLink")],1),e._v(" (also via "),o("a",{attrs:{href:"https://matrix.to/#/#frictionlessdata:matrix.okfn.org",target:"_blank",rel:"noopener noreferrer"}},[e._v("Matrix"),o("OutboundLink")],1),e._v(") or "),o("a",{attrs:{href:"https://twitter.com/frictionlessd8a",target:"_blank",rel:"noopener noreferrer"}},[e._v("Twitter"),o("OutboundLink")],1),e._v(" to say hi or ask any questions. See you there!")])])}),[],!1,null,null,null);t.default=a.exports}}]); \ No newline at end of file diff --git a/assets/js/16.67c821df.js b/assets/js/16.c870d842.js similarity index 98% rename from assets/js/16.67c821df.js rename to assets/js/16.c870d842.js index 64c95e17f..4ad49fab4 100644 --- a/assets/js/16.67c821df.js +++ b/assets/js/16.c870d842.js @@ -1 +1 @@ -(window.webpackJsonp=window.webpackJsonp||[]).push([[16],{527:function(e,t,a){e.exports=a.p+"assets/img/IMG_20230627_110531.dd426282.jpg"},528:function(e,t,a){e.exports=a.p+"assets/img/20230419_145928.f2d541cc.jpg"},529:function(e,t,a){e.exports=a.p+"assets/img/PXL_20230419_174710396.4df11ee7.jpg"},530:function(e,t,a){e.exports=a.p+"assets/img/IMG20230420143511.3025f94e.jpg"},713:function(e,t,a){"use strict";a.r(t);var n=a(29),r=Object(n.a)({},(function(){var e=this,t=e.$createElement,n=e._self._c||t;return n("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[n("p",[e._v("On April 19, 20 we attended csv,conf,v7, the best community conference for datamakers from all around the world! It was two days of fantastic talks, llamas (of course!), and above all, vibrant community encounters. We met long term friends, community members, and made new meaningful connections. Our tech lead Evgeny Karev gave a talk on April 19th to present the beta version of the Frictionless Application, making it easier for non-coders to use all the Frictionless resources, helping them improve the quality of their data. Whether you’re a data scientist, researcher, or citizen analyst, the Frictionless Application empowers you to effortlessly describe, validate, extract, and transform data, all in one user-friendly environment. If you want to have a look at the presentation of the Frictionless Application by Evgeny Karev, you can watch it "),n("a",{attrs:{href:"https://youtu.be/hk1PoedLOY4",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),n("OutboundLink")],1),e._v(".")]),e._v(" "),n("p",[n("img",{attrs:{src:a(527),alt:"Evgeny Karev presenting the Frictionless Application"}})]),e._v(" "),n("p",[e._v("Also, we can’t help but feel a sense of pride that Frictionless Data got mentioned in three different talks:")]),e._v(" "),n("p",[n("strong",[e._v("Miller: A Swiss-Army Chainsaw for CSV and More by John Kerl:")]),n("br"),e._v("\nJohn Kerl took the stage to introduce Miller, a powerful tool that simplifies working with CSV and other data formats. In his talk, he acknowledged the synergy between Miller and Frictionless Data. He highlighted how Frictionless Data’s specifications and tooling provided a solid foundation for Miller’s capabilities, enabling users to effortlessly manipulate and analyse CSV files. The collaboration between Miller and Frictionless Data is a testament to the community’s commitment to making data workflows smoother and more efficient."),n("br"),e._v("\nYou can watch (unfortunately) only parts of the recordings "),n("a",{attrs:{href:"https://youtu.be/G0_rFYz6rJg",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),n("OutboundLink")],1),e._v(".")]),e._v(" "),n("p",[n("img",{attrs:{src:a(528),alt:"Miller"}})]),e._v(" "),n("p",[n("strong",[e._v("Integrating Data Sources and Publishing Open Data with FastELT & Airflow by Augusto Herrmann:")]),n("br"),e._v("\nAugusto Herrmann, who is part of our community and whom you probably have met at one of our monthly calls, gave a talk on integrating data sources and publishing open data using FastELT and Airflow. During his presentation, Augusto emphasised the crucial role of Frictionless Data in ensuring data quality and interoperability. By implementing Frictionless Data’s specifications and tooling, he showcased how FastELT and Airflow can seamlessly process, validate, and transform data, simplifying the data integration process and making open data publishing a breeze."),n("br"),e._v("\nWatch the recording of Augusto’s talk "),n("a",{attrs:{href:"https://youtu.be/u42NrUAui8Y",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),n("OutboundLink")],1),e._v(".")]),e._v(" "),n("p",[n("img",{attrs:{src:a(529),alt:"FastELT"}})]),e._v(" "),n("p",[n("strong",[e._v("Defining The Turing Way by Malvika Sharan, Melissa Black, and Esther Plomp:")]),n("br"),e._v("\nMalvika Sharan, Melissa Black, and Esther Plomp discussed The Turing Way, an initiative aimed at providing reproducible, ethical, and inclusive research practices. In their talk, Frictionless Data was acknowledged as a useful tool to achieve these goals. By adopting Frictionless Data’s principles and tools, researchers can ensure data integrity, enhance collaboration, and promote openness in their work."),n("br"),e._v("\nWatch the recording of the talk "),n("a",{attrs:{href:"https://youtu.be/pT6W0sQJtyY",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),n("OutboundLink")],1),e._v(". And if you are interested in knowing more about the Turing Way, go and "),n("a",{attrs:{href:"https://frictionlessdata.io/blog/2022/05/05/april-community-call/",target:"_blank",rel:"noopener noreferrer"}},[e._v("have a look at the recap blog"),n("OutboundLink")],1),e._v(" of the community call of April last year, with Turing Way’s community manager Anne Steele.")]),e._v(" "),n("p",[n("img",{attrs:{src:a(530),alt:"Defining the Turing Way"}})])])}),[],!1,null,null,null);t.default=r.exports}}]); \ No newline at end of file +(window.webpackJsonp=window.webpackJsonp||[]).push([[16],{527:function(e,t,a){e.exports=a.p+"assets/img/IMG_20230627_110531.dd426282.jpg"},528:function(e,t,a){e.exports=a.p+"assets/img/20230419_145928.f2d541cc.jpg"},529:function(e,t,a){e.exports=a.p+"assets/img/PXL_20230419_174710396.4df11ee7.jpg"},530:function(e,t,a){e.exports=a.p+"assets/img/IMG20230420143511.3025f94e.jpg"},710:function(e,t,a){"use strict";a.r(t);var n=a(29),r=Object(n.a)({},(function(){var e=this,t=e.$createElement,n=e._self._c||t;return n("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[n("p",[e._v("On April 19, 20 we attended csv,conf,v7, the best community conference for datamakers from all around the world! It was two days of fantastic talks, llamas (of course!), and above all, vibrant community encounters. We met long term friends, community members, and made new meaningful connections. Our tech lead Evgeny Karev gave a talk on April 19th to present the beta version of the Frictionless Application, making it easier for non-coders to use all the Frictionless resources, helping them improve the quality of their data. Whether you’re a data scientist, researcher, or citizen analyst, the Frictionless Application empowers you to effortlessly describe, validate, extract, and transform data, all in one user-friendly environment. If you want to have a look at the presentation of the Frictionless Application by Evgeny Karev, you can watch it "),n("a",{attrs:{href:"https://youtu.be/hk1PoedLOY4",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),n("OutboundLink")],1),e._v(".")]),e._v(" "),n("p",[n("img",{attrs:{src:a(527),alt:"Evgeny Karev presenting the Frictionless Application"}})]),e._v(" "),n("p",[e._v("Also, we can’t help but feel a sense of pride that Frictionless Data got mentioned in three different talks:")]),e._v(" "),n("p",[n("strong",[e._v("Miller: A Swiss-Army Chainsaw for CSV and More by John Kerl:")]),n("br"),e._v("\nJohn Kerl took the stage to introduce Miller, a powerful tool that simplifies working with CSV and other data formats. In his talk, he acknowledged the synergy between Miller and Frictionless Data. He highlighted how Frictionless Data’s specifications and tooling provided a solid foundation for Miller’s capabilities, enabling users to effortlessly manipulate and analyse CSV files. The collaboration between Miller and Frictionless Data is a testament to the community’s commitment to making data workflows smoother and more efficient."),n("br"),e._v("\nYou can watch (unfortunately) only parts of the recordings "),n("a",{attrs:{href:"https://youtu.be/G0_rFYz6rJg",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),n("OutboundLink")],1),e._v(".")]),e._v(" "),n("p",[n("img",{attrs:{src:a(528),alt:"Miller"}})]),e._v(" "),n("p",[n("strong",[e._v("Integrating Data Sources and Publishing Open Data with FastELT & Airflow by Augusto Herrmann:")]),n("br"),e._v("\nAugusto Herrmann, who is part of our community and whom you probably have met at one of our monthly calls, gave a talk on integrating data sources and publishing open data using FastELT and Airflow. During his presentation, Augusto emphasised the crucial role of Frictionless Data in ensuring data quality and interoperability. By implementing Frictionless Data’s specifications and tooling, he showcased how FastELT and Airflow can seamlessly process, validate, and transform data, simplifying the data integration process and making open data publishing a breeze."),n("br"),e._v("\nWatch the recording of Augusto’s talk "),n("a",{attrs:{href:"https://youtu.be/u42NrUAui8Y",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),n("OutboundLink")],1),e._v(".")]),e._v(" "),n("p",[n("img",{attrs:{src:a(529),alt:"FastELT"}})]),e._v(" "),n("p",[n("strong",[e._v("Defining The Turing Way by Malvika Sharan, Melissa Black, and Esther Plomp:")]),n("br"),e._v("\nMalvika Sharan, Melissa Black, and Esther Plomp discussed The Turing Way, an initiative aimed at providing reproducible, ethical, and inclusive research practices. In their talk, Frictionless Data was acknowledged as a useful tool to achieve these goals. By adopting Frictionless Data’s principles and tools, researchers can ensure data integrity, enhance collaboration, and promote openness in their work."),n("br"),e._v("\nWatch the recording of the talk "),n("a",{attrs:{href:"https://youtu.be/pT6W0sQJtyY",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),n("OutboundLink")],1),e._v(". And if you are interested in knowing more about the Turing Way, go and "),n("a",{attrs:{href:"https://frictionlessdata.io/blog/2022/05/05/april-community-call/",target:"_blank",rel:"noopener noreferrer"}},[e._v("have a look at the recap blog"),n("OutboundLink")],1),e._v(" of the community call of April last year, with Turing Way’s community manager Anne Steele.")]),e._v(" "),n("p",[n("img",{attrs:{src:a(530),alt:"Defining the Turing Way"}})])])}),[],!1,null,null,null);t.default=r.exports}}]); \ No newline at end of file diff --git a/assets/js/160.72ed66cd.js b/assets/js/160.c098d2be.js similarity index 98% rename from assets/js/160.72ed66cd.js rename to assets/js/160.c098d2be.js index 5e515ee12..dbb7dde62 100644 --- a/assets/js/160.72ed66cd.js +++ b/assets/js/160.c098d2be.js @@ -1 +1 @@ -(window.webpackJsonp=window.webpackJsonp||[]).push([[160],{697:function(e,t,o){"use strict";o.r(t);var r=o(29),n=Object(r.a)({},(function(){var e=this,t=e.$createElement,o=e._self._c||t;return o("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[o("p",[e._v("On June 30"),o("sup",[e._v("th")]),e._v(" we had a very special community call. Instead of a project presentation this time we had the chance to meet the 3rd cohort of "),o("a",{attrs:{href:"http://fellows.frictionlessdata.io/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Frictionless Fellows"),o("OutboundLink")],1),e._v(" and hear about their reproducibility and open science journey.")]),e._v(" "),o("p",[e._v("The fellows are a group of early career researchers interested in learning about open science and data management by using the Frictionless Data tools in their own research projects. Melvin Adhiambo, Lindsay Gypin, Kevin Kidambasi, Victoria Stanley, and Guo-Qiang Zhang are almost at the end of their nine months fellowship. During the past nine months they have learnt open science principles and how to discuss them (especially with colleagues who are not convinced yet!). They also learnt data management skills, and how to correctly use metadata and data schemas. Besides using the Frictionless Data browser tools, there was also a coding component to the fellowship, as they used the Frictionless Python tools as well.")]),e._v(" "),o("p",[e._v("The Fellows also ran workshops and wrote great blog posts during the last nine months. You can read them "),o("a",{attrs:{href:"http://fellows.frictionlessdata.io/blog",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),o("OutboundLink")],1),e._v(".")]),e._v(" "),o("p",[e._v("If you are interested in knowing more about the fellows’ research field and what being a Frictionless Data Fellow meant for them, go and watch the full recording of the call:")]),e._v(" "),o("iframe",{attrs:{width:"560",height:"315",src:"https://www.youtube.com/embed/KAkUGU1BSeo",title:"YouTube video player",frameborder:"0",allow:"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture",allowfullscreen:""}}),e._v(" "),o("h1",{attrs:{id:"join-us-next-month"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#join-us-next-month"}},[e._v("#")]),e._v(" Join us next month!")]),e._v(" "),o("p",[e._v("Next community call is on July 28"),o("sup",[e._v("th")]),e._v(". Join us to hear David Raznick telling us about "),o("a",{attrs:{href:"https://flatterer.opendata.coop/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Flatterer"),o("OutboundLink")],1),e._v(", a new tool that helps convert JSON into tabular data.")]),e._v(" "),o("p",[e._v("You can sign up for the call already "),o("a",{attrs:{href:"https://docs.google.com/forms/d/e/1FAIpQLSeuNCopxXauMkrWvF6VHqOyHMcy54SfNDOseVXfWRQZWkvqjQ/viewform?usp=sf_link",target:"_blank",rel:"noopener noreferrer"}},[e._v("here."),o("OutboundLink")],1)]),e._v(" "),o("p",[e._v("Do you want to share something with the community? Let us know when you sign up.")]),e._v(" "),o("p",[e._v("Would you like to present at one of the next community calls? Please fill out "),o("a",{attrs:{href:"https://forms.gle/AWpbxyiGESNSUFK2A",target:"_blank",rel:"noopener noreferrer"}},[e._v("this form"),o("OutboundLink")],1),e._v(".")]),e._v(" "),o("p",[e._v("Join our community on "),o("a",{attrs:{href:"https://join.slack.com/t/frictionlessdata/shared_invite/zt-17kpbffnm-tRfDW_wJgOw8tJVLvZTrBg",target:"_blank",rel:"noopener noreferrer"}},[e._v("Slack"),o("OutboundLink")],1),e._v(" (also via "),o("a",{attrs:{href:"https://matrix.to/#/#frictionlessdata:matrix.okfn.org",target:"_blank",rel:"noopener noreferrer"}},[e._v("Matrix"),o("OutboundLink")],1),e._v(") or "),o("a",{attrs:{href:"https://twitter.com/frictionlessd8a",target:"_blank",rel:"noopener noreferrer"}},[e._v("Twitter"),o("OutboundLink")],1),e._v(". See you there!")])])}),[],!1,null,null,null);t.default=n.exports}}]); \ No newline at end of file +(window.webpackJsonp=window.webpackJsonp||[]).push([[160],{693:function(e,t,o){"use strict";o.r(t);var r=o(29),n=Object(r.a)({},(function(){var e=this,t=e.$createElement,o=e._self._c||t;return o("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[o("p",[e._v("On June 30"),o("sup",[e._v("th")]),e._v(" we had a very special community call. Instead of a project presentation this time we had the chance to meet the 3rd cohort of "),o("a",{attrs:{href:"http://fellows.frictionlessdata.io/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Frictionless Fellows"),o("OutboundLink")],1),e._v(" and hear about their reproducibility and open science journey.")]),e._v(" "),o("p",[e._v("The fellows are a group of early career researchers interested in learning about open science and data management by using the Frictionless Data tools in their own research projects. Melvin Adhiambo, Lindsay Gypin, Kevin Kidambasi, Victoria Stanley, and Guo-Qiang Zhang are almost at the end of their nine months fellowship. During the past nine months they have learnt open science principles and how to discuss them (especially with colleagues who are not convinced yet!). They also learnt data management skills, and how to correctly use metadata and data schemas. Besides using the Frictionless Data browser tools, there was also a coding component to the fellowship, as they used the Frictionless Python tools as well.")]),e._v(" "),o("p",[e._v("The Fellows also ran workshops and wrote great blog posts during the last nine months. You can read them "),o("a",{attrs:{href:"http://fellows.frictionlessdata.io/blog",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),o("OutboundLink")],1),e._v(".")]),e._v(" "),o("p",[e._v("If you are interested in knowing more about the fellows’ research field and what being a Frictionless Data Fellow meant for them, go and watch the full recording of the call:")]),e._v(" "),o("iframe",{attrs:{width:"560",height:"315",src:"https://www.youtube.com/embed/KAkUGU1BSeo",title:"YouTube video player",frameborder:"0",allow:"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture",allowfullscreen:""}}),e._v(" "),o("h1",{attrs:{id:"join-us-next-month"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#join-us-next-month"}},[e._v("#")]),e._v(" Join us next month!")]),e._v(" "),o("p",[e._v("Next community call is on July 28"),o("sup",[e._v("th")]),e._v(". Join us to hear David Raznick telling us about "),o("a",{attrs:{href:"https://flatterer.opendata.coop/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Flatterer"),o("OutboundLink")],1),e._v(", a new tool that helps convert JSON into tabular data.")]),e._v(" "),o("p",[e._v("You can sign up for the call already "),o("a",{attrs:{href:"https://docs.google.com/forms/d/e/1FAIpQLSeuNCopxXauMkrWvF6VHqOyHMcy54SfNDOseVXfWRQZWkvqjQ/viewform?usp=sf_link",target:"_blank",rel:"noopener noreferrer"}},[e._v("here."),o("OutboundLink")],1)]),e._v(" "),o("p",[e._v("Do you want to share something with the community? Let us know when you sign up.")]),e._v(" "),o("p",[e._v("Would you like to present at one of the next community calls? Please fill out "),o("a",{attrs:{href:"https://forms.gle/AWpbxyiGESNSUFK2A",target:"_blank",rel:"noopener noreferrer"}},[e._v("this form"),o("OutboundLink")],1),e._v(".")]),e._v(" "),o("p",[e._v("Join our community on "),o("a",{attrs:{href:"https://join.slack.com/t/frictionlessdata/shared_invite/zt-17kpbffnm-tRfDW_wJgOw8tJVLvZTrBg",target:"_blank",rel:"noopener noreferrer"}},[e._v("Slack"),o("OutboundLink")],1),e._v(" (also via "),o("a",{attrs:{href:"https://matrix.to/#/#frictionlessdata:matrix.okfn.org",target:"_blank",rel:"noopener noreferrer"}},[e._v("Matrix"),o("OutboundLink")],1),e._v(") or "),o("a",{attrs:{href:"https://twitter.com/frictionlessd8a",target:"_blank",rel:"noopener noreferrer"}},[e._v("Twitter"),o("OutboundLink")],1),e._v(". See you there!")])])}),[],!1,null,null,null);t.default=n.exports}}]); \ No newline at end of file diff --git a/assets/js/162.4a3d41ac.js b/assets/js/162.10407c12.js similarity index 99% rename from assets/js/162.4a3d41ac.js rename to assets/js/162.10407c12.js index 114e5ee08..b2204a4e5 100644 --- a/assets/js/162.4a3d41ac.js +++ b/assets/js/162.10407c12.js @@ -1 +1 @@ -(window.webpackJsonp=window.webpackJsonp||[]).push([[162],{698:function(e,t,a){"use strict";a.r(t);var r=a(29),n=Object(r.a)({},(function(){var e=this,t=e.$createElement,a=e._self._c||t;return a("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[a("p",[a("em",[e._v("Originally posted on: "),a("a",{attrs:{href:"https://medium.com/opendatacoop/announcing-flatterer-converting-structured-data-into-tabular-data-c4652eae27c9",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://medium.com/opendatacoop/announcing-flatterer-converting-structured-data-into-tabular-data-c4652eae27c9"),a("OutboundLink")],1)])]),e._v(" "),a("p",[a("em",[e._v("In this blog post, we introduce Flatterer - a new tool that helps convert JSON into tabular data. To hear more about Flatterer, "),a("a",{attrs:{href:"https://docs.google.com/forms/d/e/1FAIpQLSeuNCopxXauMkrWvF6VHqOyHMcy54SfNDOseVXfWRQZWkvqjQ/viewform",target:"_blank",rel:"noopener noreferrer"}},[e._v("sign up"),a("OutboundLink")],1),e._v(" to join David Raznick at the Frictionless Data community call on July 28th.")])]),e._v(" "),a("p",[e._v("Open data needs to be available in formats people want to work with. In our experience at "),a("a",{attrs:{href:"https://opendataservices.coop/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Open Data Services"),a("OutboundLink")],1),e._v(", we’ve found that developers often want access to structured data (for example, JSON) while analysts are used to working with flat data (in CSV files or tables).")]),e._v(" "),a("p",[e._v("More and more data is being published as JSON, but for most analysts this isn’t particularly useful. For many, working with JSON means needing to spend time converting the structured data into tables before they can get started.")]),e._v(" "),a("p",[e._v("That’s where "),a("a",{attrs:{href:"https://github.com/kindly/flatterer",target:"_blank",rel:"noopener noreferrer"}},[e._v("Flatterer"),a("OutboundLink")],1),e._v(" comes in. Flatterer is an opinionated JSON to CSV/XLSX/SQLITE/PARQUET converter. It helps people to convert JSON into relational, tabular data that can be easily analysed. It’s fast and memory efficient, and can be run either in the "),a("a",{attrs:{href:"https://flatterer.opendata.coop/",target:"_blank",rel:"noopener noreferrer"}},[e._v("command line"),a("OutboundLink")],1),e._v(" or as a "),a("a",{attrs:{href:"https://deepnote.com/@david-raznick/Flatterer-Demo-FWeGccp_QKCu1WAEGQ0mEQ",target:"_blank",rel:"noopener noreferrer"}},[e._v("Python library"),a("OutboundLink")],1),e._v(". The Python library supports creating data frames for all the flattened data, making it easy to analyse and visualise.")]),e._v(" "),a("h2",{attrs:{id:"what-does-it-do"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#what-does-it-do"}},[e._v("#")]),e._v(" What does it do?")]),e._v(" "),a("p",[e._v("With Flatterer you can:")]),e._v(" "),a("ul",[a("li",[e._v("easily convert JSON to flat relational data such as CSV, XLSX, Database Tables, Pandas Dataframes and Parquet;")]),e._v(" "),a("li",[e._v("convert JSON into data packages, so you can use Frictionless data to convert into any database format;")]),e._v(" "),a("li",[e._v("create a data dictionary that contains metadata about the conversion, including fields contained in the dataset, to help you understand the data you are looking at;")]),e._v(" "),a("li",[e._v("create a new table for each one-to-many relationship, alongside _link fields that help to join the data together.")])]),e._v(" "),a("h2",{attrs:{id:"why-we-built-it"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#why-we-built-it"}},[e._v("#")]),e._v(" Why we built it")]),e._v(" "),a("p",[e._v("When you receive a JSON file where the structure is deeply nested or not well specified, it’s hard to determine what the data contains. Even if you know the JSON structure, it can still be time consuming to work out how to flatten the JSON into a relational structure for data analysis, and to be part of a data pipeline."),a("br"),e._v("\nFlatterer aims to be the first tool to go to when faced with this problem. Although you may still need to handwrite code, Flatterer has a number of benefits over most handwritten approaches:")]),e._v(" "),a("ul",[a("li",[e._v("it’s fast – written in Rust but with Python bindings for ease of use. It can be 10x faster than hand written Python flattening;")]),e._v(" "),a("li",[e._v("it’s memory efficient – flatterer uses a custom streaming JSON parser which means that a long list of objects nested with the JSON will be streamed, so less data needs to be loaded into memory at once;")]),e._v(" "),a("li",[e._v("it gives you fast, memory efficient output to CSV/XLSX/SQLITE/PARQUET;")]),e._v(" "),a("li",[e._v("it uses best practice that has been learnt from our experience flattening JSON countless times, such as generating keys to link one-to-many tables to their parents.")])]),e._v(" "),a("h2",{attrs:{id:"using-flatterer-in-the-openownership-data-pipeline"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#using-flatterer-in-the-openownership-data-pipeline"}},[e._v("#")]),e._v(" Using Flatterer in the OpenOwnership data pipeline")]),e._v(" "),a("p",[e._v("As an example, we’ve used "),a("a",{attrs:{href:"https://github.com/kindly/flatterer",target:"_blank",rel:"noopener noreferrer"}},[e._v("Flatterer"),a("OutboundLink")],1),e._v(" to help "),a("a",{attrs:{href:"https://www.openownership.org/",target:"_blank",rel:"noopener noreferrer"}},[e._v("OpenOwnership"),a("OutboundLink")],1),e._v(" create a data pipeline to make information about who owns and controls companies available in a "),a("a",{attrs:{href:"https://bods-data.openownership.org/",target:"_blank",rel:"noopener noreferrer"}},[e._v("variety of data formats"),a("OutboundLink")],1),e._v(". In the example below, we’ve used Flatterer to convert beneficial ownership data from the Register of Enterprises of the Republic of Latvia and the OpenOwnership Register from JSON into CSV, SQLite, Postgresql, Big Query and Datasette formats.")]),e._v(" "),a("p",[a("img",{attrs:{src:"https://user-images.githubusercontent.com/74717970/179058338-08ce8ea1-9b1f-4c4c-b59c-64b04cd450f6.png",alt:"img-1-flatterer"}})]),e._v(" "),a("p",[e._v("Alongside converting the data into different formats, Flatterer has created a data dictionary that shows the fields contained in the dataset, alongside the field type and field counts. In the example below, we show how this dictionary interprets person_statement fields contained in the Beneficial Ownership Data Standard.")]),e._v(" "),a("p",[a("img",{attrs:{src:"https://user-images.githubusercontent.com/74717970/179058526-19694210-514e-4215-bf9d-f6abc7ef5400.png",alt:"img-2-flatterer"}})]),e._v(" "),a("p",[e._v("Finally, you can see Flatterer has created special _link fields, to help with joining the tables together. The example below shows how the _link field helps join "),a("a",{attrs:{href:"https://medium.com/opendatacoop/why-do-open-organisational-identifiers-matter-46af05ab30a",target:"_blank",rel:"noopener noreferrer"}},[e._v("entity identifiers"),a("OutboundLink")],1),e._v(" to statements about beneficial ownership.")]),e._v(" "),a("p",[a("img",{attrs:{src:"https://user-images.githubusercontent.com/74717970/179058657-ae4ab534-9fdb-4d6d-ad59-56521f0218e0.png",alt:"img-3-flatterer"}})]),e._v(" "),a("h2",{attrs:{id:"what-s-next"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#what-s-next"}},[e._v("#")]),e._v(" What’s next?")]),e._v(" "),a("p",[e._v("Next, we’ll be working to make Flatterer more user friendly. We’ll be exploring creating a desktop interface, improving type guessing for fields, and giving more summary statistics about the input data. We welcome feedback on the tool through "),a("a",{attrs:{href:"https://github.com/kindly/flatterer/issues",target:"_blank",rel:"noopener noreferrer"}},[e._v("GitHub"),a("OutboundLink")],1),e._v(", and are really interested to find out what kind of improvements you’d like to see.")]),e._v(" "),a("p",[e._v("More information about using Flatterer is available on "),a("a",{attrs:{href:"https://deepnote.com/@david-raznick/Flatterer-Demo-FWeGccp_QKCu1WAEGQ0mEQ",target:"_blank",rel:"noopener noreferrer"}},[e._v("deepnote"),a("OutboundLink")],1),e._v(". To hear more about Flatterer, you can join David Raznick at Frictionless Data’s monthly community call on July 28th.")]),e._v(" "),a("h4",{attrs:{id:"at-open-data-services-cooperative-we-re-always-happy-to-discuss-how-developing-or-implementing-open-data-standards-could-support-your-goals-find-out-more-about-our-work-and-get-in-touch"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#at-open-data-services-cooperative-we-re-always-happy-to-discuss-how-developing-or-implementing-open-data-standards-could-support-your-goals-find-out-more-about-our-work-and-get-in-touch"}},[e._v("#")]),e._v(" At Open Data Services Cooperative we’re always happy to discuss how developing or implementing open data standards could support your goals. Find out more about "),a("a",{attrs:{href:"https://opendataservices.coop/",target:"_blank",rel:"noopener noreferrer"}},[e._v("our work"),a("OutboundLink")],1),e._v(" and "),a("a",{attrs:{href:"https://opendataservices.coop/#contact",target:"_blank",rel:"noopener noreferrer"}},[e._v("get in touch"),a("OutboundLink")],1),e._v(".")])])}),[],!1,null,null,null);t.default=n.exports}}]); \ No newline at end of file +(window.webpackJsonp=window.webpackJsonp||[]).push([[162],{695:function(e,t,a){"use strict";a.r(t);var r=a(29),n=Object(r.a)({},(function(){var e=this,t=e.$createElement,a=e._self._c||t;return a("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[a("p",[a("em",[e._v("Originally posted on: "),a("a",{attrs:{href:"https://medium.com/opendatacoop/announcing-flatterer-converting-structured-data-into-tabular-data-c4652eae27c9",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://medium.com/opendatacoop/announcing-flatterer-converting-structured-data-into-tabular-data-c4652eae27c9"),a("OutboundLink")],1)])]),e._v(" "),a("p",[a("em",[e._v("In this blog post, we introduce Flatterer - a new tool that helps convert JSON into tabular data. To hear more about Flatterer, "),a("a",{attrs:{href:"https://docs.google.com/forms/d/e/1FAIpQLSeuNCopxXauMkrWvF6VHqOyHMcy54SfNDOseVXfWRQZWkvqjQ/viewform",target:"_blank",rel:"noopener noreferrer"}},[e._v("sign up"),a("OutboundLink")],1),e._v(" to join David Raznick at the Frictionless Data community call on July 28th.")])]),e._v(" "),a("p",[e._v("Open data needs to be available in formats people want to work with. In our experience at "),a("a",{attrs:{href:"https://opendataservices.coop/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Open Data Services"),a("OutboundLink")],1),e._v(", we’ve found that developers often want access to structured data (for example, JSON) while analysts are used to working with flat data (in CSV files or tables).")]),e._v(" "),a("p",[e._v("More and more data is being published as JSON, but for most analysts this isn’t particularly useful. For many, working with JSON means needing to spend time converting the structured data into tables before they can get started.")]),e._v(" "),a("p",[e._v("That’s where "),a("a",{attrs:{href:"https://github.com/kindly/flatterer",target:"_blank",rel:"noopener noreferrer"}},[e._v("Flatterer"),a("OutboundLink")],1),e._v(" comes in. Flatterer is an opinionated JSON to CSV/XLSX/SQLITE/PARQUET converter. It helps people to convert JSON into relational, tabular data that can be easily analysed. It’s fast and memory efficient, and can be run either in the "),a("a",{attrs:{href:"https://flatterer.opendata.coop/",target:"_blank",rel:"noopener noreferrer"}},[e._v("command line"),a("OutboundLink")],1),e._v(" or as a "),a("a",{attrs:{href:"https://deepnote.com/@david-raznick/Flatterer-Demo-FWeGccp_QKCu1WAEGQ0mEQ",target:"_blank",rel:"noopener noreferrer"}},[e._v("Python library"),a("OutboundLink")],1),e._v(". The Python library supports creating data frames for all the flattened data, making it easy to analyse and visualise.")]),e._v(" "),a("h2",{attrs:{id:"what-does-it-do"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#what-does-it-do"}},[e._v("#")]),e._v(" What does it do?")]),e._v(" "),a("p",[e._v("With Flatterer you can:")]),e._v(" "),a("ul",[a("li",[e._v("easily convert JSON to flat relational data such as CSV, XLSX, Database Tables, Pandas Dataframes and Parquet;")]),e._v(" "),a("li",[e._v("convert JSON into data packages, so you can use Frictionless data to convert into any database format;")]),e._v(" "),a("li",[e._v("create a data dictionary that contains metadata about the conversion, including fields contained in the dataset, to help you understand the data you are looking at;")]),e._v(" "),a("li",[e._v("create a new table for each one-to-many relationship, alongside _link fields that help to join the data together.")])]),e._v(" "),a("h2",{attrs:{id:"why-we-built-it"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#why-we-built-it"}},[e._v("#")]),e._v(" Why we built it")]),e._v(" "),a("p",[e._v("When you receive a JSON file where the structure is deeply nested or not well specified, it’s hard to determine what the data contains. Even if you know the JSON structure, it can still be time consuming to work out how to flatten the JSON into a relational structure for data analysis, and to be part of a data pipeline."),a("br"),e._v("\nFlatterer aims to be the first tool to go to when faced with this problem. Although you may still need to handwrite code, Flatterer has a number of benefits over most handwritten approaches:")]),e._v(" "),a("ul",[a("li",[e._v("it’s fast – written in Rust but with Python bindings for ease of use. It can be 10x faster than hand written Python flattening;")]),e._v(" "),a("li",[e._v("it’s memory efficient – flatterer uses a custom streaming JSON parser which means that a long list of objects nested with the JSON will be streamed, so less data needs to be loaded into memory at once;")]),e._v(" "),a("li",[e._v("it gives you fast, memory efficient output to CSV/XLSX/SQLITE/PARQUET;")]),e._v(" "),a("li",[e._v("it uses best practice that has been learnt from our experience flattening JSON countless times, such as generating keys to link one-to-many tables to their parents.")])]),e._v(" "),a("h2",{attrs:{id:"using-flatterer-in-the-openownership-data-pipeline"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#using-flatterer-in-the-openownership-data-pipeline"}},[e._v("#")]),e._v(" Using Flatterer in the OpenOwnership data pipeline")]),e._v(" "),a("p",[e._v("As an example, we’ve used "),a("a",{attrs:{href:"https://github.com/kindly/flatterer",target:"_blank",rel:"noopener noreferrer"}},[e._v("Flatterer"),a("OutboundLink")],1),e._v(" to help "),a("a",{attrs:{href:"https://www.openownership.org/",target:"_blank",rel:"noopener noreferrer"}},[e._v("OpenOwnership"),a("OutboundLink")],1),e._v(" create a data pipeline to make information about who owns and controls companies available in a "),a("a",{attrs:{href:"https://bods-data.openownership.org/",target:"_blank",rel:"noopener noreferrer"}},[e._v("variety of data formats"),a("OutboundLink")],1),e._v(". In the example below, we’ve used Flatterer to convert beneficial ownership data from the Register of Enterprises of the Republic of Latvia and the OpenOwnership Register from JSON into CSV, SQLite, Postgresql, Big Query and Datasette formats.")]),e._v(" "),a("p",[a("img",{attrs:{src:"https://user-images.githubusercontent.com/74717970/179058338-08ce8ea1-9b1f-4c4c-b59c-64b04cd450f6.png",alt:"img-1-flatterer"}})]),e._v(" "),a("p",[e._v("Alongside converting the data into different formats, Flatterer has created a data dictionary that shows the fields contained in the dataset, alongside the field type and field counts. In the example below, we show how this dictionary interprets person_statement fields contained in the Beneficial Ownership Data Standard.")]),e._v(" "),a("p",[a("img",{attrs:{src:"https://user-images.githubusercontent.com/74717970/179058526-19694210-514e-4215-bf9d-f6abc7ef5400.png",alt:"img-2-flatterer"}})]),e._v(" "),a("p",[e._v("Finally, you can see Flatterer has created special _link fields, to help with joining the tables together. The example below shows how the _link field helps join "),a("a",{attrs:{href:"https://medium.com/opendatacoop/why-do-open-organisational-identifiers-matter-46af05ab30a",target:"_blank",rel:"noopener noreferrer"}},[e._v("entity identifiers"),a("OutboundLink")],1),e._v(" to statements about beneficial ownership.")]),e._v(" "),a("p",[a("img",{attrs:{src:"https://user-images.githubusercontent.com/74717970/179058657-ae4ab534-9fdb-4d6d-ad59-56521f0218e0.png",alt:"img-3-flatterer"}})]),e._v(" "),a("h2",{attrs:{id:"what-s-next"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#what-s-next"}},[e._v("#")]),e._v(" What’s next?")]),e._v(" "),a("p",[e._v("Next, we’ll be working to make Flatterer more user friendly. We’ll be exploring creating a desktop interface, improving type guessing for fields, and giving more summary statistics about the input data. We welcome feedback on the tool through "),a("a",{attrs:{href:"https://github.com/kindly/flatterer/issues",target:"_blank",rel:"noopener noreferrer"}},[e._v("GitHub"),a("OutboundLink")],1),e._v(", and are really interested to find out what kind of improvements you’d like to see.")]),e._v(" "),a("p",[e._v("More information about using Flatterer is available on "),a("a",{attrs:{href:"https://deepnote.com/@david-raznick/Flatterer-Demo-FWeGccp_QKCu1WAEGQ0mEQ",target:"_blank",rel:"noopener noreferrer"}},[e._v("deepnote"),a("OutboundLink")],1),e._v(". To hear more about Flatterer, you can join David Raznick at Frictionless Data’s monthly community call on July 28th.")]),e._v(" "),a("h4",{attrs:{id:"at-open-data-services-cooperative-we-re-always-happy-to-discuss-how-developing-or-implementing-open-data-standards-could-support-your-goals-find-out-more-about-our-work-and-get-in-touch"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#at-open-data-services-cooperative-we-re-always-happy-to-discuss-how-developing-or-implementing-open-data-standards-could-support-your-goals-find-out-more-about-our-work-and-get-in-touch"}},[e._v("#")]),e._v(" At Open Data Services Cooperative we’re always happy to discuss how developing or implementing open data standards could support your goals. Find out more about "),a("a",{attrs:{href:"https://opendataservices.coop/",target:"_blank",rel:"noopener noreferrer"}},[e._v("our work"),a("OutboundLink")],1),e._v(" and "),a("a",{attrs:{href:"https://opendataservices.coop/#contact",target:"_blank",rel:"noopener noreferrer"}},[e._v("get in touch"),a("OutboundLink")],1),e._v(".")])])}),[],!1,null,null,null);t.default=n.exports}}]); \ No newline at end of file diff --git a/assets/js/163.1b796da3.js b/assets/js/163.ddd07aeb.js similarity index 98% rename from assets/js/163.1b796da3.js rename to assets/js/163.ddd07aeb.js index 805b6d024..8fca9beda 100644 --- a/assets/js/163.1b796da3.js +++ b/assets/js/163.ddd07aeb.js @@ -1 +1 @@ -(window.webpackJsonp=window.webpackJsonp||[]).push([[163],{699:function(e,t,o){"use strict";o.r(t);var a=o(29),n=Object(a.a)({},(function(){var e=this,t=e.$createElement,o=e._self._c||t;return o("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[o("p",[e._v("Dear Frictionless community,")]),e._v(" "),o("p",[e._v("I’m writing to let you all know that this is my final week working on Frictionless Data with Open Knowledge Foundation. It has been a true pleasure to get to interact with you all over the last four years! Rest assured that Frictionless Data is in good hands with the team at Open Knowledge (Evgeny, Sara, Shashi, Edgar, and the rest of the OKF tech team).")]),e._v(" "),o("p",[e._v("What’s next for me? I’m still staying in the data space, moving to product at data.world (did you know they export data as datapackages?)! Maybe you’ll see me presenting a demo at an upcoming Frictionless community call 😉")]),e._v(" "),o("p",[e._v("If you’ll allow me to reminisce for a few minutes, here are some of my favourite Frictionless memories from my time working on this project:")]),e._v(" "),o("p",[o("strong",[e._v("The Frictionless Hackathon:")]),e._v(" In October 2021, we hosted the first-ever Frictionless Hackathon (virtually of course), and it was so cool to see all the projects and contributors from around the world! You can read all about it in "),o("a",{attrs:{href:"https://frictionlessdata.io/blog/2021/10/13/hackathon-wrap/",target:"_blank",rel:"noopener noreferrer"}},[e._v("the summary blog here"),o("OutboundLink")],1),e._v(". Should we do another Hackathon? Let Sara know what you think! (Special shout-out to Oleg who set up the Hackathon software and inspired the entire event!)")]),e._v(" "),o("p",[o("strong",[e._v("Pilot collaborations")]),e._v(": We started our first Reproducible Research pilot collaboration with the Biological and Chemical Oceanographic Data Management Office (BCO-DMO) team in 2019, and learned so much from this implementation! This resulted in a new data processing pipeline for BCO-DMO data managers that used Frictionless to reproducibly clean and document data. This work ultimately led to the creation of the Frictionless Framework. You can check out all the other "),o("a",{attrs:{href:"https://frictionlessdata.io/adoption/#pilot-collaborations",target:"_blank",rel:"noopener noreferrer"}},[e._v("Pilots on the Adoption page"),o("OutboundLink")],1),e._v(" too.")]),e._v(" "),o("p",[o("strong",[e._v("Fellows")]),e._v(": Getting to mentor and teach 17 Fellows was truly a spectacular experience. These current (and future) leaders in open science and open scholarship are people to keep an eye on – they are brilliant! You can read all about their experience as Fellows on "),o("a",{attrs:{href:"https://fellows.frictionlessdata.io/blog/",target:"_blank",rel:"noopener noreferrer"}},[e._v("their blog"),o("OutboundLink")],1),e._v(".")]),e._v(" "),o("p",[o("strong",[e._v("The Frictionless Team at OKF")]),e._v(": I’ve been very lucky to get to work with the best team while being at OKF! Many of you already know how helpful and smart my colleagues are, but in case you don’t know, I will tell you! Evgeny has been carefully leading the technical development of Frictionless with a clear vision, making my job easy and fun. Sara has transformed how the community feels and works, which is no small feat! Shashi and Edgar have only been working on the project for less than a year, but their contributions to the code base and to help answer questions have already made a big impact! I will miss working with these excellent humans, and all of you in the community that have made Frictionless a special place!")]),e._v(" "),o("p",[e._v("Thank you all for being a part of the Frictionless community and for working with me in the past! I wish you all the best, and maybe I will see some of you in Buenos Aires in April for "),o("a",{attrs:{href:"https://csvconf.com/",target:"_blank",rel:"noopener noreferrer"}},[e._v("csv,conf,v7"),o("OutboundLink")],1),e._v("?")]),e._v(" "),o("p",[e._v("Cheers!")]),e._v(" "),o("p",[e._v("– "),o("a",{attrs:{href:"https://lwinfree.github.io/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Lilly"),o("OutboundLink")],1)])])}),[],!1,null,null,null);t.default=n.exports}}]); \ No newline at end of file +(window.webpackJsonp=window.webpackJsonp||[]).push([[163],{697:function(e,t,o){"use strict";o.r(t);var a=o(29),n=Object(a.a)({},(function(){var e=this,t=e.$createElement,o=e._self._c||t;return o("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[o("p",[e._v("Dear Frictionless community,")]),e._v(" "),o("p",[e._v("I’m writing to let you all know that this is my final week working on Frictionless Data with Open Knowledge Foundation. It has been a true pleasure to get to interact with you all over the last four years! Rest assured that Frictionless Data is in good hands with the team at Open Knowledge (Evgeny, Sara, Shashi, Edgar, and the rest of the OKF tech team).")]),e._v(" "),o("p",[e._v("What’s next for me? I’m still staying in the data space, moving to product at data.world (did you know they export data as datapackages?)! Maybe you’ll see me presenting a demo at an upcoming Frictionless community call 😉")]),e._v(" "),o("p",[e._v("If you’ll allow me to reminisce for a few minutes, here are some of my favourite Frictionless memories from my time working on this project:")]),e._v(" "),o("p",[o("strong",[e._v("The Frictionless Hackathon:")]),e._v(" In October 2021, we hosted the first-ever Frictionless Hackathon (virtually of course), and it was so cool to see all the projects and contributors from around the world! You can read all about it in "),o("a",{attrs:{href:"https://frictionlessdata.io/blog/2021/10/13/hackathon-wrap/",target:"_blank",rel:"noopener noreferrer"}},[e._v("the summary blog here"),o("OutboundLink")],1),e._v(". Should we do another Hackathon? Let Sara know what you think! (Special shout-out to Oleg who set up the Hackathon software and inspired the entire event!)")]),e._v(" "),o("p",[o("strong",[e._v("Pilot collaborations")]),e._v(": We started our first Reproducible Research pilot collaboration with the Biological and Chemical Oceanographic Data Management Office (BCO-DMO) team in 2019, and learned so much from this implementation! This resulted in a new data processing pipeline for BCO-DMO data managers that used Frictionless to reproducibly clean and document data. This work ultimately led to the creation of the Frictionless Framework. You can check out all the other "),o("a",{attrs:{href:"https://frictionlessdata.io/adoption/#pilot-collaborations",target:"_blank",rel:"noopener noreferrer"}},[e._v("Pilots on the Adoption page"),o("OutboundLink")],1),e._v(" too.")]),e._v(" "),o("p",[o("strong",[e._v("Fellows")]),e._v(": Getting to mentor and teach 17 Fellows was truly a spectacular experience. These current (and future) leaders in open science and open scholarship are people to keep an eye on – they are brilliant! You can read all about their experience as Fellows on "),o("a",{attrs:{href:"https://fellows.frictionlessdata.io/blog/",target:"_blank",rel:"noopener noreferrer"}},[e._v("their blog"),o("OutboundLink")],1),e._v(".")]),e._v(" "),o("p",[o("strong",[e._v("The Frictionless Team at OKF")]),e._v(": I’ve been very lucky to get to work with the best team while being at OKF! Many of you already know how helpful and smart my colleagues are, but in case you don’t know, I will tell you! Evgeny has been carefully leading the technical development of Frictionless with a clear vision, making my job easy and fun. Sara has transformed how the community feels and works, which is no small feat! Shashi and Edgar have only been working on the project for less than a year, but their contributions to the code base and to help answer questions have already made a big impact! I will miss working with these excellent humans, and all of you in the community that have made Frictionless a special place!")]),e._v(" "),o("p",[e._v("Thank you all for being a part of the Frictionless community and for working with me in the past! I wish you all the best, and maybe I will see some of you in Buenos Aires in April for "),o("a",{attrs:{href:"https://csvconf.com/",target:"_blank",rel:"noopener noreferrer"}},[e._v("csv,conf,v7"),o("OutboundLink")],1),e._v("?")]),e._v(" "),o("p",[e._v("Cheers!")]),e._v(" "),o("p",[e._v("– "),o("a",{attrs:{href:"https://lwinfree.github.io/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Lilly"),o("OutboundLink")],1)])])}),[],!1,null,null,null);t.default=n.exports}}]); \ No newline at end of file diff --git a/assets/js/164.04959c01.js b/assets/js/164.98393809.js similarity index 98% rename from assets/js/164.04959c01.js rename to assets/js/164.98393809.js index 05c9a5c8a..64e8fb313 100644 --- a/assets/js/164.04959c01.js +++ b/assets/js/164.98393809.js @@ -1 +1 @@ -(window.webpackJsonp=window.webpackJsonp||[]).push([[164],{701:function(e,t,a){"use strict";a.r(t);var r=a(29),o=Object(r.a)({},(function(){var e=this,t=e.$createElement,a=e._self._c||t;return a("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[a("p",[e._v("On the last community call on July 28"),a("sup",[e._v("th")]),e._v(", we heard David Raznick (an ex OKFer, now working at "),a("a",{attrs:{href:"https://opendataservices.coop/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Open Data Services"),a("OutboundLink")],1),e._v(") presenting Flatterer, a tool he developed to convert structured JSON data into tabular data, using Frictionless Data specifications.")]),e._v(" "),a("p",[e._v("David has been working with many different open data standards functioning with deeply nested JSON. To make the data in standard formats more human readable, users often flatten JSON files with flattening tools, but the result they get are very large spreadsheets, which can be difficult to work with.")]),e._v(" "),a("p",[e._v("Flattening tools are also often used to unflatten tabular data in JSON. That way, the data, initially written in a more human readable format, can then be used according to the standards. Unfortunately the result is not optimal, the output of flattening tools is often not user-friendly and the user would probably still need to tweak it by hand, for example modifying headers’ names and/or the way tables are joined together.")]),e._v(" "),a("p",[e._v("Flatterer aims at making these processes easier and faster. It can convert in the blink of an eye your JSON file in the tabular format of your choice: csv, xlsx, parquet, postgres and sqlite. Flatterer will convert your JSON file into a main table, with keys to link one-to-many tables to their parents. That way the data is tidy and easier to work with.")]),e._v(" "),a("p",[e._v("If you are interested in knowing more about Flatterer, have a look at David’s presentation and demo:")]),e._v(" "),a("iframe",{attrs:{width:"560",height:"315",src:"https://www.youtube.com/embed/Hi9tDGfteoA",title:"YouTube video player",frameborder:"0",allow:"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture",allowfullscreen:""}}),e._v(" "),a("p",[e._v("You can also read more about the project here: "),a("a",{attrs:{href:"https://flatterer.opendata.coop/",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://flatterer.opendata.coop/"),a("OutboundLink")],1),e._v(", or have a look at "),a("a",{attrs:{href:"https://deepnote.com/@david-raznick/Flatterer-Demo-15678671-ca7f-40a0-aed5-6004190d2611",target:"_blank",rel:"noopener noreferrer"}},[e._v("the project documentation"),a("OutboundLink")],1),e._v(".")]),e._v(" "),a("h1",{attrs:{id:"join-us-next-month"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#join-us-next-month"}},[e._v("#")]),e._v(" Join us next month!")]),e._v(" "),a("p",[e._v("Next community call is on August 25"),a("sup",[e._v("th")]),e._v(". Frictionless Data developer Shashi Gharti will discuss with the community a tool she would like to add to the Frictionless Framework. Stay tuned to know more!")]),e._v(" "),a("p",[e._v("You can sign up for the call already "),a("a",{attrs:{href:"https://docs.google.com/forms/d/e/1FAIpQLSeuNCopxXauMkrWvF6VHqOyHMcy54SfNDOseVXfWRQZWkvqjQ/viewform?usp=sf_link",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),a("OutboundLink")],1),e._v(".")]),e._v(" "),a("p",[e._v("Do you want to share something with the community? Let us know when you sign up.")]),e._v(" "),a("p",[e._v("Would you like to present at one of the next community calls? Please fill out "),a("a",{attrs:{href:"https://forms.gle/AWpbxyiGESNSUFK2A",target:"_blank",rel:"noopener noreferrer"}},[e._v("this form"),a("OutboundLink")],1),e._v(".")]),e._v(" "),a("p",[e._v("Join our community on "),a("a",{attrs:{href:"https://join.slack.com/t/frictionlessdata/shared_invite/zt-17kpbffnm-tRfDW_wJgOw8tJVLvZTrBg",target:"_blank",rel:"noopener noreferrer"}},[e._v("Slack"),a("OutboundLink")],1),e._v(" (also via "),a("a",{attrs:{href:"https://matrix.to/#/#frictionlessdata:matrix.okfn.org",target:"_blank",rel:"noopener noreferrer"}},[e._v("Matrix"),a("OutboundLink")],1),e._v(") or "),a("a",{attrs:{href:"https://twitter.com/frictionlessd8a",target:"_blank",rel:"noopener noreferrer"}},[e._v("Twitter"),a("OutboundLink")],1),e._v(". See you there!")]),e._v(" "),a("h1",{attrs:{id:"call-recording"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#call-recording"}},[e._v("#")]),e._v(" Call Recording")]),e._v(" "),a("p",[e._v("On a final note, here is the recording of the full call:")]),e._v(" "),a("iframe",{attrs:{width:"560",height:"315",src:"https://www.youtube.com/embed/hfGT6vAjjwU",title:"YouTube video player",frameborder:"0",allow:"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture",allowfullscreen:""}})])}),[],!1,null,null,null);t.default=o.exports}}]); \ No newline at end of file +(window.webpackJsonp=window.webpackJsonp||[]).push([[164],{699:function(e,t,a){"use strict";a.r(t);var r=a(29),o=Object(r.a)({},(function(){var e=this,t=e.$createElement,a=e._self._c||t;return a("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[a("p",[e._v("On the last community call on July 28"),a("sup",[e._v("th")]),e._v(", we heard David Raznick (an ex OKFer, now working at "),a("a",{attrs:{href:"https://opendataservices.coop/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Open Data Services"),a("OutboundLink")],1),e._v(") presenting Flatterer, a tool he developed to convert structured JSON data into tabular data, using Frictionless Data specifications.")]),e._v(" "),a("p",[e._v("David has been working with many different open data standards functioning with deeply nested JSON. To make the data in standard formats more human readable, users often flatten JSON files with flattening tools, but the result they get are very large spreadsheets, which can be difficult to work with.")]),e._v(" "),a("p",[e._v("Flattening tools are also often used to unflatten tabular data in JSON. That way, the data, initially written in a more human readable format, can then be used according to the standards. Unfortunately the result is not optimal, the output of flattening tools is often not user-friendly and the user would probably still need to tweak it by hand, for example modifying headers’ names and/or the way tables are joined together.")]),e._v(" "),a("p",[e._v("Flatterer aims at making these processes easier and faster. It can convert in the blink of an eye your JSON file in the tabular format of your choice: csv, xlsx, parquet, postgres and sqlite. Flatterer will convert your JSON file into a main table, with keys to link one-to-many tables to their parents. That way the data is tidy and easier to work with.")]),e._v(" "),a("p",[e._v("If you are interested in knowing more about Flatterer, have a look at David’s presentation and demo:")]),e._v(" "),a("iframe",{attrs:{width:"560",height:"315",src:"https://www.youtube.com/embed/Hi9tDGfteoA",title:"YouTube video player",frameborder:"0",allow:"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture",allowfullscreen:""}}),e._v(" "),a("p",[e._v("You can also read more about the project here: "),a("a",{attrs:{href:"https://flatterer.opendata.coop/",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://flatterer.opendata.coop/"),a("OutboundLink")],1),e._v(", or have a look at "),a("a",{attrs:{href:"https://deepnote.com/@david-raznick/Flatterer-Demo-15678671-ca7f-40a0-aed5-6004190d2611",target:"_blank",rel:"noopener noreferrer"}},[e._v("the project documentation"),a("OutboundLink")],1),e._v(".")]),e._v(" "),a("h1",{attrs:{id:"join-us-next-month"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#join-us-next-month"}},[e._v("#")]),e._v(" Join us next month!")]),e._v(" "),a("p",[e._v("Next community call is on August 25"),a("sup",[e._v("th")]),e._v(". Frictionless Data developer Shashi Gharti will discuss with the community a tool she would like to add to the Frictionless Framework. Stay tuned to know more!")]),e._v(" "),a("p",[e._v("You can sign up for the call already "),a("a",{attrs:{href:"https://docs.google.com/forms/d/e/1FAIpQLSeuNCopxXauMkrWvF6VHqOyHMcy54SfNDOseVXfWRQZWkvqjQ/viewform?usp=sf_link",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),a("OutboundLink")],1),e._v(".")]),e._v(" "),a("p",[e._v("Do you want to share something with the community? Let us know when you sign up.")]),e._v(" "),a("p",[e._v("Would you like to present at one of the next community calls? Please fill out "),a("a",{attrs:{href:"https://forms.gle/AWpbxyiGESNSUFK2A",target:"_blank",rel:"noopener noreferrer"}},[e._v("this form"),a("OutboundLink")],1),e._v(".")]),e._v(" "),a("p",[e._v("Join our community on "),a("a",{attrs:{href:"https://join.slack.com/t/frictionlessdata/shared_invite/zt-17kpbffnm-tRfDW_wJgOw8tJVLvZTrBg",target:"_blank",rel:"noopener noreferrer"}},[e._v("Slack"),a("OutboundLink")],1),e._v(" (also via "),a("a",{attrs:{href:"https://matrix.to/#/#frictionlessdata:matrix.okfn.org",target:"_blank",rel:"noopener noreferrer"}},[e._v("Matrix"),a("OutboundLink")],1),e._v(") or "),a("a",{attrs:{href:"https://twitter.com/frictionlessd8a",target:"_blank",rel:"noopener noreferrer"}},[e._v("Twitter"),a("OutboundLink")],1),e._v(". See you there!")]),e._v(" "),a("h1",{attrs:{id:"call-recording"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#call-recording"}},[e._v("#")]),e._v(" Call Recording")]),e._v(" "),a("p",[e._v("On a final note, here is the recording of the full call:")]),e._v(" "),a("iframe",{attrs:{width:"560",height:"315",src:"https://www.youtube.com/embed/hfGT6vAjjwU",title:"YouTube video player",frameborder:"0",allow:"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture",allowfullscreen:""}})])}),[],!1,null,null,null);t.default=o.exports}}]); \ No newline at end of file diff --git a/assets/js/165.56161c6a.js b/assets/js/165.98cc2fc2.js similarity index 99% rename from assets/js/165.56161c6a.js rename to assets/js/165.98cc2fc2.js index c4c622f5a..efb5e8436 100644 --- a/assets/js/165.56161c6a.js +++ b/assets/js/165.98cc2fc2.js @@ -1 +1 @@ -(window.webpackJsonp=window.webpackJsonp||[]).push([[165],{700:function(t,e,a){"use strict";a.r(e);var r=a(29),s=Object(r.a)({},(function(){var t=this,e=t.$createElement,a=t._self._c||e;return a("ContentSlotsDistributor",{attrs:{"slot-key":t.$parent.slotKey}},[a("p",[t._v("We’re releasing a first beta of Firctionless Framework (v5)!"),a("br"),t._v("\nSince the initial Frictionless Framework release we’d been collecting feedback and analyzing both high-level users’ needs and bug reports to identify shortcomings and areas that can be improved in the next version for the framework. Once that process had been done we started working on a new v5 with a goal to make the framework more bullet-proof, easy to maintain and simplify user interface. Today, this version is almost stable and ready to be published. Let’s go through the main improvements we have made:")]),t._v(" "),a("h1",{attrs:{id:"improved-metadata"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#improved-metadata"}},[t._v("#")]),t._v(" Improved Metadata")]),t._v(" "),a("p",[t._v("This year we started working on the Frictionless Application, at the same time, we were thinking about next steps for the "),a("a",{attrs:{href:"https://specs.frictionlessdata.io/",target:"_blank",rel:"noopener noreferrer"}},[t._v("Frictionless Standards"),a("OutboundLink")],1),t._v(". For both we need well-defined and an easy-to-understand metadata model. Partially it’s already published as standards like Table Schema and partially it’s going to be published as standards like File Dialect and possibly validation/transform metadata.")]),t._v(" "),a("h2",{attrs:{id:"dialect"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#dialect"}},[t._v("#")]),t._v(" Dialect")]),t._v(" "),a("p",[t._v("In v4 of the framework we had Control/Dialect/Layout concepts to describe resource details related to different formats and schemes, as well as tabular details like header rows. In v5 it’s merged into the only one concept called Dialect which is going to be standardised as a File Dialect spec. Here is an example:")]),t._v(" "),a("h4",{attrs:{id:"yaml"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#yaml"}},[t._v("#")]),t._v(" YAML")]),t._v(" "),a("div",{staticClass:"language-r extra-class"},[a("pre",{pre:!0,attrs:{class:"language-r"}},[a("code",[t._v("header"),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v(":")]),t._v(" true\nheaderRows"),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v(":")]),t._v(" "),a("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("[")]),a("span",{pre:!0,attrs:{class:"token number"}},[t._v("2")]),a("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v(" "),a("span",{pre:!0,attrs:{class:"token number"}},[t._v("3")]),a("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("]")]),t._v("\ncommentChar"),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v(":")]),t._v(" "),a("span",{pre:!0,attrs:{class:"token string"}},[t._v("'#'")]),t._v("\ncsv"),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v(":")]),t._v("\n delimiter"),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v(":")]),t._v(" "),a("span",{pre:!0,attrs:{class:"token string"}},[t._v("';'")]),t._v("\n")])])]),a("p",[t._v("A dialect descriptor can be saved and reused within a resource. Technically, it’s possible to provide different schemes and formats settings within one Dialect (e.g. for CSV and Excel) so it’s possible to create e.g. one re-usable dialect for a data package. A legacy CSV Dialect spec is supported and will be supported forever so it’s possible to provide CSV properties on the root level:")]),t._v(" "),a("h4",{attrs:{id:"yaml-2"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#yaml-2"}},[t._v("#")]),t._v(" YAML")]),t._v(" "),a("div",{staticClass:"language-r extra-class"},[a("pre",{pre:!0,attrs:{class:"language-r"}},[a("code",[t._v("header"),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v(":")]),t._v(" true\ndelimiter"),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v(":")]),t._v(" "),a("span",{pre:!0,attrs:{class:"token string"}},[t._v("';'")]),t._v("\n")])])]),a("p",[t._v("For performance and codebase maintainability reasons some marginal Layout features have been removed completely such as "),a("code",[t._v("skip/pick/limit/offsetFields/etc")]),t._v(". It’s possible to achieve the same results using the Pipeline concept as a part of the transformation workflow.")]),t._v(" "),a("p",[t._v("Read an article about "),a("a",{attrs:{href:"https://framework.frictionlessdata.io/docs/framework/dialect.html",target:"_blank",rel:"noopener noreferrer"}},[t._v("Dialect Class"),a("OutboundLink")],1),t._v(" for more information.")]),t._v(" "),a("h2",{attrs:{id:"checklist"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#checklist"}},[t._v("#")]),t._v(" Checklist")]),t._v(" "),a("p",[t._v("Checklist is a new concept introduced in v5. It’s basically a collection of validation steps and a few other settings to make “validation rules” sharable. For example:")]),t._v(" "),a("h4",{attrs:{id:"yaml-3"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#yaml-3"}},[t._v("#")]),t._v(" YAML")]),t._v(" "),a("div",{staticClass:"language-r extra-class"},[a("pre",{pre:!0,attrs:{class:"language-r"}},[a("code",[t._v("checks"),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v(":")]),t._v("\n "),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v("-")]),t._v(" type"),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v(":")]),t._v(" ascii"),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v("-")]),t._v("value\n "),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v("-")]),t._v(" type"),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v(":")]),t._v(" row_constraint\n formula"),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v(":")]),t._v(" id "),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v(">")]),t._v(" "),a("span",{pre:!0,attrs:{class:"token number"}},[t._v("1")]),t._v("\nskipErrors"),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v(":")]),t._v("\n "),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v("-")]),t._v(" duplicate"),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v("-")]),t._v("label\n")])])]),a("p",[t._v("Having and sharing this checklist it’s possible to tune data quality requirements for some data file or set of data files. This concept will provide an ability for creating data quality “libraries” within projects or domains. We can use a checklist for validation:")]),t._v(" "),a("h4",{attrs:{id:"cli"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#cli"}},[t._v("#")]),t._v(" CLI")]),t._v(" "),a("div",{staticClass:"language-r extra-class"},[a("pre",{pre:!0,attrs:{class:"language-r"}},[a("code",[t._v("frictionless validate table1.csv "),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v("-")]),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v("-")]),t._v("checklist checklist.yaml\nfrictionless validate table2.csv "),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v("-")]),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v("-")]),t._v("checklist checklist.yaml\n")])])]),a("p",[t._v("Here is a list of another changes:")]),t._v(" "),a("table",[a("thead",[a("tr",[a("th",[t._v("From (v4)")]),t._v(" "),a("th",[t._v("To (v5)")])])]),t._v(" "),a("tbody",[a("tr",[a("td",[t._v("Check(descriptor)")]),t._v(" "),a("td",[t._v("Check.from_descriptor(descriptor)")])]),t._v(" "),a("tr",[a("td",[t._v("check.code")]),t._v(" "),a("td",[t._v("check.type")])])])]),t._v(" "),a("p",[t._v("Read an article about "),a("a",{attrs:{href:"https://framework.frictionlessdata.io/docs/framework/checklist.html",target:"_blank",rel:"noopener noreferrer"}},[t._v("Checklist Class"),a("OutboundLink")],1),t._v(" for more information.")]),t._v(" "),a("h2",{attrs:{id:"pipeline"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#pipeline"}},[t._v("#")]),t._v(" Pipeline")]),t._v(" "),a("p",[t._v("In v4 Pipeline was a complex concept similar to validation Inquiry. We reworked it for v5 to be a lightweight set of validation steps that can be applied to a data resource or a data package. For example:")]),t._v(" "),a("h4",{attrs:{id:"yaml-4"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#yaml-4"}},[t._v("#")]),t._v(" YAML")]),t._v(" "),a("div",{staticClass:"language-r extra-class"},[a("pre",{pre:!0,attrs:{class:"language-r"}},[a("code",[t._v("steps"),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v(":")]),t._v("\n "),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v("-")]),t._v(" type"),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v(":")]),t._v(" table"),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v("-")]),t._v("normalize\n "),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v("-")]),t._v(" type"),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v(":")]),t._v(" cell"),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v("-")]),t._v("set\n fieldName"),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v(":")]),t._v(" version\n value"),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v(":")]),t._v(" v5\n")])])]),a("p",[t._v("Similar to the Checklist concept, Pipeline is a reusable (data-abstract) object that can be saved to a descriptor and used in some complex data workflow:")]),t._v(" "),a("h4",{attrs:{id:"cli-2"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#cli-2"}},[t._v("#")]),t._v(" CLI")]),t._v(" "),a("div",{staticClass:"language-r extra-class"},[a("pre",{pre:!0,attrs:{class:"language-r"}},[a("code",[t._v("frictionless transform table1.csv "),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v("-")]),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v("-")]),t._v("pipeline pipeline.yaml\nfrictionless transform table2.csv "),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v("-")]),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v("-")]),t._v("pipeline pipeline.yaml\n")])])]),a("p",[t._v("Here is another list of changes:")]),t._v(" "),a("table",[a("thead",[a("tr",[a("th",[t._v("From (v4)")]),t._v(" "),a("th",[t._v("To (v5)")])])]),t._v(" "),a("tbody",[a("tr",[a("td",[t._v("Step(descriptor)")]),t._v(" "),a("td",[t._v("Step.from_descriptor(descriptor)")])]),t._v(" "),a("tr",[a("td",[t._v("step.code")]),t._v(" "),a("td",[t._v("step.type")])])])]),t._v(" "),a("p",[t._v("Read an article about "),a("a",{attrs:{href:"https://framework.frictionlessdata.io/docs/framework/pipeline.html",target:"_blank",rel:"noopener noreferrer"}},[t._v("Pipeline Class"),a("OutboundLink")],1),t._v(" for more information.")]),t._v(" "),a("h2",{attrs:{id:"resource"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#resource"}},[t._v("#")]),t._v(" Resource")]),t._v(" "),a("p",[t._v("There are no changes in the Resource related to the standards although currently by default instead of "),a("code",[t._v("profile")]),t._v(" the "),a("code",[t._v("type")]),t._v(" property will be used to mark a resource as a table. It can be changed using the "),a("code",[t._v("--standards v1")]),t._v(" flag.")]),t._v(" "),a("p",[t._v("It’s now possible to set Checklist and Pipeline as a Resource property similar to Dialect and Schema:")]),t._v(" "),a("h4",{attrs:{id:"yaml-5"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#yaml-5"}},[t._v("#")]),t._v(" YAML")]),t._v(" "),a("div",{staticClass:"language-r extra-class"},[a("pre",{pre:!0,attrs:{class:"language-r"}},[a("code",[t._v("path"),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v(":")]),t._v(" table.csv\n"),a("span",{pre:!0,attrs:{class:"token comment"}},[t._v("# ...")]),t._v("\nchecklist"),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v(":")]),t._v("\n checks"),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v(":")]),t._v("\n "),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v("-")]),t._v(" type"),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v(":")]),t._v(" ascii"),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v("-")]),t._v("value\n "),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v("-")]),t._v(" type"),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v(":")]),t._v(" row_constraint\n formula"),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v(":")]),t._v(" id "),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v(">")]),t._v(" "),a("span",{pre:!0,attrs:{class:"token number"}},[t._v("1")]),t._v("\npipeline"),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v(":")]),t._v(" pipeline.yaml\n steps"),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v(":")]),t._v("\n "),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v("-")]),t._v(" type"),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v(":")]),t._v(" table"),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v("-")]),t._v("normalize\n "),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v("-")]),t._v(" type"),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v(":")]),t._v(" cell"),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v("-")]),t._v("set\n fieldName"),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v(":")]),t._v(" version\n value"),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v(":")]),t._v(" v5\n")])])]),a("p",[t._v("Or using dereference:")]),t._v(" "),a("h4",{attrs:{id:"yaml-6"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#yaml-6"}},[t._v("#")]),t._v(" YAML")]),t._v(" "),a("div",{staticClass:"language-r extra-class"},[a("pre",{pre:!0,attrs:{class:"language-r"}},[a("code",[t._v("path"),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v(":")]),t._v(" table.csv\n"),a("span",{pre:!0,attrs:{class:"token comment"}},[t._v("# ...")]),t._v("\nchecklist"),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v(":")]),t._v(" checklist.yaml\npipeline"),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v(":")]),t._v(" pipeline.yaml\n")])])]),a("p",[t._v("In this case the validation/transformation will use it by default providing an ability to ship validation rules and transformation pipelines within resources and packages. This is an important development for data publishers who want to define what they consider to be valid for their datasets as well as sharing raw data with a cleaning pipeline steps:")]),t._v(" "),a("h4",{attrs:{id:"cli-3"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#cli-3"}},[t._v("#")]),t._v(" CLI")]),t._v(" "),a("div",{staticClass:"language-r extra-class"},[a("pre",{pre:!0,attrs:{class:"language-r"}},[a("code",[t._v("frictionless validate resource.yaml "),a("span",{pre:!0,attrs:{class:"token comment"}},[t._v("# will use the checklist above")]),t._v("\nfrictionless transform resource.yaml "),a("span",{pre:!0,attrs:{class:"token comment"}},[t._v("# will use the pipeline above")]),t._v("\n")])])]),a("p",[t._v("There are minor changes in the "),a("code",[t._v("stats")]),t._v(" property. Now it uses named keys to simplify hash distinction (md5/sha256 are calculated by default and it’s not possible to change for performance reasons as it was in v4):")]),t._v(" "),a("h4",{attrs:{id:"python"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#python"}},[t._v("#")]),t._v(" Python")]),t._v(" "),a("div",{staticClass:"language-r extra-class"},[a("pre",{pre:!0,attrs:{class:"language-r"}},[a("code",[t._v("from frictionless import describe\n\nresource "),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" describe"),a("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),a("span",{pre:!0,attrs:{class:"token string"}},[t._v("'table.csv'")]),a("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v(" stats"),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v("True"),a("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\nprint"),a("span",{pre:!0,attrs:{class:"token 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row in resource:")]),t._v(" "),a("td",[t._v("for row in resource.row_stream")])])])]),t._v(" "),a("p",[t._v("Read an article about "),a("a",{attrs:{href:"https://framework.frictionlessdata.io/docs/framework/resource.html",target:"_blank",rel:"noopener noreferrer"}},[t._v("Resource Class"),a("OutboundLink")],1),t._v(" for more information.")]),t._v(" "),a("h2",{attrs:{id:"package"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#package"}},[t._v("#")]),t._v(" Package")]),t._v(" "),a("p",[t._v("There are no changes in the Package related to the standards although it’s now possible to use resource dereference:")]),t._v(" "),a("h4",{attrs:{id:"yaml-7"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#yaml-7"}},[t._v("#")]),t._v(" YAML")]),t._v(" "),a("div",{staticClass:"language-r extra-class"},[a("pre",{pre:!0,attrs:{class:"language-r"}},[a("code",[t._v("name"),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v(":")]),t._v(" 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It means that they use the type property to provide type information:")]),t._v(" "),a("table",[a("thead",[a("tr",[a("th",[t._v("From (v4)")]),t._v(" "),a("th",[t._v("To (v5)")])])]),t._v(" "),a("tbody",[a("tr",[a("td",[t._v("resource.profile")]),t._v(" "),a("td",[t._v("resource.type")])]),t._v(" "),a("tr",[a("td",[t._v("check.code")]),t._v(" "),a("td",[t._v("check.type")])]),t._v(" "),a("tr",[a("td",[t._v("control.code")]),t._v(" "),a("td",[t._v("control.type")])]),t._v(" "),a("tr",[a("td",[t._v("error.code")]),t._v(" "),a("td",[t._v("error.type")])]),t._v(" "),a("tr",[a("td",[t._v("field.type")]),t._v(" "),a("td",[t._v("field.type")])]),t._v(" "),a("tr",[a("td",[t._v("step.type")]),t._v(" "),a("td",[t._v("step.type")])])])]),t._v(" "),a("p",[t._v("The new v5 version still supports old notation in descriptors for backward-compatibility.")]),t._v(" "),a("h1",{attrs:{id:"improved-model"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#improved-model"}},[t._v("#")]),t._v(" Improved Model")]),t._v(" "),a("p",[t._v("It’s been many years that Frictionless were mixing declarative metadata and object model for historical reasons. 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So for example here is a Table Schema:")]),t._v(" "),a("h4",{attrs:{id:"yaml-9"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#yaml-9"}},[t._v("#")]),t._v(" YAML")]),t._v(" "),a("div",{staticClass:"language-r extra-class"},[a("pre",{pre:!0,attrs:{class:"language-r"}},[a("code",[t._v("fields"),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v(":")]),t._v("\n "),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v("-")]),t._v(" name"),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v(":")]),t._v(" id\n type"),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v(":")]),t._v(" integer\n "),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v("-")]),t._v(" name"),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v(":")]),t._v(" name\n type"),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v(":")]),t._v(" string\n")])])]),a("h2",{attrs:{id:"object-model"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#object-model"}},[t._v("#")]),t._v(" Object Model")]),t._v(" "),a("p",[t._v("The difference comes here we we create a metadata instance based on this descriptor. In v4 all the metadata classes were a subclasses of the dict class providing a mix between a descriptor and object model for state management. In v5 there is a clear boundary between descriptor and object model. All the state are managed as it should be in a normal Python class using class attributes:")]),t._v(" "),a("h4",{attrs:{id:"python-3"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#python-3"}},[t._v("#")]),t._v(" Python")]),t._v(" "),a("div",{staticClass:"language-r extra-class"},[a("pre",{pre:!0,attrs:{class:"language-r"}},[a("code",[t._v("from frictionless import Schema\n\nschema "),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" Schema.from_descriptor"),a("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),a("span",{pre:!0,attrs:{class:"token string"}},[t._v("'schema.yaml'")]),a("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n"),a("span",{pre:!0,attrs:{class:"token comment"}},[t._v("# Here we deal with a proper object model")]),t._v("\ndescriptor "),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" schema.to_descriptor"),a("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),a("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n"),a("span",{pre:!0,attrs:{class:"token comment"}},[t._v("# Here we export it back to be a descriptor")]),t._v("\n")])])]),a("p",[t._v("There are a few important traits of the new model:")]),t._v(" "),a("p",[t._v("it’s not possible to create a metadata instance from an invalid descriptor"),a("br"),t._v("\nit’s almost always guaranteed that a metadata instance is valid"),a("br"),t._v("\nit’s not possible to mix dicts and classes in methods like "),a("code",[t._v("package.add_resource")]),a("br"),t._v("\nit’s not possible to export an invalid descriptor"),a("br"),t._v("\nThis separation might make one to add a few additional lines of code, but it gives us much less fragile programs in the end. It’s especially important for software integrators who want to be sure that they write working code. At the same time, for quick prototyping and discovery Frictionless still provides high-level actions like "),a("code",[t._v("validate")]),t._v(" function that are more forgiving regarding user input.")]),t._v(" "),a("h2",{attrs:{id:"static-typing"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#static-typing"}},[t._v("#")]),t._v(" Static Typing")]),t._v(" "),a("p",[t._v("One of the most important consequences of “fixing” state management in Frictionless is our new ability to provide static typing for the framework codebase. This work is in progress but we have already added a lot of types and it successfully pass "),a("code",[t._v("pyright")]),t._v(" validation. 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Let’s chat in our "),a("a",{attrs:{href:"https://join.slack.com/t/frictionlessdata/shared_invite/zt-17kpbffnm-tRfDW_wJgOw8tJVLvZTrBg",target:"_blank",rel:"noopener noreferrer"}},[t._v("Slack"),a("OutboundLink")],1),t._v(" if you have questions or just want to say hi.")]),t._v(" "),a("p",[t._v("Read "),a("a",{attrs:{href:"https://framework.frictionlessdata.io/blog/2022/08-22-frictionless-framework-v5.html#:~:text=Read-,Livemark%20Docs,-for%20more%20information",target:"_blank",rel:"noopener noreferrer"}},[t._v("Livemark Docs"),a("OutboundLink")],1),t._v(" for more information.")])])}),[],!1,null,null,null);e.default=s.exports}}]); \ No newline at end of file +(window.webpackJsonp=window.webpackJsonp||[]).push([[165],{698:function(t,e,a){"use strict";a.r(e);var r=a(29),s=Object(r.a)({},(function(){var t=this,e=t.$createElement,a=t._self._c||e;return a("ContentSlotsDistributor",{attrs:{"slot-key":t.$parent.slotKey}},[a("p",[t._v("We’re releasing a first beta of Firctionless Framework (v5)!"),a("br"),t._v("\nSince the initial Frictionless Framework release we’d been collecting feedback and analyzing both high-level users’ needs and bug reports to identify shortcomings and areas that can be improved in the next version for the framework. 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Technically, it’s possible to provide different schemes and formats settings within one Dialect (e.g. for CSV and Excel) so it’s possible to create e.g. one re-usable dialect for a data package. A legacy CSV Dialect spec is supported and will be supported forever so it’s possible to provide CSV properties on the root level:")]),t._v(" "),a("h4",{attrs:{id:"yaml-2"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#yaml-2"}},[t._v("#")]),t._v(" YAML")]),t._v(" "),a("div",{staticClass:"language-r extra-class"},[a("pre",{pre:!0,attrs:{class:"language-r"}},[a("code",[t._v("header"),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v(":")]),t._v(" true\ndelimiter"),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v(":")]),t._v(" "),a("span",{pre:!0,attrs:{class:"token string"}},[t._v("';'")]),t._v("\n")])])]),a("p",[t._v("For performance and codebase maintainability reasons some marginal Layout features have been removed completely such as "),a("code",[t._v("skip/pick/limit/offsetFields/etc")]),t._v(". It’s possible to achieve the same results using the Pipeline concept as a part of the transformation workflow.")]),t._v(" "),a("p",[t._v("Read an article about "),a("a",{attrs:{href:"https://framework.frictionlessdata.io/docs/framework/dialect.html",target:"_blank",rel:"noopener noreferrer"}},[t._v("Dialect Class"),a("OutboundLink")],1),t._v(" for more information.")]),t._v(" "),a("h2",{attrs:{id:"checklist"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#checklist"}},[t._v("#")]),t._v(" Checklist")]),t._v(" "),a("p",[t._v("Checklist is a new concept introduced in v5. It’s basically a collection of validation steps and a few other settings to make “validation rules” sharable. 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This concept will provide an ability for creating data quality “libraries” within projects or domains. We can use a checklist for validation:")]),t._v(" "),a("h4",{attrs:{id:"cli"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#cli"}},[t._v("#")]),t._v(" CLI")]),t._v(" "),a("div",{staticClass:"language-r extra-class"},[a("pre",{pre:!0,attrs:{class:"language-r"}},[a("code",[t._v("frictionless validate table1.csv "),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v("-")]),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v("-")]),t._v("checklist checklist.yaml\nfrictionless validate table2.csv "),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v("-")]),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v("-")]),t._v("checklist checklist.yaml\n")])])]),a("p",[t._v("Here is a list of another changes:")]),t._v(" "),a("table",[a("thead",[a("tr",[a("th",[t._v("From (v4)")]),t._v(" "),a("th",[t._v("To (v5)")])])]),t._v(" "),a("tbody",[a("tr",[a("td",[t._v("Check(descriptor)")]),t._v(" "),a("td",[t._v("Check.from_descriptor(descriptor)")])]),t._v(" "),a("tr",[a("td",[t._v("check.code")]),t._v(" "),a("td",[t._v("check.type")])])])]),t._v(" "),a("p",[t._v("Read an article about "),a("a",{attrs:{href:"https://framework.frictionlessdata.io/docs/framework/checklist.html",target:"_blank",rel:"noopener noreferrer"}},[t._v("Checklist Class"),a("OutboundLink")],1),t._v(" for more information.")]),t._v(" "),a("h2",{attrs:{id:"pipeline"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#pipeline"}},[t._v("#")]),t._v(" Pipeline")]),t._v(" "),a("p",[t._v("In v4 Pipeline was a complex concept similar to validation Inquiry. We reworked it for v5 to be a lightweight set of validation steps that can be applied to a data resource or a data package. For example:")]),t._v(" "),a("h4",{attrs:{id:"yaml-4"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#yaml-4"}},[t._v("#")]),t._v(" YAML")]),t._v(" "),a("div",{staticClass:"language-r extra-class"},[a("pre",{pre:!0,attrs:{class:"language-r"}},[a("code",[t._v("steps"),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v(":")]),t._v("\n "),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v("-")]),t._v(" type"),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v(":")]),t._v(" table"),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v("-")]),t._v("normalize\n "),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v("-")]),t._v(" type"),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v(":")]),t._v(" cell"),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v("-")]),t._v("set\n fieldName"),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v(":")]),t._v(" version\n value"),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v(":")]),t._v(" v5\n")])])]),a("p",[t._v("Similar to the Checklist concept, Pipeline is a reusable (data-abstract) object that can be saved to a descriptor and used in some complex data workflow:")]),t._v(" "),a("h4",{attrs:{id:"cli-2"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#cli-2"}},[t._v("#")]),t._v(" CLI")]),t._v(" "),a("div",{staticClass:"language-r extra-class"},[a("pre",{pre:!0,attrs:{class:"language-r"}},[a("code",[t._v("frictionless transform table1.csv "),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v("-")]),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v("-")]),t._v("pipeline pipeline.yaml\nfrictionless transform table2.csv "),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v("-")]),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v("-")]),t._v("pipeline pipeline.yaml\n")])])]),a("p",[t._v("Here is another list of changes:")]),t._v(" "),a("table",[a("thead",[a("tr",[a("th",[t._v("From (v4)")]),t._v(" "),a("th",[t._v("To (v5)")])])]),t._v(" "),a("tbody",[a("tr",[a("td",[t._v("Step(descriptor)")]),t._v(" "),a("td",[t._v("Step.from_descriptor(descriptor)")])]),t._v(" "),a("tr",[a("td",[t._v("step.code")]),t._v(" "),a("td",[t._v("step.type")])])])]),t._v(" "),a("p",[t._v("Read an article about "),a("a",{attrs:{href:"https://framework.frictionlessdata.io/docs/framework/pipeline.html",target:"_blank",rel:"noopener noreferrer"}},[t._v("Pipeline Class"),a("OutboundLink")],1),t._v(" for more information.")]),t._v(" "),a("h2",{attrs:{id:"resource"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#resource"}},[t._v("#")]),t._v(" Resource")]),t._v(" "),a("p",[t._v("There are no changes in the Resource related to the standards although currently by default instead of "),a("code",[t._v("profile")]),t._v(" the "),a("code",[t._v("type")]),t._v(" property will be used to mark a resource as a table. It can be changed using the "),a("code",[t._v("--standards v1")]),t._v(" flag.")]),t._v(" "),a("p",[t._v("It’s now possible to set Checklist and Pipeline as a Resource property similar to Dialect and Schema:")]),t._v(" "),a("h4",{attrs:{id:"yaml-5"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#yaml-5"}},[t._v("#")]),t._v(" YAML")]),t._v(" "),a("div",{staticClass:"language-r extra-class"},[a("pre",{pre:!0,attrs:{class:"language-r"}},[a("code",[t._v("path"),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v(":")]),t._v(" table.csv\n"),a("span",{pre:!0,attrs:{class:"token comment"}},[t._v("# ...")]),t._v("\nchecklist"),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v(":")]),t._v("\n checks"),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v(":")]),t._v("\n "),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v("-")]),t._v(" type"),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v(":")]),t._v(" ascii"),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v("-")]),t._v("value\n "),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v("-")]),t._v(" type"),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v(":")]),t._v(" row_constraint\n formula"),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v(":")]),t._v(" id "),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v(">")]),t._v(" "),a("span",{pre:!0,attrs:{class:"token number"}},[t._v("1")]),t._v("\npipeline"),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v(":")]),t._v(" pipeline.yaml\n steps"),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v(":")]),t._v("\n "),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v("-")]),t._v(" type"),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v(":")]),t._v(" table"),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v("-")]),t._v("normalize\n "),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v("-")]),t._v(" type"),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v(":")]),t._v(" cell"),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v("-")]),t._v("set\n fieldName"),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v(":")]),t._v(" version\n value"),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v(":")]),t._v(" v5\n")])])]),a("p",[t._v("Or using dereference:")]),t._v(" "),a("h4",{attrs:{id:"yaml-6"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#yaml-6"}},[t._v("#")]),t._v(" YAML")]),t._v(" "),a("div",{staticClass:"language-r extra-class"},[a("pre",{pre:!0,attrs:{class:"language-r"}},[a("code",[t._v("path"),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v(":")]),t._v(" table.csv\n"),a("span",{pre:!0,attrs:{class:"token comment"}},[t._v("# ...")]),t._v("\nchecklist"),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v(":")]),t._v(" checklist.yaml\npipeline"),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v(":")]),t._v(" pipeline.yaml\n")])])]),a("p",[t._v("In this case the validation/transformation will use it by default providing an ability to ship validation rules and transformation pipelines within resources and packages. This is an important development for data publishers who want to define what they consider to be valid for their datasets as well as sharing raw data with a cleaning pipeline steps:")]),t._v(" "),a("h4",{attrs:{id:"cli-3"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#cli-3"}},[t._v("#")]),t._v(" CLI")]),t._v(" "),a("div",{staticClass:"language-r extra-class"},[a("pre",{pre:!0,attrs:{class:"language-r"}},[a("code",[t._v("frictionless validate resource.yaml "),a("span",{pre:!0,attrs:{class:"token comment"}},[t._v("# will use the checklist above")]),t._v("\nfrictionless transform resource.yaml "),a("span",{pre:!0,attrs:{class:"token comment"}},[t._v("# will use the pipeline above")]),t._v("\n")])])]),a("p",[t._v("There are minor changes in the "),a("code",[t._v("stats")]),t._v(" property. Now it uses named keys to simplify hash distinction (md5/sha256 are calculated by default and it’s not possible to change for performance reasons as it was in v4):")]),t._v(" "),a("h4",{attrs:{id:"python"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#python"}},[t._v("#")]),t._v(" Python")]),t._v(" "),a("div",{staticClass:"language-r extra-class"},[a("pre",{pre:!0,attrs:{class:"language-r"}},[a("code",[t._v("from frictionless import describe\n\nresource "),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" describe"),a("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),a("span",{pre:!0,attrs:{class:"token string"}},[t._v("'table.csv'")]),a("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v(" stats"),a("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v("True"),a("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\nprint"),a("span",{pre:!0,attrs:{class:"token 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Let’s chat in our "),a("a",{attrs:{href:"https://join.slack.com/t/frictionlessdata/shared_invite/zt-17kpbffnm-tRfDW_wJgOw8tJVLvZTrBg",target:"_blank",rel:"noopener noreferrer"}},[t._v("Slack"),a("OutboundLink")],1),t._v(" if you have questions or just want to say hi.")]),t._v(" "),a("p",[t._v("Read "),a("a",{attrs:{href:"https://framework.frictionlessdata.io/blog/2022/08-22-frictionless-framework-v5.html#:~:text=Read-,Livemark%20Docs,-for%20more%20information",target:"_blank",rel:"noopener noreferrer"}},[t._v("Livemark Docs"),a("OutboundLink")],1),t._v(" for more information.")])])}),[],!1,null,null,null);e.default=s.exports}}]); \ No newline at end of file diff --git a/assets/js/166.b05a8595.js b/assets/js/166.97b54b92.js similarity index 98% rename from assets/js/166.b05a8595.js rename to assets/js/166.97b54b92.js index 8c6a49557..8f7572c04 100644 --- a/assets/js/166.b05a8595.js +++ b/assets/js/166.97b54b92.js @@ -1 +1 @@ -(window.webpackJsonp=window.webpackJsonp||[]).push([[166],{703:function(e,t,r){"use strict";r.r(t);var o=r(29),a=Object(o.a)({},(function(){var e=this,t=e.$createElement,r=e._self._c||t;return r("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[r("p",[e._v("On the last community call on August 25"),r("sup",[e._v("th")]),e._v(", we had our very own Frictionless Data developer Shashi Gharti presenting to the community the new Frictionless GitHub integration, to read and write data packages from/to GitHub repositories.")]),e._v(" "),r("p",[e._v("Besides reading and writing packages, the integration also allows the creation of containers for data packages: the "),r("a",{attrs:{href:"https://framework.frictionlessdata.io/docs/framework/catalog.html",target:"_blank",rel:"noopener noreferrer"}},[e._v("catalog"),r("OutboundLink")],1),e._v(", a list of packages from multiple repositories in GitHub. 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If you use the Frictionless Framework v5 and its GitHub integration, please let us know! And if you have any feedback, feel free to open an issue in the "),r("a",{attrs:{href:"https://github.com/frictionlessdata/framework",target:"_blank",rel:"noopener noreferrer"}},[e._v("repository"),r("OutboundLink")],1)]),e._v(" "),r("h1",{attrs:{id:"join-us-next-month"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#join-us-next-month"}},[e._v("#")]),e._v(" Join us next month!")]),e._v(" "),r("p",[e._v("Next community call is on September 29"),r("sup",[e._v("th")]),e._v(". Frictionless Data lead developer Evgeny Karev will be presenting the Frictionless Framework version 5, so make sure not to miss it!")]),e._v(" "),r("p",[e._v("You can sign up for the call already "),r("a",{attrs:{href:"https://docs.google.com/forms/d/e/1FAIpQLSeuNCopxXauMkrWvF6VHqOyHMcy54SfNDOseVXfWRQZWkvqjQ/viewform?usp=sf_link",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),r("OutboundLink")],1),e._v(".")]),e._v(" "),r("p",[e._v("Do you want to share something with the community? Let us know when you sign up.")]),e._v(" "),r("p",[e._v("Would you like to present at one of the next community calls? 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See you there!")]),e._v(" "),r("h1",{attrs:{id:"call-recording"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#call-recording"}},[e._v("#")]),e._v(" Call Recording")]),e._v(" "),r("p",[e._v("On a final note, here is the recording of the full call:")]),e._v(" "),r("iframe",{attrs:{width:"560",height:"315",src:"https://www.youtube.com/embed/9_VwniN4JKE",title:"YouTube video player",frameborder:"0",allow:"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture",allowfullscreen:""}})])}),[],!1,null,null,null);t.default=a.exports}}]); \ No newline at end of file +(window.webpackJsonp=window.webpackJsonp||[]).push([[166],{700:function(e,t,r){"use strict";r.r(t);var o=r(29),a=Object(o.a)({},(function(){var e=this,t=e.$createElement,r=e._self._c||t;return r("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[r("p",[e._v("On the last community call on August 25"),r("sup",[e._v("th")]),e._v(", we had our very own Frictionless Data developer Shashi Gharti presenting to the community the new Frictionless GitHub integration, to read and write data packages from/to GitHub repositories.")]),e._v(" "),r("p",[e._v("Besides reading and writing packages, the integration also allows the creation of containers for data packages: the "),r("a",{attrs:{href:"https://framework.frictionlessdata.io/docs/framework/catalog.html",target:"_blank",rel:"noopener noreferrer"}},[e._v("catalog"),r("OutboundLink")],1),e._v(", a list of packages from multiple repositories in GitHub. 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Please fill out "),r("a",{attrs:{href:"https://forms.gle/AWpbxyiGESNSUFK2A",target:"_blank",rel:"noopener noreferrer"}},[e._v("this form"),r("OutboundLink")],1),e._v(".")]),e._v(" "),r("p",[e._v("Join our community on "),r("a",{attrs:{href:"https://join.slack.com/t/frictionlessdata/shared_invite/zt-17kpbffnm-tRfDW_wJgOw8tJVLvZTrBg",target:"_blank",rel:"noopener noreferrer"}},[e._v("Slack"),r("OutboundLink")],1),e._v(" (also via "),r("a",{attrs:{href:"https://matrix.to/#/#frictionlessdata:matrix.okfn.org",target:"_blank",rel:"noopener noreferrer"}},[e._v("Matrix"),r("OutboundLink")],1),e._v(") or "),r("a",{attrs:{href:"https://twitter.com/frictionlessd8a",target:"_blank",rel:"noopener noreferrer"}},[e._v("Twitter"),r("OutboundLink")],1),e._v(". See you there!")]),e._v(" "),r("h1",{attrs:{id:"call-recording"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#call-recording"}},[e._v("#")]),e._v(" Call Recording")]),e._v(" "),r("p",[e._v("On a final note, here is the recording of the full call:")]),e._v(" "),r("iframe",{attrs:{width:"560",height:"315",src:"https://www.youtube.com/embed/9_VwniN4JKE",title:"YouTube video player",frameborder:"0",allow:"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture",allowfullscreen:""}})])}),[],!1,null,null,null);t.default=a.exports}}]); \ No newline at end of file diff --git a/assets/js/167.6a39b6c8.js b/assets/js/167.8929fcb1.js similarity index 99% rename from assets/js/167.6a39b6c8.js rename to assets/js/167.8929fcb1.js index 08d3b1688..d65306bb9 100644 --- a/assets/js/167.6a39b6c8.js +++ b/assets/js/167.8929fcb1.js @@ -1 +1 @@ -(window.webpackJsonp=window.webpackJsonp||[]).push([[167],{705:function(e,t,o){"use strict";o.r(t);var r=o(29),a=Object(r.a)({},(function(){var e=this,t=e.$createElement,o=e._self._c||t;return o("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[o("p",[e._v("On our last community call on October 27"),o("sup",[e._v("th")]),e._v(", we had our very own Frictionless Data developer Shashi Gharti presenting to the community the Frictionless Zenodo integration, to read and write data packages from and to Zenodo.")]),e._v(" "),o("p",[e._v("The integration is currently in development, but we decided to present this feature already in order to gather feedback from the community. 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More info: "),o("a",{attrs:{href:"https://www.gbif.org/event/f68927-b5c1-4ac8-a4ac-7d47645/exploring-camera-trap-data",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://www.gbif.org/event/f68927-b5c1-4ac8-a4ac-7d47645/exploring-camera-trap-data"),o("OutboundLink")],1)])]),e._v(" "),o("li",[o("p",[e._v("We published an article on the Frictionless Data standards, and we talked about some community projects too, like Libraries Hacked, "),o("a",{attrs:{href:"http://data.gov.fr",target:"_blank",rel:"noopener noreferrer"}},[e._v("data.gov.fr"),o("OutboundLink")],1),e._v(", "),o("a",{attrs:{href:"http://dados.gov.br",target:"_blank",rel:"noopener noreferrer"}},[e._v("dados.gov.br"),o("OutboundLink")],1),e._v(", and BCO-DMO. 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See you there!")]),e._v(" "),o("h1",{attrs:{id:"call-recording"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#call-recording"}},[e._v("#")]),e._v(" Call Recording")]),e._v(" "),o("p",[e._v("On a final note, here is the recording of the full call:")]),e._v(" "),o("iframe",{attrs:{width:"560",height:"315",src:"https://www.youtube.com/embed/woEiTllLp7A",title:"YouTube video player",frameborder:"0",allow:"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture",allowfullscreen:""}})])}),[],!1,null,null,null);t.default=a.exports}}]); \ No newline at end of file +(window.webpackJsonp=window.webpackJsonp||[]).push([[167],{706:function(e,t,o){"use strict";o.r(t);var r=o(29),a=Object(r.a)({},(function(){var e=this,t=e.$createElement,o=e._self._c||t;return o("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[o("p",[e._v("On our last community call on October 27"),o("sup",[e._v("th")]),e._v(", we had our very own Frictionless Data developer Shashi Gharti presenting to the community the Frictionless Zenodo integration, to read and write data packages from and to Zenodo.")]),e._v(" "),o("p",[e._v("The integration is currently in development, but we decided to present this feature already in order to gather feedback from the community. 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If you use the Frictionless Framework v5 and its Zenodo integration, please let us know! We would love to hear what you think. And if you have any feedback, feel free to open an issue in the "),o("a",{attrs:{href:"https://github.com/frictionlessdata/framework",target:"_blank",rel:"noopener noreferrer"}},[e._v("repository"),o("OutboundLink")],1),e._v(".")]),e._v(" "),o("h2",{attrs:{id:"other-news-from-the-community"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#other-news-from-the-community"}},[e._v("#")]),e._v(" Other news from the community")]),e._v(" "),o("ul",[o("li",[o("p",[e._v("We are co-organizing "),o("a",{attrs:{href:"https://csvconf.com/",target:"_blank",rel:"noopener noreferrer"}},[e._v("csv,conf v7"),o("OutboundLink")],1),e._v(" in Buenos Aires in April next year. csv,conf is a community event for datamakers from all around the world. Calls for proposals are open until November 25th. 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The article is part of Common Place, a space to discuss the digital infrastructures, cultures, and actions needed to distribute, constellate, and amplify knowledge for the public good. ​ Check it out: "),o("a",{attrs:{href:"https://commonplace.knowledgefutures.org/pub/8x7oeawa/release/1?readingCollection=10ba8b01",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://commonplace.knowledgefutures.org/pub/8x7oeawa/release/1?readingCollection=10ba8b01"),o("OutboundLink")],1)])])]),e._v(" "),o("h1",{attrs:{id:"join-us-next-month"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#join-us-next-month"}},[e._v("#")]),e._v(" Join us next month!")]),e._v(" "),o("p",[e._v("Next community call is on December 1st (we are pushing it back one week because of the US Thanksgiving).")]),e._v(" "),o("p",[e._v("You can sign up for the call already "),o("a",{attrs:{href:"https://docs.google.com/forms/d/e/1FAIpQLSeuNCopxXauMkrWvF6VHqOyHMcy54SfNDOseVXfWRQZWkvqjQ/viewform?usp=sf_link",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),o("OutboundLink")],1),e._v(".")]),e._v(" "),o("p",[e._v("Do you want to share something with the community? 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The repository works as a template to create a quick API around your Frictionless Data Package. This solution is based on the "),a("a",{attrs:{href:"http://falconframework.org/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Falcon micro framework"),a("OutboundLink")],1),e._v(" and the "),a("a",{attrs:{href:"https://github.com/rgieseke/pandas-datapackage-reader",target:"_blank",rel:"noopener noreferrer"}},[e._v("Pandas Data Package Reader"),a("OutboundLink")],1),e._v(".")]),e._v(" "),a("p",[e._v("More recently Edgar Zanella from the Frictionless Data core team also worked on an "),a("a",{attrs:{href:"https://github.com/aivuk/datapackage-api",target:"_blank",rel:"noopener noreferrer"}},[e._v("experimental solution"),a("OutboundLink")],1),e._v(", converting a Data Package to SQLite database and using "),a("a",{attrs:{href:"https://datasette.io/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Datasette"),a("OutboundLink")],1),e._v(" to have a "),a("a",{attrs:{href:"https://github.com/aivuk/datapackage-api/",target:"_blank",rel:"noopener noreferrer"}},[e._v("JSON API"),a("OutboundLink")],1),e._v(" over the data. The advantage of this solution is that the way of querying the data is going to be familiar for those that knows "),a("a",{attrs:{href:"https://docs.datasette.io/en/stable/sql_queries.html",target:"_blank",rel:"noopener noreferrer"}},[e._v("SQL"),a("OutboundLink")],1),e._v(".")]),e._v(" "),a("p",[e._v("Then in November 2022, during the GLAMhack 2022 in Mendrisio, an API for Frictionless Data Packages was needed again to be able to sort data and view it on a map. The end result was a "),a("a",{attrs:{href:"https://hack.glam.opendata.ch/project/177",target:"_blank",rel:"noopener noreferrer"}},[e._v("Living Herbarium app"),a("OutboundLink")],1),e._v(".")]),e._v(" "),a("p",[e._v("So Oleg decided to pitch the idea of Frictionless Data Packages as services, as a challenge at the "),a("a",{attrs:{href:"https://hacknight.dinacon.ch/project/60",target:"_blank",rel:"noopener noreferrer"}},[e._v("DINAcon hacknights"),a("OutboundLink")],1),e._v(" in Bern. The challenge was not picked by anyone at the hackathon itself, but it sparked a conversation "),a("a",{attrs:{href:"https://frictionlessdata.slack.com/archives/C0369JLDJ1Z/p1668597797541189",target:"_blank",rel:"noopener noreferrer"}},[e._v("in our community chat"),a("OutboundLink")],1),e._v(".")]),e._v(" "),a("p",[e._v("If you are also interested in joining the conversation, just get on the thread in the community chat. If you need a bit of context, you can of course rewatch Oleg’s presentation:")]),e._v(" "),a("iframe",{attrs:{width:"560",height:"315",src:"https://www.youtube.com/embed/n_neCrY02jg",title:"YouTube video player",frameborder:"0",allow:"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share",allowfullscreen:""}}),e._v(" "),a("p",[e._v("It was also noted during the call that 2 other excellent ways to get a quick API for Frictionless Data Packages are:")]),e._v(" "),a("ul",[a("li",[a("p",[e._v("The "),a("a",{attrs:{href:"https://githubnext.com/projects/flat-data",target:"_blank",rel:"noopener noreferrer"}},[e._v("Flat Data project"),a("OutboundLink")],1),e._v(", developed on top of an idea by Simon Willison, allows (among other things) to have a quick API for your Data Package.")])]),e._v(" "),a("li",[a("p",[e._v("CKAN, since CKAN provides APIs. For example via "),a("a",{attrs:{href:"https://github.com/datalets/ckan-embed",target:"_blank",rel:"noopener noreferrer"}},[e._v("CKAN-embed"),a("OutboundLink")],1),e._v(", a widget for embedding live data searches from CKAN data portals into external websites.")])])]),e._v(" "),a("h1",{attrs:{id:"join-us-next-month"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#join-us-next-month"}},[e._v("#")]),e._v(" Join us next month!")]),e._v(" "),a("p",[e._v("Next community call is on January 26"),a("sup",[e._v("th")]),e._v(" and we are going to hear about Frictionless Data and DCAT from Matteo Fortini.")]),e._v(" "),a("p",[e._v("You can sign up for the call already "),a("a",{attrs:{href:"https://docs.google.com/forms/d/e/1FAIpQLSeuNCopxXauMkrWvF6VHqOyHMcy54SfNDOseVXfWRQZWkvqjQ/viewform?usp=sf_link",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),a("OutboundLink")],1),e._v(". Do you want to share something with the community? 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See you there!")]),e._v(" "),a("h1",{attrs:{id:"call-recording"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#call-recording"}},[e._v("#")]),e._v(" Call Recording")]),e._v(" "),a("p",[e._v("On a final note, here is the recording of the full call:")]),e._v(" "),a("iframe",{attrs:{width:"560",height:"315",src:"https://www.youtube.com/embed/hmr18OhY578",title:"YouTube video player",frameborder:"0",allow:"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share",allowfullscreen:""}})])}),[],!1,null,null,null);t.default=o.exports}}]); \ No newline at end of file +(window.webpackJsonp=window.webpackJsonp||[]).push([[169],{707:function(e,t,a){"use strict";a.r(t);var r=a(29),o=Object(r.a)({},(function(){var e=this,t=e.$createElement,a=e._self._c||t;return a("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[a("p",[e._v("On December 22"),a("sup",[e._v("nd")]),e._v(", for our last community call of the year, we had a nice discussion with Oleg Lavrovsky, an old friend of Open Knowledge Foundation, board member of the Swiss chapter, and valued member of the Frictionless Data community, about Data Package as a Service.")]),e._v(" "),a("p",[e._v("Oleg together with Thorben Westerhuys (remember his "),a("a",{attrs:{href:"https://frictionlessdata.io/blog/2021/03/29/february-virtual-hangout/#a-recap-from-our-march-community-call",target:"_blank",rel:"noopener noreferrer"}},[e._v("spatiotemporal covid 19 vaccination tracker he presented in March 2021"),a("OutboundLink")],1),e._v("?) already made a first attempt at this in 2019, as you can see in this "),a("a",{attrs:{href:"https://github.com/datalets/daats",target:"_blank",rel:"noopener noreferrer"}},[e._v("GitHub repo here"),a("OutboundLink")],1),e._v(". The repository works as a template to create a quick API around your Frictionless Data Package. This solution is based on the "),a("a",{attrs:{href:"http://falconframework.org/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Falcon micro framework"),a("OutboundLink")],1),e._v(" and the "),a("a",{attrs:{href:"https://github.com/rgieseke/pandas-datapackage-reader",target:"_blank",rel:"noopener noreferrer"}},[e._v("Pandas Data Package Reader"),a("OutboundLink")],1),e._v(".")]),e._v(" "),a("p",[e._v("More recently Edgar Zanella from the Frictionless Data core team also worked on an "),a("a",{attrs:{href:"https://github.com/aivuk/datapackage-api",target:"_blank",rel:"noopener noreferrer"}},[e._v("experimental solution"),a("OutboundLink")],1),e._v(", converting a Data Package to SQLite database and using "),a("a",{attrs:{href:"https://datasette.io/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Datasette"),a("OutboundLink")],1),e._v(" to have a "),a("a",{attrs:{href:"https://github.com/aivuk/datapackage-api/",target:"_blank",rel:"noopener noreferrer"}},[e._v("JSON API"),a("OutboundLink")],1),e._v(" over the data. The advantage of this solution is that the way of querying the data is going to be familiar for those that knows "),a("a",{attrs:{href:"https://docs.datasette.io/en/stable/sql_queries.html",target:"_blank",rel:"noopener noreferrer"}},[e._v("SQL"),a("OutboundLink")],1),e._v(".")]),e._v(" "),a("p",[e._v("Then in November 2022, during the GLAMhack 2022 in Mendrisio, an API for Frictionless Data Packages was needed again to be able to sort data and view it on a map. The end result was a "),a("a",{attrs:{href:"https://hack.glam.opendata.ch/project/177",target:"_blank",rel:"noopener noreferrer"}},[e._v("Living Herbarium app"),a("OutboundLink")],1),e._v(".")]),e._v(" "),a("p",[e._v("So Oleg decided to pitch the idea of Frictionless Data Packages as services, as a challenge at the "),a("a",{attrs:{href:"https://hacknight.dinacon.ch/project/60",target:"_blank",rel:"noopener noreferrer"}},[e._v("DINAcon hacknights"),a("OutboundLink")],1),e._v(" in Bern. The challenge was not picked by anyone at the hackathon itself, but it sparked a conversation "),a("a",{attrs:{href:"https://frictionlessdata.slack.com/archives/C0369JLDJ1Z/p1668597797541189",target:"_blank",rel:"noopener noreferrer"}},[e._v("in our community chat"),a("OutboundLink")],1),e._v(".")]),e._v(" "),a("p",[e._v("If you are also interested in joining the conversation, just get on the thread in the community chat. If you need a bit of context, you can of course rewatch Oleg’s presentation:")]),e._v(" "),a("iframe",{attrs:{width:"560",height:"315",src:"https://www.youtube.com/embed/n_neCrY02jg",title:"YouTube video player",frameborder:"0",allow:"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share",allowfullscreen:""}}),e._v(" "),a("p",[e._v("It was also noted during the call that 2 other excellent ways to get a quick API for Frictionless Data Packages are:")]),e._v(" "),a("ul",[a("li",[a("p",[e._v("The "),a("a",{attrs:{href:"https://githubnext.com/projects/flat-data",target:"_blank",rel:"noopener noreferrer"}},[e._v("Flat Data project"),a("OutboundLink")],1),e._v(", developed on top of an idea by Simon Willison, allows (among other things) to have a quick API for your Data Package.")])]),e._v(" "),a("li",[a("p",[e._v("CKAN, since CKAN provides APIs. For example via "),a("a",{attrs:{href:"https://github.com/datalets/ckan-embed",target:"_blank",rel:"noopener noreferrer"}},[e._v("CKAN-embed"),a("OutboundLink")],1),e._v(", a widget for embedding live data searches from CKAN data portals into external websites.")])])]),e._v(" "),a("h1",{attrs:{id:"join-us-next-month"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#join-us-next-month"}},[e._v("#")]),e._v(" Join us next month!")]),e._v(" "),a("p",[e._v("Next community call is on January 26"),a("sup",[e._v("th")]),e._v(" and we are going to hear about Frictionless Data and DCAT from Matteo Fortini.")]),e._v(" "),a("p",[e._v("You can sign up for the call already "),a("a",{attrs:{href:"https://docs.google.com/forms/d/e/1FAIpQLSeuNCopxXauMkrWvF6VHqOyHMcy54SfNDOseVXfWRQZWkvqjQ/viewform?usp=sf_link",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),a("OutboundLink")],1),e._v(". Do you want to share something with the community? Let us know when you sign up.")]),e._v(" "),a("p",[e._v("And if you have a cool project that you would like to show to the community, please let us know! You can just fill out "),a("a",{attrs:{href:"https://forms.gle/AWpbxyiGESNSUFK2A",target:"_blank",rel:"noopener noreferrer"}},[e._v("this form"),a("OutboundLink")],1),e._v(", or come and tell us on our community chat on "),a("a",{attrs:{href:"https://join.slack.com/t/frictionlessdata/shared_invite/zt-17kpbffnm-tRfDW_wJgOw8tJVLvZTrBg",target:"_blank",rel:"noopener noreferrer"}},[e._v("Slack"),a("OutboundLink")],1),e._v(" (also via "),a("a",{attrs:{href:"https://matrix.to/#/#frictionlessdata:matrix.okfn.org",target:"_blank",rel:"noopener noreferrer"}},[e._v("Matrix"),a("OutboundLink")],1),e._v("). See you there!")]),e._v(" "),a("h1",{attrs:{id:"call-recording"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#call-recording"}},[e._v("#")]),e._v(" Call Recording")]),e._v(" "),a("p",[e._v("On a final note, here is the recording of the full call:")]),e._v(" "),a("iframe",{attrs:{width:"560",height:"315",src:"https://www.youtube.com/embed/hmr18OhY578",title:"YouTube video player",frameborder:"0",allow:"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share",allowfullscreen:""}})])}),[],!1,null,null,null);t.default=o.exports}}]); \ No newline at end of file diff --git a/assets/js/170.e11e2e40.js b/assets/js/170.7a64b1c0.js similarity index 98% rename from assets/js/170.e11e2e40.js rename to assets/js/170.7a64b1c0.js index 00848a0d7..74839921e 100644 --- a/assets/js/170.e11e2e40.js +++ b/assets/js/170.7a64b1c0.js @@ -1 +1 @@ -(window.webpackJsonp=window.webpackJsonp||[]).push([[170],{707:function(e,t,a){"use strict";a.r(t);var o=a(29),r=Object(o.a)({},(function(){var e=this,t=e.$createElement,a=e._self._c||t;return a("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[a("p",[e._v("We are very excited to announce that we are going back to FOSDEM this year! Frictionless Data Technical Leader at Open Knowledge Foundation, Evgeny Karev, will give an overview of the main functionalities of the upcoming and much awaited Frictionless Application.")]),e._v(" "),a("p",[e._v("The talk will be featured in the Open Research Tools and Technology devroom, alongside other super interesting talks. Have a look at the schedule "),a("a",{attrs:{href:"https://fosdem.org/2023/schedule/track/open_research_tools_and_technology/",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),a("OutboundLink")],1),e._v(" to know more. This devroom provides a place and time to discuss the issues related to the creation and usage of open research technologies, with the ambition to foster discussions between designers, developers and users, bridging multiple knowledge-based communities together, and with the broader FLOSS community.")]),e._v(" "),a("p",[e._v("While FOSDEM is going back to Brussels for a physical event this year, the decision was made to keep part of the programme online to allow a broader audience to join. The Frictionless Application overview will be online as well, so you can follow it and participate in the discussion from wherever you are, even if you are not in Brussels.")]),e._v(" "),a("p",[e._v("This will be our 4th year at FOSDEM, and we are very proud to have been part of the Open Research devroom since its creation 4 years ago. To celebrate this, let’s look back at some of the great moments we shared:")]),e._v(" "),a("p",[e._v("In 2020 Lilly Winfree presented on-site “Frictionless Data for Reproducible Research” (you can watch the video recording "),a("a",{attrs:{href:"https://www.youtube.com/watch?v=lFgZTfCZu5U&t=317s",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),a("OutboundLink")],1),e._v("), a talk in which she discussed the technical ideas behind Frictionless Data for research, and showcased collaborative use cases, particularly the "),a("a",{attrs:{href:"https://frictionlessdata.io/blog/2020/02/10/frictionless-data-pipelines-for-open-ocean/",target:"_blank",rel:"noopener noreferrer"}},[e._v("BCO-DMO pilot"),a("OutboundLink")],1),e._v(", which was quite new at the time. Lilly showed how implementing Frictionless Data tooling into their data ingest pipelines allowed BCO-DMO to integrate disparate data while maintaining quality metadata in an easy to use interface. You can find all the info about this talk into the FOSDEM archive "),a("a",{attrs:{href:"https://archive.fosdem.org/2020/schedule/event/open_research_frictionless_data/",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),a("OutboundLink")],1),e._v(".")]),e._v(" "),a("p",[e._v("In 2021, for the first online FOSDEM due to Covid 19, Carles Pina i Estany presented "),a("a",{attrs:{href:"https://frictionlessdata.io/blog/2021/01/18/schema-collaboration/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Schema-collaboration"),a("OutboundLink")],1),e._v(", an open tool for reproducible research that helps data managers and researchers to collaborate on documenting datasets, built using the Frictionless Data specifications and software. Schema-collaboration was developed during the 2020 Tool Fund. For the FOSDEM presentation Carles gave an overview of the tool and did a demo as well."),a("br"),e._v("\nFind all the info (including a video recording of the presentation) on the FOSDEM archive "),a("a",{attrs:{href:"https://archive.fosdem.org/2021/schedule/event/open_research_schema_collaboration/",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),a("OutboundLink")],1),e._v(".")]),e._v(" "),a("p",[e._v("In 2022 our Technical Lead, Evgeny Karev, presented the newly released (at the time) "),a("a",{attrs:{href:"https://frictionlessdata.io/blog/2021/06/22/livemark/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Livemark"),a("OutboundLink")],1),e._v(", a tool to publish data articles with interactive tables, charts, and other elements very easily, without leaving a text editor."),a("br"),e._v("\nHave a look at the presentation and find all the information about it on the FOSDEM archive "),a("a",{attrs:{href:"https://archive.fosdem.org/2022/schedule/event/open_research_livemark/",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),a("OutboundLink")],1),e._v(".")]),e._v(" "),a("p",[e._v("And what about this year? We wanted to release the Frictionless Application for FOSDEM, but we had to push that back a little bit. But as we said above, Evgeny is still giving "),a("a",{attrs:{href:"https://fosdem.org/2023/schedule/room/dresearch/",target:"_blank",rel:"noopener noreferrer"}},[e._v("an overview of the Application"),a("OutboundLink")],1),e._v(" and some of its main features, which you should definitely not miss! See you online on Saturday, and happy FOSDEM weekend to you all!")])])}),[],!1,null,null,null);t.default=r.exports}}]); \ No newline at end of file +(window.webpackJsonp=window.webpackJsonp||[]).push([[170],{705:function(e,t,a){"use strict";a.r(t);var o=a(29),r=Object(o.a)({},(function(){var e=this,t=e.$createElement,a=e._self._c||t;return a("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[a("p",[e._v("We are very excited to announce that we are going back to FOSDEM this year! Frictionless Data Technical Leader at Open Knowledge Foundation, Evgeny Karev, will give an overview of the main functionalities of the upcoming and much awaited Frictionless Application.")]),e._v(" "),a("p",[e._v("The talk will be featured in the Open Research Tools and Technology devroom, alongside other super interesting talks. Have a look at the schedule "),a("a",{attrs:{href:"https://fosdem.org/2023/schedule/track/open_research_tools_and_technology/",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),a("OutboundLink")],1),e._v(" to know more. This devroom provides a place and time to discuss the issues related to the creation and usage of open research technologies, with the ambition to foster discussions between designers, developers and users, bridging multiple knowledge-based communities together, and with the broader FLOSS community.")]),e._v(" "),a("p",[e._v("While FOSDEM is going back to Brussels for a physical event this year, the decision was made to keep part of the programme online to allow a broader audience to join. The Frictionless Application overview will be online as well, so you can follow it and participate in the discussion from wherever you are, even if you are not in Brussels.")]),e._v(" "),a("p",[e._v("This will be our 4th year at FOSDEM, and we are very proud to have been part of the Open Research devroom since its creation 4 years ago. To celebrate this, let’s look back at some of the great moments we shared:")]),e._v(" "),a("p",[e._v("In 2020 Lilly Winfree presented on-site “Frictionless Data for Reproducible Research” (you can watch the video recording "),a("a",{attrs:{href:"https://www.youtube.com/watch?v=lFgZTfCZu5U&t=317s",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),a("OutboundLink")],1),e._v("), a talk in which she discussed the technical ideas behind Frictionless Data for research, and showcased collaborative use cases, particularly the "),a("a",{attrs:{href:"https://frictionlessdata.io/blog/2020/02/10/frictionless-data-pipelines-for-open-ocean/",target:"_blank",rel:"noopener noreferrer"}},[e._v("BCO-DMO pilot"),a("OutboundLink")],1),e._v(", which was quite new at the time. Lilly showed how implementing Frictionless Data tooling into their data ingest pipelines allowed BCO-DMO to integrate disparate data while maintaining quality metadata in an easy to use interface. You can find all the info about this talk into the FOSDEM archive "),a("a",{attrs:{href:"https://archive.fosdem.org/2020/schedule/event/open_research_frictionless_data/",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),a("OutboundLink")],1),e._v(".")]),e._v(" "),a("p",[e._v("In 2021, for the first online FOSDEM due to Covid 19, Carles Pina i Estany presented "),a("a",{attrs:{href:"https://frictionlessdata.io/blog/2021/01/18/schema-collaboration/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Schema-collaboration"),a("OutboundLink")],1),e._v(", an open tool for reproducible research that helps data managers and researchers to collaborate on documenting datasets, built using the Frictionless Data specifications and software. Schema-collaboration was developed during the 2020 Tool Fund. For the FOSDEM presentation Carles gave an overview of the tool and did a demo as well."),a("br"),e._v("\nFind all the info (including a video recording of the presentation) on the FOSDEM archive "),a("a",{attrs:{href:"https://archive.fosdem.org/2021/schedule/event/open_research_schema_collaboration/",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),a("OutboundLink")],1),e._v(".")]),e._v(" "),a("p",[e._v("In 2022 our Technical Lead, Evgeny Karev, presented the newly released (at the time) "),a("a",{attrs:{href:"https://frictionlessdata.io/blog/2021/06/22/livemark/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Livemark"),a("OutboundLink")],1),e._v(", a tool to publish data articles with interactive tables, charts, and other elements very easily, without leaving a text editor."),a("br"),e._v("\nHave a look at the presentation and find all the information about it on the FOSDEM archive "),a("a",{attrs:{href:"https://archive.fosdem.org/2022/schedule/event/open_research_livemark/",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),a("OutboundLink")],1),e._v(".")]),e._v(" "),a("p",[e._v("And what about this year? We wanted to release the Frictionless Application for FOSDEM, but we had to push that back a little bit. But as we said above, Evgeny is still giving "),a("a",{attrs:{href:"https://fosdem.org/2023/schedule/room/dresearch/",target:"_blank",rel:"noopener noreferrer"}},[e._v("an overview of the Application"),a("OutboundLink")],1),e._v(" and some of its main features, which you should definitely not miss! See you online on Saturday, and happy FOSDEM weekend to you all!")])])}),[],!1,null,null,null);t.default=r.exports}}]); \ No newline at end of file diff --git a/assets/js/175.a721089a.js b/assets/js/175.7478e44c.js similarity index 98% rename from assets/js/175.a721089a.js rename to assets/js/175.7478e44c.js index 02021d963..151ea7bea 100644 --- a/assets/js/175.a721089a.js +++ b/assets/js/175.7478e44c.js @@ -1 +1 @@ -(window.webpackJsonp=window.webpackJsonp||[]).push([[175],{716:function(e,t,a){"use strict";a.r(t);var o=a(29),r=Object(o.a)({},(function(){var e=this,t=e.$createElement,a=e._self._c||t;return a("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[a("p",[e._v("At our last community call on May 25"),a("sup",[e._v("th")]),e._v(" Augusto Herrmann presented FastETL, a free and open source software library for Apache Airflow that makes it easier to integrate heterogeneous data sources and to publish open data (e.g. to CKAN data portals) using Apache Airflow.")]),e._v(" "),a("p",[e._v("Augusto told us how the data engineering team at the Secretariat for Management and Innovation in the Brazilian federal government has been using FastETL in combination with the Frictionless Framework, and Tabular Data Packages for processing data pipelines and to publish open data.")]),e._v(" "),a("p",[e._v("Augusto and his team have developed FastETL, among other things, to be able to periodically synchronise data sources in the data lake, publish open data on open data portals, and be notified about publications in the official gazette.")]),e._v(" "),a("p",[e._v("Some of the things that you can do with FastETL are:")]),e._v(" "),a("ul",[a("li",[e._v("Full or incremental replication of tables in SQL Server, and Postgres databases (and MySQL sources).")]),e._v(" "),a("li",[e._v("Load data from GSheets and from spreadsheets on Samba/Windows networks.")]),e._v(" "),a("li",[e._v("Extract CSVs from SQL.")]),e._v(" "),a("li",[e._v("Query the Brazilian National Official Gazette’s API, and get a notification when there is a new publication in the Official Gazette.")]),e._v(" "),a("li",[e._v("Use CKAN or "),a("a",{attrs:{href:"http://dados.gov.br",target:"_blank",rel:"noopener noreferrer"}},[e._v("dados.gov.br"),a("OutboundLink")],1),e._v("’s API to update dataset metadata.")]),e._v(" "),a("li",[e._v("Use Frictionless Tabular Data Packages to write data dictionaries in OpenDocument Text format.")])]),e._v(" "),a("p",[e._v("Would you like to know more? You can have a look at Augusto’s slides on "),a("a",{attrs:{href:"https://herrmann.tech/slide-decks/2023/05/integrating-data-sources-and-publishing-open-data-with-fastetl-airflow-and-frictionless",target:"_blank",rel:"noopener noreferrer"}},[e._v("his website here"),a("OutboundLink")],1),e._v(", or check out the "),a("a",{attrs:{href:"https://github.com/gestaogovbr/FastETL",target:"_blank",rel:"noopener noreferrer"}},[e._v("FastETL GitHub Repository"),a("OutboundLink")],1),e._v("."),a("br"),e._v("\nAnd if you want to better understand how to use FastETL, have a look at Augusto’s presentation, with some great data pipeline examples:")]),e._v(" "),a("iframe",{attrs:{width:"560",height:"315",src:"https://www.youtube.com/embed/Z8bo6cyd-gw",title:"YouTube video player",frameborder:"0",allow:"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share",allowfullscreen:""}}),e._v(" "),a("h1",{attrs:{id:"join-us-next-month"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#join-us-next-month"}},[e._v("#")]),e._v(" Join us next month!")]),e._v(" "),a("p",[e._v("Next community call is on June 29"),a("sup",[e._v("th")]),e._v(", and it will be a hands-on session on strange datasets and how to describe them! Jesper Zedlitz from the German federal state of Schleswig-Holstein will be bringing one. Let us know if you would also like to bring a dataset to this call, by emailing Sara Petti sara.petti[at]"),a("a",{attrs:{href:"http://okfn.org",target:"_blank",rel:"noopener noreferrer"}},[e._v("okfn.org"),a("OutboundLink")],1),e._v(".")]),e._v(" "),a("p",[e._v("Do you have something you would like to present to the community at one of the upcoming calls? Let us know via "),a("a",{attrs:{href:"https://forms.gle/AWpbxyiGESNSUFK2A",target:"_blank",rel:"noopener noreferrer"}},[e._v("this form"),a("OutboundLink")],1),e._v(", or come and tell us on our "),a("a",{attrs:{href:"https://join.slack.com/t/frictionlessdata/shared_invite/zt-17kpbffnm-tRfDW_wJgOw8tJVLvZTrBg",target:"_blank",rel:"noopener noreferrer"}},[e._v("community chat on Slack"),a("OutboundLink")],1),e._v(" (also accessible via a "),a("a",{attrs:{href:"https://matrix.to/#/#frictionlessdata:matrix.okfn.org",target:"_blank",rel:"noopener noreferrer"}},[e._v("Matrix bridge"),a("OutboundLink")],1),e._v(" if you prefer to use an open protocol) .")]),e._v(" "),a("p",[e._v("You can sign up for the call already "),a("a",{attrs:{href:"https://docs.google.com/forms/d/e/1FAIpQLSeuNCopxXauMkrWvF6VHqOyHMcy54SfNDOseVXfWRQZWkvqjQ/viewform?usp=sf_link",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),a("OutboundLink")],1),e._v(". Do you want to share something with the community? Let us know when you sign up.")]),e._v(" "),a("h1",{attrs:{id:"call-recording"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#call-recording"}},[e._v("#")]),e._v(" Call Recording")]),e._v(" "),a("p",[e._v("On a final note, here is the recording of the full call:")]),e._v(" "),a("iframe",{attrs:{width:"560",height:"315",src:"https://www.youtube.com/embed/Z8bo6cyd-gw",title:"YouTube video player",frameborder:"0",allow:"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share",allowfullscreen:""}})])}),[],!1,null,null,null);t.default=r.exports}}]); \ No newline at end of file +(window.webpackJsonp=window.webpackJsonp||[]).push([[175],{714:function(e,t,a){"use strict";a.r(t);var o=a(29),r=Object(o.a)({},(function(){var e=this,t=e.$createElement,a=e._self._c||t;return a("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[a("p",[e._v("At our last community call on May 25"),a("sup",[e._v("th")]),e._v(" Augusto Herrmann presented FastETL, a free and open source software library for Apache Airflow that makes it easier to integrate heterogeneous data sources and to publish open data (e.g. to CKAN data portals) using Apache Airflow.")]),e._v(" "),a("p",[e._v("Augusto told us how the data engineering team at the Secretariat for Management and Innovation in the Brazilian federal government has been using FastETL in combination with the Frictionless Framework, and Tabular Data Packages for processing data pipelines and to publish open data.")]),e._v(" "),a("p",[e._v("Augusto and his team have developed FastETL, among other things, to be able to periodically synchronise data sources in the data lake, publish open data on open data portals, and be notified about publications in the official gazette.")]),e._v(" "),a("p",[e._v("Some of the things that you can do with FastETL are:")]),e._v(" "),a("ul",[a("li",[e._v("Full or incremental replication of tables in SQL Server, and Postgres databases (and MySQL sources).")]),e._v(" "),a("li",[e._v("Load data from GSheets and from spreadsheets on Samba/Windows networks.")]),e._v(" "),a("li",[e._v("Extract CSVs from SQL.")]),e._v(" "),a("li",[e._v("Query the Brazilian National Official Gazette’s API, and get a notification when there is a new publication in the Official Gazette.")]),e._v(" "),a("li",[e._v("Use CKAN or "),a("a",{attrs:{href:"http://dados.gov.br",target:"_blank",rel:"noopener noreferrer"}},[e._v("dados.gov.br"),a("OutboundLink")],1),e._v("’s API to update dataset metadata.")]),e._v(" "),a("li",[e._v("Use Frictionless Tabular Data Packages to write data dictionaries in OpenDocument Text format.")])]),e._v(" "),a("p",[e._v("Would you like to know more? You can have a look at Augusto’s slides on "),a("a",{attrs:{href:"https://herrmann.tech/slide-decks/2023/05/integrating-data-sources-and-publishing-open-data-with-fastetl-airflow-and-frictionless",target:"_blank",rel:"noopener noreferrer"}},[e._v("his website here"),a("OutboundLink")],1),e._v(", or check out the "),a("a",{attrs:{href:"https://github.com/gestaogovbr/FastETL",target:"_blank",rel:"noopener noreferrer"}},[e._v("FastETL GitHub Repository"),a("OutboundLink")],1),e._v("."),a("br"),e._v("\nAnd if you want to better understand how to use FastETL, have a look at Augusto’s presentation, with some great data pipeline examples:")]),e._v(" "),a("iframe",{attrs:{width:"560",height:"315",src:"https://www.youtube.com/embed/Z8bo6cyd-gw",title:"YouTube video player",frameborder:"0",allow:"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share",allowfullscreen:""}}),e._v(" "),a("h1",{attrs:{id:"join-us-next-month"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#join-us-next-month"}},[e._v("#")]),e._v(" Join us next month!")]),e._v(" "),a("p",[e._v("Next community call is on June 29"),a("sup",[e._v("th")]),e._v(", and it will be a hands-on session on strange datasets and how to describe them! Jesper Zedlitz from the German federal state of Schleswig-Holstein will be bringing one. Let us know if you would also like to bring a dataset to this call, by emailing Sara Petti sara.petti[at]"),a("a",{attrs:{href:"http://okfn.org",target:"_blank",rel:"noopener noreferrer"}},[e._v("okfn.org"),a("OutboundLink")],1),e._v(".")]),e._v(" "),a("p",[e._v("Do you have something you would like to present to the community at one of the upcoming calls? 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Let us know when you sign up.")]),e._v(" "),a("h1",{attrs:{id:"call-recording"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#call-recording"}},[e._v("#")]),e._v(" Call Recording")]),e._v(" "),a("p",[e._v("On a final note, here is the recording of the full call:")]),e._v(" "),a("iframe",{attrs:{width:"560",height:"315",src:"https://www.youtube.com/embed/Z8bo6cyd-gw",title:"YouTube video player",frameborder:"0",allow:"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share",allowfullscreen:""}})])}),[],!1,null,null,null);t.default=r.exports}}]); \ No newline at end of file diff --git a/assets/js/176.ad6847db.js b/assets/js/176.920f2d07.js similarity index 98% rename from assets/js/176.ad6847db.js rename to assets/js/176.920f2d07.js index 660abb6a5..a054e3d91 100644 --- a/assets/js/176.ad6847db.js +++ b/assets/js/176.920f2d07.js @@ -1 +1 @@ -(window.webpackJsonp=window.webpackJsonp||[]).push([[176],{714:function(e,t,o){"use strict";o.r(t);var a=o(29),n=Object(a.a)({},(function(){var e=this,t=e.$createElement,o=e._self._c||t;return o("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[o("p",[e._v("On June 29"),o("sup",[e._v("th")]),e._v(" we had our last monthly call, and it was kind of a special one! Instead of the usual project presentation, we had a hands-on session on strange datasets and how to describe them.")]),e._v(" "),o("p",[e._v("Our community member Jesper Zedlitz comes regularly across very weird datasets in his day-to-day work, and had asked in the May community call, whether it was possible to bring some of them to the call and check them out together with the community to try to make sense of them all together. This turned out to be an excellent idea for a fun call!")]),e._v(" "),o("p",[e._v("So what kind of problems is Jesper encountering?")]),e._v(" "),o("ul",[o("li",[o("p",[e._v("Sometimes we have extra information on the dataset, the licence, etc. at the beginning and comments at the end of the csv, so some rows need to be ignored. This is easy to do for the top part of the dataset, but it’s harder for the bottom part. 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Then you should watch the full recording of the call:")]),e._v(" "),o("iframe",{attrs:{width:"560",height:"315",src:"https://www.youtube.com/embed/WekfG2AZ-Dc",title:"YouTube video player",frameborder:"0",allow:"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share",allowfullscreen:""}}),e._v(" "),o("h1",{attrs:{id:"join-us-in-august"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#join-us-in-august"}},[e._v("#")]),e._v(" Join us in August!")]),e._v(" "),o("p",[e._v("Exceptionally we won’t have any community call in July, so see you all on August 31"),o("sup",[e._v("st")]),e._v("!")]),e._v(" "),o("p",[e._v("Do you have something you would like to present to the community at one of the upcoming calls? 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Let us know when you sign up.")])])}),[],!1,null,null,null);t.default=n.exports}}]); \ No newline at end of file +(window.webpackJsonp=window.webpackJsonp||[]).push([[176],{713:function(e,t,o){"use strict";o.r(t);var a=o(29),n=Object(a.a)({},(function(){var e=this,t=e.$createElement,o=e._self._c||t;return o("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[o("p",[e._v("On June 29"),o("sup",[e._v("th")]),e._v(" we had our last monthly call, and it was kind of a special one! Instead of the usual project presentation, we had a hands-on session on strange datasets and how to describe them.")]),e._v(" "),o("p",[e._v("Our community member Jesper Zedlitz comes regularly across very weird datasets in his day-to-day work, and had asked in the May community call, whether it was possible to bring some of them to the call and check them out together with the community to try to make sense of them all together. This turned out to be an excellent idea for a fun call!")]),e._v(" "),o("p",[e._v("So what kind of problems is Jesper encountering?")]),e._v(" "),o("ul",[o("li",[o("p",[e._v("Sometimes we have extra information on the dataset, the licence, etc. at the beginning and comments at the end of the csv, so some rows need to be ignored. This is easy to do for the top part of the dataset, but it’s harder for the bottom part. Something we will definitely need to think about for the next iteration of the Frictionless specs, for example by giving the possibility to have a “headline row”, or something like that. This was a common problem for other community members too.")])]),e._v(" "),o("li",[o("p",[e._v("Sometimes we don’t have any information at all: Jesper showed us some CSVs without any headerlines, where it’s up to you to figure out what kind of data is in there.")])]),e._v(" "),o("li",[o("p",[e._v("The dialect (e.g. weird delimiters) and character encoding are sometimes tricky too, but that’s already easy to manage with the Frictionless specs.")])])]),e._v(" "),o("p",[e._v("Do you want to know more about the strange datasets that Jesper has shown us during the call? Then you should watch the full recording of the call:")]),e._v(" "),o("iframe",{attrs:{width:"560",height:"315",src:"https://www.youtube.com/embed/WekfG2AZ-Dc",title:"YouTube video player",frameborder:"0",allow:"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share",allowfullscreen:""}}),e._v(" "),o("h1",{attrs:{id:"join-us-in-august"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#join-us-in-august"}},[e._v("#")]),e._v(" Join us in August!")]),e._v(" "),o("p",[e._v("Exceptionally we won’t have any community call in July, so see you all on August 31"),o("sup",[e._v("st")]),e._v("!")]),e._v(" "),o("p",[e._v("Do you have something you would like to present to the community at one of the upcoming calls? 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Let us know when you sign up.")])])}),[],!1,null,null,null);t.default=n.exports}}]); \ No newline at end of file diff --git a/assets/js/177.6c30d8b5.js b/assets/js/177.584bad6b.js similarity index 98% rename from assets/js/177.6c30d8b5.js rename to assets/js/177.584bad6b.js index 1cbade261..e56951c49 100644 --- a/assets/js/177.6c30d8b5.js +++ b/assets/js/177.584bad6b.js @@ -1 +1 @@ -(window.webpackJsonp=window.webpackJsonp||[]).push([[177],{715:function(e,t,o){"use strict";o.r(t);var a=o(29),r=Object(a.a)({},(function(){var e=this,t=e.$createElement,o=e._self._c||t;return o("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[o("p",[e._v("After 2 long months of absence, our monthly community call was finally back on September 28"),o("sup",[e._v("th")]),e._v(", with some very exciting news! Our Tech Lead Evgeny Karev presented the work that has absorbed so much of his and Shashi Gharti’s time in the last months: the Frictionless no-code application "),o("a",{attrs:{href:"https://opendataeditor.okfn.org/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Open Data Editor"),o("OutboundLink")],1),e._v(".")]),e._v(" "),o("p",[e._v("The problem that inspired this new tool is that still today there is no easy tool to manage and publish data for those who don’t have technical skills. The new Open Data Editor offers the possibility to access all Frictionless functionalities without having to write one single line of code, nor open your shell. Like most Frictionless products, Open Data Editor focuses on tabular data, and it can easily open big files because it uses the "),o("code",[e._v("database")]),e._v(" under the hood (similarly to CKAN). You can use it to edit metadata, and declare some rules for opening that you can share with your collaborators, making your data more reproducible.")]),e._v(" "),o("p",[e._v("You can use it to create data visualisation with VegaLite, but Open Data Editor has also an AI support, which you can use to create charts for you, in case you don’t know how to use the VegaLite specifications. You can also publish data stories, and much much more! Check out Evgeny’s presentation to see all the great features of the Open Data Editor:")]),e._v(" "),o("iframe",{attrs:{width:"560",height:"315",src:"https://www.youtube.com/embed/a0IyJPSmJyY?si=xOfI7YmS4EsVcKlp",title:"YouTube video player",frameborder:"0",allow:"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share",allowfullscreen:""}}),e._v(" "),o("p",[e._v("The application is still a work in progress, but if you would like to try it out that’s of course absolutely possible, and we would love it if you could give us feedback, so please let us know if you spot anything weird. To make the experience smoother, we have a detailed "),o("a",{attrs:{href:"https://opendataeditor.okfn.org/documentation/getting-started/",target:"_blank",rel:"noopener noreferrer"}},[e._v("documentation website"),o("OutboundLink")],1),e._v(" you can consult.")]),e._v(" "),o("h1",{attrs:{id:"join-us-in-october"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#join-us-in-october"}},[e._v("#")]),e._v(" Join us in October!")]),e._v(" "),o("p",[e._v("Next community call is on October 26"),o("sup",[e._v("th")]),e._v(", join us to hear exciting news about the "),o("a",{attrs:{href:"https://specs.frictionlessdata.io/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Frictionless specs"),o("OutboundLink")],1),e._v(" update!")]),e._v(" "),o("p",[e._v("Do you have something you would like to present to the community at one of the upcoming calls? 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Check out Evgeny’s presentation to see all the great features of the Open Data Editor:")]),e._v(" "),o("iframe",{attrs:{width:"560",height:"315",src:"https://www.youtube.com/embed/a0IyJPSmJyY?si=xOfI7YmS4EsVcKlp",title:"YouTube video player",frameborder:"0",allow:"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share",allowfullscreen:""}}),e._v(" "),o("p",[e._v("The application is still a work in progress, but if you would like to try it out that’s of course absolutely possible, and we would love it if you could give us feedback, so please let us know if you spot anything weird. 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"),s("a",{attrs:{href:"http://wq.io/",target:"_blank",rel:"noopener noreferrer"}},[t._v("wqio"),s("OutboundLink")],1),t._v(" is one example, but it is quite hard to use and a lighter version would be great. Perhaps CKAN"),s("sup",{staticClass:"footnote-ref"},[s("a",{attrs:{href:"#fn13",id:"fnref13"}},[t._v("[13]")])]),t._v(" datastore could be licensed in a more open way?")]),t._v(" "),s("p",[t._v("In terms of the next steps for us, we are currently working on a SaaS implementation of Zegami which will dramatically reduce the effort required in order to start working with Zegami. We are then planning on developing a series of APIs so developers can create their own data transformation pipelines. 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o("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[o("p",[e._v("On our last community call was on November 30"),o("sup",[e._v("th")]),e._v(", we continued discussing the Frictionless Specs update with community members. As a reminder, thanks to the generous support of "),o("a",{attrs:{href:"https://nlnet.nl/",target:"_blank",rel:"noopener noreferrer"}},[e._v("NLnet"),o("OutboundLink")],1),e._v(", the Frictionless core team at Open Knowledge Foundation, together with a working group composed of members of the community, is now working on a v2 of the Specs. Read the "),o("a",{attrs:{href:"https://frictionlessdata.io/blog/2023/11/15/frictionless-specs-update/",target:"_blank",rel:"noopener noreferrer"}},[e._v("announcement blog"),o("OutboundLink")],1),e._v(" to know more about it.")]),e._v(" "),o("h2",{attrs:{id:"ideas-that-were-shared"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#ideas-that-were-shared"}},[e._v("#")]),e._v(" Ideas that were shared")]),e._v(" "),o("p",[e._v("First of all, as captured in the last call, we took into account the group’s concerns about the shortness of the initial timeline we proposed, we were able to work out an extension of the initial time frame until mid-2024. We are now aiming at "),o("strong",[e._v("releasing v2 of the Frictionless specs by June 2024")]),e._v(".")]),e._v(" "),o("p",[e._v("The Specs update was officially "),o("a",{attrs:{href:"https://github.com/frictionlessdata/specs/issues/854",target:"_blank",rel:"noopener noreferrer"}},[e._v("announced on GitHub"),o("OutboundLink")],1),e._v(" and a GitHub team was set up. If you are interested in joining let us know. Getting involved is very low-barrier, you just need to subscribe to the GitHub issues to receive all the updates.")]),e._v(" "),o("p",[e._v("As outlined in the announcement blog, we want to "),o("strong",[e._v("put some extra effort into diversifying our group")]),e._v(", as we think that more diversity could definitely benefit our group, especially at a time in which we are rethinking parts of it. Our community is super collaborative in that sense, and new people who would be interested in standards development have been invited to join.")]),e._v(" "),o("p",[e._v("To make sure we are capturing as many branches of knowledge as possible, an idea that was brought up is to perform a quick analysis of Zenodo’s datasets by field to figure out the distribution across different fields, and see how our distribution within the working group compares to that, to understand where our gaps are.")]),e._v(" "),o("p",[e._v("We also acknowledge that the Frictionless Data project has a steep learning curve. As a community, we often operate with a developer mindset, employing specialised terminology without enough explanations. That’s why, our commitment to fostering diversity must begin with "),o("strong",[e._v("ensuring that all our documentation is accessible")]),e._v(". Achieving this goal is within reach if we all took time to review the documentation to make sure it is accessible to the broader audience possible, including beginners, and ensuring its overall coherence. We did "),o("a",{attrs:{href:"https://frictionlessdata.io/blog/2021/04/14/new-data-documentation-portal/#so-what-s-new",target:"_blank",rel:"noopener noreferrer"}},[e._v("a big documentation review with the community in the past"),o("OutboundLink")],1),e._v(", and it was a great success.")]),e._v(" "),o("p",[e._v("An essential component for achieving this objective is the "),o("strong",[e._v("development of clear onboarding materials")]),e._v(" that are easy to understand. Leveraging existing resources, such as "),o("a",{attrs:{href:"https://fellows.frictionlessdata.io/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Frictionless Fellows’ materials"),o("OutboundLink")],1),e._v(", can serve as a foundation for building a comprehensive knowledge base. 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The "),o("a",{attrs:{href:"https://specs.frictionlessdata.io/",target:"_blank",rel:"noopener noreferrer"}},[e._v("current Specs website"),o("OutboundLink")],1),e._v(" will be kept to document the v1 of the standards.")]),e._v(" "),o("h2",{attrs:{id:"immediate-next-steps"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#immediate-next-steps"}},[e._v("#")]),e._v(" Immediate next steps")]),e._v(" "),o("ul",[o("li",[o("p",[e._v("We will update the contributions guide.")])]),e._v(" "),o("li",[o("p",[e._v("In December, due to holidays and limited capacity of people, we will focus on low-hanging fruits. Of course anyone who would like to start working on an issue is very welcome to do so. If you are part of the working group you have the right permission to push PRs, so please go ahead.")])]),e._v(" "),o("li",[o("p",[e._v("We will try to simplify JSON schemas and the way extensions are generated. The Frictionless modular approach will be of course kept. Our aim is to be as technology agnostic and no-code as possible.")])]),e._v(" "),o("li",[o("p",[e._v("We will keep Specs and tooling implementing them clearly separated.")])])]),e._v(" "),o("h2",{attrs:{id:"how-to-participate"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#how-to-participate"}},[e._v("#")]),e._v(" How to participate?")]),e._v(" "),o("p",[e._v("Go and have a look at the "),o("a",{attrs:{href:"https://github.com/frictionlessdata/specs/milestone/6",target:"_blank",rel:"noopener noreferrer"}},[e._v("issue queue on GitHub"),o("OutboundLink")],1),e._v(", and feel free to jump on any of the issue to propose a solution / start a discussion.")]),e._v(" "),o("p",[e._v("Note that the v2 milestone is just an estimation to understand the scope of this release. We tried to carve out what we thought it was important to discuss about or take into consideration, but we don’t need to action every single issue on the list.")]),e._v(" "),o("h1",{attrs:{id:"announcements"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#announcements"}},[e._v("#")]),e._v(" Announcements")]),e._v(" "),o("p",[e._v("We are co-hosting the Open Research Devroom at "),o("a",{attrs:{href:"https://fosdem.org/",target:"_blank",rel:"noopener noreferrer"}},[e._v("FOSDEM 2024"),o("OutboundLink")],1),e._v(" again this year. We are looking for developers & users of free libre open source technology for scientific research, investigative journalism, data journalism, activism, OSINT."),o("br"),e._v("\nCall for Participation is open until December 10th, we strongly encourage you to apply!")]),e._v(" "),o("p",[e._v("For those who cannot make it to Brussels on February 3rd, we are going to host an online session the week after.")]),e._v(" "),o("p",[e._v("More info: "),o("a",{attrs:{href:"https://research-fosdem.github.io/",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://research-fosdem.github.io/"),o("OutboundLink")],1)]),e._v(" "),o("h1",{attrs:{id:"join-us-in-janaury"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#join-us-in-janaury"}},[e._v("#")]),e._v(" Join us in Janaury!")]),e._v(" "),o("p",[e._v("Next community call is on January 25"),o("sup",[e._v("th")]),e._v(", join us to hear all the exciting news about the "),o("a",{attrs:{href:"https://specs.frictionlessdata.io/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Frictionless specs"),o("OutboundLink")],1),e._v(" update!")]),e._v(" "),o("p",[e._v("Do you have something you would like to present to the community at one of the upcoming calls? Let us know via "),o("a",{attrs:{href:"https://forms.gle/AWpbxyiGESNSUFK2A",target:"_blank",rel:"noopener noreferrer"}},[e._v("this form"),o("OutboundLink")],1),e._v(", or come and tell us on our "),o("a",{attrs:{href:"https://join.slack.com/t/frictionlessdata/shared_invite/zt-17kpbffnm-tRfDW_wJgOw8tJVLvZTrBg",target:"_blank",rel:"noopener noreferrer"}},[e._v("community chat on Slack"),o("OutboundLink")],1),e._v("(also accessible via a "),o("a",{attrs:{href:"https://matrix.to/#/#frictionlessdata:matrix.okfn.org",target:"_blank",rel:"noopener noreferrer"}},[e._v("Matrix bridge"),o("OutboundLink")],1),e._v(" if you prefer to use an open protocol).")]),e._v(" "),o("p",[e._v("You can sign up for the call already "),o("a",{attrs:{href:"https://docs.google.com/forms/d/e/1FAIpQLSeuNCopxXauMkrWvF6VHqOyHMcy54SfNDOseVXfWRQZWkvqjQ/viewform?usp=sf_link",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),o("OutboundLink")],1),e._v(". Do you want to share something with the community? Let us know when you sign up.")]),e._v(" "),o("h1",{attrs:{id:"call-recording"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#call-recording"}},[e._v("#")]),e._v(" Call Recording")]),e._v(" "),o("p",[e._v("Here is the recording of the full call:")]),e._v(" "),o("iframe",{attrs:{width:"560",height:"315",src:"https://www.youtube.com/embed/MKFuLqyc414?si=BGEiBzaHRXHQXaJJ",title:"YouTube video player",frameborder:"0",allow:"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share",allowfullscreen:""}}),e._v(" "),o("h1",{attrs:{id:"thank-you"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#thank-you"}},[e._v("#")]),e._v(" Thank you")]),e._v(" "),o("p",[e._v("On a final note, we would like to thank all community members that joined the call and that keep all these discussions alive, and those who manifested their interest in joining the specs working group. Without you, all of this would not be possible.")])])}),[],!1,null,null,null);t.default=n.exports}}]); \ No newline at end of file +(window.webpackJsonp=window.webpackJsonp||[]).push([[180],{718:function(e,t,o){"use strict";o.r(t);var a=o(29),n=Object(a.a)({},(function(){var e=this,t=e.$createElement,o=e._self._c||t;return o("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[o("p",[e._v("On our last community call was on November 30"),o("sup",[e._v("th")]),e._v(", we continued discussing the Frictionless Specs update with community members. As a reminder, thanks to the generous support of "),o("a",{attrs:{href:"https://nlnet.nl/",target:"_blank",rel:"noopener noreferrer"}},[e._v("NLnet"),o("OutboundLink")],1),e._v(", the Frictionless core team at Open Knowledge Foundation, together with a working group composed of members of the community, is now working on a v2 of the Specs. 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The "),o("a",{attrs:{href:"https://specs.frictionlessdata.io/",target:"_blank",rel:"noopener noreferrer"}},[e._v("current Specs website"),o("OutboundLink")],1),e._v(" will be kept to document the v1 of the standards.")]),e._v(" "),o("h2",{attrs:{id:"immediate-next-steps"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#immediate-next-steps"}},[e._v("#")]),e._v(" Immediate next steps")]),e._v(" "),o("ul",[o("li",[o("p",[e._v("We will update the contributions guide.")])]),e._v(" "),o("li",[o("p",[e._v("In December, due to holidays and limited capacity of people, we will focus on low-hanging fruits. Of course anyone who would like to start working on an issue is very welcome to do so. If you are part of the working group you have the right permission to push PRs, so please go ahead.")])]),e._v(" "),o("li",[o("p",[e._v("We will try to simplify JSON schemas and the way extensions are generated. The Frictionless modular approach will be of course kept. Our aim is to be as technology agnostic and no-code as possible.")])]),e._v(" "),o("li",[o("p",[e._v("We will keep Specs and tooling implementing them clearly separated.")])])]),e._v(" "),o("h2",{attrs:{id:"how-to-participate"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#how-to-participate"}},[e._v("#")]),e._v(" How to participate?")]),e._v(" "),o("p",[e._v("Go and have a look at the "),o("a",{attrs:{href:"https://github.com/frictionlessdata/specs/milestone/6",target:"_blank",rel:"noopener noreferrer"}},[e._v("issue queue on GitHub"),o("OutboundLink")],1),e._v(", and feel free to jump on any of the issue to propose a solution / start a discussion.")]),e._v(" "),o("p",[e._v("Note that the v2 milestone is just an estimation to understand the scope of this release. We tried to carve out what we thought it was important to discuss about or take into consideration, but we don’t need to action every single issue on the list.")]),e._v(" "),o("h1",{attrs:{id:"announcements"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#announcements"}},[e._v("#")]),e._v(" Announcements")]),e._v(" "),o("p",[e._v("We are co-hosting the Open Research Devroom at "),o("a",{attrs:{href:"https://fosdem.org/",target:"_blank",rel:"noopener noreferrer"}},[e._v("FOSDEM 2024"),o("OutboundLink")],1),e._v(" again this year. We are looking for developers & users of free libre open source technology for scientific research, investigative journalism, data journalism, activism, OSINT."),o("br"),e._v("\nCall for Participation is open until December 10th, we strongly encourage you to apply!")]),e._v(" "),o("p",[e._v("For those who cannot make it to Brussels on February 3rd, we are going to host an online session the week after.")]),e._v(" "),o("p",[e._v("More info: "),o("a",{attrs:{href:"https://research-fosdem.github.io/",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://research-fosdem.github.io/"),o("OutboundLink")],1)]),e._v(" "),o("h1",{attrs:{id:"join-us-in-janaury"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#join-us-in-janaury"}},[e._v("#")]),e._v(" Join us in Janaury!")]),e._v(" "),o("p",[e._v("Next community call is on January 25"),o("sup",[e._v("th")]),e._v(", join us to hear all the exciting news about the "),o("a",{attrs:{href:"https://specs.frictionlessdata.io/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Frictionless specs"),o("OutboundLink")],1),e._v(" update!")]),e._v(" "),o("p",[e._v("Do you have something you would like to present to the community at one of the upcoming calls? Let us know via "),o("a",{attrs:{href:"https://forms.gle/AWpbxyiGESNSUFK2A",target:"_blank",rel:"noopener noreferrer"}},[e._v("this form"),o("OutboundLink")],1),e._v(", or come and tell us on our "),o("a",{attrs:{href:"https://join.slack.com/t/frictionlessdata/shared_invite/zt-17kpbffnm-tRfDW_wJgOw8tJVLvZTrBg",target:"_blank",rel:"noopener noreferrer"}},[e._v("community chat on Slack"),o("OutboundLink")],1),e._v("(also accessible via a "),o("a",{attrs:{href:"https://matrix.to/#/#frictionlessdata:matrix.okfn.org",target:"_blank",rel:"noopener noreferrer"}},[e._v("Matrix bridge"),o("OutboundLink")],1),e._v(" if you prefer to use an open protocol).")]),e._v(" "),o("p",[e._v("You can sign up for the call already "),o("a",{attrs:{href:"https://docs.google.com/forms/d/e/1FAIpQLSeuNCopxXauMkrWvF6VHqOyHMcy54SfNDOseVXfWRQZWkvqjQ/viewform?usp=sf_link",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),o("OutboundLink")],1),e._v(". Do you want to share something with the community? Let us know when you sign up.")]),e._v(" "),o("h1",{attrs:{id:"call-recording"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#call-recording"}},[e._v("#")]),e._v(" Call Recording")]),e._v(" "),o("p",[e._v("Here is the recording of the full call:")]),e._v(" "),o("iframe",{attrs:{width:"560",height:"315",src:"https://www.youtube.com/embed/MKFuLqyc414?si=BGEiBzaHRXHQXaJJ",title:"YouTube video player",frameborder:"0",allow:"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share",allowfullscreen:""}}),e._v(" "),o("h1",{attrs:{id:"thank-you"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#thank-you"}},[e._v("#")]),e._v(" Thank you")]),e._v(" "),o("p",[e._v("On a final note, we would like to thank all community members that joined the call and that keep all these discussions alive, and those who manifested their interest in joining the specs working group. Without you, all of this would not be possible.")])])}),[],!1,null,null,null);t.default=n.exports}}]); \ No newline at end of file diff --git a/assets/js/181.57db0f9a.js b/assets/js/181.c47d7742.js similarity index 99% rename from assets/js/181.57db0f9a.js rename to assets/js/181.c47d7742.js index f83676824..c5b66cb26 100644 --- a/assets/js/181.57db0f9a.js +++ b/assets/js/181.c47d7742.js @@ -1 +1 @@ -(window.webpackJsonp=window.webpackJsonp||[]).push([[181],{720:function(e,t,o){"use strict";o.r(t);var r=o(29),s=Object(r.a)({},(function(){var e=this,t=e.$createElement,o=e._self._c||t;return o("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[o("p",[e._v("On our last community call was on January 25"),o("sup",[e._v("th")]),e._v(", we continued discussing the "),o("a",{attrs:{href:"https://specs.frictionlessdata.io/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Frictionless Specs"),o("OutboundLink")],1),e._v(" update with community members. As a reminder, thanks to the generous support of "),o("a",{attrs:{href:"https://nlnet.nl/",target:"_blank",rel:"noopener noreferrer"}},[e._v("NLnet"),o("OutboundLink")],1),e._v(", the Frictionless core team, together with a working group composed of members of the community, is now working on a v2 of the Specs. Read the "),o("a",{attrs:{href:"https://frictionlessdata.io/blog/2023/11/15/frictionless-specs-update/",target:"_blank",rel:"noopener noreferrer"}},[e._v("announcement blog"),o("OutboundLink")],1),e._v(" to know more about it.")]),e._v(" "),o("h2",{attrs:{id:"working-group-discussion"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#working-group-discussion"}},[e._v("#")]),e._v(" Working group discussion")]),e._v(" "),o("p",[e._v("The first thing we discussed was ways to prioritise candidate features for future Specs updates (beyond v2). At the moment, our idea is to prioritise those issues that get more votes (aka thumbs up), among the "),o("a",{attrs:{href:"https://github.com/frictionlessdata/specs/issues?q=is%3Aissue+is%3Aopen+sort%3Areactions-%2B1-desc",target:"_blank",rel:"noopener noreferrer"}},[e._v("issues in the Specs repository"),o("OutboundLink")],1),e._v(".")]),e._v(" "),o("p",[e._v("Note, the reference for the v2 update is still "),o("a",{attrs:{href:"https://github.com/frictionlessdata/specs/milestone/6",target:"_blank",rel:"noopener noreferrer"}},[e._v("the one we initially published as a roadmap"),o("OutboundLink")],1),e._v(" in the update announcement blog. For the v2 release we will stick to that list of issues.")]),e._v(" "),o("p",[e._v("Anyone is welcome to open a Pull Request to address any of those issues. The PR will then be reviewed by the working group, according to the "),o("a",{attrs:{href:"https://frictionlessdata.io/blog/2023/11/15/frictionless-specs-update/#decision-making",target:"_blank",rel:"noopener noreferrer"}},[e._v("decision-making process"),o("OutboundLink")],1),e._v(" we have agreed upon.")]),e._v(" "),o("p",[e._v("In case of neutral response from a high number of members of the working group which prevents the achievement of the consensus, the issue gets deprioritised. In case someone has a strong interest for a certain issue that gets deprioritised, they can bring it to discussion during one of the calls.")]),e._v(" "),o("p",[e._v("Evgeny also brought up that on top of the Specs themselves, there are many non-functional things that need to be updated too. For example, the Specs navigation needs to be improved, with a clear structure of the metadata properties. Since all this non-functional stuff is still part of the Specs, and should therefore follow a defined process, it was proposed that this part would be led by OKFN, and reviewed at the end by working group members.")]),e._v(" "),o("p",[e._v("The last thing that we discussed during the calls were "),o("code",[e._v("patterns")]),e._v(". The purpose of the "),o("code",[e._v("patterns")]),e._v(" has not been super well defined within the Frictionless project, and it is now unclear to some. The idea at the time they were introduced, was for them to be best practices, but not refined enough to be part of the Specs quite yet. It was also a way to share a certain approach. But there was no clearly defined mechanism to promote the "),o("code",[e._v("patterns")]),e._v(" to be part of the Specs.")]),e._v(" "),o("p",[e._v("Since the approach with this Specs update is to simplify as much as possible and cut out what is not needed, it was decided to include the "),o("code",[e._v("patterns")]),e._v(" in the Specs documentation (as some kind of Cook Book) and not in the Specs themselves, and rename them as their current name suggests a certain formality.")]),e._v(" "),o("h1",{attrs:{id:"join-us-in-february"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#join-us-in-february"}},[e._v("#")]),e._v(" Join us in February!")]),e._v(" "),o("p",[e._v("Next community call is on February 22"),o("sup",[e._v("tnd")]),e._v(", join us to hear all the exciting news about the "),o("a",{attrs:{href:"https://specs.frictionlessdata.io/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Frictionless specs"),o("OutboundLink")],1),e._v(" update!")]),e._v(" "),o("p",[e._v("Do you have something you would like to present to the community at one of the upcoming calls? Let us know via "),o("a",{attrs:{href:"https://forms.gle/AWpbxyiGESNSUFK2A",target:"_blank",rel:"noopener noreferrer"}},[e._v("this form"),o("OutboundLink")],1),e._v(", or come and tell us on our "),o("a",{attrs:{href:"https://join.slack.com/t/frictionlessdata/shared_invite/zt-17kpbffnm-tRfDW_wJgOw8tJVLvZTrBg",target:"_blank",rel:"noopener noreferrer"}},[e._v("community chat on Slack"),o("OutboundLink")],1),e._v("(also accessible via a "),o("a",{attrs:{href:"https://matrix.to/#/#frictionlessdata:matrix.okfn.org",target:"_blank",rel:"noopener noreferrer"}},[e._v("Matrix bridge"),o("OutboundLink")],1),e._v(" if you prefer to use an open protocol).")]),e._v(" "),o("p",[e._v("You can sign up for the call already "),o("a",{attrs:{href:"https://docs.google.com/forms/d/e/1FAIpQLSeuNCopxXauMkrWvF6VHqOyHMcy54SfNDOseVXfWRQZWkvqjQ/viewform?usp=sf_link",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),o("OutboundLink")],1),e._v(". Do you want to share something with the community? Let us know when you sign up.")]),e._v(" "),o("h1",{attrs:{id:"call-recording"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#call-recording"}},[e._v("#")]),e._v(" Call Recording")]),e._v(" "),o("p",[e._v("Here is the recording of the full call:")]),e._v(" "),o("iframe",{attrs:{width:"560",height:"315",src:"https://www.youtube.com/embed/38RZZ15tIJw?si=kAocKNufgAlG64NE",title:"YouTube video player",frameborder:"0",allow:"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share",allowfullscreen:""}}),e._v(" "),o("h1",{attrs:{id:"thank-you"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#thank-you"}},[e._v("#")]),e._v(" Thank you")]),e._v(" "),o("p",[e._v("On a final note, we would like to thank all community members that joined the call and that keep all these discussions alive, and particularly to those who are putting a lot of thinking and hard work into the specs working group. Without you, all of this would not be possible.")])])}),[],!1,null,null,null);t.default=s.exports}}]); \ No newline at end of file +(window.webpackJsonp=window.webpackJsonp||[]).push([[181],{719:function(e,t,o){"use strict";o.r(t);var r=o(29),s=Object(r.a)({},(function(){var e=this,t=e.$createElement,o=e._self._c||t;return o("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[o("p",[e._v("On our last community call was on January 25"),o("sup",[e._v("th")]),e._v(", we continued discussing the "),o("a",{attrs:{href:"https://specs.frictionlessdata.io/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Frictionless Specs"),o("OutboundLink")],1),e._v(" update with community members. As a reminder, thanks to the generous support of "),o("a",{attrs:{href:"https://nlnet.nl/",target:"_blank",rel:"noopener noreferrer"}},[e._v("NLnet"),o("OutboundLink")],1),e._v(", the Frictionless core team, together with a working group composed of members of the community, is now working on a v2 of the Specs. Read the "),o("a",{attrs:{href:"https://frictionlessdata.io/blog/2023/11/15/frictionless-specs-update/",target:"_blank",rel:"noopener noreferrer"}},[e._v("announcement blog"),o("OutboundLink")],1),e._v(" to know more about it.")]),e._v(" "),o("h2",{attrs:{id:"working-group-discussion"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#working-group-discussion"}},[e._v("#")]),e._v(" Working group discussion")]),e._v(" "),o("p",[e._v("The first thing we discussed was ways to prioritise candidate features for future Specs updates (beyond v2). At the moment, our idea is to prioritise those issues that get more votes (aka thumbs up), among the "),o("a",{attrs:{href:"https://github.com/frictionlessdata/specs/issues?q=is%3Aissue+is%3Aopen+sort%3Areactions-%2B1-desc",target:"_blank",rel:"noopener noreferrer"}},[e._v("issues in the Specs repository"),o("OutboundLink")],1),e._v(".")]),e._v(" "),o("p",[e._v("Note, the reference for the v2 update is still "),o("a",{attrs:{href:"https://github.com/frictionlessdata/specs/milestone/6",target:"_blank",rel:"noopener noreferrer"}},[e._v("the one we initially published as a roadmap"),o("OutboundLink")],1),e._v(" in the update announcement blog. For the v2 release we will stick to that list of issues.")]),e._v(" "),o("p",[e._v("Anyone is welcome to open a Pull Request to address any of those issues. The PR will then be reviewed by the working group, according to the "),o("a",{attrs:{href:"https://frictionlessdata.io/blog/2023/11/15/frictionless-specs-update/#decision-making",target:"_blank",rel:"noopener noreferrer"}},[e._v("decision-making process"),o("OutboundLink")],1),e._v(" we have agreed upon.")]),e._v(" "),o("p",[e._v("In case of neutral response from a high number of members of the working group which prevents the achievement of the consensus, the issue gets deprioritised. In case someone has a strong interest for a certain issue that gets deprioritised, they can bring it to discussion during one of the calls.")]),e._v(" "),o("p",[e._v("Evgeny also brought up that on top of the Specs themselves, there are many non-functional things that need to be updated too. For example, the Specs navigation needs to be improved, with a clear structure of the metadata properties. Since all this non-functional stuff is still part of the Specs, and should therefore follow a defined process, it was proposed that this part would be led by OKFN, and reviewed at the end by working group members.")]),e._v(" "),o("p",[e._v("The last thing that we discussed during the calls were "),o("code",[e._v("patterns")]),e._v(". The purpose of the "),o("code",[e._v("patterns")]),e._v(" has not been super well defined within the Frictionless project, and it is now unclear to some. The idea at the time they were introduced, was for them to be best practices, but not refined enough to be part of the Specs quite yet. It was also a way to share a certain approach. But there was no clearly defined mechanism to promote the "),o("code",[e._v("patterns")]),e._v(" to be part of the Specs.")]),e._v(" "),o("p",[e._v("Since the approach with this Specs update is to simplify as much as possible and cut out what is not needed, it was decided to include the "),o("code",[e._v("patterns")]),e._v(" in the Specs documentation (as some kind of Cook Book) and not in the Specs themselves, and rename them as their current name suggests a certain formality.")]),e._v(" "),o("h1",{attrs:{id:"join-us-in-february"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#join-us-in-february"}},[e._v("#")]),e._v(" Join us in February!")]),e._v(" "),o("p",[e._v("Next community call is on February 22"),o("sup",[e._v("tnd")]),e._v(", join us to hear all the exciting news about the "),o("a",{attrs:{href:"https://specs.frictionlessdata.io/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Frictionless specs"),o("OutboundLink")],1),e._v(" update!")]),e._v(" "),o("p",[e._v("Do you have something you would like to present to the community at one of the upcoming calls? Let us know via "),o("a",{attrs:{href:"https://forms.gle/AWpbxyiGESNSUFK2A",target:"_blank",rel:"noopener noreferrer"}},[e._v("this form"),o("OutboundLink")],1),e._v(", or come and tell us on our "),o("a",{attrs:{href:"https://join.slack.com/t/frictionlessdata/shared_invite/zt-17kpbffnm-tRfDW_wJgOw8tJVLvZTrBg",target:"_blank",rel:"noopener noreferrer"}},[e._v("community chat on Slack"),o("OutboundLink")],1),e._v("(also accessible via a "),o("a",{attrs:{href:"https://matrix.to/#/#frictionlessdata:matrix.okfn.org",target:"_blank",rel:"noopener noreferrer"}},[e._v("Matrix bridge"),o("OutboundLink")],1),e._v(" if you prefer to use an open protocol).")]),e._v(" "),o("p",[e._v("You can sign up for the call already "),o("a",{attrs:{href:"https://docs.google.com/forms/d/e/1FAIpQLSeuNCopxXauMkrWvF6VHqOyHMcy54SfNDOseVXfWRQZWkvqjQ/viewform?usp=sf_link",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),o("OutboundLink")],1),e._v(". Do you want to share something with the community? Let us know when you sign up.")]),e._v(" "),o("h1",{attrs:{id:"call-recording"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#call-recording"}},[e._v("#")]),e._v(" Call Recording")]),e._v(" "),o("p",[e._v("Here is the recording of the full call:")]),e._v(" "),o("iframe",{attrs:{width:"560",height:"315",src:"https://www.youtube.com/embed/38RZZ15tIJw?si=kAocKNufgAlG64NE",title:"YouTube video player",frameborder:"0",allow:"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share",allowfullscreen:""}}),e._v(" "),o("h1",{attrs:{id:"thank-you"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#thank-you"}},[e._v("#")]),e._v(" Thank you")]),e._v(" "),o("p",[e._v("On a final note, we would like to thank all community members that joined the call and that keep all these discussions alive, and particularly to those who are putting a lot of thinking and hard work into the specs working group. Without you, all of this would not be possible.")])])}),[],!1,null,null,null);t.default=s.exports}}]); \ No newline at end of file diff --git a/assets/js/182.bbd38c7e.js b/assets/js/182.a686d2a7.js similarity index 99% rename from assets/js/182.bbd38c7e.js rename to assets/js/182.a686d2a7.js index da830bfb6..67088cf37 100644 --- a/assets/js/182.bbd38c7e.js +++ b/assets/js/182.a686d2a7.js @@ -1 +1 @@ -(window.webpackJsonp=window.webpackJsonp||[]).push([[182],{721:function(e,t,a){"use strict";a.r(t);var o=a(29),n=Object(o.a)({},(function(){var e=this,t=e.$createElement,a=e._self._c||t;return a("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[a("p",[e._v("On our last community call was on February 27"),a("sup",[e._v("th")]),e._v(", we continued discussing the Frictionless Specs update with community members. As a reminder, thanks to the generous support of "),a("a",{attrs:{href:"https://nlnet.nl/",target:"_blank",rel:"noopener noreferrer"}},[e._v("NLnet"),a("OutboundLink")],1),e._v(", the Frictionless core team, together with a working group composed of members of the community, is now working on a v2 of the Specs. Read the "),a("a",{attrs:{href:"https://frictionlessdata.io/blog/2023/11/15/frictionless-specs-update/",target:"_blank",rel:"noopener noreferrer"}},[e._v("announcement blog"),a("OutboundLink")],1),e._v(" to know more about it.")]),e._v(" "),a("h2",{attrs:{id:"working-group-discussion"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#working-group-discussion"}},[e._v("#")]),e._v(" Working group discussion")]),e._v(" "),a("p",[e._v("The main focus of this call was "),a("strong",[e._v("versioning")]),e._v(" and what the rules for versioning would be.")]),e._v(" "),a("p",[e._v("When announcing the Spec update, we decided there would be no breaking change introduced, and that the Data Package v2 (and all the following versions) would always support any previous version as well.")]),e._v(" "),a("p",[e._v("One thing that was highlighted during the discussion, though, is that there are some minimal aspects that we might want to modify for the sake of elegance (and to make implementers’ life easier too!). For example, Tableschema might require unique file names, and this could potentially be a breaking change. The group also pointed out that it would probably be better to do this now rather than later, when we hopefully have a much wider adoption. We would of course still be leaning towards conservatism and backwards compatibility, while having just enough flexibility to define v2 better.")]),e._v(" "),a("p",[e._v("With the working group we will need to clarify what a breaking change is: is it a change that breaks data or implementation? Kyle believes we can’t stop supporting data, and we want to be able to forever support a Data Package, even a 20 years old one (while we can afford to not support an implementation anymore). Keith thought we could decide to drop support for very old versions giving a very long timeframe for data published to update their Data Packages (e.g. 10 years).")]),e._v(" "),a("p",[e._v("There was a wide consensus on the fact that the easiest way to manage this would be to version the Spec, and introduce a version tag from v2 onwards. That way any consumer software would be able to read the version and convert it to the latest version if necessary. If no version is indicated, we would automatically assume it is a pre-v2 version.")]),e._v(" "),a("p",[e._v("Another thing we all agreed on is that the versioning would apply to Data Package as a whole, together with all the specs sitting under it. Data publishers would therefore need to indicate only one version for the Data Package.")]),e._v(" "),a("p",[e._v("Most of these considerations have been excellently captured by Peter in "),a("a",{attrs:{href:"https://github.com/frictionlessdata/specs/issues/858#issuecomment-1914820238",target:"_blank",rel:"noopener noreferrer"}},[e._v("this issue"),a("OutboundLink")],1),e._v(" already.")]),e._v(" "),a("p",[e._v("One thing the working group will need to decide is how to actually indicate the version number in the Data Package JSON. 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Without you, all of this would not be possible.")])])}),[],!1,null,null,null);t.default=n.exports}}]); \ No newline at end of file +(window.webpackJsonp=window.webpackJsonp||[]).push([[182],{720:function(e,t,a){"use strict";a.r(t);var o=a(29),n=Object(o.a)({},(function(){var e=this,t=e.$createElement,a=e._self._c||t;return a("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[a("p",[e._v("On our last community call was on February 27"),a("sup",[e._v("th")]),e._v(", we continued discussing the Frictionless Specs update with community members. As a reminder, thanks to the generous support of "),a("a",{attrs:{href:"https://nlnet.nl/",target:"_blank",rel:"noopener noreferrer"}},[e._v("NLnet"),a("OutboundLink")],1),e._v(", the Frictionless core team, together with a working group composed of members of the community, is now working on a v2 of the Specs. 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For example, Tableschema might require unique file names, and this could potentially be a breaking change. The group also pointed out that it would probably be better to do this now rather than later, when we hopefully have a much wider adoption. We would of course still be leaning towards conservatism and backwards compatibility, while having just enough flexibility to define v2 better.")]),e._v(" "),a("p",[e._v("With the working group we will need to clarify what a breaking change is: is it a change that breaks data or implementation? Kyle believes we can’t stop supporting data, and we want to be able to forever support a Data Package, even a 20 years old one (while we can afford to not support an implementation anymore). 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CSV Dialect can be used with a Tabular Data Resource to provide additional information.")])])])}),[],!1,null,null,null);t.default=i.exports}}]); \ No newline at end of file +(window.webpackJsonp=window.webpackJsonp||[]).push([[189],{728:function(a,t,e){"use strict";e.r(t);var r=e(29),i=Object(r.a)({},(function(){var a=this,t=a.$createElement,e=a._self._c||t;return e("ContentSlotsDistributor",{attrs:{"slot-key":a.$parent.slotKey}},[e("h1",{attrs:{id:"frictionless-projects"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#frictionless-projects"}},[a._v("#")]),a._v(" Frictionless Projects")]),a._v(" "),e("p",[e("big",[e("strong",[a._v("Open source projects for working with data.")])])],1),a._v(" "),e("p",[a._v("The Frictionless Data project provides a rich set of open source projects for working with data. 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Here are our recommendations:")]),a._v(" "),e("h3",{attrs:{id:"visual-interfaces"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#visual-interfaces"}},[a._v("#")]),a._v(" Visual Interfaces")]),a._v(" "),e("p",[a._v("If you prefer to use a visual interface:")]),a._v(" "),e("ul",[e("li",[e("strong",[a._v("Frictionless Application (coming soon):")]),a._v(" We’re working on our brand-new Frictionless Application that will be released in 2021. 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This creates visual quality reports and validation statuses on Github everytime you commit your data.")]),a._v(" "),e("li",[e("strong",[a._v("Datahub:")]),a._v(" For discovering, publishing, and sharing data we have "),e("a",{attrs:{href:"https://datahub.io/",target:"_blank",rel:"noopener noreferrer"}},[a._v("Datahub"),e("OutboundLink")],1),a._v(" which is built on Frictionless software. 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Using the “frictionless” command you can achieve many goals without needing to write Python code.")]),a._v(" "),e("li",[e("strong",[a._v("Livemark:")]),a._v(" For data journalists and technical writers we have a project called "),e("a",{attrs:{href:"https://livemark.frictionlessdata.io/",target:"_blank",rel:"noopener noreferrer"}},[a._v("Livemark"),e("OutboundLink")],1),a._v(". Using the “livemark” command in the CLI you can publish a website that incorporates Frictionless functions and is powered by markdown articles.")]),a._v(" "),e("li",[e("strong",[a._v("Datahub:")]),a._v(" Frictionless provides a command-line tool called "),e("a",{attrs:{href:"https://datahub.io/docs/features/data-cli",target:"_blank",rel:"noopener noreferrer"}},[a._v("Data"),e("OutboundLink")],1),a._v(" which is an important part of the Datahub project. 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This also informed the way we designed the work, as a set of connected processing and transport steps, very much outside of the publication process itself. While this was acceptable for the scope of the pilot, the real power of the approach we demonstrate here is in integrating it with pre-publication phases of data, via a combined automated and manually curated data process. Indeed, we can see via this pilot the potential to streamline the workflow demonstrated into a complete research data publication process, and would welcome the opportunity to conduct one or more pilots that build on this approach, deeply integrated into pre-publication data workflows.")]),t._v(" "),s("h2",{attrs:{id:"context"}},[s("a",{staticClass:"header-anchor",attrs:{href:"#context"}},[t._v("#")]),t._v(" Context")]),t._v(" "),s("p",[t._v("The UK Data Service offers an online repository where researchers can archive, publish and share research data, called "),s("a",{attrs:{href:"http://reshare.ukdataservice.ac.uk/",target:"_blank",rel:"noopener noreferrer"}},[t._v("Reshare"),s("OutboundLink")],1),t._v(". Reshare exposes an "),s("a",{attrs:{href:"https://www.openarchives.org/pmh/",target:"_blank",rel:"noopener noreferrer"}},[t._v("OAI-PMH"),s("OutboundLink")],1),t._v(" endpoint to facilitate metadata harvesting.")]),t._v(" "),s("p",[s("a",{attrs:{href:"http://datahub.io/",target:"_blank",rel:"noopener noreferrer"}},[t._v("datahub.io"),s("OutboundLink")],1),t._v(" is a data workflows web application build around the modular Frictionless Data toolchain, designed to find, share and publish high quality data online. Each entry has a ‘Showcase’ to display data package properties, and preview data with tables and simple visualisations. As well as the Showcase, "),s("a",{attrs:{href:"http://datahub.io/",target:"_blank",rel:"noopener noreferrer"}},[t._v("datahub.io"),s("OutboundLink")],1),t._v(" provides straight-forward direct access to import data into a variety of tools used by researchers; R, Pandas, Python, JavaScript, and SQL. "),s("a",{attrs:{href:"https://specs.frictionlessdata.io/data-package/",target:"_blank",rel:"noopener noreferrer"}},[t._v("Frictionless Data Data Packages"),s("OutboundLink")],1),t._v(" can be pushed to "),s("a",{attrs:{href:"http://datahub.io",target:"_blank",rel:"noopener noreferrer"}},[t._v("datahub.io"),s("OutboundLink")],1),t._v(" to create dataset entries.")]),t._v(" "),s("h3",{attrs:{id:"problem-we-were-trying-to-solve"}},[s("a",{staticClass:"header-anchor",attrs:{href:"#problem-we-were-trying-to-solve"}},[t._v("#")]),t._v(" Problem We Were Trying To Solve")]),t._v(" "),s("p",[t._v("We want to investigate the use of the Data Package concept, and Frictionless Data software to facilitate the reuse of data archived in Reshare.")]),t._v(" "),s("p",[t._v("We are especially interested in trialling pipelines to automate data harvesting from UKDS into "),s("a",{attrs:{href:"http://datahub.io/",target:"_blank",rel:"noopener noreferrer"}},[t._v("datahub.io"),s("OutboundLink")],1),t._v(" using Frictionless Data software such as "),s("a",{attrs:{href:"https://github.com/frictionlessdata/datapackage-pipelines",target:"_blank",rel:"noopener noreferrer"}},[t._v("datapackage-pipelines"),s("OutboundLink")],1),t._v(", and creating appropriate processors to translate widely used statistics file formats, such as "),s("a",{attrs:{href:"https://www.ibm.com/analytics/us/en/technology/spss/",target:"_blank",rel:"noopener noreferrer"}},[t._v("SPSS"),s("OutboundLink")],1),t._v(", to text-based tabular data formats such as CSV.")]),t._v(" "),s("p",[t._v("We chose the Data Package Pipelines library because it provides us with a well tested and mature framework of established processors to work with tabular data from a variety of sources and formats. Custom processors can easily be added to extend pipeline functionality. Pipelines can be configured using a simple declarative specification. Other tools supporting the underlying Frictionless Data specifications, such as "),s("a",{attrs:{href:"https://github.com/frictionlessdata/tableschema-py/",target:"_blank",rel:"noopener noreferrer"}},[t._v("tableschema"),s("OutboundLink")],1),t._v(" and "),s("a",{attrs:{href:"https://github.com/frictionlessdata/tableschema-py/",target:"_blank",rel:"noopener noreferrer"}},[t._v("goodtables"),s("OutboundLink")],1),t._v(" can be easily integrated as appropriate.")]),t._v(" "),s("p",[t._v("In this pilot we are trialling tools to:")]),t._v(" "),s("ul",[s("li",[t._v("automate data harvesting from UKDS, to "),s("a",{attrs:{href:"http://datahub.io/",target:"_blank",rel:"noopener noreferrer"}},[t._v("datahub.io"),s("OutboundLink")],1),t._v(", through a data package pipeline.")]),t._v(" "),s("li",[t._v("translate binary data formats (SPSS) to text-based tabular formats.")]),t._v(" "),s("li",[t._v("validate tabular data harvested from UKDS with goodtables.")]),t._v(" "),s("li",[t._v("fix or workaround common data issues identified from validation report, in the source-spec")]),t._v(" "),s("li",[t._v("correct file encoding")]),t._v(" "),s("li",[t._v("skip non-data rows")]),t._v(" "),s("li",[t._v("skip specified validation checks (duplicate-rows)")]),t._v(" "),s("li",[t._v("specify header rows in csv files")]),t._v(" "),s("li",[t._v("explicitly defining tabular headers")]),t._v(" "),s("li",[t._v("trial the "),s("a",{attrs:{href:"http://datahub.io/",target:"_blank",rel:"noopener noreferrer"}},[t._v("datahub.io"),s("OutboundLink")],1),t._v(" API with real-world data")]),t._v(" "),s("li",[t._v("use the Showcase features of "),s("a",{attrs:{href:"http://datahub.io/",target:"_blank",rel:"noopener noreferrer"}},[t._v("datahub.io"),s("OutboundLink")],1),t._v(" to provide instance data previews and visualisations.")])]),t._v(" "),s("h3",{attrs:{id:"the-work"}},[s("a",{staticClass:"header-anchor",attrs:{href:"#the-work"}},[t._v("#")]),t._v(" The Work")]),t._v(" "),s("h4",{attrs:{id:"what-did-we-do"}},[s("a",{staticClass:"header-anchor",attrs:{href:"#what-did-we-do"}},[t._v("#")]),t._v(" What Did We Do")]),t._v(" "),s("p",[t._v("During the pilot, we focussed on creating a reusable pipeline of processors to harvest data and dataset metadata from the UKDS Reshare service, and output valid Data Packages with tabular resources. Each pipeline processor step was created as a separate module to facilitate testing and reuse in other similar pipelines.")]),t._v(" "),s("p",[t._v("UKDS datasets were selected from "),s("a",{attrs:{href:"http://reshare.ukdataservice.ac.uk/cgi/stats/report/most_popular_eprints",target:"_blank",rel:"noopener noreferrer"}},[t._v("the UKDS list"),s("OutboundLink")],1),t._v(". Entries were selected based on the data format we intended to write processors for (.csv, .tsv, xls, or .sav), how the dataset might help demonstrate various aspects of the pipeline, and how well they might lend themselves to visualisation on "),s("a",{attrs:{href:"http://datahub.io/",target:"_blank",rel:"noopener noreferrer"}},[t._v("datahub.io"),s("OutboundLink")],1),t._v(".")]),t._v(" "),s("p",[t._v("Below is an outline of the pipeline flow from UKDS Reshare Archive to "),s("a",{attrs:{href:"http://datahub.io/",target:"_blank",rel:"noopener noreferrer"}},[t._v("datahub.io"),s("OutboundLink")],1),t._v(" entry:")]),t._v(" "),s("p",[s("img",{attrs:{src:a(435),alt:"pipeline flow from UKDS Reshare Archive to datahub.io"}}),s("br"),t._v(" "),s("em",[t._v("pipeline flow from UKDS Reshare Archive to "),s("a",{attrs:{href:"http://datahub.io/",target:"_blank",rel:"noopener noreferrer"}},[t._v("datahub.io"),s("OutboundLink")],1)])]),t._v(" "),s("h5",{attrs:{id:"specifying-an-entry"}},[s("a",{staticClass:"header-anchor",attrs:{href:"#specifying-an-entry"}},[t._v("#")]),t._v(" Specifying an Entry")]),t._v(" "),s("p",[t._v("We wanted to ensure that each UKDS dataset to be maintained on "),s("a",{attrs:{href:"http://datahub.io/",target:"_blank",rel:"noopener noreferrer"}},[t._v("datahub.io"),s("OutboundLink")],1),t._v(" could be easily configured to specify where to harvest its resource data and dataset metadata. We also wanted to add other configuration details to help customise the pipeline to work with tricky resources, and view specifications for subsequent visualisation on "),s("a",{attrs:{href:"http://datahub.io/",target:"_blank",rel:"noopener noreferrer"}},[t._v("datahub.io"),s("OutboundLink")],1),t._v(".")]),t._v(" "),s("p",[t._v("The source-spec for each Reshare entry defines a list of URLs for each resource in the dataset that we’re interested in harvesting, and the resource format (csv, tsv, xls, or spss).")]),t._v(" "),s("p",[t._v("If an OAI ID is provided, it will be used to harvest dataset metadata from the Reshare OAI endpoint.")]),t._v(" "),s("p",[t._v("As well as defining source locations, we also want to provide a way to customise downstream processor behaviour, to help work around potential resource issues.")]),t._v(" "),s("p",[t._v("Below is an example yaml source-spec for two entries, demonstrating various configuration options.")]),t._v(" "),s("div",{staticClass:"language-yaml extra-class"},[s("pre",{pre:!0,attrs:{class:"language-yaml"}},[s("code",[s("span",{pre:!0,attrs:{class:"token key atrule"}},[t._v("entries")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(":")]),t._v("\n\n "),s("span",{pre:!0,attrs:{class:"token key atrule"}},[t._v("civil-servant-survey")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(":")]),t._v(" "),s("span",{pre:!0,attrs:{class:"token comment"}},[t._v("# entry name")]),t._v("\n "),s("span",{pre:!0,attrs:{class:"token key atrule"}},[t._v("source")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(":")]),t._v(" "),s("span",{pre:!0,attrs:{class:"token comment"}},[t._v("# a list of sources")]),t._v("\n "),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("-")]),t._v(" "),s("span",{pre:!0,attrs:{class:"token key atrule"}},[t._v("url")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(":")]),t._v(" http"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(":")]),t._v("//reshare.ukdataservice.ac.uk/851401/10/Coded_SurveyData.csv\n "),s("span",{pre:!0,attrs:{class:"token key atrule"}},[t._v("format")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(":")]),t._v(" csv\n "),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("-")]),t._v(" "),s("span",{pre:!0,attrs:{class:"token key atrule"}},[t._v("url")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(":")]),t._v(" http"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(":")]),t._v("//reshare.ukdataservice.ac.uk/851401/2/key%20%283%29.csv\n "),s("span",{pre:!0,attrs:{class:"token key atrule"}},[t._v("format")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(":")]),t._v(" csv\n "),s("span",{pre:!0,attrs:{class:"token key atrule"}},[t._v("goodtables")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(":")]),t._v(" "),s("span",{pre:!0,attrs:{class:"token comment"}},[t._v("# custom processor config for goodtables")]),t._v("\n "),s("span",{pre:!0,attrs:{class:"token key atrule"}},[t._v("skip_checks")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(":")]),t._v("\n "),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("-")]),t._v(" duplicate"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("-")]),t._v("row\n "),s("span",{pre:!0,attrs:{class:"token key atrule"}},[t._v("oai-id")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(":")]),t._v(" "),s("span",{pre:!0,attrs:{class:"token number"}},[t._v("851401")]),t._v(" "),s("span",{pre:!0,attrs:{class:"token comment"}},[t._v("# OAI ID to harvest dataset metadata")]),t._v("\n\n "),s("span",{pre:!0,attrs:{class:"token key atrule"}},[t._v("uk-gov-petitions")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(":")]),t._v("\n "),s("span",{pre:!0,attrs:{class:"token key atrule"}},[t._v("source")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(":")]),t._v("\n "),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("-")]),t._v(" "),s("span",{pre:!0,attrs:{class:"token key atrule"}},[t._v("url")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(":")]),t._v(" http"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(":")]),t._v("//reshare.ukdataservice.ac.uk/851614/1/gov_pet_metadata.tab\n "),s("span",{pre:!0,attrs:{class:"token key atrule"}},[t._v("format")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(":")]),t._v(" tsv\n "),s("span",{pre:!0,attrs:{class:"token key atrule"}},[t._v("tabulator")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(":")]),t._v(" "),s("span",{pre:!0,attrs:{class:"token comment"}},[t._v("# custom processor config for tabulator")]),t._v("\n "),s("span",{pre:!0,attrs:{class:"token key atrule"}},[t._v("encoding")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(":")]),t._v(" utf"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("-")]),s("span",{pre:!0,attrs:{class:"token number"}},[t._v("8")]),t._v(" "),s("span",{pre:!0,attrs:{class:"token comment"}},[t._v("# explicitly define source file encoding")]),t._v("\n "),s("span",{pre:!0,attrs:{class:"token key atrule"}},[t._v("headers")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(":")]),t._v(" "),s("span",{pre:!0,attrs:{class:"token comment"}},[t._v("# explicitly define missing column headers")]),t._v("\n "),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("-")]),t._v(" id\n "),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("-")]),t._v(" title\n "),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("-")]),t._v(" department\n "),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("-")]),t._v(" starting\n "),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("-")]),t._v(" closing\n "),s("span",{pre:!0,attrs:{class:"token key atrule"}},[t._v("oai-id")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(":")]),t._v(" "),s("span",{pre:!0,attrs:{class:"token number"}},[t._v("851614")]),t._v("\n "),s("span",{pre:!0,attrs:{class:"token key atrule"}},[t._v("views")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(":")]),t._v("\n "),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("-")]),t._v(" views/petitions"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("-")]),t._v("view.json\n")])])]),s("p",[t._v("Sources in the first entry, "),s("em",[t._v("civil-servant-survey")]),t._v(", contain duplicate rows, which would normally fail goodtables validation. Here we will allow the "),s("code",[t._v("duplicate-row")]),t._v(" check to be skipped.")]),t._v(" "),s("p",[t._v("The source in the second entry has the wrong character encoding and no headers declared. We can fix these issues to allow the pipeline to process the resource by explicitly specifying the file encoding, and declaring the column headers.")]),t._v(" "),s("h4",{attrs:{id:"data-set-and-resource-harvest"}},[s("a",{staticClass:"header-anchor",attrs:{href:"#data-set-and-resource-harvest"}},[t._v("#")]),t._v(" Data Set and Resource Harvest")]),t._v(" "),s("p",[t._v("We identified that Reshare has an OAI-PMH2 compatible endpoint to harvest information about each Reshare data set. So we created an "),s("code",[t._v("ukds.add_oai_metadata")]),t._v(" pipeline processor. OAI metadata is compatible with Dublin Core Elements, and the processor translates this into Data Package compatible properties.")]),t._v(" "),s("p",[t._v("Resources are added to the newly created Data Package and downloaded from the URLs defined in the yaml configuration. We support adding SPSS (.sav), CSV, TSV and XLS file formats.")]),t._v(" "),s("p",[t._v("To support the widely used SPSS format, we created an "),s("code",[t._v("spss.add_spss processor")]),t._v(" that makes use of the "),s("a",{attrs:{href:"https://github.com/frictionlessdata/tableschema-spss-py",target:"_blank",rel:"noopener noreferrer"}},[t._v("tableschema-spss"),s("OutboundLink")],1),t._v(" plugin to read SPSS files and create tableschema descriptors from them.")]),t._v(" "),s("h4",{attrs:{id:"validation-reports-and-common-issues"}},[s("a",{staticClass:"header-anchor",attrs:{href:"#validation-reports-and-common-issues"}},[t._v("#")]),t._v(" Validation Reports and Common Issues")]),t._v(" "),s("p",[t._v("To help ensure data quality, we want to validate the harvested tabular data before continuing the pipeline. We created a "),s("a",{attrs:{href:"https://github.com/frictionlessdata/datapackage-pipelines-goodtables",target:"_blank",rel:"noopener noreferrer"}},[s("code",[t._v("goodtables.validate")]),t._v(" processor"),s("OutboundLink")],1),t._v(", which will write a validation report for each resource. If a resource fails to validate against its schema, or has other data issues, the pipeline will fail. Errors can be identified from validation reports, fixed, and the pipeline re-run.")]),t._v(" "),s("p",[t._v("Below are examples of issues revealed by validation that can occur when working with real-world data.")]),t._v(" "),s("h5",{attrs:{id:"au-pairing-after-the-au-pair-scheme-specifying-a-xls-sheet-and-working-around-non-data-rows"}},[s("a",{staticClass:"header-anchor",attrs:{href:"#au-pairing-after-the-au-pair-scheme-specifying-a-xls-sheet-and-working-around-non-data-rows"}},[t._v("#")]),t._v(" “Au pairing after the au pair scheme”: specifying a xls sheet, and working around non-data rows")]),t._v(" "),s("p",[t._v("The "),s("a",{attrs:{href:"http://reshare.ukdataservice.ac.uk/851656/",target:"_blank",rel:"noopener noreferrer"}},[t._v("“Au Pairing” dataset"),s("OutboundLink")],1),t._v(" has a single .xls resource we’re interested in harvesting. The file contains four sheets, and we’re interested in the second one, which contains the data. So we specify our entry:")]),t._v(" "),s("div",{staticClass:"language-yaml extra-class"},[s("pre",{pre:!0,attrs:{class:"language-yaml"}},[s("code",[s("span",{pre:!0,attrs:{class:"token key atrule"}},[t._v("au-pairing")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(":")]),t._v("\n "),s("span",{pre:!0,attrs:{class:"token key atrule"}},[t._v("source")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(":")]),t._v("\n "),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("-")]),t._v(" "),s("span",{pre:!0,attrs:{class:"token key atrule"}},[t._v("url")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(":")]),t._v(" http"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(":")]),t._v("//reshare.ukdataservice.ac.uk/851656/6/GumtreeAds_AuPairsAnalysis1.xls\n "),s("span",{pre:!0,attrs:{class:"token key atrule"}},[t._v("format")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(":")]),t._v(" xls\n "),s("span",{pre:!0,attrs:{class:"token key atrule"}},[t._v("tabulator")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(":")]),t._v("\n "),s("span",{pre:!0,attrs:{class:"token key atrule"}},[t._v("sheet")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(":")]),t._v(" "),s("span",{pre:!0,attrs:{class:"token number"}},[t._v("2")]),t._v(" "),s("span",{pre:!0,attrs:{class:"token comment"}},[t._v("# use sheet 2 in the file")]),t._v("\n "),s("span",{pre:!0,attrs:{class:"token key atrule"}},[t._v("oai-id")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(":")]),t._v(" "),s("span",{pre:!0,attrs:{class:"token number"}},[t._v("851656")]),t._v("\n")])])]),s("p",[t._v("Notice we have indicated which sheet in the file to use.")]),t._v(" "),s("p",[t._v("The data sheet has a single header row, but it also has this header row repeated at intervals throughout the data, presumably to aid the human reader when reviewing the data manually.")]),t._v(" "),s("p",[s("img",{attrs:{src:a(436),alt:""}}),s("br"),t._v(" "),s("em",[t._v("screengrab of the UKDS “Au Pairing” datasheet")])]),t._v(" "),s("p",[t._v("For machine processing, this isn’t ideal. In fact, it will fail our goodtables validation processor with the following (truncated) report:")]),t._v(" "),s("div",{staticClass:"language-yaml extra-class"},[s("pre",{pre:!0,attrs:{class:"language-yaml"}},[s("code",[s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("{")]),t._v("\n "),s("span",{pre:!0,attrs:{class:"token key atrule"}},[t._v('"time"')]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(":")]),t._v(" "),s("span",{pre:!0,attrs:{class:"token number"}},[t._v("0.466")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v("\n "),s("span",{pre:!0,attrs:{class:"token key atrule"}},[t._v('"valid"')]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(":")]),t._v(" "),s("span",{pre:!0,attrs:{class:"token boolean important"}},[t._v("false")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v("\n "),s("span",{pre:!0,attrs:{class:"token key atrule"}},[t._v('"error-count"')]),s("span",{pre:!0,attrs:{class:"token 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In this case they indicate that duplicate rows are present (the repeated header). 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We also instruct goodtables to skip the "),s("code",[t._v("duplicate-row")]),t._v(" check."),s("br"),t._v("\nThe outputted csv resource file will no longer contain rows with the duplicate header.")]),t._v(" "),s("h5",{attrs:{id:"uk-government-petitions-wrong-file-encoding-and-specifying-missing-headers"}},[s("a",{staticClass:"header-anchor",attrs:{href:"#uk-government-petitions-wrong-file-encoding-and-specifying-missing-headers"}},[t._v("#")]),t._v(" “UK government petitions”: wrong file encoding, and specifying missing headers")]),t._v(" "),s("p",[t._v("The “"),s("a",{attrs:{href:"http://gov.uk/",target:"_blank",rel:"noopener noreferrer"}},[t._v("gov.uk"),s("OutboundLink")],1),t._v(" petitions” dataset has a TSV data file we’re interested in. However, it has been saved with the wrong character encoding and attempting to open may return an error, or display some characters incorrectly.")]),t._v(" "),s("p",[t._v("Additionally, there is no header row specified at the top of the file, so the resulting data package won’t have the correct header information in the resource’s schema.")]),t._v(" "),s("p",[t._v("We can fix both of these issues in our entry specification:")]),t._v(" "),s("div",{staticClass:"language-yaml extra-class"},[s("pre",{pre:!0,attrs:{class:"language-yaml"}},[s("code",[s("span",{pre:!0,attrs:{class:"token key atrule"}},[t._v("uk-gov-petitions")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(":")]),t._v("\n "),s("span",{pre:!0,attrs:{class:"token key atrule"}},[t._v("source")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(":")]),t._v("\n "),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("-")]),t._v(" "),s("span",{pre:!0,attrs:{class:"token key atrule"}},[t._v("url")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(":")]),t._v(" http"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(":")]),t._v("//reshare.ukdataservice.ac.uk/851614/1/gov_pet_metadata.tab\n "),s("span",{pre:!0,attrs:{class:"token key atrule"}},[t._v("format")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(":")]),t._v(" tsv\n "),s("span",{pre:!0,attrs:{class:"token key atrule"}},[t._v("tabulator")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(":")]),t._v("\n "),s("span",{pre:!0,attrs:{class:"token key atrule"}},[t._v("encoding")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(":")]),t._v(" utf"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("-")]),s("span",{pre:!0,attrs:{class:"token number"}},[t._v("8")]),t._v(" "),s("span",{pre:!0,attrs:{class:"token comment"}},[t._v("# specify file encoding")]),t._v("\n "),s("span",{pre:!0,attrs:{class:"token key atrule"}},[t._v("headers")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(":")]),t._v(" "),s("span",{pre:!0,attrs:{class:"token comment"}},[t._v("# define missing headers")]),t._v("\n "),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("-")]),t._v(" id\n "),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("-")]),t._v(" title\n "),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("-")]),t._v(" department\n "),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("-")]),t._v(" starting\n "),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("-")]),t._v(" closing\n "),s("span",{pre:!0,attrs:{class:"token key atrule"}},[t._v("oai-id")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(":")]),t._v(" "),s("span",{pre:!0,attrs:{class:"token number"}},[t._v("851614")]),t._v("\n")])])]),s("p",[t._v("Above, we have defined the character encoding we want to use when opening the file, and we’ve explicitly defined the headers to use. These headers will be added to the first row of the outputted csv resource file in the data package.")]),t._v(" "),s("p",[t._v("We can also use the "),s("code",[t._v("headers")]),t._v(" parameter to define which row contains header information. By default this is the first row. However, sometimes a data file will have the headers on a different row:")]),t._v(" "),s("p",[s("img",{attrs:{src:a(437),alt:""}}),s("br"),t._v(" "),s("em",[t._v("screengrab of the UKDS “Government Petitions” datasheet")])]),t._v(" "),s("p",[t._v("This example file has its headers defined in row three, with other information, and an empty row in the first two rows. We can tell our pipeline which row contains headers by specifying it in the entry configuration:")]),t._v(" "),s("div",{staticClass:"language-yaml extra-class"},[s("pre",{pre:!0,attrs:{class:"language-yaml"}},[s("code",[s("span",{pre:!0,attrs:{class:"token key atrule"}},[t._v("example-entry")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(":")]),t._v("\n "),s("span",{pre:!0,attrs:{class:"token key atrule"}},[t._v("source")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(":")]),t._v("\n "),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("-")]),t._v(" "),s("span",{pre:!0,attrs:{class:"token key atrule"}},[t._v("url")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(":")]),t._v(" http"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(":")]),t._v("//www.newcastle.gov.uk/sites/drupalncc.newcastle.gov.uk/files/wwwfileroot/your"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("-")]),t._v("council/local_transparency/january_2012.csv\n "),s("span",{pre:!0,attrs:{class:"token key atrule"}},[t._v("format")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(":")]),t._v(" csv\n "),s("span",{pre:!0,attrs:{class:"token key atrule"}},[t._v("tabulator")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(":")]),t._v("\n "),s("span",{pre:!0,attrs:{class:"token key atrule"}},[t._v("headers")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(":")]),t._v(" "),s("span",{pre:!0,attrs:{class:"token number"}},[t._v("3")]),t._v(" "),s("span",{pre:!0,attrs:{class:"token comment"}},[t._v("# specifying which row contains headers")]),t._v("\n")])])]),s("h4",{attrs:{id:"add-data-package-views"}},[s("a",{staticClass:"header-anchor",attrs:{href:"#add-data-package-views"}},[t._v("#")]),t._v(" Add Data Package Views")]),t._v(" "),s("p",[t._v("View specs can be added to the data package to enable "),s("a",{attrs:{href:"http://datahub.io/",target:"_blank",rel:"noopener noreferrer"}},[t._v("datahub.io"),s("OutboundLink")],1),t._v(" to create visualisations from resource data in the data package. The "),s("code",[t._v("views")]),t._v(" property is a list of file paths to json files containing "),s("a",{attrs:{href:"https://specs.frictionlessdata.io/views/",target:"_blank",rel:"noopener noreferrer"}},[t._v("view-spec"),s("OutboundLink")],1),t._v(" compatible views.")]),t._v(" "),s("p",[t._v("Currently, "),s("a",{attrs:{href:"http://datahub.io/",target:"_blank",rel:"noopener noreferrer"}},[t._v("datahub.io"),s("OutboundLink")],1),t._v(" supports views written either with a ‘simple’ views-spec, or using Vega (v 2.6.5). See "),s("a",{attrs:{href:"https://datahub.io/docs/features/views",target:"_blank",rel:"noopener noreferrer"}},[t._v("datahub.io docs"),s("OutboundLink")],1),t._v(" for more details about the supported views-spec.")]),t._v(" "),s("h4",{attrs:{id:"push-to-datahub-io"}},[s("a",{staticClass:"header-anchor",attrs:{href:"#push-to-datahub-io"}},[t._v("#")]),t._v(" Push to "),s("a",{attrs:{href:"http://datahub.io/",target:"_blank",rel:"noopener noreferrer"}},[t._v("datahub.io"),s("OutboundLink")],1)]),t._v(" "),s("p",[t._v("Once the harvesting pipeline has been run the resulting data packages are pushed to "),s("a",{attrs:{href:"http://datahub.io/",target:"_blank",rel:"noopener noreferrer"}},[t._v("datahub.io"),s("OutboundLink")],1),t._v(" using the "),s("a",{attrs:{href:"https://github.com/datahq/datahub-cli",target:"_blank",rel:"noopener noreferrer"}},[s("code",[t._v("datahub.dump.to_datahub")]),s("OutboundLink")],1),t._v(" processor.")]),t._v(" "),s("p",[t._v("This creates or updates an entry for the package on datahub. If a view has been defined in the entry configuration, this will be created on the "),s("a",{attrs:{href:"http://datahub.io/",target:"_blank",rel:"noopener noreferrer"}},[t._v("datahub.io"),s("OutboundLink")],1),t._v(" entry Showcase page.")]),t._v(" "),s("h3",{attrs:{id:"review"}},[s("a",{staticClass:"header-anchor",attrs:{href:"#review"}},[t._v("#")]),t._v(" Review")]),t._v(" "),s("p",[t._v("We were able to demonstrate that a data processing pipeline using Frictionless Data tools can facilitate the automated harvesting, validation, transformation, and upload to a data package-compatible third-party service, based on a simple configuration.")]),t._v(" "),s("h3",{attrs:{id:"next-steps"}},[s("a",{staticClass:"header-anchor",attrs:{href:"#next-steps"}},[t._v("#")]),t._v(" Next Steps")]),t._v(" "),s("p",[t._v("The pilot data package pipeline runs locally in a development environment, but given each processor has been written as a separate module, these could be used within any pipeline. "),s("a",{attrs:{href:"http://datahub.io/",target:"_blank",rel:"noopener noreferrer"}},[t._v("datahub.io"),s("OutboundLink")],1),t._v(" uses datapackage-pipelines within its infrastructure, and the processors developed for this project could be used within "),s("a",{attrs:{href:"http://datahub.io/",target:"_blank",rel:"noopener noreferrer"}},[t._v("datahub.io"),s("OutboundLink")],1),t._v(" itself to facilitate the automatic harvesting of datasets from OAI-PMH enabled data sources.")]),t._v(" "),s("p",[t._v("Once a pipeline is in place, it can be scheduled to run each day (or week, month, etc.). This would ensure "),s("a",{attrs:{href:"http://datahub.io/",target:"_blank",rel:"noopener noreferrer"}},[t._v("datahub.io"),s("OutboundLink")],1),t._v(" is up-to-date with data on UKDS Reshare.")]),t._v(" "),s("p",[t._v("Working with ‘real-world’ data from UKDS Reshare has helped to identify and prioritise improvements and future features for "),s("a",{attrs:{href:"http://datahub.io/",target:"_blank",rel:"noopener noreferrer"}},[t._v("datahub.io"),s("OutboundLink")],1),t._v(".")]),t._v(" "),s("h3",{attrs:{id:"additional-resources"}},[s("a",{staticClass:"header-anchor",attrs:{href:"#additional-resources"}},[t._v("#")]),t._v(" Additional Resources")]),t._v(" "),s("ul",[s("li",[s("a",{attrs:{href:"https://github.com/frictionlessdata/pilot-ukds",target:"_blank",rel:"noopener noreferrer"}},[t._v("The main code repository for this pilot"),s("OutboundLink")],1),t._v(".")]),t._v(" "),s("li",[s("a",{attrs:{href:"https://github.com/frictionlessdata/datapackage-pipelines",target:"_blank",rel:"noopener noreferrer"}},[t._v("A framework for processing data packages in pipelines of modular components"),s("OutboundLink")],1),t._v(".")]),t._v(" "),s("li",[s("a",{attrs:{href:"https://github.com/frictionlessdata/datapackage-pipelines-spss",target:"_blank",rel:"noopener noreferrer"}},[t._v("A Data Package Pipelines processor for SPSS file formats"),s("OutboundLink")],1),t._v(".")]),t._v(" "),s("li",[s("a",{attrs:{href:"https://github.com/frictionlessdata/datapackage-pipelines-goodtables",target:"_blank",rel:"noopener noreferrer"}},[t._v("A Data Package Pipelines processor for validating tabular data using goodtables-py"),s("OutboundLink")],1),t._v(".")]),t._v(" "),s("li",[s("a",{attrs:{href:"https://github.com/datahq/datapackage-pipelines-datahub",target:"_blank",rel:"noopener noreferrer"}},[t._v("A Data Package Pipelines processor to push data packages to datahub.io"),s("OutboundLink")],1),t._v(".")])])])}),[],!1,null,null,null);e.default=n.exports}}]); \ No newline at end of file +(window.webpackJsonp=window.webpackJsonp||[]).push([[19],{432:function(t,e,a){t.exports=a.p+"assets/img/ukds-pipeline-flow.8cf26465.png"},433:function(t,e,a){t.exports=a.p+"assets/img/ukds-au-pairing-datasheet.6d159749.png"},434:function(t,e,a){t.exports=a.p+"assets/img/ukds-govt-petitions-datasheet.d9d1cb8a.png"},582:function(t,e,a){"use strict";a.r(e);var s=a(29),n=Object(s.a)({},(function(){var t=this,e=t.$createElement,s=t._self._c||e;return s("ContentSlotsDistributor",{attrs:{"slot-key":t.$parent.slotKey}},[s("p",[t._v("The UK Data Service, like many other research repository services, employs a range of closed source software solutions for the publication and consumption of research data. The data itself is often published in closed and proprietary data formats, and the data is not always, or purposefully, published in a way that enables data reuse.")]),t._v(" "),s("p",[t._v("Based on an initial exploration of user need, we identified, together with the UK Data Service, the following areas for a Frictionless Data pilot:")]),t._v(" "),s("ul",[s("li",[t._v("Conversion of data and metadata to open formats using open source tools.")]),t._v(" "),s("li",[t._v("Use the Frictionless Data toolchain to assess and report on data quality (as a proxy for reusability).")]),t._v(" "),s("li",[t._v("Demonstrate the possibility of generating visualizations from source data and metadata, described with Frictionless Data specifications.")]),t._v(" "),s("li",[t._v("Host the data with all these attributes (open formats, reusable quality, visualized) on an open source platform for data.")])]),t._v(" "),s("p",[t._v("We worked with data that was publicly accessible, and therefore in its post-publication phase. This also informed the way we designed the work, as a set of connected processing and transport steps, very much outside of the publication process itself. While this was acceptable for the scope of the pilot, the real power of the approach we demonstrate here is in integrating it with pre-publication phases of data, via a combined automated and manually curated data process. Indeed, we can see via this pilot the potential to streamline the workflow demonstrated into a complete research data publication process, and would welcome the opportunity to conduct one or more pilots that build on this approach, deeply integrated into pre-publication data workflows.")]),t._v(" "),s("h2",{attrs:{id:"context"}},[s("a",{staticClass:"header-anchor",attrs:{href:"#context"}},[t._v("#")]),t._v(" Context")]),t._v(" "),s("p",[t._v("The UK Data Service offers an online repository where researchers can archive, publish and share research data, called "),s("a",{attrs:{href:"http://reshare.ukdataservice.ac.uk/",target:"_blank",rel:"noopener noreferrer"}},[t._v("Reshare"),s("OutboundLink")],1),t._v(". Reshare exposes an "),s("a",{attrs:{href:"https://www.openarchives.org/pmh/",target:"_blank",rel:"noopener noreferrer"}},[t._v("OAI-PMH"),s("OutboundLink")],1),t._v(" endpoint to facilitate metadata harvesting.")]),t._v(" "),s("p",[s("a",{attrs:{href:"http://datahub.io/",target:"_blank",rel:"noopener noreferrer"}},[t._v("datahub.io"),s("OutboundLink")],1),t._v(" is a data workflows web application build around the modular Frictionless Data toolchain, designed to find, share and publish high quality data online. Each entry has a ‘Showcase’ to display data package properties, and preview data with tables and simple visualisations. As well as the Showcase, "),s("a",{attrs:{href:"http://datahub.io/",target:"_blank",rel:"noopener noreferrer"}},[t._v("datahub.io"),s("OutboundLink")],1),t._v(" provides straight-forward direct access to import data into a variety of tools used by researchers; R, Pandas, Python, JavaScript, and SQL. "),s("a",{attrs:{href:"https://specs.frictionlessdata.io/data-package/",target:"_blank",rel:"noopener noreferrer"}},[t._v("Frictionless Data Data Packages"),s("OutboundLink")],1),t._v(" can be pushed to "),s("a",{attrs:{href:"http://datahub.io",target:"_blank",rel:"noopener noreferrer"}},[t._v("datahub.io"),s("OutboundLink")],1),t._v(" to create dataset entries.")]),t._v(" "),s("h3",{attrs:{id:"problem-we-were-trying-to-solve"}},[s("a",{staticClass:"header-anchor",attrs:{href:"#problem-we-were-trying-to-solve"}},[t._v("#")]),t._v(" Problem We Were Trying To Solve")]),t._v(" "),s("p",[t._v("We want to investigate the use of the Data Package concept, and Frictionless Data software to facilitate the reuse of data archived in Reshare.")]),t._v(" "),s("p",[t._v("We are especially interested in trialling pipelines to automate data harvesting from UKDS into "),s("a",{attrs:{href:"http://datahub.io/",target:"_blank",rel:"noopener noreferrer"}},[t._v("datahub.io"),s("OutboundLink")],1),t._v(" using Frictionless Data software such as "),s("a",{attrs:{href:"https://github.com/frictionlessdata/datapackage-pipelines",target:"_blank",rel:"noopener noreferrer"}},[t._v("datapackage-pipelines"),s("OutboundLink")],1),t._v(", and creating appropriate processors to translate widely used statistics file formats, such as "),s("a",{attrs:{href:"https://www.ibm.com/analytics/us/en/technology/spss/",target:"_blank",rel:"noopener noreferrer"}},[t._v("SPSS"),s("OutboundLink")],1),t._v(", to text-based tabular data formats such as CSV.")]),t._v(" "),s("p",[t._v("We chose the Data Package Pipelines library because it provides us with a well tested and mature framework of established processors to work with tabular data from a variety of sources and formats. Custom processors can easily be added to extend pipeline functionality. Pipelines can be configured using a simple declarative specification. Other tools supporting the underlying Frictionless Data specifications, such as "),s("a",{attrs:{href:"https://github.com/frictionlessdata/tableschema-py/",target:"_blank",rel:"noopener noreferrer"}},[t._v("tableschema"),s("OutboundLink")],1),t._v(" and "),s("a",{attrs:{href:"https://github.com/frictionlessdata/tableschema-py/",target:"_blank",rel:"noopener noreferrer"}},[t._v("goodtables"),s("OutboundLink")],1),t._v(" can be easily integrated as appropriate.")]),t._v(" "),s("p",[t._v("In this pilot we are trialling tools to:")]),t._v(" "),s("ul",[s("li",[t._v("automate data harvesting from UKDS, to "),s("a",{attrs:{href:"http://datahub.io/",target:"_blank",rel:"noopener noreferrer"}},[t._v("datahub.io"),s("OutboundLink")],1),t._v(", through a data package pipeline.")]),t._v(" "),s("li",[t._v("translate binary data formats (SPSS) to text-based tabular formats.")]),t._v(" "),s("li",[t._v("validate tabular data harvested from UKDS with goodtables.")]),t._v(" "),s("li",[t._v("fix or workaround common data issues identified from validation report, in the source-spec")]),t._v(" "),s("li",[t._v("correct file encoding")]),t._v(" "),s("li",[t._v("skip non-data rows")]),t._v(" "),s("li",[t._v("skip specified validation checks (duplicate-rows)")]),t._v(" "),s("li",[t._v("specify header rows in csv files")]),t._v(" "),s("li",[t._v("explicitly defining tabular headers")]),t._v(" "),s("li",[t._v("trial the "),s("a",{attrs:{href:"http://datahub.io/",target:"_blank",rel:"noopener noreferrer"}},[t._v("datahub.io"),s("OutboundLink")],1),t._v(" API with real-world data")]),t._v(" "),s("li",[t._v("use the Showcase features of "),s("a",{attrs:{href:"http://datahub.io/",target:"_blank",rel:"noopener noreferrer"}},[t._v("datahub.io"),s("OutboundLink")],1),t._v(" to provide instance data previews and visualisations.")])]),t._v(" "),s("h3",{attrs:{id:"the-work"}},[s("a",{staticClass:"header-anchor",attrs:{href:"#the-work"}},[t._v("#")]),t._v(" The Work")]),t._v(" "),s("h4",{attrs:{id:"what-did-we-do"}},[s("a",{staticClass:"header-anchor",attrs:{href:"#what-did-we-do"}},[t._v("#")]),t._v(" What Did We Do")]),t._v(" "),s("p",[t._v("During the pilot, we focussed on creating a reusable pipeline of processors to harvest data and dataset metadata from the UKDS Reshare service, and output valid Data Packages with tabular resources. Each pipeline processor step was created as a separate module to facilitate testing and reuse in other similar pipelines.")]),t._v(" "),s("p",[t._v("UKDS datasets were selected from "),s("a",{attrs:{href:"http://reshare.ukdataservice.ac.uk/cgi/stats/report/most_popular_eprints",target:"_blank",rel:"noopener noreferrer"}},[t._v("the UKDS list"),s("OutboundLink")],1),t._v(". Entries were selected based on the data format we intended to write processors for (.csv, .tsv, xls, or .sav), how the dataset might help demonstrate various aspects of the pipeline, and how well they might lend themselves to visualisation on "),s("a",{attrs:{href:"http://datahub.io/",target:"_blank",rel:"noopener noreferrer"}},[t._v("datahub.io"),s("OutboundLink")],1),t._v(".")]),t._v(" "),s("p",[t._v("Below is an outline of the pipeline flow from UKDS Reshare Archive to "),s("a",{attrs:{href:"http://datahub.io/",target:"_blank",rel:"noopener noreferrer"}},[t._v("datahub.io"),s("OutboundLink")],1),t._v(" entry:")]),t._v(" "),s("p",[s("img",{attrs:{src:a(432),alt:"pipeline flow from UKDS Reshare Archive to datahub.io"}}),s("br"),t._v(" "),s("em",[t._v("pipeline flow from UKDS Reshare Archive to "),s("a",{attrs:{href:"http://datahub.io/",target:"_blank",rel:"noopener noreferrer"}},[t._v("datahub.io"),s("OutboundLink")],1)])]),t._v(" "),s("h5",{attrs:{id:"specifying-an-entry"}},[s("a",{staticClass:"header-anchor",attrs:{href:"#specifying-an-entry"}},[t._v("#")]),t._v(" Specifying an Entry")]),t._v(" "),s("p",[t._v("We wanted to ensure that each UKDS dataset to be maintained on "),s("a",{attrs:{href:"http://datahub.io/",target:"_blank",rel:"noopener noreferrer"}},[t._v("datahub.io"),s("OutboundLink")],1),t._v(" could be easily configured to specify where to harvest its resource data and dataset metadata. We also wanted to add other configuration details to help customise the pipeline to work with tricky resources, and view specifications for subsequent visualisation on "),s("a",{attrs:{href:"http://datahub.io/",target:"_blank",rel:"noopener noreferrer"}},[t._v("datahub.io"),s("OutboundLink")],1),t._v(".")]),t._v(" "),s("p",[t._v("The source-spec for each Reshare entry defines a list of URLs for each resource in the dataset that we’re interested in harvesting, and the resource format (csv, tsv, xls, or spss).")]),t._v(" "),s("p",[t._v("If an OAI ID is provided, it will be used to harvest dataset metadata from the Reshare OAI endpoint.")]),t._v(" "),s("p",[t._v("As well as defining source locations, we also want to provide a way to customise downstream processor behaviour, to help work around potential resource issues.")]),t._v(" "),s("p",[t._v("Below is an example yaml source-spec for two entries, demonstrating various configuration options.")]),t._v(" "),s("div",{staticClass:"language-yaml extra-class"},[s("pre",{pre:!0,attrs:{class:"language-yaml"}},[s("code",[s("span",{pre:!0,attrs:{class:"token key atrule"}},[t._v("entries")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(":")]),t._v("\n\n "),s("span",{pre:!0,attrs:{class:"token key atrule"}},[t._v("civil-servant-survey")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(":")]),t._v(" "),s("span",{pre:!0,attrs:{class:"token comment"}},[t._v("# entry name")]),t._v("\n "),s("span",{pre:!0,attrs:{class:"token key atrule"}},[t._v("source")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(":")]),t._v(" "),s("span",{pre:!0,attrs:{class:"token comment"}},[t._v("# a list of sources")]),t._v("\n "),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("-")]),t._v(" "),s("span",{pre:!0,attrs:{class:"token key atrule"}},[t._v("url")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(":")]),t._v(" http"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(":")]),t._v("//reshare.ukdataservice.ac.uk/851401/10/Coded_SurveyData.csv\n "),s("span",{pre:!0,attrs:{class:"token key atrule"}},[t._v("format")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(":")]),t._v(" csv\n "),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("-")]),t._v(" "),s("span",{pre:!0,attrs:{class:"token key atrule"}},[t._v("url")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(":")]),t._v(" http"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(":")]),t._v("//reshare.ukdataservice.ac.uk/851401/2/key%20%283%29.csv\n "),s("span",{pre:!0,attrs:{class:"token key atrule"}},[t._v("format")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(":")]),t._v(" csv\n "),s("span",{pre:!0,attrs:{class:"token key atrule"}},[t._v("goodtables")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(":")]),t._v(" "),s("span",{pre:!0,attrs:{class:"token comment"}},[t._v("# custom processor config for goodtables")]),t._v("\n "),s("span",{pre:!0,attrs:{class:"token key atrule"}},[t._v("skip_checks")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(":")]),t._v("\n "),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("-")]),t._v(" duplicate"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("-")]),t._v("row\n "),s("span",{pre:!0,attrs:{class:"token key atrule"}},[t._v("oai-id")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(":")]),t._v(" "),s("span",{pre:!0,attrs:{class:"token number"}},[t._v("851401")]),t._v(" "),s("span",{pre:!0,attrs:{class:"token comment"}},[t._v("# OAI ID to harvest dataset metadata")]),t._v("\n\n "),s("span",{pre:!0,attrs:{class:"token key atrule"}},[t._v("uk-gov-petitions")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(":")]),t._v("\n "),s("span",{pre:!0,attrs:{class:"token key atrule"}},[t._v("source")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(":")]),t._v("\n "),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("-")]),t._v(" "),s("span",{pre:!0,attrs:{class:"token key atrule"}},[t._v("url")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(":")]),t._v(" http"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(":")]),t._v("//reshare.ukdataservice.ac.uk/851614/1/gov_pet_metadata.tab\n "),s("span",{pre:!0,attrs:{class:"token key atrule"}},[t._v("format")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(":")]),t._v(" tsv\n "),s("span",{pre:!0,attrs:{class:"token key atrule"}},[t._v("tabulator")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(":")]),t._v(" "),s("span",{pre:!0,attrs:{class:"token comment"}},[t._v("# custom processor config for tabulator")]),t._v("\n "),s("span",{pre:!0,attrs:{class:"token key atrule"}},[t._v("encoding")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(":")]),t._v(" utf"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("-")]),s("span",{pre:!0,attrs:{class:"token number"}},[t._v("8")]),t._v(" "),s("span",{pre:!0,attrs:{class:"token comment"}},[t._v("# explicitly define source file encoding")]),t._v("\n "),s("span",{pre:!0,attrs:{class:"token key atrule"}},[t._v("headers")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(":")]),t._v(" "),s("span",{pre:!0,attrs:{class:"token comment"}},[t._v("# explicitly define missing column headers")]),t._v("\n "),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("-")]),t._v(" id\n "),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("-")]),t._v(" title\n "),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("-")]),t._v(" department\n "),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("-")]),t._v(" starting\n "),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("-")]),t._v(" closing\n "),s("span",{pre:!0,attrs:{class:"token key atrule"}},[t._v("oai-id")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(":")]),t._v(" "),s("span",{pre:!0,attrs:{class:"token number"}},[t._v("851614")]),t._v("\n "),s("span",{pre:!0,attrs:{class:"token key atrule"}},[t._v("views")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(":")]),t._v("\n "),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("-")]),t._v(" views/petitions"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("-")]),t._v("view.json\n")])])]),s("p",[t._v("Sources in the first entry, "),s("em",[t._v("civil-servant-survey")]),t._v(", contain duplicate rows, which would normally fail goodtables validation. Here we will allow the "),s("code",[t._v("duplicate-row")]),t._v(" check to be skipped.")]),t._v(" "),s("p",[t._v("The source in the second entry has the wrong character encoding and no headers declared. We can fix these issues to allow the pipeline to process the resource by explicitly specifying the file encoding, and declaring the column headers.")]),t._v(" "),s("h4",{attrs:{id:"data-set-and-resource-harvest"}},[s("a",{staticClass:"header-anchor",attrs:{href:"#data-set-and-resource-harvest"}},[t._v("#")]),t._v(" Data Set and Resource Harvest")]),t._v(" "),s("p",[t._v("We identified that Reshare has an OAI-PMH2 compatible endpoint to harvest information about each Reshare data set. So we created an "),s("code",[t._v("ukds.add_oai_metadata")]),t._v(" pipeline processor. OAI metadata is compatible with Dublin Core Elements, and the processor translates this into Data Package compatible properties.")]),t._v(" "),s("p",[t._v("Resources are added to the newly created Data Package and downloaded from the URLs defined in the yaml configuration. We support adding SPSS (.sav), CSV, TSV and XLS file formats.")]),t._v(" "),s("p",[t._v("To support the widely used SPSS format, we created an "),s("code",[t._v("spss.add_spss processor")]),t._v(" that makes use of the "),s("a",{attrs:{href:"https://github.com/frictionlessdata/tableschema-spss-py",target:"_blank",rel:"noopener noreferrer"}},[t._v("tableschema-spss"),s("OutboundLink")],1),t._v(" plugin to read SPSS files and create tableschema descriptors from them.")]),t._v(" "),s("h4",{attrs:{id:"validation-reports-and-common-issues"}},[s("a",{staticClass:"header-anchor",attrs:{href:"#validation-reports-and-common-issues"}},[t._v("#")]),t._v(" Validation Reports and Common Issues")]),t._v(" "),s("p",[t._v("To help ensure data quality, we want to validate the harvested tabular data before continuing the pipeline. We created a "),s("a",{attrs:{href:"https://github.com/frictionlessdata/datapackage-pipelines-goodtables",target:"_blank",rel:"noopener noreferrer"}},[s("code",[t._v("goodtables.validate")]),t._v(" processor"),s("OutboundLink")],1),t._v(", which will write a validation report for each resource. If a resource fails to validate against its schema, or has other data issues, the pipeline will fail. Errors can be identified from validation reports, fixed, and the pipeline re-run.")]),t._v(" "),s("p",[t._v("Below are examples of issues revealed by validation that can occur when working with real-world data.")]),t._v(" "),s("h5",{attrs:{id:"au-pairing-after-the-au-pair-scheme-specifying-a-xls-sheet-and-working-around-non-data-rows"}},[s("a",{staticClass:"header-anchor",attrs:{href:"#au-pairing-after-the-au-pair-scheme-specifying-a-xls-sheet-and-working-around-non-data-rows"}},[t._v("#")]),t._v(" “Au pairing after the au pair scheme”: specifying a xls sheet, and working around non-data rows")]),t._v(" "),s("p",[t._v("The "),s("a",{attrs:{href:"http://reshare.ukdataservice.ac.uk/851656/",target:"_blank",rel:"noopener noreferrer"}},[t._v("“Au Pairing” dataset"),s("OutboundLink")],1),t._v(" has a single .xls resource we’re interested in harvesting. The file contains four sheets, and we’re interested in the second one, which contains the data. So we specify our entry:")]),t._v(" "),s("div",{staticClass:"language-yaml extra-class"},[s("pre",{pre:!0,attrs:{class:"language-yaml"}},[s("code",[s("span",{pre:!0,attrs:{class:"token key atrule"}},[t._v("au-pairing")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(":")]),t._v("\n "),s("span",{pre:!0,attrs:{class:"token key atrule"}},[t._v("source")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(":")]),t._v("\n "),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("-")]),t._v(" "),s("span",{pre:!0,attrs:{class:"token key atrule"}},[t._v("url")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(":")]),t._v(" http"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(":")]),t._v("//reshare.ukdataservice.ac.uk/851656/6/GumtreeAds_AuPairsAnalysis1.xls\n "),s("span",{pre:!0,attrs:{class:"token key atrule"}},[t._v("format")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(":")]),t._v(" xls\n "),s("span",{pre:!0,attrs:{class:"token key atrule"}},[t._v("tabulator")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(":")]),t._v("\n "),s("span",{pre:!0,attrs:{class:"token key atrule"}},[t._v("sheet")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(":")]),t._v(" "),s("span",{pre:!0,attrs:{class:"token number"}},[t._v("2")]),t._v(" "),s("span",{pre:!0,attrs:{class:"token comment"}},[t._v("# use sheet 2 in the file")]),t._v("\n "),s("span",{pre:!0,attrs:{class:"token key atrule"}},[t._v("oai-id")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(":")]),t._v(" "),s("span",{pre:!0,attrs:{class:"token number"}},[t._v("851656")]),t._v("\n")])])]),s("p",[t._v("Notice we have indicated which sheet in the file to use.")]),t._v(" "),s("p",[t._v("The data sheet has a single header row, but it also has this header row repeated at intervals throughout the data, presumably to aid the human reader when reviewing the data manually.")]),t._v(" "),s("p",[s("img",{attrs:{src:a(433),alt:""}}),s("br"),t._v(" "),s("em",[t._v("screengrab of the UKDS “Au Pairing” datasheet")])]),t._v(" "),s("p",[t._v("For machine processing, this isn’t ideal. In fact, it will fail our goodtables validation processor with the following (truncated) report:")]),t._v(" "),s("div",{staticClass:"language-yaml extra-class"},[s("pre",{pre:!0,attrs:{class:"language-yaml"}},[s("code",[s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("{")]),t._v("\n "),s("span",{pre:!0,attrs:{class:"token key atrule"}},[t._v('"time"')]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(":")]),t._v(" "),s("span",{pre:!0,attrs:{class:"token number"}},[t._v("0.466")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v("\n "),s("span",{pre:!0,attrs:{class:"token key atrule"}},[t._v('"valid"')]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(":")]),t._v(" "),s("span",{pre:!0,attrs:{class:"token boolean important"}},[t._v("false")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v("\n "),s("span",{pre:!0,attrs:{class:"token key atrule"}},[t._v('"error-count"')]),s("span",{pre:!0,attrs:{class:"token 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We also instruct goodtables to skip the "),s("code",[t._v("duplicate-row")]),t._v(" check."),s("br"),t._v("\nThe outputted csv resource file will no longer contain rows with the duplicate header.")]),t._v(" "),s("h5",{attrs:{id:"uk-government-petitions-wrong-file-encoding-and-specifying-missing-headers"}},[s("a",{staticClass:"header-anchor",attrs:{href:"#uk-government-petitions-wrong-file-encoding-and-specifying-missing-headers"}},[t._v("#")]),t._v(" “UK government petitions”: wrong file encoding, and specifying missing headers")]),t._v(" "),s("p",[t._v("The “"),s("a",{attrs:{href:"http://gov.uk/",target:"_blank",rel:"noopener noreferrer"}},[t._v("gov.uk"),s("OutboundLink")],1),t._v(" petitions” dataset has a TSV data file we’re interested in. However, it has been saved with the wrong character encoding and attempting to open may return an error, or display some characters incorrectly.")]),t._v(" "),s("p",[t._v("Additionally, there is no header row specified at the top of the file, so the resulting data package won’t have the correct header information in the resource’s schema.")]),t._v(" "),s("p",[t._v("We can fix both of these issues in our entry specification:")]),t._v(" "),s("div",{staticClass:"language-yaml extra-class"},[s("pre",{pre:!0,attrs:{class:"language-yaml"}},[s("code",[s("span",{pre:!0,attrs:{class:"token key atrule"}},[t._v("uk-gov-petitions")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(":")]),t._v("\n "),s("span",{pre:!0,attrs:{class:"token key atrule"}},[t._v("source")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(":")]),t._v("\n "),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("-")]),t._v(" "),s("span",{pre:!0,attrs:{class:"token key atrule"}},[t._v("url")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(":")]),t._v(" http"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(":")]),t._v("//reshare.ukdataservice.ac.uk/851614/1/gov_pet_metadata.tab\n "),s("span",{pre:!0,attrs:{class:"token key atrule"}},[t._v("format")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(":")]),t._v(" tsv\n "),s("span",{pre:!0,attrs:{class:"token key atrule"}},[t._v("tabulator")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(":")]),t._v("\n "),s("span",{pre:!0,attrs:{class:"token key atrule"}},[t._v("encoding")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(":")]),t._v(" utf"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("-")]),s("span",{pre:!0,attrs:{class:"token number"}},[t._v("8")]),t._v(" "),s("span",{pre:!0,attrs:{class:"token comment"}},[t._v("# specify file encoding")]),t._v("\n "),s("span",{pre:!0,attrs:{class:"token key atrule"}},[t._v("headers")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(":")]),t._v(" "),s("span",{pre:!0,attrs:{class:"token comment"}},[t._v("# define missing headers")]),t._v("\n "),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("-")]),t._v(" id\n "),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("-")]),t._v(" title\n "),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("-")]),t._v(" department\n "),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("-")]),t._v(" starting\n "),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("-")]),t._v(" closing\n "),s("span",{pre:!0,attrs:{class:"token key atrule"}},[t._v("oai-id")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(":")]),t._v(" "),s("span",{pre:!0,attrs:{class:"token number"}},[t._v("851614")]),t._v("\n")])])]),s("p",[t._v("Above, we have defined the character encoding we want to use when opening the file, and we’ve explicitly defined the headers to use. These headers will be added to the first row of the outputted csv resource file in the data package.")]),t._v(" "),s("p",[t._v("We can also use the "),s("code",[t._v("headers")]),t._v(" parameter to define which row contains header information. By default this is the first row. However, sometimes a data file will have the headers on a different row:")]),t._v(" "),s("p",[s("img",{attrs:{src:a(434),alt:""}}),s("br"),t._v(" "),s("em",[t._v("screengrab of the UKDS “Government Petitions” datasheet")])]),t._v(" "),s("p",[t._v("This example file has its headers defined in row three, with other information, and an empty row in the first two rows. We can tell our pipeline which row contains headers by specifying it in the entry configuration:")]),t._v(" "),s("div",{staticClass:"language-yaml extra-class"},[s("pre",{pre:!0,attrs:{class:"language-yaml"}},[s("code",[s("span",{pre:!0,attrs:{class:"token key atrule"}},[t._v("example-entry")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(":")]),t._v("\n "),s("span",{pre:!0,attrs:{class:"token key atrule"}},[t._v("source")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(":")]),t._v("\n "),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("-")]),t._v(" "),s("span",{pre:!0,attrs:{class:"token key atrule"}},[t._v("url")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(":")]),t._v(" http"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(":")]),t._v("//www.newcastle.gov.uk/sites/drupalncc.newcastle.gov.uk/files/wwwfileroot/your"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("-")]),t._v("council/local_transparency/january_2012.csv\n "),s("span",{pre:!0,attrs:{class:"token key atrule"}},[t._v("format")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(":")]),t._v(" csv\n "),s("span",{pre:!0,attrs:{class:"token key atrule"}},[t._v("tabulator")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(":")]),t._v("\n "),s("span",{pre:!0,attrs:{class:"token key atrule"}},[t._v("headers")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(":")]),t._v(" "),s("span",{pre:!0,attrs:{class:"token number"}},[t._v("3")]),t._v(" "),s("span",{pre:!0,attrs:{class:"token comment"}},[t._v("# specifying which row contains headers")]),t._v("\n")])])]),s("h4",{attrs:{id:"add-data-package-views"}},[s("a",{staticClass:"header-anchor",attrs:{href:"#add-data-package-views"}},[t._v("#")]),t._v(" Add Data Package Views")]),t._v(" "),s("p",[t._v("View specs can be added to the data package to enable "),s("a",{attrs:{href:"http://datahub.io/",target:"_blank",rel:"noopener noreferrer"}},[t._v("datahub.io"),s("OutboundLink")],1),t._v(" to create visualisations from resource data in the data package. The "),s("code",[t._v("views")]),t._v(" property is a list of file paths to json files containing "),s("a",{attrs:{href:"https://specs.frictionlessdata.io/views/",target:"_blank",rel:"noopener noreferrer"}},[t._v("view-spec"),s("OutboundLink")],1),t._v(" compatible views.")]),t._v(" "),s("p",[t._v("Currently, "),s("a",{attrs:{href:"http://datahub.io/",target:"_blank",rel:"noopener noreferrer"}},[t._v("datahub.io"),s("OutboundLink")],1),t._v(" supports views written either with a ‘simple’ views-spec, or using Vega (v 2.6.5). See "),s("a",{attrs:{href:"https://datahub.io/docs/features/views",target:"_blank",rel:"noopener noreferrer"}},[t._v("datahub.io docs"),s("OutboundLink")],1),t._v(" for more details about the supported views-spec.")]),t._v(" "),s("h4",{attrs:{id:"push-to-datahub-io"}},[s("a",{staticClass:"header-anchor",attrs:{href:"#push-to-datahub-io"}},[t._v("#")]),t._v(" Push to "),s("a",{attrs:{href:"http://datahub.io/",target:"_blank",rel:"noopener noreferrer"}},[t._v("datahub.io"),s("OutboundLink")],1)]),t._v(" "),s("p",[t._v("Once the harvesting pipeline has been run the resulting data packages are pushed to "),s("a",{attrs:{href:"http://datahub.io/",target:"_blank",rel:"noopener noreferrer"}},[t._v("datahub.io"),s("OutboundLink")],1),t._v(" using the "),s("a",{attrs:{href:"https://github.com/datahq/datahub-cli",target:"_blank",rel:"noopener noreferrer"}},[s("code",[t._v("datahub.dump.to_datahub")]),s("OutboundLink")],1),t._v(" processor.")]),t._v(" "),s("p",[t._v("This creates or updates an entry for the package on datahub. If a view has been defined in the entry configuration, this will be created on the "),s("a",{attrs:{href:"http://datahub.io/",target:"_blank",rel:"noopener noreferrer"}},[t._v("datahub.io"),s("OutboundLink")],1),t._v(" entry Showcase page.")]),t._v(" "),s("h3",{attrs:{id:"review"}},[s("a",{staticClass:"header-anchor",attrs:{href:"#review"}},[t._v("#")]),t._v(" Review")]),t._v(" "),s("p",[t._v("We were able to demonstrate that a data processing pipeline using Frictionless Data tools can facilitate the automated harvesting, validation, transformation, and upload to a data package-compatible third-party service, based on a simple configuration.")]),t._v(" "),s("h3",{attrs:{id:"next-steps"}},[s("a",{staticClass:"header-anchor",attrs:{href:"#next-steps"}},[t._v("#")]),t._v(" Next Steps")]),t._v(" "),s("p",[t._v("The pilot data package pipeline runs locally in a development environment, but given each processor has been written as a separate module, these could be used within any pipeline. 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This would ensure "),s("a",{attrs:{href:"http://datahub.io/",target:"_blank",rel:"noopener noreferrer"}},[t._v("datahub.io"),s("OutboundLink")],1),t._v(" is up-to-date with data on UKDS Reshare.")]),t._v(" "),s("p",[t._v("Working with ‘real-world’ data from UKDS Reshare has helped to identify and prioritise improvements and future features for "),s("a",{attrs:{href:"http://datahub.io/",target:"_blank",rel:"noopener noreferrer"}},[t._v("datahub.io"),s("OutboundLink")],1),t._v(".")]),t._v(" "),s("h3",{attrs:{id:"additional-resources"}},[s("a",{staticClass:"header-anchor",attrs:{href:"#additional-resources"}},[t._v("#")]),t._v(" Additional Resources")]),t._v(" "),s("ul",[s("li",[s("a",{attrs:{href:"https://github.com/frictionlessdata/pilot-ukds",target:"_blank",rel:"noopener noreferrer"}},[t._v("The main code repository for this pilot"),s("OutboundLink")],1),t._v(".")]),t._v(" "),s("li",[s("a",{attrs:{href:"https://github.com/frictionlessdata/datapackage-pipelines",target:"_blank",rel:"noopener noreferrer"}},[t._v("A framework for processing data packages in pipelines of modular components"),s("OutboundLink")],1),t._v(".")]),t._v(" "),s("li",[s("a",{attrs:{href:"https://github.com/frictionlessdata/datapackage-pipelines-spss",target:"_blank",rel:"noopener noreferrer"}},[t._v("A Data Package Pipelines processor for SPSS file formats"),s("OutboundLink")],1),t._v(".")]),t._v(" "),s("li",[s("a",{attrs:{href:"https://github.com/frictionlessdata/datapackage-pipelines-goodtables",target:"_blank",rel:"noopener noreferrer"}},[t._v("A Data Package Pipelines processor for validating tabular data using goodtables-py"),s("OutboundLink")],1),t._v(".")]),t._v(" "),s("li",[s("a",{attrs:{href:"https://github.com/datahq/datapackage-pipelines-datahub",target:"_blank",rel:"noopener noreferrer"}},[t._v("A Data Package Pipelines processor to push data packages to datahub.io"),s("OutboundLink")],1),t._v(".")])])])}),[],!1,null,null,null);e.default=n.exports}}]); \ No newline at end of file diff --git a/assets/js/191.f3c4db92.js b/assets/js/191.6f606d1d.js similarity index 99% rename from assets/js/191.f3c4db92.js rename to assets/js/191.6f606d1d.js index a2bd1d734..13f4604be 100644 --- a/assets/js/191.f3c4db92.js +++ b/assets/js/191.6f606d1d.js @@ -1 +1 @@ -(window.webpackJsonp=window.webpackJsonp||[]).push([[191],{732:function(e,t,o){"use strict";o.r(t);var a=o(29),i=Object(a.a)({},(function(){var e=this,t=e.$createElement,o=e._self._c||t;return o("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[o("h1",{attrs:{id:"code-of-conduct"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#code-of-conduct"}},[e._v("#")]),e._v(" Code of Conduct")]),e._v(" "),o("h2",{attrs:{id:"introduction"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#introduction"}},[e._v("#")]),e._v(" Introduction")]),e._v(" "),o("p",[e._v("The goal of this Code of Conduct is to make explicit the type of participation that is expected, and the behaviour that is unacceptable. 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School of Data provides data literacy trainings and resources for learning how to work with data.")]),t._v(" "),o("p",[o("a",{attrs:{href:"https://schoolofdata.org",target:"_blank",rel:"noopener noreferrer"}},[t._v("School of Data"),o("OutboundLink")],1)]),t._v(" "),o("h2",{attrs:{id:"paid-support"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#paid-support"}},[t._v("#")]),t._v(" Paid Support")]),t._v(" "),o("p",[t._v("Professional, timely support is available on a paid basis from the creators of Frictionless Data at Datopian and Open Knowledge Foundation. Please get in touch via:")]),t._v(" "),o("p",[o("a",{attrs:{href:"http://datopian.com/contact",target:"_blank",rel:"noopener noreferrer"}},[t._v("Datopian"),o("OutboundLink")],1),o("br"),t._v("\nOpen Knowledge Foundation: "),o("a",{attrs:{href:"mailto:frictionlessdata@okfn.org"}},[t._v("frictionlessdata@okfn.org")])])])}),[],!1,null,null,null);a.default=r.exports}}]); \ No newline at end of file +(window.webpackJsonp=window.webpackJsonp||[]).push([[194],{734:function(t,a,o){"use strict";o.r(a);var e=o(29),r=Object(e.a)({},(function(){var t=this,a=t.$createElement,o=t._self._c||a;return o("ContentSlotsDistributor",{attrs:{"slot-key":t.$parent.slotKey}},[o("h1",{attrs:{id:"need-help"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#need-help"}},[t._v("#")]),t._v(" Need Help?")]),t._v(" "),o("p",{staticClass:"font-light text-xl"},[t._v(" We're happy to provide support! Please reach out to us by using one of the following methods:")]),t._v(" "),o("h2",{attrs:{id:"community-support"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#community-support"}},[t._v("#")]),t._v(" Community Support")]),t._v(" "),o("p",[t._v("You can ask any questions in our "),o("a",{attrs:{href:"https://join.slack.com/t/frictionlessdata/shared_invite/zt-17kpbffnm-tRfDW_wJgOw8tJVLvZTrBg",target:"_blank",rel:"noopener noreferrer"}},[t._v("Slack Community Chat room"),o("OutboundLink")],1),t._v(" (the Chat room is also accessible via "),o("a",{attrs:{href:"https://matrix.to/#/#frictionlessdata:matrix.okfn.org",target:"_blank",rel:"noopener noreferrer"}},[t._v("Matrix"),o("OutboundLink")],1),t._v("). You can also start a thread in "),o("a",{attrs:{href:"https://github.com/frictionlessdata/project/discussions",target:"_blank",rel:"noopener noreferrer"}},[t._v("GitHub Discussions"),o("OutboundLink")],1),t._v(". Frictionless is a big community that consists of people having different expertise in different domains. Feel free to ask us any questions!")]),t._v(" "),o("h2",{attrs:{id:"school-of-data"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#school-of-data"}},[t._v("#")]),t._v(" School of Data")]),t._v(" "),o("p",[t._v("School of Data is a project overseen by the Open Knowledge Foundation consisting of a network of individuals and organizations working on empowering civil society organizations, journalists and citizens with skills they need to use data effectively. School of Data provides data literacy trainings and resources for learning how to work with data.")]),t._v(" "),o("p",[o("a",{attrs:{href:"https://schoolofdata.org",target:"_blank",rel:"noopener noreferrer"}},[t._v("School of Data"),o("OutboundLink")],1)]),t._v(" "),o("h2",{attrs:{id:"paid-support"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#paid-support"}},[t._v("#")]),t._v(" Paid Support")]),t._v(" "),o("p",[t._v("Professional, timely support is available on a paid basis from the creators of Frictionless Data at Datopian and Open Knowledge Foundation. Please get in touch via:")]),t._v(" "),o("p",[o("a",{attrs:{href:"http://datopian.com/contact",target:"_blank",rel:"noopener noreferrer"}},[t._v("Datopian"),o("OutboundLink")],1),o("br"),t._v("\nOpen Knowledge Foundation: "),o("a",{attrs:{href:"mailto:frictionlessdata@okfn.org"}},[t._v("frictionlessdata@okfn.org")])])])}),[],!1,null,null,null);a.default=r.exports}}]); \ No newline at end of file diff --git a/assets/js/20.0b4e11bf.js b/assets/js/20.091e75ca.js similarity index 97% rename from assets/js/20.0b4e11bf.js rename to assets/js/20.091e75ca.js index ec292edd8..37c12f8ab 100644 --- a/assets/js/20.0b4e11bf.js +++ b/assets/js/20.091e75ca.js @@ -1 +1 @@ -(window.webpackJsonp=window.webpackJsonp||[]).push([[20],{438:function(e,t,a){e.exports=a.p+"assets/img/ckanext-validation.9e351f1c.png"},439:function(e,t,a){e.exports=a.p+"assets/img/data-validity-badges.41769a55.png"},440:function(e,t,a){e.exports=a.p+"assets/img/data-validation-on-upload.d1c30a10.png"},584:function(e,t,a){"use strict";a.r(t);var r=a(29),o=Object(r.a)({},(function(){var e=this,t=e.$createElement,r=e._self._c||t;return r("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[r("h2",{attrs:{id:"context"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#context"}},[e._v("#")]),e._v(" Context")]),e._v(" "),r("p",[e._v("One of the main goals of the Frictionless Data project is to help improve data quality by providing easy to integrate libraries and services for data validation. We have integrated data validation seamlessly with different backends like GitHub and Amazon S3 via the online service "),r("a",{attrs:{href:"https://goodtables.io/",target:"_blank",rel:"noopener noreferrer"}},[e._v("goodtables.io"),r("OutboundLink")],1),e._v(", but we also wanted to explore closer integrations with other platforms.")]),e._v(" "),r("p",[e._v("An obvious choice for that are Open Data portals. They are still one of the main forms of dissemination of Open Data, especially for governments and other organizations. They provide a single entry point to data relating to a particular region or thematic area and provide users with tools to discover and access different datasets. On the backend, publishers also have tools available for the validation and publication of datasets.")]),e._v(" "),r("p",[e._v("Data Quality varies widely across different portals, reflecting the publication processes and requirements of the hosting organizations. In general, it is difficult for users to assess the quality of the data and there is a lack of descriptors for the actual data fields. At the publisher level, while strong emphasis has been put in metadata standards and interoperability, publishers don’t generally have the same help or guidance when dealing with data quality or description.")]),e._v(" "),r("p",[e._v("We believe that data quality in Open Data portals can have a central place on both these fronts, user-centric and publisher-centric, and we started this pilot to showcase a possible implementation.")]),e._v(" "),r("p",[e._v("To field test our implementation we chose the "),r("a",{attrs:{href:"https://www.wprdc.org",target:"_blank",rel:"noopener noreferrer"}},[e._v("Western Pennsylvania Regional Data Center"),r("OutboundLink")],1),e._v(" (WPRDC), managed by the "),r("a",{attrs:{href:"http://ucsur.pitt.edu/",target:"_blank",rel:"noopener noreferrer"}},[e._v("University of Pittsburgh Center for Urban and Social Research"),r("OutboundLink")],1),e._v(". The Regional Data Center made for a good pilot as the project team takes an agile approach to managing their own CKAN instance along with support from OpenGov, members of the CKAN association. As the open data repository is used by a diverse array of data publishers (including project partners Allegheny County and the City of Pittsburgh), the Regional Data Center provides a good test case for testing the implementation across a variety of data types and publishing processes. WPRDC is a great example of a well managed Open Data portal, where datasets are actively maintained and the portal itself is just one component of a wider Open Data strategy. It also provides a good variety of publishers, including public sector agencies, academic institutions, and nonprofit organizations. The project’s partnership with the Digital Scholarship Services team at the University Library System also provides data management expertise not typically available in many open data implementations.")]),e._v(" "),r("h2",{attrs:{id:"the-work"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#the-work"}},[e._v("#")]),e._v(" The Work")]),e._v(" "),r("h3",{attrs:{id:"what-did-we-do"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#what-did-we-do"}},[e._v("#")]),e._v(" What Did We Do")]),e._v(" "),r("p",[e._v("The portal software that we chose for this pilot is "),r("a",{attrs:{href:"https://ckan.org",target:"_blank",rel:"noopener noreferrer"}},[e._v("CKAN"),r("OutboundLink")],1),e._v(", the world’s leading open source software for Open Data portals ("),r("a",{attrs:{href:"https://github.com/jalbertbowden/open-library/blob/master/lib/d2.1-state-of-the-art-report-and-evaluation-of-existing-open-data-platforms-2015-01-06-route-to-pa.pdf",target:"_blank",rel:"noopener noreferrer"}},[e._v("source"),r("OutboundLink")],1),e._v("). Open Knowledge International initially fostered the CKAN project and is now a member of the "),r("a",{attrs:{href:"https://ckan.org/about/association/",target:"_blank",rel:"noopener noreferrer"}},[e._v("CKAN Association"),r("OutboundLink")],1),e._v(".")]),e._v(" "),r("p",[e._v("We created "),r("a",{attrs:{href:"https://github.com/frictionlessdata/ckanext-validation",target:"_blank",rel:"noopener noreferrer"}},[e._v("ckanext-validation"),r("OutboundLink")],1),e._v(", a CKAN extension that provides a low level API and readily available features for data validation and reporting that can be added to any CKAN instance. This is powered by "),r("a",{attrs:{href:"https://github.com/frictionlessdata/goodtables-py",target:"_blank",rel:"noopener noreferrer"}},[e._v("goodtables"),r("OutboundLink")],1),e._v(", a library developed by Open Knowledge International to support the validation of tabular datasets.")]),e._v(" "),r("p",[e._v("The extension allows users to perform data validation against any tabular resource, such as CSV or Excel files. This generates a report that is stored against a particular resource, describing issues found with the data, both at the structural level (missing headers, blank rows, etc) and at the data schema level (wrong data types, values out of range etc).")]),e._v(" "),r("p",[r("img",{attrs:{src:a(438),alt:""}}),r("br"),e._v(" "),r("em",[e._v("data validation on CKAN made possible by ckanext-validation extension")])]),e._v(" "),r("p",[e._v("This provides a good overview of the quality of the data to users but also to publishers so they can improve the quality of the data file by addressing these issues. The reports can be easily accessed via badges that provide a quick visual indication of the quality of the data file.")]),e._v(" "),r("p",[r("img",{attrs:{src:a(439),alt:""}}),r("br"),e._v(" "),r("em",[e._v("badges indicating quality of data files on CKAN")])]),e._v(" "),r("p",[e._v("There are two default modes for performing the data validation when creating or updating resources. Data validation can be automatically performed in the background asynchronously or as part of the dataset creation in the user interface. In this case the validation will be performed immediately after uploading or linking to a new tabular file, giving quick feedback to publishers.")]),e._v(" "),r("p",[r("img",{attrs:{src:a(440),alt:""}}),r("br"),e._v(" "),r("em",[e._v("data validation on upload or linking to a new tabular file on CKAN")])]),e._v(" "),r("p",[e._v("The extension adds functionality to provide a "),r("a",{attrs:{href:"https://specs.frictionlessdata.io/table-schema/",target:"_blank",rel:"noopener noreferrer"}},[e._v("schema"),r("OutboundLink")],1),e._v(" for the data that describes the expected fields and types as well as other constraints, allowing to perform validation on the actual contents of the data. Additionally the schema is also stored with the resource metadata, so it can be displayed in the UI or accessed via the API.")]),e._v(" "),r("p",[e._v("The extension also provides some utility commands for CKAN maintainers, including the generation of "),r("a",{attrs:{href:"https://github.com/frictionlessdata/ckanext-validation#data-validation-reports",target:"_blank",rel:"noopener noreferrer"}},[e._v("reports"),r("OutboundLink")],1),e._v(" showing the number of valid and invalid tabular files, a breakdown of the error types and links to the individual resources. This gives maintainers a snapshot of the general quality of the data hosted in their CKAN instance at any given moment in time.")]),e._v(" "),r("p",[e._v("As mentioned before, we field tested the validation extension on the Western Pennsylvania Regional Data Center (WPRDC). At the moment of the import the portal hosted 258 datasets. Out of these, 221 datasets had tabular resources, totalling 626 files (mainly CSV and XLSX files). Taking into account that we only performed the default validation that only includes structural checks (ie not schema-based ones) these are the results:")]),e._v(" "),r("blockquote",[r("p",[e._v("466 resources - validation success")])]),e._v(" "),r("blockquote",[r("p",[e._v("156 resources - validation failure")])]),e._v(" "),r("blockquote",[r("p",[e._v("4 resources - validation error")])]),e._v(" "),r("p",[e._v("The errors found are due to current limitations in the validation extension with large files.")]),e._v(" "),r("p",[e._v("Here’s a breakdown of the formats:")]),e._v(" "),r("table",[r("thead",[r("tr",[r("th",{staticStyle:{"text-align":"center"}}),e._v(" "),r("th",{staticStyle:{"text-align":"center"}},[e._v("Valid resources")]),e._v(" "),r("th",{staticStyle:{"text-align":"center"}},[e._v("Invalid / Errored resources")])])]),e._v(" "),r("tbody",[r("tr",[r("td",{staticStyle:{"text-align":"center"}},[e._v("CSV")]),e._v(" "),r("td",{staticStyle:{"text-align":"center"}},[e._v("443")]),e._v(" "),r("td",{staticStyle:{"text-align":"center"}},[e._v("64")])]),e._v(" "),r("tr",[r("td",{staticStyle:{"text-align":"center"}},[e._v("XLSX")]),e._v(" "),r("td",{staticStyle:{"text-align":"center"}},[e._v("21")]),e._v(" "),r("td",{staticStyle:{"text-align":"center"}},[e._v("57")])]),e._v(" "),r("tr",[r("td",{staticStyle:{"text-align":"center"}},[e._v("XLS")]),e._v(" "),r("td",{staticStyle:{"text-align":"center"}},[e._v("2")]),e._v(" "),r("td",{staticStyle:{"text-align":"center"}},[e._v("39")])])])]),e._v(" "),r("p",[e._v("And of the error types (more information about each error type can be found in the "),r("a",{attrs:{href:"https://github.com/frictionlessdata/data-quality-spec/blob/master/spec.json",target:"_blank",rel:"noopener noreferrer"}},[e._v("Data Quality Specification"),r("OutboundLink")],1),e._v("):")]),e._v(" "),r("table",[r("thead",[r("tr",[r("th",[e._v("Type of Error")]),e._v(" "),r("th",[e._v("Error Count")])])]),e._v(" "),r("tbody",[r("tr",[r("td",[e._v("Blank row")]),e._v(" "),r("td",[e._v("19654")])]),e._v(" "),r("tr",[r("td",[e._v("Duplicate row")]),e._v(" "),r("td",[e._v("810")])]),e._v(" "),r("tr",[r("td",[e._v("Blank header")]),e._v(" "),r("td",[e._v("299")])]),e._v(" "),r("tr",[r("td",[e._v("Duplicate header")]),e._v(" "),r("td",[e._v("270")])]),e._v(" "),r("tr",[r("td",[e._v("Source error")]),e._v(" "),r("td",[e._v("30")])]),e._v(" "),r("tr",[r("td",[e._v("Extra value")]),e._v(" "),r("td",[e._v("11")])]),e._v(" "),r("tr",[r("td",[e._v("Format error")]),e._v(" "),r("td",[e._v("9")])]),e._v(" "),r("tr",[r("td",[e._v("HTTP error")]),e._v(" "),r("td",[e._v("2")])]),e._v(" "),r("tr",[r("td",[e._v("Missing value")]),e._v(" "),r("td",[e._v("1")])])])]),e._v(" "),r("p",[e._v("The highest number of errors are obviously caused by blank and duplicate rows. These are generally caused by Excel adding extra rows at the end of the file or by publishers formatting the files for human rather than machine consumption. Examples of this include adding a title in the first cell (like in this case: "),r("a",{attrs:{href:"https://data.wprdc.org/dataset/046e5b6a-0f90-4f8e-8c16-14057fd8872e/resource/b4aa617d-1cb8-42d0-8eb6-b650097cf2bf",target:"_blank",rel:"noopener noreferrer"}},[e._v("portal page"),r("OutboundLink")],1),e._v(" | "),r("a",{attrs:{href:"https://data.wprdc.org/dataset/046e5b6a-0f90-4f8e-8c16-14057fd8872e/resource/b4aa617d-1cb8-42d0-8eb6-b650097cf2bf/download/30-day-blotter-data-dictionary.xlsx",target:"_blank",rel:"noopener noreferrer"}},[e._v("file"),r("OutboundLink")],1),e._v(") or even more complex layouts ("),r("a",{attrs:{href:"https://data.wprdc.org/dataset/9c4eab3b-e05d-4af8-ad18-76e4c1a71a74/resource/21a032e9-6345-42b3-b61e-10de29280946",target:"_blank",rel:"noopener noreferrer"}},[e._v("portal page"),r("OutboundLink")],1),e._v(" | "),r("a",{attrs:{href:"https://data.wprdc.org/dataset/9c4eab3b-e05d-4af8-ad18-76e4c1a71a74/resource/21a032e9-6345-42b3-b61e-10de29280946/download/permitsummaryissuedmarch2015.xlsx",target:"_blank",rel:"noopener noreferrer"}},[e._v("file"),r("OutboundLink")],1),e._v("), with logos and links. Blank and duplicate header errors like on this case ("),r("a",{attrs:{href:"https://data.wprdc.org/dataset/543ae03d-3ef4-45c7-b766-2ed49338120f/resource/f587d617-7afa-4e79-8010-c0d2bdff4c04",target:"_blank",rel:"noopener noreferrer"}},[e._v("portal page"),r("OutboundLink")],1),e._v(" | "),r("a",{attrs:{href:"https://data.wprdc.org/dataset/543ae03d-3ef4-45c7-b766-2ed49338120f/resource/f587d617-7afa-4e79-8010-c0d2bdff4c04/download/opendata-citiparks---summer-meal-sites-2015.csv",target:"_blank",rel:"noopener noreferrer"}},[e._v("file"),r("OutboundLink")],1),e._v(") are also normally caused by Excel storing extra empty columns (and something that can not be noticed directly from Excel).")]),e._v(" "),r("p",[e._v("These errors are easy to spot and fix manually once the file has been opened for inspection but this is still an extra step that data consumers need to perform before using the data on their own processes. It is also true that they are errors that could be easily fixed automatically as part of a pre-process of data cleanup before publication. Perhaps this is something that could be developed in the validation extension in the future.")]),e._v(" "),r("p",[e._v("Other less common errors include Source errors, which include errors that prevented the file from being read by goodtables, like encoding issues or HTTP responses or HTML files incorrectly being marked as Excel files (like in this case: "),r("a",{attrs:{href:"https://data.wprdc.org/dataset/9c4eab3b-e05d-4af8-ad18-76e4c1a71a74/resource/9ea45609-e3b0-445a-8ace-0addb973fdf5",target:"_blank",rel:"noopener noreferrer"}},[e._v("portal page"),r("OutboundLink")],1),e._v(" | "),r("a",{attrs:{href:"https://data.wprdc.org/dataset/9c4eab3b-e05d-4af8-ad18-76e4c1a71a74/resource/9ea45609-e3b0-445a-8ace-0addb973fdf5/download/plipublicwebsitemonthlysummaryaugust2017.xls",target:"_blank",rel:"noopener noreferrer"}},[e._v("file"),r("OutboundLink")],1),e._v("). Extra value errors are generally caused by not properly quoting fields that contain commas, thus breaking the parser (example: "),r("a",{attrs:{href:"https://data.wprdc.org/dataset/3130f583-9499-472b-bb5a-f63a6ff6059a/resource/12d9e6e1-3657-4cad-a430-119d34b1a5b2",target:"_blank",rel:"noopener noreferrer"}},[e._v("portal page"),r("OutboundLink")],1),e._v(" | "),r("a",{attrs:{href:"https://data.wprdc.org/dataset/3130f583-9499-472b-bb5a-f63a6ff6059a/resource/12d9e6e1-3657-4cad-a430-119d34b1a5b2/download/crashdatadictionary.csv",target:"_blank",rel:"noopener noreferrer"}},[e._v("file"),r("OutboundLink")],1),e._v(").")]),e._v(" "),r("p",[e._v("Format errors are caused by labelling incorrectly the format of the hosted file, for instance CSV when it links to an Excel file ("),r("a",{attrs:{href:"https://data.wprdc.org/dataset/669b2409-bb4b-46e5-9d91-c36876b58a17/resource/e919ecd3-bb11-4883-a041-bded25dc651c",target:"_blank",rel:"noopener noreferrer"}},[e._v("portal page"),r("OutboundLink")],1),e._v(" | "),r("a",{attrs:{href:"https://data.wprdc.org/dataset/669b2409-bb4b-46e5-9d91-c36876b58a17/resource/e919ecd3-bb11-4883-a041-bded25dc651c/download/2016-cveu-inspections.xlsx",target:"_blank",rel:"noopener noreferrer"}},[e._v("file"),r("OutboundLink")],1),e._v("), CSV linking to HTML ("),r("a",{attrs:{href:"https://data.wprdc.org/dataset/libraries/resource/14babf3f-4932-4828-8b49-3c9a03bae6d0",target:"_blank",rel:"noopener noreferrer"}},[e._v("portal page"),r("OutboundLink")],1),e._v(" | "),r("a",{attrs:{href:"https://wprdc-maps.carto.com/u/wprdc/builder/1142950f-f054-4b3f-8c52-2f020e23cf78/embed",target:"_blank",rel:"noopener noreferrer"}},[e._v("file"),r("OutboundLink")],1),e._v(") or XLS linking to XLSX ("),r("a",{attrs:{href:"https://data.wprdc.org/dataset/40188e1c-6d2e-4f20-9391-607bd3054949/resource/cf0617a1-b950-4aa7-a36d-dc9da412ddf7",target:"_blank",rel:"noopener noreferrer"}},[e._v("portal page"),r("OutboundLink")],1),e._v(" | "),r("a",{attrs:{href:"https://data.wprdc.org/dataset/40188e1c-6d2e-4f20-9391-607bd3054949/resource/cf0617a1-b950-4aa7-a36d-dc9da412ddf7/download/transportation.xls",target:"_blank",rel:"noopener noreferrer"}},[e._v("file"),r("OutboundLink")],1),e._v("). These are all easily fixed at the metadata level.")]),e._v(" "),r("p",[e._v("Finally HTTP errors just show that the linked file hosted elsewhere does not exist or has been moved.")]),e._v(" "),r("p",[e._v("Again, it is important to stress that the checks performed are just "),r("a",{attrs:{href:"https://github.com/frictionlessdata/goodtables-py#validation",target:"_blank",rel:"noopener noreferrer"}},[e._v("basic and structural checks"),r("OutboundLink")],1),e._v(" that affect the general availability of the file and its general structure. The addition of standardized schemas would allow for a more thorough and precise validation, checking the data contents and ensuring that this is what was expected.")]),e._v(" "),r("p",[e._v("Also it is interesting to note that WPRDC has the excellent good practice of publishing data dictionaries describing the contents of the data files. These are generally published in CSV format and they themselves can present validation errors as well. As we saw before, using the validation extension we can assign a schema defined in the Table Schema spec to a resource. This will be used during the validation, but the information could also be used to render it nicely on the UI or export it consistently as a CSV or PDF file.")]),e._v(" "),r("p",[e._v("All the generated reports can be further analyzed using the output files stored "),r("a",{attrs:{href:"https://github.com/frictionlessdata/pilot-wprdc",target:"_blank",rel:"noopener noreferrer"}},[e._v("in this repository"),r("OutboundLink")],1),e._v(".")]),e._v(" "),r("p",[e._v("Additionally, to help browse the validation reports created from the WPRDC site we have set up a demo site that mirrors the datasets, organizations and groups hosted there (at the time we did the import).")]),e._v(" "),r("p",[e._v("All tabular resources have the validation report attached, that can be accessed clicking on the data valid / invalid badges.")]),e._v(" "),r("h2",{attrs:{id:"next-steps"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#next-steps"}},[e._v("#")]),e._v(" Next Steps")]),e._v(" "),r("h3",{attrs:{id:"areas-for-further-work"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#areas-for-further-work"}},[e._v("#")]),e._v(" Areas for further work")]),e._v(" "),r("p",[e._v("The validation extension for CKAN currently provides a very basic workflow for validation at creation and update time: basically if the validation fails in any way you are not allowed to create or edit the dataset. Maintainers can define a set of default validation options to make it more permissive but even so some publishers probably wouldn’t want to enforce all validation checks before allowing the creation of a dataset, or just apply validation to datasets from a particular organization or type. Of course the "),r("a",{attrs:{href:"https://github.com/frictionlessdata/ckanext-validation#action-functions",target:"_blank",rel:"noopener noreferrer"}},[e._v("underlying API"),r("OutboundLink")],1),e._v(" is available for extension developers to implement these workflows, but the validation extension itself could provide some of them.")]),e._v(" "),r("p",[e._v("The user interface for defining the validation options can definitely be improved, and we are planning to integrate a "),r("a",{attrs:{href:"https://github.com/frictionlessdata/ckanext-validation/issues/10",target:"_blank",rel:"noopener noreferrer"}},[e._v("Schema Creator"),r("OutboundLink")],1),e._v(" to make easier for publishers to describe their data with a schema based on the actual fields on the file. If the resource has a schema assigned, this information can be presented nicely on the UI to the users and exported in different formats.")]),e._v(" "),r("p",[e._v("The validation extension is a first iteration to demonstrate the capabilities of integrating data validation directly into CKAN, but we are keen to know about different ways in which this could be expanded or integrated in other workflows, so any feedback or thoughts is appreciated.")]),e._v(" "),r("h3",{attrs:{id:"additional-resources"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#additional-resources"}},[e._v("#")]),e._v(" Additional Resources")]),e._v(" "),r("ul",[r("li",[r("p",[e._v("Check the "),r("a",{attrs:{href:"https://github.com/frictionlessdata/ckanext-validation/blob/master/README.md#how-it-works",target:"_blank",rel:"noopener noreferrer"}},[e._v("full documentation"),r("OutboundLink")],1),e._v(" for ckanext-validation, covering all details on how to install it and configure it, features and available API")])]),e._v(" "),r("li",[r("p",[e._v("Source material:")])]),e._v(" "),r("li",[r("p",[r("a",{attrs:{href:"https://github.com/frictionlessdata/ckanext-validation",target:"_blank",rel:"noopener noreferrer"}},[e._v("ckanext-validation codebase"),r("OutboundLink")],1)])]),e._v(" "),r("li",[r("p",[r("a",{attrs:{href:"https://github.com/frictionlessdata/pilot-wprdc",target:"_blank",rel:"noopener noreferrer"}},[e._v("Western Pennsylvania Regional Data Center Github repository"),r("OutboundLink")],1)])])])])}),[],!1,null,null,null);t.default=o.exports}}]); \ No newline at end of file +(window.webpackJsonp=window.webpackJsonp||[]).push([[20],{429:function(e,t,a){e.exports=a.p+"assets/img/ckanext-validation.9e351f1c.png"},430:function(e,t,a){e.exports=a.p+"assets/img/data-validity-badges.41769a55.png"},431:function(e,t,a){e.exports=a.p+"assets/img/data-validation-on-upload.d1c30a10.png"},581:function(e,t,a){"use strict";a.r(t);var r=a(29),o=Object(r.a)({},(function(){var e=this,t=e.$createElement,r=e._self._c||t;return r("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[r("h2",{attrs:{id:"context"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#context"}},[e._v("#")]),e._v(" Context")]),e._v(" "),r("p",[e._v("One of the main goals of the Frictionless Data project is to help improve data quality by providing easy to integrate libraries and services for data validation. We have integrated data validation seamlessly with different backends like GitHub and Amazon S3 via the online service "),r("a",{attrs:{href:"https://goodtables.io/",target:"_blank",rel:"noopener noreferrer"}},[e._v("goodtables.io"),r("OutboundLink")],1),e._v(", but we also wanted to explore closer integrations with other platforms.")]),e._v(" "),r("p",[e._v("An obvious choice for that are Open Data portals. They are still one of the main forms of dissemination of Open Data, especially for governments and other organizations. They provide a single entry point to data relating to a particular region or thematic area and provide users with tools to discover and access different datasets. On the backend, publishers also have tools available for the validation and publication of datasets.")]),e._v(" "),r("p",[e._v("Data Quality varies widely across different portals, reflecting the publication processes and requirements of the hosting organizations. In general, it is difficult for users to assess the quality of the data and there is a lack of descriptors for the actual data fields. At the publisher level, while strong emphasis has been put in metadata standards and interoperability, publishers don’t generally have the same help or guidance when dealing with data quality or description.")]),e._v(" "),r("p",[e._v("We believe that data quality in Open Data portals can have a central place on both these fronts, user-centric and publisher-centric, and we started this pilot to showcase a possible implementation.")]),e._v(" "),r("p",[e._v("To field test our implementation we chose the "),r("a",{attrs:{href:"https://www.wprdc.org",target:"_blank",rel:"noopener noreferrer"}},[e._v("Western Pennsylvania Regional Data Center"),r("OutboundLink")],1),e._v(" (WPRDC), managed by the "),r("a",{attrs:{href:"http://ucsur.pitt.edu/",target:"_blank",rel:"noopener noreferrer"}},[e._v("University of Pittsburgh Center for Urban and Social Research"),r("OutboundLink")],1),e._v(". The Regional Data Center made for a good pilot as the project team takes an agile approach to managing their own CKAN instance along with support from OpenGov, members of the CKAN association. As the open data repository is used by a diverse array of data publishers (including project partners Allegheny County and the City of Pittsburgh), the Regional Data Center provides a good test case for testing the implementation across a variety of data types and publishing processes. WPRDC is a great example of a well managed Open Data portal, where datasets are actively maintained and the portal itself is just one component of a wider Open Data strategy. It also provides a good variety of publishers, including public sector agencies, academic institutions, and nonprofit organizations. The project’s partnership with the Digital Scholarship Services team at the University Library System also provides data management expertise not typically available in many open data implementations.")]),e._v(" "),r("h2",{attrs:{id:"the-work"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#the-work"}},[e._v("#")]),e._v(" The Work")]),e._v(" "),r("h3",{attrs:{id:"what-did-we-do"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#what-did-we-do"}},[e._v("#")]),e._v(" What Did We Do")]),e._v(" "),r("p",[e._v("The portal software that we chose for this pilot is "),r("a",{attrs:{href:"https://ckan.org",target:"_blank",rel:"noopener noreferrer"}},[e._v("CKAN"),r("OutboundLink")],1),e._v(", the world’s leading open source software for Open Data portals ("),r("a",{attrs:{href:"https://github.com/jalbertbowden/open-library/blob/master/lib/d2.1-state-of-the-art-report-and-evaluation-of-existing-open-data-platforms-2015-01-06-route-to-pa.pdf",target:"_blank",rel:"noopener noreferrer"}},[e._v("source"),r("OutboundLink")],1),e._v("). Open Knowledge International initially fostered the CKAN project and is now a member of the "),r("a",{attrs:{href:"https://ckan.org/about/association/",target:"_blank",rel:"noopener noreferrer"}},[e._v("CKAN Association"),r("OutboundLink")],1),e._v(".")]),e._v(" "),r("p",[e._v("We created "),r("a",{attrs:{href:"https://github.com/frictionlessdata/ckanext-validation",target:"_blank",rel:"noopener noreferrer"}},[e._v("ckanext-validation"),r("OutboundLink")],1),e._v(", a CKAN extension that provides a low level API and readily available features for data validation and reporting that can be added to any CKAN instance. This is powered by "),r("a",{attrs:{href:"https://github.com/frictionlessdata/goodtables-py",target:"_blank",rel:"noopener noreferrer"}},[e._v("goodtables"),r("OutboundLink")],1),e._v(", a library developed by Open Knowledge International to support the validation of tabular datasets.")]),e._v(" "),r("p",[e._v("The extension allows users to perform data validation against any tabular resource, such as CSV or Excel files. This generates a report that is stored against a particular resource, describing issues found with the data, both at the structural level (missing headers, blank rows, etc) and at the data schema level (wrong data types, values out of range etc).")]),e._v(" "),r("p",[r("img",{attrs:{src:a(429),alt:""}}),r("br"),e._v(" "),r("em",[e._v("data validation on CKAN made possible by ckanext-validation extension")])]),e._v(" "),r("p",[e._v("This provides a good overview of the quality of the data to users but also to publishers so they can improve the quality of the data file by addressing these issues. The reports can be easily accessed via badges that provide a quick visual indication of the quality of the data file.")]),e._v(" "),r("p",[r("img",{attrs:{src:a(430),alt:""}}),r("br"),e._v(" "),r("em",[e._v("badges indicating quality of data files on CKAN")])]),e._v(" "),r("p",[e._v("There are two default modes for performing the data validation when creating or updating resources. Data validation can be automatically performed in the background asynchronously or as part of the dataset creation in the user interface. In this case the validation will be performed immediately after uploading or linking to a new tabular file, giving quick feedback to publishers.")]),e._v(" "),r("p",[r("img",{attrs:{src:a(431),alt:""}}),r("br"),e._v(" "),r("em",[e._v("data validation on upload or linking to a new tabular file on CKAN")])]),e._v(" "),r("p",[e._v("The extension adds functionality to provide a "),r("a",{attrs:{href:"https://specs.frictionlessdata.io/table-schema/",target:"_blank",rel:"noopener noreferrer"}},[e._v("schema"),r("OutboundLink")],1),e._v(" for the data that describes the expected fields and types as well as other constraints, allowing to perform validation on the actual contents of the data. Additionally the schema is also stored with the resource metadata, so it can be displayed in the UI or accessed via the API.")]),e._v(" "),r("p",[e._v("The extension also provides some utility commands for CKAN maintainers, including the generation of "),r("a",{attrs:{href:"https://github.com/frictionlessdata/ckanext-validation#data-validation-reports",target:"_blank",rel:"noopener noreferrer"}},[e._v("reports"),r("OutboundLink")],1),e._v(" showing the number of valid and invalid tabular files, a breakdown of the error types and links to the individual resources. This gives maintainers a snapshot of the general quality of the data hosted in their CKAN instance at any given moment in time.")]),e._v(" "),r("p",[e._v("As mentioned before, we field tested the validation extension on the Western Pennsylvania Regional Data Center (WPRDC). At the moment of the import the portal hosted 258 datasets. Out of these, 221 datasets had tabular resources, totalling 626 files (mainly CSV and XLSX files). Taking into account that we only performed the default validation that only includes structural checks (ie not schema-based ones) these are the results:")]),e._v(" "),r("blockquote",[r("p",[e._v("466 resources - validation success")])]),e._v(" "),r("blockquote",[r("p",[e._v("156 resources - validation failure")])]),e._v(" "),r("blockquote",[r("p",[e._v("4 resources - validation error")])]),e._v(" "),r("p",[e._v("The errors found are due to current limitations in the validation extension with large files.")]),e._v(" "),r("p",[e._v("Here’s a breakdown of the formats:")]),e._v(" "),r("table",[r("thead",[r("tr",[r("th",{staticStyle:{"text-align":"center"}}),e._v(" "),r("th",{staticStyle:{"text-align":"center"}},[e._v("Valid resources")]),e._v(" "),r("th",{staticStyle:{"text-align":"center"}},[e._v("Invalid / Errored resources")])])]),e._v(" "),r("tbody",[r("tr",[r("td",{staticStyle:{"text-align":"center"}},[e._v("CSV")]),e._v(" "),r("td",{staticStyle:{"text-align":"center"}},[e._v("443")]),e._v(" "),r("td",{staticStyle:{"text-align":"center"}},[e._v("64")])]),e._v(" "),r("tr",[r("td",{staticStyle:{"text-align":"center"}},[e._v("XLSX")]),e._v(" "),r("td",{staticStyle:{"text-align":"center"}},[e._v("21")]),e._v(" "),r("td",{staticStyle:{"text-align":"center"}},[e._v("57")])]),e._v(" "),r("tr",[r("td",{staticStyle:{"text-align":"center"}},[e._v("XLS")]),e._v(" "),r("td",{staticStyle:{"text-align":"center"}},[e._v("2")]),e._v(" "),r("td",{staticStyle:{"text-align":"center"}},[e._v("39")])])])]),e._v(" "),r("p",[e._v("And of the error types (more information about each error type can be found in the "),r("a",{attrs:{href:"https://github.com/frictionlessdata/data-quality-spec/blob/master/spec.json",target:"_blank",rel:"noopener noreferrer"}},[e._v("Data Quality Specification"),r("OutboundLink")],1),e._v("):")]),e._v(" "),r("table",[r("thead",[r("tr",[r("th",[e._v("Type of Error")]),e._v(" "),r("th",[e._v("Error Count")])])]),e._v(" "),r("tbody",[r("tr",[r("td",[e._v("Blank row")]),e._v(" "),r("td",[e._v("19654")])]),e._v(" "),r("tr",[r("td",[e._v("Duplicate row")]),e._v(" "),r("td",[e._v("810")])]),e._v(" "),r("tr",[r("td",[e._v("Blank header")]),e._v(" "),r("td",[e._v("299")])]),e._v(" "),r("tr",[r("td",[e._v("Duplicate header")]),e._v(" "),r("td",[e._v("270")])]),e._v(" "),r("tr",[r("td",[e._v("Source error")]),e._v(" "),r("td",[e._v("30")])]),e._v(" "),r("tr",[r("td",[e._v("Extra value")]),e._v(" "),r("td",[e._v("11")])]),e._v(" "),r("tr",[r("td",[e._v("Format error")]),e._v(" "),r("td",[e._v("9")])]),e._v(" "),r("tr",[r("td",[e._v("HTTP error")]),e._v(" "),r("td",[e._v("2")])]),e._v(" "),r("tr",[r("td",[e._v("Missing value")]),e._v(" "),r("td",[e._v("1")])])])]),e._v(" "),r("p",[e._v("The highest number of errors are obviously caused by blank and duplicate rows. These are generally caused by Excel adding extra rows at the end of the file or by publishers formatting the files for human rather than machine consumption. Examples of this include adding a title in the first cell (like in this case: "),r("a",{attrs:{href:"https://data.wprdc.org/dataset/046e5b6a-0f90-4f8e-8c16-14057fd8872e/resource/b4aa617d-1cb8-42d0-8eb6-b650097cf2bf",target:"_blank",rel:"noopener noreferrer"}},[e._v("portal page"),r("OutboundLink")],1),e._v(" | "),r("a",{attrs:{href:"https://data.wprdc.org/dataset/046e5b6a-0f90-4f8e-8c16-14057fd8872e/resource/b4aa617d-1cb8-42d0-8eb6-b650097cf2bf/download/30-day-blotter-data-dictionary.xlsx",target:"_blank",rel:"noopener noreferrer"}},[e._v("file"),r("OutboundLink")],1),e._v(") or even more complex layouts ("),r("a",{attrs:{href:"https://data.wprdc.org/dataset/9c4eab3b-e05d-4af8-ad18-76e4c1a71a74/resource/21a032e9-6345-42b3-b61e-10de29280946",target:"_blank",rel:"noopener noreferrer"}},[e._v("portal page"),r("OutboundLink")],1),e._v(" | "),r("a",{attrs:{href:"https://data.wprdc.org/dataset/9c4eab3b-e05d-4af8-ad18-76e4c1a71a74/resource/21a032e9-6345-42b3-b61e-10de29280946/download/permitsummaryissuedmarch2015.xlsx",target:"_blank",rel:"noopener noreferrer"}},[e._v("file"),r("OutboundLink")],1),e._v("), with logos and links. Blank and duplicate header errors like on this case ("),r("a",{attrs:{href:"https://data.wprdc.org/dataset/543ae03d-3ef4-45c7-b766-2ed49338120f/resource/f587d617-7afa-4e79-8010-c0d2bdff4c04",target:"_blank",rel:"noopener noreferrer"}},[e._v("portal page"),r("OutboundLink")],1),e._v(" | "),r("a",{attrs:{href:"https://data.wprdc.org/dataset/543ae03d-3ef4-45c7-b766-2ed49338120f/resource/f587d617-7afa-4e79-8010-c0d2bdff4c04/download/opendata-citiparks---summer-meal-sites-2015.csv",target:"_blank",rel:"noopener noreferrer"}},[e._v("file"),r("OutboundLink")],1),e._v(") are also normally caused by Excel storing extra empty columns (and something that can not be noticed directly from Excel).")]),e._v(" "),r("p",[e._v("These errors are easy to spot and fix manually once the file has been opened for inspection but this is still an extra step that data consumers need to perform before using the data on their own processes. It is also true that they are errors that could be easily fixed automatically as part of a pre-process of data cleanup before publication. Perhaps this is something that could be developed in the validation extension in the future.")]),e._v(" "),r("p",[e._v("Other less common errors include Source errors, which include errors that prevented the file from being read by goodtables, like encoding issues or HTTP responses or HTML files incorrectly being marked as Excel files (like in this case: "),r("a",{attrs:{href:"https://data.wprdc.org/dataset/9c4eab3b-e05d-4af8-ad18-76e4c1a71a74/resource/9ea45609-e3b0-445a-8ace-0addb973fdf5",target:"_blank",rel:"noopener noreferrer"}},[e._v("portal page"),r("OutboundLink")],1),e._v(" | "),r("a",{attrs:{href:"https://data.wprdc.org/dataset/9c4eab3b-e05d-4af8-ad18-76e4c1a71a74/resource/9ea45609-e3b0-445a-8ace-0addb973fdf5/download/plipublicwebsitemonthlysummaryaugust2017.xls",target:"_blank",rel:"noopener noreferrer"}},[e._v("file"),r("OutboundLink")],1),e._v("). Extra value errors are generally caused by not properly quoting fields that contain commas, thus breaking the parser (example: "),r("a",{attrs:{href:"https://data.wprdc.org/dataset/3130f583-9499-472b-bb5a-f63a6ff6059a/resource/12d9e6e1-3657-4cad-a430-119d34b1a5b2",target:"_blank",rel:"noopener noreferrer"}},[e._v("portal page"),r("OutboundLink")],1),e._v(" | "),r("a",{attrs:{href:"https://data.wprdc.org/dataset/3130f583-9499-472b-bb5a-f63a6ff6059a/resource/12d9e6e1-3657-4cad-a430-119d34b1a5b2/download/crashdatadictionary.csv",target:"_blank",rel:"noopener noreferrer"}},[e._v("file"),r("OutboundLink")],1),e._v(").")]),e._v(" "),r("p",[e._v("Format errors are caused by labelling incorrectly the format of the hosted file, for instance CSV when it links to an Excel file ("),r("a",{attrs:{href:"https://data.wprdc.org/dataset/669b2409-bb4b-46e5-9d91-c36876b58a17/resource/e919ecd3-bb11-4883-a041-bded25dc651c",target:"_blank",rel:"noopener noreferrer"}},[e._v("portal page"),r("OutboundLink")],1),e._v(" | "),r("a",{attrs:{href:"https://data.wprdc.org/dataset/669b2409-bb4b-46e5-9d91-c36876b58a17/resource/e919ecd3-bb11-4883-a041-bded25dc651c/download/2016-cveu-inspections.xlsx",target:"_blank",rel:"noopener noreferrer"}},[e._v("file"),r("OutboundLink")],1),e._v("), CSV linking to HTML ("),r("a",{attrs:{href:"https://data.wprdc.org/dataset/libraries/resource/14babf3f-4932-4828-8b49-3c9a03bae6d0",target:"_blank",rel:"noopener noreferrer"}},[e._v("portal page"),r("OutboundLink")],1),e._v(" | "),r("a",{attrs:{href:"https://wprdc-maps.carto.com/u/wprdc/builder/1142950f-f054-4b3f-8c52-2f020e23cf78/embed",target:"_blank",rel:"noopener noreferrer"}},[e._v("file"),r("OutboundLink")],1),e._v(") or XLS linking to XLSX ("),r("a",{attrs:{href:"https://data.wprdc.org/dataset/40188e1c-6d2e-4f20-9391-607bd3054949/resource/cf0617a1-b950-4aa7-a36d-dc9da412ddf7",target:"_blank",rel:"noopener noreferrer"}},[e._v("portal page"),r("OutboundLink")],1),e._v(" | "),r("a",{attrs:{href:"https://data.wprdc.org/dataset/40188e1c-6d2e-4f20-9391-607bd3054949/resource/cf0617a1-b950-4aa7-a36d-dc9da412ddf7/download/transportation.xls",target:"_blank",rel:"noopener noreferrer"}},[e._v("file"),r("OutboundLink")],1),e._v("). These are all easily fixed at the metadata level.")]),e._v(" "),r("p",[e._v("Finally HTTP errors just show that the linked file hosted elsewhere does not exist or has been moved.")]),e._v(" "),r("p",[e._v("Again, it is important to stress that the checks performed are just "),r("a",{attrs:{href:"https://github.com/frictionlessdata/goodtables-py#validation",target:"_blank",rel:"noopener noreferrer"}},[e._v("basic and structural checks"),r("OutboundLink")],1),e._v(" that affect the general availability of the file and its general structure. The addition of standardized schemas would allow for a more thorough and precise validation, checking the data contents and ensuring that this is what was expected.")]),e._v(" "),r("p",[e._v("Also it is interesting to note that WPRDC has the excellent good practice of publishing data dictionaries describing the contents of the data files. These are generally published in CSV format and they themselves can present validation errors as well. As we saw before, using the validation extension we can assign a schema defined in the Table Schema spec to a resource. This will be used during the validation, but the information could also be used to render it nicely on the UI or export it consistently as a CSV or PDF file.")]),e._v(" "),r("p",[e._v("All the generated reports can be further analyzed using the output files stored "),r("a",{attrs:{href:"https://github.com/frictionlessdata/pilot-wprdc",target:"_blank",rel:"noopener noreferrer"}},[e._v("in this repository"),r("OutboundLink")],1),e._v(".")]),e._v(" "),r("p",[e._v("Additionally, to help browse the validation reports created from the WPRDC site we have set up a demo site that mirrors the datasets, organizations and groups hosted there (at the time we did the import).")]),e._v(" "),r("p",[e._v("All tabular resources have the validation report attached, that can be accessed clicking on the data valid / invalid badges.")]),e._v(" "),r("h2",{attrs:{id:"next-steps"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#next-steps"}},[e._v("#")]),e._v(" Next Steps")]),e._v(" "),r("h3",{attrs:{id:"areas-for-further-work"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#areas-for-further-work"}},[e._v("#")]),e._v(" Areas for further work")]),e._v(" "),r("p",[e._v("The validation extension for CKAN currently provides a very basic workflow for validation at creation and update time: basically if the validation fails in any way you are not allowed to create or edit the dataset. Maintainers can define a set of default validation options to make it more permissive but even so some publishers probably wouldn’t want to enforce all validation checks before allowing the creation of a dataset, or just apply validation to datasets from a particular organization or type. Of course the "),r("a",{attrs:{href:"https://github.com/frictionlessdata/ckanext-validation#action-functions",target:"_blank",rel:"noopener noreferrer"}},[e._v("underlying API"),r("OutboundLink")],1),e._v(" is available for extension developers to implement these workflows, but the validation extension itself could provide some of them.")]),e._v(" "),r("p",[e._v("The user interface for defining the validation options can definitely be improved, and we are planning to integrate a "),r("a",{attrs:{href:"https://github.com/frictionlessdata/ckanext-validation/issues/10",target:"_blank",rel:"noopener noreferrer"}},[e._v("Schema Creator"),r("OutboundLink")],1),e._v(" to make easier for publishers to describe their data with a schema based on the actual fields on the file. 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"),r("h3",{attrs:{id:"general-introduction"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#general-introduction"}},[a._v("#")]),a._v(" General Introduction")]),a._v(" "),r("p",[a._v("As a Developer the primary actions you want to support are:")]),a._v(" "),r("ul",[r("li",[a._v("Importing data (and metadata) in a Data Package into your system")]),a._v(" "),r("li",[a._v("Exporting data (and metadata) from your system to a Data Package")])]),a._v(" "),r("p",[a._v("Addition actions include:")]),a._v(" "),r("ul",[r("li",[a._v("Creating a Data Package from scratch")]),a._v(" "),r("li",[a._v("Validating the data in a Data Package (is the data how it says it should be)")]),a._v(" "),r("li",[a._v("Validating the metadata in a Data Package")]),a._v(" "),r("li",[a._v("Visualizing the Data Package")]),a._v(" "),r("li",[a._v("Publishing the Data Package to an online repository")])]),a._v(" "),r("h3",{attrs:{id:"programming-language"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#programming-language"}},[a._v("#")]),a._v(" Programming Language")]),a._v(" "),r("p",[a._v("This is example pseudo-code for a Data Package library in a programming language like Python or Javascript.")]),a._v(" "),r("p",[r("strong",[a._v("Importing a Data Package")])]),a._v(" "),r("div",{staticClass:"language- extra-class"},[r("pre",{pre:!0,attrs:{class:"language-text"}},[r("code",[a._v('# location of Data Package e.g. URL or path on disk\nvar location = /my/data/package/\n\n# this "imports" the Data Package providing a native DataPackage object to work with\n# Note: you usually will not load the data itself\nvar myDataPackage = new DataPackage(location)\nvar myDataResource = myDataPackage.getResource(indexOrNameOfResource)\n\n# this would return an iterator over row objects if the data was in rows\n# optional support for casting data to the right type based on Table Schema\nvar dataStream = myDataResource.stream(cast=True)\n\n# instead of an iterator you may want simply to convert to native structured data type\n# for example, in R where you have a dataframe you would do something like\nvar dataframe = myDataResource.asDataFrame()\n')])])]),r("p",[r("strong",[a._v("Accessing metadata")])]),a._v(" "),r("div",{staticClass:"language- extra-class"},[r("pre",{pre:!0,attrs:{class:"language-text"}},[r("code",[a._v("# Here we access to Data Package metadata\n# the exact accessor structure is up to you - here it an attribute called\n# metadata that acts like a dictionary\nprint myDataPackage.descriptor['title']\n")])])]),r("p",[r("strong",[a._v("Exporting a Data Package")])]),a._v(" "),r("p",[a._v("A simple functional style approach that gets across the idea:")]),a._v(" "),r("div",{staticClass:"language- extra-class"},[r("pre",{pre:!0,attrs:{class:"language-text"}},[r("code",[a._v("# e.g. a location on disk\nvar exportLocation = /path/to/where/data/package/should/go\nexport_data_package(nativeDataObject, exportLocation)\n")])])]),r("p",[a._v("A more object-oriented model fitting with our previous examples would be:")]),a._v(" "),r("div",{staticClass:"language- extra-class"},[r("pre",{pre:!0,attrs:{class:"language-text"}},[r("code",[a._v("var myDataPackage = export_data_package(nativeDataObject)\nmyDataPackage.save(exportLocation)\n\n# if the native data is more like a table a data then you might have\nvar myDataPackage = new DataPackage()\nmyDataPackage.addResourceFromNativeDataObject(nativeDataObject)\n\n# once exported to\nmyDataPackage.saveToDisk(path)\n\n# You can also provide access to the Data Package datapackage.json\n# That way clients of your library can decide how they save this themselves\nvar readyForJSONSaving = myDataPackage.dataPackageJSON()\nsaveJson(readyForJSONSaving, '/path/to/save/datapackage.json')\n")])])]),r("p",[r("strong",[a._v("Creating a Data Package from scratch")])]),a._v(" "),r("div",{staticClass:"language- extra-class"},[r("pre",{pre:!0,attrs:{class:"language-text"}},[r("code",[a._v("var myMetadata = {\n title: 'My Amazing Data'\n}\nvar myDataPackage = new DataPackage(myMetadata)\n")])])]),r("p",[r("strong",[a._v("Data Validation")])]),a._v(" "),r("p",[a._v("This is Tabular Data specific.")]),a._v(" "),r("div",{staticClass:"language- extra-class"},[r("pre",{pre:!0,attrs:{class:"language-text"}},[r("code",[a._v("var resource = myDataPackage.getResource()\n# check the data conforms to the Table Schema\nresource.validate()\n\n# more explicit version might look like\nvar schema = resource.schemaAsJSON()\nvar tsValidator = new TSValidator(schema)\n# validate a data stream\nschema.validate(resource.stream())\n")])])]),r("p",[r("strong",[a._v("Validating Metadata")])]),a._v(" "),r("h3",{attrs:{id:"specific-software-and-platforms"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#specific-software-and-platforms"}},[a._v("#")]),a._v(" Specific Software and Platforms")]),a._v(" "),r("p",[a._v("For a particular tool or platform usually all you need is simple import or export:")]),a._v(" "),r("div",{staticClass:"language- extra-class"},[r("pre",{pre:!0,attrs:{class:"language-text"}},[r("code",[a._v("# import into SQL (implemented in some language)\nimport_datapackage_into_sql(pathToDataPackage, sqlDatabaseInfo)\n\n# import into Google BigQuery\nimport_datapackage_into_bigquery(pathToDataPackage, bigQueryInfo)\n")])])]),r("h2",{attrs:{id:"examples"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#examples"}},[a._v("#")]),a._v(" Examples")]),a._v(" "),r("h3",{attrs:{id:"python"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#python"}},[a._v("#")]),a._v(" Python")]),a._v(" "),r("p",[a._v("The main Python library for working with Data Packages is "),r("code",[a._v("datapackage")]),a._v(":")]),a._v(" "),r("p",[a._v("See "),r("a",{attrs:{href:"http://github.com/frictionlessdata/datapackage-py",target:"_blank",rel:"noopener noreferrer"}},[a._v("http://github.com/frictionlessdata/datapackage-py"),r("OutboundLink")],1)]),a._v(" "),r("p",[a._v("Additional functionality such as TS and TS integration:")]),a._v(" "),r("ul",[r("li",[r("a",{attrs:{href:"https://github.com/frictionlessdata/tableschema-py",target:"_blank",rel:"noopener noreferrer"}},[a._v("https://github.com/frictionlessdata/tableschema-py"),r("OutboundLink")],1)]),a._v(" "),r("li",[r("a",{attrs:{href:"https://github.com/frictionlessdata/tableschema-sql-py",target:"_blank",rel:"noopener noreferrer"}},[a._v("https://github.com/frictionlessdata/tableschema-sql-py"),r("OutboundLink")],1)]),a._v(" "),r("li",[r("a",{attrs:{href:"https://github.com/frictionlessdata/tableschema-bigquery-py",target:"_blank",rel:"noopener noreferrer"}},[a._v("https://github.com/frictionlessdata/tableschema-bigquery-py"),r("OutboundLink")],1)])]),a._v(" "),r("p",[r("code",[a._v("tabulator")]),a._v(" is a utility library that provides a consistent interface for reading tabular data:")]),a._v(" "),r("p",[r("a",{attrs:{href:"https://github.com/frictionlessdata/tabulator-py",target:"_blank",rel:"noopener noreferrer"}},[a._v("https://github.com/frictionlessdata/tabulator-py"),r("OutboundLink")],1)]),a._v(" "),r("p",[a._v("Here’s an overview of the Python libraries available and how they fit together:")]),a._v(" "),r("p",[r("img",{attrs:{src:e(461),alt:"how the different tableschema libraries in python fit together"}}),r("br"),a._v(" "),r("em",[a._v("how the different tableschema libraries in python fit together")])]),a._v(" "),r("h3",{attrs:{id:"javascript"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#javascript"}},[a._v("#")]),a._v(" Javascript")]),a._v(" "),r("p",[a._v("Following “Node” style we have partitioned the Javascript library into pieces, see this list of libraries:")]),a._v(" "),r("ul",[r("li",[r("a",{attrs:{href:"https://github.com/frictionlessdata?language=javascript",target:"_blank",rel:"noopener noreferrer"}},[a._v("https://github.com/frictionlessdata?language=javascript"),r("OutboundLink")],1)])]),a._v(" "),r("h3",{attrs:{id:"sql-integration"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#sql-integration"}},[a._v("#")]),a._v(" SQL Integration")]),a._v(" "),r("p",[r("a",{attrs:{href:"https://github.com/frictionlessdata/tableschema-sql-py",target:"_blank",rel:"noopener noreferrer"}},[a._v("Here’s a walk-through"),r("OutboundLink")],1),a._v(" of the SQL integration for "),r("a",{attrs:{href:"https://specs.frictionlessdata.io/table-schema/",target:"_blank",rel:"noopener noreferrer"}},[a._v("Table Schema"),r("OutboundLink")],1),a._v(" written in python. This integration allows you to generate SQL tables, load and extract data based on "),r("a",{attrs:{href:"https://specs.frictionlessdata.io/table-schema/",target:"_blank",rel:"noopener noreferrer"}},[a._v("Table Schema"),r("OutboundLink")],1),a._v(" descriptors.")]),a._v(" "),r("p",[a._v("Related blog post: "),r("a",{attrs:{href:"http://okfnlabs.org/blog/2017/10/05/frictionless-data-specs-v1-updates.html",target:"_blank",rel:"noopener noreferrer"}},[a._v("http://okfnlabs.org/blog/2017/10/05/frictionless-data-specs-v1-updates.html"),r("OutboundLink")],1)])])}),[],!1,null,null,null);t.default=s.exports}}]); \ No newline at end of file diff --git a/assets/js/22.2c8e874b.js b/assets/js/22.9f4bd18f.js similarity index 97% rename from assets/js/22.2c8e874b.js rename to assets/js/22.9f4bd18f.js index 458605c8c..eda030a28 100644 --- a/assets/js/22.2c8e874b.js +++ b/assets/js/22.9f4bd18f.js @@ -1 +1 @@ -(window.webpackJsonp=window.webpackJsonp||[]).push([[22],{475:function(e,a,t){e.exports=t.p+"assets/img/data-curator.1e9d2493.png"},476:function(e,a,t){e.exports=t.p+"assets/img/data-curator-2.947fbd26.png"},477:function(e,a,t){e.exports=t.p+"assets/img/data-curator-3.4f1191bc.png"},609:function(e,a,t){"use strict";t.r(a);var r=t(29),o=Object(r.a)({},(function(){var e=this,a=e.$createElement,r=e._self._c||a;return r("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[r("h1",{attrs:{id:"data-curator-share-usable-open-data"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#data-curator-share-usable-open-data"}},[e._v("#")]),e._v(" Data Curator - share usable open data")]),e._v(" "),r("p",[e._v("Open data producers are increasingly focusing on improving open data so it can be easily used to create insight and drive positive change.")]),e._v(" "),r("p",[e._v("Open data is more likely to be used if data consumers can:")]),e._v(" "),r("ul",[r("li",[e._v("understand the structure of the data")]),e._v(" "),r("li",[e._v("understand the quality of the data")]),e._v(" "),r("li",[e._v("understand why and how the data was collected")]),e._v(" "),r("li",[e._v("look up the meaning of codes used in the data")]),e._v(" "),r("li",[e._v("access the data in an open machine-readable format")]),e._v(" "),r("li",[e._v("know how the data is licensed and how it can be reused")])]),e._v(" "),r("p",[e._v("Data Curator enables open data producers to define all this information using their desktop computer, prior to publishing it on the Internet.")]),e._v(" "),r("p",[e._v("Data Curator uses the "),r("a",{attrs:{href:"https://specs.frictionlessdata.io/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Frictionless Data specification"),r("OutboundLink")],1),e._v(" and software to package the data and supporting information in a "),r("a",{attrs:{href:"https://specs.frictionlessdata.io/tabular-data-package/",title:"Tabular Data Package specification",target:"_blank",rel:"noopener noreferrer"}},[e._v("Tabular Data Package"),r("OutboundLink")],1),e._v(".")]),e._v(" "),r("p",[r("img",{attrs:{src:t(475),alt:"Data Curator screenshot"}})]),e._v(" "),r("h2",{attrs:{id:"using-data-curator"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#using-data-curator"}},[e._v("#")]),e._v(" Using Data Curator")]),e._v(" "),r("p",[e._v("Here’s how to use Data Curator to share usable open data in a data package:")]),e._v(" "),r("ol",[r("li",[e._v("Download "),r("a",{attrs:{href:"https://github.com/ODIQueensland/data-curator/releases/latest",title:"Download Data Curator for Windows or macOS",target:"_blank",rel:"noopener noreferrer"}},[e._v("Data Curator"),r("OutboundLink")],1),e._v(" for Windows or macOS")]),e._v(" "),r("li",[e._v("In Data Curator, either:\n"),r("ul",[r("li",[e._v("create some data")]),e._v(" "),r("li",[e._v("open an Excel sheet")]),e._v(" "),r("li",[e._v("open a separated value file (e.g. CSV, TSV)")])])]),e._v(" "),r("li",[e._v("Follow the steps below…")])]),e._v(" "),r("h3",{attrs:{id:"describe-the-data"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#describe-the-data"}},[e._v("#")]),e._v(" Describe the data")]),e._v(" "),r("p",[e._v("The Frictionless Data specification allows you to describe tabular data using a "),r("a",{attrs:{href:"https://specs.frictionlessdata.io/table-schema/",title:"Table Schema specification",target:"_blank",rel:"noopener noreferrer"}},[e._v("Table Schema"),r("OutboundLink")],1),e._v(". A Table Schema allows each field in the data to be given:")]),e._v(" "),r("ul",[r("li",[e._v("a "),r("code",[e._v("name")]),e._v(", "),r("code",[e._v("title")]),e._v(" and "),r("code",[e._v("description")])]),e._v(" "),r("li",[e._v("a data "),r("code",[e._v("type")]),e._v(" (e.g. "),r("code",[e._v("string")]),e._v(", "),r("code",[e._v("integer")]),e._v(") and "),r("code",[e._v("format")]),e._v(" (e.g. "),r("code",[e._v("uri")]),e._v(", "),r("code",[e._v("email")]),e._v(")")]),e._v(" "),r("li",[e._v("one or more "),r("code",[e._v("constraints")]),e._v(" (e.g. "),r("code",[e._v("required")]),e._v(", "),r("code",[e._v("unique")]),e._v(") to limit data values and improve data validation")])]),e._v(" "),r("p",[e._v("The Table Schema also allows you to describe the characters used to represent missing values (e.g. "),r("code",[e._v("n/a")]),e._v(", "),r("code",[e._v("tba")]),e._v("), primary keys, and foreign key relationships.")]),e._v(" "),r("p",[e._v("After adding data in Data Curator, to create a Table Schema:")]),e._v(" "),r("ul",[r("li",[e._v("Give your data a header row, if it doesn’t have one")]),e._v(" "),r("li",[e._v("Set the header row to give each field a "),r("code",[e._v("name")])]),e._v(" "),r("li",[e._v("Guess column properties to give each field a "),r("code",[e._v("type")]),e._v(" and "),r("code",[e._v("format")])]),e._v(" "),r("li",[e._v("Set column properties to improve the data "),r("code",[e._v("type")]),e._v(" and "),r("code",[e._v("format")]),e._v(" guesses, and add a "),r("code",[e._v("title")]),e._v(", "),r("code",[e._v("description")]),e._v(" and "),r("code",[e._v("constraints")])]),e._v(" "),r("li",[e._v("Set table properties to give the table a "),r("code",[e._v("name")]),e._v(", define missing values, a primary key, and foreign keys.")])]),e._v(" "),r("h3",{attrs:{id:"validate-the-data"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#validate-the-data"}},[e._v("#")]),e._v(" Validate the data")]),e._v(" "),r("p",[e._v("Using Data Curator, you can validate if the data complies with the field’s "),r("code",[e._v("type")]),e._v(", "),r("code",[e._v("format")]),e._v(" and "),r("code",[e._v("contraints")]),e._v(". Errors found can be filtered in different ways so you can correct errors by row, by column or by error type.")]),e._v(" "),r("p",[e._v("In some cases data errors cannot be corrected, as they should be corrected in the source system and not as part of the data packaging process. If you’re happy to publish the data with errors, the error messages can be appended to the provenance information.")]),e._v(" "),r("h3",{attrs:{id:"provide-context"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#provide-context"}},[e._v("#")]),e._v(" Provide context")]),e._v(" "),r("p",[e._v("Data Curator lets you add provenance information to help people understand why and how the data was collected and determine if it is fit for their purpose.")]),e._v(" "),r("p",[e._v("Provenance information can be entered using "),r("a",{attrs:{href:"http://commonmark.org",title:"Markdown specification",target:"_blank",rel:"noopener noreferrer"}},[e._v("Markdown"),r("OutboundLink")],1),e._v(". You can preview the Markdown formatting in Data Curator.")]),e._v(" "),r("p",[r("img",{attrs:{src:t(476),alt:"Add provenance information screenshot"}})]),e._v(" "),r("p",[e._v("You should follow the "),r("RouterLink",{attrs:{to:"/blog/2016/04/20/publish-faq/",title:"Publishing Data Packages - FAQ"}},[e._v("Readme FAQ")]),e._v(" when writing provenance information or, even easier, cut and paste from this "),r("a",{attrs:{href:"https://github.com/ODIQueensland/data-curator/blob/develop/test/features/tools/sample-provenance-information.md",title:"Sample Provenance Information Markdown file on GitHub",target:"_blank",rel:"noopener noreferrer"}},[e._v("sample"),r("OutboundLink")],1),e._v(".")],1),e._v(" "),r("h3",{attrs:{id:"explain-the-meaning-of-codes"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#explain-the-meaning-of-codes"}},[e._v("#")]),e._v(" Explain the meaning of codes")]),e._v(" "),r("p",[e._v("Data Curator supports foreign key relationships between data. 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This is really useful if you want to share code-lists across organisations.")]),e._v(" "),r("p",[e._v("You can define foreign key relationships in Data Curator in the table properties and the relationships are checked when you validate the data.")]),e._v(" "),r("h3",{attrs:{id:"save-the-data-in-an-open-format"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#save-the-data-in-an-open-format"}},[e._v("#")]),e._v(" Save the data in an open format")]),e._v(" "),r("p",[e._v("Data Curator lets you save data as a comma, semicolon, or tab separated value file. A matching "),r("a",{attrs:{href:"https://specs.frictionlessdata.io/csv-dialect/",title:"The CSV Dialect specification",target:"_blank",rel:"noopener noreferrer"}},[e._v("CSV Dialect"),r("OutboundLink")],1),e._v(" is added to the data package.")]),e._v(" "),r("h3",{attrs:{id:"apply-an-open-license"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#apply-an-open-license"}},[e._v("#")]),e._v(" Apply an open license")]),e._v(" "),r("p",[e._v("Applying a license, waiver, or public domain mark to a "),r("a",{attrs:{href:"https://specs.frictionlessdata.io/data-package/#licenses",title:"The licenses property in the Data Package specification",target:"_blank",rel:"noopener noreferrer"}},[e._v("data package"),r("OutboundLink")],1),e._v(" and its "),r("a",{attrs:{href:"https://specs.frictionlessdata.io/data-resource/#optional-properties",title:"The licenses property in the Data Resource specification",target:"_blank",rel:"noopener noreferrer"}},[e._v("resources"),r("OutboundLink")],1),e._v(" helps people understand how they can use, modify, and share the contents of the data package.")]),e._v(" "),r("p",[r("img",{attrs:{src:t(477),alt:"Apply open license to data package screenshot"}})]),e._v(" "),r("p",[e._v("Although there are many ways to "),r("RouterLink",{attrs:{to:"/blog/2018/03/27/applying-licenses/",title:"Guide to applying licenses, waivers or public domain marks to data packages"}},[e._v("apply a licence, waiver or public domain mark")]),e._v(" to a data package, Data Curator only allows you to use open licences - after all, its purpose is to share usable open data.")],1),e._v(" "),r("h3",{attrs:{id:"export-the-data-package"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#export-the-data-package"}},[e._v("#")]),e._v(" Export the data package")]),e._v(" "),r("p",[e._v("To ensure only usable open data is shared, Data Curator applies some checks before allowing a data package to be exported. These go beyond the mandatory requirements* in the Frictionless Data specification.")]),e._v(" "),r("p",[e._v("To export a tabular data package, it must have:")]),e._v(" "),r("ul",[r("li",[e._v("a header row")]),e._v(" "),r("li",[e._v("a table schema*")]),e._v(" "),r("li",[e._v("a table (resource) "),r("code",[e._v("name")]),e._v("*")]),e._v(" "),r("li",[e._v("a data package "),r("code",[e._v("name")]),e._v("*")]),e._v(" "),r("li",[e._v("provenance information")]),e._v(" "),r("li",[e._v("an open licence applied to the data package")])]),e._v(" "),r("p",[e._v("If a data package "),r("code",[e._v("version")]),e._v(" is used, it must follow the "),r("a",{attrs:{href:"https://specs.frictionlessdata.io/patterns/#data-package-version",title:"Data Package Version pattern",target:"_blank",rel:"noopener noreferrer"}},[e._v("data package version pattern"),r("OutboundLink")],1),e._v(".")]),e._v(" "),r("p",[e._v("Before exporting a data package you should:")]),e._v(" "),r("ul",[r("li",[e._v("add a "),r("code",[e._v("title")]),e._v(" and "),r("code",[e._v("description")]),e._v(" to each field, table and data package")]),e._v(" "),r("li",[e._v("acknowledge any data sources and contributors")]),e._v(" "),r("li",[e._v("validate the data and add any known errors to the provenance information")])]),e._v(" "),r("p",[e._v("The data package is exported as a "),r("code",[e._v("datapackage.zip")]),e._v(" file that contains the:")]),e._v(" "),r("ul",[r("li",[e._v("data files in a "),r("code",[e._v("/data")]),e._v(" directory")]),e._v(" "),r("li",[e._v("data package, table (resource), table schema, and csv dialect properties in a"),r("code",[e._v("datapackage.json")]),e._v(" file")]),e._v(" "),r("li",[e._v("provenance information in a "),r("code",[e._v("README.md")]),e._v(" file")])]),e._v(" "),r("h3",{attrs:{id:"share-the-data"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#share-the-data"}},[e._v("#")]),e._v(" Share the data")]),e._v(" "),r("p",[e._v("Share the "),r("code",[e._v("datapackage.zip")]),e._v(" with open data consumers by publishing it on the Internet or on an open data platform. Some platforms support uploading, displaying, and downloading data packages.")]),e._v(" "),r("p",[e._v("Open data consumers will be able to read the data package with one of the many applications and software libraries that work with data packages, including Data Curator.")]),e._v(" "),r("h2",{attrs:{id:"get-started"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#get-started"}},[e._v("#")]),e._v(" Get Started")]),e._v(" "),r("p",[r("strong",[r("a",{attrs:{href:"https://github.com/ODIQueensland/data-curator/releases/latest",title:"Download Data Curator for Windows or macOS",target:"_blank",rel:"noopener noreferrer"}},[e._v("Download Data Curator"),r("OutboundLink")],1)]),e._v(" for Windows or macOS and start sharing usable open data.")]),e._v(" "),r("h2",{attrs:{id:"who-made-data-curator"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#who-made-data-curator"}},[e._v("#")]),e._v(" Who made Data Curator?")]),e._v(" "),r("p",[e._v("Data Curator was made possible with funding from the "),r("a",{attrs:{href:"https://www.qld.gov.au",target:"_blank",rel:"noopener noreferrer"}},[e._v("Queensland Government"),r("OutboundLink")],1),e._v(" and the guidance of the Open Data Policy team within the Department of Housing and Public Works. We’re grateful for the ideas and testing provided by open data champions in the Department of Environment and Science, and the Department of Transport and Main Roads.")]),e._v(" "),r("p",[e._v("The project was led by "),r("a",{attrs:{href:"https://theodi.org/article/open-data-pathway-introducing-country-level-statistics/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Stephen Gates"),r("OutboundLink")],1),e._v(" from the "),r("a",{attrs:{href:"https://www.linkedin.com/company/odiaustraliannetwork/about/",target:"_blank",rel:"noopener noreferrer"}},[e._v("ODI Australian Network"),r("OutboundLink")],1),e._v(". Software development was coordinated by Gavin Kennedy and performed by Matt Mulholland from the "),r("a",{attrs:{href:"https://www.qcif.edu.au",target:"_blank",rel:"noopener noreferrer"}},[e._v("Queensland Cyber Infrastructure Foundation"),r("OutboundLink")],1),e._v(" (QCIF).")]),e._v(" "),r("p",[e._v("Data Curator uses the Frictionless Data software libraries maintained by "),r("a",{attrs:{href:"https://okfn.org",target:"_blank",rel:"noopener noreferrer"}},[e._v("Open Knowledge International"),r("OutboundLink")],1),e._v(" and we’re extremely grateful for the support provided by "),r("a",{attrs:{href:"https://github.com/orgs/frictionlessdata/teams/core/members",target:"_blank",rel:"noopener noreferrer"}},[e._v("the team"),r("OutboundLink")],1),e._v(".")]),e._v(" "),r("p",[e._v("Data Curator started life as "),r("a",{attrs:{href:"http://comma-chameleon.io",title:"Comma Chameleon - A desktop CSV editor for data publishers\n",target:"_blank",rel:"noopener noreferrer"}},[e._v("Comma Chameleon"),r("OutboundLink")],1),e._v(", an "),r("a",{attrs:{href:"https://youtu.be/wIIw0cTeUG0",title:"Stuart Harrison explains Comma Chameleon at CSVConf",target:"_blank",rel:"noopener noreferrer"}},[e._v("experiment"),r("OutboundLink")],1),e._v(" by "),r("a",{attrs:{href:"https://theodi.org",title:"The Open Data Institute",target:"_blank",rel:"noopener noreferrer"}},[e._v("the ODI"),r("OutboundLink")],1),e._v(". The ODI and the ODI Australian Network agreed to take the software in "),r("a",{attrs:{href:"https://theodi.org/article/odi-toolbox-application-experiments-from-comma-chameleon-to-data-curator/",title:"Stephen Fortune explains why Data Curator is a fork of Comma Chameleon",target:"_blank",rel:"noopener noreferrer"}},[e._v("different directions"),r("OutboundLink")],1),e._v(".")])])}),[],!1,null,null,null);a.default=o.exports}}]); \ No newline at end of file +(window.webpackJsonp=window.webpackJsonp||[]).push([[22],{469:function(e,a,t){e.exports=t.p+"assets/img/data-curator.1e9d2493.png"},470:function(e,a,t){e.exports=t.p+"assets/img/data-curator-2.947fbd26.png"},471:function(e,a,t){e.exports=t.p+"assets/img/data-curator-3.4f1191bc.png"},608:function(e,a,t){"use strict";t.r(a);var r=t(29),o=Object(r.a)({},(function(){var e=this,a=e.$createElement,r=e._self._c||a;return r("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[r("h1",{attrs:{id:"data-curator-share-usable-open-data"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#data-curator-share-usable-open-data"}},[e._v("#")]),e._v(" Data Curator - share usable open data")]),e._v(" "),r("p",[e._v("Open data producers are increasingly focusing on improving open data so it can be easily used to create insight and drive positive change.")]),e._v(" "),r("p",[e._v("Open data is more likely to be used if data consumers can:")]),e._v(" "),r("ul",[r("li",[e._v("understand the structure of the data")]),e._v(" "),r("li",[e._v("understand the quality of the data")]),e._v(" "),r("li",[e._v("understand why and how the data was collected")]),e._v(" "),r("li",[e._v("look up the meaning of codes used in the data")]),e._v(" "),r("li",[e._v("access the data in an open machine-readable format")]),e._v(" "),r("li",[e._v("know how the data is licensed and how it can be reused")])]),e._v(" "),r("p",[e._v("Data Curator enables open data producers to define all this information using their desktop computer, prior to publishing it on the Internet.")]),e._v(" "),r("p",[e._v("Data Curator uses the "),r("a",{attrs:{href:"https://specs.frictionlessdata.io/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Frictionless Data specification"),r("OutboundLink")],1),e._v(" and software to package the data and supporting information in a "),r("a",{attrs:{href:"https://specs.frictionlessdata.io/tabular-data-package/",title:"Tabular Data Package specification",target:"_blank",rel:"noopener noreferrer"}},[e._v("Tabular Data Package"),r("OutboundLink")],1),e._v(".")]),e._v(" "),r("p",[r("img",{attrs:{src:t(469),alt:"Data Curator screenshot"}})]),e._v(" "),r("h2",{attrs:{id:"using-data-curator"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#using-data-curator"}},[e._v("#")]),e._v(" Using Data Curator")]),e._v(" "),r("p",[e._v("Here’s how to use Data Curator to share usable open data in a data package:")]),e._v(" "),r("ol",[r("li",[e._v("Download "),r("a",{attrs:{href:"https://github.com/ODIQueensland/data-curator/releases/latest",title:"Download Data Curator for Windows or macOS",target:"_blank",rel:"noopener noreferrer"}},[e._v("Data Curator"),r("OutboundLink")],1),e._v(" for Windows or macOS")]),e._v(" "),r("li",[e._v("In Data Curator, either:\n"),r("ul",[r("li",[e._v("create some data")]),e._v(" "),r("li",[e._v("open an Excel sheet")]),e._v(" "),r("li",[e._v("open a separated value file (e.g. CSV, TSV)")])])]),e._v(" "),r("li",[e._v("Follow the steps below…")])]),e._v(" "),r("h3",{attrs:{id:"describe-the-data"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#describe-the-data"}},[e._v("#")]),e._v(" Describe the data")]),e._v(" "),r("p",[e._v("The Frictionless Data specification allows you to describe tabular data using a "),r("a",{attrs:{href:"https://specs.frictionlessdata.io/table-schema/",title:"Table Schema specification",target:"_blank",rel:"noopener noreferrer"}},[e._v("Table Schema"),r("OutboundLink")],1),e._v(". A Table Schema allows each field in the data to be given:")]),e._v(" "),r("ul",[r("li",[e._v("a "),r("code",[e._v("name")]),e._v(", "),r("code",[e._v("title")]),e._v(" and "),r("code",[e._v("description")])]),e._v(" "),r("li",[e._v("a data "),r("code",[e._v("type")]),e._v(" (e.g. "),r("code",[e._v("string")]),e._v(", "),r("code",[e._v("integer")]),e._v(") and "),r("code",[e._v("format")]),e._v(" (e.g. "),r("code",[e._v("uri")]),e._v(", "),r("code",[e._v("email")]),e._v(")")]),e._v(" "),r("li",[e._v("one or more "),r("code",[e._v("constraints")]),e._v(" (e.g. "),r("code",[e._v("required")]),e._v(", "),r("code",[e._v("unique")]),e._v(") to limit data values and improve data validation")])]),e._v(" "),r("p",[e._v("The Table Schema also allows you to describe the characters used to represent missing values (e.g. "),r("code",[e._v("n/a")]),e._v(", "),r("code",[e._v("tba")]),e._v("), primary keys, and foreign key relationships.")]),e._v(" "),r("p",[e._v("After adding data in Data Curator, to create a Table Schema:")]),e._v(" "),r("ul",[r("li",[e._v("Give your data a header row, if it doesn’t have one")]),e._v(" "),r("li",[e._v("Set the header row to give each field a "),r("code",[e._v("name")])]),e._v(" "),r("li",[e._v("Guess column properties to give each field a "),r("code",[e._v("type")]),e._v(" and "),r("code",[e._v("format")])]),e._v(" "),r("li",[e._v("Set column properties to improve the data "),r("code",[e._v("type")]),e._v(" and "),r("code",[e._v("format")]),e._v(" guesses, and add a "),r("code",[e._v("title")]),e._v(", "),r("code",[e._v("description")]),e._v(" and "),r("code",[e._v("constraints")])]),e._v(" "),r("li",[e._v("Set table properties to give the table a "),r("code",[e._v("name")]),e._v(", define missing values, a primary key, and foreign keys.")])]),e._v(" "),r("h3",{attrs:{id:"validate-the-data"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#validate-the-data"}},[e._v("#")]),e._v(" Validate the data")]),e._v(" "),r("p",[e._v("Using Data Curator, you can validate if the data complies with the field’s "),r("code",[e._v("type")]),e._v(", "),r("code",[e._v("format")]),e._v(" and "),r("code",[e._v("contraints")]),e._v(". Errors found can be filtered in different ways so you can correct errors by row, by column or by error type.")]),e._v(" "),r("p",[e._v("In some cases data errors cannot be corrected, as they should be corrected in the source system and not as part of the data packaging process. If you’re happy to publish the data with errors, the error messages can be appended to the provenance information.")]),e._v(" "),r("h3",{attrs:{id:"provide-context"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#provide-context"}},[e._v("#")]),e._v(" Provide context")]),e._v(" "),r("p",[e._v("Data Curator lets you add provenance information to help people understand why and how the data was collected and determine if it is fit for their purpose.")]),e._v(" "),r("p",[e._v("Provenance information can be entered using "),r("a",{attrs:{href:"http://commonmark.org",title:"Markdown specification",target:"_blank",rel:"noopener noreferrer"}},[e._v("Markdown"),r("OutboundLink")],1),e._v(". You can preview the Markdown formatting in Data Curator.")]),e._v(" "),r("p",[r("img",{attrs:{src:t(470),alt:"Add provenance information screenshot"}})]),e._v(" "),r("p",[e._v("You should follow the "),r("RouterLink",{attrs:{to:"/blog/2016/04/20/publish-faq/",title:"Publishing Data Packages - FAQ"}},[e._v("Readme FAQ")]),e._v(" when writing provenance information or, even easier, cut and paste from this "),r("a",{attrs:{href:"https://github.com/ODIQueensland/data-curator/blob/develop/test/features/tools/sample-provenance-information.md",title:"Sample Provenance Information Markdown file on GitHub",target:"_blank",rel:"noopener noreferrer"}},[e._v("sample"),r("OutboundLink")],1),e._v(".")],1),e._v(" "),r("h3",{attrs:{id:"explain-the-meaning-of-codes"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#explain-the-meaning-of-codes"}},[e._v("#")]),e._v(" Explain the meaning of codes")]),e._v(" "),r("p",[e._v("Data Curator supports foreign key relationships between data. 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This is really useful if you want to share code-lists across organisations.")]),e._v(" "),r("p",[e._v("You can define foreign key relationships in Data Curator in the table properties and the relationships are checked when you validate the data.")]),e._v(" "),r("h3",{attrs:{id:"save-the-data-in-an-open-format"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#save-the-data-in-an-open-format"}},[e._v("#")]),e._v(" Save the data in an open format")]),e._v(" "),r("p",[e._v("Data Curator lets you save data as a comma, semicolon, or tab separated value file. A matching "),r("a",{attrs:{href:"https://specs.frictionlessdata.io/csv-dialect/",title:"The CSV Dialect specification",target:"_blank",rel:"noopener noreferrer"}},[e._v("CSV Dialect"),r("OutboundLink")],1),e._v(" is added to the data package.")]),e._v(" "),r("h3",{attrs:{id:"apply-an-open-license"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#apply-an-open-license"}},[e._v("#")]),e._v(" Apply an open license")]),e._v(" "),r("p",[e._v("Applying a license, waiver, or public domain mark to a "),r("a",{attrs:{href:"https://specs.frictionlessdata.io/data-package/#licenses",title:"The licenses property in the Data Package specification",target:"_blank",rel:"noopener noreferrer"}},[e._v("data package"),r("OutboundLink")],1),e._v(" and its "),r("a",{attrs:{href:"https://specs.frictionlessdata.io/data-resource/#optional-properties",title:"The licenses property in the Data Resource specification",target:"_blank",rel:"noopener noreferrer"}},[e._v("resources"),r("OutboundLink")],1),e._v(" helps people understand how they can use, modify, and share the contents of the data package.")]),e._v(" "),r("p",[r("img",{attrs:{src:t(471),alt:"Apply open license to data package screenshot"}})]),e._v(" "),r("p",[e._v("Although there are many ways to "),r("RouterLink",{attrs:{to:"/blog/2018/03/27/applying-licenses/",title:"Guide to applying licenses, waivers or public domain marks to data packages"}},[e._v("apply a licence, waiver or public domain mark")]),e._v(" to a data package, Data Curator only allows you to use open licences - after all, its purpose is to share usable open data.")],1),e._v(" "),r("h3",{attrs:{id:"export-the-data-package"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#export-the-data-package"}},[e._v("#")]),e._v(" Export the data package")]),e._v(" "),r("p",[e._v("To ensure only usable open data is shared, Data Curator applies some checks before allowing a data package to be exported. These go beyond the mandatory requirements* in the Frictionless Data specification.")]),e._v(" "),r("p",[e._v("To export a tabular data package, it must have:")]),e._v(" "),r("ul",[r("li",[e._v("a header row")]),e._v(" "),r("li",[e._v("a table schema*")]),e._v(" "),r("li",[e._v("a table (resource) "),r("code",[e._v("name")]),e._v("*")]),e._v(" "),r("li",[e._v("a data package "),r("code",[e._v("name")]),e._v("*")]),e._v(" "),r("li",[e._v("provenance information")]),e._v(" "),r("li",[e._v("an open licence applied to the data package")])]),e._v(" "),r("p",[e._v("If a data package "),r("code",[e._v("version")]),e._v(" is used, it must follow the "),r("a",{attrs:{href:"https://specs.frictionlessdata.io/patterns/#data-package-version",title:"Data Package Version pattern",target:"_blank",rel:"noopener noreferrer"}},[e._v("data package version pattern"),r("OutboundLink")],1),e._v(".")]),e._v(" "),r("p",[e._v("Before exporting a data package you should:")]),e._v(" "),r("ul",[r("li",[e._v("add a "),r("code",[e._v("title")]),e._v(" and "),r("code",[e._v("description")]),e._v(" to each field, table and data package")]),e._v(" "),r("li",[e._v("acknowledge any data sources and contributors")]),e._v(" "),r("li",[e._v("validate the data and add any known errors to the provenance information")])]),e._v(" "),r("p",[e._v("The data package is exported as a "),r("code",[e._v("datapackage.zip")]),e._v(" file that contains the:")]),e._v(" "),r("ul",[r("li",[e._v("data files in a "),r("code",[e._v("/data")]),e._v(" directory")]),e._v(" "),r("li",[e._v("data package, table (resource), table schema, and csv dialect properties in a"),r("code",[e._v("datapackage.json")]),e._v(" file")]),e._v(" "),r("li",[e._v("provenance information in a "),r("code",[e._v("README.md")]),e._v(" file")])]),e._v(" "),r("h3",{attrs:{id:"share-the-data"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#share-the-data"}},[e._v("#")]),e._v(" Share the data")]),e._v(" "),r("p",[e._v("Share the "),r("code",[e._v("datapackage.zip")]),e._v(" with open data consumers by publishing it on the Internet or on an open data platform. Some platforms support uploading, displaying, and downloading data packages.")]),e._v(" "),r("p",[e._v("Open data consumers will be able to read the data package with one of the many applications and software libraries that work with data packages, including Data Curator.")]),e._v(" "),r("h2",{attrs:{id:"get-started"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#get-started"}},[e._v("#")]),e._v(" Get Started")]),e._v(" "),r("p",[r("strong",[r("a",{attrs:{href:"https://github.com/ODIQueensland/data-curator/releases/latest",title:"Download Data Curator for Windows or macOS",target:"_blank",rel:"noopener noreferrer"}},[e._v("Download Data Curator"),r("OutboundLink")],1)]),e._v(" for Windows or macOS and start sharing usable open data.")]),e._v(" "),r("h2",{attrs:{id:"who-made-data-curator"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#who-made-data-curator"}},[e._v("#")]),e._v(" Who made Data Curator?")]),e._v(" "),r("p",[e._v("Data Curator was made possible with funding from the "),r("a",{attrs:{href:"https://www.qld.gov.au",target:"_blank",rel:"noopener noreferrer"}},[e._v("Queensland Government"),r("OutboundLink")],1),e._v(" and the guidance of the Open Data Policy team within the Department of Housing and Public Works. 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Sign up, and starting playing with data.")]),a._v(" "),r("hr",{staticClass:"footnotes-sep"}),a._v(" "),r("section",{staticClass:"footnotes"},[r("ol",{staticClass:"footnotes-list"},[r("li",{staticClass:"footnote-item",attrs:{id:"fn1"}},[r("p",[a._v("Tabular Data Package specifications: "),r("a",{attrs:{href:"https://specs.frictionlessdata.io/tabular-data-package",target:"_blank",rel:"noopener noreferrer"}},[a._v("https://specs.frictionlessdata.io/tabular-data-package"),r("OutboundLink")],1),a._v(" "),r("a",{staticClass:"footnote-backref",attrs:{href:"#fnref1"}},[a._v("↩︎")])])]),a._v(" "),r("li",{staticClass:"footnote-item",attrs:{id:"fn2"}},[r("p",[a._v("RDF: Resource Description Framework: "),r("a",{attrs:{href:"https://www.w3.org/RDF/",target:"_blank",rel:"noopener noreferrer"}},[a._v("https://www.w3.org/RDF/"),r("OutboundLink")],1),a._v(" "),r("a",{staticClass:"footnote-backref",attrs:{href:"#fnref2"}},[a._v("↩︎")])])]),a._v(" "),r("li",{staticClass:"footnote-item",attrs:{id:"fn3"}},[r("p",[a._v("SKOS: Simple Knowledge Organization System: "),r("a",{attrs:{href:"https://www.w3.org/2004/02/skos/",target:"_blank",rel:"noopener noreferrer"}},[a._v("https://www.w3.org/2004/02/skos/"),r("OutboundLink")],1),a._v(" "),r("a",{staticClass:"footnote-backref",attrs:{href:"#fnref3"}},[a._v("↩︎")])])]),a._v(" "),r("li",{staticClass:"footnote-item",attrs:{id:"fn4"}},[r("p",[a._v("OWL Web Ontology Language: "),r("a",{attrs:{href:"https://www.w3.org/TR/owl-ref/",target:"_blank",rel:"noopener noreferrer"}},[a._v("https://www.w3.org/TR/owl-ref/"),r("OutboundLink")],1),a._v(" "),r("a",{staticClass:"footnote-backref",attrs:{href:"#fnref4"}},[a._v("↩︎")])])])])])])}),[],!1,null,null,null);t.default=o.exports}}]); \ No newline at end of file +(window.webpackJsonp=window.webpackJsonp||[]).push([[32],{414:function(a,t,e){a.exports=e.p+"assets/img/data-world-1.5a4d0891.png"},415:function(a,t,e){a.exports=e.p+"assets/img/data-world-2.6312c262.png"},567:function(a,t,e){"use strict";e.r(t);var r=e(29),o=Object(r.a)({},(function(){var a=this,t=a.$createElement,r=a._self._c||t;return r("ContentSlotsDistributor",{attrs:{"slot-key":a.$parent.slotKey}},[r("p",[a._v("At "),r("a",{attrs:{href:"https://data.world",target:"_blank",rel:"noopener noreferrer"}},[a._v("data.world"),r("OutboundLink")],1),a._v(", we deal with a great diversity of data, both in terms of content and in terms of source format - most people working with data are emailing each other spreadsheets or CSVs, and not formally defining schema or semantics for what’s contained in these data files.")]),a._v(" "),r("p",[a._v("When "),r("a",{attrs:{href:"https://data.world",target:"_blank",rel:"noopener noreferrer"}},[a._v("data.world"),r("OutboundLink")],1),a._v(" ingests tabular data, we “virtualize” the tables away from their source format, and build layers of type and semantic information on top of the raw data. What this allows us to do is to produce a clean Tabular Data Package"),r("sup",{staticClass:"footnote-ref"},[r("a",{attrs:{href:"#fn1",id:"fnref1"}},[a._v("[1]")])]),a._v(" for any dataset, whether the input is CSV files, Excel Spreadsheets, JSON data, SQLite Database files - any format that we know how to extract tabular information from - we can present it as cleaned-up CSV data with a "),r("code",[a._v("datapackage.json")]),a._v(" that describes the schema and metadata of the contents.")]),a._v(" "),r("p",[r("img",{attrs:{src:e(414),alt:"Available Data"}}),a._v(" "),r("br"),a._v(" "),r("em",[a._v("Tabular Data Package structure on disk")])]),a._v(" "),r("p",[a._v("We would also like to see graph data packages developed as part of the Frictionless Data specifications, or “Universal Data Packages” that can encapsulate both tabular and graph data. 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It might be helpful to use the "),r("code",[a._v("datapackage.json")]),a._v(" descriptor to catalog the standard taxonomies and ontologies that were in use, for example, it would be useful to know if a file contained SKOS"),r("sup",{staticClass:"footnote-ref"},[r("a",{attrs:{href:"#fn3",id:"fnref3"}},[a._v("[3]")])]),a._v(" vocabularies, or OWL"),r("sup",{staticClass:"footnote-ref"},[r("a",{attrs:{href:"#fn4",id:"fnref4"}},[a._v("[4]")])]),a._v(" classes.")]),a._v(" "),r("p",[a._v("In the coming days, we want to continue to enrich the metadata we include in Tabular Data Packages exported from "),r("a",{attrs:{href:"https://data.world",target:"_blank",rel:"noopener noreferrer"}},[a._v("data.world"),r("OutboundLink")],1),a._v(", and we’re looking into using "),r("code",[a._v("datapackage.json")]),a._v(" as an import format as well as an export option.")]),a._v(" "),r("p",[r("a",{attrs:{href:"https://data.world",target:"_blank",rel:"noopener noreferrer"}},[a._v("data.world"),r("OutboundLink")],1),a._v(" works with lots of data across many domains - what’s great about Frictionless Data is that it’s a lightweight set of content specifications that can be a starting point for building domain-specific content standards - it really helps with the “first mile” of standardizing data and making it interoperable.")]),a._v(" "),r("p",[r("img",{attrs:{src:e(415),alt:"Available Data"}}),a._v(" "),r("br"),a._v(" "),r("em",[a._v("Tabular datasets can be downloaded as Tabular Data Packages")])]),a._v(" "),r("p",[a._v("In a certain sense, a Tabular Data Package is sort of like an open-source, cross-platform, accessible replacement for spreadsheets that can act as a “binder” for several related tables of data. "),r("strong",[a._v("I could easily imagine web or desktop-based tools that look and function much like a traditional spreadsheet, but use Data Packages as their serialization format.")])]),a._v(" "),r("p",[a._v("To read more about Data Package integration at "),r("a",{attrs:{href:"https://data.world",target:"_blank",rel:"noopener noreferrer"}},[a._v("data.world"),r("OutboundLink")],1),a._v(", read our post: "),r("a",{attrs:{href:"https://meta.data.world/try-this-frictionless-data-world-ad36b6422ceb#.rbbf8k40t",target:"_blank",rel:"noopener noreferrer"}},[a._v("Try This: Frictionless data.world"),r("OutboundLink")],1),a._v(". Sign up, and starting playing with data.")]),a._v(" "),r("hr",{staticClass:"footnotes-sep"}),a._v(" "),r("section",{staticClass:"footnotes"},[r("ol",{staticClass:"footnotes-list"},[r("li",{staticClass:"footnote-item",attrs:{id:"fn1"}},[r("p",[a._v("Tabular Data Package specifications: "),r("a",{attrs:{href:"https://specs.frictionlessdata.io/tabular-data-package",target:"_blank",rel:"noopener noreferrer"}},[a._v("https://specs.frictionlessdata.io/tabular-data-package"),r("OutboundLink")],1),a._v(" "),r("a",{staticClass:"footnote-backref",attrs:{href:"#fnref1"}},[a._v("↩︎")])])]),a._v(" "),r("li",{staticClass:"footnote-item",attrs:{id:"fn2"}},[r("p",[a._v("RDF: Resource Description Framework: "),r("a",{attrs:{href:"https://www.w3.org/RDF/",target:"_blank",rel:"noopener noreferrer"}},[a._v("https://www.w3.org/RDF/"),r("OutboundLink")],1),a._v(" "),r("a",{staticClass:"footnote-backref",attrs:{href:"#fnref2"}},[a._v("↩︎")])])]),a._v(" "),r("li",{staticClass:"footnote-item",attrs:{id:"fn3"}},[r("p",[a._v("SKOS: Simple Knowledge Organization System: "),r("a",{attrs:{href:"https://www.w3.org/2004/02/skos/",target:"_blank",rel:"noopener noreferrer"}},[a._v("https://www.w3.org/2004/02/skos/"),r("OutboundLink")],1),a._v(" "),r("a",{staticClass:"footnote-backref",attrs:{href:"#fnref3"}},[a._v("↩︎")])])]),a._v(" "),r("li",{staticClass:"footnote-item",attrs:{id:"fn4"}},[r("p",[a._v("OWL Web Ontology Language: "),r("a",{attrs:{href:"https://www.w3.org/TR/owl-ref/",target:"_blank",rel:"noopener noreferrer"}},[a._v("https://www.w3.org/TR/owl-ref/"),r("OutboundLink")],1),a._v(" "),r("a",{staticClass:"footnote-backref",attrs:{href:"#fnref4"}},[a._v("↩︎")])])])])])])}),[],!1,null,null,null);t.default=o.exports}}]); \ No newline at end of file diff --git a/assets/js/33.72bf6e52.js b/assets/js/33.56a20d3a.js similarity index 97% rename from assets/js/33.72bf6e52.js rename to assets/js/33.56a20d3a.js index 336c1616d..30574a4c2 100644 --- a/assets/js/33.72bf6e52.js +++ b/assets/js/33.56a20d3a.js @@ -1 +1 @@ -(window.webpackJsonp=window.webpackJsonp||[]).push([[33],{429:function(e,t,a){e.exports=a.p+"assets/img/elife1.bec2a182.png"},430:function(e,t,a){e.exports=a.p+"assets/img/elife3.4a4230d3.png"},577:function(e,t,a){"use strict";a.r(t);var r=a(29),s=Object(r.a)({},(function(){var e=this,t=e.$createElement,r=e._self._c||t;return r("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[r("h2",{attrs:{id:"context"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#context"}},[e._v("#")]),e._v(" Context")]),e._v(" "),r("p",[r("a",{attrs:{href:"https://elifesciences.org/",target:"_blank",rel:"noopener noreferrer"}},[e._v("eLife"),r("OutboundLink")],1),e._v(" is a non-profit organisation with a mission to help scientists accelerate discovery by operating a platform for research communication that encourages and recognises the most responsible behaviours in science. eLife publishes important research in all areas of life and biomedical sciences. The research is selected and evaluated by working scientists and is made freely available to all readers.")]),e._v(" "),r("h3",{attrs:{id:"problem-we-were-trying-to-solve"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#problem-we-were-trying-to-solve"}},[e._v("#")]),e._v(" Problem We Were Trying To Solve")]),e._v(" "),r("p",[e._v("Having met at csv,conf,v3 in Portland in May 2017, eLife’s "),r("a",{attrs:{href:"https://www.youtube.com/watch?v=YYWNSWNq-do&list=PLg5zZXwt2ZW5UIz13oI56vfZjF6mvpIXN&index=27",target:"_blank",rel:"noopener noreferrer"}},[e._v("Naomi Penfold"),r("OutboundLink")],1),e._v(" and Open Knowledge International’s "),r("a",{attrs:{href:"https://www.youtube.com/watch?v=Gk2F4hncAgY&index=35&list=PLg5zZXwt2ZW5UIz13oI56vfZjF6mvpIXN",target:"_blank",rel:"noopener noreferrer"}},[e._v("Adrià Mercader"),r("OutboundLink")],1),e._v(" determined that eLife would be a good candidate for a Frictionless Data pilot. eLife has a strong emphasis on research data, and stood to benefit from the data validation service offered by Frictionless Data’s goodtables.")]),e._v(" "),r("h2",{attrs:{id:"the-work"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#the-work"}},[e._v("#")]),e._v(" The Work")]),e._v(" "),r("p",[e._v("In order to assess the potential for a goodtables integration at eLife, we first needed to measure the quality of source data shared directly through eLife.")]),e._v(" "),r("h3",{attrs:{id:"software"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#software"}},[e._v("#")]),e._v(" Software")]),e._v(" "),r("p",[e._v("To explore the data published in the eLife platform we used the goodtables library"),r("sup",{staticClass:"footnote-ref"},[r("a",{attrs:{href:"#fn1",id:"fnref1"}},[e._v("[1]")])]),e._v(". Both the goodtables python library and web service"),r("sup",{staticClass:"footnote-ref"},[r("a",{attrs:{href:"#fn2",id:"fnref2"}},[e._v("[2]")])]),e._v(" were developed by Open Knowledge International to support the validation of tabular datasets both in terms of structure and also with respect to a published schema. You can read more about them "),r("a",{attrs:{href:"http://okfnlabs.org/blog/2015/02/20/introducing-goodtables.html",target:"_blank",rel:"noopener noreferrer"}},[e._v("in this introductory blog post"),r("OutboundLink")],1),e._v(".")]),e._v(" "),r("h3",{attrs:{id:"what-did-we-do"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#what-did-we-do"}},[e._v("#")]),e._v(" What Did We Do")]),e._v(" "),r("p",[e._v("The first stage was to perform validation on all files made available through the eLife API in order to generate a report on data quality - this would allow us to understand the current state of eLife-published data and present the possibility of doing more exciting things with the data such as more comprehensive tests or visualisations.")]),e._v(" "),r("p",[e._v("The process:")]),e._v(" "),r("ul",[r("li",[e._v("We downloaded a big subset of the articles metadata made available via the eLife public API"),r("sup",{staticClass:"footnote-ref"},[r("a",{attrs:{href:"#fn3",id:"fnref3"}},[e._v("[3]")])]),e._v(".")]),e._v(" "),r("li",[e._v("We parsed all metadata files in order to extract the data files linked to each article, regardless of whether it was an additional file or a figure source. This gave us a direct link to each data file linked to the parent article.")]),e._v(" "),r("li",[e._v("We then ran the validation process on each file, storing the resulting report for future analysis.")])]),e._v(" "),r("p",[e._v("All scripts used in the process as well as the outputs can be found in "),r("a",{attrs:{href:"https://github.com/frictionlessdata/pilot-elife",target:"_blank",rel:"noopener noreferrer"}},[e._v("our pilot repository"),r("OutboundLink")],1),e._v(".")]),e._v(" "),r("p",[e._v("Here are some high-level statistics for the process:")]),e._v(" "),r("p",[e._v("We analyzed 3910 articles, 1085 of which had data files. The most common format was Microsoft Excel Open XML Format Spreadsheet (xlsx), with 89% of all 4318 files being published on this format. Older versions of Excel and CSV files made up the rest.")]),e._v(" "),r("p",[r("img",{attrs:{src:a(429),alt:"datasets analysed by eLife image"}}),e._v(" "),r("em",[e._v("A summary of the eLife research articles analysed as part of the Frictionless Data pilot work")])]),e._v(" "),r("p",[e._v("In terms of validation, more than 75% of the articles analyzed contained at least one invalid file. Of course valid data is an arbitrary term based on the tests that are set within goodtables and results need to be reviewed to adjust the checks performed. For instance errors raised by blank rows are really common on Excel files as people add a title on the first row, leaving an empty row before the data, or empty rows are detected at the end of the sheet.")]),e._v(" "),r("p",[e._v("Other errors raised that might actually point to genuine errors included duplicated headers, extra headers, missing values, incorrect format values (e.g. date format instead of gene name) to give just some examples. Here’s a summary of the raw number of errors encountered. For a more complete description of each error, see the Data Quality Spec"),r("sup",{staticClass:"footnote-ref"},[r("a",{attrs:{href:"#fn4",id:"fnref4"}},[e._v("[4]")])]),e._v(":")]),e._v(" "),r("table",[r("thead",[r("tr",[r("th",[e._v("Error Type")]),e._v(" "),r("th",[e._v("Count")])])]),e._v(" "),r("tbody",[r("tr",[r("td",[e._v("Blank rows")]),e._v(" "),r("td",[e._v("45748")])]),e._v(" "),r("tr",[r("td",[e._v("Duplicate rows")]),e._v(" "),r("td",[e._v("9384")])]),e._v(" "),r("tr",[r("td",[e._v("Duplicate headers")]),e._v(" "),r("td",[e._v("6672")])]),e._v(" "),r("tr",[r("td",[e._v("Blank headers")]),e._v(" "),r("td",[e._v("2479")])]),e._v(" "),r("tr",[r("td",[e._v("Missing values")]),e._v(" "),r("td",[e._v("1032")])]),e._v(" "),r("tr",[r("td",[e._v("Extra values")]),e._v(" "),r("td",[e._v("39")])]),e._v(" "),r("tr",[r("td",[e._v("Source errors")]),e._v(" "),r("td",[e._v("11")])]),e._v(" "),r("tr",[r("td",[e._v("Format errors")]),e._v(" "),r("td",[e._v("4")])])])]),e._v(" "),r("h2",{attrs:{id:"review"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#review"}},[e._v("#")]),e._v(" Review")]),e._v(" "),r("h3",{attrs:{id:"how-effective-was-it"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#how-effective-was-it"}},[e._v("#")]),e._v(" How Effective Was It")]),e._v(" "),r("p",[e._v("Following analysis of a sample of the results, the vast majority of the errors appear to be due to the data being presented in nice-looking tables, using formatting to make particular elements more visually clear, as opposed to a machine-readable format:")]),e._v(" "),r("p",[r("img",{attrs:{src:a(430),alt:"example tables image shared by Naomi"}}),e._v(" "),r("em",[e._v("Data from Maddox et al. was shared in a machine-readable format (top), and adapted here to demonstrate how such data are often shared in a format that looks nice to the human reader (bottom)."),r("br"),e._v("\nSource: Source data"),r("br"),e._v("\nThe data file is presented as is and adapted from Maddox et al. eLife 2015;4:e04995 under the Creative Commons Attribution License (CC BY 4.0).")])]),e._v(" "),r("p",[e._v("This is not limited to the academic field of course, and the tendency to present data in spreadsheets so it is visually appealing is perhaps more prevalent in other areas. Perhaps because consumers of the data are even less likely to have the data processed by machines or because the data is collated by people with no experience of having to use it in their work.")]),e._v(" "),r("p",[e._v("In general the eLife datasets had better quality than for instance those created by government organisations, where structural issues like missing headers, extra cells, etc are much more common. So although the results here have been good, the community could derive substantial benefit from researchers going that extra mile to make files more machine-friendly and embrace more robust data description techniques like Data Packages.")]),e._v(" "),r("p",[e._v("Because these types of ‘errors’ are so common we have introduced default "),r("code",[e._v("ignore blank rows")]),e._v(" and "),r("code",[e._v("ignore duplicate rows")]),e._v(" options in "),r("a",{attrs:{href:"https://try.goodtables.io",target:"_blank",rel:"noopener noreferrer"}},[e._v("our standalone validator"),r("OutboundLink")],1),e._v(" since this helps bring more complex errors to the surface and focusses attention on the errors which may be less trivial to resolve. Excluding duplicate and blank rows as well as duplicate headers (the most common but also relatively simple errors), 6.4% (277/4318) of data files had errors remaining, affecting 10% of research articles (112/1085).")]),e._v(" "),r("p",[e._v("Having said this, the relevance of these errors should not be underplayed as "),r("code",[e._v("blank rows")]),e._v(", "),r("code",[e._v("duplicate rows")]),e._v(" and other human-centered formatting preferences can still result in errors that prevent machine readability. Although the errors were often minor and easy to fix in our case, these seemingly simple errors can be obstructive to anyone trying to reuse data in a more computational workflow. Any computational analysis software, such as R"),r("sup",{staticClass:"footnote-ref"},[r("a",{attrs:{href:"#fn5",id:"fnref5"}},[e._v("[5]")])]),e._v(", requires that all column headers are variables and rows are individual observations i.e. we need variables in columns and observations in rows for any R analysis.")]),e._v(" "),r("p",[e._v("Much less frequent errors were related to difficulties retrieving and opening data files. It was certainly helpful to flag articles with files that were not actually possible to open (source-error), and the eLife production team are resolving these issues. While only representing a small number of datasets, this is one use key case for goodtables: enabling publishers to regularly check continued data availability after publication.")]),e._v(" "),r("p",[e._v("The use case for authors is clear — to identify how a dataset could be reshaped to make it reusable. However, this demands extra work if reshaping is a job added at the point of sharing work. In fact, it is important that any issues are resolved before final publication, to avoid adding updated versions of publications/datasets. Tools that reduce this burden by making it easy to quickly edit a datafile to resolve the errors are of interest moving forward. In the meantime, it may be helpful to consider some key best practises as datasets are collected.")]),e._v(" "),r("p",[e._v("Overall, the findings from this pilot demonstrate that there are different ways of producing data for sharing: datasets are predominantly presented in an Excel file with human aesthetics in mind, rather than structured for use by a statistical program. We found few issues with the data itself beyond presentation preferences. This is encouraging and is a great starting point for venturing forward with helping researchers to make greater use of open data.")]),e._v(" "),r("h2",{attrs:{id:"next-steps"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#next-steps"}},[e._v("#")]),e._v(" Next Steps")]),e._v(" "),r("h3",{attrs:{id:"areas-for-further-work"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#areas-for-further-work"}},[e._v("#")]),e._v(" Areas for further work")]),e._v(" "),r("p",[e._v("Libraries such as goodtables help to flag the gap between the current situation and the ideal situation, which is machine-readability. Most of the issues identified by goodtables in the datasets shared with eLife relate to structuring the data for human visual consumption: adding space around the table, merging header cells, etc. We encourage researchers to make data as easy to consume as possible, and recognise that datasets built primarily to look good to humans may only be sufficient for low-level reuse.")]),e._v(" "),r("p",[e._v("Moving forward, we are interested in tools and workflows that help to improve data quality earlier in the research lifecycle or make it easy to reshape at the point of sharing or reuse.")]),e._v(" "),r("h2",{attrs:{id:"find-out-more"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#find-out-more"}},[e._v("#")]),e._v(" Find Out More")]),e._v(" "),r("p",[r("a",{attrs:{href:"https://github.com/frictionlessdata/pilot-elife",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://github.com/frictionlessdata/pilot-elife"),r("OutboundLink")],1)]),e._v(" "),r("p",[e._v("Parts of this post are "),r("a",{attrs:{href:"https://elifesciences.org/labs/b6de9fb0/data-reusability-a-pilot-with-goodtables",target:"_blank",rel:"noopener noreferrer"}},[e._v("cross-posted"),r("OutboundLink")],1),e._v(" on eLife Labs"),r("sup",{staticClass:"footnote-ref"},[r("a",{attrs:{href:"#fn6",id:"fnref6"}},[e._v("[6]")])]),e._v(".")]),e._v(" "),r("hr",{staticClass:"footnotes-sep"}),e._v(" "),r("section",{staticClass:"footnotes"},[r("ol",{staticClass:"footnotes-list"},[r("li",{staticClass:"footnote-item",attrs:{id:"fn1"}},[r("p",[e._v("goodtables Python library: "),r("a",{attrs:{href:"http://github.com/frictionlessdata/goodtables-py",target:"_blank",rel:"noopener noreferrer"}},[e._v("http://github.com/frictionlessdata/goodtables-py"),r("OutboundLink")],1),e._v(" "),r("a",{staticClass:"footnote-backref",attrs:{href:"#fnref1"}},[e._v("↩︎")])])]),e._v(" "),r("li",{staticClass:"footnote-item",attrs:{id:"fn2"}},[r("p",[e._v("goodtables web service: "),r("a",{attrs:{href:"http://goodtables.io",target:"_blank",rel:"noopener noreferrer"}},[e._v("http://goodtables.io"),r("OutboundLink")],1),e._v(" "),r("a",{staticClass:"footnote-backref",attrs:{href:"#fnref2"}},[e._v("↩︎")])])]),e._v(" "),r("li",{staticClass:"footnote-item",attrs:{id:"fn3"}},[r("p",[e._v("eLife Public API: "),r("a",{attrs:{href:"https://api.elifesciences.org/",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://api.elifesciences.org/"),r("OutboundLink")],1),e._v(" "),r("a",{staticClass:"footnote-backref",attrs:{href:"#fnref3"}},[e._v("↩︎")])])]),e._v(" "),r("li",{staticClass:"footnote-item",attrs:{id:"fn4"}},[r("p",[e._v("Data Quality Spec: "),r("a",{attrs:{href:"https://github.com/frictionlessdata/data-quality-spec/blob/master/spec.json",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://github.com/frictionlessdata/data-quality-spec/blob/master/spec.json"),r("OutboundLink")],1),e._v(" "),r("a",{staticClass:"footnote-backref",attrs:{href:"#fnref4"}},[e._v("↩︎")])])]),e._v(" "),r("li",{staticClass:"footnote-item",attrs:{id:"fn5"}},[r("p",[e._v("R Programming Language: Popular open-source programming language and platform for data analysis: "),r("a",{attrs:{href:"https://www.r-project.org",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://www.r-project.org"),r("OutboundLink")],1),e._v(" "),r("a",{staticClass:"footnote-backref",attrs:{href:"#fnref5"}},[e._v("↩︎")])])]),e._v(" "),r("li",{staticClass:"footnote-item",attrs:{id:"fn6"}},[r("p",[e._v("eLife Labs: "),r("a",{attrs:{href:"https://elifesciences.org/labs",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://elifesciences.org/labs"),r("OutboundLink")],1),e._v(" "),r("a",{staticClass:"footnote-backref",attrs:{href:"#fnref6"}},[e._v("↩︎")])])])])])])}),[],!1,null,null,null);t.default=s.exports}}]); \ No newline at end of file +(window.webpackJsonp=window.webpackJsonp||[]).push([[33],{423:function(e,t,a){e.exports=a.p+"assets/img/elife1.bec2a182.png"},424:function(e,t,a){e.exports=a.p+"assets/img/elife3.4a4230d3.png"},575:function(e,t,a){"use strict";a.r(t);var r=a(29),s=Object(r.a)({},(function(){var e=this,t=e.$createElement,r=e._self._c||t;return r("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[r("h2",{attrs:{id:"context"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#context"}},[e._v("#")]),e._v(" Context")]),e._v(" "),r("p",[r("a",{attrs:{href:"https://elifesciences.org/",target:"_blank",rel:"noopener noreferrer"}},[e._v("eLife"),r("OutboundLink")],1),e._v(" is a non-profit organisation with a mission to help scientists accelerate discovery by operating a platform for research communication that encourages and recognises the most responsible behaviours in science. eLife publishes important research in all areas of life and biomedical sciences. The research is selected and evaluated by working scientists and is made freely available to all readers.")]),e._v(" "),r("h3",{attrs:{id:"problem-we-were-trying-to-solve"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#problem-we-were-trying-to-solve"}},[e._v("#")]),e._v(" Problem We Were Trying To Solve")]),e._v(" "),r("p",[e._v("Having met at csv,conf,v3 in Portland in May 2017, eLife’s "),r("a",{attrs:{href:"https://www.youtube.com/watch?v=YYWNSWNq-do&list=PLg5zZXwt2ZW5UIz13oI56vfZjF6mvpIXN&index=27",target:"_blank",rel:"noopener noreferrer"}},[e._v("Naomi Penfold"),r("OutboundLink")],1),e._v(" and Open Knowledge International’s "),r("a",{attrs:{href:"https://www.youtube.com/watch?v=Gk2F4hncAgY&index=35&list=PLg5zZXwt2ZW5UIz13oI56vfZjF6mvpIXN",target:"_blank",rel:"noopener noreferrer"}},[e._v("Adrià Mercader"),r("OutboundLink")],1),e._v(" determined that eLife would be a good candidate for a Frictionless Data pilot. eLife has a strong emphasis on research data, and stood to benefit from the data validation service offered by Frictionless Data’s goodtables.")]),e._v(" "),r("h2",{attrs:{id:"the-work"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#the-work"}},[e._v("#")]),e._v(" The Work")]),e._v(" "),r("p",[e._v("In order to assess the potential for a goodtables integration at eLife, we first needed to measure the quality of source data shared directly through eLife.")]),e._v(" "),r("h3",{attrs:{id:"software"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#software"}},[e._v("#")]),e._v(" Software")]),e._v(" "),r("p",[e._v("To explore the data published in the eLife platform we used the goodtables library"),r("sup",{staticClass:"footnote-ref"},[r("a",{attrs:{href:"#fn1",id:"fnref1"}},[e._v("[1]")])]),e._v(". Both the goodtables python library and web service"),r("sup",{staticClass:"footnote-ref"},[r("a",{attrs:{href:"#fn2",id:"fnref2"}},[e._v("[2]")])]),e._v(" were developed by Open Knowledge International to support the validation of tabular datasets both in terms of structure and also with respect to a published schema. You can read more about them "),r("a",{attrs:{href:"http://okfnlabs.org/blog/2015/02/20/introducing-goodtables.html",target:"_blank",rel:"noopener noreferrer"}},[e._v("in this introductory blog post"),r("OutboundLink")],1),e._v(".")]),e._v(" "),r("h3",{attrs:{id:"what-did-we-do"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#what-did-we-do"}},[e._v("#")]),e._v(" What Did We Do")]),e._v(" "),r("p",[e._v("The first stage was to perform validation on all files made available through the eLife API in order to generate a report on data quality - this would allow us to understand the current state of eLife-published data and present the possibility of doing more exciting things with the data such as more comprehensive tests or visualisations.")]),e._v(" "),r("p",[e._v("The process:")]),e._v(" "),r("ul",[r("li",[e._v("We downloaded a big subset of the articles metadata made available via the eLife public API"),r("sup",{staticClass:"footnote-ref"},[r("a",{attrs:{href:"#fn3",id:"fnref3"}},[e._v("[3]")])]),e._v(".")]),e._v(" "),r("li",[e._v("We parsed all metadata files in order to extract the data files linked to each article, regardless of whether it was an additional file or a figure source. This gave us a direct link to each data file linked to the parent article.")]),e._v(" "),r("li",[e._v("We then ran the validation process on each file, storing the resulting report for future analysis.")])]),e._v(" "),r("p",[e._v("All scripts used in the process as well as the outputs can be found in "),r("a",{attrs:{href:"https://github.com/frictionlessdata/pilot-elife",target:"_blank",rel:"noopener noreferrer"}},[e._v("our pilot repository"),r("OutboundLink")],1),e._v(".")]),e._v(" "),r("p",[e._v("Here are some high-level statistics for the process:")]),e._v(" "),r("p",[e._v("We analyzed 3910 articles, 1085 of which had data files. The most common format was Microsoft Excel Open XML Format Spreadsheet (xlsx), with 89% of all 4318 files being published on this format. Older versions of Excel and CSV files made up the rest.")]),e._v(" "),r("p",[r("img",{attrs:{src:a(423),alt:"datasets analysed by eLife image"}}),e._v(" "),r("em",[e._v("A summary of the eLife research articles analysed as part of the Frictionless Data pilot work")])]),e._v(" "),r("p",[e._v("In terms of validation, more than 75% of the articles analyzed contained at least one invalid file. Of course valid data is an arbitrary term based on the tests that are set within goodtables and results need to be reviewed to adjust the checks performed. For instance errors raised by blank rows are really common on Excel files as people add a title on the first row, leaving an empty row before the data, or empty rows are detected at the end of the sheet.")]),e._v(" "),r("p",[e._v("Other errors raised that might actually point to genuine errors included duplicated headers, extra headers, missing values, incorrect format values (e.g. date format instead of gene name) to give just some examples. Here’s a summary of the raw number of errors encountered. For a more complete description of each error, see the Data Quality Spec"),r("sup",{staticClass:"footnote-ref"},[r("a",{attrs:{href:"#fn4",id:"fnref4"}},[e._v("[4]")])]),e._v(":")]),e._v(" "),r("table",[r("thead",[r("tr",[r("th",[e._v("Error Type")]),e._v(" "),r("th",[e._v("Count")])])]),e._v(" "),r("tbody",[r("tr",[r("td",[e._v("Blank rows")]),e._v(" "),r("td",[e._v("45748")])]),e._v(" "),r("tr",[r("td",[e._v("Duplicate rows")]),e._v(" "),r("td",[e._v("9384")])]),e._v(" "),r("tr",[r("td",[e._v("Duplicate headers")]),e._v(" "),r("td",[e._v("6672")])]),e._v(" "),r("tr",[r("td",[e._v("Blank headers")]),e._v(" "),r("td",[e._v("2479")])]),e._v(" "),r("tr",[r("td",[e._v("Missing values")]),e._v(" "),r("td",[e._v("1032")])]),e._v(" "),r("tr",[r("td",[e._v("Extra values")]),e._v(" "),r("td",[e._v("39")])]),e._v(" "),r("tr",[r("td",[e._v("Source errors")]),e._v(" "),r("td",[e._v("11")])]),e._v(" "),r("tr",[r("td",[e._v("Format errors")]),e._v(" "),r("td",[e._v("4")])])])]),e._v(" "),r("h2",{attrs:{id:"review"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#review"}},[e._v("#")]),e._v(" Review")]),e._v(" "),r("h3",{attrs:{id:"how-effective-was-it"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#how-effective-was-it"}},[e._v("#")]),e._v(" How Effective Was It")]),e._v(" "),r("p",[e._v("Following analysis of a sample of the results, the vast majority of the errors appear to be due to the data being presented in nice-looking tables, using formatting to make particular elements more visually clear, as opposed to a machine-readable format:")]),e._v(" "),r("p",[r("img",{attrs:{src:a(424),alt:"example tables image shared by Naomi"}}),e._v(" "),r("em",[e._v("Data from Maddox et al. was shared in a machine-readable format (top), and adapted here to demonstrate how such data are often shared in a format that looks nice to the human reader (bottom)."),r("br"),e._v("\nSource: Source data"),r("br"),e._v("\nThe data file is presented as is and adapted from Maddox et al. eLife 2015;4:e04995 under the Creative Commons Attribution License (CC BY 4.0).")])]),e._v(" "),r("p",[e._v("This is not limited to the academic field of course, and the tendency to present data in spreadsheets so it is visually appealing is perhaps more prevalent in other areas. Perhaps because consumers of the data are even less likely to have the data processed by machines or because the data is collated by people with no experience of having to use it in their work.")]),e._v(" "),r("p",[e._v("In general the eLife datasets had better quality than for instance those created by government organisations, where structural issues like missing headers, extra cells, etc are much more common. So although the results here have been good, the community could derive substantial benefit from researchers going that extra mile to make files more machine-friendly and embrace more robust data description techniques like Data Packages.")]),e._v(" "),r("p",[e._v("Because these types of ‘errors’ are so common we have introduced default "),r("code",[e._v("ignore blank rows")]),e._v(" and "),r("code",[e._v("ignore duplicate rows")]),e._v(" options in "),r("a",{attrs:{href:"https://try.goodtables.io",target:"_blank",rel:"noopener noreferrer"}},[e._v("our standalone validator"),r("OutboundLink")],1),e._v(" since this helps bring more complex errors to the surface and focusses attention on the errors which may be less trivial to resolve. Excluding duplicate and blank rows as well as duplicate headers (the most common but also relatively simple errors), 6.4% (277/4318) of data files had errors remaining, affecting 10% of research articles (112/1085).")]),e._v(" "),r("p",[e._v("Having said this, the relevance of these errors should not be underplayed as "),r("code",[e._v("blank rows")]),e._v(", "),r("code",[e._v("duplicate rows")]),e._v(" and other human-centered formatting preferences can still result in errors that prevent machine readability. Although the errors were often minor and easy to fix in our case, these seemingly simple errors can be obstructive to anyone trying to reuse data in a more computational workflow. Any computational analysis software, such as R"),r("sup",{staticClass:"footnote-ref"},[r("a",{attrs:{href:"#fn5",id:"fnref5"}},[e._v("[5]")])]),e._v(", requires that all column headers are variables and rows are individual observations i.e. we need variables in columns and observations in rows for any R analysis.")]),e._v(" "),r("p",[e._v("Much less frequent errors were related to difficulties retrieving and opening data files. It was certainly helpful to flag articles with files that were not actually possible to open (source-error), and the eLife production team are resolving these issues. While only representing a small number of datasets, this is one use key case for goodtables: enabling publishers to regularly check continued data availability after publication.")]),e._v(" "),r("p",[e._v("The use case for authors is clear — to identify how a dataset could be reshaped to make it reusable. However, this demands extra work if reshaping is a job added at the point of sharing work. In fact, it is important that any issues are resolved before final publication, to avoid adding updated versions of publications/datasets. Tools that reduce this burden by making it easy to quickly edit a datafile to resolve the errors are of interest moving forward. In the meantime, it may be helpful to consider some key best practises as datasets are collected.")]),e._v(" "),r("p",[e._v("Overall, the findings from this pilot demonstrate that there are different ways of producing data for sharing: datasets are predominantly presented in an Excel file with human aesthetics in mind, rather than structured for use by a statistical program. We found few issues with the data itself beyond presentation preferences. This is encouraging and is a great starting point for venturing forward with helping researchers to make greater use of open data.")]),e._v(" "),r("h2",{attrs:{id:"next-steps"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#next-steps"}},[e._v("#")]),e._v(" Next Steps")]),e._v(" "),r("h3",{attrs:{id:"areas-for-further-work"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#areas-for-further-work"}},[e._v("#")]),e._v(" Areas for further work")]),e._v(" "),r("p",[e._v("Libraries such as goodtables help to flag the gap between the current situation and the ideal situation, which is machine-readability. 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Read more about datapackage-m "),o("RouterLink",{attrs:{to:"/blog/2018/07/20/nimblelearn/"}},[e._v("here")]),e._v(", and check out the documentation for datapackage-connector on our "),o("a",{attrs:{href:"https://github.com/nimblelearn/datapackage-connector",target:"_blank",rel:"noopener noreferrer"}},[e._v("GitHub repo"),o("OutboundLink")],1),e._v(".")],1)])}),[],!1,null,null,null);a.default=r.exports}}]); \ No newline at end of file diff --git a/assets/js/37.f28f2173.js b/assets/js/37.45c937fd.js similarity index 95% rename from assets/js/37.f28f2173.js rename to assets/js/37.45c937fd.js index 836fca478..18ae36e8d 100644 --- a/assets/js/37.f28f2173.js +++ b/assets/js/37.45c937fd.js @@ -1 +1 @@ -(window.webpackJsonp=window.webpackJsonp||[]).push([[37],{489:function(e,t,a){e.exports=a.p+"assets/img/FDdarwin1.8b2fb42c.png"},490:function(e,t,a){e.exports=a.p+"assets/img/FDdarwin2.3a98a635.png"},618:function(e,t,a){"use strict";a.r(t);var o=a(29),i=Object(o.a)({},(function(){var e=this,t=e.$createElement,o=e._self._c||t;return o("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[o("p",[e._v("This blog is part of a series showcasing projects developed during the 2019 Frictionless Data Tool Fund.")]),e._v(" "),o("p",[e._v("Originally published "),o("a",{attrs:{href:"https://blog.okfn.org/2019/12/09/andre-heughebaert-frictionless-darwincore/",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://blog.okfn.org/2019/12/09/andre-heughebaert-frictionless-darwincore/"),o("OutboundLink")],1)]),e._v(" "),o("p",[o("em",[e._v("The 2019 Frictionless Data Tool Fund provided four mini-grants of $5,000 to support individuals or organisations in developing an open tool for reproducible research built using the Frictionless Data specifications and software. This fund is part of the Frictionless Data for Reproducible Research project, which is funded by the Sloan Foundation. This project applies our work in Frictionless Data to data-driven research disciplines, in order to facilitate reproducible data workflows in research contexts.")])]),e._v(" "),o("p",[e._v("André Heughebaert is an open biodiversity data advocate in his work and his free time. He is an IT Software Engineer at the Belgian Biodiversity Platform and is also the Belgian GBIF (Global Biodiversity Information Facility) Node manager. During this time, he has worked with the Darwin Core Standards and Open Biodiversity data on a daily basis. This work inspired him to apply for the Tool Fund, where he has developed a tool to convert DarwinCore Archives into Frictionless Data Packages.")]),e._v(" "),o("p",[e._v("The DarwinCore Archive (DwCA) is a standardised container for biodiversity data and metadata largely used amongst the GBIF community, which consists of more than 1,500 institutions around the world. The DwCA is used to publish biodiversity data about observations, collections specimens, species checklists and sampling events. However, this domain specific standard has some limitations, mainly the star schema (core table + extensions), rules that are sometimes too permissive, and a lack of controlled vocabularies for certain terms. These limitations encouraged André to investigate emerging open data standards. In 2016, he discovered Frictionless Data and published his first data package on historical data from 1815 Napoleonic Campaign of Belgium. He was then encouraged to create a tool that would, in part, build a bridge between these two open data ecosystems.")]),e._v(" "),o("p",[e._v("As a result, the Frictionless DarwinCore tool converts DwCA into Frictionless Data Packages, and also gives access to the vast Frictionless Data software ecosystem enabling constraints validation and support of a fully relational data schema. Technically speaking, the tool is implemented as a Python library, and is exposed as a Command Line Interface. The tool automatically converts:")]),e._v(" "),o("ul",[o("li",[e._v("DwCA data schema into datapackage.json")]),e._v(" "),o("li",[e._v("EML metadata into human readable markdown readme file")]),e._v(" "),o("li",[e._v("data files are converted when necessary, this is when default values are described")])]),e._v(" "),o("p",[e._v("The resulting zip file complies to both DarwinCore and Frictionless specifications.")]),e._v(" "),o("p",[o("img",{attrs:{src:a(489),alt:"flow"}}),e._v(" "),o("br"),e._v(" "),o("em",[e._v("Frictionless DarwinCore Flow")])]),e._v(" "),o("p",[e._v("André hopes that bridging the two standards will give an excellent opportunity for the GBIF community to provide open biodiversity data to a wider audience. He says this is also a good opportunity to discover the Frictionless Data specifications and assess their applicability to the biodiversity domain. In fact, on 9th October 2019, André presented the tool at a GBIF Global Nodes meeting. It was perceived by the nodes managers community as an exploratory and pioneering work. While the command line interface offers a simple user interface for non-programmers, others might prefer the more flexible and sophisticated Python API. André encourages anyone working with DarwinCore data, including all data publishers and data users of GBIF network, to try out the new tool.")]),e._v(" "),o("p",[e._v("“I’m quite optimistic that the project will feed the necessary reflection on the evolution of our biodiversity standards and data flows.”")]),e._v(" "),o("p",[e._v("To get started, installation of the tool is done through a single pip install command (full directions can be found in the project README). Central to the tool is a table of DarwinCore terms linking a Data Package type, format and constraints for every DwC term. The tool can be used as CLI directly from your terminal window or as Python Library for developers. The tool can work with either locally stored or online DwCA. Once converted to Tabular DataPackage, the DwC data can then be ingested and further processed by software such as Goodtables, OpenRefine or any other Frictionless Data software.")]),e._v(" "),o("p",[e._v("André has aspirations to take the Frictionless DarwinCore tool further by encapsulating the tool in a web-service that will directly deliver Goodtables reports from a DwCA, which will make it even more user friendly. Additional ideas for further improvement would be including an import pathway for DarwinCore data into Open Refine, which is a popular tool in the GBIF community. André’s long term hope is that the Data Package will become an optional format for data download on "),o("a",{attrs:{href:"http://GBIF.org",target:"_blank",rel:"noopener noreferrer"}},[e._v("GBIF.org"),o("OutboundLink")],1),e._v(".")]),e._v(" "),o("p",[o("img",{attrs:{src:a(490),alt:"workflow"}}),e._v(" "),o("br")]),e._v(" "),o("p",[e._v("Further reading:")]),e._v(" "),o("p",[e._v("Repository: "),o("a",{attrs:{href:"https://github.com/frictionlessdata/FrictionlessDarwinCore",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://github.com/frictionlessdata/FrictionlessDarwinCore"),o("OutboundLink")],1)]),e._v(" "),o("p",[e._v("Project blog: "),o("a",{attrs:{href:"https://andrejjh.github.io/fdwc.github.io/",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://andrejjh.github.io/fdwc.github.io/"),o("OutboundLink")],1)])])}),[],!1,null,null,null);t.default=i.exports}}]); \ No newline at end of file +(window.webpackJsonp=window.webpackJsonp||[]).push([[37],{483:function(e,t,a){e.exports=a.p+"assets/img/FDdarwin1.8b2fb42c.png"},484:function(e,t,a){e.exports=a.p+"assets/img/FDdarwin2.3a98a635.png"},617:function(e,t,a){"use strict";a.r(t);var o=a(29),i=Object(o.a)({},(function(){var e=this,t=e.$createElement,o=e._self._c||t;return o("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[o("p",[e._v("This blog is part of a series showcasing projects developed during the 2019 Frictionless Data Tool Fund.")]),e._v(" "),o("p",[e._v("Originally published "),o("a",{attrs:{href:"https://blog.okfn.org/2019/12/09/andre-heughebaert-frictionless-darwincore/",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://blog.okfn.org/2019/12/09/andre-heughebaert-frictionless-darwincore/"),o("OutboundLink")],1)]),e._v(" "),o("p",[o("em",[e._v("The 2019 Frictionless Data Tool Fund provided four mini-grants of $5,000 to support individuals or organisations in developing an open tool for reproducible research built using the Frictionless Data specifications and software. This fund is part of the Frictionless Data for Reproducible Research project, which is funded by the Sloan Foundation. This project applies our work in Frictionless Data to data-driven research disciplines, in order to facilitate reproducible data workflows in research contexts.")])]),e._v(" "),o("p",[e._v("André Heughebaert is an open biodiversity data advocate in his work and his free time. He is an IT Software Engineer at the Belgian Biodiversity Platform and is also the Belgian GBIF (Global Biodiversity Information Facility) Node manager. During this time, he has worked with the Darwin Core Standards and Open Biodiversity data on a daily basis. This work inspired him to apply for the Tool Fund, where he has developed a tool to convert DarwinCore Archives into Frictionless Data Packages.")]),e._v(" "),o("p",[e._v("The DarwinCore Archive (DwCA) is a standardised container for biodiversity data and metadata largely used amongst the GBIF community, which consists of more than 1,500 institutions around the world. The DwCA is used to publish biodiversity data about observations, collections specimens, species checklists and sampling events. However, this domain specific standard has some limitations, mainly the star schema (core table + extensions), rules that are sometimes too permissive, and a lack of controlled vocabularies for certain terms. These limitations encouraged André to investigate emerging open data standards. In 2016, he discovered Frictionless Data and published his first data package on historical data from 1815 Napoleonic Campaign of Belgium. He was then encouraged to create a tool that would, in part, build a bridge between these two open data ecosystems.")]),e._v(" "),o("p",[e._v("As a result, the Frictionless DarwinCore tool converts DwCA into Frictionless Data Packages, and also gives access to the vast Frictionless Data software ecosystem enabling constraints validation and support of a fully relational data schema. Technically speaking, the tool is implemented as a Python library, and is exposed as a Command Line Interface. The tool automatically converts:")]),e._v(" "),o("ul",[o("li",[e._v("DwCA data schema into datapackage.json")]),e._v(" "),o("li",[e._v("EML metadata into human readable markdown readme file")]),e._v(" "),o("li",[e._v("data files are converted when necessary, this is when default values are described")])]),e._v(" "),o("p",[e._v("The resulting zip file complies to both DarwinCore and Frictionless specifications.")]),e._v(" "),o("p",[o("img",{attrs:{src:a(483),alt:"flow"}}),e._v(" "),o("br"),e._v(" "),o("em",[e._v("Frictionless DarwinCore Flow")])]),e._v(" "),o("p",[e._v("André hopes that bridging the two standards will give an excellent opportunity for the GBIF community to provide open biodiversity data to a wider audience. He says this is also a good opportunity to discover the Frictionless Data specifications and assess their applicability to the biodiversity domain. In fact, on 9th October 2019, André presented the tool at a GBIF Global Nodes meeting. It was perceived by the nodes managers community as an exploratory and pioneering work. While the command line interface offers a simple user interface for non-programmers, others might prefer the more flexible and sophisticated Python API. André encourages anyone working with DarwinCore data, including all data publishers and data users of GBIF network, to try out the new tool.")]),e._v(" "),o("p",[e._v("“I’m quite optimistic that the project will feed the necessary reflection on the evolution of our biodiversity standards and data flows.”")]),e._v(" "),o("p",[e._v("To get started, installation of the tool is done through a single pip install command (full directions can be found in the project README). Central to the tool is a table of DarwinCore terms linking a Data Package type, format and constraints for every DwC term. The tool can be used as CLI directly from your terminal window or as Python Library for developers. The tool can work with either locally stored or online DwCA. Once converted to Tabular DataPackage, the DwC data can then be ingested and further processed by software such as Goodtables, OpenRefine or any other Frictionless Data software.")]),e._v(" "),o("p",[e._v("André has aspirations to take the Frictionless DarwinCore tool further by encapsulating the tool in a web-service that will directly deliver Goodtables reports from a DwCA, which will make it even more user friendly. Additional ideas for further improvement would be including an import pathway for DarwinCore data into Open Refine, which is a popular tool in the GBIF community. André’s long term hope is that the Data Package will become an optional format for data download on "),o("a",{attrs:{href:"http://GBIF.org",target:"_blank",rel:"noopener noreferrer"}},[e._v("GBIF.org"),o("OutboundLink")],1),e._v(".")]),e._v(" "),o("p",[o("img",{attrs:{src:a(484),alt:"workflow"}}),e._v(" "),o("br")]),e._v(" "),o("p",[e._v("Further reading:")]),e._v(" "),o("p",[e._v("Repository: "),o("a",{attrs:{href:"https://github.com/frictionlessdata/FrictionlessDarwinCore",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://github.com/frictionlessdata/FrictionlessDarwinCore"),o("OutboundLink")],1)]),e._v(" "),o("p",[e._v("Project blog: "),o("a",{attrs:{href:"https://andrejjh.github.io/fdwc.github.io/",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://andrejjh.github.io/fdwc.github.io/"),o("OutboundLink")],1)])])}),[],!1,null,null,null);t.default=i.exports}}]); \ No newline at end of file diff --git a/assets/js/38.60d749aa.js b/assets/js/38.9d06cc6a.js similarity index 97% rename from assets/js/38.60d749aa.js rename to assets/js/38.9d06cc6a.js index a8d256e21..ba472aa4c 100644 --- a/assets/js/38.60d749aa.js +++ b/assets/js/38.9d06cc6a.js @@ -1 +1 @@ -(window.webpackJsonp=window.webpackJsonp||[]).push([[38],{507:function(e,a,t){e.exports=t.p+"assets/img/schema-1.d455d7fb.png"},508:function(e,a,t){e.exports=t.p+"assets/img/schema-2.97aad0c8.png"},631:function(e,a,t){"use strict";t.r(a);var r=t(29),o=Object(r.a)({},(function(){var e=this,a=e.$createElement,r=e._self._c||a;return r("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[r("p",[e._v("In June 2019, "),r("a",{attrs:{href:"https://etalab.gouv.fr",target:"_blank",rel:"noopener noreferrer"}},[e._v("Etalab"),r("OutboundLink")],1),e._v(", a department of the French interministerial digital service (DINUM), launched "),r("a",{attrs:{href:"https://schema.data.gouv.fr",target:"_blank",rel:"noopener noreferrer"}},[e._v("schema.data.gouv.fr"),r("OutboundLink")],1),e._v(", a platform listing schemas for France. It could be described as what Johan Richer recently called a "),r("a",{attrs:{href:"https://frictionlessdata.io/blog/2020/04/23/table-schema-catalog/",target:"_blank",rel:"noopener noreferrer"}},[e._v("schema catalog"),r("OutboundLink")],1),e._v(". This project is an initiative of "),r("a",{attrs:{href:"http://data.gouv.fr",target:"_blank",rel:"noopener noreferrer"}},[e._v("data.gouv.fr"),r("OutboundLink")],1),e._v(", the French open data platform, which is developed and maintained by Etalab.")]),e._v(" "),r("p",[r("img",{attrs:{src:"/img/blog/schema.gouv.fr.png",alt:"schema.gouv.fr homepage"}})]),e._v(" "),r("h2",{attrs:{id:"what-s-a-schema"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#what-s-a-schema"}},[e._v("#")]),e._v(" What’s a schema?")]),e._v(" "),r("p",[e._v("A schema declares a data model in a clear and precise manner, the various fields and types in a structured and consistent manner, according to a specification. For example, "),r("a",{attrs:{href:"https://specs.frictionlessdata.io/table-schema/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Table Schema"),r("OutboundLink")],1),e._v(" is a simple language to declare a schema for tabular data.")]),e._v(" "),r("p",[e._v("Schemas are well suited for a wide range of applications: validating data against a schema, documenting a data model, consolidating data from multiple sources, generating example datasets, or proposing tailored input forms. This wide range of applications makes schemas an important tool for both producers and reusers.")]),e._v(" "),r("h2",{attrs:{id:"advancing-open-data-quality"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#advancing-open-data-quality"}},[e._v("#")]),e._v(" Advancing open data quality")]),e._v(" "),r("p",[e._v("A common complaint of open data reusers has been the lack of quality of the data and data structure changes over time, without notice. The OKFN spoke about this issue in mid-2017 in a blog post, "),r("a",{attrs:{href:"https://blog.okfn.org/2017/05/31/open-data-quality-the-next-shift-in-open-data/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Open data quality – the next shift in open data?"),r("OutboundLink")],1)]),e._v(" "),r("p",[e._v("With "),r("a",{attrs:{href:"schema.data.gouv.fr"}},[e._v("schema.data.gouv.fr")]),e._v(", Etalab promotes high-quality open data: producers are encouraged to discuss and come up with an appropriate schema for the data they want to publish, and to document it with a recognised specification. Producers will then be able to make sure that the data they publish conforms to the schema over time. Reusers benefit from high-quality documentation, a stable data structure, and increased quality of the data.")]),e._v(" "),r("h2",{attrs:{id:"impacts"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#impacts"}},[e._v("#")]),e._v(" Impacts")]),e._v(" "),r("p",[e._v("The first impact of the launch of "),r("a",{attrs:{href:"https://schema.data.gouv.fr",target:"_blank",rel:"noopener noreferrer"}},[e._v("schema.data.gouv.fr"),r("OutboundLink")],1),e._v(" has "),r("strong",[e._v("put at the forefront the challenge of open data quality")]),e._v(". It acknowledges that this is not a solved problem and that producers should embrace schemas, validators, documentation, automated testing to raise the quality of the data they publish. It’s also a recognition of the efforts already made by the community, for example the “Socle commun des données locales” (Common Ground of Local Data) by "),r("a",{attrs:{href:"http://www.opendatafrance.net",target:"_blank",rel:"noopener noreferrer"}},[e._v("OpenDataFrance"),r("OutboundLink")],1),e._v(".")]),e._v(" "),r("p",[e._v("To help producers discover schemas and how it can be helpful for them, we published in March 2020 a "),r("a",{attrs:{href:"https://guides.etalab.gouv.fr/producteurs-schemas/",target:"_blank",rel:"noopener noreferrer"}},[e._v("long guide"),r("OutboundLink")],1),e._v(" going over steps producers are encouraged to follow when creating a schema: discovery, discussions, implementation, publication and finally referencing the schema on "),r("a",{attrs:{href:"http://schema.data.gouv.fr",target:"_blank",rel:"noopener noreferrer"}},[e._v("schema.data.gouv.fr"),r("OutboundLink")],1),e._v(".")]),e._v(" "),r("p",[e._v("Since the launch, producers worked with their reusers and published various schemas: "),r("a",{attrs:{href:"https://schema.data.gouv.fr/etalab/schema-lieux-covoiturage/latest.html",target:"_blank",rel:"noopener noreferrer"}},[e._v("carpooling places"),r("OutboundLink")],1),e._v(" or "),r("a",{attrs:{href:"https://schema.data.gouv.fr/arsante/schema-dae/latest.html",target:"_blank",rel:"noopener noreferrer"}},[e._v("defibrillators"),r("OutboundLink")],1),e._v(" to name a few. People had in-depth discussions about their data model, encouraged by the thoroughness of the Table Schema specification. Producers worked hard to clean their data and finally reached a point where their dataset is 100% aligned with the schema, without any errors.")]),e._v(" "),r("h2",{attrs:{id:"what-s-next"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#what-s-next"}},[e._v("#")]),e._v(" What’s next")]),e._v(" "),r("p",[e._v("Here are a few things we are working on and hope to be able to finish in the coming years.")]),e._v(" "),r("h3",{attrs:{id:"improved-data-models-defined-in-the-law"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#improved-data-models-defined-in-the-law"}},[e._v("#")]),e._v(" Improved data models defined in the law")]),e._v(" "),r("p",[e._v("Right now, when data models are introduced by law, the data model is often described by a table. We’d like to offer a schema when these laws are published, to ease adoption by the community and improve discoverability.")]),e._v(" "),r("h3",{attrs:{id:"integration-with-data-gouv-fr"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#integration-with-data-gouv-fr"}},[e._v("#")]),e._v(" Integration with "),r("a",{attrs:{href:"http://data.gouv.fr",target:"_blank",rel:"noopener noreferrer"}},[e._v("data.gouv.fr"),r("OutboundLink")],1)]),e._v(" "),r("p",[e._v("The "),r("a",{attrs:{href:"http://schema.data.gouv.fr",target:"_blank",rel:"noopener noreferrer"}},[e._v("schema.data.gouv.fr"),r("OutboundLink")],1),e._v(" initiative is mainly based on published datasets on the French open data platform "),r("a",{attrs:{href:"http://data.gouv.fr",target:"_blank",rel:"noopener noreferrer"}},[e._v("data.gouv.fr"),r("OutboundLink")],1),e._v(". However, these tools are still quite separated today. In the coming months, we would like to strengthen the link between "),r("a",{attrs:{href:"http://schema.data.gouv.fr",target:"_blank",rel:"noopener noreferrer"}},[e._v("schema.data.gouv.fr"),r("OutboundLink")],1),e._v(" and "),r("a",{attrs:{href:"http://data.gouv.fr",target:"_blank",rel:"noopener noreferrer"}},[e._v("data.gouv.fr"),r("OutboundLink")],1),e._v(" by promoting existing schemas directly on the open data platform.")]),e._v(" "),r("p",[e._v("First, we would like to inform users of the existence of a consolidated dataset based on an existing schema and provide them with its quality report. Such a feature is newly available on "),r("a",{attrs:{href:"http://schema.data.gouv.fr",target:"_blank",rel:"noopener noreferrer"}},[e._v("schema.data.gouv.fr"),r("OutboundLink")],1),e._v(". The same feature will arrive soon on "),r("a",{attrs:{href:"http://data.gouv.fr",target:"_blank",rel:"noopener noreferrer"}},[e._v("data.gouv.fr"),r("OutboundLink")],1),e._v(".")]),e._v(" "),r("p",[r("img",{attrs:{src:t(507),alt:"Screenshot à prévoir"}})]),e._v(" "),r("p",[e._v("Second, we’re looking into integrating schemas into the data publishing process on "),r("a",{attrs:{href:"http://data.gouv.fr",target:"_blank",rel:"noopener noreferrer"}},[e._v("data.gouv.fr"),r("OutboundLink")],1),e._v(". We could help users by letting them know that a schema corresponding to their dataset already exists. We could suggest them what changes to make to get their data directly validated. We already started doing this with a simple implementation: we post comments on datasets which are supposed to follow a schema, letting producers know if the data is valid and if not, enabling them to access a report to troubleshoot.")]),e._v(" "),r("p",[e._v("Another possibility would be to offer a new service on "),r("a",{attrs:{href:"http://data.gouv.fr",target:"_blank",rel:"noopener noreferrer"}},[e._v("data.gouv.fr"),r("OutboundLink")],1),e._v(" such as the generation of data from an automatically generated form. This is the goal of the ongoing development of "),r("a",{attrs:{href:"https://csv-gg.etalab.studio/?schema=etalab%2Fschema-lieux-covoiturage",target:"_blank",rel:"noopener noreferrer"}},[e._v("CSV-GG"),r("OutboundLink")],1),e._v(" allowing to generate a form from an existing Table Schema. This could help users to directly produce validated data.")]),e._v(" "),r("p",[r("img",{attrs:{src:t(508),alt:"screenshot à prévoir"}})]),e._v(" "),r("h3",{attrs:{id:"automation"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#automation"}},[e._v("#")]),e._v(" Automation")]),e._v(" "),r("p",[e._v("In the longer term, we also plan to automate data consolidation based on a schema as much as possible. For that, we need to better know and understand available resources on the platform. This could be done by systematically analyzing the content of a new resource and try to fetch metadata such as headers or type of data for each column.")]),e._v(" "),r("p",[e._v("These metadata could then be used to identify datasets with similar structures and link them to an existing schema or propose to create a new one if it does not already exist.")]),e._v(" "),r("p",[e._v("We could also take advantage of the tool "),r("a",{attrs:{href:"https://github.com/etalab/csvapi",target:"_blank",rel:"noopener noreferrer"}},[e._v("CSVAPI"),r("OutboundLink")],1),e._v(" which is actually in use on "),r("a",{attrs:{href:"http://data.gouv.fr",target:"_blank",rel:"noopener noreferrer"}},[e._v("data.gouv.fr"),r("OutboundLink")],1),e._v(" to preview data of a specific dataset. CSVAPI could evolve to offer new features such as highlighting quality problems directly in the dataset or navigating through different datasets with same - or partial - structures. The schema associated with a dataset could also help having a better preview by associating a type to each field. For example, a postal code could be recognized as such and the leading zero would not be cropped.")]),e._v(" "),r("h2",{attrs:{id:"conclusion"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#conclusion"}},[e._v("#")]),e._v(" Conclusion")]),e._v(" "),r("p",[e._v("All of the features mentioned in this article are intended to promote the usefulness and the value of schemas and lead to the creation of new ones. We hope this will result in an increase of the overall quality of the data hosted on "),r("a",{attrs:{href:"http://data.gouv.fr",target:"_blank",rel:"noopener noreferrer"}},[e._v("data.gouv.fr"),r("OutboundLink")],1),e._v(".")]),e._v(" "),r("p",[e._v("Furthermore, we strongly believe that these features will help to link different users and producers with similar interests and therefore be in line with the community-based nature of "),r("a",{attrs:{href:"http://data.gouv.fr",target:"_blank",rel:"noopener noreferrer"}},[e._v("data.gouv.fr"),r("OutboundLink")],1),e._v(".")])])}),[],!1,null,null,null);a.default=o.exports}}]); \ No newline at end of file +(window.webpackJsonp=window.webpackJsonp||[]).push([[38],{501:function(e,a,t){e.exports=t.p+"assets/img/schema-1.d455d7fb.png"},502:function(e,a,t){e.exports=t.p+"assets/img/schema-2.97aad0c8.png"},629:function(e,a,t){"use strict";t.r(a);var r=t(29),o=Object(r.a)({},(function(){var e=this,a=e.$createElement,r=e._self._c||a;return r("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[r("p",[e._v("In June 2019, "),r("a",{attrs:{href:"https://etalab.gouv.fr",target:"_blank",rel:"noopener noreferrer"}},[e._v("Etalab"),r("OutboundLink")],1),e._v(", a department of the French interministerial digital service (DINUM), launched "),r("a",{attrs:{href:"https://schema.data.gouv.fr",target:"_blank",rel:"noopener noreferrer"}},[e._v("schema.data.gouv.fr"),r("OutboundLink")],1),e._v(", a platform listing schemas for France. It could be described as what Johan Richer recently called a "),r("a",{attrs:{href:"https://frictionlessdata.io/blog/2020/04/23/table-schema-catalog/",target:"_blank",rel:"noopener noreferrer"}},[e._v("schema catalog"),r("OutboundLink")],1),e._v(". This project is an initiative of "),r("a",{attrs:{href:"http://data.gouv.fr",target:"_blank",rel:"noopener noreferrer"}},[e._v("data.gouv.fr"),r("OutboundLink")],1),e._v(", the French open data platform, which is developed and maintained by Etalab.")]),e._v(" "),r("p",[r("img",{attrs:{src:"/img/blog/schema.gouv.fr.png",alt:"schema.gouv.fr homepage"}})]),e._v(" "),r("h2",{attrs:{id:"what-s-a-schema"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#what-s-a-schema"}},[e._v("#")]),e._v(" What’s a schema?")]),e._v(" "),r("p",[e._v("A schema declares a data model in a clear and precise manner, the various fields and types in a structured and consistent manner, according to a specification. For example, "),r("a",{attrs:{href:"https://specs.frictionlessdata.io/table-schema/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Table Schema"),r("OutboundLink")],1),e._v(" is a simple language to declare a schema for tabular data.")]),e._v(" "),r("p",[e._v("Schemas are well suited for a wide range of applications: validating data against a schema, documenting a data model, consolidating data from multiple sources, generating example datasets, or proposing tailored input forms. This wide range of applications makes schemas an important tool for both producers and reusers.")]),e._v(" "),r("h2",{attrs:{id:"advancing-open-data-quality"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#advancing-open-data-quality"}},[e._v("#")]),e._v(" Advancing open data quality")]),e._v(" "),r("p",[e._v("A common complaint of open data reusers has been the lack of quality of the data and data structure changes over time, without notice. The OKFN spoke about this issue in mid-2017 in a blog post, "),r("a",{attrs:{href:"https://blog.okfn.org/2017/05/31/open-data-quality-the-next-shift-in-open-data/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Open data quality – the next shift in open data?"),r("OutboundLink")],1)]),e._v(" "),r("p",[e._v("With "),r("a",{attrs:{href:"schema.data.gouv.fr"}},[e._v("schema.data.gouv.fr")]),e._v(", Etalab promotes high-quality open data: producers are encouraged to discuss and come up with an appropriate schema for the data they want to publish, and to document it with a recognised specification. Producers will then be able to make sure that the data they publish conforms to the schema over time. Reusers benefit from high-quality documentation, a stable data structure, and increased quality of the data.")]),e._v(" "),r("h2",{attrs:{id:"impacts"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#impacts"}},[e._v("#")]),e._v(" Impacts")]),e._v(" "),r("p",[e._v("The first impact of the launch of "),r("a",{attrs:{href:"https://schema.data.gouv.fr",target:"_blank",rel:"noopener noreferrer"}},[e._v("schema.data.gouv.fr"),r("OutboundLink")],1),e._v(" has "),r("strong",[e._v("put at the forefront the challenge of open data quality")]),e._v(". It acknowledges that this is not a solved problem and that producers should embrace schemas, validators, documentation, automated testing to raise the quality of the data they publish. It’s also a recognition of the efforts already made by the community, for example the “Socle commun des données locales” (Common Ground of Local Data) by "),r("a",{attrs:{href:"http://www.opendatafrance.net",target:"_blank",rel:"noopener noreferrer"}},[e._v("OpenDataFrance"),r("OutboundLink")],1),e._v(".")]),e._v(" "),r("p",[e._v("To help producers discover schemas and how it can be helpful for them, we published in March 2020 a "),r("a",{attrs:{href:"https://guides.etalab.gouv.fr/producteurs-schemas/",target:"_blank",rel:"noopener noreferrer"}},[e._v("long guide"),r("OutboundLink")],1),e._v(" going over steps producers are encouraged to follow when creating a schema: discovery, discussions, implementation, publication and finally referencing the schema on "),r("a",{attrs:{href:"http://schema.data.gouv.fr",target:"_blank",rel:"noopener noreferrer"}},[e._v("schema.data.gouv.fr"),r("OutboundLink")],1),e._v(".")]),e._v(" "),r("p",[e._v("Since the launch, producers worked with their reusers and published various schemas: "),r("a",{attrs:{href:"https://schema.data.gouv.fr/etalab/schema-lieux-covoiturage/latest.html",target:"_blank",rel:"noopener noreferrer"}},[e._v("carpooling places"),r("OutboundLink")],1),e._v(" or "),r("a",{attrs:{href:"https://schema.data.gouv.fr/arsante/schema-dae/latest.html",target:"_blank",rel:"noopener noreferrer"}},[e._v("defibrillators"),r("OutboundLink")],1),e._v(" to name a few. People had in-depth discussions about their data model, encouraged by the thoroughness of the Table Schema specification. Producers worked hard to clean their data and finally reached a point where their dataset is 100% aligned with the schema, without any errors.")]),e._v(" "),r("h2",{attrs:{id:"what-s-next"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#what-s-next"}},[e._v("#")]),e._v(" What’s next")]),e._v(" "),r("p",[e._v("Here are a few things we are working on and hope to be able to finish in the coming years.")]),e._v(" "),r("h3",{attrs:{id:"improved-data-models-defined-in-the-law"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#improved-data-models-defined-in-the-law"}},[e._v("#")]),e._v(" Improved data models defined in the law")]),e._v(" "),r("p",[e._v("Right now, when data models are introduced by law, the data model is often described by a table. We’d like to offer a schema when these laws are published, to ease adoption by the community and improve discoverability.")]),e._v(" "),r("h3",{attrs:{id:"integration-with-data-gouv-fr"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#integration-with-data-gouv-fr"}},[e._v("#")]),e._v(" Integration with "),r("a",{attrs:{href:"http://data.gouv.fr",target:"_blank",rel:"noopener noreferrer"}},[e._v("data.gouv.fr"),r("OutboundLink")],1)]),e._v(" "),r("p",[e._v("The "),r("a",{attrs:{href:"http://schema.data.gouv.fr",target:"_blank",rel:"noopener noreferrer"}},[e._v("schema.data.gouv.fr"),r("OutboundLink")],1),e._v(" initiative is mainly based on published datasets on the French open data platform "),r("a",{attrs:{href:"http://data.gouv.fr",target:"_blank",rel:"noopener noreferrer"}},[e._v("data.gouv.fr"),r("OutboundLink")],1),e._v(". However, these tools are still quite separated today. In the coming months, we would like to strengthen the link between "),r("a",{attrs:{href:"http://schema.data.gouv.fr",target:"_blank",rel:"noopener noreferrer"}},[e._v("schema.data.gouv.fr"),r("OutboundLink")],1),e._v(" and "),r("a",{attrs:{href:"http://data.gouv.fr",target:"_blank",rel:"noopener noreferrer"}},[e._v("data.gouv.fr"),r("OutboundLink")],1),e._v(" by promoting existing schemas directly on the open data platform.")]),e._v(" "),r("p",[e._v("First, we would like to inform users of the existence of a consolidated dataset based on an existing schema and provide them with its quality report. Such a feature is newly available on "),r("a",{attrs:{href:"http://schema.data.gouv.fr",target:"_blank",rel:"noopener noreferrer"}},[e._v("schema.data.gouv.fr"),r("OutboundLink")],1),e._v(". The same feature will arrive soon on "),r("a",{attrs:{href:"http://data.gouv.fr",target:"_blank",rel:"noopener noreferrer"}},[e._v("data.gouv.fr"),r("OutboundLink")],1),e._v(".")]),e._v(" "),r("p",[r("img",{attrs:{src:t(501),alt:"Screenshot à prévoir"}})]),e._v(" "),r("p",[e._v("Second, we’re looking into integrating schemas into the data publishing process on "),r("a",{attrs:{href:"http://data.gouv.fr",target:"_blank",rel:"noopener noreferrer"}},[e._v("data.gouv.fr"),r("OutboundLink")],1),e._v(". We could help users by letting them know that a schema corresponding to their dataset already exists. We could suggest them what changes to make to get their data directly validated. We already started doing this with a simple implementation: we post comments on datasets which are supposed to follow a schema, letting producers know if the data is valid and if not, enabling them to access a report to troubleshoot.")]),e._v(" "),r("p",[e._v("Another possibility would be to offer a new service on "),r("a",{attrs:{href:"http://data.gouv.fr",target:"_blank",rel:"noopener noreferrer"}},[e._v("data.gouv.fr"),r("OutboundLink")],1),e._v(" such as the generation of data from an automatically generated form. This is the goal of the ongoing development of "),r("a",{attrs:{href:"https://csv-gg.etalab.studio/?schema=etalab%2Fschema-lieux-covoiturage",target:"_blank",rel:"noopener noreferrer"}},[e._v("CSV-GG"),r("OutboundLink")],1),e._v(" allowing to generate a form from an existing Table Schema. This could help users to directly produce validated data.")]),e._v(" "),r("p",[r("img",{attrs:{src:t(502),alt:"screenshot à prévoir"}})]),e._v(" "),r("h3",{attrs:{id:"automation"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#automation"}},[e._v("#")]),e._v(" Automation")]),e._v(" "),r("p",[e._v("In the longer term, we also plan to automate data consolidation based on a schema as much as possible. For that, we need to better know and understand available resources on the platform. This could be done by systematically analyzing the content of a new resource and try to fetch metadata such as headers or type of data for each column.")]),e._v(" "),r("p",[e._v("These metadata could then be used to identify datasets with similar structures and link them to an existing schema or propose to create a new one if it does not already exist.")]),e._v(" "),r("p",[e._v("We could also take advantage of the tool "),r("a",{attrs:{href:"https://github.com/etalab/csvapi",target:"_blank",rel:"noopener noreferrer"}},[e._v("CSVAPI"),r("OutboundLink")],1),e._v(" which is actually in use on "),r("a",{attrs:{href:"http://data.gouv.fr",target:"_blank",rel:"noopener noreferrer"}},[e._v("data.gouv.fr"),r("OutboundLink")],1),e._v(" to preview data of a specific dataset. CSVAPI could evolve to offer new features such as highlighting quality problems directly in the dataset or navigating through different datasets with same - or partial - structures. The schema associated with a dataset could also help having a better preview by associating a type to each field. For example, a postal code could be recognized as such and the leading zero would not be cropped.")]),e._v(" "),r("h2",{attrs:{id:"conclusion"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#conclusion"}},[e._v("#")]),e._v(" Conclusion")]),e._v(" "),r("p",[e._v("All of the features mentioned in this article are intended to promote the usefulness and the value of schemas and lead to the creation of new ones. We hope this will result in an increase of the overall quality of the data hosted on "),r("a",{attrs:{href:"http://data.gouv.fr",target:"_blank",rel:"noopener noreferrer"}},[e._v("data.gouv.fr"),r("OutboundLink")],1),e._v(".")]),e._v(" "),r("p",[e._v("Furthermore, we strongly believe that these features will help to link different users and producers with similar interests and therefore be in line with the community-based nature of "),r("a",{attrs:{href:"http://data.gouv.fr",target:"_blank",rel:"noopener noreferrer"}},[e._v("data.gouv.fr"),r("OutboundLink")],1),e._v(".")])])}),[],!1,null,null,null);a.default=o.exports}}]); \ No newline at end of file diff --git a/assets/js/39.efc74ba0.js b/assets/js/39.33041130.js similarity index 95% rename from assets/js/39.efc74ba0.js rename to assets/js/39.33041130.js index 6753a2076..ce39b2bd4 100644 --- a/assets/js/39.efc74ba0.js +++ b/assets/js/39.33041130.js @@ -1 +1 @@ -(window.webpackJsonp=window.webpackJsonp||[]).push([[39],{509:function(e,t,a){e.exports=a.p+"assets/img/duplication_error.070ab48c.png"},510:function(e,t,a){e.exports=a.p+"assets/img/goodtables_lon-out-of-bounds.ef393c01.png"},640:function(e,t,a){"use strict";a.r(t);var o=a(29),s=Object(o.a)({},(function(){var e=this,t=e.$createElement,o=e._self._c||t;return o("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[o("p",[o("em",[e._v("This post was originally published on the "),o("a",{attrs:{href:"https://blog.bco-dmo.org/2020/09/14/goodtables",target:"_blank",rel:"noopener noreferrer"}},[e._v("BCO-DMO blog"),o("OutboundLink")],1),e._v(".")])]),e._v(" "),o("p",[e._v("Earlier this year, the "),o("a",{attrs:{href:"https://www.bco-dmo.org/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Biological and Chemical Oceanography Data Management Office (BCO-DMO)"),o("OutboundLink")],1),e._v(" completed a pilot project with the "),o("a",{attrs:{href:"https://okfn.org/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Open Knowledge Foundation (OKF)"),o("OutboundLink")],1),e._v(" to "),o("a",{attrs:{href:"https://blog.okfn.org/2020/02/10/frictionless-data-pipelines-for-ocean-science/",target:"_blank",rel:"noopener noreferrer"}},[e._v("streamline the data curation processes for oceanographic datasets using Frictionless Data Pipelines (FDP)"),o("OutboundLink")],1),e._v(". The goal of this pilot was to construct reproducible workflows that transformed the original data submitted to the office into archive-quality, "),o("a",{attrs:{href:"https://doi.org/10.1038/sdata.2016.18",target:"_blank",rel:"noopener noreferrer"}},[e._v("FAIR-compliant"),o("OutboundLink")],1),e._v(" versions. FDP lets a user define an order of processing steps to perform on some data, and the project developed new processing steps specific to the needs of these oceanographic datasets. These ordered processing steps are saved into a configuration file that is then available to be used anytime the archived version of the dataset must be reproduced. The primary value of these configuration files is that they capture and make the curation process at BCO-DMO transparent. Subsequently, we found additional value internally by using FDP in three other areas. First, they made the curation process across our data managers much more consistent versus the ad-hoc data processing scripts they individually produced before FDP. Second, we found that data managers saved time because they could reuse pre-existing pipelines to process newer versions submitted for pre-existing datasets. Finally, the configuration files helped us keep track of what processes were used in case a bug or error was ever found in the processing code. This project exceeded our goal of using FDP on at least 80% of data submissions to BCO-DMO to where we now use it almost 100% of the time.")]),e._v(" "),o("p",[e._v("As a major deliverable from BCO-DMO’s "),o("a",{attrs:{href:"https://www.nsf.gov/awardsearch/showAward?AWD_ID=1924618",target:"_blank",rel:"noopener noreferrer"}},[e._v("recent NSF award"),o("OutboundLink")],1),e._v(" the office planned to refactor its entire data infrastructure using techniques that would allow BCO-DMO to respond more rapidly to technological change. Using Frictionless Data as a backbone for data transport is a large piece of that transformation. Continuing to work with OKF, both groups sought to continue our collaboration by focusing on how to improve the data submission process at BCO-DMO.")]),e._v(" "),o("p",[o("img",{attrs:{src:a(509),alt:"Duplication error"}})]),e._v(" "),o("p",[o("em",[e._v("Goodtables noticed a duplicate row in an uploaded tabular data file.")])]),e._v(" "),o("p",[e._v("Part of what makes BCO-DMO a successful data curation office is our hands-on work helping researchers achieve compliance with the "),o("a",{attrs:{href:"https://www.nsf.gov/pubs/2017/nsf17037/nsf17037.jsp",target:"_blank",rel:"noopener noreferrer"}},[e._v("NSF’s Sample and Data Policy coming from their Ocean Sciences division"),o("OutboundLink")],1),e._v(". Yet, a steady and constant queue of data submissions means that it can take some weeks before our data managers can thoroughly review data submissions and provide necessary feedback to submitters. In response, BCO-DMO has been creating a lightweight web application for submitting data while ensuring such a tool preserves the easy experience of submitting data that presently exists. Working with OKF, we wanted to expedite the data review process by providing data submitters with as much immediate feedback as possible by using Frictionless Data’s "),o("a",{attrs:{href:"https://goodtables.io/",target:"_blank",rel:"noopener noreferrer"}},[e._v("GoodTables project"),o("OutboundLink")],1),e._v(".")]),e._v(" "),o("p",[e._v("Through a data submission platform, researchers would be able to upload data to BCO-DMO and, if tabular, get immediate feedback from Goodtables about whether it was correctly formatted or any other quality issues existed. With these reports at their disposal, submitters could update their submissions without having to wait for a BCO-DMO data manager to review. For small and minor changes this saves the submitter the headache of having to wait for simple feedback. The goal is to catch submitters at a time where they are focused on this data submission so that they don’t have to return weeks later and reconstitute their headspace around these data again. We catch them when their head is in the game.")]),e._v(" "),o("p",[e._v("Goodtables provides us a framework to branch out beyond simple tabular validation by developing data profiles. These profiles would let a submitter specify the type of data they are submitting. Is the data a bottle or CTD file? Does it contain latitude, longitude time or depth observations? These questions, optional for submitters to answer, would provide even further validation steps to get improved feedback immediately. For example, specifying that a file contains latitude or longitude columns could detect whether all values fall within valid bounds. Or that a depth column contains values above the surface. Or that the column pertaining to the time of an observation has inconsistent formatting across some of the rows. BCO-DMO can expand on this platform to continue to add new and better quality checks that submitters can use.")]),e._v(" "),o("p",[o("img",{attrs:{src:a(510),alt:"Out-of-bounds longitude"}}),o("br"),e._v(" "),o("em",[e._v("Goodtables noticed a longitude that is outside a range of -180 to 180. This happended because BCO-DMO recommends using decimal degrees format between -180 to 180 and defined a Goodtables check for longitude fields.")])])])}),[],!1,null,null,null);t.default=s.exports}}]); \ No newline at end of file +(window.webpackJsonp=window.webpackJsonp||[]).push([[39],{503:function(e,t,a){e.exports=a.p+"assets/img/duplication_error.070ab48c.png"},504:function(e,t,a){e.exports=a.p+"assets/img/goodtables_lon-out-of-bounds.ef393c01.png"},639:function(e,t,a){"use strict";a.r(t);var o=a(29),s=Object(o.a)({},(function(){var e=this,t=e.$createElement,o=e._self._c||t;return o("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[o("p",[o("em",[e._v("This post was originally published on the "),o("a",{attrs:{href:"https://blog.bco-dmo.org/2020/09/14/goodtables",target:"_blank",rel:"noopener noreferrer"}},[e._v("BCO-DMO blog"),o("OutboundLink")],1),e._v(".")])]),e._v(" "),o("p",[e._v("Earlier this year, the "),o("a",{attrs:{href:"https://www.bco-dmo.org/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Biological and Chemical Oceanography Data Management Office (BCO-DMO)"),o("OutboundLink")],1),e._v(" completed a pilot project with the "),o("a",{attrs:{href:"https://okfn.org/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Open Knowledge Foundation (OKF)"),o("OutboundLink")],1),e._v(" to "),o("a",{attrs:{href:"https://blog.okfn.org/2020/02/10/frictionless-data-pipelines-for-ocean-science/",target:"_blank",rel:"noopener noreferrer"}},[e._v("streamline the data curation processes for oceanographic datasets using Frictionless Data Pipelines (FDP)"),o("OutboundLink")],1),e._v(". The goal of this pilot was to construct reproducible workflows that transformed the original data submitted to the office into archive-quality, "),o("a",{attrs:{href:"https://doi.org/10.1038/sdata.2016.18",target:"_blank",rel:"noopener noreferrer"}},[e._v("FAIR-compliant"),o("OutboundLink")],1),e._v(" versions. FDP lets a user define an order of processing steps to perform on some data, and the project developed new processing steps specific to the needs of these oceanographic datasets. These ordered processing steps are saved into a configuration file that is then available to be used anytime the archived version of the dataset must be reproduced. The primary value of these configuration files is that they capture and make the curation process at BCO-DMO transparent. Subsequently, we found additional value internally by using FDP in three other areas. First, they made the curation process across our data managers much more consistent versus the ad-hoc data processing scripts they individually produced before FDP. Second, we found that data managers saved time because they could reuse pre-existing pipelines to process newer versions submitted for pre-existing datasets. Finally, the configuration files helped us keep track of what processes were used in case a bug or error was ever found in the processing code. This project exceeded our goal of using FDP on at least 80% of data submissions to BCO-DMO to where we now use it almost 100% of the time.")]),e._v(" "),o("p",[e._v("As a major deliverable from BCO-DMO’s "),o("a",{attrs:{href:"https://www.nsf.gov/awardsearch/showAward?AWD_ID=1924618",target:"_blank",rel:"noopener noreferrer"}},[e._v("recent NSF award"),o("OutboundLink")],1),e._v(" the office planned to refactor its entire data infrastructure using techniques that would allow BCO-DMO to respond more rapidly to technological change. Using Frictionless Data as a backbone for data transport is a large piece of that transformation. Continuing to work with OKF, both groups sought to continue our collaboration by focusing on how to improve the data submission process at BCO-DMO.")]),e._v(" "),o("p",[o("img",{attrs:{src:a(503),alt:"Duplication error"}})]),e._v(" "),o("p",[o("em",[e._v("Goodtables noticed a duplicate row in an uploaded tabular data file.")])]),e._v(" "),o("p",[e._v("Part of what makes BCO-DMO a successful data curation office is our hands-on work helping researchers achieve compliance with the "),o("a",{attrs:{href:"https://www.nsf.gov/pubs/2017/nsf17037/nsf17037.jsp",target:"_blank",rel:"noopener noreferrer"}},[e._v("NSF’s Sample and Data Policy coming from their Ocean Sciences division"),o("OutboundLink")],1),e._v(". Yet, a steady and constant queue of data submissions means that it can take some weeks before our data managers can thoroughly review data submissions and provide necessary feedback to submitters. 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BCO-DMO can expand on this platform to continue to add new and better quality checks that submitters can use.")]),e._v(" "),o("p",[o("img",{attrs:{src:a(504),alt:"Out-of-bounds longitude"}}),o("br"),e._v(" "),o("em",[e._v("Goodtables noticed a longitude that is outside a range of -180 to 180. 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It allows you to create notebooks that hold and deliver your data, as well as text, images and inline scripts for doing analysis and visualization. The people you share it with can read, execute and even edit a copy of your notebook and publish the remixed version as a fork.")]),t._v(" "),o("p",[t._v("One of the main challenges we face with data is that it’s hard to share it with others. Tools like Jupyter (iPython notebook)"),o("sup",{staticClass:"footnote-ref"},[o("a",{attrs:{href:"#fn1",id:"fnref1"}},[t._v("[1]")])]),t._v(" make it much easier and more affordable to do analysis (with the help of open source projects like numpy"),o("sup",{staticClass:"footnote-ref"},[o("a",{attrs:{href:"#fn2",id:"fnref2"}},[t._v("[2]")])]),t._v(" and pandas"),o("sup",{staticClass:"footnote-ref"},[o("a",{attrs:{href:"#fn3",id:"fnref3"}},[t._v("[3]")])]),t._v("). What they don’t do is allow you to "),o("em",[t._v("cheaply and easily share that with the world")]),t._v(". "),o("strong",[t._v("If it were as easy to share data and analysis as it is to share pictures of your breakfast, the world would be a more enlightened place.")]),t._v(" Dataship is helping to build that world.")]),t._v(" "),o("p",[t._v("Every notebook on Dataship is also a Data Package"),o("sup",{staticClass:"footnote-ref"},[o("a",{attrs:{href:"#fn4",id:"fnref4"}},[t._v("[4]")])]),t._v(". Like other Data Packages it can be downloaded, along with its data, just by giving its URL to software like data-cli"),o("sup",{staticClass:"footnote-ref"},[o("a",{attrs:{href:"#fn5",id:"fnref5"}},[t._v("[5]")])]),t._v(". Additionally, working with existing Data Packages is easy. Just as you can fork other notebooks, you can also fork existing Data Packages, even when they’re located somewhere else, like GitHub.")]),t._v(" "),o("p",[o("img",{attrs:{src:a(400),alt:"Dataship GIF"}}),t._v(" "),o("br"),t._v(" "),o("em",[t._v("Dataship in action")])]),t._v(" "),o("p",[t._v("Every cell in a notebook is represented by a resource entry"),o("sup",{staticClass:"footnote-ref"},[o("a",{attrs:{href:"#fn6",id:"fnref6"}},[t._v("[6]")])]),t._v(" in an underlying Data Package. This also allows for interesting possibilities. One of these is executable Data Packages. Since the code is included inline and its dependencies are explicit and bounded, very simple software could be written to execute a Data Package-based notebook from the command line, printing the results to the console and writing images to the current directory.")]),t._v(" "),o("p",[t._v("It would be useful to have a JavaScript version of some of the functionality in goodtables"),o("sup",{staticClass:"footnote-ref"},[o("a",{attrs:{href:"#fn7",id:"fnref7"}},[t._v("[7]")])]),t._v(" available for use, specifically header detection in parsed csv contents (output of PapaParse), as well as an option in dpm to not put things in a ‘datapackages’ folder, as I rarely need this when downloading a dataset.")]),t._v(" "),o("p",[t._v("dpm, mentioned above, is now deprecated. Check out DataHub’s "),o("a",{attrs:{href:"https://github.com/datahq/data-cli",target:"_blank",rel:"noopener noreferrer"}},[t._v("data-cli"),o("OutboundLink")],1)]),t._v(" "),o("p",[t._v("My next task will be building and integrating the machine learning and neural network components into Dataship. After that I’ll be focusing on features that allow organizations to store private encrypted data, in addition to the default public storage. The focus of the platform will always be open data, but hosting closed data sources will allow us to nudge people towards sharing, when it makes sense.")]),t._v(" "),o("p",[t._v("As for additional use cases, the volume of personal data is growing exponentially- from medical data to internet activity and media consumption. These are just a few existing examples. The rise of the Internet of Things will only accelerate this. People are also beginning to see the value in controlling their data themselves. Providing mechanisms for doing this will likely become important over the next ten years.")]),t._v(" "),o("hr",{staticClass:"footnotes-sep"}),t._v(" "),o("section",{staticClass:"footnotes"},[o("ol",{staticClass:"footnotes-list"},[o("li",{staticClass:"footnote-item",attrs:{id:"fn1"}},[o("p",[t._v("Jupyter Notebook: "),o("a",{attrs:{href:"http://jupyter.org/",target:"_blank",rel:"noopener noreferrer"}},[t._v("http://jupyter.org/"),o("OutboundLink")],1),t._v(" "),o("a",{staticClass:"footnote-backref",attrs:{href:"#fnref1"}},[t._v("↩︎")])])]),t._v(" "),o("li",{staticClass:"footnote-item",attrs:{id:"fn2"}},[o("p",[t._v("NumPy: Python package for scientific computing: "),o("a",{attrs:{href:"http://www.numpy.org",target:"_blank",rel:"noopener noreferrer"}},[t._v("http://www.numpy.org"),o("OutboundLink")],1),t._v(" "),o("a",{staticClass:"footnote-backref",attrs:{href:"#fnref2"}},[t._v("↩︎")])])]),t._v(" "),o("li",{staticClass:"footnote-item",attrs:{id:"fn3"}},[o("p",[t._v("Pandas: Python package for data analysis: "),o("a",{attrs:{href:"http://pandas.pydata.org/",target:"_blank",rel:"noopener noreferrer"}},[t._v("http://pandas.pydata.org/"),o("OutboundLink")],1),t._v(" "),o("a",{staticClass:"footnote-backref",attrs:{href:"#fnref3"}},[t._v("↩︎")])])]),t._v(" "),o("li",{staticClass:"footnote-item",attrs:{id:"fn4"}},[o("p",[t._v("Data Packages: "),t._v(" "),o("a",{staticClass:"footnote-backref",attrs:{href:"#fnref4"}},[t._v("↩︎")])])]),t._v(" "),o("li",{staticClass:"footnote-item",attrs:{id:"fn5"}},[o("p",[t._v("DataHub’s data commandline tool: "),o("a",{attrs:{href:"https://github.com/datahq/data-cli",target:"_blank",rel:"noopener noreferrer"}},[t._v("https://github.com/datahq/data-cli"),o("OutboundLink")],1),t._v(" "),o("a",{staticClass:"footnote-backref",attrs:{href:"#fnref5"}},[t._v("↩︎")])])]),t._v(" "),o("li",{staticClass:"footnote-item",attrs:{id:"fn6"}},[o("p",[t._v("Data Package Resource: "),o("a",{attrs:{href:"https://specs.frictionlessdata.io/data-package/#resource-information",target:"_blank",rel:"noopener noreferrer"}},[t._v("https://specs.frictionlessdata.io/data-package/#resource-information"),o("OutboundLink")],1),t._v(" "),o("a",{staticClass:"footnote-backref",attrs:{href:"#fnref6"}},[t._v("↩︎")])])]),t._v(" "),o("li",{staticClass:"footnote-item",attrs:{id:"fn7"}},[o("p",[t._v("goodtables: "),o("a",{attrs:{href:"http://try.goodtables.io",target:"_blank",rel:"noopener noreferrer"}},[t._v("http://try.goodtables.io"),o("OutboundLink")],1),t._v(" "),o("a",{staticClass:"footnote-backref",attrs:{href:"#fnref7"}},[t._v("↩︎")])])])])])])}),[],!1,null,null,null);e.default=s.exports}}]); \ No newline at end of file diff --git a/assets/js/45.70edfea5.js b/assets/js/45.18c8dc64.js similarity index 98% rename from assets/js/45.70edfea5.js rename to assets/js/45.18c8dc64.js index b30c1e72b..7e737c05d 100644 --- a/assets/js/45.70edfea5.js +++ b/assets/js/45.18c8dc64.js @@ -1 +1 @@ -(window.webpackJsonp=window.webpackJsonp||[]).push([[45],{414:function(e,t,a){e.exports=a.p+"assets/img/cmso-1.25f92f63.png"},567:function(e,t,a){"use strict";a.r(t);var r=a(29),o=Object(r.a)({},(function(){var e=this,t=e.$createElement,r=e._self._c||t;return r("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[r("p",[e._v("Researchers worldwide try to understand how cells move, a process extremely important for many physiological and pathological conditions. "),r("a",{attrs:{href:"https://en.wikipedia.org/wiki/Cell_migration",target:"_blank",rel:"noopener noreferrer"}},[e._v("Cell migration"),r("OutboundLink")],1),e._v(" is in fact involved in many processes, like wound healing,neuronal development and cancer invasion. The "),r("a",{attrs:{href:"https://cmso.science/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Cell Migration Standardization Organization"),r("OutboundLink")],1),e._v(" (CMSO) is a community building standards for cell migration data, in order to enable data sharing in the field. The organization has three main working groups:")]),e._v(" "),r("ul",[r("li",[e._v("Minimal reporting requirement (developing "),r("a",{attrs:{href:"https://github.com/CellMigStandOrg/MIACME",target:"_blank",rel:"noopener noreferrer"}},[e._v("MIACME"),r("OutboundLink")],1),e._v(", i.e. the Minimum Information About a Cell Migration Experiment)")]),e._v(" "),r("li",[e._v("Controlled Vocabularies")]),e._v(" "),r("li",[e._v("Data Formats and APIs")])]),e._v(" "),r("p",[e._v("In our last working group, we discussed where the Data Package specifications"),r("sup",{staticClass:"footnote-ref"},[r("a",{attrs:{href:"#fn1",id:"fnref1"}},[e._v("[1]")])]),e._v(" could be used or expanded for the definition of a standard format and the corresponding libraries to interact with these standards. In particular, we have started to address the standardization of cell tracking data. This is data produced using tracking software that reconstructs cell movement in time based on images from a microscope.")]),e._v(" "),r("p",[r("img",{attrs:{src:a(414),alt:"Diagram"}}),e._v(" "),r("br"),e._v(" "),r("em",[e._v("In pink, the "),r("a",{attrs:{href:"http://isa-tools.org/",target:"_blank",rel:"noopener noreferrer"}},[e._v("ISA"),r("OutboundLink")],1),e._v(" (Investigation Study Assay) model to annotate the experimental metadata; in blue, the "),r("a",{attrs:{href:"http://www.openmicroscopy.org/",target:"_blank",rel:"noopener noreferrer"}},[e._v("OME"),r("OutboundLink")],1),e._v(" (Open Microscopy Environment) model for the imaging data; in green, our biotracks format based on the Data Package specification for the analytics data (cell tracking, positions, features etc.);in purple, CV: Controlled Vocabulary; and in turquoise, "),r("a",{attrs:{href:"https://github.com/CellMigStandOrg/MIACME",target:"_blank",rel:"noopener noreferrer"}},[e._v("MIACME"),r("OutboundLink")],1),e._v(": Minimum Information About a Cell Migration Experiment. "),r("a",{attrs:{href:"https://creativecommons.org/licenses/by-sa/4.0/",target:"_blank",rel:"noopener noreferrer"}},[e._v("CC BY-SA 4.0"),r("OutboundLink")],1),e._v(" Credit: Paola Masuzzo (text) and CMSO (diagram).")])]),e._v(" "),r("p",[e._v("CMSO deals specifically with cell migration data (a subject of cell biology). Our main challenge lies in the heterogeneity of the data. This diversity has its origin in two factors:")]),e._v(" "),r("ul",[r("li",[r("strong",[e._v("Experimentally")]),e._v(": Cell migration data can be produced using many diverse techniques (imaging, non-imaging, dynamic, static, high-throughput/screening, etc.)")]),e._v(" "),r("li",[r("strong",[e._v("Analytically")]),e._v(": These data are produced using many diverse software packages, each of these writing data to specific (sometimes proprietary) file formats.")])]),e._v(" "),r("p",[e._v("This diversity hampers (or at least makes very difficult) procedures like meta-analysis, data integration, data mining, and last but not least, data "),r("em",[e._v("reproducibility")]),e._v(".")]),e._v(" "),r("p",[e._v("CMSO has developed and is about to release the first specification of a "),r("a",{attrs:{href:"https://cellmigstandorg.github.io/Tracks/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Cell Tracking format"),r("OutboundLink")],1),e._v(". This specification is built on a tabular representation, i.e. data are stored in tables. Current v0.1 of this specification can be seen at "),r("a",{attrs:{href:"https://cellmigstandorg.github.io/Tracks/v0.1/",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),r("OutboundLink")],1),e._v(".")]),e._v(" "),r("p",[e._v("CMSO is using the "),r("em",[e._v("Tabular")]),e._v(" Data Package"),r("sup",{staticClass:"footnote-ref"},[r("a",{attrs:{href:"#fn2",id:"fnref2"}},[e._v("[2]")])]),e._v(" specification to represent cell migration-derived tracking data, as illustrated"),r("br"),e._v(" "),r("a",{attrs:{href:"https://github.com/CellMigStandOrg/biotracks/",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),r("OutboundLink")],1),e._v(". The specification is used for two goals:")]),e._v(" "),r("ol",[r("li",[r("strong",[e._v("Create a Data Package representation")]),e._v(" where the data—in our case objects (e.g. cells detected in microscopy images), links and optionally tracks—are stored in CSV files, while metadata and schema"),r("sup",{staticClass:"footnote-ref"},[r("a",{attrs:{href:"#fn3",id:"fnref3"}},[e._v("[3]")])]),e._v(" information are stored in a JSON file.")]),e._v(" "),r("li",[r("strong",[e._v("Write")]),e._v(" this Data Package to a pandas"),r("sup",{staticClass:"footnote-ref"},[r("a",{attrs:{href:"#fn4",id:"fnref4"}},[e._v("[4]")])]),e._v(" dataframe, to aid quick inspection and visualization.")])]),e._v(" "),r("p",[e._v("You can see some examples "),r("a",{attrs:{href:"https://github.com/CellMigStandOrg/biotracks/tree/master/examples",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),r("OutboundLink")],1),e._v(".")]),e._v(" "),r("p",[e._v("I am an Open Science fan and advocate, so I try to keep up to date with the initiatives of the"),r("br"),e._v(" "),r("a",{attrs:{href:"https://okfn.org",target:"_blank",rel:"noopener noreferrer"}},[e._v("Open Knowledge International"),r("OutboundLink")],1),e._v(" teams. I think I first became aware of Frictionless Data when I saw a tweet and I checked the specs out. Also, CMSO really wanted to keep a possible specification and file format light and simple. So different people of the team must have googled for ‘CSV and JSON formats’ or something like that, and Frictionless Data popped out 😃.")]),e._v(" "),r("p",[e._v("I have opened a couple of issues on the "),r("a",{attrs:{href:"https://github.com/frictionlessdata/specs",target:"_blank",rel:"noopener noreferrer"}},[e._v("GitHub page of the spec"),r("OutboundLink")],1),e._v(", detailing what I would like to see developed in the Frictionless Data project. The CMSO is not sure yet if the Data Package representation will be the one we’ll go for in the very end, because we would first like to know how sustainable/sustained this spec will be in the future.")]),e._v(" "),r("p",[e._v("CMSO is looking into expanding the "),r("a",{attrs:{href:"https://github.com/CellMigStandOrg/biotracks/tree/master/examples",target:"_blank",rel:"noopener noreferrer"}},[e._v("list of examples"),r("OutboundLink")],1),e._v(" we have so far in terms of tracking software. Personally, I would like to choose a reference data set (a live-cell, time-lapse microscopy data set), and run different cell tracking algorithms/software packages on it. Then I want to put the results into a common, light and easy-to-interpret CSV+JSON format (the biotracks format), and show people how data containerization"),r("sup",{staticClass:"footnote-ref"},[r("a",{attrs:{href:"#fn5",id:"fnref5"}},[e._v("[5]")])]),e._v(" can be the way to go to enable research data exchange and knowledge discovery at large.")]),e._v(" "),r("p",[e._v("With most other specifications, cell tracking data are stored in tabular format, but metadata are never kept together with the data, which makes data interpretation and sharing very difficult. The Frictionless Data specifications take good care of this aspect. Some other formats are based on XML"),r("sup",{staticClass:"footnote-ref"},[r("a",{attrs:{href:"#fn6",id:"fnref6"}},[e._v("[6]")])]),e._v(" annotation, which certainly does the job, but are perhaps heavier (even though perhaps more sustainable in the long term). I hate Excel formats, and unfortunately I need to parse those too. I love the integration with Python"),r("sup",{staticClass:"footnote-ref"},[r("a",{attrs:{href:"#fn7",id:"fnref7"}},[e._v("[7]")])]),e._v(" and the pandas"),r("sup",{staticClass:"footnote-ref"},[r("a",{attrs:{href:"#fn4",id:"fnref4:1"}},[e._v("[4:1]")])]),e._v(" system, this is a big plus when doing data science.")]),e._v(" "),r("p",[e._v("As a researcher, I mostly deal with research data. I am pretty sure if this could work for cell migration data, it could work for many cell biology disciplines as well. I recommend speaking to more researchers and data producers to determine additional use cases!")]),e._v(" "),r("hr",{staticClass:"footnotes-sep"}),e._v(" "),r("section",{staticClass:"footnotes"},[r("ol",{staticClass:"footnotes-list"},[r("li",{staticClass:"footnote-item",attrs:{id:"fn1"}},[r("p",[e._v("Data Package: "),r("a",{attrs:{href:"https://specs.frictionlessdata.io/data-package",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://specs.frictionlessdata.io/data-package"),r("OutboundLink")],1),e._v(" "),r("a",{staticClass:"footnote-backref",attrs:{href:"#fnref1"}},[e._v("↩︎")])])]),e._v(" "),r("li",{staticClass:"footnote-item",attrs:{id:"fn2"}},[r("p",[e._v("Tabular Data Package: "),r("a",{attrs:{href:"https://specs.frictionlessdata.io/tabular-data-package",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://specs.frictionlessdata.io/tabular-data-package"),r("OutboundLink")],1),e._v(" "),r("a",{staticClass:"footnote-backref",attrs:{href:"#fnref2"}},[e._v("↩︎")])])]),e._v(" "),r("li",{staticClass:"footnote-item",attrs:{id:"fn3"}},[r("p",[e._v("Table Schema: "),r("a",{attrs:{href:"https://specs.frictionlessdata.io/table-schema",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://specs.frictionlessdata.io/table-schema"),r("OutboundLink")],1),e._v(" "),r("a",{staticClass:"footnote-backref",attrs:{href:"#fnref3"}},[e._v("↩︎")])])]),e._v(" "),r("li",{staticClass:"footnote-item",attrs:{id:"fn4"}},[r("p",[e._v("Pandas: Python package for data analysis: "),r("a",{attrs:{href:"http://pandas.pydata.org/",target:"_blank",rel:"noopener noreferrer"}},[e._v("http://pandas.pydata.org/"),r("OutboundLink")],1),e._v(" "),r("a",{staticClass:"footnote-backref",attrs:{href:"#fnref4"}},[e._v("↩︎")]),e._v(" "),r("a",{staticClass:"footnote-backref",attrs:{href:"#fnref4:1"}},[e._v("↩︎")])])]),e._v(" "),r("li",{staticClass:"footnote-item",attrs:{id:"fn5"}},[r("p",[e._v("Design Philosophy: "),r("a",{attrs:{href:"https://specs.frictionlessdata.io/",target:"_blank",rel:"noopener noreferrer"}},[e._v("specs"),r("OutboundLink")],1),e._v(" "),r("a",{staticClass:"footnote-backref",attrs:{href:"#fnref5"}},[e._v("↩︎")])])]),e._v(" "),r("li",{staticClass:"footnote-item",attrs:{id:"fn6"}},[r("p",[e._v("Extensible Markup Language: "),r("a",{attrs:{href:"https://en.wikipedia.org/wiki/XML",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://en.wikipedia.org/wiki/XML"),r("OutboundLink")],1),e._v(" "),r("a",{staticClass:"footnote-backref",attrs:{href:"#fnref6"}},[e._v("↩︎")])])]),e._v(" "),r("li",{staticClass:"footnote-item",attrs:{id:"fn7"}},[r("p",[e._v("Data Package-aware libraries in Python: "),r("a",{attrs:{href:"https://github.com/frictionlessdata/datapackage-py",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://github.com/frictionlessdata/datapackage-py"),r("OutboundLink")],1),e._v(", "),r("a",{attrs:{href:"https://github.com/frictionlessdata/tableschema-py",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://github.com/frictionlessdata/tableschema-py"),r("OutboundLink")],1),e._v(", "),r("a",{attrs:{href:"https://github.com/frictionlessdata/goodtables-py",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://github.com/frictionlessdata/goodtables-py"),r("OutboundLink")],1),e._v(" "),r("a",{staticClass:"footnote-backref",attrs:{href:"#fnref7"}},[e._v("↩︎")])])])])])])}),[],!1,null,null,null);t.default=o.exports}}]); \ No newline at end of file +(window.webpackJsonp=window.webpackJsonp||[]).push([[45],{416:function(e,t,a){e.exports=a.p+"assets/img/cmso-1.25f92f63.png"},568:function(e,t,a){"use strict";a.r(t);var r=a(29),o=Object(r.a)({},(function(){var e=this,t=e.$createElement,r=e._self._c||t;return r("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[r("p",[e._v("Researchers worldwide try to understand how cells move, a process extremely important for many physiological and pathological conditions. "),r("a",{attrs:{href:"https://en.wikipedia.org/wiki/Cell_migration",target:"_blank",rel:"noopener noreferrer"}},[e._v("Cell migration"),r("OutboundLink")],1),e._v(" is in fact involved in many processes, like wound healing,neuronal development and cancer invasion. The "),r("a",{attrs:{href:"https://cmso.science/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Cell Migration Standardization Organization"),r("OutboundLink")],1),e._v(" (CMSO) is a community building standards for cell migration data, in order to enable data sharing in the field. The organization has three main working groups:")]),e._v(" "),r("ul",[r("li",[e._v("Minimal reporting requirement (developing "),r("a",{attrs:{href:"https://github.com/CellMigStandOrg/MIACME",target:"_blank",rel:"noopener noreferrer"}},[e._v("MIACME"),r("OutboundLink")],1),e._v(", i.e. the Minimum Information About a Cell Migration Experiment)")]),e._v(" "),r("li",[e._v("Controlled Vocabularies")]),e._v(" "),r("li",[e._v("Data Formats and APIs")])]),e._v(" "),r("p",[e._v("In our last working group, we discussed where the Data Package specifications"),r("sup",{staticClass:"footnote-ref"},[r("a",{attrs:{href:"#fn1",id:"fnref1"}},[e._v("[1]")])]),e._v(" could be used or expanded for the definition of a standard format and the corresponding libraries to interact with these standards. In particular, we have started to address the standardization of cell tracking data. This is data produced using tracking software that reconstructs cell movement in time based on images from a microscope.")]),e._v(" "),r("p",[r("img",{attrs:{src:a(416),alt:"Diagram"}}),e._v(" "),r("br"),e._v(" "),r("em",[e._v("In pink, the "),r("a",{attrs:{href:"http://isa-tools.org/",target:"_blank",rel:"noopener noreferrer"}},[e._v("ISA"),r("OutboundLink")],1),e._v(" (Investigation Study Assay) model to annotate the experimental metadata; in blue, the "),r("a",{attrs:{href:"http://www.openmicroscopy.org/",target:"_blank",rel:"noopener noreferrer"}},[e._v("OME"),r("OutboundLink")],1),e._v(" (Open Microscopy Environment) model for the imaging data; in green, our biotracks format based on the Data Package specification for the analytics data (cell tracking, positions, features etc.);in purple, CV: Controlled Vocabulary; and in turquoise, "),r("a",{attrs:{href:"https://github.com/CellMigStandOrg/MIACME",target:"_blank",rel:"noopener noreferrer"}},[e._v("MIACME"),r("OutboundLink")],1),e._v(": Minimum Information About a Cell Migration Experiment. "),r("a",{attrs:{href:"https://creativecommons.org/licenses/by-sa/4.0/",target:"_blank",rel:"noopener noreferrer"}},[e._v("CC BY-SA 4.0"),r("OutboundLink")],1),e._v(" Credit: Paola Masuzzo (text) and CMSO (diagram).")])]),e._v(" "),r("p",[e._v("CMSO deals specifically with cell migration data (a subject of cell biology). Our main challenge lies in the heterogeneity of the data. This diversity has its origin in two factors:")]),e._v(" "),r("ul",[r("li",[r("strong",[e._v("Experimentally")]),e._v(": Cell migration data can be produced using many diverse techniques (imaging, non-imaging, dynamic, static, high-throughput/screening, etc.)")]),e._v(" "),r("li",[r("strong",[e._v("Analytically")]),e._v(": These data are produced using many diverse software packages, each of these writing data to specific (sometimes proprietary) file formats.")])]),e._v(" "),r("p",[e._v("This diversity hampers (or at least makes very difficult) procedures like meta-analysis, data integration, data mining, and last but not least, data "),r("em",[e._v("reproducibility")]),e._v(".")]),e._v(" "),r("p",[e._v("CMSO has developed and is about to release the first specification of a "),r("a",{attrs:{href:"https://cellmigstandorg.github.io/Tracks/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Cell Tracking format"),r("OutboundLink")],1),e._v(". This specification is built on a tabular representation, i.e. data are stored in tables. Current v0.1 of this specification can be seen at "),r("a",{attrs:{href:"https://cellmigstandorg.github.io/Tracks/v0.1/",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),r("OutboundLink")],1),e._v(".")]),e._v(" "),r("p",[e._v("CMSO is using the "),r("em",[e._v("Tabular")]),e._v(" Data Package"),r("sup",{staticClass:"footnote-ref"},[r("a",{attrs:{href:"#fn2",id:"fnref2"}},[e._v("[2]")])]),e._v(" specification to represent cell migration-derived tracking data, as illustrated"),r("br"),e._v(" "),r("a",{attrs:{href:"https://github.com/CellMigStandOrg/biotracks/",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),r("OutboundLink")],1),e._v(". The specification is used for two goals:")]),e._v(" "),r("ol",[r("li",[r("strong",[e._v("Create a Data Package representation")]),e._v(" where the data—in our case objects (e.g. cells detected in microscopy images), links and optionally tracks—are stored in CSV files, while metadata and schema"),r("sup",{staticClass:"footnote-ref"},[r("a",{attrs:{href:"#fn3",id:"fnref3"}},[e._v("[3]")])]),e._v(" information are stored in a JSON file.")]),e._v(" "),r("li",[r("strong",[e._v("Write")]),e._v(" this Data Package to a pandas"),r("sup",{staticClass:"footnote-ref"},[r("a",{attrs:{href:"#fn4",id:"fnref4"}},[e._v("[4]")])]),e._v(" dataframe, to aid quick inspection and visualization.")])]),e._v(" "),r("p",[e._v("You can see some examples "),r("a",{attrs:{href:"https://github.com/CellMigStandOrg/biotracks/tree/master/examples",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),r("OutboundLink")],1),e._v(".")]),e._v(" "),r("p",[e._v("I am an Open Science fan and advocate, so I try to keep up to date with the initiatives of the"),r("br"),e._v(" "),r("a",{attrs:{href:"https://okfn.org",target:"_blank",rel:"noopener noreferrer"}},[e._v("Open Knowledge International"),r("OutboundLink")],1),e._v(" teams. I think I first became aware of Frictionless Data when I saw a tweet and I checked the specs out. Also, CMSO really wanted to keep a possible specification and file format light and simple. So different people of the team must have googled for ‘CSV and JSON formats’ or something like that, and Frictionless Data popped out 😃.")]),e._v(" "),r("p",[e._v("I have opened a couple of issues on the "),r("a",{attrs:{href:"https://github.com/frictionlessdata/specs",target:"_blank",rel:"noopener noreferrer"}},[e._v("GitHub page of the spec"),r("OutboundLink")],1),e._v(", detailing what I would like to see developed in the Frictionless Data project. The CMSO is not sure yet if the Data Package representation will be the one we’ll go for in the very end, because we would first like to know how sustainable/sustained this spec will be in the future.")]),e._v(" "),r("p",[e._v("CMSO is looking into expanding the "),r("a",{attrs:{href:"https://github.com/CellMigStandOrg/biotracks/tree/master/examples",target:"_blank",rel:"noopener noreferrer"}},[e._v("list of examples"),r("OutboundLink")],1),e._v(" we have so far in terms of tracking software. Personally, I would like to choose a reference data set (a live-cell, time-lapse microscopy data set), and run different cell tracking algorithms/software packages on it. Then I want to put the results into a common, light and easy-to-interpret CSV+JSON format (the biotracks format), and show people how data containerization"),r("sup",{staticClass:"footnote-ref"},[r("a",{attrs:{href:"#fn5",id:"fnref5"}},[e._v("[5]")])]),e._v(" can be the way to go to enable research data exchange and knowledge discovery at large.")]),e._v(" "),r("p",[e._v("With most other specifications, cell tracking data are stored in tabular format, but metadata are never kept together with the data, which makes data interpretation and sharing very difficult. The Frictionless Data specifications take good care of this aspect. Some other formats are based on XML"),r("sup",{staticClass:"footnote-ref"},[r("a",{attrs:{href:"#fn6",id:"fnref6"}},[e._v("[6]")])]),e._v(" annotation, which certainly does the job, but are perhaps heavier (even though perhaps more sustainable in the long term). I hate Excel formats, and unfortunately I need to parse those too. I love the integration with Python"),r("sup",{staticClass:"footnote-ref"},[r("a",{attrs:{href:"#fn7",id:"fnref7"}},[e._v("[7]")])]),e._v(" and the pandas"),r("sup",{staticClass:"footnote-ref"},[r("a",{attrs:{href:"#fn4",id:"fnref4:1"}},[e._v("[4:1]")])]),e._v(" system, this is a big plus when doing data science.")]),e._v(" "),r("p",[e._v("As a researcher, I mostly deal with research data. I am pretty sure if this could work for cell migration data, it could work for many cell biology disciplines as well. I recommend speaking to more researchers and data producers to determine additional use cases!")]),e._v(" "),r("hr",{staticClass:"footnotes-sep"}),e._v(" "),r("section",{staticClass:"footnotes"},[r("ol",{staticClass:"footnotes-list"},[r("li",{staticClass:"footnote-item",attrs:{id:"fn1"}},[r("p",[e._v("Data Package: "),r("a",{attrs:{href:"https://specs.frictionlessdata.io/data-package",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://specs.frictionlessdata.io/data-package"),r("OutboundLink")],1),e._v(" "),r("a",{staticClass:"footnote-backref",attrs:{href:"#fnref1"}},[e._v("↩︎")])])]),e._v(" "),r("li",{staticClass:"footnote-item",attrs:{id:"fn2"}},[r("p",[e._v("Tabular Data Package: "),r("a",{attrs:{href:"https://specs.frictionlessdata.io/tabular-data-package",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://specs.frictionlessdata.io/tabular-data-package"),r("OutboundLink")],1),e._v(" "),r("a",{staticClass:"footnote-backref",attrs:{href:"#fnref2"}},[e._v("↩︎")])])]),e._v(" "),r("li",{staticClass:"footnote-item",attrs:{id:"fn3"}},[r("p",[e._v("Table Schema: "),r("a",{attrs:{href:"https://specs.frictionlessdata.io/table-schema",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://specs.frictionlessdata.io/table-schema"),r("OutboundLink")],1),e._v(" "),r("a",{staticClass:"footnote-backref",attrs:{href:"#fnref3"}},[e._v("↩︎")])])]),e._v(" "),r("li",{staticClass:"footnote-item",attrs:{id:"fn4"}},[r("p",[e._v("Pandas: Python package for data analysis: "),r("a",{attrs:{href:"http://pandas.pydata.org/",target:"_blank",rel:"noopener noreferrer"}},[e._v("http://pandas.pydata.org/"),r("OutboundLink")],1),e._v(" "),r("a",{staticClass:"footnote-backref",attrs:{href:"#fnref4"}},[e._v("↩︎")]),e._v(" "),r("a",{staticClass:"footnote-backref",attrs:{href:"#fnref4:1"}},[e._v("↩︎")])])]),e._v(" "),r("li",{staticClass:"footnote-item",attrs:{id:"fn5"}},[r("p",[e._v("Design Philosophy: "),r("a",{attrs:{href:"https://specs.frictionlessdata.io/",target:"_blank",rel:"noopener noreferrer"}},[e._v("specs"),r("OutboundLink")],1),e._v(" "),r("a",{staticClass:"footnote-backref",attrs:{href:"#fnref5"}},[e._v("↩︎")])])]),e._v(" "),r("li",{staticClass:"footnote-item",attrs:{id:"fn6"}},[r("p",[e._v("Extensible Markup Language: "),r("a",{attrs:{href:"https://en.wikipedia.org/wiki/XML",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://en.wikipedia.org/wiki/XML"),r("OutboundLink")],1),e._v(" "),r("a",{staticClass:"footnote-backref",attrs:{href:"#fnref6"}},[e._v("↩︎")])])]),e._v(" "),r("li",{staticClass:"footnote-item",attrs:{id:"fn7"}},[r("p",[e._v("Data Package-aware libraries in Python: "),r("a",{attrs:{href:"https://github.com/frictionlessdata/datapackage-py",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://github.com/frictionlessdata/datapackage-py"),r("OutboundLink")],1),e._v(", "),r("a",{attrs:{href:"https://github.com/frictionlessdata/tableschema-py",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://github.com/frictionlessdata/tableschema-py"),r("OutboundLink")],1),e._v(", "),r("a",{attrs:{href:"https://github.com/frictionlessdata/goodtables-py",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://github.com/frictionlessdata/goodtables-py"),r("OutboundLink")],1),e._v(" "),r("a",{staticClass:"footnote-backref",attrs:{href:"#fnref7"}},[e._v("↩︎")])])])])])])}),[],!1,null,null,null);t.default=o.exports}}]); \ No newline at end of file diff --git a/assets/js/47.852889fe.js b/assets/js/47.7ce0d603.js similarity index 99% rename from assets/js/47.852889fe.js rename to assets/js/47.7ce0d603.js index c4d37875f..5d341426e 100644 --- a/assets/js/47.852889fe.js +++ b/assets/js/47.7ce0d603.js @@ -1 +1 @@ -(window.webpackJsonp=window.webpackJsonp||[]).push([[47],{418:function(e,t,a){e.exports=a.p+"assets/img/adbio.ea0206c3.png"},571:function(e,t,a){"use strict";a.r(t);var o=a(29),i=Object(o.a)({},(function(){var e=this,t=e.$createElement,o=e._self._c||t;return o("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[o("h2",{attrs:{id:"context"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#context"}},[e._v("#")]),e._v(" Context")]),e._v(" "),o("h3",{attrs:{id:"problem-we-were-trying-to-solve"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#problem-we-were-trying-to-solve"}},[e._v("#")]),e._v(" Problem We Were Trying To Solve")]),e._v(" "),o("p",[e._v("Sam Payne and his team at the Pacific Northwest National Laboratory (PNNL) have designed an application called "),o("a",{attrs:{href:"https://adbio.pnnl.gov/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Active Data Biology"),o("OutboundLink")],1),e._v(" (ADBio) which is an interactive web-based suite of tools for analyzing high-throughput omics (a set of related fields of study in biology). The goal is to visualize and analyze datasets while still enabling seamless collaboration between computational and non-computational domain experts. The tool provides several views on the same data facilitating different avenues of investigation.")]),e._v(" "),o("p",[e._v("One of the high level goals of ADBio was to make collaborative data analysis work in a similar manner to collaborative software development (versioned, asynchronous, flexible, sharable, global). You can read more of the motivation in the Open Knowledge International blog post "),o("a",{attrs:{href:"https://blog.okfn.org/2016/11/29/git-for-data-analysis-why-version-control-is-essential-collaboration-public-trust/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Git for Data Analysis – why version control is essential for collaboration and for gaining public trust"),o("OutboundLink")],1),e._v(" written by Sam Payne as part of the pilot. To facilitate this goal, Sam and his team used version-controlled repositories as the storage mechanism for all required resources. Data, software (for conducting analyses), and insights (gained from these analyses) for the project all get checked into the same repository. ADBio pulls data and software directly from the repository and serves up an interactive visualization for data exploration. Any insight you choose to record gets checked back into the repository.")]),e._v(" "),o("p",[o("img",{attrs:{src:a(418),alt:"ADBio"}})]),e._v(" "),o("p",[e._v("When we were first approached by Sam and his team, they outlined several use cases for which it might be valuable to have formal Data Package support (with the benefit of the associated tooling) within their framework. In the end, we decided to work on the first: "),o("em",[e._v("validating metadata associated with ADBio repositories")]),e._v(".")]),e._v(" "),o("h3",{attrs:{id:"use-case-validating-metadata"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#use-case-validating-metadata"}},[e._v("#")]),e._v(" Use Case: Validating Metadata")]),e._v(" "),o("p",[e._v("To initiate a project in Active Data Biology, users start with a dataset of quantitative molecular measurements across multiple samples combined with metadata for each sample. Each repository on ADBio contains these two types of files. For clinical experiments, the metadata may include information about a participant’s age, gender, disease stage, etc. For an environmental experiment, this may be geographical location, temperature, time of day, etc. One "),o("a",{attrs:{href:"https://github.com/ActiveDataBio/ADB-User-Study/blob/master/metadata.tsv",target:"_blank",rel:"noopener noreferrer"}},[e._v("example"),o("OutboundLink")],1),e._v(" of a metadata file can be found at on the ADB-User-Study project repository under the "),o("a",{attrs:{href:"https://github.com/ActiveDataBio/",target:"_blank",rel:"noopener noreferrer"}},[e._v("ActiveDataBio organization on GitHub"),o("OutboundLink")],1),e._v(".")]),e._v(" "),o("p",[e._v("The metadata file can be updated or expanded during the course of analysis. This is currently not easily done within ADBio. Moreover, the researchers lacked any formal schema describing the metadata file and its contents. It was suggested that having a Data Package formalizing the metadata file would be a benefit. This would also enable validation of the contents, according to the schema stored as part of the Data Package. Finally, the researchers also requested the development of a web UI to edit the metadata file that would be an application within the ADBio suite. Users could then update the schema online, and it would be versioned through GitHub like everything else. Scenario")]),e._v(" "),o("p",[e._v("A user gets updated survival information for patients in a clinical study and wants to update the metadata associated with this experiment. Within ADBio, the user opens the “Metadata” app and enters new information into the user interface. When finished, user clicks a ‘save’ button and the data is validated against the schema. If it fails, the specific cells are highlighted and annotated with failure codes. If it passes, the new metadata file is checked into the repository with a user-specified comment for the commit message.")]),e._v(" "),o("h2",{attrs:{id:"the-work"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#the-work"}},[e._v("#")]),e._v(" The Work")]),e._v(" "),o("h3",{attrs:{id:"what-did-we-do"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#what-did-we-do"}},[e._v("#")]),e._v(" What Did We Do")]),e._v(" "),o("p",[e._v("This was a valuable pilot for several reasons. For one, the researchers interests in openness and the value of public, versioned infrastructure like GitHub for tabular, flat file datasets aligned well with the overall interests of the project. OKI’s first step was to start a new repository to track progress "),o("a",{attrs:{href:"https://github.com/frictionlessdata/pilot-pnnl",target:"_blank",rel:"noopener noreferrer"}},[e._v("in the open"),o("OutboundLink")],1),e._v(". In addition, OKI also created their own "),o("a",{attrs:{href:"https://github.com/frictionlessdata/ADB-User-Study",target:"_blank",rel:"noopener noreferrer"}},[e._v("“fork” (i.e. versioned copy) of the repository"),o("OutboundLink")],1),e._v(" in which PNNL stored their exemplar metadata file.")]),e._v(" "),o("h3",{attrs:{id:"data"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#data"}},[e._v("#")]),e._v(" Data")]),e._v(" "),o("p",[e._v("The "),o("code",[e._v("metadata.tsv")]),e._v(" file is specially formatted compared to other TSV (tab-separated values) files in that it contains two extra rows below the header for describing a column’s "),o("em",[e._v("methods")]),e._v(" and "),o("em",[e._v("descriptions")]),e._v(". While this is a neat way of storing metadata for each column, it is not particularly standard as ordinarily, we would expect all rows below the header contain actual data. Nevertheless, it provided a great start to the development of a custom schema. We used the information stored in these rows to generate a "),o("a",{attrs:{href:"https://specs.frictionlessdata.io/table-schema/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Table Schema"),o("OutboundLink")],1),e._v(" for the data compatible with our software ("),o("a",{attrs:{href:"https://github.com/frictionlessdata/ADB-User-Study/blob/master/metadata-schema.json",target:"_blank",rel:"noopener noreferrer"}},[e._v("the schema"),o("OutboundLink")],1),e._v(").")]),e._v(" "),o("p",[e._v("For instance, if a column in the original metadata.tsv file had the text "),o("code",[e._v("categorical")]),e._v(" in its "),o("code",[e._v("#methods")]),e._v(" row, we knew that this translated very well to our "),o("a",{attrs:{href:"https://specs.frictionlessdata.io/table-schema/#constraints",target:"_blank",rel:"noopener noreferrer"}},[e._v("enum (short for enumerated list) constraint"),o("OutboundLink")],1),e._v(". However, this was not enough. We had to infer from the values below in the dataset which values were actually valid categorical values for that column. So, for example, the "),o("code",[e._v("PlatinumStatus")]),e._v(" column could only be one of "),o("code",[e._v("Resistant")]),e._v(", "),o("code",[e._v("Sensitive")]),e._v(", or "),o("code",[e._v("Tooearly")]),e._v(" leading to the following constraint definition in Table Schema:")]),e._v(" "),o("div",{staticClass:"language- extra-class"},[o("pre",{pre:!0,attrs:{class:"language-text"}},[o("code",[e._v('"constraints": {\n "enum": [\n "Resistant",\n "Sensitive",\n "Tooearly"\n ]\n}\n')])])]),o("p",[e._v("More straightforward was the translation of the "),o("code",[e._v("#descriptions")]),e._v(" row; each description was translated directly into a "),o("a",{attrs:{href:"https://specs.frictionlessdata.io/table-schema/#description",target:"_blank",rel:"noopener noreferrer"}},[e._v("description attribute"),o("OutboundLink")],1),e._v(" on the column:")]),e._v(" "),o("div",{staticClass:"language- extra-class"},[o("pre",{pre:!0,attrs:{class:"language-text"}},[o("code",[e._v('"description": "It describes whether the patient was resistant to platinum (chemotherapy) treatment",\n')])])]),o("p",[e._v("What the "),o("code",[e._v("metadata.tsv")]),e._v(" file did not record at all was any information about the “type” of value expected for each column. For instance, the "),o("code",[e._v("days_to_death")]),e._v(" column would never contain a value that was of a “geopoint” type, but rather always a number (and a whole number at that). Likewise, the "),o("code",[e._v("additional_immuno_therapy")]),e._v(" column would always be a True/False (i.e. boolean) value. With PNNL’s domain expertise, OKI added these expectations to the schema so that "),o("code",[e._v("days_to_death")]),e._v(" could be relied upon to always be an integer and "),o("code",[e._v("additional_immuno_therapy")]),e._v(" a boolean (True/False) value.")]),e._v(" "),o("div",{staticClass:"language- extra-class"},[o("pre",{pre:!0,attrs:{class:"language-text"}},[o("code",[e._v('{\n "name": "additional_immuno_therapy",\n "type": "boolean"\n}\n')])])]),o("p",[e._v("Up to this point, the dataset provided by PNNL was adequately described by our specifications. One challenge was how to deal, though, with the many missing values in the dataset. While we had discussion on the "),o("a",{attrs:{href:"https://github.com/frictionlessdata/specs/issues/97",target:"_blank",rel:"noopener noreferrer"}},[e._v("topic"),o("OutboundLink")],1),e._v(", we had not yet established a formal way of specifying. In part due to observed usage and the needs of the pilot, we formalized an approach to recording information about which values signal missing data in "),o("a",{attrs:{href:"https://twitter.com/OKFNLabs/status/765568650699018241",target:"_blank",rel:"noopener noreferrer"}},[e._v("mid-August 2016"),o("OutboundLink")],1),e._v(". We added this information to the Table Schema:")]),e._v(" "),o("div",{staticClass:"language- extra-class"},[o("pre",{pre:!0,attrs:{class:"language-text"}},[o("code",[e._v('"missingValues": [\n "[Not Applicable]",\n "[Not Available]",\n "[Pending]"\n]\n')])])]),o("h3",{attrs:{id:"software"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#software"}},[e._v("#")]),e._v(" Software")]),e._v(" "),o("p",[e._v("Goodtables had "),o("a",{attrs:{href:"http://okfnlabs.org/blog/2015/02/20/introducing-goodtables.html",target:"_blank",rel:"noopener noreferrer"}},[e._v("existed"),o("OutboundLink")],1),e._v(" as a Python library and web application developed by Open Knowledge International to support the validation of tabular datasets both in terms of structure and also with respect to a published schema as described above. This software was put to good use in a local government context.")]),e._v(" "),o("p",[e._v("For this pilot, and in coordination with other work in the project, we took the opportunity to drastically improve the software to support the online, automated validation referenced in the above use case. We took as inspiration the workflow in use in software development environments around the world—continuous automated testing—and applied to data. This involved not only updating the Python library to reflect the specification development to date, but the design of a new data publishing workflow that is applicable beyond PNNL’s needs. It is designed to be extensible, so that custom checks and custom backends (e.g. other places where one might publish a dataset) can take advantage of this workflow. For example, in addition to datasets stored on GitHub, the new goodtables supports the automated validation of datasets stored on S3 and we are currently working on validation of datasets stored on CKAN.")]),e._v(" "),o("p",[e._v("Goodtables supports validation of tabular data in GitHub repositories to solve the use case for Active Data Biology. On every update to the dataset, a validation task is run on the stored data.")]),e._v(" "),o("h2",{attrs:{id:"review"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#review"}},[e._v("#")]),e._v(" Review")]),e._v(" "),o("h3",{attrs:{id:"how-effective-was-it"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#how-effective-was-it"}},[e._v("#")]),e._v(" How Effective Was It")]),e._v(" "),o("p",[e._v("The omics team at PNNL are still investigating the use of "),o("a",{attrs:{href:"http://goodtables.io",target:"_blank",rel:"noopener noreferrer"}},[e._v("goodtables.io"),o("OutboundLink")],1),e._v(" for their use case, but early reports are positive:")]),e._v(" "),o("blockquote",[o("p",[e._v("We created a schema and started testing it. So far so good! I think this is going to work for a lot of projects which want to store data in a repo.")])]),e._v(" "),o("p",[e._v("As a real test of the generality of goodtables, we also tried to apply it to another project. This second project is a public repository describing measurements of metabolites in ion mobility mass spectrometry. Here, we are again using flat files for structured data. The data is actually a library of information describing metabolites, and we know that the library will be growing. So it was very similar to the ADBio project, in that the curated data would be continually updated. (see "),o("a",{attrs:{href:"https://github.com/PNNL-Comp-Mass-Spec/MetabolomicsCCS",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://github.com/PNNL-Comp-Mass-Spec/MetabolomicsCCS"),o("OutboundLink")],1),e._v(" for the project itself, and "),o("a",{attrs:{href:"https://github.com/PNNL-Comp-Mass-Spec/metaboliteValidation",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://github.com/PNNL-Comp-Mass-Spec/metaboliteValidation"),o("OutboundLink")],1),e._v(" for a validation script that leverages goodtables).")]),e._v(" "),o("p",[e._v("Of course, technical issues that they have encountered have been translated in GitHub issues and are being addressed:")]),e._v(" "),o("ul",[o("li",[o("a",{attrs:{href:"https://github.com/frictionlessdata/goodtables.io/issues/233",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://github.com/frictionlessdata/goodtables.io/issues/233"),o("OutboundLink")],1)]),e._v(" "),o("li",[o("a",{attrs:{href:"https://github.com/frictionlessdata/goodtables.io/pull/235",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://github.com/frictionlessdata/goodtables.io/pull/235"),o("OutboundLink")],1)]),e._v(" "),o("li",[o("a",{attrs:{href:"https://github.com/frictionlessdata/goodtables.io/issues/232",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://github.com/frictionlessdata/goodtables.io/issues/232"),o("OutboundLink")],1)])])])}),[],!1,null,null,null);t.default=i.exports}}]); \ No newline at end of file +(window.webpackJsonp=window.webpackJsonp||[]).push([[47],{418:function(e,t,a){e.exports=a.p+"assets/img/adbio.ea0206c3.png"},570:function(e,t,a){"use strict";a.r(t);var o=a(29),i=Object(o.a)({},(function(){var e=this,t=e.$createElement,o=e._self._c||t;return o("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[o("h2",{attrs:{id:"context"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#context"}},[e._v("#")]),e._v(" Context")]),e._v(" "),o("h3",{attrs:{id:"problem-we-were-trying-to-solve"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#problem-we-were-trying-to-solve"}},[e._v("#")]),e._v(" Problem We Were Trying To Solve")]),e._v(" "),o("p",[e._v("Sam Payne and his team at the Pacific Northwest National Laboratory (PNNL) have designed an application called "),o("a",{attrs:{href:"https://adbio.pnnl.gov/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Active Data Biology"),o("OutboundLink")],1),e._v(" (ADBio) which is an interactive web-based suite of tools for analyzing high-throughput omics (a set of related fields of study in biology). The goal is to visualize and analyze datasets while still enabling seamless collaboration between computational and non-computational domain experts. The tool provides several views on the same data facilitating different avenues of investigation.")]),e._v(" "),o("p",[e._v("One of the high level goals of ADBio was to make collaborative data analysis work in a similar manner to collaborative software development (versioned, asynchronous, flexible, sharable, global). You can read more of the motivation in the Open Knowledge International blog post "),o("a",{attrs:{href:"https://blog.okfn.org/2016/11/29/git-for-data-analysis-why-version-control-is-essential-collaboration-public-trust/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Git for Data Analysis – why version control is essential for collaboration and for gaining public trust"),o("OutboundLink")],1),e._v(" written by Sam Payne as part of the pilot. To facilitate this goal, Sam and his team used version-controlled repositories as the storage mechanism for all required resources. Data, software (for conducting analyses), and insights (gained from these analyses) for the project all get checked into the same repository. ADBio pulls data and software directly from the repository and serves up an interactive visualization for data exploration. Any insight you choose to record gets checked back into the repository.")]),e._v(" "),o("p",[o("img",{attrs:{src:a(418),alt:"ADBio"}})]),e._v(" "),o("p",[e._v("When we were first approached by Sam and his team, they outlined several use cases for which it might be valuable to have formal Data Package support (with the benefit of the associated tooling) within their framework. In the end, we decided to work on the first: "),o("em",[e._v("validating metadata associated with ADBio repositories")]),e._v(".")]),e._v(" "),o("h3",{attrs:{id:"use-case-validating-metadata"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#use-case-validating-metadata"}},[e._v("#")]),e._v(" Use Case: Validating Metadata")]),e._v(" "),o("p",[e._v("To initiate a project in Active Data Biology, users start with a dataset of quantitative molecular measurements across multiple samples combined with metadata for each sample. Each repository on ADBio contains these two types of files. For clinical experiments, the metadata may include information about a participant’s age, gender, disease stage, etc. For an environmental experiment, this may be geographical location, temperature, time of day, etc. One "),o("a",{attrs:{href:"https://github.com/ActiveDataBio/ADB-User-Study/blob/master/metadata.tsv",target:"_blank",rel:"noopener noreferrer"}},[e._v("example"),o("OutboundLink")],1),e._v(" of a metadata file can be found at on the ADB-User-Study project repository under the "),o("a",{attrs:{href:"https://github.com/ActiveDataBio/",target:"_blank",rel:"noopener noreferrer"}},[e._v("ActiveDataBio organization on GitHub"),o("OutboundLink")],1),e._v(".")]),e._v(" "),o("p",[e._v("The metadata file can be updated or expanded during the course of analysis. This is currently not easily done within ADBio. Moreover, the researchers lacked any formal schema describing the metadata file and its contents. It was suggested that having a Data Package formalizing the metadata file would be a benefit. This would also enable validation of the contents, according to the schema stored as part of the Data Package. Finally, the researchers also requested the development of a web UI to edit the metadata file that would be an application within the ADBio suite. Users could then update the schema online, and it would be versioned through GitHub like everything else. Scenario")]),e._v(" "),o("p",[e._v("A user gets updated survival information for patients in a clinical study and wants to update the metadata associated with this experiment. Within ADBio, the user opens the “Metadata” app and enters new information into the user interface. When finished, user clicks a ‘save’ button and the data is validated against the schema. If it fails, the specific cells are highlighted and annotated with failure codes. If it passes, the new metadata file is checked into the repository with a user-specified comment for the commit message.")]),e._v(" "),o("h2",{attrs:{id:"the-work"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#the-work"}},[e._v("#")]),e._v(" The Work")]),e._v(" "),o("h3",{attrs:{id:"what-did-we-do"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#what-did-we-do"}},[e._v("#")]),e._v(" What Did We Do")]),e._v(" "),o("p",[e._v("This was a valuable pilot for several reasons. For one, the researchers interests in openness and the value of public, versioned infrastructure like GitHub for tabular, flat file datasets aligned well with the overall interests of the project. OKI’s first step was to start a new repository to track progress "),o("a",{attrs:{href:"https://github.com/frictionlessdata/pilot-pnnl",target:"_blank",rel:"noopener noreferrer"}},[e._v("in the open"),o("OutboundLink")],1),e._v(". In addition, OKI also created their own "),o("a",{attrs:{href:"https://github.com/frictionlessdata/ADB-User-Study",target:"_blank",rel:"noopener noreferrer"}},[e._v("“fork” (i.e. versioned copy) of the repository"),o("OutboundLink")],1),e._v(" in which PNNL stored their exemplar metadata file.")]),e._v(" "),o("h3",{attrs:{id:"data"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#data"}},[e._v("#")]),e._v(" Data")]),e._v(" "),o("p",[e._v("The "),o("code",[e._v("metadata.tsv")]),e._v(" file is specially formatted compared to other TSV (tab-separated values) files in that it contains two extra rows below the header for describing a column’s "),o("em",[e._v("methods")]),e._v(" and "),o("em",[e._v("descriptions")]),e._v(". While this is a neat way of storing metadata for each column, it is not particularly standard as ordinarily, we would expect all rows below the header contain actual data. Nevertheless, it provided a great start to the development of a custom schema. We used the information stored in these rows to generate a "),o("a",{attrs:{href:"https://specs.frictionlessdata.io/table-schema/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Table Schema"),o("OutboundLink")],1),e._v(" for the data compatible with our software ("),o("a",{attrs:{href:"https://github.com/frictionlessdata/ADB-User-Study/blob/master/metadata-schema.json",target:"_blank",rel:"noopener noreferrer"}},[e._v("the schema"),o("OutboundLink")],1),e._v(").")]),e._v(" "),o("p",[e._v("For instance, if a column in the original metadata.tsv file had the text "),o("code",[e._v("categorical")]),e._v(" in its "),o("code",[e._v("#methods")]),e._v(" row, we knew that this translated very well to our "),o("a",{attrs:{href:"https://specs.frictionlessdata.io/table-schema/#constraints",target:"_blank",rel:"noopener noreferrer"}},[e._v("enum (short for enumerated list) constraint"),o("OutboundLink")],1),e._v(". However, this was not enough. We had to infer from the values below in the dataset which values were actually valid categorical values for that column. So, for example, the "),o("code",[e._v("PlatinumStatus")]),e._v(" column could only be one of "),o("code",[e._v("Resistant")]),e._v(", "),o("code",[e._v("Sensitive")]),e._v(", or "),o("code",[e._v("Tooearly")]),e._v(" leading to the following constraint definition in Table Schema:")]),e._v(" "),o("div",{staticClass:"language- extra-class"},[o("pre",{pre:!0,attrs:{class:"language-text"}},[o("code",[e._v('"constraints": {\n "enum": [\n "Resistant",\n "Sensitive",\n "Tooearly"\n ]\n}\n')])])]),o("p",[e._v("More straightforward was the translation of the "),o("code",[e._v("#descriptions")]),e._v(" row; each description was translated directly into a "),o("a",{attrs:{href:"https://specs.frictionlessdata.io/table-schema/#description",target:"_blank",rel:"noopener noreferrer"}},[e._v("description attribute"),o("OutboundLink")],1),e._v(" on the column:")]),e._v(" "),o("div",{staticClass:"language- extra-class"},[o("pre",{pre:!0,attrs:{class:"language-text"}},[o("code",[e._v('"description": "It describes whether the patient was resistant to platinum (chemotherapy) treatment",\n')])])]),o("p",[e._v("What the "),o("code",[e._v("metadata.tsv")]),e._v(" file did not record at all was any information about the “type” of value expected for each column. For instance, the "),o("code",[e._v("days_to_death")]),e._v(" column would never contain a value that was of a “geopoint” type, but rather always a number (and a whole number at that). Likewise, the "),o("code",[e._v("additional_immuno_therapy")]),e._v(" column would always be a True/False (i.e. boolean) value. With PNNL’s domain expertise, OKI added these expectations to the schema so that "),o("code",[e._v("days_to_death")]),e._v(" could be relied upon to always be an integer and "),o("code",[e._v("additional_immuno_therapy")]),e._v(" a boolean (True/False) value.")]),e._v(" "),o("div",{staticClass:"language- extra-class"},[o("pre",{pre:!0,attrs:{class:"language-text"}},[o("code",[e._v('{\n "name": "additional_immuno_therapy",\n "type": "boolean"\n}\n')])])]),o("p",[e._v("Up to this point, the dataset provided by PNNL was adequately described by our specifications. One challenge was how to deal, though, with the many missing values in the dataset. While we had discussion on the "),o("a",{attrs:{href:"https://github.com/frictionlessdata/specs/issues/97",target:"_blank",rel:"noopener noreferrer"}},[e._v("topic"),o("OutboundLink")],1),e._v(", we had not yet established a formal way of specifying. In part due to observed usage and the needs of the pilot, we formalized an approach to recording information about which values signal missing data in "),o("a",{attrs:{href:"https://twitter.com/OKFNLabs/status/765568650699018241",target:"_blank",rel:"noopener noreferrer"}},[e._v("mid-August 2016"),o("OutboundLink")],1),e._v(". We added this information to the Table Schema:")]),e._v(" "),o("div",{staticClass:"language- extra-class"},[o("pre",{pre:!0,attrs:{class:"language-text"}},[o("code",[e._v('"missingValues": [\n "[Not Applicable]",\n "[Not Available]",\n "[Pending]"\n]\n')])])]),o("h3",{attrs:{id:"software"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#software"}},[e._v("#")]),e._v(" Software")]),e._v(" "),o("p",[e._v("Goodtables had "),o("a",{attrs:{href:"http://okfnlabs.org/blog/2015/02/20/introducing-goodtables.html",target:"_blank",rel:"noopener noreferrer"}},[e._v("existed"),o("OutboundLink")],1),e._v(" as a Python library and web application developed by Open Knowledge International to support the validation of tabular datasets both in terms of structure and also with respect to a published schema as described above. This software was put to good use in a local government context.")]),e._v(" "),o("p",[e._v("For this pilot, and in coordination with other work in the project, we took the opportunity to drastically improve the software to support the online, automated validation referenced in the above use case. We took as inspiration the workflow in use in software development environments around the world—continuous automated testing—and applied to data. This involved not only updating the Python library to reflect the specification development to date, but the design of a new data publishing workflow that is applicable beyond PNNL’s needs. It is designed to be extensible, so that custom checks and custom backends (e.g. other places where one might publish a dataset) can take advantage of this workflow. For example, in addition to datasets stored on GitHub, the new goodtables supports the automated validation of datasets stored on S3 and we are currently working on validation of datasets stored on CKAN.")]),e._v(" "),o("p",[e._v("Goodtables supports validation of tabular data in GitHub repositories to solve the use case for Active Data Biology. On every update to the dataset, a validation task is run on the stored data.")]),e._v(" "),o("h2",{attrs:{id:"review"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#review"}},[e._v("#")]),e._v(" Review")]),e._v(" "),o("h3",{attrs:{id:"how-effective-was-it"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#how-effective-was-it"}},[e._v("#")]),e._v(" How Effective Was It")]),e._v(" "),o("p",[e._v("The omics team at PNNL are still investigating the use of "),o("a",{attrs:{href:"http://goodtables.io",target:"_blank",rel:"noopener noreferrer"}},[e._v("goodtables.io"),o("OutboundLink")],1),e._v(" for their use case, but early reports are positive:")]),e._v(" "),o("blockquote",[o("p",[e._v("We created a schema and started testing it. So far so good! I think this is going to work for a lot of projects which want to store data in a repo.")])]),e._v(" "),o("p",[e._v("As a real test of the generality of goodtables, we also tried to apply it to another project. This second project is a public repository describing measurements of metabolites in ion mobility mass spectrometry. Here, we are again using flat files for structured data. The data is actually a library of information describing metabolites, and we know that the library will be growing. So it was very similar to the ADBio project, in that the curated data would be continually updated. (see "),o("a",{attrs:{href:"https://github.com/PNNL-Comp-Mass-Spec/MetabolomicsCCS",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://github.com/PNNL-Comp-Mass-Spec/MetabolomicsCCS"),o("OutboundLink")],1),e._v(" for the project itself, and "),o("a",{attrs:{href:"https://github.com/PNNL-Comp-Mass-Spec/metaboliteValidation",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://github.com/PNNL-Comp-Mass-Spec/metaboliteValidation"),o("OutboundLink")],1),e._v(" for a validation script that leverages goodtables).")]),e._v(" "),o("p",[e._v("Of course, technical issues that they have encountered have been translated in GitHub issues and are being addressed:")]),e._v(" "),o("ul",[o("li",[o("a",{attrs:{href:"https://github.com/frictionlessdata/goodtables.io/issues/233",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://github.com/frictionlessdata/goodtables.io/issues/233"),o("OutboundLink")],1)]),e._v(" "),o("li",[o("a",{attrs:{href:"https://github.com/frictionlessdata/goodtables.io/pull/235",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://github.com/frictionlessdata/goodtables.io/pull/235"),o("OutboundLink")],1)]),e._v(" "),o("li",[o("a",{attrs:{href:"https://github.com/frictionlessdata/goodtables.io/issues/232",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://github.com/frictionlessdata/goodtables.io/issues/232"),o("OutboundLink")],1)])])])}),[],!1,null,null,null);t.default=i.exports}}]); \ No newline at end of file diff --git a/assets/js/48.8ca4767a.js b/assets/js/48.c8ca2de5.js similarity index 99% rename from assets/js/48.8ca4767a.js rename to assets/js/48.c8ca2de5.js index 1e49585e6..e5b162bff 100644 --- a/assets/js/48.8ca4767a.js +++ b/assets/js/48.c8ca2de5.js @@ -1 +1 @@ -(window.webpackJsonp=window.webpackJsonp||[]).push([[48],{425:function(t,a,s){t.exports=s.p+"assets/img/camimage1.409b3ea6.png"},575:function(t,a,s){"use strict";s.r(a);var e=s(29),n=Object(e.a)({},(function(){var t=this,a=t.$createElement,e=t._self._c||a;return e("ContentSlotsDistributor",{attrs:{"slot-key":t.$parent.slotKey}},[e("h2",{attrs:{id:"context"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#context"}},[t._v("#")]),t._v(" Context")]),t._v(" "),e("h3",{attrs:{id:"problem-we-were-trying-to-solve"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#problem-we-were-trying-to-solve"}},[t._v("#")]),t._v(" Problem We Were Trying To Solve")]),t._v(" "),e("p",[t._v("Stephen Eglen was looking to investigate the applicability of the Data Package concept to facilitate data reuse in the field of Computational Neuroscience. The following figure describes the kind of data he is collecting. He was eventually seeking to get around 100–160 fields like this. Each circle is a cell drawn approximately to scale. The units of measurement are microns (micrometers).")]),t._v(" "),e("p",[e("img",{attrs:{src:s(425),alt:"ADBio"}})]),t._v(" "),e("p",[t._v("The basic data are quite simple: two columns of numbers that describes the center of each circle where each circle represents a "),e("a",{attrs:{href:"https://en.wikipedia.org/wiki/Retinal_ganglion_cell",target:"_blank",rel:"noopener noreferrer"}},[t._v("retinal ganglion cell"),e("OutboundLink")],1),t._v(". The metadata that adds context to this data are as follows: the radius of each circle is 10um representing the average radius of this cell type; the dashed line represents the sample window which is the region of space within which the cells were sampled; the species from which the cells were sampled is a cat.")]),t._v(" "),e("p",[t._v("The key question posed by the collection of such data in large quantities is “where to store all these metadata”. 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Proc R Soc Lond B Biol Sci 212:177–195.")]),t._v(" "),e("li",[t._v("Unit of measurement: micron.")]),t._v(" "),e("li",[t._v("(Optionally) A raw image from where the data where taken. e.g. "),e("a",{attrs:{href:"http://www.damtp.cam.ac.uk/user/sje30/data/mosaics/w81_scan/w81all.png",target:"_blank",rel:"noopener noreferrer"}},[t._v("http://www.damtp.cam.ac.uk/user/sje30/data/mosaics/w81_scan/w81all.png"),e("OutboundLink")],1)])]),t._v(" "),e("p",[t._v("The long-term goal was to build a website/repository containing about 100+ examples of these “retinal mosaics”. 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could"),e("br"),t._v("\nread this metadata—could provide an “object-oriented” style of working with experimental data.")]),t._v(" "),e("p",[t._v("We have not tried this out on multiple samples (this is forthcoming), so we don’t have much information yet on"),e("br"),t._v("\nthe usefulness of this approach, but the exercise raised several important issues to potentially address with the"),e("br"),t._v("\nData Package format:")]),t._v(" "),e("ol",[e("li",[t._v("Stephen’s request for a specified location for storing units in a structured way comes up often: "),e("a",{attrs:{href:"https://github.com/frictionlessdata/specs/issues/216",target:"_blank",rel:"noopener noreferrer"}},[t._v("https://github.com/frictionlessdata/specs/issues/216"),e("OutboundLink")],1)]),t._v(" "),e("li",[t._v("More iterations, with more of a variety of data sources could help in trialling this")]),t._v(" "),e("li",[t._v("Stephen wanted to store a non-tabular data file (an image) with the tabular datasets that comprise his datasets. This is currently not allowed, but the subsequent definition of a Tabular Data Resource could pave the way for a method of specifying types of different resources and the kind of processing, validation or otherwise, that could be done with each.")])]),t._v(" "),e("h2",{attrs:{id:"next-steps"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#next-steps"}},[t._v("#")]),t._v(" Next Steps")]),t._v(" "),e("h3",{attrs:{id:"areas-for-future-work"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#areas-for-future-work"}},[t._v("#")]),t._v(" Areas for future work")]),t._v(" "),e("p",[t._v("Stephen now has about 100 retinal mosaics that might make for a nice use case of the Data Package. In addition, The Frictionless Data Tool Fund has funded the development of the next version of the R Data Package. This will make some of the improvements brought to the Data Package specifications in the past few months available in the R library.")])])}),[],!1,null,null,null);a.default=n.exports}}]); \ No newline at end of file +(window.webpackJsonp=window.webpackJsonp||[]).push([[48],{419:function(t,a,s){t.exports=s.p+"assets/img/camimage1.409b3ea6.png"},573:function(t,a,s){"use strict";s.r(a);var e=s(29),n=Object(e.a)({},(function(){var t=this,a=t.$createElement,e=t._self._c||a;return e("ContentSlotsDistributor",{attrs:{"slot-key":t.$parent.slotKey}},[e("h2",{attrs:{id:"context"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#context"}},[t._v("#")]),t._v(" Context")]),t._v(" "),e("h3",{attrs:{id:"problem-we-were-trying-to-solve"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#problem-we-were-trying-to-solve"}},[t._v("#")]),t._v(" Problem We Were Trying To Solve")]),t._v(" "),e("p",[t._v("Stephen Eglen was looking to investigate the applicability of the Data Package concept to facilitate data reuse in the field of Computational Neuroscience. The following figure describes the kind of data he is collecting. He was eventually seeking to get around 100–160 fields like this. Each circle is a cell drawn approximately to scale. The units of measurement are microns (micrometers).")]),t._v(" "),e("p",[e("img",{attrs:{src:s(419),alt:"ADBio"}})]),t._v(" "),e("p",[t._v("The basic data are quite simple: two columns of numbers that describes the center of each circle where each circle represents a "),e("a",{attrs:{href:"https://en.wikipedia.org/wiki/Retinal_ganglion_cell",target:"_blank",rel:"noopener noreferrer"}},[t._v("retinal ganglion cell"),e("OutboundLink")],1),t._v(". The metadata that adds context to this data are as follows: the radius of each circle is 10um representing the average radius of this cell type; the dashed line represents the sample window which is the region of space within which the cells were sampled; the species from which the cells were sampled is a cat.")]),t._v(" "),e("p",[t._v("The key question posed by the collection of such data in large quantities is “where to store all these metadata”. More formally, Stephen wanted a way to include the following metadata with sampled data:")]),t._v(" "),e("ol",[e("li",[t._v("Cell type: on center retinal ganglion cells")]),t._v(" "),e("li",[t._v("Species: cat")]),t._v(" "),e("li",[t._v("Radius of "),e("a",{attrs:{href:"https://en.wikipedia.org/wiki/Soma_(biology)"}},[t._v("soma")]),t._v(": 10 um")]),t._v(" "),e("li",[t._v("Citation to original paper where data were presented: Wässle H, Boycott BB, Illing RB (1981) Morphology and mosaic of on- and off-beta cells in the cat retina and some functional considerations. Proc R Soc Lond B Biol Sci 212:177–195.")]),t._v(" "),e("li",[t._v("Unit of measurement: micron.")]),t._v(" "),e("li",[t._v("(Optionally) A raw image from where the data where taken. e.g. "),e("a",{attrs:{href:"http://www.damtp.cam.ac.uk/user/sje30/data/mosaics/w81_scan/w81all.png",target:"_blank",rel:"noopener noreferrer"}},[t._v("http://www.damtp.cam.ac.uk/user/sje30/data/mosaics/w81_scan/w81all.png"),e("OutboundLink")],1)])]),t._v(" "),e("p",[t._v("The long-term goal was to build a website/repository containing about 100+ examples of these “retinal mosaics”. The website would allow people to view the mosaics, download the data files, or run online some simple analysis.")]),t._v(" "),e("h2",{attrs:{id:"the-work"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#the-work"}},[t._v("#")]),t._v(" The Work")]),t._v(" "),e("h3",{attrs:{id:"what-did-we-do"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#what-did-we-do"}},[t._v("#")]),t._v(" What Did We Do")]),t._v(" "),e("p",[t._v("The Data Package specification is meant to be a container for data providing a consistent interface to tabular data. The Data Package specification outlines a number of different fields that can be stored within a descriptor file, "),e("code",[t._v("datapackage.json")]),t._v(". For instance, for this example, we can assign a title to this Data Package by setting the field "),e("code",[t._v("title")]),t._v(" to “Example Retinal Mosaic”:")]),t._v(" "),e("p",[e("code",[t._v('"title" : "Example Retinal Mosaic"')])]),t._v(" "),e("p",[t._v("We can also, for instance, set a "),e("code",[t._v("homepage")]),t._v(" for the dataset which is a URL for the home on the web that is related to this Data Package.")]),t._v(" "),e("p",[e("code",[t._v('"homepage": "http://www.damtp.cam.ac.uk/user/sje30/data/mosaics/w81_scan/"')])]),t._v(" "),e("p",[t._v("Some of the other metadata Stephen required do not, however, map well to existing specified fields in a Data Package. For instance, as the Data Package is not specialized for storing biological data, there is no field for “species”. (The Digital Curation Centre maintains a database of domain-specific metadata standards for biology and other fields.) 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This is currently not allowed, but the subsequent definition of a Tabular Data Resource could pave the way for a method of specifying types of different resources and the kind of processing, validation or otherwise, that could be done with each.")])]),t._v(" "),e("h2",{attrs:{id:"next-steps"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#next-steps"}},[t._v("#")]),t._v(" Next Steps")]),t._v(" "),e("h3",{attrs:{id:"areas-for-future-work"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#areas-for-future-work"}},[t._v("#")]),t._v(" Areas for future work")]),t._v(" "),e("p",[t._v("Stephen now has about 100 retinal mosaics that might make for a nice use case of the Data Package. In addition, The Frictionless Data Tool Fund has funded the development of the next version of the R Data Package. 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It incorporates a straightforward UI to modify and validate data package descriptors. With its easy to use interface we are able to:")]),t._v(" "),e("ul",[e("li",[t._v("Load/validate/save a data package")]),t._v(" "),e("li",[t._v("Update a data package metadata")]),t._v(" "),e("li",[t._v("Add/remove/modify data package resources")]),t._v(" "),e("li",[t._v("Add/remove/modify data resource fields")]),t._v(" "),e("li",[t._v("Set type/format for data values")])]),t._v(" "),e("p",[e("img",{attrs:{src:s(441),alt:"ADBio"}})]),t._v(" "),e("p",[t._v("On the figure above we have loaded the "),e("code",[t._v("refit-cleaned")]),t._v(" data package into the Packagist UI to make changes to the data package as needed.")]),t._v(" "),e("h3",{attrs:{id:"publishing-a-data-package-to-amazon-s3"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#publishing-a-data-package-to-amazon-s3"}},[t._v("#")]),t._v(" Publishing a data package to Amazon S3")]),t._v(" "),e("blockquote",[e("p",[t._v("Link to the published package: "),e("a",{attrs:{href:"https://s3.eu-central-1.amazonaws.com/pilot-dm4t/pilot-dm4t/packages/refit-cleaned/datapackage.json",target:"_blank",rel:"noopener noreferrer"}},[t._v("https://s3.eu-central-1.amazonaws.com/pilot-dm4t/pilot-dm4t/packages/refit-cleaned/datapackage.json"),e("OutboundLink")],1)])]),t._v(" "),e("p",[t._v("In this section we will show how data packages can be moved from one data storage system to another. 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For example to the Amazon S3:")]),t._v(" "),e("blockquote",[e("p",[t._v("pipelines-spec.yml")])]),t._v(" "),e("div",{staticClass:"language-yaml extra-class"},[e("pre",{pre:!0,attrs:{class:"language-yaml"}},[e("code",[e("span",{pre:!0,attrs:{class:"token key atrule"}},[t._v("refit-cleaned")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(":")]),t._v("\n\n "),e("span",{pre:!0,attrs:{class:"token comment"}},[t._v("# Initial steps are omitted")]),t._v("\n\n "),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("-")]),t._v(" "),e("span",{pre:!0,attrs:{class:"token key atrule"}},[t._v("run")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(":")]),t._v(" aws.dump.to_s3\n "),e("span",{pre:!0,attrs:{class:"token key atrule"}},[t._v("parameters")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(":")]),t._v("\n "),e("span",{pre:!0,attrs:{class:"token key atrule"}},[t._v("bucket")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(":")]),t._v(" pilot"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("-")]),t._v("dm4t\n "),e("span",{pre:!0,attrs:{class:"token key atrule"}},[t._v("path")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(":")]),t._v(" pilot"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("-")]),t._v("dm4t/packages/refit"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("-")]),t._v("cleaned\n")])])]),e("p",[t._v("Running this command again:")]),t._v(" "),e("div",{staticClass:"language-bash extra-class"},[e("pre",{pre:!0,attrs:{class:"language-bash"}},[e("code",[t._v("$ dpp run ./refit-cleaned\n")])])]),e("p",[t._v("And now our data package is published to Amazon the S3 remote storage:")]),t._v(" "),e("p",[e("img",{attrs:{src:"https://i.imgur.com/5Z7EPDR.pnghttps://",alt:"screenshot of S3 storage"}})]),t._v(" "),e("h3",{attrs:{id:"getting-insight-from-data-using-python-libraries"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#getting-insight-from-data-using-python-libraries"}},[t._v("#")]),t._v(" Getting insight from data using Python libraries")]),t._v(" "),e("blockquote",[e("p",[t._v("Link to the demostration script: "),e("a",{attrs:{href:"https://github.com/frictionlessdata/pilot-dm4t/blob/delivery/scripts/refit-cleaned.py",target:"_blank",rel:"noopener noreferrer"}},[t._v("https://github.com/frictionlessdata/pilot-dm4t/blob/delivery/scripts/refit-cleaned.py"),e("OutboundLink")],1)])]),t._v(" "),e("p",[t._v("The Frictionless Data projects provides various Python (along with other 8 languages) libraries to work with data package programatically. We used the "),e("code",[t._v("datapackage")]),t._v(" library to analyse the "),e("code",[t._v("refit-cleaned")]),t._v(" data package:")]),t._v(" "),e("div",{staticClass:"language-python extra-class"},[e("pre",{pre:!0,attrs:{class:"language-python"}},[e("code",[e("span",{pre:!0,attrs:{class:"token keyword"}},[t._v("import")]),t._v(" datetime\n"),e("span",{pre:!0,attrs:{class:"token keyword"}},[t._v("import")]),t._v(" statistics\n"),e("span",{pre:!0,attrs:{class:"token keyword"}},[t._v("from")]),t._v(" datapackage "),e("span",{pre:!0,attrs:{class:"token keyword"}},[t._v("import")]),t._v(" Package\n\n"),e("span",{pre:!0,attrs:{class:"token comment"}},[t._v("# Get aggregates")]),t._v("\nconsumption "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" "),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("{")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("}")]),t._v("\npackage "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" 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punctuation"}},[t._v(")")]),t._v("\n")])])]),e("p",[t._v("Now we could run it in the command line:")]),t._v(" "),e("div",{staticClass:"language-bash extra-class"},[e("pre",{pre:!0,attrs:{class:"language-bash"}},[e("code",[t._v("$ python examles/refit-cleaned.py\nAverage consumption at 00 hours: "),e("span",{pre:!0,attrs:{class:"token number"}},[t._v("232")]),t._v("\nAverage consumption at 01 hours: "),e("span",{pre:!0,attrs:{class:"token number"}},[t._v("213")]),t._v("\nAverage consumption at 02 hours: "),e("span",{pre:!0,attrs:{class:"token number"}},[t._v("247")]),t._v("\nAverage consumption at 03 hours: "),e("span",{pre:!0,attrs:{class:"token number"}},[t._v("335")]),t._v("\nAverage consumption at 04 hours: "),e("span",{pre:!0,attrs:{class:"token number"}},[t._v("215")]),t._v("\nAverage consumption at 05 hours: "),e("span",{pre:!0,attrs:{class:"token number"}},[t._v("690")]),t._v("\nAverage consumption at 06 hours: "),e("span",{pre:!0,attrs:{class:"token number"}},[t._v("722")]),t._v("\nAverage consumption at 07 hours: "),e("span",{pre:!0,attrs:{class:"token number"}},[t._v("648")]),t._v("\nAverage consumption at 08 hours: "),e("span",{pre:!0,attrs:{class:"token number"}},[t._v("506")]),t._v("\nAverage consumption at 09 hours: "),e("span",{pre:!0,attrs:{class:"token number"}},[t._v("464")]),t._v("\nAverage consumption at "),e("span",{pre:!0,attrs:{class:"token number"}},[t._v("10")]),t._v(" hours: "),e("span",{pre:!0,attrs:{class:"token number"}},[t._v("364")]),t._v("\nAverage consumption at "),e("span",{pre:!0,attrs:{class:"token number"}},[t._v("11")]),t._v(" hours: "),e("span",{pre:!0,attrs:{class:"token number"}},[t._v("569")]),t._v("\nAverage consumption at "),e("span",{pre:!0,attrs:{class:"token number"}},[t._v("12")]),t._v(" hours: "),e("span",{pre:!0,attrs:{class:"token number"}},[t._v("520")]),t._v("\nAverage consumption at "),e("span",{pre:!0,attrs:{class:"token number"}},[t._v("13")]),t._v(" hours: 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"),e("span",{pre:!0,attrs:{class:"token number"}},[t._v("19")]),t._v(" hours: "),e("span",{pre:!0,attrs:{class:"token number"}},[t._v("732")]),t._v("\nAverage consumption at "),e("span",{pre:!0,attrs:{class:"token number"}},[t._v("20")]),t._v(" hours: "),e("span",{pre:!0,attrs:{class:"token number"}},[t._v("471")]),t._v("\nAverage consumption at "),e("span",{pre:!0,attrs:{class:"token number"}},[t._v("21")]),t._v(" hours: "),e("span",{pre:!0,attrs:{class:"token number"}},[t._v("478")]),t._v("\nAverage consumption at "),e("span",{pre:!0,attrs:{class:"token number"}},[t._v("22")]),t._v(" hours: "),e("span",{pre:!0,attrs:{class:"token number"}},[t._v("325")]),t._v("\nAverage consumption at "),e("span",{pre:!0,attrs:{class:"token number"}},[t._v("23")]),t._v(" hours: "),e("span",{pre:!0,attrs:{class:"token number"}},[t._v("231")]),t._v("\n")])])]),e("p",[t._v("Here we we’re able to get the averages for electricity consumption grouped by hour. We could have achieved this in different ways, but using the Frictionless Data specs and software provides some important advantages:")]),t._v(" "),e("ul",[e("li",[t._v("The fact that we have data wrapped into a data package has allowed us to validate and read the data already converted for its correct types (e.g native python "),e("code",[t._v("datetime")]),t._v(" object). No need for any kind of string parsing.")]),t._v(" "),e("li",[t._v("The Frictionless Data software uses file streams under the hood. This means that only the current row is kept in memory, so we’re able to handle datasets bigger than the available RAM memory.")])]),t._v(" "),e("h3",{attrs:{id:"exporting-data-to-an-elasticsearch-cluster"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#exporting-data-to-an-elasticsearch-cluster"}},[t._v("#")]),t._v(" Exporting data to an ElasticSearch cluster")]),t._v(" "),e("blockquote",[e("p",[t._v("Link to the export script: "),e("a",{attrs:{href:"https://github.com/frictionlessdata/pilot-dm4t/blob/delivery/scripts/refit-cleaned.py",target:"_blank",rel:"noopener noreferrer"}},[t._v("https://github.com/frictionlessdata/pilot-dm4t/blob/delivery/scripts/refit-cleaned.py"),e("OutboundLink")],1)])]),t._v(" "),e("p",[t._v("The Frictionless Data software provides plugins to export data to various backends like SQL, BigQuery etc. We will export the first resource from our data package for future analysis:")]),t._v(" "),e("div",{staticClass:"language-python 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boolean"}},[t._v("True")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v(" "),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("[")]),e("span",{pre:!0,attrs:{class:"token string"}},[t._v("'Unix'")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("]")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n\n")])])]),e("p",[t._v("Now we are able to check that our documents are indexed:")]),t._v(" "),e("div",{staticClass:"language-bash extra-class"},[e("pre",{pre:!0,attrs:{class:"language-bash"}},[e("code",[t._v("$ http http://localhost:9200/_cat/indices?v\n")])])]),e("h3",{attrs:{id:"getting-insight-from-data-using-kibana"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#getting-insight-from-data-using-kibana"}},[t._v("#")]),t._v(" Getting insight from data using Kibana")]),t._v(" "),e("p",[t._v("To demonstrate how the Frictionless Data specs and software empower the usage of other analytics tools, we will use ElasticSearch/Kibana project. On the previous step we have imported our data package into an ElasticSearch cluster. It allows us to visualize data using a simple UI:")]),t._v(" "),e("p",[e("img",{attrs:{src:"https://i.imgur.com/Fm373F4.png",alt:"screenshot of elasticsearch cluster"}})]),t._v(" "),e("p",[t._v("In this screenshot we see the distribution of the average electricity comsumption. This is just an example of what you can do by having the ability to easily load datasets into other analytical software.")]),t._v(" "),e("h2",{attrs:{id:"review"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#review"}},[t._v("#")]),t._v(" Review")]),t._v(" "),e("h3",{attrs:{id:"the-results"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#the-results"}},[t._v("#")]),t._v(" The results")]),t._v(" "),e("p",[t._v("In this pilot, we have been able to demonstrate the the following:")]),t._v(" "),e("ul",[e("li",[t._v("Packaging the "),e("code",[t._v("refit-cleaned")]),t._v(" dataset as a data package using the Data Package Pipelines library")]),t._v(" "),e("li",[t._v("Validating the data package using the Goodtables library")]),t._v(" "),e("li",[t._v("Modifying data packages metadata using the Packagist UI")]),t._v(" "),e("li",[t._v("Uploading the dataset to Amazon S3 and ElasticSearch cluster using Frictionless Data tools")]),t._v(" "),e("li",[t._v("Reading and analysing in Python the created Data Package using the Frictionless Data library")])]),t._v(" "),e("h3",{attrs:{id:"current-limitations"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#current-limitations"}},[t._v("#")]),t._v(" Current limitations")]),t._v(" "),e("p",[t._v("The central challenge of working with these datasets is the size. Publishing the results of these research projects as flat files for immediate analysis is beneficial, however, the scale of each of these datasets (gigabytes of data, millions of rows) is a challenge to deal with no matter how you are storing. Processing this data through Data Package pipelines takes a long time.")]),t._v(" "),e("h3",{attrs:{id:"next-steps"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#next-steps"}},[t._v("#")]),t._v(" Next Steps")]),t._v(" "),e("ul",[e("li",[t._v("Improve the speed of the data package creation step")])]),t._v(" "),e("h3",{attrs:{id:"find-out-more"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#find-out-more"}},[t._v("#")]),t._v(" Find Out More")]),t._v(" "),e("ul",[e("li",[e("a",{attrs:{href:"https://github.com/frictionlessdata/pilot-pnnl",target:"_blank",rel:"noopener noreferrer"}},[t._v("https://github.com/frictionlessdata/pilot-pnnl"),e("OutboundLink")],1)])]),t._v(" "),e("h3",{attrs:{id:"source-material"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#source-material"}},[t._v("#")]),t._v(" Source Material")]),t._v(" "),e("ul",[e("li",[e("a",{attrs:{href:"https://app.hubspot.com/sales/2281421/deal/146418008",target:"_blank",rel:"noopener noreferrer"}},[t._v("https://app.hubspot.com/sales/2281421/deal/146418008"),e("OutboundLink")],1)]),t._v(" "),e("li",[e("a",{attrs:{href:"https://discuss.okfn.org/c/working-groups/open-archaeology",target:"_blank",rel:"noopener noreferrer"}},[t._v("https://discuss.okfn.org/c/working-groups/open-archaeology"),e("OutboundLink")],1)]),t._v(" "),e("li",[e("a",{attrs:{href:"https://github.com/frictionlessdata/pilot-open-archaeology",target:"_blank",rel:"noopener noreferrer"}},[t._v("https://github.com/frictionlessdata/pilot-open-archaeology"),e("OutboundLink")],1)])])])}),[],!1,null,null,null);a.default=n.exports}}]); \ No newline at end of file +(window.webpackJsonp=window.webpackJsonp||[]).push([[49],{435:function(t,a,s){t.exports=s.p+"assets/img/packagist.835e6f2c.png"},584:function(t,a,s){"use strict";s.r(a);var e=s(29),n=Object(e.a)({},(function(){var t=this,a=t.$createElement,e=t._self._c||a;return e("ContentSlotsDistributor",{attrs:{"slot-key":t.$parent.slotKey}},[e("h1",{attrs:{id:"dm4t-pilot"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#dm4t-pilot"}},[t._v("#")]),t._v(" DM4T Pilot")]),t._v(" "),e("h2",{attrs:{id:"pilot-name"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#pilot-name"}},[t._v("#")]),t._v(" Pilot Name")]),t._v(" "),e("p",[t._v("Data Management for TEDDINET (DM4T)")]),t._v(" "),e("h2",{attrs:{id:"authors"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#authors"}},[t._v("#")]),t._v(" Authors")]),t._v(" "),e("p",[t._v("Julian Padget (DM4T), Dan Fowler (OKI), Evgeny Karev (OKI)")]),t._v(" "),e("h2",{attrs:{id:"field"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#field"}},[t._v("#")]),t._v(" Field")]),t._v(" "),e("p",[t._v("Energy Data")]),t._v(" "),e("h2",{attrs:{id:"fd-tech-involved"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#fd-tech-involved"}},[t._v("#")]),t._v(" FD Tech Involved")]),t._v(" "),e("ul",[e("li",[t._v("Frictionless Data specs: "),e("a",{attrs:{href:"http://specs.frictionlessdata.io/",target:"_blank",rel:"noopener noreferrer"}},[t._v("http://specs.frictionlessdata.io/"),e("OutboundLink")],1)]),t._v(" "),e("li",[t._v("Data Package Pipelines: "),e("a",{attrs:{href:"https://github.com/frictionlessdata/datapackage-pipelines",target:"_blank",rel:"noopener noreferrer"}},[t._v("https://github.com/frictionlessdata/datapackage-pipelines"),e("OutboundLink")],1)]),t._v(" "),e("li",[t._v("Goodtables: "),e("a",{attrs:{href:"https://github.com/frictionlessdata/goodtables-py",target:"_blank",rel:"noopener noreferrer"}},[t._v("https://github.com/frictionlessdata/goodtables-py"),e("OutboundLink")],1)])]),t._v(" "),e("p",[e("code",[t._v("packagist")]),t._v(" has now moved to "),e("a",{attrs:{href:"http://create.frictionlessdata.io",target:"_blank",rel:"noopener noreferrer"}},[t._v("create.frictionlessdata.io"),e("OutboundLink")],1)]),t._v(" "),e("h2",{attrs:{id:"context"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#context"}},[t._v("#")]),t._v(" Context")]),t._v(" "),e("h3",{attrs:{id:"problem-we-were-trying-to-solve"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#problem-we-were-trying-to-solve"}},[t._v("#")]),t._v(" Problem We Were Trying To Solve")]),t._v(" "),e("p",[t._v("Open Knowledge International and the Data Management for TEDDINET project (DM4T) agreed to work together on a proof-of-concept pilot to attempt to use Frictionless Data approaches to address some of the data legacy issues facing the TEDDINET project, a research network addressing the challenges of transforming energy demand in our buildings, as a key component of the transition to an affordable, low carbon energy system. The problem as described on the DM4T Website:")]),t._v(" "),e("blockquote",[e("p",[t._v("The Engineering and Physical Sciences Research Council (EPSRC), the UK’s main agency for funding research in engineering and the physical sciences, funded 22 projects over two calls in 2010 and 2012 to investigate Transforming Energy Demand through Digital Innovation’ (TEDDI) as a means to find out how people use energy in homes and what can be done reduce energy consumption. A lot of data is being collected at different levels of detail in a variety of housing throughout the UK, but the level of detail are largely defined by the needs of each individual project. At the same time, the Research Councils UK (RCUK) are defining guidelines for what happens to data generated by projects they fund which require researchers to take concrete actions to store, preserve, and document their data for future reference.")])]),t._v(" "),e("blockquote",[e("p",[t._v("The problem, however, is that there is relatively little awareness, limited experience and only emerging practice of how to incorporate data management into much of physical science research. This is in contrast to established procedures for data formats and sharing in the biosciences, stemming from international collaboration on the Human Genome Project, and in the social sciences, where data from national surveys, including census data, have been centrally archived for many years. Consequently, current solutions may be able to meet a minimal interpretation of the requirements, but not effectively deliver the desired data legacy.")])]),t._v(" "),e("p",[t._v("The DM4T group selected three suitable datasets to on which to base this work and provided domain knowledge to ensure the pilot is applicable to real use cases.")]),t._v(" "),e("p",[t._v("Output was tracked here: "),e("a",{attrs:{href:"https://github.com/frictionlessdata/pilot-dm4t/issues",target:"_blank",rel:"noopener noreferrer"}},[t._v("https://github.com/frictionlessdata/pilot-dm4t/issues"),e("OutboundLink")],1)]),t._v(" "),e("h2",{attrs:{id:"the-work"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#the-work"}},[t._v("#")]),t._v(" The work")]),t._v(" "),e("p",[t._v("We will use the "),e("code",[t._v("refit-cleaned")]),t._v(" dataset to show the Frictionless Data specs and software capabilities. For this work, we limited the size of this dataset in order to preserve a reasonable showcasing time. 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There were 20 houses in total, and each house had a different mix of devices plugged into the electrical load sensor. The dataset was distributed as a zipped file (~500MB) containing 20 CSVs with a combined ~120 million rows.")]),t._v(" "),e("div",{staticClass:"language- extra-class"},[e("pre",{pre:!0,attrs:{class:"language-text"}},[e("code",[t._v("Time,Unix,Aggregate,Appliance1,Appliance2,Appliance3,Appliance4,Appliance5,Appliance6,Appliance7,Appliance8,Appliance9\n2013-10-09 13:06:17,1381323977,523,74,0,69,0,0,0,0,0,1\n2013-10-09 13:06:31,1381323991,526,75,0,69,0,0,0,0,0,1\n2013-10-09 13:06:46,1381324006,540,74,0,68,0,0,0,0,0,1\n2013-10-09 13:07:01,1381324021,532,74,0,68,0,0,0,0,0,1\n2013-10-09 13:07:15,1381324035,540,74,0,69,0,0,0,0,0,1\n2013-10-09 13:07:18,1381324038,539,74,0,69,0,0,0,0,0,1\n2013-10-09 13:07:30,1381324050,537,74,0,69,0,0,0,0,0,1\n2013-10-09 13:07:32,1381324052,537,74,0,69,0,0,0,0,0,1\n2013-10-09 13:07:44,1381324064,548,74,0,69,0,0,0,0,0,1\n")])])]),e("p",[t._v("Given that these datasets were already provided in well structured CSV files, it was straightforward to translate the data dictionary found in the dataset’s README into the relevant fields in the datapackage.json. We did not need to alter the CSVs that comprise the dataset.")]),t._v(" "),e("h3",{attrs:{id:"creating-a-data-package-using-datapackage-pipelines"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#creating-a-data-package-using-datapackage-pipelines"}},[t._v("#")]),t._v(" Creating a data package using Datapackage Pipelines")]),t._v(" "),e("blockquote",[e("p",[t._v("Link to the Datapackage Pipelines project: "),e("a",{attrs:{href:"https://github.com/frictionlessdata/datapackage-pipelines",target:"_blank",rel:"noopener noreferrer"}},[t._v("https://github.com/frictionlessdata/datapackage-pipelines"),e("OutboundLink")],1)])]),t._v(" "),e("p",[t._v("Datapackage Pipelines is a framework for declarative stream-processing of tabular data. It is built upon the concepts and tooling of the Frictionless Data project. The basic concept in this framework is the pipeline. A pipeline has a list of processing steps, and it generates a single data package as its output. 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We could have achieved this in different ways, but using the Frictionless Data specs and software provides some important advantages:")]),t._v(" "),e("ul",[e("li",[t._v("The fact that we have data wrapped into a data package has allowed us to validate and read the data already converted for its correct types (e.g native python "),e("code",[t._v("datetime")]),t._v(" object). No need for any kind of string parsing.")]),t._v(" "),e("li",[t._v("The Frictionless Data software uses file streams under the hood. 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"),e("p",[t._v("To demonstrate how the Frictionless Data specs and software empower the usage of other analytics tools, we will use ElasticSearch/Kibana project. On the previous step we have imported our data package into an ElasticSearch cluster. It allows us to visualize data using a simple UI:")]),t._v(" "),e("p",[e("img",{attrs:{src:"https://i.imgur.com/Fm373F4.png",alt:"screenshot of elasticsearch cluster"}})]),t._v(" "),e("p",[t._v("In this screenshot we see the distribution of the average electricity comsumption. This is just an example of what you can do by having the ability to easily load datasets into other analytical software.")]),t._v(" "),e("h2",{attrs:{id:"review"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#review"}},[t._v("#")]),t._v(" Review")]),t._v(" "),e("h3",{attrs:{id:"the-results"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#the-results"}},[t._v("#")]),t._v(" The results")]),t._v(" "),e("p",[t._v("In this pilot, we have been able to demonstrate the the following:")]),t._v(" "),e("ul",[e("li",[t._v("Packaging the "),e("code",[t._v("refit-cleaned")]),t._v(" dataset as a data package using the Data Package Pipelines library")]),t._v(" "),e("li",[t._v("Validating the data package using the Goodtables library")]),t._v(" "),e("li",[t._v("Modifying data packages metadata using the Packagist UI")]),t._v(" "),e("li",[t._v("Uploading the dataset to Amazon S3 and ElasticSearch cluster using Frictionless Data tools")]),t._v(" "),e("li",[t._v("Reading and analysing in Python the created Data Package using the Frictionless Data library")])]),t._v(" "),e("h3",{attrs:{id:"current-limitations"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#current-limitations"}},[t._v("#")]),t._v(" Current limitations")]),t._v(" "),e("p",[t._v("The central challenge of working with these datasets is the size. Publishing the results of these research projects as flat files for immediate analysis is beneficial, however, the scale of each of these datasets (gigabytes of data, millions of rows) is a challenge to deal with no matter how you are storing. Processing this data through Data Package pipelines takes a long time.")]),t._v(" "),e("h3",{attrs:{id:"next-steps"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#next-steps"}},[t._v("#")]),t._v(" Next Steps")]),t._v(" "),e("ul",[e("li",[t._v("Improve the speed of the data package creation step")])]),t._v(" "),e("h3",{attrs:{id:"find-out-more"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#find-out-more"}},[t._v("#")]),t._v(" Find Out More")]),t._v(" "),e("ul",[e("li",[e("a",{attrs:{href:"https://github.com/frictionlessdata/pilot-pnnl",target:"_blank",rel:"noopener noreferrer"}},[t._v("https://github.com/frictionlessdata/pilot-pnnl"),e("OutboundLink")],1)])]),t._v(" "),e("h3",{attrs:{id:"source-material"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#source-material"}},[t._v("#")]),t._v(" Source Material")]),t._v(" "),e("ul",[e("li",[e("a",{attrs:{href:"https://app.hubspot.com/sales/2281421/deal/146418008",target:"_blank",rel:"noopener 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strict";t.r(a);var o=t(29),r=Object(o.a)({},(function(){var e=this,a=e.$createElement,o=e._self._c||a;return o("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[o("h2",{attrs:{id:"include-a-data-schema"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#include-a-data-schema"}},[e._v("#")]),e._v(" Include a data schema")]),e._v(" "),o("p",[e._v("Simply put, a schema is a blueprint that tells us how your data is structured, and what type of content is to be expected in it. 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Depending on how complex is your data, this can be time consuming, but it’s still easier than creating the descriptor JSON file from scratch.This option is time consuming, as a user has to manually create each field of a data resource. However, this is simpler than learning how to create a JSON file from scratch.")]),e._v(" "),o("ol",{attrs:{start:"3"}},[o("li",[o("strong",[e._v("Load a Data Package")]),e._v(" option")])]),e._v(" "),o("p",[e._v("With this option, you can load a pre-existing "),o("em",[e._v("datapackage.json")]),e._v(" file to view and edit its metadata and resource fields.")]),e._v(" "),o("p",[e._v("Our "),o("a",{attrs:{href:"https://github.com/frictionlessdata/example-data-packages/blob/master/gross-domestic-product-all/data/gdp.csv",target:"_blank",rel:"noopener noreferrer"}},[e._v("Gross Domestic Product dataset for all countries (1960 - 2014)"),o("OutboundLink")],1),e._v(" dataset is publicly available on GitHub.")]),e._v(" "),o("p",[e._v("Obtain a link to the raw CSV file by clicking on the Raw button at the top right corner of the GitHub file preview page, as shown in figure 1 below. 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Click on the prompt to load the fields.")]),e._v(" "),o("p",[o("img",{attrs:{src:t(479),alt:"annotated in red, a prompt to add all fields inferred from your data resource"}}),o("br"),e._v(" "),o("em",[e._v("Figure 2: annotated in red, a prompt to add all fields inferred from your data resource.")])]),e._v(" "),o("p",[e._v("The page that follows looks like Figure 3 below. Each column from the GDP dataset has been mapped to a "),o("em",[e._v("field")]),e._v(". The data type for each column has been inferred correctly, and we can preview data under each field by hovering over the field name. It is also possible to edit all sections of our data resource’s fields as we can see below.")]),e._v(" "),o("p",[o("img",{attrs:{src:t(480),alt:"all fields inferred from your data resource"}}),o("br"),e._v(" "),o("em",[e._v("Figure 3: all fields inferred from your data resource.")])]),e._v(" "),o("p",[e._v("You can now edit data types and formats as necessary, and optionally add titles and descriptive information to your fields. For example, the data type for our {Year} field should be "),o("em",[o("strong",[e._v("year")])]),e._v(" and not "),o("em",[o("strong",[e._v("integer")])]),e._v(". Our {Value} column has numeric information with decimal places.")]),e._v(" "),o("p",[e._v("By definition, values under the "),o("em",[o("strong",[e._v("integer")])]),e._v(" data type are whole numbers. The "),o("em",[o("strong",[e._v("number")])]),e._v(" data type is more appropriate for the {Value} column. When in doubt about what data type to use, consult the "),o("a",{attrs:{href:"https://specs.frictionlessdata.io/table-schema/#types-and-formats",target:"_blank",rel:"noopener noreferrer"}},[e._v("Table Schema data types cheat sheet"),o("OutboundLink")],1),e._v(".")]),e._v(" "),o("p",[e._v("Click on the "),o("img",{attrs:{src:t(481),alt:""}}),e._v(" icon to pick a suitable profile for your data resource. "),o("a",{attrs:{href:"https://specs.frictionlessdata.io/profiles/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Here’s more information about Frictionless Data profiles"),o("OutboundLink")],1),e._v(".")]),e._v(" "),o("p",[e._v("If your dataset has other data resources, add them by scrolling to the bottom of the page, clicking on Add Resource, and repeating the same process as we just did.")]),e._v(" "),o("p",[e._v("If your dataset has other data resources, add them by scrolling to the bottom of the page, clicking on "),o("strong",[e._v("Add Resource")]),e._v(", and repeating the same process as we just did.")]),e._v(" "),o("p",[o("img",{attrs:{src:t(482),alt:"Prompt to add more data resources"}}),o("br"),e._v(" "),o("em",[e._v("Figure 4: Prompt to add more data resources.")])]),e._v(" "),o("hr"),e._v(" "),o("h2",{attrs:{id:"add-descriptive-metadata"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#add-descriptive-metadata"}},[e._v("#")]),e._v(" Add descriptive metadata")]),e._v(" "),o("p",[e._v("In the previous section, we described metadata for each of our datasets, but we’re still missing metadata for our collection of datasets. You can add it via the "),o("strong",[e._v("Metadata")]),e._v(" section on the left side bar, describing things like the dataset name, description, author, license, etc.")]),e._v(" "),o("p",[o("img",{attrs:{src:t(483),alt:"Add Data Package Metadata"}})]),e._v(" "),o("p",[e._v("The "),o("strong",[e._v("Profile")]),e._v(" section under metadata allows us to specify what kind of data collection we are packaging.")]),e._v(" "),o("ul",[o("li",[o("p",[o("em",[e._v("Data Package")]),o("br"),e._v("\nThis is the base, more general profile. Use it if your dataset contains resources of mixed formats, like tabular and geographical data. The base requirement for a valid Data Package profile is the "),o("em",[e._v("datapackage.json")]),e._v(" file. See the "),o("a",{attrs:{href:"https://specs.frictionlessdata.io/data-package/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Data Package specification"),o("OutboundLink")],1),e._v(" for more information.")])]),e._v(" "),o("li",[o("p",[o("em",[e._v("Tabular Data Package")]),o("br"),e._v("\nIf your data contains only tabular resources like CSVs and spreadsheets, use the Tabular Data Package profile. See the "),o("a",{attrs:{href:"https://specs.frictionlessdata.io/tabular-data-package/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Tabular Data Package specification"),o("OutboundLink")],1),e._v(" for more information.")])]),e._v(" "),o("li",[o("p",[o("em",[e._v("Fiscal Data Package")]),o("br"),e._v("\nIf your data contains fiscal information like budgets and expenditure data, use the Fiscal Data Package profile. See the "),o("a",{attrs:{href:"https://specs.frictionlessdata.io/fiscal-data-package/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Fiscal Data Package specification"),o("OutboundLink")],1),e._v(" for more information.")])])]),e._v(" "),o("p",[e._v("In our example, as we only have a CSV data resource, the "),o("em",[e._v("Tabular Data Package")]),e._v(" profile is the best option.")]),e._v(" "),o("p",[e._v("In the "),o("strong",[e._v("Keywords")]),e._v(" section, you can add any keywords that helps make your data collection more discoverable. For our dataset, we might use the keywords "),o("em",[e._v("GDP, National Accounts, National GDP, Regional GDP")]),e._v(". Other datasets could include the country name, dataset area (e.g. “health” or “environmental”), etc.")]),e._v(" "),o("p",[e._v("Now that we have created a Data Package, we can "),o("strong",[e._v("Validate")]),e._v(" or "),o("strong",[e._v("Download")]),e._v(" it. But first, let’s see what our datapackage.json file looks like. With every addition and modification, the "),o("a",{attrs:{href:"https://create.frictionlessdata.io",target:"_blank",rel:"noopener noreferrer"}},[e._v("Data Package Creator"),o("OutboundLink")],1),e._v(" has been populating the "),o("em",[e._v("datapackage.json")]),e._v(" file for us. Click on the "),o("strong",[e._v("{···}")]),e._v(" icon to view the "),o("em",[e._v("datapackage.json")]),e._v(" file. As you can see below, any edit we make to the description of the Value field reflects on the JSON file in real time.")]),e._v(" "),o("p",[e._v("The "),o("strong",[e._v("Validate")]),e._v(" button allows us to confirm whether we chose the correct Profile for our Data Package. The two possible outcomes at this stage are:")]),e._v(" "),o("p",[o("img",{attrs:{src:t(484),alt:"Data Package is Invalid"}})]),e._v(" "),o("p",[e._v("This message appears when there is some validation error like if we miss some required attribute (e.g. the data package name), or have picked an incorrect profile (e.g. Tabular Data Package with geographical data)… Review the metadata and profiles to find the mistake and try validating again.")]),e._v(" "),o("p",[o("img",{attrs:{src:t(485),alt:"Data Package is Valid"}})]),e._v(" "),o("p",[e._v("All good! This message means that your data package is valid, and we can download it.")]),e._v(" "),o("hr"),e._v(" "),o("h2",{attrs:{id:"create-data-packages"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#create-data-packages"}},[e._v("#")]),e._v(" Create Data Packages")]),e._v(" "),o("p",[e._v("As we said earlier, the base requirement for a valid Data Package profile is the "),o("em",[e._v("datapackage.json")]),e._v(" file, which contains your data schema and metadata. We call this the descriptor file. You can download your descriptor file by clicking on the "),o("strong",[e._v("Download")]),e._v(" button.")]),e._v(" "),o("ul",[o("li",[o("p",[e._v("If your data resources, like ours, were linked from an online public source, sharing the "),o("em",[e._v("datapackage.json")]),e._v(" file is sufficient, since it contains URLs to your data resources.")])]),e._v(" "),o("li",[o("p",[e._v("If you manually created a data resource and its fields, remember to add all your data resources and the downloaded "),o("em",[e._v("datapackage.json")]),e._v(" file in one folder before sharing it.")])])]),e._v(" "),o("p",[e._v("The way to structure your dataset depends on your data, and what extra artifacts it contains (e.g. images, scripts, reports, etc.). In this section, we’ll show a complete example with:")]),e._v(" "),o("ul",[o("li",[o("strong",[e._v("Data files")]),e._v(": The files with the actual data (e.g. CSV, XLS, GeoJSON, …)")]),e._v(" "),o("li",[o("strong",[e._v("Documentation")]),e._v(": How was the data collected, any caveats, how to update it, etc.")]),e._v(" "),o("li",[o("strong",[e._v("Metadata")]),e._v(": Where the data comes from, what’s in the files, what’s their source and license, etc.")]),e._v(" "),o("li",[o("strong",[e._v("Scripts")]),e._v(": Software scripts that were used to generate, update, or modify the data.")])]),e._v(" "),o("p",[e._v("Your final Data Package file directory should look like this:")]),e._v(" "),o("div",{staticClass:"language- extra-class"},[o("pre",{pre:!0,attrs:{class:"language-text"}},[o("code",[e._v("data/\n dataresource1.csv\n dataresource2.csv\ndatapackage.json\n")])])]),o("ul",[o("li",[o("p",[o("strong",[e._v("data/")]),e._v(": All data files are contained in this folder. In our example, there is only one: "),o("code",[e._v("data/gdp.csv")]),e._v(" .")])]),e._v(" "),o("li",[o("p",[o("strong",[e._v("datapackage.json")]),e._v(": This file describes the dataset’s metadata. For example, what is the dataset, where are its files, what they contain, what each column means (for tabular data), what’s the source, license, and authors, and so on. As it’s a machine-readable specification, other software can import and validate your files.")])])]),e._v(" "),o("p",[e._v("Congratulations! You have now created a schema for your data, and combined it with descriptive metadata and your data collection to create your first data package!")]),e._v(" "),o("hr"),e._v(" "),o("h2",{attrs:{id:"validate-your-packaged-data-automatically"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#validate-your-packaged-data-automatically"}},[e._v("#")]),e._v(" Validate your packaged data automatically")]),e._v(" "),o("p",[e._v("Running continuous checks on data provides regular feedback and contributes to better data quality as errors can be flagged and fixed early on.")]),e._v(" "),o("p",[e._v("In this section, you will learn how to setup automatic tabular data validation using goodtables, so your data is validated every time it’s updated. Although not strictly necessary, it’s useful to "),o("RouterLink",{attrs:{to:"/blog/2018/03/07/well-packaged-datasets/"}},[e._v("know about Data Packages and Table Schema")]),e._v(" before proceeding, as they allow you to describe your data in more detail, allowing more advanced validations.")],1),e._v(" "),o("p",[e._v("We will show how to set up automated tabular data validations for data published on:")]),e._v(" "),o("ul",[o("li",[o("a",{attrs:{href:"https://ckan.org/",target:"_blank",rel:"noopener noreferrer"}},[e._v("CKAN"),o("OutboundLink")],1),e._v(", an open source platform for publishing data in the open, that makes it easy to discover, use and share data;")]),e._v(" "),o("li",[o("a",{attrs:{href:"https://github.com/",target:"_blank",rel:"noopener noreferrer"}},[e._v("GitHub"),o("OutboundLink")],1),e._v(", a web platform for collaborating on projects as well as publishing, sharing and storing resources, such as data files;")]),e._v(" "),o("li",[o("a",{attrs:{href:"https://aws.amazon.com/s3/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Amazon S3"),o("OutboundLink")],1),e._v(", a data storage service by Amazon.")])]),e._v(" "),o("p",[e._v("Even if you don’t use any of these platforms, you can still setup the validation using "),o("a",{attrs:{href:"https://github.com/frictionlessdata/goodtables-py",title:"Goodtables.py",target:"_blank",rel:"noopener noreferrer"}},[e._v("goodtables-py"),o("OutboundLink")],1),e._v(", it will just require some technical knowledge")]),e._v(" "),o("h3",{attrs:{id:"validate-tabular-data-automatically-on-ckan"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#validate-tabular-data-automatically-on-ckan"}},[e._v("#")]),e._v(" Validate tabular data automatically on CKAN")]),e._v(" "),o("p",[o("a",{attrs:{href:"https://ckan.org/",target:"_blank",rel:"noopener noreferrer"}},[e._v("CKAN"),o("OutboundLink")],1),e._v(" is an open source platform for publishing data online. It is widely used across the planet, including by the federal governments of the USA, United Kingdom, Brazil, and others.")]),e._v(" "),o("p",[e._v("To automatically validate tabular data on CKAN, enable the "),o("a",{attrs:{href:"https://github.com/frictionlessdata/ckanext-validation",target:"_blank",rel:"noopener noreferrer"}},[e._v("ckanext-validation"),o("OutboundLink")],1),e._v(" extension, which uses goodtables to run continuous checks on your data. The "),o("a",{attrs:{href:"https://github.com/frictionlessdata/ckanext-validation",target:"_blank",rel:"noopener noreferrer"}},[e._v("ckanext-validation"),o("OutboundLink")],1),e._v(" extension:")]),e._v(" "),o("ul",[o("li",[e._v("Adds a badge next to each dataset showing the status of their validation (valid or invalid), and")]),e._v(" "),o("li",[e._v("allows users to access the validation report, making it possible for errors to be identified and fixed.")])]),e._v(" "),o("p",[o("img",{attrs:{src:t(486),alt:"annotated in red, automated validation checks on datasets in CKAN"}}),o("br"),e._v(" "),o("em",[e._v("Figure 8: annotated in red, automated validation checks on datasets in CKAN.")])]),e._v(" "),o("p",[e._v("The installation and usage instructions for "),o("a",{attrs:{href:"https://github.com/frictionlessdata/ckanext-validation",target:"_blank",rel:"noopener noreferrer"}},[e._v("ckanext-validation"),o("OutboundLink")],1),e._v(" extension are available on "),o("a",{attrs:{href:"https://github.com/frictionlessdata/ckanext-validation",target:"_blank",rel:"noopener noreferrer"}},[e._v("Github"),o("OutboundLink")],1),e._v(".")]),e._v(" "),o("h3",{attrs:{id:"validate-tabular-data-automatically-on-github"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#validate-tabular-data-automatically-on-github"}},[e._v("#")]),e._v(" Validate tabular data automatically on GitHub")]),e._v(" "),o("p",[e._v("If your data is hosted on GitHub, you can use "),o("a",{attrs:{href:"https://goodtables.io",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://goodtables.io"),o("OutboundLink")],1),e._v(" to automatically validate it on every change.")]),e._v(" "),o("p",[e._v("For this section, you will first need to create a "),o("a",{attrs:{href:"https://help.github.com/articles/create-a-repo/",target:"_blank",rel:"noopener noreferrer"}},[e._v("GitHub repository"),o("OutboundLink")],1),e._v(" and add tabular data to it.")]),e._v(" "),o("p",[e._v("Once you have tabular data in your Github repository:")]),e._v(" "),o("ol",[o("li",[e._v("Login on "),o("a",{attrs:{href:"https://goodtables.io",target:"_blank",rel:"noopener noreferrer"}},[e._v("goodtables.io"),o("OutboundLink")],1),e._v(" using your GitHub account and accept the permissions confirmation.")]),e._v(" "),o("li",[e._v("Once we’ve synchronized your repository list, go to the "),o("a",{attrs:{href:"https://goodtables.io/settings",target:"_blank",rel:"noopener noreferrer"}},[e._v("Manage Sources"),o("OutboundLink")],1),e._v(" page and enable the repository with the data you want to validate.\n"),o("ul",[o("li",[e._v("If you can’t find the repository, try clicking on the Refresh button on the Manage Sources page")])])])]),e._v(" "),o("p",[e._v("Goodtables will then validate all tabular data files (CSV, XLS, XLSX, ODS) and "),o("RouterLink",{attrs:{to:"/data-package/"}},[e._v("data packages")]),e._v(" in the repository. These validations will be executed on every change, including pull requests.")],1),e._v(" "),o("h3",{attrs:{id:"validate-tabular-data-automatically-on-amazon-s3"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#validate-tabular-data-automatically-on-amazon-s3"}},[e._v("#")]),e._v(" Validate tabular data automatically on Amazon S3")]),e._v(" "),o("p",[e._v("If your data is hosted on Amazon S3, you can use "),o("a",{attrs:{href:"https://goodtables.io",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://goodtables.io"),o("OutboundLink")],1),e._v(" to automatically validate it on every change.")]),e._v(" "),o("p",[e._v("It is a technical process to set up, as you need to know how to configure your Amazon S3 bucket. However, once it’s configured, the validations happen automatically on any tabular data created or updated. Find the detailed instructions "),o("a",{attrs:{href:"https://docs.goodtables.io/getting_started/s3.html",title:"Validating data on Amazon S3",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),o("OutboundLink")],1),e._v(".")]),e._v(" "),o("h3",{attrs:{id:"custom-setup-of-automatic-tabular-data-validation"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#custom-setup-of-automatic-tabular-data-validation"}},[e._v("#")]),e._v(" Custom setup of automatic tabular data validation")]),e._v(" "),o("p",[e._v("If you don’t use any of the oficially supported data publishing platforms, you can use "),o("a",{attrs:{href:"https://github.com/frictionlessdata/goodtables-py",title:"Goodtables.py",target:"_blank",rel:"noopener noreferrer"}},[e._v("goodtables-py"),o("OutboundLink")],1),e._v(" directly to validate your data. This is the most flexible option, as you can configure exactly when, and how your tabular data is validated. For example, if your data come from an external source, you could validate it once before you process it (so you catch errors in the source data), and once after cleaning, just before you publish it, so you catch errors introduced by your data processing.")]),e._v(" "),o("p",[e._v("The instructions on how to do this are technical, and can be found on "),o("a",{attrs:{href:"https://github.com/frictionlessdata/goodtables-py",title:"Goodtables.py",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://github.com/frictionlessdata/goodtables-py"),o("OutboundLink")],1),e._v(".")]),e._v(" "),o("hr"),e._v(" "),o("h2",{attrs:{id:"publish-packaged-data"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#publish-packaged-data"}},[e._v("#")]),e._v(" Publish packaged data")]),e._v(" "),o("p",[e._v("Creating and Sharing Data Packages is important for both data publishers and data users because it provides a common and open specification to describe your dataset’s metadata. This facilitates data reuse, as users don’t need to understand each data publisher’s specific metadata format, and as the specification is machine-readable, it also allows tools to parse the metadata. This enables software to:")]),e._v(" "),o("ul",[o("li",[e._v("Import the data packages into different tools and languages, like Python and R")]),e._v(" "),o("li",[e._v("Validate the data contents according to the schema described in the data package")]),e._v(" "),o("li",[e._v("Convert the data package into other formats, for example loading it into a SQL database for further analysis")])]),e._v(" "),o("p",[e._v("Although these reasons are not unique to publishing data as data packages, here’s why we think data publishers should consider publishing in this format:")]),e._v(" "),o("ul",[o("li",[o("p",[e._v("Archiving data collections using data packages ensure data publishers can update data more efficiently at any time. The associated schema is a guide on existing data fields and acceptable data types for individual tabular data resources and can be easily built upon.")])]),e._v(" "),o("li",[o("p",[e._v("Sharing data with descriptive metadata and its associated schema provides context for your data no matter where it is used, and significantly cuts down on time spent researching data provenance before using acquired data.")])]),e._v(" "),o("li",[o("p",[e._v("Data Packages allow for accountability and enrich the feedback process as data publishers can add metadata with contact information for users to reach out to them and licensing to spell out accepted use of published data.")])])]),e._v(" "),o("p",[e._v("If don’t need your own data portal, there are many platforms where you can publish your data (if you need your own, check "),o("a",{attrs:{href:"https://ckan.org/",target:"_blank",rel:"noopener noreferrer"}},[e._v("CKAN"),o("OutboundLink")],1),e._v("). In the section below, we dive into a few options. Read along and decide what option is most suitable:")]),e._v(" "),o("h3",{attrs:{id:"publish-packaged-data-in-our-community-ckan-instance"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#publish-packaged-data-in-our-community-ckan-instance"}},[e._v("#")]),e._v(" Publish packaged data in our community CKAN instance")]),e._v(" "),o("p",[e._v("CKAN is an open source platform for publishing data that makes it easy to discover, use and share data. "),o("a",{attrs:{href:"https://datahub.ckan.io",target:"_blank",rel:"noopener noreferrer"}},[e._v("datahub.ckan.io"),o("OutboundLink")],1),e._v(" is a public instance of CKAN that allows anyone to publish their data.")]),e._v(" "),o("p",[e._v("Here’s why you should consider creating an organization on "),o("a",{attrs:{href:"https://datahub.ckan.io",target:"_blank",rel:"noopener noreferrer"}},[e._v("datahub.ckan.io"),o("OutboundLink")],1),e._v(" and publishing datasets therein:")]),e._v(" "),o("ul",[o("li",[o("a",{attrs:{href:"https://datahub.ckan.io",target:"_blank",rel:"noopener noreferrer"}},[e._v("datahub.ckan.io"),o("OutboundLink")],1),e._v(" is free for all to use! The file upload size limit on the platform is currently 100mb.")]),e._v(" "),o("li",[e._v("The decision on whether to publicly or privately publish datasets rests with data publishers.")]),e._v(" "),o("li",[o("a",{attrs:{href:"https://datahub.ckan.io",target:"_blank",rel:"noopener noreferrer"}},[e._v("datahub.ckan.io"),o("OutboundLink")],1),e._v(" organizations allow for multiple users to collaborate with varied privileges:")]),e._v(" "),o("li",[o("strong",[e._v("Admin")]),e._v(": Can add/edit and delete datasets, as well as manage organization members.")]),e._v(" "),o("li",[o("strong",[e._v("Editor")]),e._v(": Can add and edit datasets, but not manage organization members.")]),e._v(" "),o("li",[o("strong",[e._v("Member")]),e._v(": Can view the organization’s private datasets, but not add new datasets.")])]),e._v(" "),o("p",[e._v("To publish data on "),o("a",{attrs:{href:"https://datahub.ckan.io",target:"_blank",rel:"noopener noreferrer"}},[e._v("datahub.ckan.io"),o("OutboundLink")],1),e._v(":")]),e._v(" "),o("ol",[o("li",[o("p",[e._v("Request for a new Organization to be created on the platform for you via "),o("a",{attrs:{href:"https://discuss.okfn.org/c/open-knowledge-labs/datahub",target:"_blank",rel:"noopener noreferrer"}},[e._v("our community page"),o("OutboundLink")],1),e._v("."),o("br"),e._v("\nThis is required only to ensure spammers don’t take up space and hog resources on the platform.")]),e._v(" "),o("p",[e._v("The request format is simple and requires:")]),e._v(" "),o("ul",[o("li",[o("p",[o("strong",[e._v("Title")]),e._v(": This will be the name of your Organization on "),o("a",{attrs:{href:"https://datahub.ckan.io",target:"_blank",rel:"noopener noreferrer"}},[e._v("datahub.ckan.io"),o("OutboundLink")],1),e._v(" i.e."),o("br"),o("br"),e._v(" "),o("em",[e._v("My New Organization")])])]),e._v(" "),o("li",[o("p",[o("strong",[e._v("Slug")]),e._v(": This is an acronym, word or hyphenated phrase that will be added to the end of the "),o("a",{attrs:{href:"https://datahub.ckan.io",target:"_blank",rel:"noopener noreferrer"}},[e._v("datahub.ckan.io"),o("OutboundLink")],1),e._v(" url to uniquely identify your Organization and associate your data collections with it i.e."),o("br"),o("br"),e._v(" "),o("em",[e._v("my-new-organization")])])]),e._v(" "),o("li",[o("p",[o("strong",[e._v("Username")]),e._v(": The username you provide is associated with an email address on "),o("a",{attrs:{href:"https://datahub.ckan.io",target:"_blank",rel:"noopener noreferrer"}},[e._v("datahub.ckan.io"),o("OutboundLink")],1),e._v(" and allows us to give you admin access to your Organization on "),o("a",{attrs:{href:"https://datahub.ckan.io",target:"_blank",rel:"noopener noreferrer"}},[e._v("datahub.ckan.io"),o("OutboundLink")],1),e._v(".")])])])]),e._v(" "),o("li",[o("p",[e._v("Log In and add new datasets")])])]),e._v(" "),o("p",[e._v("Adding datasets on "),o("a",{attrs:{href:"https://datahub.ckan.io",target:"_blank",rel:"noopener noreferrer"}},[e._v("datahub.ckan.io"),o("OutboundLink")],1),e._v(" is no different from using any other CKAN platform, but "),o("a",{attrs:{href:"http://okfnlabs.org/blog/2016/07/25/publish-data-packages-to-datahub-ckan.html",target:"_blank",rel:"noopener noreferrer"}},[e._v("here’s a good guide by Dan Fowler"),o("OutboundLink")],1),e._v(" for first timers.")]),e._v(" "),o("p",[e._v("3.Publish and share public datasets widely.")]),e._v(" "),o("p",[e._v("On "),o("a",{attrs:{href:"https://datahub.ckan.io",target:"_blank",rel:"noopener noreferrer"}},[e._v("datahub.ckan.io"),o("OutboundLink")],1),e._v(", you can either publish datasets privately, meaning only members of your organization have access to them, or publicly, as open data. "),o("a",{attrs:{href:"http://okfnlabs.org/blog/2016/07/25/publish-data-packages-to-datahub-ckan.html",target:"_blank",rel:"noopener noreferrer"}},[e._v("Find out more"),o("OutboundLink")],1),e._v(".")]),e._v(" "),o("h3",{attrs:{id:"publish-packaged-data-on-datahub-io"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#publish-packaged-data-on-datahub-io"}},[e._v("#")]),e._v(" Publish packaged data on "),o("a",{attrs:{href:"http://DataHub.io",target:"_blank",rel:"noopener noreferrer"}},[e._v("DataHub.io"),o("OutboundLink")],1)]),e._v(" "),o("p",[o("a",{attrs:{href:"http://DataHub.io",target:"_blank",rel:"noopener noreferrer"}},[e._v("DataHub.io"),o("OutboundLink")],1),e._v(" is a platform for finding, sharing and publishing high quality data online.")]),e._v(" "),o("p",[o("a",{attrs:{href:"https://datahub.io",target:"_blank",rel:"noopener noreferrer"}},[e._v("DataHub.io"),o("OutboundLink")],1),e._v(" and "),o("a",{attrs:{href:"https://datahub.ckan.io",target:"_blank",rel:"noopener noreferrer"}},[e._v("datahub.ckan.io"),o("OutboundLink")],1),e._v(" share the same name for historical reasons. "),o("a",{attrs:{href:"https://datahub.ckan.io",target:"_blank",rel:"noopener noreferrer"}},[e._v("Datahub.ckan.io"),o("OutboundLink")],1),e._v(" used to be the DataHub, but was moved to its current address, and the current DataHub uses new software developed from scratch.")]),e._v(" "),o("ol",[o("li",[e._v("Set up a data publisher / user account on "),o("a",{attrs:{href:"https://datahub.io",target:"_blank",rel:"noopener noreferrer"}},[e._v("DataHub.io"),o("OutboundLink")],1)])]),e._v(" "),o("p",[e._v("Join the "),o("a",{attrs:{href:"https://gitter.im/datahubio/chat",target:"_blank",rel:"noopener noreferrer"}},[e._v("datahub.io community group"),o("OutboundLink")],1),e._v(", introduce yourself and request for an account.")]),e._v(" "),o("ol",{attrs:{start:"2"}},[o("li",[e._v("Publish Datasets on "),o("a",{attrs:{href:"https://datahub.io",target:"_blank",rel:"noopener noreferrer"}},[e._v("DataHub.io"),o("OutboundLink")],1)])]),e._v(" "),o("p",[o("a",{attrs:{href:"http://datahub.io/docs/getting-started/publishing-data",target:"_blank",rel:"noopener noreferrer"}},[e._v("This post"),o("OutboundLink")],1),e._v(" provides helpful information on publishing datasets on "),o("a",{attrs:{href:"https://datahub.io",target:"_blank",rel:"noopener noreferrer"}},[e._v("DataHub.io"),o("OutboundLink")],1),e._v(".")]),e._v(" "),o("h3",{attrs:{id:"publish-packaged-data-on-github"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#publish-packaged-data-on-github"}},[e._v("#")]),e._v(" Publish packaged data on GitHub")]),e._v(" "),o("p",[e._v("GitHub is the largest repository of source code, with "),o("a",{attrs:{href:"https://github.com/blog/2345-celebrating-nine-years-of-github-with-an-anniversary-sale",target:"_blank",rel:"noopener noreferrer"}},[e._v("more than 20 million"),o("br"),e._v("\nusers"),o("OutboundLink")],1),e._v(". Although the focus is on hosting source code, any type of file can be hosted. Documents, thesis, images, shapefiles, you can even host an entire static website with "),o("a",{attrs:{href:"https://pages.github.com/",target:"_blank",rel:"noopener noreferrer"}},[e._v("GitHub Pages"),o("OutboundLink")],1),e._v(".")]),e._v(" "),o("p",[e._v("By using GitHub, you get all the advantages of using a version control system as Git, where every modification to your files is tracked. You also get an issue ticketing system, wiki pages, milestones tracking, and other useful"),o("br"),e._v("\ncollaboration tools.")]),e._v(" "),o("p",[e._v("** What types of datasets can be hosted on GitHub?**")]),e._v(" "),o("p",[e._v("Although GitHub offers many useful functionalities, not all datasets are a good fit for it. The main limitations are:")]),e._v(" "),o("ul",[o("li",[e._v("Individual files have less than 100 MB")]),e._v(" "),o("li",[e._v("Entire repository have less than 1 GB\n"),o("ul",[o("li",[e._v("The repository size includes not only the current files, but all of their previous versions.")])])])]),e._v(" "),o("p",[e._v("You can store larger files using "),o("a",{attrs:{href:"https://git-lfs.github.com/",target:"_blank",rel:"noopener noreferrer"}},[e._v("git-lfs"),o("OutboundLink")],1),e._v(", but we won’t go in details about it in this section.")]),e._v(" "),o("p",[e._v("It’s also useful if your data files use text-based file formats like CSV or GeoJSON, as then git is able to show you exactly what changed between two versions of the files. However, even if you use binary file formats like XLS, GitHub is still useful.")]),e._v(" "),o("p",[e._v("** Step 1. Organise your dataset folder structure **")]),e._v(" "),o("p",[e._v("The way to structure your dataset depends on your data, and what extra artifacts it contains (e.g. images, scripts, reports, etc.). In this section, we’ll show a complete example with:")]),e._v(" "),o("ul",[o("li",[o("strong",[e._v("Data files")]),e._v(": The files with the actual data (e.g. CSV, XLS, GeoJSON, …)")]),e._v(" "),o("li",[o("strong",[e._v("Documentation")]),e._v(": How was the data collected, any caveats, how to update it, etc.")]),e._v(" "),o("li",[o("strong",[e._v("Metadata")]),e._v(": Where the data comes from, what’s in the files, what’s their source and license, etc.")]),e._v(" "),o("li",[o("strong",[e._v("Scripts")]),e._v(": Software scripts that were used to generate, update, or modify the data.")])]),e._v(" "),o("p",[e._v("Even though we’ll see an example that has all of these different types of files, this isn’t always the case. For example, datasets that were manually collected might not have any scripts.")]),e._v(" "),o("p",[e._v("Consider this folder structure:")]),e._v(" "),o("div",{staticClass:"language- extra-class"},[o("pre",{pre:!0,attrs:{class:"language-text"}},[o("code",[e._v("data/\n schools.csv\n cities.csv\ndocs/\n screenshot.png\nscripts/\n clean_data.py\nMakefile\ndatapackage.json\nREADME.md\n")])])]),o("ul",[o("li",[o("strong",[e._v("data/")]),e._v(": All data files are contained in this folder. In our example, there are two: "),o("code",[e._v("data/schools.csv")]),e._v(" and "),o("code",[e._v("data/cities.csv")]),e._v(".")]),e._v(" "),o("li",[o("strong",[e._v("docs/")]),e._v(": Images, sample analysis, and other documentation files regarding the dataset. The main documentation is in "),o("code",[e._v("README.md")]),e._v(", but in this folder you can add any images used in the README, and other writings about the dataset.")]),e._v(" "),o("li",[o("strong",[e._v("scripts/")]),e._v(": All scripts are contained in this folder. There could be scripts to scrape the data, join different files, clean them, etc. Depending on the programming language you use, you might also add requirements files like "),o("code",[e._v("requirements.txt")]),e._v(" for Python, or "),o("code",[e._v("package.json")]),e._v(" for NodeJS.")]),e._v(" "),o("li",[o("strong",[e._v("Makefile")]),e._v(": The scripts are only part of the puzzle, we also need to know how to run them. In which order they should be executed, which one should I run to update the data, and so on. You could document this information textually in the "),o("code",[e._v("README.md")]),e._v(" file, but the "),o("code",[e._v("Makefile")]),e._v(" allows you to have executable documentation. You can think of it as a script to run the scripts. If you have never written a Makefile, read "),o("a",{attrs:{href:"https://bost.ocks.org/mike/make/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Why Use Make"),o("OutboundLink")],1),e._v(".")]),e._v(" "),o("li",[o("strong",[e._v("datapackage.json")]),e._v(": This file describes the dataset’s metadata. For example, what is the dataset, where are its files, what they contain, what each column means (for tabular data), what’s the source, license, and authors, and so on. As it’s a machine-readable specification, other software can import and validate your files. See "),o("RouterLink",{attrs:{to:"/blog/2018/03/07/well-packaged-datasets/"}},[e._v("how to create a data package")]),e._v(" on instructions on writing this file.")],1),e._v(" "),o("li",[o("strong",[o("a",{attrs:{href:"http://README.md",target:"_blank",rel:"noopener noreferrer"}},[e._v("README.md"),o("OutboundLink")],1)]),e._v(": This is where the dataset is described for humans. We recommend the following sections:\n"),o("ul",[o("li",[o("strong",[e._v("Introduction")]),e._v(": A short description of the dataset, what it contains, the time or geographical area it covers")]),e._v(" "),o("li",[o("strong",[e._v("Data")]),e._v(": What the data structure? Does it use any codes? How do you define missing values (e.g. ‘N/A’ or ‘-1’)")]),e._v(" "),o("li",[o("strong",[e._v("Preparation")]),e._v(": How was the data collected? How do I update the data? Was it modified in any way? If you have a "),o("code",[e._v("Makefile")]),e._v(", this section will mostly document how to run it. Otherwise you can describe how to run the scripts, or how to collect the data manually.")]),e._v(" "),o("li",[o("strong",[e._v("License")]),e._v(": There are two issues here: the license of the data itself, and the license of the package you are creating (including any scripts). Our recommendation is to license the package you created as "),o("a",{attrs:{href:"https://creativecommons.org/publicdomain/zero/1.0/",title:"Creative Commons Public Domain Dedication",target:"_blank",rel:"noopener noreferrer"}},[e._v("CC0"),o("OutboundLink")],1),e._v(", and add any relevant information or disclaimers regarding the source data’s license.")])])])]),e._v(" "),o("p",[e._v("To summarize, these are the folders, files, and their respective contents in this structure:")]),e._v(" "),o("table",[o("thead",[o("tr",[o("th",[e._v("Path")]),e._v(" "),o("th",[e._v("Type")]),e._v(" "),o("th",[e._v("Contents")])])]),e._v(" "),o("tbody",[o("tr",[o("td",[e._v("data/")]),e._v(" "),o("td",[e._v("Data")]),e._v(" "),o("td",[e._v("Dataset’s data files.")])]),e._v(" "),o("tr",[o("td",[e._v("docs/")]),e._v(" "),o("td",[e._v("Documentation")]),e._v(" "),o("td",[e._v("Images, analysis, and other documentation files.")])]),e._v(" "),o("tr",[o("td",[e._v("scripts/")]),e._v(" "),o("td",[e._v("Scripts")]),e._v(" "),o("td",[e._v("Scripts used for creating, modifying, or analysing the dataset.")])]),e._v(" "),o("tr",[o("td",[e._v("Makefile")]),e._v(" "),o("td",[e._v("Scripts")]),e._v(" "),o("td",[e._v("Executable documentation on how to run the scripts.")])]),e._v(" "),o("tr",[o("td",[e._v("datapackage.json")]),e._v(" "),o("td",[e._v("Metadata")]),e._v(" "),o("td",[e._v("Data Package descriptor file.")])]),e._v(" "),o("tr",[o("td",[o("a",{attrs:{href:"http://README.md",target:"_blank",rel:"noopener noreferrer"}},[e._v("README.md"),o("OutboundLink")],1)]),e._v(" "),o("td",[e._v("Documentation")]),e._v(" "),o("td",[e._v("Textual description of the dataset with description, preparation steps, license, etc.")])])])]),e._v(" "),o("p",[e._v("** Step 2. Upload the dataset to GitHub **")]),e._v(" "),o("ol",[o("li",[e._v("Login (or create) a new account on GitHub")]),e._v(" "),o("li",[e._v("Create "),o("a",{attrs:{href:"https://github.com/new",title:"GitHub New Repository",target:"_blank",rel:"noopener noreferrer"}},[e._v("a new repository"),o("OutboundLink")],1),e._v(" "),o("ul",[o("li",[e._v("Write a short description about the dataset")])])]),e._v(" "),o("li",[e._v("On your repository page, click on the “Upload files” link")]),e._v(" "),o("li",[e._v("Upload the files you created in the previous step\n"),o("ul",[o("li",[e._v("If your have files larger than 25 MB, you’ll need to either "),o("a",{attrs:{href:"https://help.github.com/articles/adding-a-file-to-a-repository-using-the-command-line/",title:"Adding a file to a repository using the command line",target:"_blank",rel:"noopener noreferrer"}},[e._v("upload using the command line"),o("OutboundLink")],1),e._v(", or the "),o("a",{attrs:{href:"https://desktop.github.com/",target:"_blank",rel:"noopener noreferrer"}},[e._v("GitHub Desktop client"),o("OutboundLink")],1),e._v(".")])])])]),e._v(" "),o("p",[e._v("** (Optional) Step 3. Enable automatic tabular data validation **")]),e._v(" "),o("p",[e._v("You can automatically validate your tabular data files using "),o("a",{attrs:{href:"https://goodtables.io",target:"_blank",rel:"noopener noreferrer"}},[e._v("goodtables.io"),o("OutboundLink")],1),e._v(". This will take only a few minutes, and will ensure you’ll always know when there are errors with your dataset, maintaining its quality. "),o("a",{attrs:{href:"/blog/2018/03/12/automatically-validated-tabular-data"}},[e._v("Read the walkthrough here")]),e._v(".")]),e._v(" "),o("p",[e._v("The sample datasets used in this example, that is, List of schools in Birmingham, UK are available "),o("a",{attrs:{href:"https://github.com/vitorbaptista/birmingham_schools",target:"_blank",rel:"noopener noreferrer"}},[e._v("in this repository"),o("OutboundLink")],1),e._v(".")]),e._v(" "),o("hr"),e._v(" "),o("h2",{attrs:{id:"share-packaged-data-effectively"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#share-packaged-data-effectively"}},[e._v("#")]),e._v(" Share packaged data effectively")]),e._v(" "),o("p",[e._v("Publishing packaged data is not enough. To avoid hiding useful information in open archives online, it is necessary to engage communities that could be interested in your data. Community engagement should not be viewed as a one-off assignment, but rather, as a continuous effort to increase the impact of your data publishing work.")]),e._v(" "),o("p",[e._v("Some best practices:")]),e._v(" "),o("ol",[o("li",[o("p",[e._v("Publish quickly, update often."),o("br"),e._v("\nThe true value of published data lies in its use and reuse by open data communities. Publish data as soon as possible and update it regularly so users have access to the latest information.")])]),e._v(" "),o("li",[o("p",[e._v("Set up feedback loops."),o("br"),e._v("\nYour data publishing platform should aim to get community “buy in”, by encouraging participatory processes. Feedback loops are important because:")])])]),e._v(" "),o("ul",[o("li",[e._v("they allow data users to ask for clarifications and request for additional information about specific datasets, if need be.")]),e._v(" "),o("li",[e._v("they allow data publishers to understand what communities need and publish data driven by user demand, increasing the chance it’ll be used")]),e._v(" "),o("li",[e._v("they provide an avenue for data publishers to learn how their data is used, so they can gauge its impact.")])]),e._v(" "),o("p",[e._v("Examples of feedback loops that data publishers can set up include:")]),e._v(" "),o("ul",[o("li",[e._v("Adding a comments section in a data platform. Needless to say, the comments section should be monitored closely to ensure that responses are sent in time, and that discussions remain respectful and on topic.")]),e._v(" "),o("li",[e._v("A dedicated social platform channel, such as a Google Group or Facebook group, with a prominently placed link from the data platform for sharing updates, collating and responding to feedback.")]),e._v(" "),o("li",[e._v("An e-mail address where users can contact the people responsible for the datasets for clarifications, suggestions, or to report errors.")])]),e._v(" "),o("ol",{attrs:{start:"3"}},[o("li",[e._v("Meet open data communities in the places they already meet")])]),e._v(" "),o("p",[e._v("Communities thrive when there’s continued discourse over similar interests."),o("br"),e._v("\nData publishers should be active in existing networks, as supporters and collaborators in community data initiatives. Some of the ways this can be done, leveraging on Open Knowledge communities and others, include:")]),e._v(" "),o("ul",[o("li",[o("p",[e._v("Kickstarting and joining discussions in online forums")])]),e._v(" "),o("li",[o("p",[e._v("Blogs"),o("br"),e._v("\nAs a data publisher, running a data blog is a great way to create awareness about the data you publish, and an avenue to highlight how data users are drawing insight from it. This encourages use and reuse of your data. If you don’t run a data blog, there are plenty of open data blogs that welcome external contributions i.e. "),o("a",{attrs:{href:"https://blog.okfn.org/submit/",target:"_blank",rel:"noopener noreferrer"}},[e._v("here’s how"),o("OutboundLink")],1),e._v(" you can contribute guest posts on "),o("a",{attrs:{href:"http://blog.okfn.org",target:"_blank",rel:"noopener noreferrer"}},[e._v("blog.okfn.org"),o("OutboundLink")],1),e._v(".")])]),e._v(" "),o("li",[o("p",[e._v("Open Knowledge Discuss"),o("br"),e._v("\nThe Open Knowledge discussion platform is a great place to invoke and contribute to conversation on specific subjects. "),o("a",{attrs:{href:"https://discuss.okfn.org",target:"_blank",rel:"noopener noreferrer"}},[e._v("Dive in"),o("OutboundLink")],1),e._v("!")])]),e._v(" "),o("li",[o("p",[e._v("Gitter"),o("br"),e._v("\nGitter is a chat platform that’s well suited for more technical discussions around open data. If you are looking to engage technical data users, consider joining our "),o("a",{attrs:{href:"https://gitter.im/okfn/chat",target:"_blank",rel:"noopener noreferrer"}},[e._v("Open Knowledge Foundation channel"),o("OutboundLink")],1),e._v(" or the "),o("a",{attrs:{href:"https://gitter.im/frictionlessdata/chat",target:"_blank",rel:"noopener noreferrer"}},[e._v("Frictionless Data project channel"),o("OutboundLink")],1),e._v(".")])]),e._v(" "),o("li",[o("p",[e._v("In-person meetups"),o("br"),e._v("\nOrganizing and participating in meetups, hackathons and domain-specific conferences is a good way to engage with communities.")])]),e._v(" "),o("li",[o("p",[e._v("Community calls, webinars and podcasts")])])]),e._v(" "),o("p",[e._v("Finally, to maintain an active community of data users as a data publisher:")]),e._v(" "),o("ul",[o("li",[e._v("Keep your datasets updated and highlight changes that might be of interest to the community. For example, if the changes are relevant to a specific data request, reach out and let the user know.")]),e._v(" "),o("li",[e._v("Have a human representative play an active role in community activities. Bots can be fun and efficient, but they are limited and can get in the way of meaningful interactions.")]),e._v(" "),o("li",[e._v("Be flexible and transparent. Listen to your community needs and respond appropriately and in timely fashion i.e. consider publishing datasets that are in high demand first, or more regularly. Archive, rather than delete datasets, but if one must be deleted, issue a forewarning and explain why.")]),e._v(" "),o("li",[e._v("Set up a sharing system to regularly showcase notable data use cases by the the community i.e. fortnightly to inspire other community members.")])])])}),[],!1,null,null,null);a.default=r.exports}}]); \ No newline at end of file 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strict";t.r(a);var o=t(29),r=Object(o.a)({},(function(){var e=this,a=e.$createElement,o=e._self._c||a;return o("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[o("h2",{attrs:{id:"include-a-data-schema"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#include-a-data-schema"}},[e._v("#")]),e._v(" Include a data schema")]),e._v(" "),o("p",[e._v("Simply put, a schema is a blueprint that tells us how your data is structured, and what type of content is to be expected in it. You can think of it as a data dictionary. Having a table schema at hand makes it possible to run more precise validation checks on your data, both at a structural and content level.")]),e._v(" "),o("p",[e._v("For this section, we will use the "),o("a",{attrs:{href:"https://create.frictionlessdata.io",target:"_blank",rel:"noopener noreferrer"}},[e._v("Data Package Creator"),o("OutboundLink")],1),e._v(" and "),o("a",{attrs:{href:"http://datahub.io/core/gdp",target:"_blank",rel:"noopener noreferrer"}},[e._v("Gross Domestic Product dataset for all countries (1960 - 2014)"),o("OutboundLink")],1),e._v(".")]),e._v(" "),o("p",[o("strong",[e._v("Data Package")]),e._v(" is a format that makes it possible to put your data collection and relevant information that provides context about your data in one container before you share it. 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There are three ways in which a user can add data resources on "),o("a",{attrs:{href:"https://create.frictionlessdata.io",target:"_blank",rel:"noopener noreferrer"}},[e._v("Data Package Creator"),o("OutboundLink")],1),e._v(":")]),e._v(" "),o("ol",[o("li",[e._v("Provide a hyperlink to your data resource (highly recommended).")])]),e._v(" "),o("p",[e._v("If your data resource is publicly available, like on GitHub or in a data repository, simply obtain the URL and paste it in the "),o("strong",[e._v("Path")]),e._v(" section. To learn how to publish your data resource online, check the publish your dataset section.")]),e._v(" "),o("ol",{attrs:{start:"2"}},[o("li",[e._v("Create your data resource within the service.")])]),e._v(" "),o("p",[e._v("If your data resource isn’t published online, you’ll have to define its fields from scratch. Depending on how complex is your data, this can be time consuming, but it’s still easier than creating the descriptor JSON file from scratch.This option is time consuming, as a user has to manually create each field of a data resource. However, this is simpler than learning how to create a JSON file from scratch.")]),e._v(" "),o("ol",{attrs:{start:"3"}},[o("li",[o("strong",[e._v("Load a Data Package")]),e._v(" option")])]),e._v(" "),o("p",[e._v("With this option, you can load a pre-existing "),o("em",[e._v("datapackage.json")]),e._v(" file to view and edit its metadata and resource fields.")]),e._v(" "),o("p",[e._v("Our "),o("a",{attrs:{href:"https://github.com/frictionlessdata/example-data-packages/blob/master/gross-domestic-product-all/data/gdp.csv",target:"_blank",rel:"noopener noreferrer"}},[e._v("Gross Domestic Product dataset for all countries (1960 - 2014)"),o("OutboundLink")],1),e._v(" dataset is publicly available on GitHub.")]),e._v(" "),o("p",[e._v("Obtain a link to the raw CSV file by clicking on the Raw button at the top right corner of the GitHub file preview page, as shown in figure 1 below. The resulting hyperlink looks like "),o("a",{attrs:{href:"https://raw.githubusercontent.com/datasets/continent-codes/master/data/continent-codes.csv",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://raw.githubusercontent.com/datasets/continent-codes/master/data/continent-codes.csv"),o("OutboundLink")],1)]),e._v(" "),o("p",[o("img",{attrs:{src:t(472),alt:"Above, raw button highlighted in red"}}),o("br"),e._v(" "),o("em",[e._v("Figure 1: Above, raw button highlighted in red.")])]),e._v(" "),o("p",[e._v("Paste your hyperlink in the "),o("em",[e._v("Path")]),e._v(" section and click on the "),o("em",[e._v("Load")]),e._v(" button. Each column in your table translates to a "),o("em",[e._v("field")]),e._v(". You should be prompted to add all fields identified in your data resource, as in Figure 2 below. Click on the prompt to load the fields.")]),e._v(" "),o("p",[o("img",{attrs:{src:t(473),alt:"annotated in red, a prompt to add all fields inferred from your data resource"}}),o("br"),e._v(" "),o("em",[e._v("Figure 2: annotated in red, a prompt to add all fields inferred from your data resource.")])]),e._v(" "),o("p",[e._v("The page that follows looks like Figure 3 below. Each column from the GDP dataset has been mapped to a "),o("em",[e._v("field")]),e._v(". The data type for each column has been inferred correctly, and we can preview data under each field by hovering over the field name. It is also possible to edit all sections of our data resource’s fields as we can see below.")]),e._v(" "),o("p",[o("img",{attrs:{src:t(474),alt:"all fields inferred from your data resource"}}),o("br"),e._v(" "),o("em",[e._v("Figure 3: all fields inferred from your data resource.")])]),e._v(" "),o("p",[e._v("You can now edit data types and formats as necessary, and optionally add titles and descriptive information to your fields. For example, the data type for our {Year} field should be "),o("em",[o("strong",[e._v("year")])]),e._v(" and not "),o("em",[o("strong",[e._v("integer")])]),e._v(". Our {Value} column has numeric information with decimal places.")]),e._v(" "),o("p",[e._v("By definition, values under the "),o("em",[o("strong",[e._v("integer")])]),e._v(" data type are whole numbers. The "),o("em",[o("strong",[e._v("number")])]),e._v(" data type is more appropriate for the {Value} column. When in doubt about what data type to use, consult the "),o("a",{attrs:{href:"https://specs.frictionlessdata.io/table-schema/#types-and-formats",target:"_blank",rel:"noopener noreferrer"}},[e._v("Table Schema data types cheat sheet"),o("OutboundLink")],1),e._v(".")]),e._v(" "),o("p",[e._v("Click on the "),o("img",{attrs:{src:t(475),alt:""}}),e._v(" icon to pick a suitable profile for your data resource. "),o("a",{attrs:{href:"https://specs.frictionlessdata.io/profiles/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Here’s more information about Frictionless Data profiles"),o("OutboundLink")],1),e._v(".")]),e._v(" "),o("p",[e._v("If your dataset has other data resources, add them by scrolling to the bottom of the page, clicking on Add Resource, and repeating the same process as we just did.")]),e._v(" "),o("p",[e._v("If your dataset has other data resources, add them by scrolling to the bottom of the page, clicking on "),o("strong",[e._v("Add Resource")]),e._v(", and repeating the same process as we just did.")]),e._v(" "),o("p",[o("img",{attrs:{src:t(476),alt:"Prompt to add more data resources"}}),o("br"),e._v(" "),o("em",[e._v("Figure 4: Prompt to add more data resources.")])]),e._v(" "),o("hr"),e._v(" "),o("h2",{attrs:{id:"add-descriptive-metadata"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#add-descriptive-metadata"}},[e._v("#")]),e._v(" Add descriptive metadata")]),e._v(" "),o("p",[e._v("In the previous section, we described metadata for each of our datasets, but we’re still missing metadata for our collection of datasets. You can add it via the "),o("strong",[e._v("Metadata")]),e._v(" section on the left side bar, describing things like the dataset name, description, author, license, etc.")]),e._v(" "),o("p",[o("img",{attrs:{src:t(477),alt:"Add Data Package Metadata"}})]),e._v(" "),o("p",[e._v("The "),o("strong",[e._v("Profile")]),e._v(" section under metadata allows us to specify what kind of data collection we are packaging.")]),e._v(" "),o("ul",[o("li",[o("p",[o("em",[e._v("Data Package")]),o("br"),e._v("\nThis is the base, more general profile. Use it if your dataset contains resources of mixed formats, like tabular and geographical data. The base requirement for a valid Data Package profile is the "),o("em",[e._v("datapackage.json")]),e._v(" file. See the "),o("a",{attrs:{href:"https://specs.frictionlessdata.io/data-package/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Data Package specification"),o("OutboundLink")],1),e._v(" for more information.")])]),e._v(" "),o("li",[o("p",[o("em",[e._v("Tabular Data Package")]),o("br"),e._v("\nIf your data contains only tabular resources like CSVs and spreadsheets, use the Tabular Data Package profile. See the "),o("a",{attrs:{href:"https://specs.frictionlessdata.io/tabular-data-package/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Tabular Data Package specification"),o("OutboundLink")],1),e._v(" for more information.")])]),e._v(" "),o("li",[o("p",[o("em",[e._v("Fiscal Data Package")]),o("br"),e._v("\nIf your data contains fiscal information like budgets and expenditure data, use the Fiscal Data Package profile. See the "),o("a",{attrs:{href:"https://specs.frictionlessdata.io/fiscal-data-package/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Fiscal Data Package specification"),o("OutboundLink")],1),e._v(" for more information.")])])]),e._v(" "),o("p",[e._v("In our example, as we only have a CSV data resource, the "),o("em",[e._v("Tabular Data Package")]),e._v(" profile is the best option.")]),e._v(" "),o("p",[e._v("In the "),o("strong",[e._v("Keywords")]),e._v(" section, you can add any keywords that helps make your data collection more discoverable. For our dataset, we might use the keywords "),o("em",[e._v("GDP, National Accounts, National GDP, Regional GDP")]),e._v(". Other datasets could include the country name, dataset area (e.g. “health” or “environmental”), etc.")]),e._v(" "),o("p",[e._v("Now that we have created a Data Package, we can "),o("strong",[e._v("Validate")]),e._v(" or "),o("strong",[e._v("Download")]),e._v(" it. But first, let’s see what our datapackage.json file looks like. With every addition and modification, the "),o("a",{attrs:{href:"https://create.frictionlessdata.io",target:"_blank",rel:"noopener noreferrer"}},[e._v("Data Package Creator"),o("OutboundLink")],1),e._v(" has been populating the "),o("em",[e._v("datapackage.json")]),e._v(" file for us. Click on the "),o("strong",[e._v("{···}")]),e._v(" icon to view the "),o("em",[e._v("datapackage.json")]),e._v(" file. As you can see below, any edit we make to the description of the Value field reflects on the JSON file in real time.")]),e._v(" "),o("p",[e._v("The "),o("strong",[e._v("Validate")]),e._v(" button allows us to confirm whether we chose the correct Profile for our Data Package. The two possible outcomes at this stage are:")]),e._v(" "),o("p",[o("img",{attrs:{src:t(478),alt:"Data Package is Invalid"}})]),e._v(" "),o("p",[e._v("This message appears when there is some validation error like if we miss some required attribute (e.g. the data package name), or have picked an incorrect profile (e.g. Tabular Data Package with geographical data)… Review the metadata and profiles to find the mistake and try validating again.")]),e._v(" "),o("p",[o("img",{attrs:{src:t(479),alt:"Data Package is Valid"}})]),e._v(" "),o("p",[e._v("All good! This message means that your data package is valid, and we can download it.")]),e._v(" "),o("hr"),e._v(" "),o("h2",{attrs:{id:"create-data-packages"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#create-data-packages"}},[e._v("#")]),e._v(" Create Data Packages")]),e._v(" "),o("p",[e._v("As we said earlier, the base requirement for a valid Data Package profile is the "),o("em",[e._v("datapackage.json")]),e._v(" file, which contains your data schema and metadata. We call this the descriptor file. You can download your descriptor file by clicking on the "),o("strong",[e._v("Download")]),e._v(" button.")]),e._v(" "),o("ul",[o("li",[o("p",[e._v("If your data resources, like ours, were linked from an online public source, sharing the "),o("em",[e._v("datapackage.json")]),e._v(" file is sufficient, since it contains URLs to your data resources.")])]),e._v(" "),o("li",[o("p",[e._v("If you manually created a data resource and its fields, remember to add all your data resources and the downloaded "),o("em",[e._v("datapackage.json")]),e._v(" file in one folder before sharing it.")])])]),e._v(" "),o("p",[e._v("The way to structure your dataset depends on your data, and what extra artifacts it contains (e.g. images, scripts, reports, etc.). In this section, we’ll show a complete example with:")]),e._v(" "),o("ul",[o("li",[o("strong",[e._v("Data files")]),e._v(": The files with the actual data (e.g. CSV, XLS, GeoJSON, …)")]),e._v(" "),o("li",[o("strong",[e._v("Documentation")]),e._v(": How was the data collected, any caveats, how to update it, etc.")]),e._v(" "),o("li",[o("strong",[e._v("Metadata")]),e._v(": Where the data comes from, what’s in the files, what’s their source and license, etc.")]),e._v(" "),o("li",[o("strong",[e._v("Scripts")]),e._v(": Software scripts that were used to generate, update, or modify the data.")])]),e._v(" "),o("p",[e._v("Your final Data Package file directory should look like this:")]),e._v(" "),o("div",{staticClass:"language- extra-class"},[o("pre",{pre:!0,attrs:{class:"language-text"}},[o("code",[e._v("data/\n dataresource1.csv\n dataresource2.csv\ndatapackage.json\n")])])]),o("ul",[o("li",[o("p",[o("strong",[e._v("data/")]),e._v(": All data files are contained in this folder. In our example, there is only one: "),o("code",[e._v("data/gdp.csv")]),e._v(" .")])]),e._v(" "),o("li",[o("p",[o("strong",[e._v("datapackage.json")]),e._v(": This file describes the dataset’s metadata. For example, what is the dataset, where are its files, what they contain, what each column means (for tabular data), what’s the source, license, and authors, and so on. As it’s a machine-readable specification, other software can import and validate your files.")])])]),e._v(" "),o("p",[e._v("Congratulations! You have now created a schema for your data, and combined it with descriptive metadata and your data collection to create your first data package!")]),e._v(" "),o("hr"),e._v(" "),o("h2",{attrs:{id:"validate-your-packaged-data-automatically"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#validate-your-packaged-data-automatically"}},[e._v("#")]),e._v(" Validate your packaged data automatically")]),e._v(" "),o("p",[e._v("Running continuous checks on data provides regular feedback and contributes to better data quality as errors can be flagged and fixed early on.")]),e._v(" "),o("p",[e._v("In this section, you will learn how to setup automatic tabular data validation using goodtables, so your data is validated every time it’s updated. Although not strictly necessary, it’s useful to "),o("RouterLink",{attrs:{to:"/blog/2018/03/07/well-packaged-datasets/"}},[e._v("know about Data Packages and Table Schema")]),e._v(" before proceeding, as they allow you to describe your data in more detail, allowing more advanced validations.")],1),e._v(" "),o("p",[e._v("We will show how to set up automated tabular data validations for data published on:")]),e._v(" "),o("ul",[o("li",[o("a",{attrs:{href:"https://ckan.org/",target:"_blank",rel:"noopener noreferrer"}},[e._v("CKAN"),o("OutboundLink")],1),e._v(", an open source platform for publishing data in the open, that makes it easy to discover, use and share data;")]),e._v(" "),o("li",[o("a",{attrs:{href:"https://github.com/",target:"_blank",rel:"noopener noreferrer"}},[e._v("GitHub"),o("OutboundLink")],1),e._v(", a web platform for collaborating on projects as well as publishing, sharing and storing resources, such as data files;")]),e._v(" "),o("li",[o("a",{attrs:{href:"https://aws.amazon.com/s3/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Amazon S3"),o("OutboundLink")],1),e._v(", a data storage service by Amazon.")])]),e._v(" "),o("p",[e._v("Even if you don’t use any of these platforms, you can still setup the validation using "),o("a",{attrs:{href:"https://github.com/frictionlessdata/goodtables-py",title:"Goodtables.py",target:"_blank",rel:"noopener noreferrer"}},[e._v("goodtables-py"),o("OutboundLink")],1),e._v(", it will just require some technical knowledge")]),e._v(" "),o("h3",{attrs:{id:"validate-tabular-data-automatically-on-ckan"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#validate-tabular-data-automatically-on-ckan"}},[e._v("#")]),e._v(" Validate tabular data automatically on CKAN")]),e._v(" "),o("p",[o("a",{attrs:{href:"https://ckan.org/",target:"_blank",rel:"noopener noreferrer"}},[e._v("CKAN"),o("OutboundLink")],1),e._v(" is an open source platform for publishing data online. It is widely used across the planet, including by the federal governments of the USA, United Kingdom, Brazil, and others.")]),e._v(" "),o("p",[e._v("To automatically validate tabular data on CKAN, enable the "),o("a",{attrs:{href:"https://github.com/frictionlessdata/ckanext-validation",target:"_blank",rel:"noopener noreferrer"}},[e._v("ckanext-validation"),o("OutboundLink")],1),e._v(" extension, which uses goodtables to run continuous checks on your data. The "),o("a",{attrs:{href:"https://github.com/frictionlessdata/ckanext-validation",target:"_blank",rel:"noopener noreferrer"}},[e._v("ckanext-validation"),o("OutboundLink")],1),e._v(" extension:")]),e._v(" "),o("ul",[o("li",[e._v("Adds a badge next to each dataset showing the status of their validation (valid or invalid), and")]),e._v(" "),o("li",[e._v("allows users to access the validation report, making it possible for errors to be identified and fixed.")])]),e._v(" "),o("p",[o("img",{attrs:{src:t(480),alt:"annotated in red, automated validation checks on datasets in CKAN"}}),o("br"),e._v(" "),o("em",[e._v("Figure 8: annotated in red, automated validation checks on datasets in CKAN.")])]),e._v(" "),o("p",[e._v("The installation and usage instructions for "),o("a",{attrs:{href:"https://github.com/frictionlessdata/ckanext-validation",target:"_blank",rel:"noopener noreferrer"}},[e._v("ckanext-validation"),o("OutboundLink")],1),e._v(" extension are available on "),o("a",{attrs:{href:"https://github.com/frictionlessdata/ckanext-validation",target:"_blank",rel:"noopener noreferrer"}},[e._v("Github"),o("OutboundLink")],1),e._v(".")]),e._v(" "),o("h3",{attrs:{id:"validate-tabular-data-automatically-on-github"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#validate-tabular-data-automatically-on-github"}},[e._v("#")]),e._v(" Validate tabular data automatically on GitHub")]),e._v(" "),o("p",[e._v("If your data is hosted on GitHub, you can use "),o("a",{attrs:{href:"https://goodtables.io",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://goodtables.io"),o("OutboundLink")],1),e._v(" to automatically validate it on every change.")]),e._v(" "),o("p",[e._v("For this section, you will first need to create a "),o("a",{attrs:{href:"https://help.github.com/articles/create-a-repo/",target:"_blank",rel:"noopener noreferrer"}},[e._v("GitHub repository"),o("OutboundLink")],1),e._v(" and add tabular data to it.")]),e._v(" "),o("p",[e._v("Once you have tabular data in your Github repository:")]),e._v(" "),o("ol",[o("li",[e._v("Login on "),o("a",{attrs:{href:"https://goodtables.io",target:"_blank",rel:"noopener noreferrer"}},[e._v("goodtables.io"),o("OutboundLink")],1),e._v(" using your GitHub account and accept the permissions confirmation.")]),e._v(" "),o("li",[e._v("Once we’ve synchronized your repository list, go to the "),o("a",{attrs:{href:"https://goodtables.io/settings",target:"_blank",rel:"noopener noreferrer"}},[e._v("Manage Sources"),o("OutboundLink")],1),e._v(" page and enable the repository with the data you want to validate.\n"),o("ul",[o("li",[e._v("If you can’t find the repository, try clicking on the Refresh button on the Manage Sources page")])])])]),e._v(" "),o("p",[e._v("Goodtables will then validate all tabular data files (CSV, XLS, XLSX, ODS) and "),o("RouterLink",{attrs:{to:"/data-package/"}},[e._v("data packages")]),e._v(" in the repository. These validations will be executed on every change, including pull requests.")],1),e._v(" "),o("h3",{attrs:{id:"validate-tabular-data-automatically-on-amazon-s3"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#validate-tabular-data-automatically-on-amazon-s3"}},[e._v("#")]),e._v(" Validate tabular data automatically on Amazon S3")]),e._v(" "),o("p",[e._v("If your data is hosted on Amazon S3, you can use "),o("a",{attrs:{href:"https://goodtables.io",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://goodtables.io"),o("OutboundLink")],1),e._v(" to automatically validate it on every change.")]),e._v(" "),o("p",[e._v("It is a technical process to set up, as you need to know how to configure your Amazon S3 bucket. However, once it’s configured, the validations happen automatically on any tabular data created or updated. Find the detailed instructions "),o("a",{attrs:{href:"https://docs.goodtables.io/getting_started/s3.html",title:"Validating data on Amazon S3",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),o("OutboundLink")],1),e._v(".")]),e._v(" "),o("h3",{attrs:{id:"custom-setup-of-automatic-tabular-data-validation"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#custom-setup-of-automatic-tabular-data-validation"}},[e._v("#")]),e._v(" Custom setup of automatic tabular data validation")]),e._v(" "),o("p",[e._v("If you don’t use any of the oficially supported data publishing platforms, you can use "),o("a",{attrs:{href:"https://github.com/frictionlessdata/goodtables-py",title:"Goodtables.py",target:"_blank",rel:"noopener noreferrer"}},[e._v("goodtables-py"),o("OutboundLink")],1),e._v(" directly to validate your data. This is the most flexible option, as you can configure exactly when, and how your tabular data is validated. For example, if your data come from an external source, you could validate it once before you process it (so you catch errors in the source data), and once after cleaning, just before you publish it, so you catch errors introduced by your data processing.")]),e._v(" "),o("p",[e._v("The instructions on how to do this are technical, and can be found on "),o("a",{attrs:{href:"https://github.com/frictionlessdata/goodtables-py",title:"Goodtables.py",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://github.com/frictionlessdata/goodtables-py"),o("OutboundLink")],1),e._v(".")]),e._v(" "),o("hr"),e._v(" "),o("h2",{attrs:{id:"publish-packaged-data"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#publish-packaged-data"}},[e._v("#")]),e._v(" Publish packaged data")]),e._v(" "),o("p",[e._v("Creating and Sharing Data Packages is important for both data publishers and data users because it provides a common and open specification to describe your dataset’s metadata. This facilitates data reuse, as users don’t need to understand each data publisher’s specific metadata format, and as the specification is machine-readable, it also allows tools to parse the metadata. This enables software to:")]),e._v(" "),o("ul",[o("li",[e._v("Import the data packages into different tools and languages, like Python and R")]),e._v(" "),o("li",[e._v("Validate the data contents according to the schema described in the data package")]),e._v(" "),o("li",[e._v("Convert the data package into other formats, for example loading it into a SQL database for further analysis")])]),e._v(" "),o("p",[e._v("Although these reasons are not unique to publishing data as data packages, here’s why we think data publishers should consider publishing in this format:")]),e._v(" "),o("ul",[o("li",[o("p",[e._v("Archiving data collections using data packages ensure data publishers can update data more efficiently at any time. The associated schema is a guide on existing data fields and acceptable data types for individual tabular data resources and can be easily built upon.")])]),e._v(" "),o("li",[o("p",[e._v("Sharing data with descriptive metadata and its associated schema provides context for your data no matter where it is used, and significantly cuts down on time spent researching data provenance before using acquired data.")])]),e._v(" "),o("li",[o("p",[e._v("Data Packages allow for accountability and enrich the feedback process as data publishers can add metadata with contact information for users to reach out to them and licensing to spell out accepted use of published data.")])])]),e._v(" "),o("p",[e._v("If don’t need your own data portal, there are many platforms where you can publish your data (if you need your own, check "),o("a",{attrs:{href:"https://ckan.org/",target:"_blank",rel:"noopener noreferrer"}},[e._v("CKAN"),o("OutboundLink")],1),e._v("). In the section below, we dive into a few options. Read along and decide what option is most suitable:")]),e._v(" "),o("h3",{attrs:{id:"publish-packaged-data-in-our-community-ckan-instance"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#publish-packaged-data-in-our-community-ckan-instance"}},[e._v("#")]),e._v(" Publish packaged data in our community CKAN instance")]),e._v(" "),o("p",[e._v("CKAN is an open source platform for publishing data that makes it easy to discover, use and share data. "),o("a",{attrs:{href:"https://datahub.ckan.io",target:"_blank",rel:"noopener noreferrer"}},[e._v("datahub.ckan.io"),o("OutboundLink")],1),e._v(" is a public instance of CKAN that allows anyone to publish their data.")]),e._v(" "),o("p",[e._v("Here’s why you should consider creating an organization on "),o("a",{attrs:{href:"https://datahub.ckan.io",target:"_blank",rel:"noopener noreferrer"}},[e._v("datahub.ckan.io"),o("OutboundLink")],1),e._v(" and publishing datasets therein:")]),e._v(" "),o("ul",[o("li",[o("a",{attrs:{href:"https://datahub.ckan.io",target:"_blank",rel:"noopener noreferrer"}},[e._v("datahub.ckan.io"),o("OutboundLink")],1),e._v(" is free for all to use! The file upload size limit on the platform is currently 100mb.")]),e._v(" "),o("li",[e._v("The decision on whether to publicly or privately publish datasets rests with data publishers.")]),e._v(" "),o("li",[o("a",{attrs:{href:"https://datahub.ckan.io",target:"_blank",rel:"noopener noreferrer"}},[e._v("datahub.ckan.io"),o("OutboundLink")],1),e._v(" organizations allow for multiple users to collaborate with varied privileges:")]),e._v(" "),o("li",[o("strong",[e._v("Admin")]),e._v(": Can add/edit and delete datasets, as well as manage organization members.")]),e._v(" "),o("li",[o("strong",[e._v("Editor")]),e._v(": Can add and edit datasets, but not manage organization members.")]),e._v(" "),o("li",[o("strong",[e._v("Member")]),e._v(": Can view the organization’s private datasets, but not add new datasets.")])]),e._v(" "),o("p",[e._v("To publish data on "),o("a",{attrs:{href:"https://datahub.ckan.io",target:"_blank",rel:"noopener noreferrer"}},[e._v("datahub.ckan.io"),o("OutboundLink")],1),e._v(":")]),e._v(" "),o("ol",[o("li",[o("p",[e._v("Request for a new Organization to be created on the platform for you via "),o("a",{attrs:{href:"https://discuss.okfn.org/c/open-knowledge-labs/datahub",target:"_blank",rel:"noopener noreferrer"}},[e._v("our community page"),o("OutboundLink")],1),e._v("."),o("br"),e._v("\nThis is required only to ensure spammers don’t take up space and hog resources on the platform.")]),e._v(" "),o("p",[e._v("The request format is simple and requires:")]),e._v(" "),o("ul",[o("li",[o("p",[o("strong",[e._v("Title")]),e._v(": This will be the name of your Organization on "),o("a",{attrs:{href:"https://datahub.ckan.io",target:"_blank",rel:"noopener noreferrer"}},[e._v("datahub.ckan.io"),o("OutboundLink")],1),e._v(" i.e."),o("br"),o("br"),e._v(" "),o("em",[e._v("My New Organization")])])]),e._v(" "),o("li",[o("p",[o("strong",[e._v("Slug")]),e._v(": This is an acronym, word or hyphenated phrase that will be added to the end of the "),o("a",{attrs:{href:"https://datahub.ckan.io",target:"_blank",rel:"noopener noreferrer"}},[e._v("datahub.ckan.io"),o("OutboundLink")],1),e._v(" url to uniquely identify your Organization and associate your data collections with it i.e."),o("br"),o("br"),e._v(" "),o("em",[e._v("my-new-organization")])])]),e._v(" "),o("li",[o("p",[o("strong",[e._v("Username")]),e._v(": The username you provide is associated with an email address on "),o("a",{attrs:{href:"https://datahub.ckan.io",target:"_blank",rel:"noopener noreferrer"}},[e._v("datahub.ckan.io"),o("OutboundLink")],1),e._v(" and allows us to give you admin access to your Organization on "),o("a",{attrs:{href:"https://datahub.ckan.io",target:"_blank",rel:"noopener noreferrer"}},[e._v("datahub.ckan.io"),o("OutboundLink")],1),e._v(".")])])])]),e._v(" "),o("li",[o("p",[e._v("Log In and add new datasets")])])]),e._v(" "),o("p",[e._v("Adding datasets on "),o("a",{attrs:{href:"https://datahub.ckan.io",target:"_blank",rel:"noopener noreferrer"}},[e._v("datahub.ckan.io"),o("OutboundLink")],1),e._v(" is no different from using any other CKAN platform, but "),o("a",{attrs:{href:"http://okfnlabs.org/blog/2016/07/25/publish-data-packages-to-datahub-ckan.html",target:"_blank",rel:"noopener noreferrer"}},[e._v("here’s a good guide by Dan Fowler"),o("OutboundLink")],1),e._v(" for first timers.")]),e._v(" "),o("p",[e._v("3.Publish and share public datasets widely.")]),e._v(" "),o("p",[e._v("On "),o("a",{attrs:{href:"https://datahub.ckan.io",target:"_blank",rel:"noopener noreferrer"}},[e._v("datahub.ckan.io"),o("OutboundLink")],1),e._v(", you can either publish datasets privately, meaning only members of your organization have access to them, or publicly, as open data. "),o("a",{attrs:{href:"http://okfnlabs.org/blog/2016/07/25/publish-data-packages-to-datahub-ckan.html",target:"_blank",rel:"noopener noreferrer"}},[e._v("Find out more"),o("OutboundLink")],1),e._v(".")]),e._v(" "),o("h3",{attrs:{id:"publish-packaged-data-on-datahub-io"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#publish-packaged-data-on-datahub-io"}},[e._v("#")]),e._v(" Publish packaged data on "),o("a",{attrs:{href:"http://DataHub.io",target:"_blank",rel:"noopener noreferrer"}},[e._v("DataHub.io"),o("OutboundLink")],1)]),e._v(" "),o("p",[o("a",{attrs:{href:"http://DataHub.io",target:"_blank",rel:"noopener noreferrer"}},[e._v("DataHub.io"),o("OutboundLink")],1),e._v(" is a platform for finding, sharing and publishing high quality data online.")]),e._v(" "),o("p",[o("a",{attrs:{href:"https://datahub.io",target:"_blank",rel:"noopener noreferrer"}},[e._v("DataHub.io"),o("OutboundLink")],1),e._v(" and "),o("a",{attrs:{href:"https://datahub.ckan.io",target:"_blank",rel:"noopener noreferrer"}},[e._v("datahub.ckan.io"),o("OutboundLink")],1),e._v(" share the same name for historical reasons. "),o("a",{attrs:{href:"https://datahub.ckan.io",target:"_blank",rel:"noopener noreferrer"}},[e._v("Datahub.ckan.io"),o("OutboundLink")],1),e._v(" used to be the DataHub, but was moved to its current address, and the current DataHub uses new software developed from scratch.")]),e._v(" "),o("ol",[o("li",[e._v("Set up a data publisher / user account on "),o("a",{attrs:{href:"https://datahub.io",target:"_blank",rel:"noopener noreferrer"}},[e._v("DataHub.io"),o("OutboundLink")],1)])]),e._v(" "),o("p",[e._v("Join the "),o("a",{attrs:{href:"https://gitter.im/datahubio/chat",target:"_blank",rel:"noopener noreferrer"}},[e._v("datahub.io community group"),o("OutboundLink")],1),e._v(", introduce yourself and request for an account.")]),e._v(" "),o("ol",{attrs:{start:"2"}},[o("li",[e._v("Publish Datasets on "),o("a",{attrs:{href:"https://datahub.io",target:"_blank",rel:"noopener noreferrer"}},[e._v("DataHub.io"),o("OutboundLink")],1)])]),e._v(" "),o("p",[o("a",{attrs:{href:"http://datahub.io/docs/getting-started/publishing-data",target:"_blank",rel:"noopener noreferrer"}},[e._v("This post"),o("OutboundLink")],1),e._v(" provides helpful information on publishing datasets on "),o("a",{attrs:{href:"https://datahub.io",target:"_blank",rel:"noopener noreferrer"}},[e._v("DataHub.io"),o("OutboundLink")],1),e._v(".")]),e._v(" "),o("h3",{attrs:{id:"publish-packaged-data-on-github"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#publish-packaged-data-on-github"}},[e._v("#")]),e._v(" Publish packaged data on GitHub")]),e._v(" "),o("p",[e._v("GitHub is the largest repository of source code, with "),o("a",{attrs:{href:"https://github.com/blog/2345-celebrating-nine-years-of-github-with-an-anniversary-sale",target:"_blank",rel:"noopener noreferrer"}},[e._v("more than 20 million"),o("br"),e._v("\nusers"),o("OutboundLink")],1),e._v(". Although the focus is on hosting source code, any type of file can be hosted. Documents, thesis, images, shapefiles, you can even host an entire static website with "),o("a",{attrs:{href:"https://pages.github.com/",target:"_blank",rel:"noopener noreferrer"}},[e._v("GitHub Pages"),o("OutboundLink")],1),e._v(".")]),e._v(" "),o("p",[e._v("By using GitHub, you get all the advantages of using a version control system as Git, where every modification to your files is tracked. You also get an issue ticketing system, wiki pages, milestones tracking, and other useful"),o("br"),e._v("\ncollaboration tools.")]),e._v(" "),o("p",[e._v("** What types of datasets can be hosted on GitHub?**")]),e._v(" "),o("p",[e._v("Although GitHub offers many useful functionalities, not all datasets are a good fit for it. The main limitations are:")]),e._v(" "),o("ul",[o("li",[e._v("Individual files have less than 100 MB")]),e._v(" "),o("li",[e._v("Entire repository have less than 1 GB\n"),o("ul",[o("li",[e._v("The repository size includes not only the current files, but all of their previous versions.")])])])]),e._v(" "),o("p",[e._v("You can store larger files using "),o("a",{attrs:{href:"https://git-lfs.github.com/",target:"_blank",rel:"noopener noreferrer"}},[e._v("git-lfs"),o("OutboundLink")],1),e._v(", but we won’t go in details about it in this section.")]),e._v(" "),o("p",[e._v("It’s also useful if your data files use text-based file formats like CSV or GeoJSON, as then git is able to show you exactly what changed between two versions of the files. However, even if you use binary file formats like XLS, GitHub is still useful.")]),e._v(" "),o("p",[e._v("** Step 1. Organise your dataset folder structure **")]),e._v(" "),o("p",[e._v("The way to structure your dataset depends on your data, and what extra artifacts it contains (e.g. images, scripts, reports, etc.). In this section, we’ll show a complete example with:")]),e._v(" "),o("ul",[o("li",[o("strong",[e._v("Data files")]),e._v(": The files with the actual data (e.g. CSV, XLS, GeoJSON, …)")]),e._v(" "),o("li",[o("strong",[e._v("Documentation")]),e._v(": How was the data collected, any caveats, how to update it, etc.")]),e._v(" "),o("li",[o("strong",[e._v("Metadata")]),e._v(": Where the data comes from, what’s in the files, what’s their source and license, etc.")]),e._v(" "),o("li",[o("strong",[e._v("Scripts")]),e._v(": Software scripts that were used to generate, update, or modify the data.")])]),e._v(" "),o("p",[e._v("Even though we’ll see an example that has all of these different types of files, this isn’t always the case. For example, datasets that were manually collected might not have any scripts.")]),e._v(" "),o("p",[e._v("Consider this folder structure:")]),e._v(" "),o("div",{staticClass:"language- extra-class"},[o("pre",{pre:!0,attrs:{class:"language-text"}},[o("code",[e._v("data/\n schools.csv\n cities.csv\ndocs/\n screenshot.png\nscripts/\n clean_data.py\nMakefile\ndatapackage.json\nREADME.md\n")])])]),o("ul",[o("li",[o("strong",[e._v("data/")]),e._v(": All data files are contained in this folder. In our example, there are two: "),o("code",[e._v("data/schools.csv")]),e._v(" and "),o("code",[e._v("data/cities.csv")]),e._v(".")]),e._v(" "),o("li",[o("strong",[e._v("docs/")]),e._v(": Images, sample analysis, and other documentation files regarding the dataset. The main documentation is in "),o("code",[e._v("README.md")]),e._v(", but in this folder you can add any images used in the README, and other writings about the dataset.")]),e._v(" "),o("li",[o("strong",[e._v("scripts/")]),e._v(": All scripts are contained in this folder. There could be scripts to scrape the data, join different files, clean them, etc. Depending on the programming language you use, you might also add requirements files like "),o("code",[e._v("requirements.txt")]),e._v(" for Python, or "),o("code",[e._v("package.json")]),e._v(" for NodeJS.")]),e._v(" "),o("li",[o("strong",[e._v("Makefile")]),e._v(": The scripts are only part of the puzzle, we also need to know how to run them. In which order they should be executed, which one should I run to update the data, and so on. You could document this information textually in the "),o("code",[e._v("README.md")]),e._v(" file, but the "),o("code",[e._v("Makefile")]),e._v(" allows you to have executable documentation. You can think of it as a script to run the scripts. If you have never written a Makefile, read "),o("a",{attrs:{href:"https://bost.ocks.org/mike/make/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Why Use Make"),o("OutboundLink")],1),e._v(".")]),e._v(" "),o("li",[o("strong",[e._v("datapackage.json")]),e._v(": This file describes the dataset’s metadata. For example, what is the dataset, where are its files, what they contain, what each column means (for tabular data), what’s the source, license, and authors, and so on. As it’s a machine-readable specification, other software can import and validate your files. See "),o("RouterLink",{attrs:{to:"/blog/2018/03/07/well-packaged-datasets/"}},[e._v("how to create a data package")]),e._v(" on instructions on writing this file.")],1),e._v(" "),o("li",[o("strong",[o("a",{attrs:{href:"http://README.md",target:"_blank",rel:"noopener noreferrer"}},[e._v("README.md"),o("OutboundLink")],1)]),e._v(": This is where the dataset is described for humans. We recommend the following sections:\n"),o("ul",[o("li",[o("strong",[e._v("Introduction")]),e._v(": A short description of the dataset, what it contains, the time or geographical area it covers")]),e._v(" "),o("li",[o("strong",[e._v("Data")]),e._v(": What the data structure? Does it use any codes? How do you define missing values (e.g. ‘N/A’ or ‘-1’)")]),e._v(" "),o("li",[o("strong",[e._v("Preparation")]),e._v(": How was the data collected? How do I update the data? Was it modified in any way? If you have a "),o("code",[e._v("Makefile")]),e._v(", this section will mostly document how to run it. Otherwise you can describe how to run the scripts, or how to collect the data manually.")]),e._v(" "),o("li",[o("strong",[e._v("License")]),e._v(": There are two issues here: the license of the data itself, and the license of the package you are creating (including any scripts). Our recommendation is to license the package you created as "),o("a",{attrs:{href:"https://creativecommons.org/publicdomain/zero/1.0/",title:"Creative Commons Public Domain Dedication",target:"_blank",rel:"noopener noreferrer"}},[e._v("CC0"),o("OutboundLink")],1),e._v(", and add any relevant information or disclaimers regarding the source data’s license.")])])])]),e._v(" "),o("p",[e._v("To summarize, these are the folders, files, and their respective contents in this structure:")]),e._v(" "),o("table",[o("thead",[o("tr",[o("th",[e._v("Path")]),e._v(" "),o("th",[e._v("Type")]),e._v(" "),o("th",[e._v("Contents")])])]),e._v(" "),o("tbody",[o("tr",[o("td",[e._v("data/")]),e._v(" "),o("td",[e._v("Data")]),e._v(" "),o("td",[e._v("Dataset’s data files.")])]),e._v(" "),o("tr",[o("td",[e._v("docs/")]),e._v(" "),o("td",[e._v("Documentation")]),e._v(" "),o("td",[e._v("Images, analysis, and other documentation files.")])]),e._v(" "),o("tr",[o("td",[e._v("scripts/")]),e._v(" "),o("td",[e._v("Scripts")]),e._v(" "),o("td",[e._v("Scripts used for creating, modifying, or analysing the dataset.")])]),e._v(" "),o("tr",[o("td",[e._v("Makefile")]),e._v(" "),o("td",[e._v("Scripts")]),e._v(" "),o("td",[e._v("Executable documentation on how to run the scripts.")])]),e._v(" "),o("tr",[o("td",[e._v("datapackage.json")]),e._v(" "),o("td",[e._v("Metadata")]),e._v(" "),o("td",[e._v("Data Package descriptor file.")])]),e._v(" "),o("tr",[o("td",[o("a",{attrs:{href:"http://README.md",target:"_blank",rel:"noopener noreferrer"}},[e._v("README.md"),o("OutboundLink")],1)]),e._v(" "),o("td",[e._v("Documentation")]),e._v(" "),o("td",[e._v("Textual description of the dataset with description, preparation steps, license, etc.")])])])]),e._v(" "),o("p",[e._v("** Step 2. Upload the dataset to GitHub **")]),e._v(" "),o("ol",[o("li",[e._v("Login (or create) a new account on GitHub")]),e._v(" "),o("li",[e._v("Create "),o("a",{attrs:{href:"https://github.com/new",title:"GitHub New Repository",target:"_blank",rel:"noopener noreferrer"}},[e._v("a new repository"),o("OutboundLink")],1),e._v(" "),o("ul",[o("li",[e._v("Write a short description about the dataset")])])]),e._v(" "),o("li",[e._v("On your repository page, click on the “Upload files” link")]),e._v(" "),o("li",[e._v("Upload the files you created in the previous step\n"),o("ul",[o("li",[e._v("If your have files larger than 25 MB, you’ll need to either "),o("a",{attrs:{href:"https://help.github.com/articles/adding-a-file-to-a-repository-using-the-command-line/",title:"Adding a file to a repository using the command line",target:"_blank",rel:"noopener noreferrer"}},[e._v("upload using the command line"),o("OutboundLink")],1),e._v(", or the "),o("a",{attrs:{href:"https://desktop.github.com/",target:"_blank",rel:"noopener noreferrer"}},[e._v("GitHub Desktop client"),o("OutboundLink")],1),e._v(".")])])])]),e._v(" "),o("p",[e._v("** (Optional) Step 3. Enable automatic tabular data validation **")]),e._v(" "),o("p",[e._v("You can automatically validate your tabular data files using "),o("a",{attrs:{href:"https://goodtables.io",target:"_blank",rel:"noopener noreferrer"}},[e._v("goodtables.io"),o("OutboundLink")],1),e._v(". This will take only a few minutes, and will ensure you’ll always know when there are errors with your dataset, maintaining its quality. "),o("a",{attrs:{href:"/blog/2018/03/12/automatically-validated-tabular-data"}},[e._v("Read the walkthrough here")]),e._v(".")]),e._v(" "),o("p",[e._v("The sample datasets used in this example, that is, List of schools in Birmingham, UK are available "),o("a",{attrs:{href:"https://github.com/vitorbaptista/birmingham_schools",target:"_blank",rel:"noopener noreferrer"}},[e._v("in this repository"),o("OutboundLink")],1),e._v(".")]),e._v(" "),o("hr"),e._v(" "),o("h2",{attrs:{id:"share-packaged-data-effectively"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#share-packaged-data-effectively"}},[e._v("#")]),e._v(" Share packaged data effectively")]),e._v(" "),o("p",[e._v("Publishing packaged data is not enough. To avoid hiding useful information in open archives online, it is necessary to engage communities that could be interested in your data. Community engagement should not be viewed as a one-off assignment, but rather, as a continuous effort to increase the impact of your data publishing work.")]),e._v(" "),o("p",[e._v("Some best practices:")]),e._v(" "),o("ol",[o("li",[o("p",[e._v("Publish quickly, update often."),o("br"),e._v("\nThe true value of published data lies in its use and reuse by open data communities. Publish data as soon as possible and update it regularly so users have access to the latest information.")])]),e._v(" "),o("li",[o("p",[e._v("Set up feedback loops."),o("br"),e._v("\nYour data publishing platform should aim to get community “buy in”, by encouraging participatory processes. Feedback loops are important because:")])])]),e._v(" "),o("ul",[o("li",[e._v("they allow data users to ask for clarifications and request for additional information about specific datasets, if need be.")]),e._v(" "),o("li",[e._v("they allow data publishers to understand what communities need and publish data driven by user demand, increasing the chance it’ll be used")]),e._v(" "),o("li",[e._v("they provide an avenue for data publishers to learn how their data is used, so they can gauge its impact.")])]),e._v(" "),o("p",[e._v("Examples of feedback loops that data publishers can set up include:")]),e._v(" "),o("ul",[o("li",[e._v("Adding a comments section in a data platform. Needless to say, the comments section should be monitored closely to ensure that responses are sent in time, and that discussions remain respectful and on topic.")]),e._v(" "),o("li",[e._v("A dedicated social platform channel, such as a Google Group or Facebook group, with a prominently placed link from the data platform for sharing updates, collating and responding to feedback.")]),e._v(" "),o("li",[e._v("An e-mail address where users can contact the people responsible for the datasets for clarifications, suggestions, or to report errors.")])]),e._v(" "),o("ol",{attrs:{start:"3"}},[o("li",[e._v("Meet open data communities in the places they already meet")])]),e._v(" "),o("p",[e._v("Communities thrive when there’s continued discourse over similar interests."),o("br"),e._v("\nData publishers should be active in existing networks, as supporters and collaborators in community data initiatives. Some of the ways this can be done, leveraging on Open Knowledge communities and others, include:")]),e._v(" "),o("ul",[o("li",[o("p",[e._v("Kickstarting and joining discussions in online forums")])]),e._v(" "),o("li",[o("p",[e._v("Blogs"),o("br"),e._v("\nAs a data publisher, running a data blog is a great way to create awareness about the data you publish, and an avenue to highlight how data users are drawing insight from it. This encourages use and reuse of your data. If you don’t run a data blog, there are plenty of open data blogs that welcome external contributions i.e. "),o("a",{attrs:{href:"https://blog.okfn.org/submit/",target:"_blank",rel:"noopener noreferrer"}},[e._v("here’s how"),o("OutboundLink")],1),e._v(" you can contribute guest posts on "),o("a",{attrs:{href:"http://blog.okfn.org",target:"_blank",rel:"noopener noreferrer"}},[e._v("blog.okfn.org"),o("OutboundLink")],1),e._v(".")])]),e._v(" "),o("li",[o("p",[e._v("Open Knowledge Discuss"),o("br"),e._v("\nThe Open Knowledge discussion platform is a great place to invoke and contribute to conversation on specific subjects. "),o("a",{attrs:{href:"https://discuss.okfn.org",target:"_blank",rel:"noopener noreferrer"}},[e._v("Dive in"),o("OutboundLink")],1),e._v("!")])]),e._v(" "),o("li",[o("p",[e._v("Gitter"),o("br"),e._v("\nGitter is a chat platform that’s well suited for more technical discussions around open data. If you are looking to engage technical data users, consider joining our "),o("a",{attrs:{href:"https://gitter.im/okfn/chat",target:"_blank",rel:"noopener noreferrer"}},[e._v("Open Knowledge Foundation channel"),o("OutboundLink")],1),e._v(" or the "),o("a",{attrs:{href:"https://gitter.im/frictionlessdata/chat",target:"_blank",rel:"noopener noreferrer"}},[e._v("Frictionless Data project channel"),o("OutboundLink")],1),e._v(".")])]),e._v(" "),o("li",[o("p",[e._v("In-person meetups"),o("br"),e._v("\nOrganizing and participating in meetups, hackathons and domain-specific conferences is a good way to engage with communities.")])]),e._v(" "),o("li",[o("p",[e._v("Community calls, webinars and podcasts")])])]),e._v(" "),o("p",[e._v("Finally, to maintain an active community of data users as a data publisher:")]),e._v(" "),o("ul",[o("li",[e._v("Keep your datasets updated and highlight changes that might be of interest to the community. For example, if the changes are relevant to a specific data request, reach out and let the user know.")]),e._v(" "),o("li",[e._v("Have a human representative play an active role in community activities. Bots can be fun and efficient, but they are limited and can get in the way of meaningful interactions.")]),e._v(" "),o("li",[e._v("Be flexible and transparent. Listen to your community needs and respond appropriately and in timely fashion i.e. consider publishing datasets that are in high demand first, or more regularly. 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This Fund is part of the Frictionless Data for Reproducible Research project, which is funded by the Sloan Foundation. This project applies our work in Frictionless Data to data-driven research disciplines, in order to facilitate reproducible data workflows in research contexts.")])]),e._v(" "),r("p",[e._v("Open Referral creates standards for health, human, and social services data – the data found in community resource directories used to help find resources for people in need. In many organizations, this data lives in a multitude of formats, from handwritten notes to Excel files on a laptop to Microsoft SQL databases in the cloud. For community resource directories to be maximally useful to the public, this disparate data must be converted into an interoperable format. Many organizations have decided to use Open Referral’s Human Services Data Specification (HSDS) as that format. However, to accurately represent this data, HSDS uses multiple linked tables, which can be challenging to work with. To make this process easier, Greg Bloom and Shelby Switzer from Open Referral decided to implement datapackage bundling of their CSV files using the Frictionless Data Tool Fund.")]),e._v(" "),r("p",[e._v("In order to accurately represent the relationships between organizations, the services they provide, and the locations they are offered, Open Referral aims to use their Human Service Data Specification (HSDS) makes sense of disparate data by linking multiple CSV files together by foreign keys. Open Referral used Frictionless Data’s datapackage to specify the tables’ contents and relationships in a single machine-readable file, so that this standardized format could transport HSDS-compliant data in a way that all of the teams who work with this data can use: CSVs of linked data.")]),e._v(" "),r("p",[e._v("In the Tool Fund, Open Referral worked on their HSDS Transformer tool, which enables a group or person to transform data into an HSDS-compliant data package, so that it can then be combined with other data or used in any number of applications. The HSDS-Transformer is a Ruby library that can be used during the extract, transform, load (ETL) workflow of raw community resource data. This library extracts the community resource data, transforms that data into HSDS-compliant CSVs, and generates a datapackage.json that describes the data output. The Transformer can also output the datapackage as a zip file, called HSDS Zip, enabling systems to send and receive a single compressed file rather than multiple files. The Transformer can be spun up in a docker container — and once it’s live, the API can deliver a payload that includes links to the source data and to the configuration file that maps the source data to HSDS fields. The Transformer then grabs the source data and uses the configuration file to transform the data and return a zip file of the HSDS-compliant datapackage.")]),e._v(" "),r("p",[r("img",{attrs:{src:t(494),alt:"DemoApp"}}),e._v(" "),r("br"),e._v(" "),r("em",[e._v("A demo app consuming the API generated from the HSDS Zip")])]),e._v(" "),r("p",[e._v("The Open Referral team has also been working on projects related to the HSDS Transformer and HSDS Zip. For example, the HSDS Validator checks that a given datapackage of community service data is HSDS-compliant. Additionally, they have used these tools in the field with a project in Miami. For this project, the HSDS Transformer was used to transform data from a Microsoft SQL Server into an HSDS Zip. Then that zipped datapackage was used to populate a Human Services Data API with a generated developer portal and OpenAPI Specification.")]),e._v(" "),r("p",[e._v("Further, as part of this work, the team also contributed to the original source code for the datapackage-rb Ruby gem. They added a new feature to infer a datapackage.json schema from a given set of CSVs, so that you can generate the json file automatically from your dataset.")]),e._v(" "),r("p",[e._v("Greg and Shelby are eager for the Open Referral community to use these new tools and provide feedback. To use these tools currently, users should either be a Ruby developer who can use the gem as part of another Ruby project, or be familiar enough with Docker and HTTP APIs to start a Docker container and make an HTTP request to it. You can use the HSDS Transformer as a Ruby gem in another project or as a standalone API. In the future, the project might expand to include hosting the HSDS Transformer as a cloud service that anyone can use to transform their data, eliminating many of these technical requirements.")]),e._v(" "),r("p",[e._v("Interested in using these new tools? Open Referral wants to hear your feedback. For example, would it be useful to develop an extract-transform-load API, hosted in the cloud, that enables recurring transformation of nonstandardized human service directory data source into an HSDS-compliant datapackage? You can reach them via their GitHub repos.")]),e._v(" "),r("p",[e._v("Further reading:")]),e._v(" "),r("p",[e._v("Repository: "),r("a",{attrs:{href:"https://github.com/openreferral/hsds-transformer",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://github.com/openreferral/hsds-transformer"),r("OutboundLink")],1),r("br"),e._v("\nHSDS Transformer: "),r("a",{attrs:{href:"https://openreferral.github.io/hsds-transformer/",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://openreferral.github.io/hsds-transformer/"),r("OutboundLink")],1)])])}),[],!1,null,null,null);a.default=o.exports}}]); \ No newline at end of file +(window.webpackJsonp=window.webpackJsonp||[]).push([[51],{488:function(e,a,t){e.exports=t.p+"assets/img/OR.8407f111.png"},619:function(e,a,t){"use strict";t.r(a);var r=t(29),o=Object(r.a)({},(function(){var e=this,a=e.$createElement,r=e._self._c||a;return r("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[r("p",[e._v("This blog is part of a series showcasing projects developed during the 2019 Tool Fund."),r("br"),e._v(" "),r("br"),e._v("\nOriginally published at "),r("a",{attrs:{href:"https://blog.okfn.org/2020/01/15/frictionless-data-tool-fund-update-shelby-switzer-and-greg-bloom-open-referral/",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://blog.okfn.org/2020/01/15/frictionless-data-tool-fund-update-shelby-switzer-and-greg-bloom-open-referral/"),r("OutboundLink")],1)]),e._v(" "),r("p",[r("em",[e._v("The 2019 Tool Fund provided four mini-grants of $5,000 to support individuals or organisations in developing an open tool for reproducible research built using the Frictionless Data specifications and software. This Fund is part of the Frictionless Data for Reproducible Research project, which is funded by the Sloan Foundation. This project applies our work in Frictionless Data to data-driven research disciplines, in order to facilitate reproducible data workflows in research contexts.")])]),e._v(" "),r("p",[e._v("Open Referral creates standards for health, human, and social services data – the data found in community resource directories used to help find resources for people in need. In many organizations, this data lives in a multitude of formats, from handwritten notes to Excel files on a laptop to Microsoft SQL databases in the cloud. For community resource directories to be maximally useful to the public, this disparate data must be converted into an interoperable format. Many organizations have decided to use Open Referral’s Human Services Data Specification (HSDS) as that format. However, to accurately represent this data, HSDS uses multiple linked tables, which can be challenging to work with. To make this process easier, Greg Bloom and Shelby Switzer from Open Referral decided to implement datapackage bundling of their CSV files using the Frictionless Data Tool Fund.")]),e._v(" "),r("p",[e._v("In order to accurately represent the relationships between organizations, the services they provide, and the locations they are offered, Open Referral aims to use their Human Service Data Specification (HSDS) makes sense of disparate data by linking multiple CSV files together by foreign keys. Open Referral used Frictionless Data’s datapackage to specify the tables’ contents and relationships in a single machine-readable file, so that this standardized format could transport HSDS-compliant data in a way that all of the teams who work with this data can use: CSVs of linked data.")]),e._v(" "),r("p",[e._v("In the Tool Fund, Open Referral worked on their HSDS Transformer tool, which enables a group or person to transform data into an HSDS-compliant data package, so that it can then be combined with other data or used in any number of applications. The HSDS-Transformer is a Ruby library that can be used during the extract, transform, load (ETL) workflow of raw community resource data. This library extracts the community resource data, transforms that data into HSDS-compliant CSVs, and generates a datapackage.json that describes the data output. The Transformer can also output the datapackage as a zip file, called HSDS Zip, enabling systems to send and receive a single compressed file rather than multiple files. The Transformer can be spun up in a docker container — and once it’s live, the API can deliver a payload that includes links to the source data and to the configuration file that maps the source data to HSDS fields. The Transformer then grabs the source data and uses the configuration file to transform the data and return a zip file of the HSDS-compliant datapackage.")]),e._v(" "),r("p",[r("img",{attrs:{src:t(488),alt:"DemoApp"}}),e._v(" "),r("br"),e._v(" "),r("em",[e._v("A demo app consuming the API generated from the HSDS Zip")])]),e._v(" "),r("p",[e._v("The Open Referral team has also been working on projects related to the HSDS Transformer and HSDS Zip. For example, the HSDS Validator checks that a given datapackage of community service data is HSDS-compliant. Additionally, they have used these tools in the field with a project in Miami. For this project, the HSDS Transformer was used to transform data from a Microsoft SQL Server into an HSDS Zip. Then that zipped datapackage was used to populate a Human Services Data API with a generated developer portal and OpenAPI Specification.")]),e._v(" "),r("p",[e._v("Further, as part of this work, the team also contributed to the original source code for the datapackage-rb Ruby gem. They added a new feature to infer a datapackage.json schema from a given set of CSVs, so that you can generate the json file automatically from your dataset.")]),e._v(" "),r("p",[e._v("Greg and Shelby are eager for the Open Referral community to use these new tools and provide feedback. To use these tools currently, users should either be a Ruby developer who can use the gem as part of another Ruby project, or be familiar enough with Docker and HTTP APIs to start a Docker container and make an HTTP request to it. You can use the HSDS Transformer as a Ruby gem in another project or as a standalone API. In the future, the project might expand to include hosting the HSDS Transformer as a cloud service that anyone can use to transform their data, eliminating many of these technical requirements.")]),e._v(" "),r("p",[e._v("Interested in using these new tools? Open Referral wants to hear your feedback. For example, would it be useful to develop an extract-transform-load API, hosted in the cloud, that enables recurring transformation of nonstandardized human service directory data source into an HSDS-compliant datapackage? You can reach them via their GitHub repos.")]),e._v(" "),r("p",[e._v("Further reading:")]),e._v(" "),r("p",[e._v("Repository: "),r("a",{attrs:{href:"https://github.com/openreferral/hsds-transformer",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://github.com/openreferral/hsds-transformer"),r("OutboundLink")],1),r("br"),e._v("\nHSDS Transformer: "),r("a",{attrs:{href:"https://openreferral.github.io/hsds-transformer/",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://openreferral.github.io/hsds-transformer/"),r("OutboundLink")],1)])])}),[],!1,null,null,null);a.default=o.exports}}]); \ No newline at end of file diff --git a/assets/js/52.f8dc673f.js b/assets/js/52.2f176438.js similarity index 99% rename from assets/js/52.f8dc673f.js rename to assets/js/52.2f176438.js index b9b12f80f..011dffdeb 100644 --- a/assets/js/52.f8dc673f.js +++ b/assets/js/52.2f176438.js @@ -1 +1 @@ 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strict";a.r(t);var o=a(29),r=Object(o.a)({},(function(){var e=this,t=e.$createElement,o=e._self._c||t;return o("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[o("p",[e._v("This blog post describes a Frictionless Data Pilot with the Public Utility Data Liberation project. Pilot projects are part of the "),o("a",{attrs:{href:"https://frictionlessdata.io/reproducible-research/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Frictionless Data for Reproducible Research project"),o("OutboundLink")],1),e._v(". Written by Zane Selvans, Christina Gosnell, and Lilly Winfree.")]),e._v(" "),o("p",[e._v("The Public Utility Data Liberation project, "),o("a",{attrs:{href:"https://catalyst.coop/pudl/",target:"_blank",rel:"noopener noreferrer"}},[e._v("PUDL"),o("OutboundLink")],1),e._v(", aims to make US energy data easier to access and use. Much of this data, including information about the cost of electricity, how much fuel is being burned, powerplant usage, and emissions, is not well documented or is in difficult to use formats. Last year, PUDL joined forces with the Frictionless Data for Reproducible Research team as a Pilot project to release this public utility data. PUDL takes the original spreadsheets, CSV files, and databases and turns them into unified Frictionless [tabular data packages("),o("a",{attrs:{href:"https://frictionlessdata.io/docs/tabular-data-package/",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://frictionlessdata.io/docs/tabular-data-package/"),o("OutboundLink")],1),e._v(")] that can be used to populate a database, or read in directly with Python, R, Microsoft Access, and many other tools.")]),e._v(" "),o("p",[o("img",{attrs:{src:a(502),alt:"Catalyst Logo"}})]),e._v(" "),o("h2",{attrs:{id:"what-is-pudl"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#what-is-pudl"}},[e._v("#")]),e._v(" What is PUDL?")]),e._v(" "),o("p",[e._v("The PUDL project, which is coordinated by "),o("a",{attrs:{href:"https://catalyst.coop/pudl/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Catalyst Cooperative"),o("OutboundLink")],1),e._v(", is focused on creating an energy utility data product that can serve a wide range of users. PUDL was inspired to make this data more accessible because the current US utility data ecosystem fragmented, and commercial products are expensive. There are hundreds of gigabytes of information available from government agencies, but they are often difficult to work with, and different sources can be hard to combine.")]),e._v(" "),o("p",[e._v("PUDL users include researchers, activists, journalists, and policy makers. They have a wide range of technical backgrounds, from grassroots organizers who might only feel comfortable with spreadsheets, to PhDs with cloud computing resources, so it was important to provide data that would work for all users.")]),e._v(" "),o("p",[e._v("Before PUDL, much of this data was freely available to download from various sources, but it was typically messy and not well documented. This led to a lack of uniformity and reproducibility amongst projects that were using this data. The users were scraping the data together in their own way, making it hard to compare analyses or understand outcomes. Therefore, one of the goals for PUDL was to minimize these duplicated efforts, and enable the creation of lasting, cumulative outputs.")]),e._v(" "),o("h2",{attrs:{id:"what-were-the-main-pilot-goals"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#what-were-the-main-pilot-goals"}},[e._v("#")]),e._v(" What were the main Pilot goals?")]),e._v(" "),o("p",[e._v("The main focus of this Pilot was to create a way to openly share the utility data in a reproducible way that would be understandable to PUDL’s many potential users. The first change Catalyst identified they wanted to make during the Pilot was with their data storage medium. PUDL was previously creating a Postgresql database as the main data output. However many users, even those with technical experience, found setting up the separate database software a major hurdle that prevented them from accessing and using the processed data. They also desired a static, archivable, platform-independent format. Therefore, Catalyst decided to transition PUDL away from PostgreSQL, and instead try Frictionless Tabular Data Packages. They also wanted a way to share the processed data without needing to commit to long-term maintenance and curation, meaning they needed the outputs to continue being useful to users even if they only had minimal resources to dedicate to the maintenance and updates. The team decided to package their data into Tabular Data Packages and identified Zenodo as a good option for openly hosting that packaged data.")]),e._v(" "),o("p",[e._v("Catalyst also recognized that most users only want to download the outputs and use them directly, and did not care about reproducing the data processing pipeline themselves, but it was still important to provide the processing pipeline code publicly to support transparency and reproducibility. Therefore, in this Pilot, they focused on transitioning their existing ETL pipeline from outputting a PostgreSQL database, that was defined using SQLAlchemy, to outputting datapackages which could then be archived publicly on Zenodo. Importantly, they needed this pipeline to maintain the metadata, information about data type, and database structural information that had already been accumulated. This rich metadata needed to be stored alongside the data itself, so future users could understand where the data came from and understand its meaning. The Catalyst team used Tabular Data Packages to record and store this metadata (see the code here: "),o("a",{attrs:{href:"https://github.com/catalyst-cooperative/pudl/blob/master/src/pudl/load/metadata.py",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://github.com/catalyst-cooperative/pudl/blob/master/src/pudl/load/metadata.py"),o("OutboundLink")],1),e._v(").")]),e._v(" "),o("p",[e._v("Another complicating factor is that many of the PUDL datasets are fairly entangled with each other. The PUDL team ideally wanted users to be able to pick and choose which datasets they actually wanted to download and use without requiring them to download it all (currently about 100GB of data when uncompressed). However, they were worried that if single datasets were downloaded, the users might miss that some of the datasets were meant to be used together. So, the PUDL team created information, which they call “glue”, that shows which datasets are linked together and that should ideally be used in tandem.")]),e._v(" "),o("p",[e._v("The cumulation of this Pilot was a release of the PUDL data (access it here – "),o("a",{attrs:{href:"https://zenodo.org/record/3672068",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://zenodo.org/record/3672068"),o("OutboundLink")],1),e._v(" and read the corresponding documentation here – "),o("a",{attrs:{href:"https://catalystcoop-pudl.readthedocs.io/en/v0.3.2/",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://catalystcoop-pudl.readthedocs.io/en/v0.3.2/"),o("OutboundLink")],1),e._v("), which includes integrated data from the EIA Form 860, EIA Form 923, The EPA Continuous Emissions Monitoring System (CEMS), The EPA Integrated Planning Model (IPM), and FERC Form 1.")]),e._v(" "),o("h2",{attrs:{id:"what-problems-were-encountered-during-this-pilot"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#what-problems-were-encountered-during-this-pilot"}},[e._v("#")]),e._v(" What problems were encountered during this Pilot?")]),e._v(" "),o("p",[e._v("One issue that the group encountered during the Pilot was that the data types available in Postgres are substantially richer than those natively in the Tabular Data Package standard. However, this issue is an endemic problem of wanting to work with several different platforms, and so the team compromised and worked with the least common denominator. In the future, PUDL might store several different sets of data types for use in different contexts, for example, one for freezing the data out into data packages, one for SQLite, and one for Pandas.")]),e._v(" "),o("p",[e._v("Another problem encountered during the Pilot resulted from testing the limits of the draft Tabular Data Package specifications. There were aspects of the specifications that the Catalyst team assumed were fully implemented in the reference (Python) implementation of the Frictionless toolset, but were in fact still works in progress. This work led the Frictionless team to start a documentation improvement project, including a revision of the specifications website to incorporate this feedback.")]),e._v(" "),o("p",[e._v("Through the pilot, the teams worked to implement new Frictionless features, including the specification of composite primary keys and foreign key references that point to external data packages. Other new Frictionless functionality that was created with this Pilot included partitioning of large resources into resource groups in which all resources use identical table schemas, and adding gzip compression of resources. The Pilot also focused on implementing more complete validation through goodtables, including bytes/hash checks, foreign keys checks, and primary keys checks, though there is still more work to be done here.")]),e._v(" "),o("h2",{attrs:{id:"future-directions"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#future-directions"}},[e._v("#")]),e._v(" Future Directions")]),e._v(" "),o("p",[e._v("A common problem with using publicly available energy data is that the federal agencies creating the data do not use version control or maintain change logs for the data they publish, but they do frequently go back years after the fact to revise or alter previously published data — with no notification. To combat this problem, Catalyst is using data packages to encapsulate the raw inputs to the ETL process. They are setting up a process which will periodically check to see if the federal agencies’ posted data has been updated or changed, create an archive, and upload it to Zenodo. They will also store metadata in non-tabular data packages, indicating which information is stored in each file (year, state, month, etc.) so that there can be a uniform process of querying those raw input data packages. This will mean the raw inputs won’t have to be archived alongside every data release. Instead one can simply refer to these other versioned archives of the inputs. Catalyst hopes these version controlled raw archives will also be useful to other researchers.")]),e._v(" "),o("p",[e._v("Another next step for Catalyst will be to make the ETL and new dataset integration more modular to hopefully make it easier for others to integrate new datasets. For instance, they are planning on integrating the EIA 861 and the ISO/RTO LMP data next. Other future plans include simplifying metadata storage, using Docker to containerize the ETL process for better reproducibility, and setting up a "),o("a",{attrs:{href:"https://pangeo.io/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Pangeo"),o("OutboundLink")],1),e._v(" instance for live interactive data access without requiring anyone to download any data at all. The team would also like to build visualizations that sit on top of the database, making an interactive, regularly updated map of US coal plants and their operating costs, compared to new renewable energy in the same area. They would also like to visualize power plant operational attributes from EPA CEMS (e.g., ramp rates, min/max operating loads, relationship between load factor and heat rate, marginal additional fuel required for a startup event…).")]),e._v(" "),o("p",[e._v("Have you used PUDL? The team would love to hear feedback from users of the published data so that they can understand how to improve it, based on real user experiences. If you are integrating other US energy/electricity data of interest, please talk to the PUDL team about whether they might want to integrate it into PUDL to help ensure that it’s all more standardized and can be maintained long term. Also let them know what other datasets you would find useful (E.g. FERC EQR, FERC 714, PHMSA Pipelines, MSHA mines…). If you have questions, please ask them on GitHub ("),o("a",{attrs:{href:"https://github.com/catalyst-cooperative/pudl",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://github.com/catalyst-cooperative/pudl"),o("OutboundLink")],1),e._v(") so that the answers will be public for others to find as well.")])])}),[],!1,null,null,null);t.default=r.exports}}]); \ No newline at end of file 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strict";a.r(t);var o=a(29),r=Object(o.a)({},(function(){var e=this,t=e.$createElement,o=e._self._c||t;return o("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[o("p",[e._v("This blog post describes a Frictionless Data Pilot with the Public Utility Data Liberation project. Pilot projects are part of the "),o("a",{attrs:{href:"https://frictionlessdata.io/reproducible-research/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Frictionless Data for Reproducible Research project"),o("OutboundLink")],1),e._v(". Written by Zane Selvans, Christina Gosnell, and Lilly Winfree.")]),e._v(" "),o("p",[e._v("The Public Utility Data Liberation project, "),o("a",{attrs:{href:"https://catalyst.coop/pudl/",target:"_blank",rel:"noopener noreferrer"}},[e._v("PUDL"),o("OutboundLink")],1),e._v(", aims to make US energy data easier to access and use. Much of this data, including information about the cost of electricity, how much fuel is being burned, powerplant usage, and emissions, is not well documented or is in difficult to use formats. Last year, PUDL joined forces with the Frictionless Data for Reproducible Research team as a Pilot project to release this public utility data. PUDL takes the original spreadsheets, CSV files, and databases and turns them into unified Frictionless [tabular data packages("),o("a",{attrs:{href:"https://frictionlessdata.io/docs/tabular-data-package/",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://frictionlessdata.io/docs/tabular-data-package/"),o("OutboundLink")],1),e._v(")] that can be used to populate a database, or read in directly with Python, R, Microsoft Access, and many other tools.")]),e._v(" "),o("p",[o("img",{attrs:{src:a(489),alt:"Catalyst Logo"}})]),e._v(" "),o("h2",{attrs:{id:"what-is-pudl"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#what-is-pudl"}},[e._v("#")]),e._v(" What is PUDL?")]),e._v(" "),o("p",[e._v("The PUDL project, which is coordinated by "),o("a",{attrs:{href:"https://catalyst.coop/pudl/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Catalyst Cooperative"),o("OutboundLink")],1),e._v(", is focused on creating an energy utility data product that can serve a wide range of users. PUDL was inspired to make this data more accessible because the current US utility data ecosystem fragmented, and commercial products are expensive. There are hundreds of gigabytes of information available from government agencies, but they are often difficult to work with, and different sources can be hard to combine.")]),e._v(" "),o("p",[e._v("PUDL users include researchers, activists, journalists, and policy makers. They have a wide range of technical backgrounds, from grassroots organizers who might only feel comfortable with spreadsheets, to PhDs with cloud computing resources, so it was important to provide data that would work for all users.")]),e._v(" "),o("p",[e._v("Before PUDL, much of this data was freely available to download from various sources, but it was typically messy and not well documented. This led to a lack of uniformity and reproducibility amongst projects that were using this data. The users were scraping the data together in their own way, making it hard to compare analyses or understand outcomes. Therefore, one of the goals for PUDL was to minimize these duplicated efforts, and enable the creation of lasting, cumulative outputs.")]),e._v(" "),o("h2",{attrs:{id:"what-were-the-main-pilot-goals"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#what-were-the-main-pilot-goals"}},[e._v("#")]),e._v(" What were the main Pilot goals?")]),e._v(" "),o("p",[e._v("The main focus of this Pilot was to create a way to openly share the utility data in a reproducible way that would be understandable to PUDL’s many potential users. The first change Catalyst identified they wanted to make during the Pilot was with their data storage medium. PUDL was previously creating a Postgresql database as the main data output. However many users, even those with technical experience, found setting up the separate database software a major hurdle that prevented them from accessing and using the processed data. They also desired a static, archivable, platform-independent format. Therefore, Catalyst decided to transition PUDL away from PostgreSQL, and instead try Frictionless Tabular Data Packages. They also wanted a way to share the processed data without needing to commit to long-term maintenance and curation, meaning they needed the outputs to continue being useful to users even if they only had minimal resources to dedicate to the maintenance and updates. The team decided to package their data into Tabular Data Packages and identified Zenodo as a good option for openly hosting that packaged data.")]),e._v(" "),o("p",[e._v("Catalyst also recognized that most users only want to download the outputs and use them directly, and did not care about reproducing the data processing pipeline themselves, but it was still important to provide the processing pipeline code publicly to support transparency and reproducibility. Therefore, in this Pilot, they focused on transitioning their existing ETL pipeline from outputting a PostgreSQL database, that was defined using SQLAlchemy, to outputting datapackages which could then be archived publicly on Zenodo. Importantly, they needed this pipeline to maintain the metadata, information about data type, and database structural information that had already been accumulated. This rich metadata needed to be stored alongside the data itself, so future users could understand where the data came from and understand its meaning. The Catalyst team used Tabular Data Packages to record and store this metadata (see the code here: "),o("a",{attrs:{href:"https://github.com/catalyst-cooperative/pudl/blob/master/src/pudl/load/metadata.py",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://github.com/catalyst-cooperative/pudl/blob/master/src/pudl/load/metadata.py"),o("OutboundLink")],1),e._v(").")]),e._v(" "),o("p",[e._v("Another complicating factor is that many of the PUDL datasets are fairly entangled with each other. The PUDL team ideally wanted users to be able to pick and choose which datasets they actually wanted to download and use without requiring them to download it all (currently about 100GB of data when uncompressed). However, they were worried that if single datasets were downloaded, the users might miss that some of the datasets were meant to be used together. So, the PUDL team created information, which they call “glue”, that shows which datasets are linked together and that should ideally be used in tandem.")]),e._v(" "),o("p",[e._v("The cumulation of this Pilot was a release of the PUDL data (access it here – "),o("a",{attrs:{href:"https://zenodo.org/record/3672068",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://zenodo.org/record/3672068"),o("OutboundLink")],1),e._v(" and read the corresponding documentation here – "),o("a",{attrs:{href:"https://catalystcoop-pudl.readthedocs.io/en/v0.3.2/",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://catalystcoop-pudl.readthedocs.io/en/v0.3.2/"),o("OutboundLink")],1),e._v("), which includes integrated data from the EIA Form 860, EIA Form 923, The EPA Continuous Emissions Monitoring System (CEMS), The EPA Integrated Planning Model (IPM), and FERC Form 1.")]),e._v(" "),o("h2",{attrs:{id:"what-problems-were-encountered-during-this-pilot"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#what-problems-were-encountered-during-this-pilot"}},[e._v("#")]),e._v(" What problems were encountered during this Pilot?")]),e._v(" "),o("p",[e._v("One issue that the group encountered during the Pilot was that the data types available in Postgres are substantially richer than those natively in the Tabular Data Package standard. However, this issue is an endemic problem of wanting to work with several different platforms, and so the team compromised and worked with the least common denominator. In the future, PUDL might store several different sets of data types for use in different contexts, for example, one for freezing the data out into data packages, one for SQLite, and one for Pandas.")]),e._v(" "),o("p",[e._v("Another problem encountered during the Pilot resulted from testing the limits of the draft Tabular Data Package specifications. There were aspects of the specifications that the Catalyst team assumed were fully implemented in the reference (Python) implementation of the Frictionless toolset, but were in fact still works in progress. This work led the Frictionless team to start a documentation improvement project, including a revision of the specifications website to incorporate this feedback.")]),e._v(" "),o("p",[e._v("Through the pilot, the teams worked to implement new Frictionless features, including the specification of composite primary keys and foreign key references that point to external data packages. Other new Frictionless functionality that was created with this Pilot included partitioning of large resources into resource groups in which all resources use identical table schemas, and adding gzip compression of resources. The Pilot also focused on implementing more complete validation through goodtables, including bytes/hash checks, foreign keys checks, and primary keys checks, though there is still more work to be done here.")]),e._v(" "),o("h2",{attrs:{id:"future-directions"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#future-directions"}},[e._v("#")]),e._v(" Future Directions")]),e._v(" "),o("p",[e._v("A common problem with using publicly available energy data is that the federal agencies creating the data do not use version control or maintain change logs for the data they publish, but they do frequently go back years after the fact to revise or alter previously published data — with no notification. To combat this problem, Catalyst is using data packages to encapsulate the raw inputs to the ETL process. They are setting up a process which will periodically check to see if the federal agencies’ posted data has been updated or changed, create an archive, and upload it to Zenodo. They will also store metadata in non-tabular data packages, indicating which information is stored in each file (year, state, month, etc.) so that there can be a uniform process of querying those raw input data packages. This will mean the raw inputs won’t have to be archived alongside every data release. Instead one can simply refer to these other versioned archives of the inputs. Catalyst hopes these version controlled raw archives will also be useful to other researchers.")]),e._v(" "),o("p",[e._v("Another next step for Catalyst will be to make the ETL and new dataset integration more modular to hopefully make it easier for others to integrate new datasets. For instance, they are planning on integrating the EIA 861 and the ISO/RTO LMP data next. Other future plans include simplifying metadata storage, using Docker to containerize the ETL process for better reproducibility, and setting up a "),o("a",{attrs:{href:"https://pangeo.io/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Pangeo"),o("OutboundLink")],1),e._v(" instance for live interactive data access without requiring anyone to download any data at all. The team would also like to build visualizations that sit on top of the database, making an interactive, regularly updated map of US coal plants and their operating costs, compared to new renewable energy in the same area. They would also like to visualize power plant operational attributes from EPA CEMS (e.g., ramp rates, min/max operating loads, relationship between load factor and heat rate, marginal additional fuel required for a startup event…).")]),e._v(" "),o("p",[e._v("Have you used PUDL? The team would love to hear feedback from users of the published data so that they can understand how to improve it, based on real user experiences. If you are integrating other US energy/electricity data of interest, please talk to the PUDL team about whether they might want to integrate it into PUDL to help ensure that it’s all more standardized and can be maintained long term. Also let them know what other datasets you would find useful (E.g. FERC EQR, FERC 714, PHMSA Pipelines, MSHA mines…). If you have questions, please ask them on GitHub ("),o("a",{attrs:{href:"https://github.com/catalyst-cooperative/pudl",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://github.com/catalyst-cooperative/pudl"),o("OutboundLink")],1),e._v(") so that the answers will be public for others to find as well.")])])}),[],!1,null,null,null);t.default=r.exports}}]); \ No newline at end of file diff --git a/assets/js/53.202bb7e3.js b/assets/js/53.c39f5f20.js similarity index 84% rename from assets/js/53.202bb7e3.js rename to assets/js/53.c39f5f20.js index f2fc47880..e9f3f501c 100644 --- a/assets/js/53.202bb7e3.js +++ b/assets/js/53.c39f5f20.js @@ -1 +1 @@ -(window.webpackJsonp=window.webpackJsonp||[]).push([[53],{506:function(t,e,o){t.exports=o.p+"assets/img/community.33c3b55f.jpeg"},629:function(t,e,o){"use strict";o.r(e);var n=o(29),r=Object(n.a)({},(function(){var t=this,e=t.$createElement,n=t._self._c||e;return n("ContentSlotsDistributor",{attrs:{"slot-key":t.$parent.slotKey}},[n("p",[t._v("We are hosting another round of our virtual community hangout to share recent developments in the Frictionless Data community and it’s also an avenue to connect with other community members. 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If you would like to attend the hangout, "),n("a",{attrs:{href:"https://us02web.zoom.us/meeting/register/tZMsf-qrrjopHtGZwMyM7tCmp_YyPlNms6wK",target:"_blank",rel:"noopener noreferrer"}},[t._v("you can sign up for the event here"),n("OutboundLink")],1)]),t._v(" "),n("p",[t._v("Looking forward to seeing you there!")])])}),[],!1,null,null,null);e.default=r.exports}}]); \ No newline at end of file +(window.webpackJsonp=window.webpackJsonp||[]).push([[53],{500:function(t,e,o){t.exports=o.p+"assets/img/community.33c3b55f.jpeg"},628:function(t,e,o){"use strict";o.r(e);var n=o(29),r=Object(n.a)({},(function(){var t=this,e=t.$createElement,n=t._self._c||e;return n("ContentSlotsDistributor",{attrs:{"slot-key":t.$parent.slotKey}},[n("p",[t._v("We are hosting another round of our virtual community hangout to share recent developments in the Frictionless Data community and it’s also an avenue to connect with other community members. 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If you would like to attend the hangout, "),n("a",{attrs:{href:"https://us02web.zoom.us/meeting/register/tZMsf-qrrjopHtGZwMyM7tCmp_YyPlNms6wK",target:"_blank",rel:"noopener noreferrer"}},[t._v("you can sign up for the event here"),n("OutboundLink")],1)]),t._v(" "),n("p",[t._v("Looking forward to seeing you there!")])])}),[],!1,null,null,null);e.default=r.exports}}]); \ No newline at end of file diff --git a/assets/js/54.9e9cbb98.js b/assets/js/54.4d996240.js similarity index 98% rename from assets/js/54.9e9cbb98.js rename to assets/js/54.4d996240.js index b4a93296f..d42baf538 100644 --- a/assets/js/54.9e9cbb98.js +++ b/assets/js/54.4d996240.js @@ -1 +1 @@ -(window.webpackJsonp=window.webpackJsonp||[]).push([[54],{514:function(e,a,t){e.exports=t.p+"assets/img/fellows-img-1.967b02cd.png"},688:function(e,a,t){"use strict";t.r(a);var r=t(29),o=Object(r.a)({},(function(){var e=this,a=e.$createElement,r=e._self._c||a;return r("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[r("p",[e._v("With the Frictionless Data Reproducible Research Fellows Programme, supported by the Sloan Foundation and Open Knowledge Foundation, we are recruiting and training early career researchers to become champions of the Frictionless Data tools and approaches in their field. Fellows learn about Frictionless Data, including how to use Frictionless tools in their domains to improve reproducible research workflows, and how to advocate for open science.")]),e._v(" "),r("p",[e._v("As part of their training, we asked the 3rd cohort of Frictionless Fellows to package their research data in Frictionless "),r("a",{attrs:{href:"https://specs.frictionlessdata.io/data-package/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Data Packages"),r("OutboundLink")],1),e._v(". Here’s what they reported on their experience:")]),e._v(" "),r("h2",{attrs:{id:"victoria"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#victoria"}},[e._v("#")]),e._v(" Victoria")]),e._v(" "),r("p",[e._v("Constantly under the impression that I’m six months behind on lab work, I am capital Q - Queen - of bad data practices. My computer is a graveyard of poorly labeled .csv files, featuring illustrative headers such as “redo,” “negative pressure why?” and “weird - see notes.” I was vaguely aware of the existence of data packages, but like learning Italian or traveling more, implementing them in my workflow got slotted in the category of “would be nice if I had the time.” That clemency, however, was not extended to my research lifeblood - molecular spectroscopy databases, you disorganised beauties you - nor to collaborators who often invoked the following feeling:")]),e._v(" "),r("p",[r("img",{attrs:{src:t(514),alt:"fellows-img-1"}})]),e._v(" "),r("p",[e._v("Particularly in fields where measurables aren’t tangible macro concepts (see: population) but abstract and insular conventions with many varied representations, clear descriptors of multivariate data are a must in order for that data to be easily used and reproduced. This is where data packages come in; they bundle up your data with a human and machine readable file containing, at minimum, standardised information regarding structure and contents. In this lil’ post here, we’re going to walk through this process together by packaging data together with its metadata, and then validating the data using Frictionless tools.")]),e._v(" "),r("p",[e._v("Keep on reading about Victoria’s experience packaging data in her blog "),r("a",{attrs:{href:"https://fellows.frictionlessdata.io/blog/victoria-datapackage-blog/",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),r("OutboundLink")],1),e._v(".")]),e._v(" "),r("h2",{attrs:{id:"lindsay"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#lindsay"}},[e._v("#")]),e._v(" Lindsay")]),e._v(" "),r("p",[e._v("The first tenet of the American Library Association’s Bill of Rights states: “Books and other library resources should be provided for the interest, information, and enlightenment of all people of the community the library serves” (American Library Association). Libraries are supposed to be for everyone. Unfortunately, like many other institutions, libraries were founded upon outdated and racist patriarchal heteronormative ideals that ostracise users from marginalized backgrounds. Most academic libraries in the United States use the Library of Congress Classification System to organize books, a system that inadvertently centers christian, heterosexual white males. Critical librarianship, or critical cataloging is “a movement of library workers dedicated to bringing social justice principles into our work in libraries” "),r("a",{attrs:{href:"http://critlib.org/about/",target:"_blank",rel:"noopener noreferrer"}},[e._v("critlib"),r("OutboundLink")],1),e._v(". I would like to use data science principles to explore bias in library MARC (machine readable catalog) records.")]),e._v(" "),r("p",[e._v("Read Lindsay’s Data Package blog "),r("a",{attrs:{href:"https://fellows.frictionlessdata.io/blog/lindsay-datapackage-blog/",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),r("OutboundLink")],1),e._v(".")]),e._v(" "),r("h2",{attrs:{id:"zarena"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#zarena"}},[e._v("#")]),e._v(" Zarena")]),e._v(" "),r("p",[e._v("As a social science researcher studying the research landscape in Central Asian countries, I decided to share a part of my dataset with key bibliometric information about the journal articles published by Kyrgyzstani authors between 1991-2021. The data I am going to share comes from the "),r("a",{attrs:{href:"https://www.lens.org/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Lens"),r("OutboundLink")],1),e._v(" platform. To ensure the data quality, and to comply with the "),r("a",{attrs:{href:"https://howtofair.dk/what-is-fair/#fair-principles",target:"_blank",rel:"noopener noreferrer"}},[e._v("FAIR principles"),r("OutboundLink")],1),e._v(", before sharing my data, I created a data package that consists of the cleaned raw data, "),r("a",{attrs:{href:"https://specs.frictionlessdata.io/data-resource/#metadata-properties",target:"_blank",rel:"noopener noreferrer"}},[e._v("metadata"),r("OutboundLink")],1),e._v(", and "),r("a",{attrs:{href:"https://specs.frictionlessdata.io/table-schema/#language",target:"_blank",rel:"noopener noreferrer"}},[e._v("schema"),r("OutboundLink")],1),e._v(".")]),e._v(" "),r("p",[e._v("I tested two methods to create such a package. First, I tried to use the "),r("a",{attrs:{href:"https://framework.frictionlessdata.io/",target:"_blank",rel:"noopener noreferrer"}},[e._v("data package programming libraries"),r("OutboundLink")],1),e._v(". This method lets you do more than just to create a data package (e.g., describe, extract, transform, and validate your data). But I found the programming libraries a bit complicated. So, I ended up using the second method, that is the browser tool "),r("a",{attrs:{href:"https://create.frictionlessdata.io/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Frictionless Data Package Creator"),r("OutboundLink")],1),e._v(". It lets you create a data package without ay technical knowledge. The tool is comparatively simple and easy to navigate. It allows you to clean your dataset, change datatypes, provide a short description to your data as well as to add and edit associated metadata…")]),e._v(" "),r("p",[e._v("Keep on reading about how Zarena packaged here data in her blog "),r("a",{attrs:{href:"https://fellows.frictionlessdata.io/blog/zarena-datapackage-blog/",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),r("OutboundLink")],1),e._v(".")]),e._v(" "),r("h2",{attrs:{id:"kevin"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#kevin"}},[e._v("#")]),e._v(" Kevin")]),e._v(" "),r("p",[e._v("My research aims at understanding the transmission mechanisms of neglected vector-borne diseases. I mostly deal with data on the distribution and diversity of vectors of diseases and their infection status. The metadata would include but not be limited to the date of sample collection, location and GPS coordinates of the sites of sample collection, type of sample (blood or fly sample), the concentration of RNA or DNA extracted from the samples, and the infection status of the samples (whether the samples are infected with pathogens or not) as well as the blood meal sources of the insect vectors. All these datasets are supposed to be presented in a way that it can be understood by whoever accesses it and that information regarding the licensing and other attribution information can easily be accessed. One way to reduce friction when dealing with such huge datasets is to put them in a container that groups all the descriptive data and schema together. A schema tells us how the data is structured and the type of content that is expected in it. All this is contained in a data package that can be generated by a data package creator.")]),e._v(" "),r("p",[e._v("I am going to take you through a step by step process on how I created a data package for my dataset on sandflies diversity, infection status, and their blood-meal sources, using Frictionless Data Package Creator…")]),e._v(" "),r("p",[e._v("Read Kevin’s blog "),r("a",{attrs:{href:"https://fellows.frictionlessdata.io/blog/kk-datapackage-blog/",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),r("OutboundLink")],1),e._v(" to know more about how he created data packages for his data.")]),e._v(" "),r("h2",{attrs:{id:"guo-qiang"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#guo-qiang"}},[e._v("#")]),e._v(" Guo Qiang")]),e._v(" "),r("p",[e._v("The dataset I am going to package is from a project which we have recently completed – “"),r("a",{attrs:{href:"https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1003731",target:"_blank",rel:"noopener noreferrer"}},[e._v("Menopausal hormone therapy and women’s health: An umbrella review"),r("OutboundLink")],1),e._v("” which summarizes the clinical evidence on various health effects of menopausal hormone therapy in menopausal women. The full datasets are publicly available in the "),r("a",{attrs:{href:"https://osf.io/dsy37/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Open Science Framework"),r("OutboundLink")],1),e._v(". I am going to use one of the datasets –All-Cause Mortality.xlsx, which summarizes all the clinical trials published until 2017 investigating the effect of menopausal hormone therapy on all-cause mortality in menopausal women – to illustrate the process of creating a Data Package.")]),e._v(" "),r("p",[e._v("As the Data Package Creator currently accepts only .csv format, first I need to convert All-Cause Mortality.xlsx to .csv format…")]),e._v(" "),r("p",[e._v("Keep on reading about Guo Qiang’s experience of packaging is data in his blog "),r("a",{attrs:{href:"https://fellows.frictionlessdata.io/blog/guo-qiang-datapackage-blog/",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),r("OutboundLink")],1),e._v(".")]),e._v(" "),r("h2",{attrs:{id:"melvin"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#melvin"}},[e._v("#")]),e._v(" Melvin")]),e._v(" "),r("p",[e._v("Being a soil science student, I felt using soil data would be useful for me to better understand this process of packaging data for future use. I got data on the impact of fertiliser recommendations on yield and felt it would be great to use it. However, this wasn’t such a good idea as I got so many error messages and clean-ups to do to suit the tabular data accepted by the data package creator ("),r("a",{attrs:{href:"http://create.frictionlessdata.io",target:"_blank",rel:"noopener noreferrer"}},[e._v("create.frictionlessdata.io"),r("OutboundLink")],1),e._v("). Similarly in case you want to create a data package using someone else’s data it should either have a licence or ask to use the data.Afterwards, I got around to working with a different data set that was more straightforward and easy to work with.The data was on the infection prevalence of ‘Ca. Anaplasma camelii’ in camels and camel keds evaluated in different seasons within a year…")]),e._v(" "),r("p",[e._v("To read about the errors that Melvin got and what she learned from them, read her blog "),r("a",{attrs:{href:"https://fellows.frictionlessdata.io/blog/melvin-datapackage-blog/",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),r("OutboundLink")],1),e._v(".")]),e._v(" "),r("hr"),e._v(" "),r("p",[e._v("You can read all the Frictionless Data Fellows’ blogs on the dedicated website: "),r("a",{attrs:{href:"https://fellows.frictionlessdata.io/",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://fellows.frictionlessdata.io/"),r("OutboundLink")],1)])])}),[],!1,null,null,null);a.default=o.exports}}]); \ No newline at end of file +(window.webpackJsonp=window.webpackJsonp||[]).push([[54],{508:function(e,a,t){e.exports=t.p+"assets/img/fellows-img-1.967b02cd.png"},688:function(e,a,t){"use strict";t.r(a);var r=t(29),o=Object(r.a)({},(function(){var e=this,a=e.$createElement,r=e._self._c||a;return r("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[r("p",[e._v("With the Frictionless Data Reproducible Research Fellows Programme, supported by the Sloan Foundation and Open Knowledge Foundation, we are recruiting and training early career researchers to become champions of the Frictionless Data tools and approaches in their field. Fellows learn about Frictionless Data, including how to use Frictionless tools in their domains to improve reproducible research workflows, and how to advocate for open science.")]),e._v(" "),r("p",[e._v("As part of their training, we asked the 3rd cohort of Frictionless Fellows to package their research data in Frictionless "),r("a",{attrs:{href:"https://specs.frictionlessdata.io/data-package/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Data Packages"),r("OutboundLink")],1),e._v(". Here’s what they reported on their experience:")]),e._v(" "),r("h2",{attrs:{id:"victoria"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#victoria"}},[e._v("#")]),e._v(" Victoria")]),e._v(" "),r("p",[e._v("Constantly under the impression that I’m six months behind on lab work, I am capital Q - Queen - of bad data practices. My computer is a graveyard of poorly labeled .csv files, featuring illustrative headers such as “redo,” “negative pressure why?” and “weird - see notes.” I was vaguely aware of the existence of data packages, but like learning Italian or traveling more, implementing them in my workflow got slotted in the category of “would be nice if I had the time.” That clemency, however, was not extended to my research lifeblood - molecular spectroscopy databases, you disorganised beauties you - nor to collaborators who often invoked the following feeling:")]),e._v(" "),r("p",[r("img",{attrs:{src:t(508),alt:"fellows-img-1"}})]),e._v(" "),r("p",[e._v("Particularly in fields where measurables aren’t tangible macro concepts (see: population) but abstract and insular conventions with many varied representations, clear descriptors of multivariate data are a must in order for that data to be easily used and reproduced. This is where data packages come in; they bundle up your data with a human and machine readable file containing, at minimum, standardised information regarding structure and contents. In this lil’ post here, we’re going to walk through this process together by packaging data together with its metadata, and then validating the data using Frictionless tools.")]),e._v(" "),r("p",[e._v("Keep on reading about Victoria’s experience packaging data in her blog "),r("a",{attrs:{href:"https://fellows.frictionlessdata.io/blog/victoria-datapackage-blog/",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),r("OutboundLink")],1),e._v(".")]),e._v(" "),r("h2",{attrs:{id:"lindsay"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#lindsay"}},[e._v("#")]),e._v(" Lindsay")]),e._v(" "),r("p",[e._v("The first tenet of the American Library Association’s Bill of Rights states: “Books and other library resources should be provided for the interest, information, and enlightenment of all people of the community the library serves” (American Library Association). Libraries are supposed to be for everyone. Unfortunately, like many other institutions, libraries were founded upon outdated and racist patriarchal heteronormative ideals that ostracise users from marginalized backgrounds. Most academic libraries in the United States use the Library of Congress Classification System to organize books, a system that inadvertently centers christian, heterosexual white males. Critical librarianship, or critical cataloging is “a movement of library workers dedicated to bringing social justice principles into our work in libraries” "),r("a",{attrs:{href:"http://critlib.org/about/",target:"_blank",rel:"noopener noreferrer"}},[e._v("critlib"),r("OutboundLink")],1),e._v(". I would like to use data science principles to explore bias in library MARC (machine readable catalog) records.")]),e._v(" "),r("p",[e._v("Read Lindsay’s Data Package blog "),r("a",{attrs:{href:"https://fellows.frictionlessdata.io/blog/lindsay-datapackage-blog/",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),r("OutboundLink")],1),e._v(".")]),e._v(" "),r("h2",{attrs:{id:"zarena"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#zarena"}},[e._v("#")]),e._v(" Zarena")]),e._v(" "),r("p",[e._v("As a social science researcher studying the research landscape in Central Asian countries, I decided to share a part of my dataset with key bibliometric information about the journal articles published by Kyrgyzstani authors between 1991-2021. The data I am going to share comes from the "),r("a",{attrs:{href:"https://www.lens.org/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Lens"),r("OutboundLink")],1),e._v(" platform. To ensure the data quality, and to comply with the "),r("a",{attrs:{href:"https://howtofair.dk/what-is-fair/#fair-principles",target:"_blank",rel:"noopener noreferrer"}},[e._v("FAIR principles"),r("OutboundLink")],1),e._v(", before sharing my data, I created a data package that consists of the cleaned raw data, "),r("a",{attrs:{href:"https://specs.frictionlessdata.io/data-resource/#metadata-properties",target:"_blank",rel:"noopener noreferrer"}},[e._v("metadata"),r("OutboundLink")],1),e._v(", and "),r("a",{attrs:{href:"https://specs.frictionlessdata.io/table-schema/#language",target:"_blank",rel:"noopener noreferrer"}},[e._v("schema"),r("OutboundLink")],1),e._v(".")]),e._v(" "),r("p",[e._v("I tested two methods to create such a package. First, I tried to use the "),r("a",{attrs:{href:"https://framework.frictionlessdata.io/",target:"_blank",rel:"noopener noreferrer"}},[e._v("data package programming libraries"),r("OutboundLink")],1),e._v(". This method lets you do more than just to create a data package (e.g., describe, extract, transform, and validate your data). But I found the programming libraries a bit complicated. So, I ended up using the second method, that is the browser tool "),r("a",{attrs:{href:"https://create.frictionlessdata.io/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Frictionless Data Package Creator"),r("OutboundLink")],1),e._v(". It lets you create a data package without ay technical knowledge. The tool is comparatively simple and easy to navigate. It allows you to clean your dataset, change datatypes, provide a short description to your data as well as to add and edit associated metadata…")]),e._v(" "),r("p",[e._v("Keep on reading about how Zarena packaged here data in her blog "),r("a",{attrs:{href:"https://fellows.frictionlessdata.io/blog/zarena-datapackage-blog/",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),r("OutboundLink")],1),e._v(".")]),e._v(" "),r("h2",{attrs:{id:"kevin"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#kevin"}},[e._v("#")]),e._v(" Kevin")]),e._v(" "),r("p",[e._v("My research aims at understanding the transmission mechanisms of neglected vector-borne diseases. I mostly deal with data on the distribution and diversity of vectors of diseases and their infection status. The metadata would include but not be limited to the date of sample collection, location and GPS coordinates of the sites of sample collection, type of sample (blood or fly sample), the concentration of RNA or DNA extracted from the samples, and the infection status of the samples (whether the samples are infected with pathogens or not) as well as the blood meal sources of the insect vectors. All these datasets are supposed to be presented in a way that it can be understood by whoever accesses it and that information regarding the licensing and other attribution information can easily be accessed. One way to reduce friction when dealing with such huge datasets is to put them in a container that groups all the descriptive data and schema together. A schema tells us how the data is structured and the type of content that is expected in it. All this is contained in a data package that can be generated by a data package creator.")]),e._v(" "),r("p",[e._v("I am going to take you through a step by step process on how I created a data package for my dataset on sandflies diversity, infection status, and their blood-meal sources, using Frictionless Data Package Creator…")]),e._v(" "),r("p",[e._v("Read Kevin’s blog "),r("a",{attrs:{href:"https://fellows.frictionlessdata.io/blog/kk-datapackage-blog/",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),r("OutboundLink")],1),e._v(" to know more about how he created data packages for his data.")]),e._v(" "),r("h2",{attrs:{id:"guo-qiang"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#guo-qiang"}},[e._v("#")]),e._v(" Guo Qiang")]),e._v(" "),r("p",[e._v("The dataset I am going to package is from a project which we have recently completed – “"),r("a",{attrs:{href:"https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1003731",target:"_blank",rel:"noopener noreferrer"}},[e._v("Menopausal hormone therapy and women’s health: An umbrella review"),r("OutboundLink")],1),e._v("” which summarizes the clinical evidence on various health effects of menopausal hormone therapy in menopausal women. The full datasets are publicly available in the "),r("a",{attrs:{href:"https://osf.io/dsy37/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Open Science Framework"),r("OutboundLink")],1),e._v(". I am going to use one of the datasets –All-Cause Mortality.xlsx, which summarizes all the clinical trials published until 2017 investigating the effect of menopausal hormone therapy on all-cause mortality in menopausal women – to illustrate the process of creating a Data Package.")]),e._v(" "),r("p",[e._v("As the Data Package Creator currently accepts only .csv format, first I need to convert All-Cause Mortality.xlsx to .csv format…")]),e._v(" "),r("p",[e._v("Keep on reading about Guo Qiang’s experience of packaging is data in his blog "),r("a",{attrs:{href:"https://fellows.frictionlessdata.io/blog/guo-qiang-datapackage-blog/",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),r("OutboundLink")],1),e._v(".")]),e._v(" "),r("h2",{attrs:{id:"melvin"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#melvin"}},[e._v("#")]),e._v(" Melvin")]),e._v(" "),r("p",[e._v("Being a soil science student, I felt using soil data would be useful for me to better understand this process of packaging data for future use. 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Anaplasma camelii’ in camels and camel keds evaluated in different seasons within a year…")]),e._v(" "),r("p",[e._v("To read about the errors that Melvin got and what she learned from them, read her blog "),r("a",{attrs:{href:"https://fellows.frictionlessdata.io/blog/melvin-datapackage-blog/",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),r("OutboundLink")],1),e._v(".")]),e._v(" "),r("hr"),e._v(" "),r("p",[e._v("You can read all the Frictionless Data Fellows’ blogs on the dedicated website: "),r("a",{attrs:{href:"https://fellows.frictionlessdata.io/",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://fellows.frictionlessdata.io/"),r("OutboundLink")],1)])])}),[],!1,null,null,null);a.default=o.exports}}]); \ No newline at end of file diff --git a/assets/js/57.84990213.js b/assets/js/57.5b565d5f.js similarity index 99% rename from assets/js/57.84990213.js rename to assets/js/57.5b565d5f.js index 16b4ce5f2..16122efee 100644 --- a/assets/js/57.84990213.js +++ b/assets/js/57.5b565d5f.js @@ -1 +1 @@ -(window.webpackJsonp=window.webpackJsonp||[]).push([[57],{554:function(t,a,e){"use strict";e.r(a);var r=e(29),s=Object(r.a)({},(function(){var t=this,a=t.$createElement,e=t._self._c||a;return e("ContentSlotsDistributor",{attrs:{"slot-key":t.$parent.slotKey}},[e("h1",{attrs:{id:"frictionless-adoption"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#frictionless-adoption"}},[t._v("#")]),t._v(" Frictionless Adoption")]),t._v(" "),e("p",[e("big",[e("strong",[t._v("Projects and collaborations that use Frictionless.")])])],1),t._v(" "),e("p",[t._v("The Frictionless Data project provides software and standards to work with data. 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You can read more details about this grant "),e("a",{attrs:{href:"https://blog.okfn.org/2018/07/12/sloan-foundation-funds-frictionless-data-for-reproducible-research/",target:"_blank",rel:"noopener noreferrer"}},[t._v("here"),e("OutboundLink")],1),t._v(".")]),t._v(" "),e("h4",{attrs:{id:"pilot-collaborations-2"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#pilot-collaborations-2"}},[t._v("#")]),t._v(" Pilot Collaborations")]),t._v(" "),e("p",[t._v("Pilots are intensive, hands-on collaborations with researcher teams to resolve their research data management workflow issues with Frictionless Data software and specs. 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On this page we share projects and collaborations that use Frictionless, including collaborations with the Frictionless Team and also community projects that use our toolkit.")]),t._v(" "),e("div",{staticClass:"custom-block tip"},[e("p",{staticClass:"custom-block-title"},[t._v("TIP")]),t._v(" "),e("p",[t._v("If you use Frictionless in your work and want to share it with community, please write to the Frictionless Team using any available contact provided on this site and we will add your project to this page.")])]),t._v(" "),e("h2",{attrs:{id:"pilot-collaborations"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#pilot-collaborations"}},[t._v("#")]),t._v(" Pilot Collaborations")]),t._v(" "),e("p",[t._v("We work closely with data researchers and institutions to help them integrate Frictionless into their workflow. 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")])])]),t._v(" "),e("div",{staticClass:"w-full md:w-1/3 feature flex justify-center"},[e("div",{staticClass:"px-8 text-center"},[e("a",{attrs:{href:"/blog/2020/04/23/table-schema-catalog/",target:"_blank"}},[e("img",{staticStyle:{width:"200px",border:"dashed 1px #555",padding:"10px","border-radius":"10px"},attrs:{src:"/img/adoption/validata.png"}}),t._v(" "),e("h3",[t._v("Validata")])]),t._v(" "),e("p",[e("a",{attrs:{href:"https://www.opendatafrance.net/",target:"_blank"}},[t._v("OpenDataFrance")]),t._v(" created "),e("a",{attrs:{href:"http://validata.fr/",target:"_blank"}},[t._v("Validata")]),t._v(", a platform for local public administration in France to validate CSV files on the web, using the tabular data package specification.")])])])])]),t._v(" "),e("h2",{attrs:{id:"find-frictionless-datasets"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#find-frictionless-datasets"}},[t._v("#")]),t._v(" Find Frictionless Datasets")]),t._v(" "),e("p",[t._v("Where can I find Frictionless Datasets?")]),t._v(" "),e("ul",[e("li",[t._v("The Frictionless team maintains a list of Frictionless Datasets from GitHub on this site: "),e("a",{attrs:{href:"http://data-package.frictionlessdata.io",target:"_blank",rel:"noopener noreferrer"}},[t._v("data-package.frictionlessdata.io"),e("OutboundLink")],1)]),t._v(" "),e("li",[t._v("You can find Frictionless Datasets on Zenodo by "),e("a",{attrs:{href:"https://zenodo.org/search?page=1&size=20&q=keywords:%22frictionlessdata%22",target:"_blank",rel:"noopener noreferrer"}},[t._v("searching for #frictionlessdata"),e("OutboundLink")],1)]),t._v(" "),e("li",[t._v("There are several external databases that allow export of data as datapackages, including "),e("a",{attrs:{href:"https://datahub.io/",target:"_blank",rel:"noopener noreferrer"}},[t._v("datahub.io"),e("OutboundLink")],1),t._v(", "),e("a",{attrs:{href:"https://data.world/",target:"_blank",rel:"noopener noreferrer"}},[t._v("data.world"),e("OutboundLink")],1),t._v(", and "),e("a",{attrs:{href:"http://intermine.org/",target:"_blank",rel:"noopener noreferrer"}},[t._v("Intermine"),e("OutboundLink")],1),t._v("."),e("br"),t._v(" "),e("em",[t._v("Don’t see your database listed here? Let us know!")])])]),t._v(" "),e("h2",{attrs:{id:"grant-funded-work"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#grant-funded-work"}},[t._v("#")]),t._v(" Grant-funded work")]),t._v(" "),e("h3",{attrs:{id:"frictionless-data-for-reproducible-research"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#frictionless-data-for-reproducible-research"}},[t._v("#")]),t._v(" Frictionless Data for Reproducible Research")]),t._v(" "),e("p",[t._v("From September 2018 til December 2021, the Frictionless Data team focused on enhanced dissemination and training activities, and further iterations on our software and specifications via a range of collaborations with research partners. We aimed to use Frictionless tooling to resolve research data workflow issues, create a new wave of open science advocates, and teach about FAIR data management. This pivotal work was funded by the Alfred P. Sloan Foundation and overseen by the Frictionless team at the Open Knowledge Foundation. You can read more details about this grant "),e("a",{attrs:{href:"https://blog.okfn.org/2018/07/12/sloan-foundation-funds-frictionless-data-for-reproducible-research/",target:"_blank",rel:"noopener noreferrer"}},[t._v("here"),e("OutboundLink")],1),t._v(".")]),t._v(" "),e("h4",{attrs:{id:"pilot-collaborations-2"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#pilot-collaborations-2"}},[t._v("#")]),t._v(" Pilot Collaborations")]),t._v(" "),e("p",[t._v("Pilots are intensive, hands-on collaborations with researcher teams to resolve their research data management workflow issues with Frictionless Data software and specs. You can read about the Pilot projects on our "),e("RouterLink",{attrs:{to:"/tag/pilot/"}},[t._v("blog")]),t._v(".")],1),t._v(" "),e("h4",{attrs:{id:"tool-fund"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#tool-fund"}},[t._v("#")]),t._v(" Tool Fund")]),t._v(" "),e("p",[t._v("The Tool Fund is a $5000 grant to develop an open tool for reproducible science or research built using the Frictionless Data codebase. Learn more by reading "),e("RouterLink",{attrs:{to:"/tag/tool-fund/"}},[t._v("Tool Fund Blogs")]),t._v(" or by visiting the "),e("a",{attrs:{href:"https://toolfund.frictionlessdata.io/",target:"_blank",rel:"noopener noreferrer"}},[t._v("Tool Fund site"),e("OutboundLink")],1),t._v(".")],1),t._v(" "),e("h4",{attrs:{id:"fellows-programme"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#fellows-programme"}},[t._v("#")]),t._v(" Fellows Programme")]),t._v(" "),e("p",[t._v("The "),e("a",{attrs:{href:"https://fellows.frictionlessdata.io/",target:"_blank",rel:"noopener noreferrer"}},[t._v("Fellows Programme"),e("OutboundLink")],1),t._v(" trains early career researchers to become champions of the Frictionless Data tools and approaches in their field. Read more about the Programme, including Fellows biographies and the programme syllabus, on the "),e("a",{attrs:{href:"https://fellows.frictionlessdata.io/",target:"_blank",rel:"noopener noreferrer"}},[t._v("Fellows website"),e("OutboundLink")],1)]),t._v(" "),e("h3",{attrs:{id:"data-institutions-website-update"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#data-institutions-website-update"}},[t._v("#")]),t._v(" Data Institutions - Website Update")]),t._v(" "),e("p",[t._v("In 2021, we partnered with the Open Data Institute (ODI) to improve our existing documentation and add new features on Frictionless Data to create a better user experience for all. Working with a series of feedback sessions from our community members, we created our new "),e("a",{attrs:{href:"https://framework.frictionlessdata.io/",target:"_blank",rel:"noopener noreferrer"}},[t._v("documentation portal"),e("OutboundLink")],1),t._v(" for the Frictionless Framework and several new tutorials. Read more about this grant "),e("a",{attrs:{href:"https://blog.okfn.org/2021/04/14/unveiling-the-new-frictionless-data-documentation-portal/",target:"_blank",rel:"noopener noreferrer"}},[t._v("here"),e("OutboundLink")],1),t._v(".")]),t._v(" "),e("h3",{attrs:{id:"frictionless-field-guide"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#frictionless-field-guide"}},[t._v("#")]),t._v(" Frictionless Field Guide")]),t._v(" "),e("p",[t._v("In 2017, OKF received funding from the Open Data Institute to create a Frictionless Data Field Guide. This guide provided step-by-step instructions for improving data publishing workflows. The "),e("a",{attrs:{href:"/tag/field-guide"}},[t._v("field guide")]),t._v(" introduced new ways of working informed by the Frictionless Data suite of software that data publishers can use independently, or adapt into existing personal and organisational workflows. You can read more details about this work "),e("a",{attrs:{href:"https://blog.okfn.org/2018/03/27/improving-your-data-publishing-workflow-with-the-frictionless-data-field-guide/",target:"_blank",rel:"noopener noreferrer"}},[t._v("here"),e("OutboundLink")],1),t._v(".")]),t._v(" "),e("h3",{attrs:{id:"data-package-integrations"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#data-package-integrations"}},[t._v("#")]),t._v(" Data Package Integrations")]),t._v(" "),e("p",[t._v("In 2016, Google funded OKF to work on tool integration for Data Packages as part of our broader work on Frictionless Data to support the open data community. You can read more about this work "),e("a",{attrs:{href:"https://blog.okfn.org/2016/02/01/google-funds-frictionless-data-initiative-at-open-knowledge/",target:"_blank",rel:"noopener noreferrer"}},[t._v("here"),e("OutboundLink")],1),t._v(".")]),t._v(" "),e("h3",{attrs:{id:"data-packages-development"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#data-packages-development"}},[t._v("#")]),t._v(" Data Packages Development")]),t._v(" "),e("p",[t._v("In 2016, OKF received funding from The Alfred P. Sloan Foundation to work on a broad range of activities to enable better research and more effective civic tech through Frictionless Data. The funding targeted standards work, tooling, and infrastructure around “data packages” as well as piloting and outreach activities to support researchers and civic technologists in addressing real problems encountered when working with data. 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It is standard practice to include some (if possible all) of the following sections: "),e("strong",[a._v("Introduction, Data, Preparation, License")]),a._v(".")]),a._v(" "),e("p",[a._v("We SHOULD NOT include the title of the Data Package at the top of the README.")]),a._v(" "),e("p",[a._v("Each section other than the introduction should be headed with its name using level 2 heading in markdown e.g. for the data section you would have the following markdown in your README:")]),a._v(" "),e("div",{staticClass:"language- extra-class"},[e("pre",{pre:!0,attrs:{class:"language-text"}},[e("code",[a._v("## Data\n")])])]),e("h4",{attrs:{id:"introduction"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#introduction"}},[a._v("#")]),a._v(" Introduction")]),a._v(" "),e("p",[a._v("Start with a short description of the dataset (the first sentence and first paragraph should be extractable to provide short standalone descriptions).")]),a._v(" "),e("p",[a._v("Unlike other sections "),e("strong",[a._v("this section SHOULD NOT have a heading")]),a._v(" as it starts the README. 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In particular, any instructions about how to run any preparation and processing scripts to generate the data should go here.")]),a._v(" "),e("h4",{attrs:{id:"license"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#license"}},[a._v("#")]),a._v(" License")]),a._v(" "),e("p",[a._v("Put additional information on the permissions and licensing of the data in the Data Package in the License section.")]),a._v(" "),e("p",[a._v("Since licensing information is often not clear from the data producers, the guideline here is to license the Data Package under the Public Domain Dedication and License, and then to add any relevant information or disclaimers regarding the source data.")]),a._v(" "),e("p",[a._v("See, for example:")]),a._v(" "),e("ul",[e("li",[e("a",{attrs:{href:"http://datahub.io/core/corruption-perceptions-index#readme",target:"_blank",rel:"noopener noreferrer"}},[a._v("http://datahub.io/core/corruption-perceptions-index#readme"),e("OutboundLink")],1)]),a._v(" "),e("li",[e("a",{attrs:{href:"http://datahub.io/core/geo-nuts-administrative-boundaries#readme",target:"_blank",rel:"noopener noreferrer"}},[a._v("http://datahub.io/core/geo-nuts-administrative-boundaries#readme"),e("OutboundLink")],1)])]),a._v(" "),e("p",[a._v("See also the following thread "),e("a",{attrs:{href:"https://discuss.okfn.org/t/copyright-on-data-sources/189",target:"_blank",rel:"noopener noreferrer"}},[a._v("https://discuss.okfn.org/t/copyright-on-data-sources/189"),e("OutboundLink")],1)]),a._v(" "),e("hr"),a._v(" "),e("h2",{attrs:{id:"validate-and-preview-your-data-package"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#validate-and-preview-your-data-package"}},[a._v("#")]),a._v(" Validate and preview your Data Package")]),a._v(" "),e("p",[a._v("Use the "),e("a",{attrs:{href:"http://create.frictionlessdata.io",target:"_blank",rel:"noopener noreferrer"}},[a._v("Data Package Creator"),e("OutboundLink")],1),a._v(" to check that your "),e("code",[a._v("datapackage.json")]),a._v(" and Data Package are good to go. 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In particular, any instructions about how to run any preparation and processing scripts to generate the data should go here.")]),a._v(" "),e("h4",{attrs:{id:"license"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#license"}},[a._v("#")]),a._v(" License")]),a._v(" "),e("p",[a._v("Put additional information on the permissions and licensing of the data in the Data Package in the License section.")]),a._v(" "),e("p",[a._v("Since licensing information is often not clear from the data producers, the guideline here is to license the Data Package under the Public Domain Dedication and License, and then to add any relevant information or disclaimers regarding the source data.")]),a._v(" "),e("p",[a._v("See, for example:")]),a._v(" "),e("ul",[e("li",[e("a",{attrs:{href:"http://datahub.io/core/corruption-perceptions-index#readme",target:"_blank",rel:"noopener noreferrer"}},[a._v("http://datahub.io/core/corruption-perceptions-index#readme"),e("OutboundLink")],1)]),a._v(" "),e("li",[e("a",{attrs:{href:"http://datahub.io/core/geo-nuts-administrative-boundaries#readme",target:"_blank",rel:"noopener noreferrer"}},[a._v("http://datahub.io/core/geo-nuts-administrative-boundaries#readme"),e("OutboundLink")],1)])]),a._v(" "),e("p",[a._v("See also the following thread "),e("a",{attrs:{href:"https://discuss.okfn.org/t/copyright-on-data-sources/189",target:"_blank",rel:"noopener noreferrer"}},[a._v("https://discuss.okfn.org/t/copyright-on-data-sources/189"),e("OutboundLink")],1)]),a._v(" "),e("hr"),a._v(" "),e("h2",{attrs:{id:"validate-and-preview-your-data-package"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#validate-and-preview-your-data-package"}},[a._v("#")]),a._v(" Validate and preview your Data Package")]),a._v(" "),e("p",[a._v("Use the "),e("a",{attrs:{href:"http://create.frictionlessdata.io",target:"_blank",rel:"noopener noreferrer"}},[a._v("Data Package Creator"),e("OutboundLink")],1),a._v(" to check that your "),e("code",[a._v("datapackage.json")]),a._v(" and Data Package are good to go. 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strict";t.r(a);var o=t(29),r=Object(o.a)({},(function(){var e=this,a=e.$createElement,o=e._self._c||a;return o("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[o("p",[e._v("When sharing multiple datasets on a specific subject with a varied audience, it is important to ensure that whoever accesses the data understands the context around it, and can quickly access licensing and other attribution information.")]),e._v(" "),o("p",[e._v("In this section, you will learn how to collate related datasets in one place, and easily create a schema that contains descriptive metadata for your data collection.")]),e._v(" "),o("h2",{attrs:{id:"write-a-table-schema"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#write-a-table-schema"}},[e._v("#")]),e._v(" Write a Table Schema")]),e._v(" "),o("p",[e._v("Simply put, a schema is a blueprint that tells us how your data is structured, and what type of content is to be expected in it. You can think of it as a data dictionary. Having a table schema at hand makes it possible to run more precise validation checks on your data, both at a structural and content level.")]),e._v(" "),o("p",[e._v("For this section, we will use the "),o("a",{attrs:{href:"https://create.frictionlessdata.io",target:"_blank",rel:"noopener noreferrer"}},[e._v("Data Package Creator"),o("OutboundLink")],1),e._v(" and "),o("a",{attrs:{href:"http://datahub.io/core/gdp",target:"_blank",rel:"noopener noreferrer"}},[e._v("Gross Domestic Product dataset for all countries (1960 - 2014)"),o("OutboundLink")],1),e._v(".")]),e._v(" "),o("p",[o("strong",[e._v("Data Package")]),e._v(" is a format that makes it possible to put your data collection and relevant information that provides context about your data in one container before you share it. All contextual information, such as metadata and your data schema, is published in a JSON file named "),o("em",[e._v("datapackage.json")]),e._v(".")]),e._v(" "),o("p",[o("strong",[e._v("Data Package Creator")]),e._v(" is an online service that facilitates the creation and editing of data packages. The service automatically generates a "),o("em",[e._v("datapackage.json")]),e._v(" file for you as you add and edit data that is part of your data collection. We refer to each piece of data in a data collection as a "),o("strong",[e._v("data resource")]),e._v(".")]),e._v(" "),o("p",[o("a",{attrs:{href:"https://create.frictionlessdata.io",target:"_blank",rel:"noopener noreferrer"}},[e._v("Data Package Creator"),o("OutboundLink")],1),e._v(" loads with dummy data to make it easy to understand how metadata and sample resources help generate the "),o("em",[e._v("datapackage.json")]),e._v(" file. There are three ways in which a user can add data resources on "),o("a",{attrs:{href:"https://create.frictionlessdata.io",target:"_blank",rel:"noopener noreferrer"}},[e._v("Data Package Creator"),o("OutboundLink")],1),e._v(":")]),e._v(" "),o("ol",[o("li",[e._v("Provide a hyperlink to your data resource (highly recommended).")])]),e._v(" "),o("p",[e._v("If your data resource is publicly available, like on GitHub or in a data repository, simply obtain the URL and paste it in the "),o("strong",[e._v("Path")]),e._v(" section. To learn how to publish your data resource online, check the publish your dataset section.")]),e._v(" "),o("ol",{attrs:{start:"2"}},[o("li",[e._v("Create your data resource within the service.")])]),e._v(" "),o("p",[e._v("If your data resource isn’t published online, you’ll have to define its fields from scratch. Depending on how complex is your data, this can be time consuming, but it’s still easier than creating the descriptor JSON file from scratch.This option is time consuming, as a user has to manually create each field of a data resource. However, this is simpler than learning how to create a JSON file from scratch.")]),e._v(" "),o("ol",{attrs:{start:"3"}},[o("li",[o("strong",[e._v("Load a Data Package")]),e._v(" option")])]),e._v(" "),o("p",[e._v("With this option, you can load a pre-existing "),o("em",[e._v("datapackage.json")]),e._v(" file to view and edit its metadata and resource fields.")]),e._v(" "),o("hr"),e._v(" "),o("p",[e._v("Let’s use our "),o("a",{attrs:{href:"https://github.com/frictionlessdata/example-data-packages/blob/master/gross-domestic-product-all/data/gdp.csv",target:"_blank",rel:"noopener noreferrer"}},[e._v("Gross Domestic Product dataset for all countries (1960 - 2014)"),o("OutboundLink")],1),e._v(" dataset, which is publicly available on GitHub.")]),e._v(" "),o("p",[e._v("Obtain a link to the raw CSV file by clicking on the Raw button at the top right corner of the GitHub file preview page, as shown in figure 1 below. The resulting hyperlink looks like "),o("code",[e._v("https://raw.githubusercontent.com/datasets/continent-codes/master/data/continent-codes.csv")])]),e._v(" "),o("figure",[o("img",{attrs:{src:t(442),alt:"Above, raw button highlighted in red"}}),e._v(" "),o("figcaption",[e._v("\n Figure 1: Above, raw button highlighted in red.\n ")])]),e._v(" "),o("p",[e._v("Paste your hyperlink in the "),o("em",[e._v("Path")]),e._v(" section and click on the "),o("em",[e._v("Load")]),e._v(" button. Each column in your table translates to a "),o("em",[e._v("field")]),e._v(". You should be prompted to add all fields identified in your data resource, as in Figure 2 below. Click on the prompt to load the fields.")]),e._v(" "),o("figure",[o("img",{attrs:{src:t(443),alt:"annotated in red, a prompt to add all fields inferred from your data resource"}}),e._v(" "),o("figcaption",[e._v("\n Figure 2: annotated in red, a prompt to add all fields inferred from your data resource.\n ")])]),e._v(" "),o("p",[e._v("The page that follows looks like Figure 3 below. Each column from the GDP dataset has been mapped to a "),o("em",[e._v("field")]),e._v(". The data type for each column has been inferred correctly, and we can preview data under each field by hovering over the field name. It is also possible to edit all sections of our data resource’s fields as we can see below.")]),e._v(" "),o("figure",[o("img",{attrs:{src:t(444),alt:"all fields inferred from your data resource"}}),e._v(" "),o("figcaption",[e._v("\n Figure 3: all fields inferred from your data resource.\n ")])]),e._v(" "),o("p",[e._v("You can now edit data types and formats as necessary, and optionally add titles and descriptive information to your fields. For example, the data type for our {Year} field should be "),o("em",[o("strong",[e._v("year")])]),e._v(" and not "),o("em",[o("strong",[e._v("integer")])]),e._v(". Our {Value} column has numeric information with decimal places.")]),e._v(" "),o("p",[e._v("By definition, values under the "),o("em",[o("strong",[e._v("integer")])]),e._v(" data type are whole numbers. The "),o("em",[o("strong",[e._v("number")])]),e._v(" data type is more appropriate for the {Value} column. When in doubt about what data type to use, consult the "),o("a",{attrs:{href:"https://specs.frictionlessdata.io/table-schema/#types-and-formats",target:"_blank",rel:"noopener noreferrer"}},[e._v("Table Schema data types cheat sheet"),o("OutboundLink")],1),e._v(".")]),e._v(" "),o("p",[e._v("Click on the "),o("img",{attrs:{src:t(445),alt:"settings"}}),e._v(" icon to pick a suitable profile for your data resource. "),o("a",{attrs:{href:"https://specs.frictionlessdata.io/profiles/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Here’s more information about Frictionless Data profiles"),o("OutboundLink")],1),e._v(".")]),e._v(" "),o("p",[e._v("If your dataset has other data resources, add them by scrolling to the bottom of the page, clicking on Add Resource, and repeating the same process as we just did.")]),e._v(" "),o("p",[e._v("If your dataset has other data resources, add them by scrolling to the bottom of the page, clicking on "),o("strong",[e._v("Add Resource")]),e._v(", and repeating the same process as we just did.")]),e._v(" "),o("figure",[o("img",{attrs:{src:t(446),alt:"Prompt to add more data resources"}}),e._v(" "),o("figcaption",[e._v("\n Figure 4: Prompt to add more data resources.\n ")])]),e._v(" "),o("h2",{attrs:{id:"add-your-dataset-s-metadata"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#add-your-dataset-s-metadata"}},[e._v("#")]),e._v(" Add your dataset’s metadata")]),e._v(" "),o("p",[e._v("In the previous section, we described metadata for each of our datasets, but we’re still missing metadata for our collection of datasets. You can add it via the "),o("strong",[e._v("Metadata")]),e._v(" section on the left side bar, describing things like the dataset name, description, author, license, etc.")]),e._v(" "),o("figure",[o("img",{attrs:{src:t(447),alt:"Add Data Package Metadata "}})]),e._v(" "),o("p",[e._v("The "),o("strong",[e._v("Profile")]),e._v(" section under metadata allows us to specify what kind of data collection we are packaging.")]),e._v(" "),o("ul",[o("li",[o("p",[o("em",[e._v("Data Package")]),o("br"),e._v("\nThis is the base, more general profile. Use it if your dataset contains resources of mixed formats, like tabular and geographical data. The base requirement for a valid Data Package profile is the "),o("em",[e._v("datapackage.json")]),e._v(" file. See the "),o("a",{attrs:{href:"https://specs.frictionlessdata.io/data-package/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Data Package specification"),o("OutboundLink")],1),e._v(" for more information.")])]),e._v(" "),o("li",[o("p",[o("em",[e._v("Tabular Data Package")]),o("br"),e._v("\nIf your data contains only tabular resources like CSVs and spreadsheets, use the Tabular Data Package profile. See the "),o("a",{attrs:{href:"https://specs.frictionlessdata.io/tabular-data-package/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Tabular Data Package specification"),o("OutboundLink")],1),e._v(" for more information.")])]),e._v(" "),o("li",[o("p",[o("em",[e._v("Fiscal Data Package")]),o("br"),e._v("\nIf your data contains fiscal information like budgets and expenditure data, use the Fiscal Data Package profile. See the "),o("a",{attrs:{href:"https://specs.frictionlessdata.io/fiscal-data-package/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Fiscal Data Package specification"),o("OutboundLink")],1),e._v(" for more information.")])])]),e._v(" "),o("p",[e._v("In our example, as we only have a CSV data resource, the "),o("em",[e._v("Tabular Data Package")]),e._v(" profile is the best option.")]),e._v(" "),o("p",[e._v("In the "),o("strong",[e._v("Keywords")]),e._v(" section, you can add any keywords that helps make your data collection more discoverable. For our dataset, we might use the keywords "),o("em",[e._v("GDP, National Accounts, National GDP, Regional GDP")]),e._v(". Other datasets could include the country name, dataset area (e.g. “health” or “environmental”), etc.")]),e._v(" "),o("p",[e._v("Now that we have created a Data Package, we can "),o("strong",[e._v("Validate")]),e._v(" or "),o("strong",[e._v("Download")]),e._v(" it. But first, let’s see what our datapackage.json file looks like. With every addition and modification, the "),o("a",{attrs:{href:"https://create.frictionlessdata.io",target:"_blank",rel:"noopener noreferrer"}},[e._v("Data Package Creator"),o("OutboundLink")],1),e._v(" has been populating the "),o("em",[e._v("datapackage.json")]),e._v(" file for us. Click on the "),o("strong",[e._v("{···}")]),e._v(" icon to view the "),o("em",[e._v("datapackage.json")]),e._v(" file. As you can see below, any edit we make to the description of the Value field reflects on the JSON file in real time.")]),e._v(" "),o("p",[e._v("The "),o("strong",[e._v("Validate")]),e._v(" button allows us to confirm whether we chose the correct Profile for our Data Package. The two possible outcomes at this stage are:")]),e._v(" "),o("figure",[o("img",{attrs:{src:t(448),alt:"Data Package is Invalid"}})]),e._v(" "),o("p",[e._v("This message appears when there is some validation error like if we miss some required attribute (e.g. the data package name), or have picked an incorrect profile (e.g. Tabular Data Package with geographical data)… Review the metadata and profiles to find the mistake and try validating again.")]),e._v(" "),o("figure",[o("img",{attrs:{src:t(449),alt:"Data Package is Valid"}})]),e._v(" "),o("p",[e._v("All good! This message means that your data package is valid, and we can download it.")]),e._v(" "),o("h2",{attrs:{id:"download-your-data-package"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#download-your-data-package"}},[e._v("#")]),e._v(" Download your Data Package")]),e._v(" "),o("p",[e._v("As we said earlier, the base requirement for a valid Data Package profile is the "),o("em",[e._v("datapackage.json")]),e._v(" file, which contains your data schema and metadata. We call this the descriptor file. You can download your descriptor file by clicking on the "),o("strong",[e._v("Download")]),e._v(" button.")]),e._v(" "),o("ul",[o("li",[o("p",[e._v("If your data resources, like ours, were linked from an online public source, sharing the "),o("em",[e._v("datapackage.json")]),e._v(" file is sufficient, since it contains URLs to your data resources.")])]),e._v(" "),o("li",[o("p",[e._v("If you manually created a data resource and its fields, remember to add all your data resources and the downloaded "),o("em",[e._v("datapackage.json")]),e._v(" file in one folder before sharing it.")])])]),e._v(" "),o("p",[e._v("The way to structure your dataset depends on your data, and what extra artifacts it contains (e.g. images, scripts, reports, etc.). In this section, we’ll show a complete example with:")]),e._v(" "),o("ul",[o("li",[o("strong",[e._v("Data files")]),e._v(": The files with the actual data (e.g. CSV, XLS, GeoJSON, …)")]),e._v(" "),o("li",[o("strong",[e._v("Documentation")]),e._v(": How was the data collected, any caveats, how to update it, etc.")]),e._v(" "),o("li",[o("strong",[e._v("Metadata")]),e._v(": Where the data comes from, what’s in the files, what’s their source and license, etc.")]),e._v(" "),o("li",[o("strong",[e._v("Scripts")]),e._v(": Software scripts that were used to generate, update, or modify the data.")])]),e._v(" "),o("p",[e._v("Your final Data Package file directory should look like this:")]),e._v(" "),o("div",{staticClass:"language- extra-class"},[o("pre",{pre:!0,attrs:{class:"language-text"}},[o("code",[e._v("data/\n dataresource1.csv\n dataresource2.csv\ndatapackage.json\n")])])]),o("ul",[o("li",[o("p",[o("strong",[e._v("data/")]),e._v(": All data files are contained in this folder. In our example, there is only one: "),o("code",[e._v("data/gdp.csv")]),e._v(" .")])]),e._v(" "),o("li",[o("p",[o("strong",[e._v("datapackage.json")]),e._v(": This file describes the dataset’s metadata. For example, what is the dataset, where are its files, what they contain, what each column means (for tabular data), what’s the source, license, and authors, and so on. As it’s a machine-readable specification, other software can import and validate your files.")])])]),e._v(" "),o("p",[e._v("Congratulations! You have now created a schema for your data, and combined it with descriptive metadata and your data collection to create your first data package!")])])}),[],!1,null,null,null);a.default=r.exports}}]); \ No newline at end of file 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strict";t.r(a);var o=t(29),r=Object(o.a)({},(function(){var e=this,a=e.$createElement,o=e._self._c||a;return o("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[o("p",[e._v("When sharing multiple datasets on a specific subject with a varied audience, it is important to ensure that whoever accesses the data understands the context around it, and can quickly access licensing and other attribution information.")]),e._v(" "),o("p",[e._v("In this section, you will learn how to collate related datasets in one place, and easily create a schema that contains descriptive metadata for your data collection.")]),e._v(" "),o("h2",{attrs:{id:"write-a-table-schema"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#write-a-table-schema"}},[e._v("#")]),e._v(" Write a Table Schema")]),e._v(" "),o("p",[e._v("Simply put, a schema is a blueprint that tells us how your data is structured, and what type of content is to be expected in it. You can think of it as a data dictionary. Having a table schema at hand makes it possible to run more precise validation checks on your data, both at a structural and content level.")]),e._v(" "),o("p",[e._v("For this section, we will use the "),o("a",{attrs:{href:"https://create.frictionlessdata.io",target:"_blank",rel:"noopener noreferrer"}},[e._v("Data Package Creator"),o("OutboundLink")],1),e._v(" and "),o("a",{attrs:{href:"http://datahub.io/core/gdp",target:"_blank",rel:"noopener noreferrer"}},[e._v("Gross Domestic Product dataset for all countries (1960 - 2014)"),o("OutboundLink")],1),e._v(".")]),e._v(" "),o("p",[o("strong",[e._v("Data Package")]),e._v(" is a format that makes it possible to put your data collection and relevant information that provides context about your data in one container before you share it. All contextual information, such as metadata and your data schema, is published in a JSON file named "),o("em",[e._v("datapackage.json")]),e._v(".")]),e._v(" "),o("p",[o("strong",[e._v("Data Package Creator")]),e._v(" is an online service that facilitates the creation and editing of data packages. The service automatically generates a "),o("em",[e._v("datapackage.json")]),e._v(" file for you as you add and edit data that is part of your data collection. We refer to each piece of data in a data collection as a "),o("strong",[e._v("data resource")]),e._v(".")]),e._v(" "),o("p",[o("a",{attrs:{href:"https://create.frictionlessdata.io",target:"_blank",rel:"noopener noreferrer"}},[e._v("Data Package Creator"),o("OutboundLink")],1),e._v(" loads with dummy data to make it easy to understand how metadata and sample resources help generate the "),o("em",[e._v("datapackage.json")]),e._v(" file. There are three ways in which a user can add data resources on "),o("a",{attrs:{href:"https://create.frictionlessdata.io",target:"_blank",rel:"noopener noreferrer"}},[e._v("Data Package Creator"),o("OutboundLink")],1),e._v(":")]),e._v(" "),o("ol",[o("li",[e._v("Provide a hyperlink to your data resource (highly recommended).")])]),e._v(" "),o("p",[e._v("If your data resource is publicly available, like on GitHub or in a data repository, simply obtain the URL and paste it in the "),o("strong",[e._v("Path")]),e._v(" section. To learn how to publish your data resource online, check the publish your dataset section.")]),e._v(" "),o("ol",{attrs:{start:"2"}},[o("li",[e._v("Create your data resource within the service.")])]),e._v(" "),o("p",[e._v("If your data resource isn’t published online, you’ll have to define its fields from scratch. Depending on how complex is your data, this can be time consuming, but it’s still easier than creating the descriptor JSON file from scratch.This option is time consuming, as a user has to manually create each field of a data resource. However, this is simpler than learning how to create a JSON file from scratch.")]),e._v(" "),o("ol",{attrs:{start:"3"}},[o("li",[o("strong",[e._v("Load a Data Package")]),e._v(" option")])]),e._v(" "),o("p",[e._v("With this option, you can load a pre-existing "),o("em",[e._v("datapackage.json")]),e._v(" file to view and edit its metadata and resource fields.")]),e._v(" "),o("hr"),e._v(" "),o("p",[e._v("Let’s use our "),o("a",{attrs:{href:"https://github.com/frictionlessdata/example-data-packages/blob/master/gross-domestic-product-all/data/gdp.csv",target:"_blank",rel:"noopener noreferrer"}},[e._v("Gross Domestic Product dataset for all countries (1960 - 2014)"),o("OutboundLink")],1),e._v(" dataset, which is publicly available on GitHub.")]),e._v(" "),o("p",[e._v("Obtain a link to the raw CSV file by clicking on the Raw button at the top right corner of the GitHub file preview page, as shown in figure 1 below. The resulting hyperlink looks like "),o("code",[e._v("https://raw.githubusercontent.com/datasets/continent-codes/master/data/continent-codes.csv")])]),e._v(" "),o("figure",[o("img",{attrs:{src:t(436),alt:"Above, raw button highlighted in red"}}),e._v(" "),o("figcaption",[e._v("\n Figure 1: Above, raw button highlighted in red.\n ")])]),e._v(" "),o("p",[e._v("Paste your hyperlink in the "),o("em",[e._v("Path")]),e._v(" section and click on the "),o("em",[e._v("Load")]),e._v(" button. Each column in your table translates to a "),o("em",[e._v("field")]),e._v(". You should be prompted to add all fields identified in your data resource, as in Figure 2 below. Click on the prompt to load the fields.")]),e._v(" "),o("figure",[o("img",{attrs:{src:t(437),alt:"annotated in red, a prompt to add all fields inferred from your data resource"}}),e._v(" "),o("figcaption",[e._v("\n Figure 2: annotated in red, a prompt to add all fields inferred from your data resource.\n ")])]),e._v(" "),o("p",[e._v("The page that follows looks like Figure 3 below. Each column from the GDP dataset has been mapped to a "),o("em",[e._v("field")]),e._v(". The data type for each column has been inferred correctly, and we can preview data under each field by hovering over the field name. It is also possible to edit all sections of our data resource’s fields as we can see below.")]),e._v(" "),o("figure",[o("img",{attrs:{src:t(438),alt:"all fields inferred from your data resource"}}),e._v(" "),o("figcaption",[e._v("\n Figure 3: all fields inferred from your data resource.\n ")])]),e._v(" "),o("p",[e._v("You can now edit data types and formats as necessary, and optionally add titles and descriptive information to your fields. For example, the data type for our {Year} field should be "),o("em",[o("strong",[e._v("year")])]),e._v(" and not "),o("em",[o("strong",[e._v("integer")])]),e._v(". Our {Value} column has numeric information with decimal places.")]),e._v(" "),o("p",[e._v("By definition, values under the "),o("em",[o("strong",[e._v("integer")])]),e._v(" data type are whole numbers. The "),o("em",[o("strong",[e._v("number")])]),e._v(" data type is more appropriate for the {Value} column. When in doubt about what data type to use, consult the "),o("a",{attrs:{href:"https://specs.frictionlessdata.io/table-schema/#types-and-formats",target:"_blank",rel:"noopener noreferrer"}},[e._v("Table Schema data types cheat sheet"),o("OutboundLink")],1),e._v(".")]),e._v(" "),o("p",[e._v("Click on the "),o("img",{attrs:{src:t(439),alt:"settings"}}),e._v(" icon to pick a suitable profile for your data resource. "),o("a",{attrs:{href:"https://specs.frictionlessdata.io/profiles/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Here’s more information about Frictionless Data profiles"),o("OutboundLink")],1),e._v(".")]),e._v(" "),o("p",[e._v("If your dataset has other data resources, add them by scrolling to the bottom of the page, clicking on Add Resource, and repeating the same process as we just did.")]),e._v(" "),o("p",[e._v("If your dataset has other data resources, add them by scrolling to the bottom of the page, clicking on "),o("strong",[e._v("Add Resource")]),e._v(", and repeating the same process as we just did.")]),e._v(" "),o("figure",[o("img",{attrs:{src:t(440),alt:"Prompt to add more data resources"}}),e._v(" "),o("figcaption",[e._v("\n Figure 4: Prompt to add more data resources.\n ")])]),e._v(" "),o("h2",{attrs:{id:"add-your-dataset-s-metadata"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#add-your-dataset-s-metadata"}},[e._v("#")]),e._v(" Add your dataset’s metadata")]),e._v(" "),o("p",[e._v("In the previous section, we described metadata for each of our datasets, but we’re still missing metadata for our collection of datasets. You can add it via the "),o("strong",[e._v("Metadata")]),e._v(" section on the left side bar, describing things like the dataset name, description, author, license, etc.")]),e._v(" "),o("figure",[o("img",{attrs:{src:t(441),alt:"Add Data Package Metadata "}})]),e._v(" "),o("p",[e._v("The "),o("strong",[e._v("Profile")]),e._v(" section under metadata allows us to specify what kind of data collection we are packaging.")]),e._v(" "),o("ul",[o("li",[o("p",[o("em",[e._v("Data Package")]),o("br"),e._v("\nThis is the base, more general profile. Use it if your dataset contains resources of mixed formats, like tabular and geographical data. The base requirement for a valid Data Package profile is the "),o("em",[e._v("datapackage.json")]),e._v(" file. See the "),o("a",{attrs:{href:"https://specs.frictionlessdata.io/data-package/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Data Package specification"),o("OutboundLink")],1),e._v(" for more information.")])]),e._v(" "),o("li",[o("p",[o("em",[e._v("Tabular Data Package")]),o("br"),e._v("\nIf your data contains only tabular resources like CSVs and spreadsheets, use the Tabular Data Package profile. See the "),o("a",{attrs:{href:"https://specs.frictionlessdata.io/tabular-data-package/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Tabular Data Package specification"),o("OutboundLink")],1),e._v(" for more information.")])]),e._v(" "),o("li",[o("p",[o("em",[e._v("Fiscal Data Package")]),o("br"),e._v("\nIf your data contains fiscal information like budgets and expenditure data, use the Fiscal Data Package profile. See the "),o("a",{attrs:{href:"https://specs.frictionlessdata.io/fiscal-data-package/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Fiscal Data Package specification"),o("OutboundLink")],1),e._v(" for more information.")])])]),e._v(" "),o("p",[e._v("In our example, as we only have a CSV data resource, the "),o("em",[e._v("Tabular Data Package")]),e._v(" profile is the best option.")]),e._v(" "),o("p",[e._v("In the "),o("strong",[e._v("Keywords")]),e._v(" section, you can add any keywords that helps make your data collection more discoverable. For our dataset, we might use the keywords "),o("em",[e._v("GDP, National Accounts, National GDP, Regional GDP")]),e._v(". Other datasets could include the country name, dataset area (e.g. “health” or “environmental”), etc.")]),e._v(" "),o("p",[e._v("Now that we have created a Data Package, we can "),o("strong",[e._v("Validate")]),e._v(" or "),o("strong",[e._v("Download")]),e._v(" it. But first, let’s see what our datapackage.json file looks like. With every addition and modification, the "),o("a",{attrs:{href:"https://create.frictionlessdata.io",target:"_blank",rel:"noopener noreferrer"}},[e._v("Data Package Creator"),o("OutboundLink")],1),e._v(" has been populating the "),o("em",[e._v("datapackage.json")]),e._v(" file for us. Click on the "),o("strong",[e._v("{···}")]),e._v(" icon to view the "),o("em",[e._v("datapackage.json")]),e._v(" file. As you can see below, any edit we make to the description of the Value field reflects on the JSON file in real time.")]),e._v(" "),o("p",[e._v("The "),o("strong",[e._v("Validate")]),e._v(" button allows us to confirm whether we chose the correct Profile for our Data Package. The two possible outcomes at this stage are:")]),e._v(" "),o("figure",[o("img",{attrs:{src:t(442),alt:"Data Package is Invalid"}})]),e._v(" "),o("p",[e._v("This message appears when there is some validation error like if we miss some required attribute (e.g. the data package name), or have picked an incorrect profile (e.g. Tabular Data Package with geographical data)… Review the metadata and profiles to find the mistake and try validating again.")]),e._v(" "),o("figure",[o("img",{attrs:{src:t(443),alt:"Data Package is Valid"}})]),e._v(" "),o("p",[e._v("All good! This message means that your data package is valid, and we can download it.")]),e._v(" "),o("h2",{attrs:{id:"download-your-data-package"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#download-your-data-package"}},[e._v("#")]),e._v(" Download your Data Package")]),e._v(" "),o("p",[e._v("As we said earlier, the base requirement for a valid Data Package profile is the "),o("em",[e._v("datapackage.json")]),e._v(" file, which contains your data schema and metadata. We call this the descriptor file. You can download your descriptor file by clicking on the "),o("strong",[e._v("Download")]),e._v(" button.")]),e._v(" "),o("ul",[o("li",[o("p",[e._v("If your data resources, like ours, were linked from an online public source, sharing the "),o("em",[e._v("datapackage.json")]),e._v(" file is sufficient, since it contains URLs to your data resources.")])]),e._v(" "),o("li",[o("p",[e._v("If you manually created a data resource and its fields, remember to add all your data resources and the downloaded "),o("em",[e._v("datapackage.json")]),e._v(" file in one folder before sharing it.")])])]),e._v(" "),o("p",[e._v("The way to structure your dataset depends on your data, and what extra artifacts it contains (e.g. images, scripts, reports, etc.). In this section, we’ll show a complete example with:")]),e._v(" "),o("ul",[o("li",[o("strong",[e._v("Data files")]),e._v(": The files with the actual data (e.g. CSV, XLS, GeoJSON, …)")]),e._v(" "),o("li",[o("strong",[e._v("Documentation")]),e._v(": How was the data collected, any caveats, how to update it, etc.")]),e._v(" "),o("li",[o("strong",[e._v("Metadata")]),e._v(": Where the data comes from, what’s in the files, what’s their source and license, etc.")]),e._v(" "),o("li",[o("strong",[e._v("Scripts")]),e._v(": Software scripts that were used to generate, update, or modify the data.")])]),e._v(" "),o("p",[e._v("Your final Data Package file directory should look like this:")]),e._v(" "),o("div",{staticClass:"language- extra-class"},[o("pre",{pre:!0,attrs:{class:"language-text"}},[o("code",[e._v("data/\n dataresource1.csv\n dataresource2.csv\ndatapackage.json\n")])])]),o("ul",[o("li",[o("p",[o("strong",[e._v("data/")]),e._v(": All data files are contained in this folder. In our example, there is only one: "),o("code",[e._v("data/gdp.csv")]),e._v(" .")])]),e._v(" "),o("li",[o("p",[o("strong",[e._v("datapackage.json")]),e._v(": This file describes the dataset’s metadata. For example, what is the dataset, where are its files, what they contain, what each column means (for tabular data), what’s the source, license, and authors, and so on. As it’s a machine-readable specification, other software can import and validate your files.")])])]),e._v(" "),o("p",[e._v("Congratulations! You have now created a schema for your data, and combined it with descriptive metadata and your data collection to create your first data package!")])])}),[],!1,null,null,null);a.default=r.exports}}]); \ No newline at end of file diff --git a/assets/js/60.bc4d3fa1.js b/assets/js/60.39521d34.js similarity index 99% rename from assets/js/60.bc4d3fa1.js rename to assets/js/60.39521d34.js index df6928812..bd3fa0fad 100644 --- a/assets/js/60.bc4d3fa1.js +++ b/assets/js/60.39521d34.js @@ -1 +1 @@ -(window.webpackJsonp=window.webpackJsonp||[]).push([[60],{555:function(a,t,s){"use strict";s.r(t);var e=s(29),n=Object(e.a)({},(function(){var a=this,t=a.$createElement,s=a._self._c||t;return s("ContentSlotsDistributor",{attrs:{"slot-key":a.$parent.slotKey}},[s("p",[a._v("This tutorial will show you how to install the Python library for working with Data Packages and Table Schema, load a CSV file, infer its schema, and write a Tabular Data Package.")]),a._v(" "),s("h2",{attrs:{id:"setup"}},[s("a",{staticClass:"header-anchor",attrs:{href:"#setup"}},[a._v("#")]),a._v(" Setup")]),a._v(" "),s("p",[a._v("For this tutorial, we will need the "),s("a",{attrs:{href:"https://github.com/frictionlessdata/datapackage-py",target:"_blank",rel:"noopener noreferrer"}},[a._v("Data Package library"),s("OutboundLink")],1),a._v(" ("),s("a",{attrs:{href:"https://pypi.python.org/pypi/datapackage",target:"_blank",rel:"noopener noreferrer"}},[a._v("PyPI"),s("OutboundLink")],1),a._v(") library.")]),a._v(" "),s("div",{staticClass:"language-bash extra-class"},[s("pre",{pre:!0,attrs:{class:"language-bash"}},[s("code",[a._v("pip "),s("span",{pre:!0,attrs:{class:"token function"}},[a._v("install")]),a._v(" datapackage\n")])])]),s("h2",{attrs:{id:"creating-basic-metadata"}},[s("a",{staticClass:"header-anchor",attrs:{href:"#creating-basic-metadata"}},[a._v("#")]),a._v(" Creating basic metadata")]),a._v(" "),s("p",[a._v("You can start using the library by importing "),s("code",[a._v("datapackage")]),a._v(".")]),a._v(" "),s("div",{staticClass:"language-python extra-class"},[s("pre",{pre:!0,attrs:{class:"language-python"}},[s("code",[s("span",{pre:!0,attrs:{class:"token keyword"}},[a._v("import")]),a._v(" datapackage\n")])])]),s("p",[a._v("The Package() class allows you to work with data packages. Use it to create a blank datapackage called package like so:")]),a._v(" "),s("div",{staticClass:"language-python extra-class"},[s("pre",{pre:!0,attrs:{class:"language-python"}},[s("code",[a._v("package "),s("span",{pre:!0,attrs:{class:"token operator"}},[a._v("=")]),a._v(" datapackage"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[a._v(".")]),a._v("Package"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[a._v("(")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[a._v(")")]),a._v("\n")])])]),s("p",[a._v("You can then add useful metadata by adding keys to metadata dict attribute. Below, we are adding the required "),s("code",[a._v("name")]),a._v(" key as well as a human-readable "),s("code",[a._v("title")]),a._v(" key. For the keys supported, please consult the full "),s("a",{attrs:{href:"https://specs.frictionlessdata.io/data-package/#metadata",target:"_blank",rel:"noopener noreferrer"}},[a._v("Data Package spec"),s("OutboundLink")],1),a._v(". 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from assets/js/61.061c8843.js rename to assets/js/61.d8ece9c6.js index 444bcd720..d281e3e9d 100644 --- a/assets/js/61.061c8843.js +++ b/assets/js/61.d8ece9c6.js @@ -1 +1 @@ -(window.webpackJsonp=window.webpackJsonp||[]).push([[61],{556:function(t,a,e){"use strict";e.r(a);var o=e(29),r=Object(o.a)({},(function(){var t=this,a=t.$createElement,e=t._self._c||a;return e("ContentSlotsDistributor",{attrs:{"slot-key":t.$parent.slotKey}},[e("p",[t._v("You can publish "),e("strong",[t._v("all and any kind of data")]),t._v(" as Data packages. 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You’ll need to create this file and then place it in the directory you created.")]),t._v(" "),e("p",[e("em",[t._v("Don’t worry if you don’t know what JSON is - we provide some tools such as "),e("a",{attrs:{href:"http://create.frictionlessdata.io",target:"_blank",rel:"noopener noreferrer"}},[t._v("Data Package Creator"),e("OutboundLink")],1),t._v(" that can automatically create this file for you.")])]),t._v(" "),e("p",[t._v("There are 2 options for creating the "),e("code",[t._v("datapackage.json")]),t._v(":")]),t._v(" "),e("p",[e("strong",[t._v("Option 1")]),t._v(": Use the online "),e("a",{attrs:{href:"http://create.frictionlessdata.io",target:"_blank",rel:"noopener noreferrer"}},[t._v("datapackage.json creator tool"),e("OutboundLink")],1),t._v(" - just answer a few questions and give it your data files and it will spit out a datapackage.json for you to include in your project")]),t._v(" "),e("p",[e("strong",[t._v("Option 2")]),t._v(": Do it yourself - if you’re familiar with JSON you can create this yourself. 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With this new knowledge, now you can {achieve a benefit}.")]),t._v(" "),e("p",[t._v("Now go {do something}")]),t._v(" "),e("h2",{attrs:{id:"learn-more"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#learn-more"}},[t._v("#")]),t._v(" Learn more")]),t._v(" "),e("h3",{attrs:{id:"related-guides"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#related-guides"}},[t._v("#")]),t._v(" Related Guides")]),t._v(" "),e("ul",[e("li",[t._v("Tabular Data Package guide - "),e("a",{attrs:{href:"http://frictionlessdata.io/docs/tabular-data-package/",target:"_blank",rel:"noopener noreferrer"}},[t._v("http://frictionlessdata.io/docs/tabular-data-package/"),e("OutboundLink")],1)])]),t._v(" "),e("h3",{attrs:{id:"references"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#references"}},[t._v("#")]),t._v(" References")]),t._v(" "),e("ul",[e("li",[e("RouterLink",{attrs:{to:"/specs/tabular-data-package/"}},[t._v("Tabular Data Package specification")])],1)])])])}),[],!1,null,null,null);a.default=o.exports}}]); \ No newline at end of file diff --git a/assets/js/68.4cf46b70.js b/assets/js/68.28f21aa8.js similarity index 98% rename from assets/js/68.4cf46b70.js rename to assets/js/68.28f21aa8.js index fc871888a..8b797dc99 100644 --- a/assets/js/68.4cf46b70.js +++ b/assets/js/68.28f21aa8.js @@ -1 +1 @@ -(window.webpackJsonp=window.webpackJsonp||[]).push([[68],{574:function(e,t,a){"use strict";a.r(t);var o=a(29),r=Object(o.a)({},(function(){var e=this,t=e.$createElement,a=e._self._c||t;return a("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[a("p",[e._v("The Workforce Data Initiative aims to modernize the US workforce through the use of data. 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strict";s.r(a);var e=s(29),o=Object(e.a)({},(function(){var t=this,a=t.$createElement,e=t._self._c||a;return e("ContentSlotsDistributor",{attrs:{"slot-key":t.$parent.slotKey}},[e("p",[t._v("Tabular data (e.g. data stored in "),e("RouterLink",{attrs:{to:"/blog/2018/07/09/csv/"}},[t._v("CSV")]),t._v(" and Excel worksheets) is one of the most common forms of data available on the web. This guide will walk through validating tabular data using Frictionless Data software.")],1),t._v(" "),e("p",[t._v("This guide show how you can validate your tabular data and check both:")]),t._v(" "),e("ul",[e("li",[t._v("Structure: are there too many rows or columns in some places?")]),t._v(" "),e("li",[t._v("Schema: does the data fit its schema. Are the values in the date column actually dates? Are all the numbers greater than zero?")])]),t._v(" "),e("p",[t._v("We will walk through two methods of performing validation:")]),t._v(" "),e("ul",[e("li",[t._v("Web service: an online service called "),e("strong",[t._v("goodtables")]),t._v(". This option requires no technical knowledge or expertise.")]),t._v(" "),e("li",[t._v("Using the "),e("a",{attrs:{href:"https://github.com/frictionlessdata/goodtables-py",target:"_blank",rel:"noopener noreferrer"}},[t._v("Python goodtables library"),e("OutboundLink")],1),t._v(". This allows you full control over the validation process but requires knowledge of Python.")])]),t._v(" "),e("h2",{attrs:{id:"goodtables"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#goodtables"}},[t._v("#")]),t._v(" goodtables")]),t._v(" "),e("p",[e("a",{attrs:{href:"http://goodtables.io/",target:"_blank",rel:"noopener noreferrer"}},[t._v("goodtables"),e("OutboundLink")],1),t._v(" is a free, open-source, hosted service for validating tabular data. goodtables checks your data for its "),e("em",[t._v("structure")]),t._v(", and, optionally, its adherence to a specified "),e("em",[t._v("schema")]),t._v(". 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For instance, a poorly structured tabular dataset may consist of a header row with too many (or too few) columns when compared to of data rows with an equal amount of columns.")]),t._v(" "),e("p",[t._v("You can also provide a schema for your tabular data defined using JSON Table Schema.")]),t._v(" "),e("p",[e("img",{attrs:{src:s(457),alt:"goodtables Provide Schema"}})]),t._v(" "),e("p",[t._v("Briefly, the format allows users to specify not only the types of information within each column in a tabular dataset, but also expected values. 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"),e("a",{attrs:{href:"http://goodtables.io",target:"_blank",rel:"noopener noreferrer"}},[t._v("goodtables.io"),e("OutboundLink")],1),t._v(" provides support for different backends. At this time, users can use it to check any datasets hosted on GitHub and Amazon S3 buckets, automatically running validation against data files every time they are updated, and providing a user friendly report of any issues found.")]),t._v(" "),e("p",[e("img",{attrs:{src:s(458),alt:"Data Valid"}})]),t._v(" "),e("p",[t._v("Start your continuous data validation here: "),e("a",{attrs:{href:"https://goodtables.io",target:"_blank",rel:"noopener noreferrer"}},[t._v("https://goodtables.io"),e("OutboundLink")],1)]),t._v(" "),e("p",[t._v("Blog post on goodtables python library and goodtables web service: "),e("a",{attrs:{href:"http://okfnlabs.org/blog/2017/05/22/introducing-the-new-goodtables-library-and-goodtablesio.html",target:"_blank",rel:"noopener noreferrer"}},[t._v("http://okfnlabs.org/blog/2017/05/22/introducing-the-new-goodtables-library-and-goodtablesio.html"),e("OutboundLink")],1)]),t._v(" "),e("p",[t._v("See the "),e("code",[t._v("README.md")]),t._v(" for more information.")]),t._v(" "),e("p",[t._v("Find more examples on validating tabular data in the "),e("a",{attrs:{href:"/tag/field-guide"}},[t._v("Frictionless Data Field Guide")])])])}),[],!1,null,null,null);a.default=o.exports}}]); \ No newline at end of file diff --git a/assets/js/70.4383c25b.js b/assets/js/70.542e1289.js similarity index 98% rename from assets/js/70.4383c25b.js rename to assets/js/70.542e1289.js index 83389005b..70c7aa497 100644 --- a/assets/js/70.4383c25b.js +++ b/assets/js/70.542e1289.js @@ -1 +1 @@ -(window.webpackJsonp=window.webpackJsonp||[]).push([[70],{579:function(e,t,a){"use strict";a.r(t);var o=a(29),r=Object(o.a)({},(function(){var e=this,t=e.$createElement,a=e._self._c||t;return a("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[a("p",[e._v("This grantee profile features Matt Thompson for our series of Frictionless Data Tool Fund posts, written to shine a light on Frictionless Data’s Tool Fund grantees, their work and to let our technical community know how they can get involved.")]),e._v(" "),a("p",[e._v("My name is Matt Thompson, I am from Bristol, UK, and work as a lecturer in Creative Computing at Bath Spa University. 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We came away excited about the possibilities that the Frictionless Data software enable for the datasets we’re working with.")]),e._v(" "),a("p",[e._v("When the "),a("a",{attrs:{href:"https://blog.okfn.org/2017/03/01/announcing-the-frictionless-data-tool-fund/",target:"_blank",rel:"noopener noreferrer"}},[e._v("call for applications"),a("OutboundLink")],1),e._v(" for Frictionless Data’s Tool Fund was made in May 2017, I was already building tools in Clojure for working with Frictionless Data as part of DM4T, and I decided to apply to enable me to flesh them out into well-tested, documented libraries.")]),e._v(" "),a("p",[e._v("The problem we had with the DM4T project is that the same kinds of data were being collected by many different projects run by different universities across the country. 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This information would be contained in datasets that are curated by different people, and so we need uniform metadata in order to be able to make these kinds of queries.")]),e._v(" "),a("p",[e._v("We run Clojure events and workshops twice a month as part of the "),a("a",{attrs:{href:"https://bristolclojurians.github.io",target:"_blank",rel:"noopener noreferrer"}},[e._v("Bristol Clojurians group"),a("OutboundLink")],1),e._v(", so interested people can drop in and discuss the work we’re doing with Frictionless Data. 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We are supported by a national network of volunteers, most of whom are experienced professionals in the fields of Computer Science, Mathematics, Medicine, Journalism, Agriculture etc."),a("br"),e._v("\nOur team consists of community members who are interested in open data, linked data technologies, coding, data journalism, and who do their best in applying scientific results into community activities.")]),e._v(" "),a("p",[e._v("We were very excited when we read about "),a("RouterLink",{attrs:{to:"/"}},[e._v("Frictionless Data")]),e._v(" on the "),a("a",{attrs:{href:"https://blog.okfn.org",target:"_blank",rel:"noopener noreferrer"}},[e._v("Open Knowledge International blog"),a("OutboundLink")],1),e._v(" and have been following the progress of the project carefully. When we saw the Frictionless Data Tool Fund had been "),a("a",{attrs:{href:"https://blog.okfn.org/2017/03/01/announcing-the-frictionless-data-tool-fund/",target:"_blank",rel:"noopener noreferrer"}},[e._v("announced"),a("OutboundLink")],1),e._v(", we were certain that we wanted to be part of this project and build tools that can help in removing the friction in working with data.")],1),e._v(" "),a("p",[e._v("People and organizations working with data are interested in analyzing, visualizing or building apps based on data, but they end up spending most of their time cleaning and preparing the data.")]),e._v(" "),a("p",[e._v("Frictionless Data software and specifications aim to make data ready for use. There are a lot of open data repositories in different formats and they often include dirty and missing data with null values, dates, currencies, units and identifiers which are represented differently and need effort to make them usable. Moreover datasets from different sources have different licenses, are often not up to date and inconsistencies make including in in one analysis difficult.")]),e._v(" "),a("p",[e._v("We received the Frictionless Data Tool Fund grant for the development of libraries in R language. 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Comments, forks, and pull requests welcome in the two repositories.")]),e._v(" "),a("p",[e._v("Users will be able to load a data package into R in seconds so that data can be used for analysis and visualizations, invalid data can be fixed and generally friction is removed in working with data especially when shifting from one language to another. For example you will be able to get and analyze a data package in R which will in turn make visualizations in other graphical interfaces easier.")]),e._v(" "),a("p",[e._v("We are also very delighted to be hosting "),a("a",{attrs:{href:"http://2018.okfestival.org/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Open Knowledge Festival"),a("OutboundLink")],1),e._v(" (OKFest) in Thessaloniki, Greece from 3-6 May 2018. OKFest is expected to bring together over 1,500 people from more than 60 countries. 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I have been a Golang enthusiast for a while now, but I hadn’t thought of applying to the fund until I had a quick chat with "),a("a",{attrs:{href:"https://github.com/nazareno",target:"_blank",rel:"noopener noreferrer"}},[e._v("Nazareno Andrade"),a("OutboundLink")],1),e._v(" that started with Golang and ended with: “what about the "),a("a",{attrs:{href:"https://toolfund.frictionlessdata.io/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Frictionless Data Tool Fund"),a("OutboundLink")],1),e._v("?”")]),e._v(" "),a("p",[e._v("Go has a lot to deliver in terms of approximating simplicity of reading/writing, correctness, and performance. 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We are going to use issues to track development progress and next steps.")])])}),[],!1,null,null,null);t.default=n.exports}}]); \ No newline at end of file +(window.webpackJsonp=window.webpackJsonp||[]).push([[72],{579:function(e,t,a){"use strict";a.r(t);var o=a(29),n=Object(o.a)({},(function(){var e=this,t=e.$createElement,a=e._self._c||t;return a("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[a("p",[e._v("This grantee profile features Daniel Fireman for our series of Frictionless Data Tool Fund posts, written to shine a light on Frictionless Data’s Tool Fund grantees, their work and to let our technical community know how they can get involved.")]),e._v(" "),a("p",[e._v("I was born in "),a("a",{attrs:{href:"https://www.google.com/search?site=&tbm=isch&source=hp&biw=1600&bih=783&q=Macei%C3%B3&oq=Macei%C3%B3&gs_l=img.3..0l7j0i30k1l3.707.4892.0.5214.9.7.0.0.0.0.245.904.0j1j3.4.0....0...1.1.64.img..5.4.903.0..35i39k1.p1SYqvZtcYw",target:"_blank",rel:"noopener noreferrer"}},[e._v("Maceió"),a("OutboundLink")],1),e._v(", a sunny coastal city in the Northeast of Brazil. 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In this tutorial, "),e("a",{attrs:{href:"https://gr.linkedin.com/in/kleanthis-koupidis-8348b88b",target:"_blank",rel:"noopener noreferrer"}},[t._v("Kleanthis Koupidis"),e("OutboundLink")],1),t._v(", a Data Scientist and Statistician at Open Knowledge Greece, explains how to create Data Packages in R.")]),t._v(" "),e("h2",{attrs:{id:"creating-data-packages-in-r"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#creating-data-packages-in-r"}},[t._v("#")]),t._v(" Creating Data Packages in R")]),t._v(" "),e("p",[t._v("This tutorial will show you how to install the R library for working with Data Packages and Table Schema, load a CSV file, infer its schema, and write a Tabular Data Package.")]),t._v(" "),e("h2",{attrs:{id:"load"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#load"}},[t._v("#")]),t._v(" Load")]),t._v(" "),e("p",[t._v("For this tutorial, we will need the Data Package R library ("),e("a",{attrs:{href:"https://github.com/frictionlessdata/datapackage-r",target:"_blank",rel:"noopener noreferrer"}},[t._v("datapackage.r"),e("OutboundLink")],1),t._v(")."),e("br"),t._v("\nYou can start using the library by loading "),e("code",[t._v("datapackage.r")]),t._v(".")]),t._v(" "),e("div",{staticClass:"language-r extra-class"},[e("pre",{pre:!0,attrs:{class:"language-r"}},[e("code",[t._v(" library"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),t._v("datapackage.r"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n")])])]),e("p",[t._v("You can add useful metadata by adding keys to metadata dict attribute. 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You can read more about this in "),e("a",{attrs:{href:"https://frictionlessdata.io/articles/open-knowledge-greece/",target:"_blank",rel:"noopener noreferrer"}},[a._v("their grantee profile"),e("OutboundLink")],1),a._v(". 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"),e("a",{attrs:{href:"https://cran.r-project.org/package=devtools",target:"_blank",rel:"noopener noreferrer"}},[a._v("Devtools library"),e("OutboundLink")],1),a._v(" is also required to install the datapackage.r library from github.")]),a._v(" "),e("div",{staticClass:"language-bash extra-class"},[e("pre",{pre:!0,attrs:{class:"language-bash"}},[e("code",[a._v(" "),e("span",{pre:!0,attrs:{class:"token comment"}},[a._v("# Install devtools package if not already")]),a._v("\n install.packages"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[a._v("(")]),e("span",{pre:!0,attrs:{class:"token string"}},[a._v('"devtools"')]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[a._v(")")]),a._v("\n")])])]),e("p",[a._v("And then install the development version of "),e("a",{attrs:{href:"https://github.com/frictionlessdata/datapackage-r",target:"_blank",rel:"noopener noreferrer"}},[a._v("datapackage.r"),e("OutboundLink")],1),a._v(" from github.")]),a._v(" "),e("div",{staticClass:"language-bash extra-class"},[e("pre",{pre:!0,attrs:{class:"language-bash"}},[e("code",[a._v(" devtools::install_github"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[a._v("(")]),e("span",{pre:!0,attrs:{class:"token string"}},[a._v('"frictionlessdata/datapackage-r"')]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[a._v(")")]),a._v("\n")])])]),e("h2",{attrs:{id:"load"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#load"}},[a._v("#")]),a._v(" Load")]),a._v(" "),e("p",[a._v("You can start using the library by loading "),e("code",[a._v("datapackage.r")]),a._v(".")]),a._v(" "),e("div",{staticClass:"language-r extra-class"},[e("pre",{pre:!0,attrs:{class:"language-r"}},[e("code",[a._v(" library"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[a._v("(")]),a._v("datapackage.r"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[a._v(")")]),a._v("\n")])])]),e("h2",{attrs:{id:"reading-basic-metadata"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#reading-basic-metadata"}},[a._v("#")]),a._v(" Reading Basic Metadata")]),a._v(" "),e("p",[a._v("In this case, we are using an example Tabular Data Package containing the periodic table stored on "),e("a",{attrs:{href:"https://github.com/frictionlessdata/example-data-packages/tree/master/periodic-table",target:"_blank",rel:"noopener noreferrer"}},[a._v("GitHub"),e("OutboundLink")],1),a._v(" ("),e("a",{attrs:{href:"https://raw.githubusercontent.com/frictionlessdata/example-data-packages/master/periodic-table/datapackage.json",target:"_blank",rel:"noopener noreferrer"}},[a._v("datapackage.json"),e("OutboundLink")],1),a._v(", "),e("a",{attrs:{href:"https://raw.githubusercontent.com/frictionlessdata/example-data-packages/master/periodic-table/data.csv",target:"_blank",rel:"noopener noreferrer"}},[a._v("data.csv"),e("OutboundLink")],1),a._v("). This dataset includes the atomic number, symbol, element name, atomic mass, and the metallicity of the element. Here are the first five rows:")]),a._v(" "),e("div",{staticClass:"language-r extra-class"},[e("pre",{pre:!0,attrs:{class:"language-r"}},[e("code",[a._v(" url "),e("span",{pre:!0,attrs:{class:"token operator"}},[a._v("=")]),a._v(" "),e("span",{pre:!0,attrs:{class:"token string"}},[a._v("'https://raw.githubusercontent.com/okgreece/datapackage-r/master/vignettes/example_data/data.csv'")]),a._v("\n pt_data "),e("span",{pre:!0,attrs:{class:"token operator"}},[a._v("=")]),a._v(" read.csv2"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[a._v("(")]),a._v("url"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[a._v(",")]),a._v(" sep "),e("span",{pre:!0,attrs:{class:"token operator"}},[a._v("=")]),a._v(" "),e("span",{pre:!0,attrs:{class:"token string"}},[a._v("','")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[a._v(")")]),a._v("\n knitr"),e("span",{pre:!0,attrs:{class:"token operator"}},[a._v("::")]),a._v("kable"),e("span",{pre:!0,attrs:{class:"token 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punctuation"}},[a._v(")")]),a._v(" "),e("span",{pre:!0,attrs:{class:"token comment"}},[a._v("# commit changes")]),a._v("\n\n "),e("span",{pre:!0,attrs:{class:"token comment"}},[a._v("## [1] TRUE")]),a._v("\n")])])]),e("p",[a._v("At the most basic level, Data Packages provide a standardized format for general metadata (for example, the dataset title, source, author, and/or description) about your dataset. Now that you have loaded this Data Package, you have access to this "),e("code",[a._v("metadata")]),a._v(" using the metadata dict attribute. Note that these fields are optional and may not be specified for all Data Packages. For more information on which fields are supported, see "),e("a",{attrs:{href:"https://specs.frictionlessdata.io/data-package/",target:"_blank",rel:"noopener noreferrer"}},[a._v("the full Data Package standard"),e("OutboundLink")],1),a._v(".")]),a._v(" "),e("div",{staticClass:"language-r extra-class"},[e("pre",{pre:!0,attrs:{class:"language-r"}},[e("code",[a._v(" datapackage"),e("span",{pre:!0,attrs:{class:"token operator"}},[a._v("$")]),a._v("descriptor"),e("span",{pre:!0,attrs:{class:"token operator"}},[a._v("$")]),a._v("title\n\n "),e("span",{pre:!0,attrs:{class:"token comment"}},[a._v('## [1] "Periodic Table"')]),a._v("\n")])])]),e("h2",{attrs:{id:"reading-data"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#reading-data"}},[a._v("#")]),a._v(" Reading Data")]),a._v(" "),e("p",[a._v("Now that you have loaded your Data Package, you can read its data. A Data Package can contain multiple files which are accessible via the "),e("code",[a._v("resources")]),a._v(" attribute. The "),e("code",[a._v("resources")]),a._v(" attribute is an array of objects containing information (e.g. path, schema, description) about each file in the package.")]),a._v(" "),e("p",[a._v("You can access the data in a given resource in the "),e("code",[a._v("resources")]),a._v(" array by reading the "),e("code",[a._v("data")]),a._v(" attribute.")]),a._v(" "),e("div",{staticClass:"language-r extra-class"},[e("pre",{pre:!0,attrs:{class:"language-r"}},[e("code",[a._v(" table "),e("span",{pre:!0,attrs:{class:"token operator"}},[a._v("=")]),a._v(" datapackage"),e("span",{pre:!0,attrs:{class:"token operator"}},[a._v("$")]),a._v("resources"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[a._v("[")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[a._v("[")]),e("span",{pre:!0,attrs:{class:"token number"}},[a._v("1")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[a._v("]")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[a._v("]")]),e("span",{pre:!0,attrs:{class:"token operator"}},[a._v("$")]),a._v("table\n periodic_table_data "),e("span",{pre:!0,attrs:{class:"token operator"}},[a._v("=")]),a._v(" table"),e("span",{pre:!0,attrs:{class:"token operator"}},[a._v("$")]),a._v("read"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[a._v("(")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[a._v(")")]),a._v("\n")])])]),e("p",[a._v("You can further manipulate list objects in R by using")]),a._v(" "),e("div",{staticClass:"language-r extra-class"},[e("pre",{pre:!0,attrs:{class:"language-r"}},[e("code",[a._v(" "),e("span",{pre:!0,attrs:{class:"token punctuation"}},[a._v("[")]),a._v("purrr"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[a._v("]")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[a._v("(")]),a._v("https"),e("span",{pre:!0,attrs:{class:"token operator"}},[a._v(":")]),e("span",{pre:!0,attrs:{class:"token operator"}},[a._v("/")]),e("span",{pre:!0,attrs:{class:"token operator"}},[a._v("/")]),a._v("cran.r"),e("span",{pre:!0,attrs:{class:"token operator"}},[a._v("-")]),a._v("project.org"),e("span",{pre:!0,attrs:{class:"token operator"}},[a._v("/")]),a._v("package"),e("span",{pre:!0,attrs:{class:"token operator"}},[a._v("=")]),a._v("purrr"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[a._v(")")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[a._v(",")]),a._v(" "),e("span",{pre:!0,attrs:{class:"token punctuation"}},[a._v("[")]),a._v("rlist"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[a._v("]")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[a._v("(")]),a._v("https"),e("span",{pre:!0,attrs:{class:"token operator"}},[a._v(":")]),e("span",{pre:!0,attrs:{class:"token operator"}},[a._v("/")]),e("span",{pre:!0,attrs:{class:"token operator"}},[a._v("/")]),a._v("cran.r"),e("span",{pre:!0,attrs:{class:"token operator"}},[a._v("-")]),a._v("project.org"),e("span",{pre:!0,attrs:{class:"token operator"}},[a._v("/")]),a._v("package"),e("span",{pre:!0,attrs:{class:"token operator"}},[a._v("=")]),a._v("rlist"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[a._v(")")]),a._v(" packages.\n")])])]),e("h2",{attrs:{id:"loading-into-an-sql-database"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#loading-into-an-sql-database"}},[a._v("#")]),a._v(" Loading into an SQL database")]),a._v(" "),e("p",[e("a",{attrs:{href:"https://specs.frictionlessdata.io/tabular-data-package/",target:"_blank",rel:"noopener noreferrer"}},[a._v("Tabular Data Packages"),e("OutboundLink")],1),a._v(" contains schema information about its data using "),e("a",{attrs:{href:"https://specs.frictionlessdata.io/table-schema/",target:"_blank",rel:"noopener noreferrer"}},[a._v("Table Schema"),e("OutboundLink")],1),a._v(". This means you can easily import your Data Package into the SQL backend of your choice. In this case, we are creating an "),e("a",{attrs:{href:"http://sqlite.org/",target:"_blank",rel:"noopener noreferrer"}},[a._v("SQLite"),e("OutboundLink")],1),a._v(" database.")]),a._v(" "),e("p",[a._v("To create a new SQLite database and load the data into SQL we will need "),e("a",{attrs:{href:"https://cran.r-project.org/package=DBI",target:"_blank",rel:"noopener noreferrer"}},[a._v("DBI"),e("OutboundLink")],1),a._v(" package and "),e("a",{attrs:{href:"https://cran.r-project.org/package=RSQLite",target:"_blank",rel:"noopener noreferrer"}},[a._v("RSQLite"),e("OutboundLink")],1),a._v(" package, which contains "),e("a",{attrs:{href:"https://www.sqlite.org/",target:"_blank",rel:"noopener noreferrer"}},[a._v("SQLite"),e("OutboundLink")],1),a._v(" (no external software is needed).")]),a._v(" "),e("p",[a._v("You can install and load them by using:")]),a._v(" "),e("div",{staticClass:"language-r extra-class"},[e("pre",{pre:!0,attrs:{class:"language-r"}},[e("code",[a._v(" 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data to a data frame object to copy them to database table.")]),a._v(" "),e("div",{staticClass:"language-r extra-class"},[e("pre",{pre:!0,attrs:{class:"language-r"}},[e("code",[a._v(" "),e("span",{pre:!0,attrs:{class:"token comment"}},[a._v("# install data.table package if not already")]),a._v("\n "),e("span",{pre:!0,attrs:{class:"token comment"}},[a._v('# install.packages("data.table")')]),a._v("\n\n periodic_table_sql "),e("span",{pre:!0,attrs:{class:"token operator"}},[a._v("=")]),a._v(" data.table"),e("span",{pre:!0,attrs:{class:"token operator"}},[a._v("::")]),a._v("rbindlist"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[a._v("(")]),a._v("periodic_table_data"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[a._v(")")]),a._v("\n periodic_table_sql "),e("span",{pre:!0,attrs:{class:"token operator"}},[a._v("=")]),a._v(" setNames"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[a._v("(")]),a._v("periodic_table_sql"),e("span",{pre:!0,attrs:{class:"token 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"),e("div",{staticClass:"language-r extra-class"},[e("pre",{pre:!0,attrs:{class:"language-r"}},[e("code",[a._v("\n dbGetQuery"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[a._v("(")]),a._v("dp.database"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[a._v(",")]),a._v(" "),e("span",{pre:!0,attrs:{class:"token string"}},[a._v("'SELECT * FROM periodic_table_sql LIMIT 5'")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[a._v(")")]),a._v("\n\n "),e("span",{pre:!0,attrs:{class:"token comment"}},[a._v("## atomic number symbol name atomic mass metal or nonmetal?")]),a._v("\n "),e("span",{pre:!0,attrs:{class:"token comment"}},[a._v("## 1 1 H Hydrogen 1.007940 nonmetal")]),a._v("\n "),e("span",{pre:!0,attrs:{class:"token comment"}},[a._v("## 2 2 He Helium 4.002602 noble gas")]),a._v("\n "),e("span",{pre:!0,attrs:{class:"token comment"}},[a._v("## 3 3 Li Lithium 6.941000 alkali metal")]),a._v("\n "),e("span",{pre:!0,attrs:{class:"token comment"}},[a._v("## 4 4 Be Beryllium 9.012182 alkaline earth metal")]),a._v("\n "),e("span",{pre:!0,attrs:{class:"token comment"}},[a._v("## 5 5 B Boron 10.811000 metalloid")]),a._v("\n")])])]),e("p",[a._v("Or return all elements with an atomic number of less than 10:")]),a._v(" "),e("div",{staticClass:"language-r extra-class"},[e("pre",{pre:!0,attrs:{class:"language-r"}},[e("code",[a._v(" dbGetQuery"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[a._v("(")]),a._v("dp.database"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[a._v(",")]),a._v(" "),e("span",{pre:!0,attrs:{class:"token string"}},[a._v("'SELECT * FROM periodic_table_sql WHERE \"atomic number\" < 10'")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[a._v(")")]),a._v("\n\n "),e("span",{pre:!0,attrs:{class:"token comment"}},[a._v("## atomic number symbol name atomic mass metal or nonmetal?")]),a._v("\n "),e("span",{pre:!0,attrs:{class:"token comment"}},[a._v("## 1 1 H Hydrogen 1.007940 nonmetal")]),a._v("\n "),e("span",{pre:!0,attrs:{class:"token comment"}},[a._v("## 2 2 He Helium 4.002602 noble gas")]),a._v("\n "),e("span",{pre:!0,attrs:{class:"token comment"}},[a._v("## 3 3 Li Lithium 6.941000 alkali metal")]),a._v("\n "),e("span",{pre:!0,attrs:{class:"token comment"}},[a._v("## 4 4 Be Beryllium 9.012182 alkaline earth metal")]),a._v("\n "),e("span",{pre:!0,attrs:{class:"token comment"}},[a._v("## 5 5 B Boron 10.811000 metalloid")]),a._v("\n "),e("span",{pre:!0,attrs:{class:"token comment"}},[a._v("## 6 6 C Carbon 12.010700 nonmetal")]),a._v("\n "),e("span",{pre:!0,attrs:{class:"token comment"}},[a._v("## 7 7 N Nitrogen 14.006700 nonmetal")]),a._v("\n "),e("span",{pre:!0,attrs:{class:"token comment"}},[a._v("## 8 8 O Oxygen 15.999400 nonmetal")]),a._v("\n "),e("span",{pre:!0,attrs:{class:"token comment"}},[a._v("## 9 9 F Fluorine 18.998403 halogen")]),a._v("\n")])])]),e("p",[a._v("More about using databases, SQLite in R you can find in vignettes of 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Now that you have loaded this Data Package, you have access to this "),e("code",[a._v("metadata")]),a._v(" using the metadata dict attribute. Note that these fields are optional and may not be specified for all Data Packages. For more information on which fields are supported, see "),e("a",{attrs:{href:"https://specs.frictionlessdata.io/data-package/",target:"_blank",rel:"noopener noreferrer"}},[a._v("the full Data Package standard"),e("OutboundLink")],1),a._v(".")]),a._v(" "),e("div",{staticClass:"language-r extra-class"},[e("pre",{pre:!0,attrs:{class:"language-r"}},[e("code",[a._v(" datapackage"),e("span",{pre:!0,attrs:{class:"token operator"}},[a._v("$")]),a._v("descriptor"),e("span",{pre:!0,attrs:{class:"token operator"}},[a._v("$")]),a._v("title\n\n "),e("span",{pre:!0,attrs:{class:"token comment"}},[a._v('## [1] "Periodic Table"')]),a._v("\n")])])]),e("h2",{attrs:{id:"reading-data"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#reading-data"}},[a._v("#")]),a._v(" Reading Data")]),a._v(" "),e("p",[a._v("Now that you have loaded your Data Package, you can read its data. A Data Package can contain multiple files which are accessible via the "),e("code",[a._v("resources")]),a._v(" attribute. The "),e("code",[a._v("resources")]),a._v(" attribute is an array of objects containing information (e.g. path, schema, description) about each file in the package.")]),a._v(" "),e("p",[a._v("You can access the data in a given resource in the "),e("code",[a._v("resources")]),a._v(" array by reading the "),e("code",[a._v("data")]),a._v(" attribute.")]),a._v(" "),e("div",{staticClass:"language-r extra-class"},[e("pre",{pre:!0,attrs:{class:"language-r"}},[e("code",[a._v(" table "),e("span",{pre:!0,attrs:{class:"token operator"}},[a._v("=")]),a._v(" datapackage"),e("span",{pre:!0,attrs:{class:"token operator"}},[a._v("$")]),a._v("resources"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[a._v("[")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[a._v("[")]),e("span",{pre:!0,attrs:{class:"token number"}},[a._v("1")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[a._v("]")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[a._v("]")]),e("span",{pre:!0,attrs:{class:"token operator"}},[a._v("$")]),a._v("table\n periodic_table_data "),e("span",{pre:!0,attrs:{class:"token operator"}},[a._v("=")]),a._v(" table"),e("span",{pre:!0,attrs:{class:"token operator"}},[a._v("$")]),a._v("read"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[a._v("(")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[a._v(")")]),a._v("\n")])])]),e("p",[a._v("You can further manipulate list objects in R by using")]),a._v(" "),e("div",{staticClass:"language-r extra-class"},[e("pre",{pre:!0,attrs:{class:"language-r"}},[e("code",[a._v(" "),e("span",{pre:!0,attrs:{class:"token punctuation"}},[a._v("[")]),a._v("purrr"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[a._v("]")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[a._v("(")]),a._v("https"),e("span",{pre:!0,attrs:{class:"token operator"}},[a._v(":")]),e("span",{pre:!0,attrs:{class:"token operator"}},[a._v("/")]),e("span",{pre:!0,attrs:{class:"token operator"}},[a._v("/")]),a._v("cran.r"),e("span",{pre:!0,attrs:{class:"token operator"}},[a._v("-")]),a._v("project.org"),e("span",{pre:!0,attrs:{class:"token operator"}},[a._v("/")]),a._v("package"),e("span",{pre:!0,attrs:{class:"token operator"}},[a._v("=")]),a._v("purrr"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[a._v(")")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[a._v(",")]),a._v(" "),e("span",{pre:!0,attrs:{class:"token punctuation"}},[a._v("[")]),a._v("rlist"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[a._v("]")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[a._v("(")]),a._v("https"),e("span",{pre:!0,attrs:{class:"token operator"}},[a._v(":")]),e("span",{pre:!0,attrs:{class:"token operator"}},[a._v("/")]),e("span",{pre:!0,attrs:{class:"token operator"}},[a._v("/")]),a._v("cran.r"),e("span",{pre:!0,attrs:{class:"token operator"}},[a._v("-")]),a._v("project.org"),e("span",{pre:!0,attrs:{class:"token operator"}},[a._v("/")]),a._v("package"),e("span",{pre:!0,attrs:{class:"token operator"}},[a._v("=")]),a._v("rlist"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[a._v(")")]),a._v(" packages.\n")])])]),e("h2",{attrs:{id:"loading-into-an-sql-database"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#loading-into-an-sql-database"}},[a._v("#")]),a._v(" Loading into an SQL database")]),a._v(" "),e("p",[e("a",{attrs:{href:"https://specs.frictionlessdata.io/tabular-data-package/",target:"_blank",rel:"noopener noreferrer"}},[a._v("Tabular Data Packages"),e("OutboundLink")],1),a._v(" contains schema information about its data using "),e("a",{attrs:{href:"https://specs.frictionlessdata.io/table-schema/",target:"_blank",rel:"noopener noreferrer"}},[a._v("Table Schema"),e("OutboundLink")],1),a._v(". This means you can easily import your Data Package into the SQL backend of your choice. In this case, we are creating an "),e("a",{attrs:{href:"http://sqlite.org/",target:"_blank",rel:"noopener noreferrer"}},[a._v("SQLite"),e("OutboundLink")],1),a._v(" database.")]),a._v(" "),e("p",[a._v("To create a new SQLite database and load the data into SQL we will need "),e("a",{attrs:{href:"https://cran.r-project.org/package=DBI",target:"_blank",rel:"noopener noreferrer"}},[a._v("DBI"),e("OutboundLink")],1),a._v(" package and "),e("a",{attrs:{href:"https://cran.r-project.org/package=RSQLite",target:"_blank",rel:"noopener noreferrer"}},[a._v("RSQLite"),e("OutboundLink")],1),a._v(" package, which contains "),e("a",{attrs:{href:"https://www.sqlite.org/",target:"_blank",rel:"noopener noreferrer"}},[a._v("SQLite"),e("OutboundLink")],1),a._v(" (no external software is needed).")]),a._v(" "),e("p",[a._v("You can install and load them by using:")]),a._v(" "),e("div",{staticClass:"language-r extra-class"},[e("pre",{pre:!0,attrs:{class:"language-r"}},[e("code",[a._v(" 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extra-class"},[e("pre",{pre:!0,attrs:{class:"language-r"}},[e("code",[a._v(" dp.database "),e("span",{pre:!0,attrs:{class:"token operator"}},[a._v("=")]),a._v(" dbConnect"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[a._v("(")]),a._v("RSQLite"),e("span",{pre:!0,attrs:{class:"token operator"}},[a._v("::")]),a._v("SQLite"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[a._v("(")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[a._v(")")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[a._v(",")]),a._v(" "),e("span",{pre:!0,attrs:{class:"token string"}},[a._v('""')]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[a._v(")")]),a._v(" "),e("span",{pre:!0,attrs:{class:"token comment"}},[a._v("# temporary database")]),a._v("\n")])])]),e("p",[a._v("We will use "),e("a",{attrs:{href:"https://cran.r-project.org/package=RSQLite",target:"_blank",rel:"noopener noreferrer"}},[a._v("data.table"),e("OutboundLink")],1),a._v(" package to convert the list object with the data to a data frame object to copy them to database table.")]),a._v(" "),e("div",{staticClass:"language-r extra-class"},[e("pre",{pre:!0,attrs:{class:"language-r"}},[e("code",[a._v(" "),e("span",{pre:!0,attrs:{class:"token comment"}},[a._v("# install data.table package if not already")]),a._v("\n "),e("span",{pre:!0,attrs:{class:"token comment"}},[a._v('# install.packages("data.table")')]),a._v("\n\n periodic_table_sql "),e("span",{pre:!0,attrs:{class:"token operator"}},[a._v("=")]),a._v(" data.table"),e("span",{pre:!0,attrs:{class:"token operator"}},[a._v("::")]),a._v("rbindlist"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[a._v("(")]),a._v("periodic_table_data"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[a._v(")")]),a._v("\n periodic_table_sql "),e("span",{pre:!0,attrs:{class:"token operator"}},[a._v("=")]),a._v(" setNames"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[a._v("(")]),a._v("periodic_table_sql"),e("span",{pre:!0,attrs:{class:"token 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"),e("div",{staticClass:"language-r extra-class"},[e("pre",{pre:!0,attrs:{class:"language-r"}},[e("code",[a._v("\n dbGetQuery"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[a._v("(")]),a._v("dp.database"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[a._v(",")]),a._v(" "),e("span",{pre:!0,attrs:{class:"token string"}},[a._v("'SELECT * FROM periodic_table_sql LIMIT 5'")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[a._v(")")]),a._v("\n\n "),e("span",{pre:!0,attrs:{class:"token comment"}},[a._v("## atomic number symbol name atomic mass metal or nonmetal?")]),a._v("\n "),e("span",{pre:!0,attrs:{class:"token comment"}},[a._v("## 1 1 H Hydrogen 1.007940 nonmetal")]),a._v("\n "),e("span",{pre:!0,attrs:{class:"token comment"}},[a._v("## 2 2 He Helium 4.002602 noble gas")]),a._v("\n "),e("span",{pre:!0,attrs:{class:"token comment"}},[a._v("## 3 3 Li Lithium 6.941000 alkali metal")]),a._v("\n "),e("span",{pre:!0,attrs:{class:"token comment"}},[a._v("## 4 4 Be Beryllium 9.012182 alkaline earth metal")]),a._v("\n "),e("span",{pre:!0,attrs:{class:"token comment"}},[a._v("## 5 5 B Boron 10.811000 metalloid")]),a._v("\n")])])]),e("p",[a._v("Or return all elements with an atomic number of less than 10:")]),a._v(" "),e("div",{staticClass:"language-r extra-class"},[e("pre",{pre:!0,attrs:{class:"language-r"}},[e("code",[a._v(" dbGetQuery"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[a._v("(")]),a._v("dp.database"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[a._v(",")]),a._v(" "),e("span",{pre:!0,attrs:{class:"token string"}},[a._v("'SELECT * FROM periodic_table_sql WHERE \"atomic number\" < 10'")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[a._v(")")]),a._v("\n\n "),e("span",{pre:!0,attrs:{class:"token comment"}},[a._v("## atomic number symbol name atomic mass metal or nonmetal?")]),a._v("\n "),e("span",{pre:!0,attrs:{class:"token comment"}},[a._v("## 1 1 H Hydrogen 1.007940 nonmetal")]),a._v("\n "),e("span",{pre:!0,attrs:{class:"token comment"}},[a._v("## 2 2 He Helium 4.002602 noble gas")]),a._v("\n "),e("span",{pre:!0,attrs:{class:"token comment"}},[a._v("## 3 3 Li Lithium 6.941000 alkali metal")]),a._v("\n "),e("span",{pre:!0,attrs:{class:"token comment"}},[a._v("## 4 4 Be Beryllium 9.012182 alkaline earth metal")]),a._v("\n "),e("span",{pre:!0,attrs:{class:"token comment"}},[a._v("## 5 5 B Boron 10.811000 metalloid")]),a._v("\n "),e("span",{pre:!0,attrs:{class:"token comment"}},[a._v("## 6 6 C Carbon 12.010700 nonmetal")]),a._v("\n "),e("span",{pre:!0,attrs:{class:"token comment"}},[a._v("## 7 7 N Nitrogen 14.006700 nonmetal")]),a._v("\n "),e("span",{pre:!0,attrs:{class:"token comment"}},[a._v("## 8 8 O Oxygen 15.999400 nonmetal")]),a._v("\n "),e("span",{pre:!0,attrs:{class:"token comment"}},[a._v("## 9 9 F Fluorine 18.998403 halogen")]),a._v("\n")])])]),e("p",[a._v("More about using databases, SQLite in R you can find in vignettes of 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No newline at end of file diff --git a/assets/js/75.ec5d7d14.js b/assets/js/75.591b6129.js similarity index 99% rename from assets/js/75.ec5d7d14.js rename to assets/js/75.591b6129.js index 9ef1385ae..893fc5e1d 100644 --- a/assets/js/75.ec5d7d14.js +++ b/assets/js/75.591b6129.js @@ -1 +1 @@ -(window.webpackJsonp=window.webpackJsonp||[]).push([[75],{588:function(t,a,s){"use strict";s.r(a);var e=s(29),n=Object(e.a)({},(function(){var t=this,a=t.$createElement,s=t._self._c||a;return s("ContentSlotsDistributor",{attrs:{"slot-key":t.$parent.slotKey}},[s("p",[t._v("Daniel Fireman was one of 2017’s "),s("a",{attrs:{href:"https://toolfund.frictionlessdata.io",target:"_blank",rel:"noopener noreferrer"}},[t._v("Frictionless Data Tool Fund"),s("OutboundLink")],1),t._v(" grantees tasked with extending implementation of core Frictionless Data libraries in Go programming language. 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In this post, Fireman will show you how to install and use the "),s("a",{attrs:{href:"http://golang.org",target:"_blank",rel:"noopener noreferrer"}},[t._v("Go"),s("OutboundLink")],1),t._v(" libraries for working with "),s("a",{attrs:{href:"https://specs.frictionlessdata.io/tabular-data-package/",target:"_blank",rel:"noopener noreferrer"}},[t._v("Tabular Data Packages"),s("OutboundLink")],1),t._v(".")],1),t._v(" "),s("p",[t._v("Our goal in this tutorial is to load a data package from the web and read its metadata and contents.")]),t._v(" "),s("h2",{attrs:{id:"setup"}},[s("a",{staticClass:"header-anchor",attrs:{href:"#setup"}},[t._v("#")]),t._v(" Setup")]),t._v(" "),s("p",[t._v("For this tutorial, we will need the "),s("a",{attrs:{href:"https://github.com/frictionlessdata/datapackage-go",target:"_blank",rel:"noopener noreferrer"}},[t._v("datapackage-go"),s("OutboundLink")],1),t._v(" and "),s("a",{attrs:{href:"https://github.com/frictionlessdata/tableschema-go",target:"_blank",rel:"noopener noreferrer"}},[t._v("tableschema-go"),s("OutboundLink")],1),t._v(" packages, which provide all the functionality to deal with a Data Package’s metadata and its contents.")]),t._v(" "),s("p",[t._v("We are going to use the "),s("a",{attrs:{href:"https://golang.github.io/dep/",target:"_blank",rel:"noopener noreferrer"}},[t._v("dep tool"),s("OutboundLink")],1),t._v(" to manage the dependencies of our new project:")]),t._v(" "),s("div",{staticClass:"language-sh extra-class"},[s("pre",{pre:!0,attrs:{class:"language-sh"}},[s("code",[t._v("$ "),s("span",{pre:!0,attrs:{class:"token builtin class-name"}},[t._v("cd")]),t._v(" "),s("span",{pre:!0,attrs:{class:"token variable"}},[t._v("$GOPATH")]),t._v("/src/newdataproj\n$ dep init\n")])])]),s("h2",{attrs:{id:"the-periodic-table-data-package"}},[s("a",{staticClass:"header-anchor",attrs:{href:"#the-periodic-table-data-package"}},[t._v("#")]),t._v(" The Periodic Table Data Package")]),t._v(" "),s("p",[t._v("A "),s("a",{attrs:{href:"https://specs.frictionlessdata.io/data-package/",target:"_blank",rel:"noopener noreferrer"}},[t._v("Data Package"),s("OutboundLink")],1),t._v(" is a simple container format used to describe and package a collection of data. It consists of two parts:")]),t._v(" "),s("ul",[s("li",[t._v("Metadata that describes the structure and contents of the package")]),t._v(" "),s("li",[t._v("Resources such as data files that form the contents of the package")])]),t._v(" "),s("p",[t._v("In this tutorial, we are using a "),s("a",{attrs:{href:"https://specs.frictionlessdata.io/tabular-data-package/",target:"_blank",rel:"noopener noreferrer"}},[t._v("Tabular Data Package"),s("OutboundLink")],1),t._v(" containing the periodic table. The package descriptor ("),s("a",{attrs:{href:"https://raw.githubusercontent.com/frictionlessdata/example-data-packages/62d47b454d95a95b6029214b9533de79401e953a/periodic-table/datapackage.json",target:"_blank",rel:"noopener noreferrer"}},[t._v("datapackage.json"),s("OutboundLink")],1),t._v(") and contents ("),s("a",{attrs:{href:"https://raw.githubusercontent.com/frictionlessdata/example-data-packages/62d47b454d95a95b6029214b9533de79401e953a/periodic-table/data.csv",target:"_blank",rel:"noopener noreferrer"}},[t._v("data.csv"),s("OutboundLink")],1),t._v(") are stored on GitHub. This dataset includes the atomic number, symbol, element name, atomic mass, and the metallicity of the element. Here are the header and the first three rows:")]),t._v(" "),s("table",[s("thead",[s("tr",[s("th",[t._v("atomic number")]),t._v(" "),s("th",[t._v("symbol")]),t._v(" "),s("th",[t._v("name")]),t._v(" "),s("th",[t._v("atomic mass")]),t._v(" "),s("th",[t._v("metal or nonmetal?")])])]),t._v(" "),s("tbody",[s("tr",[s("td",[t._v("1")]),t._v(" "),s("td",[t._v("H")]),t._v(" "),s("td",[t._v("Hydrogen")]),t._v(" "),s("td",[t._v("1.00794")]),t._v(" "),s("td",[t._v("nonmetal")])]),t._v(" "),s("tr",[s("td",[t._v("2")]),t._v(" "),s("td",[t._v("He")]),t._v(" "),s("td",[t._v("Helium")]),t._v(" "),s("td",[t._v("4.002602")]),t._v(" "),s("td",[t._v("noble gas")])]),t._v(" "),s("tr",[s("td",[t._v("3")]),t._v(" "),s("td",[t._v("Li")]),t._v(" "),s("td",[t._v("Lithium")]),t._v(" "),s("td",[t._v("6.941")]),t._v(" "),s("td",[t._v("alkali metal")])])])]),t._v(" "),s("h2",{attrs:{id:"inspecting-package-metadata"}},[s("a",{staticClass:"header-anchor",attrs:{href:"#inspecting-package-metadata"}},[t._v("#")]),t._v(" Inspecting Package Metadata")]),t._v(" "),s("p",[t._v("Let’s start off by creating the "),s("code",[t._v("main.go")]),t._v(", which loads the data package and inspects some of its metadata.")]),t._v(" "),s("div",{staticClass:"language-go extra-class"},[s("pre",{pre:!0,attrs:{class:"language-go"}},[s("code",[s("span",{pre:!0,attrs:{class:"token keyword"}},[t._v("package")]),t._v(" main\n\n"),s("span",{pre:!0,attrs:{class:"token keyword"}},[t._v("import")]),t._v(" "),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),t._v("\n "),s("span",{pre:!0,attrs:{class:"token string"}},[t._v('"fmt"')]),t._v("\n\n "),s("span",{pre:!0,attrs:{class:"token string"}},[t._v('"github.com/frictionlessdata/datapackage-go/datapackage"')]),t._v("\n"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n\n"),s("span",{pre:!0,attrs:{class:"token keyword"}},[t._v("func")]),t._v(" "),s("span",{pre:!0,attrs:{class:"token function"}},[t._v("main")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v(" "),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("{")]),t._v("\n pkg"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v(" err "),s("span",{pre:!0,attrs:{class:"token operator"}},[t._v(":=")]),t._v(" datapackage"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(".")]),s("span",{pre:!0,attrs:{class:"token function"}},[t._v("Load")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),s("span",{pre:!0,attrs:{class:"token string"}},[t._v('"https://raw.githubusercontent.com/frictionlessdata/example-data-packages/62d47b454d95a95b6029214b9533de79401e953a/periodic-table/datapackage.json"')]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n "),s("span",{pre:!0,attrs:{class:"token keyword"}},[t._v("if")]),t._v(" err "),s("span",{pre:!0,attrs:{class:"token operator"}},[t._v("!=")]),t._v(" "),s("span",{pre:!0,attrs:{class:"token boolean"}},[t._v("nil")]),t._v(" "),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("{")]),t._v("\n "),s("span",{pre:!0,attrs:{class:"token function"}},[t._v("panic")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),t._v("err"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n "),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("}")]),t._v("\n fmt"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(".")]),s("span",{pre:!0,attrs:{class:"token function"}},[t._v("Println")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),s("span",{pre:!0,attrs:{class:"token string"}},[t._v('"Package loaded successfully."')]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("}")]),t._v("\n")])])]),s("p",[t._v("Before running the code, you need to tell the dep tool to update our project dependencies. Don’t worry; you won’t need to do it again in this tutorial.")]),t._v(" "),s("div",{staticClass:"language-sh extra-class"},[s("pre",{pre:!0,attrs:{class:"language-sh"}},[s("code",[t._v("$ dep ensure\n$ go run main.go\nPackage loaded successfully.\n")])])]),s("p",[t._v("Now that you have loaded the periodic table Data Package, you have access to its "),s("code",[t._v("title")]),t._v(" and "),s("code",[t._v("name")]),t._v(" fields through the "),s("a",{attrs:{href:"https://godoc.org/github.com/frictionlessdata/datapackage-go/datapackage#Package.Descriptor",target:"_blank",rel:"noopener noreferrer"}},[t._v("Package.Descriptor() function"),s("OutboundLink")],1),t._v(". To do so, let’s change our main function to (omitting error handling for the sake of brevity, but we know it is "),s("em",[t._v("very")]),t._v(" important):")]),t._v(" "),s("div",{staticClass:"language-go extra-class"},[s("pre",{pre:!0,attrs:{class:"language-go"}},[s("code",[s("span",{pre:!0,attrs:{class:"token keyword"}},[t._v("func")]),t._v(" "),s("span",{pre:!0,attrs:{class:"token function"}},[t._v("main")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v(" "),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("{")]),t._v("\n pkg"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v(" "),s("span",{pre:!0,attrs:{class:"token boolean"}},[t._v("_")]),t._v(" "),s("span",{pre:!0,attrs:{class:"token operator"}},[t._v(":=")]),t._v(" datapackage"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(".")]),s("span",{pre:!0,attrs:{class:"token function"}},[t._v("Load")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),s("span",{pre:!0,attrs:{class:"token string"}},[t._v('"https://raw.githubusercontent.com/frictionlessdata/example-data-packages/62d47b454d95a95b6029214b9533de79401e953a/periodic-table/datapackage.json"')]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n fmt"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(".")]),s("span",{pre:!0,attrs:{class:"token function"}},[t._v("Println")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),s("span",{pre:!0,attrs:{class:"token string"}},[t._v('"Name:"')]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v(" pkg"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(".")]),s("span",{pre:!0,attrs:{class:"token function"}},[t._v("Descriptor")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("[")]),s("span",{pre:!0,attrs:{class:"token string"}},[t._v('"name"')]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("]")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n fmt"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(".")]),s("span",{pre:!0,attrs:{class:"token function"}},[t._v("Println")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),s("span",{pre:!0,attrs:{class:"token string"}},[t._v('"Title:"')]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v(" pkg"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(".")]),s("span",{pre:!0,attrs:{class:"token function"}},[t._v("Descriptor")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("[")]),s("span",{pre:!0,attrs:{class:"token string"}},[t._v('"title"')]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("]")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("}")]),t._v("\n")])])]),s("p",[t._v("And rerun the program:")]),t._v(" "),s("div",{staticClass:"language-sh extra-class"},[s("pre",{pre:!0,attrs:{class:"language-sh"}},[s("code",[t._v("$ go run main.go\nName: period-table\nTitle: Periodic Table\n")])])]),s("p",[t._v("And as you can see, the printed fields match the "),s("a",{attrs:{href:"https://raw.githubusercontent.com/frictionlessdata/example-data-packages/62d47b454d95a95b6029214b9533de79401e953a/periodic-table/datapackage.json",target:"_blank",rel:"noopener noreferrer"}},[t._v("package descriptor"),s("OutboundLink")],1),t._v(". For more information about the Data Package structure, please take a look at the "),s("a",{attrs:{href:"https://specs.frictionlessdata.io/data-package/",target:"_blank",rel:"noopener noreferrer"}},[t._v("specification"),s("OutboundLink")],1),t._v(".")]),t._v(" "),s("h2",{attrs:{id:"quick-look-at-the-data"}},[s("a",{staticClass:"header-anchor",attrs:{href:"#quick-look-at-the-data"}},[t._v("#")]),t._v(" Quick Look At the Data")]),t._v(" "),s("p",[t._v("Now that you have loaded your Data Package, it is time to process its contents. The package content consists of one or more resources. You can access "),s("a",{attrs:{href:"https://godoc.org/github.com/frictionlessdata/datapackage-go/datapackage#Resource",target:"_blank",rel:"noopener noreferrer"}},[t._v("Resources"),s("OutboundLink")],1),t._v(" via the "),s("a",{attrs:{href:"https://godoc.org/github.com/frictionlessdata/datapackage-go/datapackage#Package.GetResource()",target:"_blank",rel:"noopener noreferrer"}},[t._v("Package.GetResource()"),s("OutboundLink")],1),t._v(" method. 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noreferrer"}},[t._v("Resource.ReadAll()"),s("OutboundLink")],1),t._v(" method loads the whole table in memory as raw strings and returns it as a Go "),s("code",[t._v("[][]string")]),t._v(". This can be quick useful to take a quick look or perform a visual sanity check at the data.")]),t._v(" "),s("h2",{attrs:{id:"processing-the-data-package-s-content"}},[s("a",{staticClass:"header-anchor",attrs:{href:"#processing-the-data-package-s-content"}},[t._v("#")]),t._v(" Processing the Data Package’s Content")]),t._v(" "),s("p",[t._v("Even though the string representation can be useful for a quick sanity check, you probably want to use actual language types to process the data. Don’t worry, you won’t need to fight the casting battle yourself. 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It consists of two parts:")]),t._v(" "),s("ul",[s("li",[t._v("Metadata that describes the structure and contents of the package")]),t._v(" "),s("li",[t._v("Resources such as data files that form the contents of the package")])]),t._v(" "),s("p",[t._v("In this tutorial, we are using a "),s("a",{attrs:{href:"https://specs.frictionlessdata.io/tabular-data-package/",target:"_blank",rel:"noopener noreferrer"}},[t._v("Tabular Data Package"),s("OutboundLink")],1),t._v(" containing the periodic table. The package descriptor ("),s("a",{attrs:{href:"https://raw.githubusercontent.com/frictionlessdata/example-data-packages/62d47b454d95a95b6029214b9533de79401e953a/periodic-table/datapackage.json",target:"_blank",rel:"noopener noreferrer"}},[t._v("datapackage.json"),s("OutboundLink")],1),t._v(") and contents ("),s("a",{attrs:{href:"https://raw.githubusercontent.com/frictionlessdata/example-data-packages/62d47b454d95a95b6029214b9533de79401e953a/periodic-table/data.csv",target:"_blank",rel:"noopener noreferrer"}},[t._v("data.csv"),s("OutboundLink")],1),t._v(") are stored on GitHub. This dataset includes the atomic number, symbol, element name, atomic mass, and the metallicity of the element. Here are the header and the first three rows:")]),t._v(" "),s("table",[s("thead",[s("tr",[s("th",[t._v("atomic number")]),t._v(" "),s("th",[t._v("symbol")]),t._v(" "),s("th",[t._v("name")]),t._v(" "),s("th",[t._v("atomic mass")]),t._v(" "),s("th",[t._v("metal or nonmetal?")])])]),t._v(" "),s("tbody",[s("tr",[s("td",[t._v("1")]),t._v(" "),s("td",[t._v("H")]),t._v(" "),s("td",[t._v("Hydrogen")]),t._v(" "),s("td",[t._v("1.00794")]),t._v(" "),s("td",[t._v("nonmetal")])]),t._v(" "),s("tr",[s("td",[t._v("2")]),t._v(" "),s("td",[t._v("He")]),t._v(" "),s("td",[t._v("Helium")]),t._v(" "),s("td",[t._v("4.002602")]),t._v(" "),s("td",[t._v("noble gas")])]),t._v(" "),s("tr",[s("td",[t._v("3")]),t._v(" "),s("td",[t._v("Li")]),t._v(" "),s("td",[t._v("Lithium")]),t._v(" "),s("td",[t._v("6.941")]),t._v(" "),s("td",[t._v("alkali metal")])])])]),t._v(" "),s("h2",{attrs:{id:"inspecting-package-metadata"}},[s("a",{staticClass:"header-anchor",attrs:{href:"#inspecting-package-metadata"}},[t._v("#")]),t._v(" Inspecting Package Metadata")]),t._v(" "),s("p",[t._v("Let’s start off by creating the "),s("code",[t._v("main.go")]),t._v(", which loads the data package and inspects some of its metadata.")]),t._v(" "),s("div",{staticClass:"language-go extra-class"},[s("pre",{pre:!0,attrs:{class:"language-go"}},[s("code",[s("span",{pre:!0,attrs:{class:"token keyword"}},[t._v("package")]),t._v(" main\n\n"),s("span",{pre:!0,attrs:{class:"token keyword"}},[t._v("import")]),t._v(" "),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),t._v("\n "),s("span",{pre:!0,attrs:{class:"token string"}},[t._v('"fmt"')]),t._v("\n\n "),s("span",{pre:!0,attrs:{class:"token string"}},[t._v('"github.com/frictionlessdata/datapackage-go/datapackage"')]),t._v("\n"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n\n"),s("span",{pre:!0,attrs:{class:"token keyword"}},[t._v("func")]),t._v(" "),s("span",{pre:!0,attrs:{class:"token function"}},[t._v("main")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v(" "),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("{")]),t._v("\n pkg"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v(" err "),s("span",{pre:!0,attrs:{class:"token operator"}},[t._v(":=")]),t._v(" datapackage"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(".")]),s("span",{pre:!0,attrs:{class:"token function"}},[t._v("Load")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),s("span",{pre:!0,attrs:{class:"token string"}},[t._v('"https://raw.githubusercontent.com/frictionlessdata/example-data-packages/62d47b454d95a95b6029214b9533de79401e953a/periodic-table/datapackage.json"')]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n "),s("span",{pre:!0,attrs:{class:"token keyword"}},[t._v("if")]),t._v(" err "),s("span",{pre:!0,attrs:{class:"token operator"}},[t._v("!=")]),t._v(" "),s("span",{pre:!0,attrs:{class:"token boolean"}},[t._v("nil")]),t._v(" "),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("{")]),t._v("\n "),s("span",{pre:!0,attrs:{class:"token function"}},[t._v("panic")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),t._v("err"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n "),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("}")]),t._v("\n fmt"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(".")]),s("span",{pre:!0,attrs:{class:"token function"}},[t._v("Println")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),s("span",{pre:!0,attrs:{class:"token string"}},[t._v('"Package loaded successfully."')]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("}")]),t._v("\n")])])]),s("p",[t._v("Before running the code, you need to tell the dep tool to update our project dependencies. Don’t worry; you won’t need to do it again in this tutorial.")]),t._v(" "),s("div",{staticClass:"language-sh extra-class"},[s("pre",{pre:!0,attrs:{class:"language-sh"}},[s("code",[t._v("$ dep ensure\n$ go run main.go\nPackage loaded successfully.\n")])])]),s("p",[t._v("Now that you have loaded the periodic table Data Package, you have access to its "),s("code",[t._v("title")]),t._v(" and "),s("code",[t._v("name")]),t._v(" fields through the "),s("a",{attrs:{href:"https://godoc.org/github.com/frictionlessdata/datapackage-go/datapackage#Package.Descriptor",target:"_blank",rel:"noopener noreferrer"}},[t._v("Package.Descriptor() function"),s("OutboundLink")],1),t._v(". To do so, let’s change our main function to (omitting error handling for the sake of brevity, but we know it is "),s("em",[t._v("very")]),t._v(" important):")]),t._v(" "),s("div",{staticClass:"language-go extra-class"},[s("pre",{pre:!0,attrs:{class:"language-go"}},[s("code",[s("span",{pre:!0,attrs:{class:"token keyword"}},[t._v("func")]),t._v(" "),s("span",{pre:!0,attrs:{class:"token function"}},[t._v("main")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v(" "),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("{")]),t._v("\n pkg"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v(" "),s("span",{pre:!0,attrs:{class:"token boolean"}},[t._v("_")]),t._v(" "),s("span",{pre:!0,attrs:{class:"token operator"}},[t._v(":=")]),t._v(" datapackage"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(".")]),s("span",{pre:!0,attrs:{class:"token function"}},[t._v("Load")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),s("span",{pre:!0,attrs:{class:"token string"}},[t._v('"https://raw.githubusercontent.com/frictionlessdata/example-data-packages/62d47b454d95a95b6029214b9533de79401e953a/periodic-table/datapackage.json"')]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n fmt"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(".")]),s("span",{pre:!0,attrs:{class:"token function"}},[t._v("Println")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),s("span",{pre:!0,attrs:{class:"token string"}},[t._v('"Name:"')]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v(" pkg"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(".")]),s("span",{pre:!0,attrs:{class:"token function"}},[t._v("Descriptor")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("[")]),s("span",{pre:!0,attrs:{class:"token string"}},[t._v('"name"')]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("]")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n fmt"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(".")]),s("span",{pre:!0,attrs:{class:"token function"}},[t._v("Println")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),s("span",{pre:!0,attrs:{class:"token string"}},[t._v('"Title:"')]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v(" pkg"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(".")]),s("span",{pre:!0,attrs:{class:"token function"}},[t._v("Descriptor")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("[")]),s("span",{pre:!0,attrs:{class:"token string"}},[t._v('"title"')]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("]")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("}")]),t._v("\n")])])]),s("p",[t._v("And rerun the program:")]),t._v(" "),s("div",{staticClass:"language-sh extra-class"},[s("pre",{pre:!0,attrs:{class:"language-sh"}},[s("code",[t._v("$ go run main.go\nName: period-table\nTitle: Periodic Table\n")])])]),s("p",[t._v("And as you can see, the printed fields match the "),s("a",{attrs:{href:"https://raw.githubusercontent.com/frictionlessdata/example-data-packages/62d47b454d95a95b6029214b9533de79401e953a/periodic-table/datapackage.json",target:"_blank",rel:"noopener noreferrer"}},[t._v("package descriptor"),s("OutboundLink")],1),t._v(". For more information about the Data Package structure, please take a look at the "),s("a",{attrs:{href:"https://specs.frictionlessdata.io/data-package/",target:"_blank",rel:"noopener noreferrer"}},[t._v("specification"),s("OutboundLink")],1),t._v(".")]),t._v(" "),s("h2",{attrs:{id:"quick-look-at-the-data"}},[s("a",{staticClass:"header-anchor",attrs:{href:"#quick-look-at-the-data"}},[t._v("#")]),t._v(" Quick Look At the Data")]),t._v(" "),s("p",[t._v("Now that you have loaded your Data Package, it is time to process its contents. The package content consists of one or more resources. You can access "),s("a",{attrs:{href:"https://godoc.org/github.com/frictionlessdata/datapackage-go/datapackage#Resource",target:"_blank",rel:"noopener noreferrer"}},[t._v("Resources"),s("OutboundLink")],1),t._v(" via the "),s("a",{attrs:{href:"https://godoc.org/github.com/frictionlessdata/datapackage-go/datapackage#Package.GetResource()",target:"_blank",rel:"noopener noreferrer"}},[t._v("Package.GetResource()"),s("OutboundLink")],1),t._v(" method. 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noreferrer"}},[t._v("Resource.ReadAll()"),s("OutboundLink")],1),t._v(" method loads the whole table in memory as raw strings and returns it as a Go "),s("code",[t._v("[][]string")]),t._v(". This can be quick useful to take a quick look or perform a visual sanity check at the data.")]),t._v(" "),s("h2",{attrs:{id:"processing-the-data-package-s-content"}},[s("a",{staticClass:"header-anchor",attrs:{href:"#processing-the-data-package-s-content"}},[t._v("#")]),t._v(" Processing the Data Package’s Content")]),t._v(" "),s("p",[t._v("Even though the string representation can be useful for a quick sanity check, you probably want to use actual language types to process the data. Don’t worry, you won’t need to fight the casting battle yourself. 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This may make, or give the perception that, the license is legally binding. Please check your specific situation before publishing the data.")]),e._v(" "),t("h3",{attrs:{id:"software-may-not-fully-support-the-frictionless-data-specification"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#software-may-not-fully-support-the-frictionless-data-specification"}},[e._v("#")]),e._v(" Software may not fully support the Frictionless Data specification")]),e._v(" "),t("p",[e._v("Be aware that some data platforms or software may not fully support the Frictionless Data specification. This may result in license information being lost or other issues. 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You can read more about this in "),s("RouterLink",{attrs:{to:"/blog/2017/10/24/georges-labreche/"}},[t._v("his grantee profile")]),t._v(".")],1),t._v(" "),s("p",[t._v("In this post, Labrèche will show you how to install and use the "),s("a",{attrs:{href:"https://www.java.com/en/",target:"_blank",rel:"noopener noreferrer"}},[t._v("Java"),s("OutboundLink")],1),t._v(" libraries for working with "),s("a",{attrs:{href:"https://specs.frictionlessdata.io/tabular-data-package/",target:"_blank",rel:"noopener noreferrer"}},[t._v("Tabular Data Packages"),s("OutboundLink")],1),t._v(".")]),t._v(" "),s("p",[t._v("Our goal in this tutorial is to load tabular data from a CSV file, infer data types and the table’s schema.")]),t._v(" "),s("h2",{attrs:{id:"setup"}},[s("a",{staticClass:"header-anchor",attrs:{href:"#setup"}},[t._v("#")]),t._v(" Setup")]),t._v(" "),s("p",[t._v("First things first, you’ll want to grab "),s("a",{attrs:{href:"https://github.com/frictionlessdata/datapackage-java",target:"_blank",rel:"noopener noreferrer"}},[t._v("datapackage-java"),s("OutboundLink")],1),t._v(" and the "),s("a",{attrs:{href:"https://github.com/frictionlessdata/tableschema-java",target:"_blank",rel:"noopener noreferrer"}},[t._v("tableschema-java"),s("OutboundLink")],1),t._v(" libraries.")]),t._v(" "),s("h2",{attrs:{id:"the-data"}},[s("a",{staticClass:"header-anchor",attrs:{href:"#the-data"}},[t._v("#")]),t._v(" The Data")]),t._v(" "),s("p",[t._v("For our example, we will use a "),s("a",{attrs:{href:"https://specs.frictionlessdata.io/tabular-data-package/",target:"_blank",rel:"noopener noreferrer"}},[t._v("Tabular Data Package"),s("OutboundLink")],1),t._v(" containing the periodic table. 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At the end, the best score so far is returned.")]),t._v(" "),s("p",[t._v("The inference algorithm traverses all of the table’s rows and attempts to cast every single value of the table. When dealing with large tables, you might want to limit the number of rows that the inference algorithm processes:")]),t._v(" "),s("div",{staticClass:"language-java extra-class"},[s("pre",{pre:!0,attrs:{class:"language-java"}},[s("code",[t._v("\n"),s("span",{pre:!0,attrs:{class:"token comment"}},[t._v("// Only process the first 25 rows for type inference.")]),t._v("\n"),s("span",{pre:!0,attrs:{class:"token class-name"}},[t._v("Schema")]),t._v(" schema "),s("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" table"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(".")]),s("span",{pre:!0,attrs:{class:"token function"}},[t._v("inferSchema")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),s("span",{pre:!0,attrs:{class:"token number"}},[t._v("25")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(";")]),t._v("\n\n")])])]),s("p",[t._v("Be sure to go through "),s("code",[t._v("tableschema-java")]),t._v("'s "),s("a",{attrs:{href:"https://github.com/frictionlessdata/tableschema-java/blob/master/README.md",target:"_blank",rel:"noopener noreferrer"}},[t._v("README"),s("OutboundLink")],1),t._v(" as well to learn more about how to operate with "),s("a",{attrs:{href:"https://specs.frictionlessdata.io/table-schema/",target:"_blank",rel:"noopener noreferrer"}},[t._v("Table Schema"),s("OutboundLink")],1),t._v(".")]),t._v(" "),s("h2",{attrs:{id:"contributing"}},[s("a",{staticClass:"header-anchor",attrs:{href:"#contributing"}},[t._v("#")]),t._v(" Contributing")]),t._v(" "),s("p",[t._v("In case you discovered an issue that you’d like to contribute a fix for, or if you would like to extend functionality:")]),t._v(" "),s("div",{staticClass:"language-sh extra-class"},[s("pre",{pre:!0,attrs:{class:"language-sh"}},[s("code",[t._v("\n"),s("span",{pre:!0,attrs:{class:"token comment"}},[t._v("# install jabba and maven2")]),t._v("\n$ "),s("span",{pre:!0,attrs:{class:"token builtin class-name"}},[t._v("cd")]),t._v(" tableschema-java\n$ jabba "),s("span",{pre:!0,attrs:{class:"token function"}},[t._v("install")]),t._v(" "),s("span",{pre:!0,attrs:{class:"token number"}},[t._v("1.8")]),t._v("\n$ jabba use "),s("span",{pre:!0,attrs:{class:"token number"}},[t._v("1.8")]),t._v("\n$ mvn "),s("span",{pre:!0,attrs:{class:"token function"}},[t._v("install")]),t._v(" -DskipTests"),s("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v("true -Dmaven.javadoc.skip"),s("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v("true -B -V\n$ mvn "),s("span",{pre:!0,attrs:{class:"token builtin class-name"}},[t._v("test")]),t._v(" -B\n\n")])])]),s("p",[t._v("Make sure that all tests pass, and submit a PR with your contributions once you’re ready.")]),t._v(" "),s("p",[t._v("We also welcome your feedback and questions via our "),s("a",{attrs:{href:"http://gitter.im/frictionlessdata/chat",target:"_blank",rel:"noopener noreferrer"}},[t._v("Frictionless Data Gitter chat"),s("OutboundLink")],1),t._v(" or via "),s("a",{attrs:{href:"https://github.com/frictionlessdata/datapackage-java/issues",target:"_blank",rel:"noopener noreferrer"}},[t._v("GitHub issues"),s("OutboundLink")],1),t._v(" on the datapackage-java repository.")])])}),[],!1,null,null,null);a.default=n.exports}}]); \ No newline at end of file diff --git a/assets/js/8.4d18202d.js b/assets/js/8.243cb0bd.js similarity index 94% rename from assets/js/8.4d18202d.js rename to assets/js/8.243cb0bd.js index 7641c9382..3400a1d1d 100644 --- a/assets/js/8.4d18202d.js +++ b/assets/js/8.243cb0bd.js @@ -1 +1 @@ -(window.webpackJsonp=window.webpackJsonp||[]).push([[8],{495:function(e,t,a){e.exports=a.p+"assets/img/bcodmoLogo.958d74b9.jpg"},496:function(e,t,a){e.exports=a.p+"assets/img/bcodmo1.1e0069cf.png"},497:function(e,t,a){e.exports=a.p+"assets/img/bcodmo2.1e6fde83.png"},498:function(e,t,a){e.exports=a.p+"assets/img/bcodmo3.a2871755.png"},499:function(e,t,a){e.exports=a.p+"assets/img/bcodmo4.74b606a5.png"},500:function(e,t,a){e.exports=a.p+"assets/img/bcodmo5.ab522411.png"},501:function(e,t,a){e.exports=a.p+"assets/img/bcodmo6.c90593b8.png"},621:function(e,t,a){"use strict";a.r(t);var o=a(29),n=Object(o.a)({},(function(){var e=this,t=e.$createElement,o=e._self._c||t;return o("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[o("p",[e._v("This blog post describes a Frictionless Data Pilot with the Biological and Chemical Oceanography Data Management Office (BCO-DMO). 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The result of this effort is a database containing over "),o("strong",[e._v("9,000 datasets from a variety of oceanographic and limnological measurements")]),e._v(" including those from: in situ sampling, moorings, floats and gliders, sediment traps; laboratory and mesocosm experiments; satellite images; derived parameters and model output; and synthesis products from data integration efforts. The project has worked with over 2,600 data contributors representing over 1,000 funded projects.")]),e._v(" "),o("p",[e._v("As the catalog of data holdings continued to grow in both size and the variety of data types it curates, BCO-DMO needed to retool its data infrastructure with three goals. First, to improve the transportation of data to, from, and within BCO-DMO’s ecosystem. Second, to support reproducibility of research by making all curation activities of the office completely transparent and traceable. Finally, to improve the efficiency and consistency across data management staff. Until recently, data curation activities in the office were largely dependent on the individual capabilities of each data manager. While some of the staff were fluent in Python and other scripting languages, others were dependent on in-house custom developed tools. These in-house tools were extremely useful and flexible, but they were developed for an aging computing paradigm grounded in physical hardware accessing local data resources on disk. While locally stored data is still the convention at BCO-DMO, the distributed nature of the web coupled with the challenges of big data stretched this toolset beyond its original intention.")]),e._v(" "),o("p",[e._v("In 2015, we were introduced to the idea of data containerization and the Frictionless Data project in a "),o("a",{attrs:{href:"https://www.rd-alliance.org/data-packages-bof-p6-bof-session.html",target:"_blank",rel:"noopener noreferrer"}},[e._v("Data Packages BoF"),o("OutboundLink")],1),e._v(" at the "),o("a",{attrs:{href:"https://www.rd-alliance.org/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Research Data Alliance"),o("OutboundLink")],1),e._v(" conference in Paris, France. After evaluating the Frictionless Data specifications and tools, BCO-DMO developed a strategy to underpin its new data infrastructure on the ideas behind this project.")]),e._v(" "),o("p",[e._v("While the concept of data packaging is not new, the simplicity and extendibility of the Frictionless Data implementation made it easy to adopt within an existing infrastructure. "),o("strong",[e._v("BCO-DMO identified the Data Package Pipelines (DPP) project in the Frictionless Data toolset as key to achieving its data curation goals.")]),e._v(" DPP implements the philosophy of declarative workflows which trade code in a specific programming language that tells a computer how a task should be completed, for imperative, structured statements that detail what should be done. These structured statements abstract the user writing the statements from the actual code executing them, and are useful for reproducibility over long periods of time where programming languages age, change or algorithms improve. This flexibility was appealing because it meant the intent of the data manager could be translated into many varying programming (and data) languages over time without having to refactor older workflows. In data management, that means that one of the languages a DPP workflow captures is provenance – a common need across oceanographic datasets for reproducibility. DPP Workflows translated into records of provenance explicitly communicates to data submitters and future data users what BCO-DMO had done during the curation phase. Secondly, because workflow steps need to be interpreted by computers into code that carries out the instructions, it helped data management staff converge on a declarative language they could all share. This convergence meant cohesiveness, consistency, and efficiency across the team if we could implement DPP in a way they could all use.")]),e._v(" "),o("p",[o("strong",[e._v("In 2018, BCO-DMO formed a partnership with Open Knowledge Foundation (OKF) to develop a web application that would help any BCO-DMO data manager use the declarative language they had developed in a consistent way.")]),e._v(" Why develop a web application for DPP? As the data management staff evaluated DPP and Frictionless Data, they found that there was a learning curve to setting up the DPP environment and a deep understanding of the Frictionlessdata ‘Data Package’ specification was required. The web application abstracted this required knowledge to achieve two main goals: 1) consistently structured Data Packages (datapackage.json) with all the required metadata employed at BCO-DMO, and 2) efficiencies of time by eliminating typos and syntax errors made by data managers. Thus, the partnership with OKF focused on making the needs of scientific research data a possibility within the Frictionless Data ecosystem of specs and tools.")]),e._v(" "),o("p",[o("a",{attrs:{href:"https://github.com/frictionlessdata/datapackage-pipelines",target:"_blank",rel:"noopener noreferrer"}},[e._v("Data Package Pipelines"),o("OutboundLink")],1),e._v(" is implemented in Python and comes with some built-in processors that can be used in a workflow. BCO-DMO took its own declarative language and identified gaps in the built-in processors. For these gaps, BCO-DMO and OKF developed Python implementations for the missing declarations to support the curation of oceanographic data, and the result was a new set of processors made available on "),o("a",{attrs:{href:"https://github.com/BCODMO/bcodmo_processors",target:"_blank",rel:"noopener noreferrer"}},[e._v("Github"),o("OutboundLink")],1),e._v(".")]),e._v(" "),o("p",[e._v("Some notable BCO-DMO processors are:")]),e._v(" "),o("p",[o("a",{attrs:{href:"https://github.com/BCODMO/bcodmo_processors#bcodmo_pipeline_processorsboolean_add_computed_field",target:"_blank",rel:"noopener noreferrer"}},[e._v("boolean_add_computed_field"),o("OutboundLink")],1),e._v("– Computes a new field to add to the data whether a particular row satisfies a certain set of criteria."),o("br"),e._v("\nExample: Where Cruise_ID = ‘AT39-05’ and Station = 6, set Latitude to 22.1645.")]),e._v(" "),o("p",[o("a",{attrs:{href:"https://github.com/BCODMO/bcodmo_processors#bcodmo_pipeline_processorsconvert_date",target:"_blank",rel:"noopener noreferrer"}},[e._v("convert_date"),o("OutboundLink")],1),e._v(" – Converts any number of fields containing date information into a single date field with display format and timezone options. Often data information is reported in multiple columns such as "),o("code",[e._v("year")]),e._v(", "),o("code",[e._v("month")]),e._v(", "),o("code",[e._v("day")]),e._v(", "),o("code",[e._v("hours_local_time")]),e._v(", "),o("code",[e._v("minutes_local_time")]),e._v(", "),o("code",[e._v("seconds_local_time")]),e._v(". For spatio-temporal datasets, it’s important to know the UTC date and time of the recorded data to ensure that searches for data with a time range are accurate. Here, these columns are combined to form an ISO 8601-compliant UTC datetime value.")]),e._v(" "),o("p",[o("a",{attrs:{href:"https://github.com/BCODMO/bcodmo_processors#bcodmo_pipeline_processorsconvert_to_decimal_degrees",target:"_blank",rel:"noopener noreferrer"}},[e._v("convert_to_decimal_degrees"),o("OutboundLink")],1),e._v(" – Convert a single field containing coordinate information from degrees-minutes-seconds or degrees-decimal_minutes to decimal_degrees. The standard representation at BCO-DMO for spatial data conforms to the decimal degrees specification.")]),e._v(" "),o("p",[o("a",{attrs:{href:"https://github.com/BCODMO/bcodmo_processors#bcodmo_pipeline_processorsreorder_fields",target:"_blank",rel:"noopener noreferrer"}},[e._v("reorder_fields"),o("OutboundLink")],1),e._v(" – Changes the order of columns within the data. This is a convention within the oceanographic data community to put certain columns at the beginning of tabular data to help contextualize the following columns. Examples of columns that are typically moved to the beginning are: dates, locations, instrument or vessel identifiers, and depth at collection.")]),e._v(" "),o("p",[e._v("The remaining processors used by BCO-DMO can be found at "),o("a",{attrs:{href:"https://github.com/BCODMO/bcodmo_processors",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://github.com/BCODMO/bcodmo_processors"),o("OutboundLink")],1),e._v(".")]),e._v(" "),o("p",[e._v("How does Laminar work?"),o("br"),e._v("\nIn our collaboration with OKF, BCO-DMO developed use cases based on real-world data submissions. One such example is a recent Arctic Nitrogen Fixation Rates dataset.")]),e._v(" "),o("p",[o("img",{attrs:{src:a(496),alt:"Arctic dataset"}})]),e._v(" "),o("p",[e._v("The original dataset shown above needed the following curation steps to make the data more interoperable and reusable:")]),e._v(" "),o("p",[e._v("Convert lat/lon to decimal degrees"),o("br"),e._v("\nAdd timestamp (UTC) in ISO format"),o("br"),e._v("\n‘Collection Depth’ with value “surface” should be changed to 0"),o("br"),e._v("\nRemove parenthesis and units from column names (field descriptions and units captured in metadata)."),o("br"),e._v("\nRemove spaces from column names"),o("br"),e._v("\nThe web application, named Laminar, built on top of DPP helps Data Managers at BCO-DMO perform these operations in a consistent way. First, Laminar prompts us to name and describe the current pipeline being developed, and assumes that the data manager wants to load some data in to start the pipeline, and prompts for a source location.")]),e._v(" "),o("p",[o("img",{attrs:{src:a(497),alt:"Laminar"}})]),e._v(" "),o("p",[e._v("After providing a name and description of our DPP workflow, we provide a data source to load, and give it the name, ‘nfix’.")]),e._v(" "),o("p",[e._v("In subsequent pipeline steps, we refer to ‘nfix’ as the resource we want to transform. For example, to convert the latitude and longitude into decimal degrees, we add a new step to the pipeline, select the ‘Convert to decimal degrees’ processor, a proxy for our custom processor convert_to_decimal_degrees’, select the ‘nfix’ resource, select a field form that ‘nfix’ data source, and specify the Python regex pattern identifying where the values for the degrees, minutes and seconds can be found in each value of the latitude column.")]),e._v(" "),o("p",[o("img",{attrs:{src:a(498),alt:"processor step"}})]),e._v(" "),o("p",[e._v("Similarly, in step 7 of this pipeline, we want to generate an ISO 8601-compliant UTC datetime value by combining the pre-existing ‘Date’ and ‘Local Time’ columns. This step is depicted below:")]),e._v(" "),o("p",[o("img",{attrs:{src:a(499),alt:"date processing step"}})]),e._v(" "),o("p",[e._v("After the pipeline is completed, the interface displays all steps, and lets the data manager execute the pipeline by clicking the green ‘play’ button at the bottom. This button then generates the pipeline-spec.yaml file, executes the pipeline, and can display the resulting dataset.")]),e._v(" "),o("p",[o("img",{attrs:{src:a(500),alt:"all steps"}})]),e._v(" "),o("p",[o("img",{attrs:{src:a(501),alt:"data"}})]),e._v(" "),o("p",[e._v("The resulting DPP workflow contained 223 lines across this 12-step operation, and for a data manager, the web application reduces the chance of error if this pipelines was being generated by hand. Ultimately, our work with OKF helped us develop processors that follow the DPP conventions.")]),e._v(" "),o("p",[e._v("Our goal for the pilot project with OKF was to have BCO-DMO data managers using the Laminar for processing 80% of the data submissions we receive. The pilot was so successful, that data managers have processed 95% of new data submissions to the repository using the application.")]),e._v(" "),o("p",[e._v("This is exciting from a data management processing perspective because the use of Laminar is more sustainable, and acted to bring the team together to determine best strategies for processing, documentation, etc. This increase in consistency and efficiency is welcomed from an administrative perspective and helps with the training of any new data managers coming to the team.")]),e._v(" "),o("p",[e._v("The OKF team are excellent partners, who were the catalysts to a successful project. The next steps for BCO-DMO are to build on the success of The Frictionlessdata Data Package Pipelines by implementing the Frictionlessdata Goodtables specification for data validation to help us develop submission guidelines for common data types. Special thanks to the OKF team – Lilly Winfree, Evgeny Karev, and Jo Barrett.")])])}),[],!1,null,null,null);t.default=n.exports}}]); \ No newline at end of file +(window.webpackJsonp=window.webpackJsonp||[]).push([[8],{490:function(e,t,a){e.exports=a.p+"assets/img/bcodmoLogo.958d74b9.jpg"},491:function(e,t,a){e.exports=a.p+"assets/img/bcodmo1.1e0069cf.png"},492:function(e,t,a){e.exports=a.p+"assets/img/bcodmo2.1e6fde83.png"},493:function(e,t,a){e.exports=a.p+"assets/img/bcodmo3.a2871755.png"},494:function(e,t,a){e.exports=a.p+"assets/img/bcodmo4.74b606a5.png"},495:function(e,t,a){e.exports=a.p+"assets/img/bcodmo5.ab522411.png"},496:function(e,t,a){e.exports=a.p+"assets/img/bcodmo6.c90593b8.png"},621:function(e,t,a){"use strict";a.r(t);var o=a(29),n=Object(o.a)({},(function(){var e=this,t=e.$createElement,o=e._self._c||t;return o("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[o("p",[e._v("This blog post describes a Frictionless Data Pilot with the Biological and Chemical Oceanography Data Management Office (BCO-DMO). Pilot projects are part of the "),o("a",{attrs:{href:"https://frictionlessdata.io/reproducible-research/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Frictionless Data for Reproducible Research project"),o("OutboundLink")],1),e._v(". Written by the BCO-DMO team members Adam Shepherd, Amber York, Danie Kinkade, and development by Conrad Schloer.")]),e._v(" "),o("p",[o("img",{attrs:{src:a(490),alt:"BCO-DMO logo"}})]),e._v(" "),o("p",[e._v("Scientific research is implicitly reliant upon the creation, management, analysis, synthesis, and interpretation of data. When properly stewarded, data hold great potential to demonstrate the reproducibility of scientific results and accelerate scientific discovery. "),o("a",{attrs:{href:"https://www.bco-dmo.org/",target:"_blank",rel:"noopener noreferrer"}},[e._v("The Biological and Chemical Oceanography Data Management Office (BCO-DMO)"),o("OutboundLink")],1),e._v(" is a publicly accessible earth science data repository established by the National Science Foundation "),o("a",{attrs:{href:"https://www.nsf.gov/",target:"_blank",rel:"noopener noreferrer"}},[e._v("(NSF)"),o("OutboundLink")],1),e._v(" for the curation of biological, chemical, and biogeochemical oceanographic data from research in coastal, marine, and laboratory environments. With the groundswell surrounding the "),o("a",{attrs:{href:"https://doi.org/10.1038/sdata.2016.18",target:"_blank",rel:"noopener noreferrer"}},[e._v("FAIR data principles"),o("OutboundLink")],1),e._v(", BCO-DMO recognized an opportunity to improve its curation services to better support reproducibility of results, while increasing process efficiencies for incoming data submissions. "),o("strong",[e._v("In 2019, BCO-DMO worked with the Frictionless Data team at Open Knowledge Foundation to develop a web application called Laminar for creating Frictionlessdata Data Package Pipelines that help data managers process data efficiently while recording the provenance of their activities to support reproducibility of results.")])]),e._v(" "),o("p",[e._v("The mission of BCO-DMO is to provide investigators with data management services that span the full data lifecycle from data management planning, to data publication, and archiving.")]),e._v(" "),o("p",[e._v("BCO-DMO provides free access to oceanographic data through a web-based catalog with tools and features facilitating assessment of fitness for purpose. The result of this effort is a database containing over "),o("strong",[e._v("9,000 datasets from a variety of oceanographic and limnological measurements")]),e._v(" including those from: in situ sampling, moorings, floats and gliders, sediment traps; laboratory and mesocosm experiments; satellite images; derived parameters and model output; and synthesis products from data integration efforts. The project has worked with over 2,600 data contributors representing over 1,000 funded projects.")]),e._v(" "),o("p",[e._v("As the catalog of data holdings continued to grow in both size and the variety of data types it curates, BCO-DMO needed to retool its data infrastructure with three goals. First, to improve the transportation of data to, from, and within BCO-DMO’s ecosystem. Second, to support reproducibility of research by making all curation activities of the office completely transparent and traceable. Finally, to improve the efficiency and consistency across data management staff. Until recently, data curation activities in the office were largely dependent on the individual capabilities of each data manager. While some of the staff were fluent in Python and other scripting languages, others were dependent on in-house custom developed tools. These in-house tools were extremely useful and flexible, but they were developed for an aging computing paradigm grounded in physical hardware accessing local data resources on disk. While locally stored data is still the convention at BCO-DMO, the distributed nature of the web coupled with the challenges of big data stretched this toolset beyond its original intention.")]),e._v(" "),o("p",[e._v("In 2015, we were introduced to the idea of data containerization and the Frictionless Data project in a "),o("a",{attrs:{href:"https://www.rd-alliance.org/data-packages-bof-p6-bof-session.html",target:"_blank",rel:"noopener noreferrer"}},[e._v("Data Packages BoF"),o("OutboundLink")],1),e._v(" at the "),o("a",{attrs:{href:"https://www.rd-alliance.org/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Research Data Alliance"),o("OutboundLink")],1),e._v(" conference in Paris, France. After evaluating the Frictionless Data specifications and tools, BCO-DMO developed a strategy to underpin its new data infrastructure on the ideas behind this project.")]),e._v(" "),o("p",[e._v("While the concept of data packaging is not new, the simplicity and extendibility of the Frictionless Data implementation made it easy to adopt within an existing infrastructure. "),o("strong",[e._v("BCO-DMO identified the Data Package Pipelines (DPP) project in the Frictionless Data toolset as key to achieving its data curation goals.")]),e._v(" DPP implements the philosophy of declarative workflows which trade code in a specific programming language that tells a computer how a task should be completed, for imperative, structured statements that detail what should be done. These structured statements abstract the user writing the statements from the actual code executing them, and are useful for reproducibility over long periods of time where programming languages age, change or algorithms improve. This flexibility was appealing because it meant the intent of the data manager could be translated into many varying programming (and data) languages over time without having to refactor older workflows. In data management, that means that one of the languages a DPP workflow captures is provenance – a common need across oceanographic datasets for reproducibility. DPP Workflows translated into records of provenance explicitly communicates to data submitters and future data users what BCO-DMO had done during the curation phase. Secondly, because workflow steps need to be interpreted by computers into code that carries out the instructions, it helped data management staff converge on a declarative language they could all share. This convergence meant cohesiveness, consistency, and efficiency across the team if we could implement DPP in a way they could all use.")]),e._v(" "),o("p",[o("strong",[e._v("In 2018, BCO-DMO formed a partnership with Open Knowledge Foundation (OKF) to develop a web application that would help any BCO-DMO data manager use the declarative language they had developed in a consistent way.")]),e._v(" Why develop a web application for DPP? As the data management staff evaluated DPP and Frictionless Data, they found that there was a learning curve to setting up the DPP environment and a deep understanding of the Frictionlessdata ‘Data Package’ specification was required. The web application abstracted this required knowledge to achieve two main goals: 1) consistently structured Data Packages (datapackage.json) with all the required metadata employed at BCO-DMO, and 2) efficiencies of time by eliminating typos and syntax errors made by data managers. Thus, the partnership with OKF focused on making the needs of scientific research data a possibility within the Frictionless Data ecosystem of specs and tools.")]),e._v(" "),o("p",[o("a",{attrs:{href:"https://github.com/frictionlessdata/datapackage-pipelines",target:"_blank",rel:"noopener noreferrer"}},[e._v("Data Package Pipelines"),o("OutboundLink")],1),e._v(" is implemented in Python and comes with some built-in processors that can be used in a workflow. BCO-DMO took its own declarative language and identified gaps in the built-in processors. For these gaps, BCO-DMO and OKF developed Python implementations for the missing declarations to support the curation of oceanographic data, and the result was a new set of processors made available on "),o("a",{attrs:{href:"https://github.com/BCODMO/bcodmo_processors",target:"_blank",rel:"noopener noreferrer"}},[e._v("Github"),o("OutboundLink")],1),e._v(".")]),e._v(" "),o("p",[e._v("Some notable BCO-DMO processors are:")]),e._v(" "),o("p",[o("a",{attrs:{href:"https://github.com/BCODMO/bcodmo_processors#bcodmo_pipeline_processorsboolean_add_computed_field",target:"_blank",rel:"noopener noreferrer"}},[e._v("boolean_add_computed_field"),o("OutboundLink")],1),e._v("– Computes a new field to add to the data whether a particular row satisfies a certain set of criteria."),o("br"),e._v("\nExample: Where Cruise_ID = ‘AT39-05’ and Station = 6, set Latitude to 22.1645.")]),e._v(" "),o("p",[o("a",{attrs:{href:"https://github.com/BCODMO/bcodmo_processors#bcodmo_pipeline_processorsconvert_date",target:"_blank",rel:"noopener noreferrer"}},[e._v("convert_date"),o("OutboundLink")],1),e._v(" – Converts any number of fields containing date information into a single date field with display format and timezone options. Often data information is reported in multiple columns such as "),o("code",[e._v("year")]),e._v(", "),o("code",[e._v("month")]),e._v(", "),o("code",[e._v("day")]),e._v(", "),o("code",[e._v("hours_local_time")]),e._v(", "),o("code",[e._v("minutes_local_time")]),e._v(", "),o("code",[e._v("seconds_local_time")]),e._v(". For spatio-temporal datasets, it’s important to know the UTC date and time of the recorded data to ensure that searches for data with a time range are accurate. Here, these columns are combined to form an ISO 8601-compliant UTC datetime value.")]),e._v(" "),o("p",[o("a",{attrs:{href:"https://github.com/BCODMO/bcodmo_processors#bcodmo_pipeline_processorsconvert_to_decimal_degrees",target:"_blank",rel:"noopener noreferrer"}},[e._v("convert_to_decimal_degrees"),o("OutboundLink")],1),e._v(" – Convert a single field containing coordinate information from degrees-minutes-seconds or degrees-decimal_minutes to decimal_degrees. The standard representation at BCO-DMO for spatial data conforms to the decimal degrees specification.")]),e._v(" "),o("p",[o("a",{attrs:{href:"https://github.com/BCODMO/bcodmo_processors#bcodmo_pipeline_processorsreorder_fields",target:"_blank",rel:"noopener noreferrer"}},[e._v("reorder_fields"),o("OutboundLink")],1),e._v(" – Changes the order of columns within the data. This is a convention within the oceanographic data community to put certain columns at the beginning of tabular data to help contextualize the following columns. Examples of columns that are typically moved to the beginning are: dates, locations, instrument or vessel identifiers, and depth at collection.")]),e._v(" "),o("p",[e._v("The remaining processors used by BCO-DMO can be found at "),o("a",{attrs:{href:"https://github.com/BCODMO/bcodmo_processors",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://github.com/BCODMO/bcodmo_processors"),o("OutboundLink")],1),e._v(".")]),e._v(" "),o("p",[e._v("How does Laminar work?"),o("br"),e._v("\nIn our collaboration with OKF, BCO-DMO developed use cases based on real-world data submissions. One such example is a recent Arctic Nitrogen Fixation Rates dataset.")]),e._v(" "),o("p",[o("img",{attrs:{src:a(491),alt:"Arctic dataset"}})]),e._v(" "),o("p",[e._v("The original dataset shown above needed the following curation steps to make the data more interoperable and reusable:")]),e._v(" "),o("p",[e._v("Convert lat/lon to decimal degrees"),o("br"),e._v("\nAdd timestamp (UTC) in ISO format"),o("br"),e._v("\n‘Collection Depth’ with value “surface” should be changed to 0"),o("br"),e._v("\nRemove parenthesis and units from column names (field descriptions and units captured in metadata)."),o("br"),e._v("\nRemove spaces from column names"),o("br"),e._v("\nThe web application, named Laminar, built on top of DPP helps Data Managers at BCO-DMO perform these operations in a consistent way. First, Laminar prompts us to name and describe the current pipeline being developed, and assumes that the data manager wants to load some data in to start the pipeline, and prompts for a source location.")]),e._v(" "),o("p",[o("img",{attrs:{src:a(492),alt:"Laminar"}})]),e._v(" "),o("p",[e._v("After providing a name and description of our DPP workflow, we provide a data source to load, and give it the name, ‘nfix’.")]),e._v(" "),o("p",[e._v("In subsequent pipeline steps, we refer to ‘nfix’ as the resource we want to transform. For example, to convert the latitude and longitude into decimal degrees, we add a new step to the pipeline, select the ‘Convert to decimal degrees’ processor, a proxy for our custom processor convert_to_decimal_degrees’, select the ‘nfix’ resource, select a field form that ‘nfix’ data source, and specify the Python regex pattern identifying where the values for the degrees, minutes and seconds can be found in each value of the latitude column.")]),e._v(" "),o("p",[o("img",{attrs:{src:a(493),alt:"processor step"}})]),e._v(" "),o("p",[e._v("Similarly, in step 7 of this pipeline, we want to generate an ISO 8601-compliant UTC datetime value by combining the pre-existing ‘Date’ and ‘Local Time’ columns. This step is depicted below:")]),e._v(" "),o("p",[o("img",{attrs:{src:a(494),alt:"date processing step"}})]),e._v(" "),o("p",[e._v("After the pipeline is completed, the interface displays all steps, and lets the data manager execute the pipeline by clicking the green ‘play’ button at the bottom. This button then generates the pipeline-spec.yaml file, executes the pipeline, and can display the resulting dataset.")]),e._v(" "),o("p",[o("img",{attrs:{src:a(495),alt:"all steps"}})]),e._v(" "),o("p",[o("img",{attrs:{src:a(496),alt:"data"}})]),e._v(" "),o("p",[e._v("The resulting DPP workflow contained 223 lines across this 12-step operation, and for a data manager, the web application reduces the chance of error if this pipelines was being generated by hand. Ultimately, our work with OKF helped us develop processors that follow the DPP conventions.")]),e._v(" "),o("p",[e._v("Our goal for the pilot project with OKF was to have BCO-DMO data managers using the Laminar for processing 80% of the data submissions we receive. The pilot was so successful, that data managers have processed 95% of new data submissions to the repository using the application.")]),e._v(" "),o("p",[e._v("This is exciting from a data management processing perspective because the use of Laminar is more sustainable, and acted to bring the team together to determine best strategies for processing, documentation, etc. This increase in consistency and efficiency is welcomed from an administrative perspective and helps with the training of any new data managers coming to the team.")]),e._v(" "),o("p",[e._v("The OKF team are excellent partners, who were the catalysts to a successful project. The next steps for BCO-DMO are to build on the success of The Frictionlessdata Data Package Pipelines by implementing the Frictionlessdata Goodtables specification for data validation to help us develop submission guidelines for common data types. Special thanks to the OKF team – Lilly Winfree, Evgeny Karev, and Jo Barrett.")])])}),[],!1,null,null,null);t.default=n.exports}}]); \ No newline at end of file diff --git a/assets/js/80.3113bfdb.js b/assets/js/80.ec49c7bb.js similarity index 99% rename from assets/js/80.3113bfdb.js rename to assets/js/80.ec49c7bb.js index 86bb3cacc..a13be1a76 100644 --- a/assets/js/80.3113bfdb.js +++ b/assets/js/80.ec49c7bb.js @@ -1 +1 @@ -(window.webpackJsonp=window.webpackJsonp||[]).push([[80],{599:function(t,a,s){"use strict";s.r(a);var e=s(29),n=Object(e.a)({},(function(){var t=this,a=t.$createElement,s=t._self._c||a;return s("ContentSlotsDistributor",{attrs:{"slot-key":t.$parent.slotKey}},[s("p",[t._v("Matt Thompson was one of 2017’s "),s("a",{attrs:{href:"https://toolfund.frictionlessdata.io",target:"_blank",rel:"noopener noreferrer"}},[t._v("Frictionless Data Tool Fund"),s("OutboundLink")],1),t._v(" grantees tasked with extending implementation of core Frictionless Data "),s("a",{attrs:{href:"https://github.com/frictionlessdata/datapackage-clj",target:"_blank",rel:"noopener noreferrer"}},[t._v("data package"),s("OutboundLink")],1),t._v(" and "),s("a",{attrs:{href:"https://github.com/frictionlessdata/tableschema-clj",target:"_blank",rel:"noopener noreferrer"}},[t._v("table schema"),s("OutboundLink")],1),t._v(" libraries in Clojure programming language. 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In this post, Thompson will show you how to set up and use the "),s("a",{attrs:{href:"http://clojure.org",target:"_blank",rel:"noopener noreferrer"}},[t._v("Clojure"),s("OutboundLink")],1),t._v(" libraries for working with "),s("a",{attrs:{href:"https://specs.frictionlessdata.io/tabular-data-package/",target:"_blank",rel:"noopener noreferrer"}},[t._v("Tabular Data Packages"),s("OutboundLink")],1),t._v(".")],1),t._v(" "),s("p",[t._v("This tutorial uses a worked example of downloading a data package from a remote location on the web, and using the Frictionless Data tools to read its contents and metadata into Clojure data structures.")]),t._v(" "),s("h2",{attrs:{id:"setup"}},[s("a",{staticClass:"header-anchor",attrs:{href:"#setup"}},[t._v("#")]),t._v(" Setup")]),t._v(" "),s("p",[t._v("First, we need to set up the project structure using the "),s("a",{attrs:{href:"http://leiningen.org",target:"_blank",rel:"noopener noreferrer"}},[t._v("Leiningen"),s("OutboundLink")],1),t._v(" tool. If you don’t have Leiningen set up on your system, follow the link to download and install it. Once it is set up, run the following command from the command line to create the folders and files for a basic Clojure project:")]),t._v(" "),s("div",{staticClass:"language-sh extra-class"},[s("pre",{pre:!0,attrs:{class:"language-sh"}},[s("code",[t._v("\nlein new periodic-table\n\n")])])]),s("p",[t._v("This will create the "),s("em",[t._v("periodic-table")]),t._v(" folder. Inside the "),s("em",[t._v("periodic-table/src/periodic-table")]),t._v(" folder should be a file named "),s("em",[t._v("core.clj")]),t._v(". This is the file you need to edit during this tutorial.")]),t._v(" "),s("h2",{attrs:{id:"the-data"}},[s("a",{staticClass:"header-anchor",attrs:{href:"#the-data"}},[t._v("#")]),t._v(" The Data")]),t._v(" "),s("p",[t._v("For this tutorial, we will use a pre-created data package, the Periodic Table Data Package hosted by the Frictionless Data project. 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Clojure libraries for Frictionless Data.")]),t._v(" "),s("p",[t._v("We welcome your feedback and questions via our "),s("a",{attrs:{href:"http://gitter.im/frictionlessdata/chat",target:"_blank",rel:"noopener noreferrer"}},[t._v("Frictionless Data Gitter chat"),s("OutboundLink")],1),t._v(" or via "),s("a",{attrs:{href:"https://github.com/frictionlessdata/datapackage-clj/issues",target:"_blank",rel:"noopener noreferrer"}},[t._v("GitHub issues"),s("OutboundLink")],1),t._v(" on the "),s("a",{attrs:{href:"https://github.com/frictionlessdata/datapackage-clj",target:"_blank",rel:"noopener noreferrer"}},[t._v("datapackage-clj"),s("OutboundLink")],1),t._v(" repository.")])])}),[],!1,null,null,null);a.default=n.exports}}]); \ No newline at end of file +(window.webpackJsonp=window.webpackJsonp||[]).push([[80],{596:function(t,a,s){"use strict";s.r(a);var e=s(29),n=Object(e.a)({},(function(){var t=this,a=t.$createElement,s=t._self._c||a;return 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If you don’t have Leiningen set up on your system, follow the link to download and install it. Once it is set up, run the following command from the command line to create the folders and files for a basic Clojure project:")]),t._v(" "),s("div",{staticClass:"language-sh extra-class"},[s("pre",{pre:!0,attrs:{class:"language-sh"}},[s("code",[t._v("\nlein new periodic-table\n\n")])])]),s("p",[t._v("This will create the "),s("em",[t._v("periodic-table")]),t._v(" folder. Inside the "),s("em",[t._v("periodic-table/src/periodic-table")]),t._v(" folder should be a file named "),s("em",[t._v("core.clj")]),t._v(". This is the file you need to edit during this tutorial.")]),t._v(" "),s("h2",{attrs:{id:"the-data"}},[s("a",{staticClass:"header-anchor",attrs:{href:"#the-data"}},[t._v("#")]),t._v(" The Data")]),t._v(" "),s("p",[t._v("For this tutorial, we will use a pre-created data package, the Periodic Table Data Package hosted by the Frictionless Data project. 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Clojure libraries for Frictionless Data.")]),t._v(" "),s("p",[t._v("We welcome your feedback and questions via our "),s("a",{attrs:{href:"http://gitter.im/frictionlessdata/chat",target:"_blank",rel:"noopener noreferrer"}},[t._v("Frictionless Data Gitter chat"),s("OutboundLink")],1),t._v(" or via "),s("a",{attrs:{href:"https://github.com/frictionlessdata/datapackage-clj/issues",target:"_blank",rel:"noopener noreferrer"}},[t._v("GitHub issues"),s("OutboundLink")],1),t._v(" on the "),s("a",{attrs:{href:"https://github.com/frictionlessdata/datapackage-clj",target:"_blank",rel:"noopener noreferrer"}},[t._v("datapackage-clj"),s("OutboundLink")],1),t._v(" repository.")])])}),[],!1,null,null,null);a.default=n.exports}}]); \ No newline at end of file diff --git a/assets/js/81.8026f04a.js b/assets/js/81.f80c9b50.js similarity index 99% rename from assets/js/81.8026f04a.js rename to assets/js/81.f80c9b50.js index 5c1b324e7..ec7f87bec 100644 --- a/assets/js/81.8026f04a.js +++ b/assets/js/81.f80c9b50.js @@ -1 +1 @@ -(window.webpackJsonp=window.webpackJsonp||[]).push([[81],{596:function(e,t,r){"use strict";r.r(t);var a=r(29),o=Object(a.a)({},(function(){var e=this,t=e.$createElement,r=e._self._c||t;return r("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[r("p",[e._v("This page provides an overview CSV (Comma Separated Values) format for data.")]),e._v(" "),r("p",[e._v("CSV is a very old, very simple and very common “standard” for (tabular) data."),r("br"),e._v("\nWe say “standard” in quotes because there was never a formal standard for CSV,"),r("br"),e._v("\nthough in 2005 someone did put together a "),r("a",{attrs:{href:"http://tools.ietf.org/html/rfc4180",target:"_blank",rel:"noopener noreferrer"}},[e._v("RFC"),r("OutboundLink")],1),e._v(" for it.")]),e._v(" "),r("p",[e._v("CSV is supported by a "),r("strong",[e._v("huge")]),e._v(" number of tools from spreadsheets like Excel,"),r("br"),e._v("\nOpenOffice and Google Docs to complex databases to almost all programming"),r("br"),e._v("\nlanguages. As such it is probably the most widely supported structured data"),r("br"),e._v("\nformat in the world.")]),e._v(" "),r("hr"),e._v(" "),r("h2",{attrs:{id:"the-format"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#the-format"}},[e._v("#")]),e._v(" The Format")]),e._v(" "),r("p",[e._v("Key points are:")]),e._v(" "),r("ul",[r("li",[e._v("CSV is probably the simplest possible structured format for data")]),e._v(" "),r("li",[e._v("CSV strikes a delicate balance, remaining readable by both machines & humans")]),e._v(" "),r("li",[e._v("CSV is a two dimensional structure consisting of rows of data, each row"),r("br"),e._v("\ncontaining multiple cells. Rows are (usually) separated by line terminators"),r("br"),e._v("\nso each row corresponds to one line. Cells within a row are separated by"),r("br"),e._v("\ncommas (hence the C(ommmas) part)\n"),r("ul",[r("li",[e._v("Note that strictly we’re really talking about DSV files in that we can"),r("br"),e._v("\nallow ‘delimiters’ between cells other than a comma. However, many people"),r("br"),e._v("\nand many programs still call such data CSV (since comma is so common as the"),r("br"),e._v("\ndelimiter)")])])]),e._v(" "),r("li",[e._v("CSV is a “text-based” format, i.e. a CSV file "),r("em",[e._v("is")]),e._v(" a text file. This makes it"),r("br"),e._v("\namenable for processing with all kinds of text-oriented tools (from text"),r("br"),e._v("\neditors to "),r("a",{attrs:{href:"https://github.com/rgrp/command-line-data-wrangling",target:"_blank",rel:"noopener noreferrer"}},[e._v("unix tools like sed, grep etc"),r("OutboundLink")],1),e._v(")")])]),e._v(" "),r("h3",{attrs:{id:"what-a-csv-looks-like"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#what-a-csv-looks-like"}},[e._v("#")]),e._v(" What a CSV looks like")]),e._v(" "),r("p",[e._v("If you open up a CSV file in a text editor it would look something like:")]),e._v(" "),r("div",{staticClass:"language- extra-class"},[r("pre",[r("code",[e._v('A,B,C\n1,2,3\n4,"5,3",6\n')])])]),r("p",[e._v("Here there are 3 rows each of 3 columns. Notice how the second column in the last line is"),r("br"),e._v("\n“quoted” because the content of that value actually contains a “,” character. Without"),r("br"),e._v("\nthe quotes this character would be interpreted as a column separator. To avoid this"),r("br"),e._v("\nconfusion we put quotes around the whole value. The result is that we have 3 rows each"),r("br"),e._v("\nof 3 columns (Note a CSV file does not "),r("em",[e._v("have")]),e._v(" to have"),r("br"),e._v("\nthe same number of columns in each row).")]),e._v(" "),r("h3",{attrs:{id:"dialects-of-csvs"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#dialects-of-csvs"}},[e._v("#")]),e._v(" Dialects of CSVs")]),e._v(" "),r("p",[e._v("As mentioned above, CSV files can have quite a bit of variation in"),r("br"),e._v("\nstructure. Key options are:")]),e._v(" "),r("ul",[r("li",[e._v("Field delimiter: rather than comma "),r("code",[e._v(",")]),e._v(" people often use things like "),r("code",[e._v("\\t")]),r("br"),e._v("\n(tab), "),r("code",[e._v(";")]),e._v(" or "),r("code",[e._v("|")])]),e._v(" "),r("li",[e._v("Record terminator / line terminator: is "),r("code",[e._v("\\n")]),e._v(" (unix), "),r("code",[e._v("\\n\\r")]),e._v(" (dos) or something else …")]),e._v(" "),r("li",[e._v("How do you quote records that contain your delimiter")])]),e._v(" "),r("p",[e._v("You can read more in the "),r("a",{attrs:{href:"https://specs.frictionlessdata.io/csv-dialect/",target:"_blank",rel:"noopener noreferrer"}},[e._v("CSV Dialect Description Format"),r("OutboundLink")],1),e._v(" which defines"),r("br"),e._v("\na small JSON-oriented structure for specifying what options a CSV uses.")]),e._v(" "),r("h3",{attrs:{id:"what-is-missing-in-csv"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#what-is-missing-in-csv"}},[e._v("#")]),e._v(" What is Missing in CSV?")]),e._v(" "),r("ul",[r("li",[e._v("CSV lacks any way to specify type information: that is, there is no way to"),r("br"),e._v("\ndistinguish “1” the string from 1 the number. This shortcoming can be"),r("br"),e._v("\naddressed by adding some form of simple schema. For example "),r("RouterLink",{attrs:{to:"/table-schema/"}},[e._v("Table"),r("br"),e._v("\nSchema")]),e._v(" provides a very simple way to describe your schema externally"),r("br"),e._v("\nwhilst "),r("a",{attrs:{href:"http://jenit.github.io/linked-csv/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Linked CSV"),r("OutboundLink")],1),e._v(" is an example of doing this “inline” (that"),r("br"),e._v("\nis, in the CSV).")],1),e._v(" "),r("li",[e._v("No support for relationships between different “tables”. This is similar to"),r("br"),e._v("\nthe previous point and again "),r("RouterLink",{attrs:{to:"/table-schema/"}},[e._v("Table Schema")]),e._v(" provides a way to address"),r("br"),e._v("\nthis by providing additional schema information externally.")],1),e._v(" "),r("li",[e._v("CSV is really only for tabular data – it is not so good for data with"),r("br"),e._v("\nnesting or where structure is not especially tabular (though remember most"),r("br"),e._v("\ndata can be put into tabular form if you try hard enough!)")])]),e._v(" "),r("h3",{attrs:{id:"links"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#links"}},[e._v("#")]),e._v(" Links")]),e._v(" "),r("p",[e._v("Specifications and overviews:")]),e._v(" "),r("ul",[r("li",[r("a",{attrs:{href:"http://tools.ietf.org/html/rfc4180",target:"_blank",rel:"noopener noreferrer"}},[e._v("RFC specification of CSV"),r("OutboundLink")],1)]),e._v(" "),r("li",[e._v("[CSV Dialect Description Format][csvddf]")]),e._v(" "),r("li",[r("a",{attrs:{href:"http://en.wikipedia.org/wiki/Comma-separated_values",target:"_blank",rel:"noopener noreferrer"}},[e._v("CSV on Wikipedia"),r("OutboundLink")],1)])]),e._v(" "),r("hr"),e._v(" "),r("h2",{attrs:{id:"tools"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#tools"}},[e._v("#")]),e._v(" Tools")]),e._v(" "),r("p",[e._v("The great thing about CSV is the huge level of tool support. The following is"),r("br"),e._v("\nnot intended to be comprehensive but is more at the electic end of the spectrum.")]),e._v(" "),r("h3",{attrs:{id:"desktop"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#desktop"}},[e._v("#")]),e._v(" Desktop")]),e._v(" "),r("p",[e._v("All spreadsheet programs including Excel, OpenOffice, Google Docs"),r("br"),e._v("\nSpreadsheets supporting opening, editing and saving CSVs.")]),e._v(" "),r("h3",{attrs:{id:"view-a-csv-file-in-your-browser"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#view-a-csv-file-in-your-browser"}},[e._v("#")]),e._v(" View a CSV file in your Browser")]),e._v(" "),r("p",[e._v("You can view a CSV file (saving you the hassle of downloading it and opening"),r("br"),e._v("\nit). Options include:")]),e._v(" "),r("ul",[r("li",[r("p",[e._v("You can use datapipes: "),r("a",{attrs:{href:"http://datapipes.okfnlabs.org/csv/html",target:"_blank",rel:"noopener noreferrer"}},[e._v("http://datapipes.okfnlabs.org/csv/html"),r("OutboundLink")],1)]),e._v(" "),r("p",[e._v("Just paste your CSV file and away you go.")])]),e._v(" "),r("li",[r("p",[e._v("Install this "),r("a",{attrs:{href:"https://chrome.google.com/webstore/detail/recline-csv-viewer/ibfcfelnbfhlbpelldnngdcklnndhael",target:"_blank",rel:"noopener noreferrer"}},[e._v("Chrome Browser Extension"),r("OutboundLink")],1),e._v(". This can be used both"),r("br"),e._v("\nfor online files and for files on your local disk (if you open them with your"),r("br"),e._v("\nbrowser!)")])])]),e._v(" "),r("h3",{attrs:{id:"unix-command-line-manipulation"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#unix-command-line-manipulation"}},[e._v("#")]),e._v(" Unix Command Line Manipulation")]),e._v(" "),r("p",[e._v("See")]),e._v(" "),r("ul",[r("li",[e._v("Using "),r("a",{attrs:{href:"https://github.com/rgrp/command-line-data-wrangling",target:"_blank",rel:"noopener noreferrer"}},[e._v("unix command line tools on CSV"),r("OutboundLink")],1)]),e._v(" "),r("li",[e._v("The wonderful "),r("a",{attrs:{href:"http://csvkit.readthedocs.org/",target:"_blank",rel:"noopener noreferrer"}},[e._v("csvkit"),r("OutboundLink")],1),e._v(" (python)")])]),e._v(" "),r("h3",{attrs:{id:"power-tools"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#power-tools"}},[e._v("#")]),e._v(" Power Tools")]),e._v(" "),r("ul",[r("li",[r("a",{attrs:{href:"http://openrefine.org/",target:"_blank",rel:"noopener noreferrer"}},[e._v("OpenRefine"),r("OutboundLink")],1),e._v(" is a powerful tool for editing and manipulating data and works"),r("br"),e._v("\nvery well with CSV")]),e._v(" "),r("li",[r("a",{attrs:{href:"http://explorer.okfnlabs.org/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Data Explorer"),r("OutboundLink")],1),e._v(" supports importing CSVs and manipulating and changing"),r("br"),e._v("\nthem using javascript in the browser")])]),e._v(" "),r("h3",{attrs:{id:"libraries"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#libraries"}},[e._v("#")]),e._v(" Libraries")]),e._v(" "),r("p",[e._v("This is heavily biased towards python!")]),e._v(" "),r("h4",{attrs:{id:"python"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#python"}},[e._v("#")]),e._v(" Python")]),e._v(" "),r("ul",[r("li",[e._v("Built in csv library is good")]),e._v(" "),r("li",[e._v("The wonderful "),r("a",{attrs:{href:"http://csvkit.readthedocs.org/",target:"_blank",rel:"noopener noreferrer"}},[e._v("csvkit"),r("OutboundLink")],1),e._v(" (python)")]),e._v(" "),r("li",[r("a",{attrs:{href:"http://messytables.readthedocs.org",target:"_blank",rel:"noopener noreferrer"}},[e._v("messytables"),r("OutboundLink")],1),e._v(" (python) - convert lots of badly structured data into CSV (or"),r("br"),e._v("\nother formats)")])]),e._v(" "),r("h4",{attrs:{id:"node"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#node"}},[e._v("#")]),e._v(" Node")]),e._v(" "),r("p",[e._v("Nothing in standard lib yet and best option seems to be:")]),e._v(" "),r("ul",[r("li",[r("a",{attrs:{href:"https://github.com/wdavidw/node-csv",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://github.com/wdavidw/node-csv"),r("OutboundLink")],1)])]),e._v(" "),r("hr"),e._v(" "),r("h2",{attrs:{id:"tips-and-tricks"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#tips-and-tricks"}},[e._v("#")]),e._v(" Tips and Tricks")]),e._v(" "),r("h3",{attrs:{id:"csvs-and-git"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#csvs-and-git"}},[e._v("#")]),e._v(" CSVs and Git")]),e._v(" "),r("p",[e._v("Get git to handle CSV diffs in a sensible way (very useful if you are "),r("a",{attrs:{href:"http://blog.okfn.org/2013/07/02/git-and-github-for-data/",target:"_blank",rel:"noopener noreferrer"}},[e._v("using"),r("br"),e._v("\ngit or another version control system to store data"),r("OutboundLink")],1),e._v(").")]),e._v(" "),r("p",[e._v("Make these changes to config files:")]),e._v(" "),r("div",{staticClass:"language- extra-class"},[r("pre",[r("code",[e._v('# ~/.config/git/attributes\n*.csv diff=csv\n\n# ~/.gitconfig\n[diff "csv"]\n wordRegex = [^,\\n]+[,\\n]|[,]\n')])])]),r("p",[e._v("Then do:")]),e._v(" "),r("div",{staticClass:"language- extra-class"},[r("pre",[r("code",[e._v("git diff --word-diff\n# make it even nicer\ngit diff --word-diff --color-words\n")])])]),r("p",[e._v("Credit for these fixups to "),r("a",{attrs:{href:"http://opendata.stackexchange.com/questions/748/is-there-a-git-for-data",target:"_blank",rel:"noopener noreferrer"}},[e._v("contributors on this question on"),r("br"),e._v("\nStackExchange"),r("OutboundLink")],1),r("br"),e._v("\nand to "),r("a",{attrs:{href:"http://theodi.org/blog/adapting-git-simple-data",target:"_blank",rel:"noopener noreferrer"}},[e._v("James Smith"),r("OutboundLink")],1),e._v(".")])])}),[],!1,null,null,null);t.default=o.exports}}]); \ No newline at end of file +(window.webpackJsonp=window.webpackJsonp||[]).push([[81],{597:function(e,t,r){"use strict";r.r(t);var a=r(29),o=Object(a.a)({},(function(){var e=this,t=e.$createElement,r=e._self._c||t;return r("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[r("p",[e._v("This page provides an overview CSV (Comma Separated Values) format for data.")]),e._v(" "),r("p",[e._v("CSV is a very old, very simple and very common “standard” for (tabular) data."),r("br"),e._v("\nWe say “standard” in quotes because there was never a formal standard for CSV,"),r("br"),e._v("\nthough in 2005 someone did put together a "),r("a",{attrs:{href:"http://tools.ietf.org/html/rfc4180",target:"_blank",rel:"noopener noreferrer"}},[e._v("RFC"),r("OutboundLink")],1),e._v(" for it.")]),e._v(" "),r("p",[e._v("CSV is supported by a "),r("strong",[e._v("huge")]),e._v(" number of tools from spreadsheets like Excel,"),r("br"),e._v("\nOpenOffice and Google Docs to complex databases to almost all programming"),r("br"),e._v("\nlanguages. As such it is probably the most widely supported structured data"),r("br"),e._v("\nformat in the world.")]),e._v(" "),r("hr"),e._v(" "),r("h2",{attrs:{id:"the-format"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#the-format"}},[e._v("#")]),e._v(" The Format")]),e._v(" "),r("p",[e._v("Key points are:")]),e._v(" "),r("ul",[r("li",[e._v("CSV is probably the simplest possible structured format for data")]),e._v(" "),r("li",[e._v("CSV strikes a delicate balance, remaining readable by both machines & humans")]),e._v(" "),r("li",[e._v("CSV is a two dimensional structure consisting of rows of data, each row"),r("br"),e._v("\ncontaining multiple cells. Rows are (usually) separated by line terminators"),r("br"),e._v("\nso each row corresponds to one line. Cells within a row are separated by"),r("br"),e._v("\ncommas (hence the C(ommmas) part)\n"),r("ul",[r("li",[e._v("Note that strictly we’re really talking about DSV files in that we can"),r("br"),e._v("\nallow ‘delimiters’ between cells other than a comma. However, many people"),r("br"),e._v("\nand many programs still call such data CSV (since comma is so common as the"),r("br"),e._v("\ndelimiter)")])])]),e._v(" "),r("li",[e._v("CSV is a “text-based” format, i.e. a CSV file "),r("em",[e._v("is")]),e._v(" a text file. This makes it"),r("br"),e._v("\namenable for processing with all kinds of text-oriented tools (from text"),r("br"),e._v("\neditors to "),r("a",{attrs:{href:"https://github.com/rgrp/command-line-data-wrangling",target:"_blank",rel:"noopener noreferrer"}},[e._v("unix tools like sed, grep etc"),r("OutboundLink")],1),e._v(")")])]),e._v(" "),r("h3",{attrs:{id:"what-a-csv-looks-like"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#what-a-csv-looks-like"}},[e._v("#")]),e._v(" What a CSV looks like")]),e._v(" "),r("p",[e._v("If you open up a CSV file in a text editor it would look something like:")]),e._v(" "),r("div",{staticClass:"language- extra-class"},[r("pre",[r("code",[e._v('A,B,C\n1,2,3\n4,"5,3",6\n')])])]),r("p",[e._v("Here there are 3 rows each of 3 columns. Notice how the second column in the last line is"),r("br"),e._v("\n“quoted” because the content of that value actually contains a “,” character. Without"),r("br"),e._v("\nthe quotes this character would be interpreted as a column separator. To avoid this"),r("br"),e._v("\nconfusion we put quotes around the whole value. The result is that we have 3 rows each"),r("br"),e._v("\nof 3 columns (Note a CSV file does not "),r("em",[e._v("have")]),e._v(" to have"),r("br"),e._v("\nthe same number of columns in each row).")]),e._v(" "),r("h3",{attrs:{id:"dialects-of-csvs"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#dialects-of-csvs"}},[e._v("#")]),e._v(" Dialects of CSVs")]),e._v(" "),r("p",[e._v("As mentioned above, CSV files can have quite a bit of variation in"),r("br"),e._v("\nstructure. Key options are:")]),e._v(" "),r("ul",[r("li",[e._v("Field delimiter: rather than comma "),r("code",[e._v(",")]),e._v(" people often use things like "),r("code",[e._v("\\t")]),r("br"),e._v("\n(tab), "),r("code",[e._v(";")]),e._v(" or "),r("code",[e._v("|")])]),e._v(" "),r("li",[e._v("Record terminator / line terminator: is "),r("code",[e._v("\\n")]),e._v(" (unix), "),r("code",[e._v("\\n\\r")]),e._v(" (dos) or something else …")]),e._v(" "),r("li",[e._v("How do you quote records that contain your delimiter")])]),e._v(" "),r("p",[e._v("You can read more in the "),r("a",{attrs:{href:"https://specs.frictionlessdata.io/csv-dialect/",target:"_blank",rel:"noopener noreferrer"}},[e._v("CSV Dialect Description Format"),r("OutboundLink")],1),e._v(" which defines"),r("br"),e._v("\na small JSON-oriented structure for specifying what options a CSV uses.")]),e._v(" "),r("h3",{attrs:{id:"what-is-missing-in-csv"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#what-is-missing-in-csv"}},[e._v("#")]),e._v(" What is Missing in CSV?")]),e._v(" "),r("ul",[r("li",[e._v("CSV lacks any way to specify type information: that is, there is no way to"),r("br"),e._v("\ndistinguish “1” the string from 1 the number. This shortcoming can be"),r("br"),e._v("\naddressed by adding some form of simple schema. For example "),r("RouterLink",{attrs:{to:"/table-schema/"}},[e._v("Table"),r("br"),e._v("\nSchema")]),e._v(" provides a very simple way to describe your schema externally"),r("br"),e._v("\nwhilst "),r("a",{attrs:{href:"http://jenit.github.io/linked-csv/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Linked CSV"),r("OutboundLink")],1),e._v(" is an example of doing this “inline” (that"),r("br"),e._v("\nis, in the CSV).")],1),e._v(" "),r("li",[e._v("No support for relationships between different “tables”. This is similar to"),r("br"),e._v("\nthe previous point and again "),r("RouterLink",{attrs:{to:"/table-schema/"}},[e._v("Table Schema")]),e._v(" provides a way to address"),r("br"),e._v("\nthis by providing additional schema information externally.")],1),e._v(" "),r("li",[e._v("CSV is really only for tabular data – it is not so good for data with"),r("br"),e._v("\nnesting or where structure is not especially tabular (though remember most"),r("br"),e._v("\ndata can be put into tabular form if you try hard enough!)")])]),e._v(" "),r("h3",{attrs:{id:"links"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#links"}},[e._v("#")]),e._v(" Links")]),e._v(" "),r("p",[e._v("Specifications and overviews:")]),e._v(" "),r("ul",[r("li",[r("a",{attrs:{href:"http://tools.ietf.org/html/rfc4180",target:"_blank",rel:"noopener noreferrer"}},[e._v("RFC specification of CSV"),r("OutboundLink")],1)]),e._v(" "),r("li",[e._v("[CSV Dialect Description Format][csvddf]")]),e._v(" "),r("li",[r("a",{attrs:{href:"http://en.wikipedia.org/wiki/Comma-separated_values",target:"_blank",rel:"noopener noreferrer"}},[e._v("CSV on Wikipedia"),r("OutboundLink")],1)])]),e._v(" "),r("hr"),e._v(" "),r("h2",{attrs:{id:"tools"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#tools"}},[e._v("#")]),e._v(" Tools")]),e._v(" "),r("p",[e._v("The great thing about CSV is the huge level of tool support. The following is"),r("br"),e._v("\nnot intended to be comprehensive but is more at the electic end of the spectrum.")]),e._v(" "),r("h3",{attrs:{id:"desktop"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#desktop"}},[e._v("#")]),e._v(" Desktop")]),e._v(" "),r("p",[e._v("All spreadsheet programs including Excel, OpenOffice, Google Docs"),r("br"),e._v("\nSpreadsheets supporting opening, editing and saving CSVs.")]),e._v(" "),r("h3",{attrs:{id:"view-a-csv-file-in-your-browser"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#view-a-csv-file-in-your-browser"}},[e._v("#")]),e._v(" View a CSV file in your Browser")]),e._v(" "),r("p",[e._v("You can view a CSV file (saving you the hassle of downloading it and opening"),r("br"),e._v("\nit). Options include:")]),e._v(" "),r("ul",[r("li",[r("p",[e._v("You can use datapipes: "),r("a",{attrs:{href:"http://datapipes.okfnlabs.org/csv/html",target:"_blank",rel:"noopener noreferrer"}},[e._v("http://datapipes.okfnlabs.org/csv/html"),r("OutboundLink")],1)]),e._v(" "),r("p",[e._v("Just paste your CSV file and away you go.")])]),e._v(" "),r("li",[r("p",[e._v("Install this "),r("a",{attrs:{href:"https://chrome.google.com/webstore/detail/recline-csv-viewer/ibfcfelnbfhlbpelldnngdcklnndhael",target:"_blank",rel:"noopener noreferrer"}},[e._v("Chrome Browser Extension"),r("OutboundLink")],1),e._v(". This can be used both"),r("br"),e._v("\nfor online files and for files on your local disk (if you open them with your"),r("br"),e._v("\nbrowser!)")])])]),e._v(" "),r("h3",{attrs:{id:"unix-command-line-manipulation"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#unix-command-line-manipulation"}},[e._v("#")]),e._v(" Unix Command Line Manipulation")]),e._v(" "),r("p",[e._v("See")]),e._v(" "),r("ul",[r("li",[e._v("Using "),r("a",{attrs:{href:"https://github.com/rgrp/command-line-data-wrangling",target:"_blank",rel:"noopener noreferrer"}},[e._v("unix command line tools on CSV"),r("OutboundLink")],1)]),e._v(" "),r("li",[e._v("The wonderful "),r("a",{attrs:{href:"http://csvkit.readthedocs.org/",target:"_blank",rel:"noopener noreferrer"}},[e._v("csvkit"),r("OutboundLink")],1),e._v(" (python)")])]),e._v(" "),r("h3",{attrs:{id:"power-tools"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#power-tools"}},[e._v("#")]),e._v(" Power Tools")]),e._v(" "),r("ul",[r("li",[r("a",{attrs:{href:"http://openrefine.org/",target:"_blank",rel:"noopener noreferrer"}},[e._v("OpenRefine"),r("OutboundLink")],1),e._v(" is a powerful tool for editing and manipulating data and works"),r("br"),e._v("\nvery well with CSV")]),e._v(" "),r("li",[r("a",{attrs:{href:"http://explorer.okfnlabs.org/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Data Explorer"),r("OutboundLink")],1),e._v(" supports importing CSVs and manipulating and changing"),r("br"),e._v("\nthem using javascript in the browser")])]),e._v(" "),r("h3",{attrs:{id:"libraries"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#libraries"}},[e._v("#")]),e._v(" Libraries")]),e._v(" "),r("p",[e._v("This is heavily biased towards python!")]),e._v(" "),r("h4",{attrs:{id:"python"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#python"}},[e._v("#")]),e._v(" Python")]),e._v(" "),r("ul",[r("li",[e._v("Built in csv library is good")]),e._v(" "),r("li",[e._v("The wonderful "),r("a",{attrs:{href:"http://csvkit.readthedocs.org/",target:"_blank",rel:"noopener noreferrer"}},[e._v("csvkit"),r("OutboundLink")],1),e._v(" (python)")]),e._v(" "),r("li",[r("a",{attrs:{href:"http://messytables.readthedocs.org",target:"_blank",rel:"noopener noreferrer"}},[e._v("messytables"),r("OutboundLink")],1),e._v(" (python) - convert lots of badly structured data into CSV (or"),r("br"),e._v("\nother formats)")])]),e._v(" "),r("h4",{attrs:{id:"node"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#node"}},[e._v("#")]),e._v(" Node")]),e._v(" "),r("p",[e._v("Nothing in standard lib yet and best option seems to be:")]),e._v(" "),r("ul",[r("li",[r("a",{attrs:{href:"https://github.com/wdavidw/node-csv",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://github.com/wdavidw/node-csv"),r("OutboundLink")],1)])]),e._v(" "),r("hr"),e._v(" "),r("h2",{attrs:{id:"tips-and-tricks"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#tips-and-tricks"}},[e._v("#")]),e._v(" Tips and Tricks")]),e._v(" "),r("h3",{attrs:{id:"csvs-and-git"}},[r("a",{staticClass:"header-anchor",attrs:{href:"#csvs-and-git"}},[e._v("#")]),e._v(" CSVs and Git")]),e._v(" "),r("p",[e._v("Get git to handle CSV diffs in a sensible way (very useful if you are "),r("a",{attrs:{href:"http://blog.okfn.org/2013/07/02/git-and-github-for-data/",target:"_blank",rel:"noopener noreferrer"}},[e._v("using"),r("br"),e._v("\ngit or another version control system to store data"),r("OutboundLink")],1),e._v(").")]),e._v(" "),r("p",[e._v("Make these changes to config files:")]),e._v(" "),r("div",{staticClass:"language- extra-class"},[r("pre",[r("code",[e._v('# ~/.config/git/attributes\n*.csv diff=csv\n\n# ~/.gitconfig\n[diff "csv"]\n wordRegex = [^,\\n]+[,\\n]|[,]\n')])])]),r("p",[e._v("Then do:")]),e._v(" "),r("div",{staticClass:"language- extra-class"},[r("pre",[r("code",[e._v("git diff --word-diff\n# make it even nicer\ngit diff --word-diff --color-words\n")])])]),r("p",[e._v("Credit for these fixups to "),r("a",{attrs:{href:"http://opendata.stackexchange.com/questions/748/is-there-a-git-for-data",target:"_blank",rel:"noopener noreferrer"}},[e._v("contributors on this question on"),r("br"),e._v("\nStackExchange"),r("OutboundLink")],1),r("br"),e._v("\nand to "),r("a",{attrs:{href:"http://theodi.org/blog/adapting-git-simple-data",target:"_blank",rel:"noopener noreferrer"}},[e._v("James Smith"),r("OutboundLink")],1),e._v(".")])])}),[],!1,null,null,null);t.default=o.exports}}]); \ No newline at end of file diff --git a/assets/js/82.ea0e1c19.js b/assets/js/82.2f296314.js similarity index 99% rename from assets/js/82.ea0e1c19.js rename to assets/js/82.2f296314.js index dfcc1bf95..26d2eb22b 100644 --- a/assets/js/82.ea0e1c19.js +++ b/assets/js/82.2f296314.js @@ -1 +1 @@ -(window.webpackJsonp=window.webpackJsonp||[]).push([[82],{601:function(e,t,a){"use strict";a.r(t);var o=a(29),n=Object(o.a)({},(function(){var e=this,t=e.$createElement,a=e._self._c||t;return a("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[a("p",[a("em",[e._v("This grantee profile features Oleg Lavrovsky for our series of Frictionless Data Tool Fund posts, written to shine a light on Frictionless Data’s Tool Fund grantees, their work and to let our technical community know how they can get involved.")])]),e._v(" "),a("p",[e._v("We are digital natives, dazzled by the boundless information and cultural resources of electronic networks, tuned in to a life on - and offline, dimly aware of all kinds of borders being rewritten. I was born in the Soviet Union and grew up in Canada, immersed in the wonders of creative code on Apple II and DOS-era personal computers, doing fun things in programming environments from "),a("a",{attrs:{href:"https://www.scullinsteel.com/apple2/",target:"_blank",rel:"noopener noreferrer"}},[e._v("BASIC"),a("OutboundLink")],1),e._v(" to C++/C#/.NET (hey "),a("a",{attrs:{href:"https://github.com/ooswald",target:"_blank",rel:"noopener noreferrer"}},[e._v("@ooswald"),a("OutboundLink")],1),e._v("!) to Perl (hey "),a("a",{attrs:{href:"https://github.com/virtualsue",target:"_blank",rel:"noopener noreferrer"}},[e._v("@virtualsue"),a("OutboundLink")],1),e._v("!) to Java (hey "),a("a",{attrs:{href:"https://github.com/timcolson",target:"_blank",rel:"noopener noreferrer"}},[e._v("@timcolson"),a("OutboundLink")],1),e._v("!) to JavaScript (hey "),a("a",{attrs:{href:"https://github.com/jermolene",target:"_blank",rel:"noopener noreferrer"}},[e._v("@jermolene"),a("OutboundLink")],1),e._v("!) to Python (hey "),a("a",{attrs:{href:"https://github.com/gasman",target:"_blank",rel:"noopener noreferrer"}},[e._v("@gasman"),a("OutboundLink")],1),e._v("!), all of which find some use in the freelance work I now do based in my adoptive home of Switzerland - a country of "),a("a",{attrs:{href:"https://en.wikipedia.org/wiki/Swiss_people",target:"_blank",rel:"noopener noreferrer"}},[e._v("plurality"),a("OutboundLink")],1),e._v(".")]),e._v(" "),a("p",[e._v("Over the years, I have tried other languages like Clojure and Pascal, Groovy and Go, Erlang and Haskell, Scala and R, even ARM C/C++ and x86 assembly. Some have stuck in my dev chain, others have not. As far as possible, I hope to keep a beginner’s mind open to new paradigms, a solid craft of working on code and data with care, and the wisdom to avoid jumping off every tempting new thing on the horizon.")]),e._v(" "),a("p",[e._v("I first came across tendrils of Open Knowledge ten years ago while living in Oxford, a vibrant community of thinkers and civic reformers. After we started a "),a("a",{attrs:{href:"https://oxhack.org/",target:"_blank",rel:"noopener noreferrer"}},[e._v("hackspace"),a("OutboundLink")],1),e._v(", I got more involved in extracurricular open source activities, joined barcamps and hackathons, started contributing to projects. I started to see so-called ‘big IT’ or ‘enterprise software’ challenges to be, on many levels, problems of incompatible or intractable data standards. It was in the U.K. that I also discovered civic tech and open data activism.")]),e._v(" "),a("p",[e._v("Helping to start a Swiss "),a("a",{attrs:{href:"http://make.opendata.ch/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Open Knowledge chapter"),a("OutboundLink")],1),e._v(" presented me with the opportunity to be involved in an ambitious and exciting techno-political movement, and to learn from some of the most deeply ethical and forward-thinking people in Information Technology. Running the "),a("a",{attrs:{href:"http://forum.schoolofdata.ch/",target:"_blank",rel:"noopener noreferrer"}},[e._v("School of Data"),a("OutboundLink")],1),e._v(" working group and supporting many projects in the Swiss "),a("a",{attrs:{href:"https://opendata.ch",target:"_blank",rel:"noopener noreferrer"}},[e._v("Opendata.ch"),a("OutboundLink")],1),e._v(" association and international network is today no longer just a weekend activity: it is my "),a("code",[e._v("master")]),e._v(" branch.")]),e._v(" "),a("p",[e._v("I first heard the term "),a("em",[e._v("frictionless")]),e._v(" from a "),a("a",{attrs:{href:"https://andrewjtaggart.com/",target:"_blank",rel:"noopener noreferrer"}},[e._v("philosopher"),a("OutboundLink")],1),e._v(" who warned of a world where IT removes friction to the point where we live anywhere, and do anything, at the cost of social alienation - and, along with it, grave consequences to our well-being. There are parallels here to “closed datasets”, which may well be padlocked for a reason. Throwing them into the wind may deprive them of the nurturing care of the original owners. The open data community offers them a softer landing.")]),e._v(" "),a("p",[e._v("Some of the conversations that led to "),a("em",[e._v("Frictionless Data")]),e._v(" took place at "),a("a",{attrs:{href:"https://opendata.ch/2013/09/okcon-2013-some-swiss-highlights/",target:"_blank",rel:"noopener noreferrer"}},[e._v("OKCon 2013"),a("OutboundLink")],1),e._v(" in Geneva, where I was busy "),a("a",{attrs:{href:"https://make.opendata.ch/legal/",target:"_blank",rel:"noopener noreferrer"}},[e._v("mining the Law"),a("OutboundLink")],1),e._v(". Max Ogden mentioned related ideas in his "),a("a",{attrs:{href:"https://vimeo.com/channels/okcon2013/79932550",target:"_blank",rel:"noopener noreferrer"}},[e._v("talk"),a("OutboundLink")],1),e._v(" there on "),a("a",{attrs:{href:"https://datproject.org/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Dat Project"),a("OutboundLink")],1),e._v(". It later became a regular topic in the "),a("a",{attrs:{href:"http://okfnlabs.org/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Open Knowledge Labs hangouts"),a("OutboundLink")],1),e._v(" and elsewhere. My first impression was mixed: I liked the idea in principle, but found it hard to foresee what the standardization process could accomplish. It took me a couple of years to catch up, gain experience in putting the "),a("a",{attrs:{href:"http://opendefinition.org/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Open Definition"),a("OutboundLink")],1),e._v(" to use, struggle with some of the fundamental issues myself - just to wholly accept the idea of an open data ecosystem.")]),e._v(" "),a("p",[e._v("Working with more unwieldy data as well as having an interest in Data Science, and the great vibe of a growing community all led me to test the waters with the "),a("a",{attrs:{href:"https://julialang.org/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Julia language"),a("OutboundLink")],1),e._v(". I quickly became a fan, and started looking for ways to include it in my workflow. Thanks to the collaboration enabled by the Frictionless Data Tool Fund, I will now be able to focus on this goal and start connecting the dots more quickly. More bridges need to be built to help open data users use Julia’s computing environment, and Julia users could use sturdier access to open data.")]),e._v(" "),a("p",[e._v("There are two high level use cases which I think are particularly interesting when it comes to Frictionless Data: strongly typed and easy to validate dataset schema leading to a “light” version of semantic interoperability, helping data analysts, developers, even automated agents, to see at a glance how compatible datasets might be. Take a look at "),a("RouterLink",{attrs:{to:"/blog/2016/11/15/dataship/"}},[e._v("dataship")]),e._v(", "),a("RouterLink",{attrs:{to:"/blog/2016/11/15/open-power-system-data/"}},[e._v("open power system data")]),e._v(" and other case studies at "),a("RouterLink",{attrs:{to:"/"}},[e._v("Frictionlessdata.io")]),e._v(" for examples. The other is the pipelines approach which, as a "),a("a",{attrs:{href:"https://en.wikipedia.org/wiki/Pipeline_(Unix)"}},[e._v(" feature of Unix")]),e._v(" and "),a("a",{attrs:{href:"https://docs.microsoft.com/en-us/powershell/scripting/learn/understanding-the-powershell-pipeline?view=powershell-7",target:"_blank",rel:"noopener noreferrer"}},[e._v("other OS"),a("OutboundLink")],1),e._v(" is the basis for an incredibly powerful system building tool, now laying the foundation of a rich and reliable world of "),a("a",{attrs:{href:"http://datahub.io/blog/core-data-essential-datasets-for-data-wranglers-and-data-scientists",target:"_blank",rel:"noopener noreferrer"}},[e._v("shared data"),a("OutboundLink")],1),e._v(".")],1),e._v(" "),a("p",[e._v("At a more practical level, I have been using Data Packages to publish data for "),a("a",{attrs:{href:"http://hack.opendata.ch",target:"_blank",rel:"noopener noreferrer"}},[e._v("hackathons"),a("OutboundLink")],1),e._v(", School of Data "),a("a",{attrs:{href:"http://schoolofdata.ch",target:"_blank",rel:"noopener noreferrer"}},[e._v("workshops"),a("OutboundLink")],1),e._v(" and other activities in my Open Knowledge chapter, and regularly explaining the concepts and training people to use Frictionless Data tools in the Open Data module I teach at the "),a("a",{attrs:{href:"https://www.bfh.ch/en/home.html",target:"_blank",rel:"noopener noreferrer"}},[e._v("Bern University of Applied Sciences"),a("OutboundLink")],1),e._v(". I have built support for them into "),a("a",{attrs:{href:"http://datalets.ch/dribdat",target:"_blank",rel:"noopener noreferrer"}},[e._v("Dribdat"),a("OutboundLink")],1),e._v(", a tool we use for connecting the dots between people, code and data.")]),e._v(" "),a("p",[e._v("Over the years, I have made small contributions to OKI’s codebases on projects like "),a("a",{attrs:{href:"https://ckan.org/",target:"_blank",rel:"noopener noreferrer"}},[e._v("CKAN"),a("OutboundLink")],1),e._v(". Contributing to the Frictionless Data project clears the way to the frontlines of development: putting better tools in users’ hands, committing directly to the needs of the community, setting an elevated expectation of responsibility and quality. That said, I am a novice in Julia. But my initial ambition is modest: make a working set of tools, produce a stable "),a("a",{attrs:{href:"https://blog.okfn.org/2017/09/05/frictionless-data-v1-0/",target:"_blank",rel:"noopener noreferrer"}},[e._v("v1.0 specification"),a("OutboundLink")],1),e._v(" release. Run tests, get reviewed, interact with the community, and iterate. This project will be a learning process, and my intention is to widen the goalposts as much as I can for others to follow.")]),e._v(" "),a("p",[e._v("The Julia language also needs to be better known, so I will start threads on the "),a("a",{attrs:{href:"https://discuss.okfn.org/u/loleg",target:"_blank",rel:"noopener noreferrer"}},[e._v("OKI forums"),a("OutboundLink")],1),e._v(", at the "),a("a",{attrs:{href:"https://schoolofdata.org/",target:"_blank",rel:"noopener noreferrer"}},[e._v("School of Data"),a("OutboundLink")],1),e._v(", in technical and academic circles. I am likewise really looking forward to representing Frictionless Data in the diverse and wide-ranging "),a("a",{attrs:{href:"https://julialang.org/community/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Julia community"),a("OutboundLink")],1),e._v(", sharing whatever questions and needs arise both ways. The specifications, libraries and tools will help to preserve key information on widely used datasets, foster a more in-depth technical discussion between everyone involved in data sharing, and open the door to more critical feedback loops between creators, publishers and users of open data.")]),e._v(" "),a("p",[e._v("I will be developing the "),a("a",{attrs:{href:"https://github.com/loleg/datapackage-jl",target:"_blank",rel:"noopener noreferrer"}},[e._v("datapackage-jl"),a("OutboundLink")],1),e._v(" and "),a("a",{attrs:{href:"https://github.com/loleg/tableschema-jl",target:"_blank",rel:"noopener noreferrer"}},[e._v("tableschema-jl"),a("OutboundLink")],1),e._v(" libraries on GitHub, and you can follow me on "),a("a",{attrs:{href:"http://github.com/loleg/",target:"_blank",rel:"noopener noreferrer"}},[e._v("GitHub"),a("OutboundLink")],1),e._v(" to see how this develops and read stories about putting Frictionless Data libraries to use. Please feel free to "),a("a",{attrs:{href:"http://datalets.ch/",target:"_blank",rel:"noopener noreferrer"}},[e._v("write me a note"),a("OutboundLink")],1),e._v(", send in your use case, respond to anything I’m working on or writing about, share a tricky dataset or any other kind of challenge - and "),a("a",{attrs:{href:"https://gitter.im/frictionlessdata/chat",target:"_blank",rel:"noopener noreferrer"}},[e._v("let’s chat"),a("OutboundLink")],1),e._v("!")])])}),[],!1,null,null,null);t.default=n.exports}}]); \ No newline at end of file +(window.webpackJsonp=window.webpackJsonp||[]).push([[82],{600:function(e,t,a){"use strict";a.r(t);var o=a(29),n=Object(o.a)({},(function(){var e=this,t=e.$createElement,a=e._self._c||t;return a("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[a("p",[a("em",[e._v("This grantee profile features Oleg Lavrovsky for our series of Frictionless Data Tool Fund posts, written to shine a light on Frictionless Data’s Tool Fund grantees, their work and to let our technical community know how they can get involved.")])]),e._v(" "),a("p",[e._v("We are digital natives, dazzled by the boundless information and cultural resources of electronic networks, tuned in to a life on - and offline, dimly aware of all kinds of borders being rewritten. I was born in the Soviet Union and grew up in Canada, immersed in the wonders of creative code on Apple II and DOS-era personal computers, doing fun things in programming environments from "),a("a",{attrs:{href:"https://www.scullinsteel.com/apple2/",target:"_blank",rel:"noopener noreferrer"}},[e._v("BASIC"),a("OutboundLink")],1),e._v(" to C++/C#/.NET (hey "),a("a",{attrs:{href:"https://github.com/ooswald",target:"_blank",rel:"noopener noreferrer"}},[e._v("@ooswald"),a("OutboundLink")],1),e._v("!) to Perl (hey "),a("a",{attrs:{href:"https://github.com/virtualsue",target:"_blank",rel:"noopener noreferrer"}},[e._v("@virtualsue"),a("OutboundLink")],1),e._v("!) to Java (hey "),a("a",{attrs:{href:"https://github.com/timcolson",target:"_blank",rel:"noopener noreferrer"}},[e._v("@timcolson"),a("OutboundLink")],1),e._v("!) to JavaScript (hey "),a("a",{attrs:{href:"https://github.com/jermolene",target:"_blank",rel:"noopener noreferrer"}},[e._v("@jermolene"),a("OutboundLink")],1),e._v("!) to Python (hey "),a("a",{attrs:{href:"https://github.com/gasman",target:"_blank",rel:"noopener noreferrer"}},[e._v("@gasman"),a("OutboundLink")],1),e._v("!), all of which find some use in the freelance work I now do based in my adoptive home of Switzerland - a country of "),a("a",{attrs:{href:"https://en.wikipedia.org/wiki/Swiss_people",target:"_blank",rel:"noopener noreferrer"}},[e._v("plurality"),a("OutboundLink")],1),e._v(".")]),e._v(" "),a("p",[e._v("Over the years, I have tried other languages like Clojure and Pascal, Groovy and Go, Erlang and Haskell, Scala and R, even ARM C/C++ and x86 assembly. Some have stuck in my dev chain, others have not. As far as possible, I hope to keep a beginner’s mind open to new paradigms, a solid craft of working on code and data with care, and the wisdom to avoid jumping off every tempting new thing on the horizon.")]),e._v(" "),a("p",[e._v("I first came across tendrils of Open Knowledge ten years ago while living in Oxford, a vibrant community of thinkers and civic reformers. After we started a "),a("a",{attrs:{href:"https://oxhack.org/",target:"_blank",rel:"noopener noreferrer"}},[e._v("hackspace"),a("OutboundLink")],1),e._v(", I got more involved in extracurricular open source activities, joined barcamps and hackathons, started contributing to projects. I started to see so-called ‘big IT’ or ‘enterprise software’ challenges to be, on many levels, problems of incompatible or intractable data standards. It was in the U.K. that I also discovered civic tech and open data activism.")]),e._v(" "),a("p",[e._v("Helping to start a Swiss "),a("a",{attrs:{href:"http://make.opendata.ch/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Open Knowledge chapter"),a("OutboundLink")],1),e._v(" presented me with the opportunity to be involved in an ambitious and exciting techno-political movement, and to learn from some of the most deeply ethical and forward-thinking people in Information Technology. Running the "),a("a",{attrs:{href:"http://forum.schoolofdata.ch/",target:"_blank",rel:"noopener noreferrer"}},[e._v("School of Data"),a("OutboundLink")],1),e._v(" working group and supporting many projects in the Swiss "),a("a",{attrs:{href:"https://opendata.ch",target:"_blank",rel:"noopener noreferrer"}},[e._v("Opendata.ch"),a("OutboundLink")],1),e._v(" association and international network is today no longer just a weekend activity: it is my "),a("code",[e._v("master")]),e._v(" branch.")]),e._v(" "),a("p",[e._v("I first heard the term "),a("em",[e._v("frictionless")]),e._v(" from a "),a("a",{attrs:{href:"https://andrewjtaggart.com/",target:"_blank",rel:"noopener noreferrer"}},[e._v("philosopher"),a("OutboundLink")],1),e._v(" who warned of a world where IT removes friction to the point where we live anywhere, and do anything, at the cost of social alienation - and, along with it, grave consequences to our well-being. There are parallels here to “closed datasets”, which may well be padlocked for a reason. Throwing them into the wind may deprive them of the nurturing care of the original owners. The open data community offers them a softer landing.")]),e._v(" "),a("p",[e._v("Some of the conversations that led to "),a("em",[e._v("Frictionless Data")]),e._v(" took place at "),a("a",{attrs:{href:"https://opendata.ch/2013/09/okcon-2013-some-swiss-highlights/",target:"_blank",rel:"noopener noreferrer"}},[e._v("OKCon 2013"),a("OutboundLink")],1),e._v(" in Geneva, where I was busy "),a("a",{attrs:{href:"https://make.opendata.ch/legal/",target:"_blank",rel:"noopener noreferrer"}},[e._v("mining the Law"),a("OutboundLink")],1),e._v(". Max Ogden mentioned related ideas in his "),a("a",{attrs:{href:"https://vimeo.com/channels/okcon2013/79932550",target:"_blank",rel:"noopener noreferrer"}},[e._v("talk"),a("OutboundLink")],1),e._v(" there on "),a("a",{attrs:{href:"https://datproject.org/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Dat Project"),a("OutboundLink")],1),e._v(". It later became a regular topic in the "),a("a",{attrs:{href:"http://okfnlabs.org/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Open Knowledge Labs hangouts"),a("OutboundLink")],1),e._v(" and elsewhere. My first impression was mixed: I liked the idea in principle, but found it hard to foresee what the standardization process could accomplish. It took me a couple of years to catch up, gain experience in putting the "),a("a",{attrs:{href:"http://opendefinition.org/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Open Definition"),a("OutboundLink")],1),e._v(" to use, struggle with some of the fundamental issues myself - just to wholly accept the idea of an open data ecosystem.")]),e._v(" "),a("p",[e._v("Working with more unwieldy data as well as having an interest in Data Science, and the great vibe of a growing community all led me to test the waters with the "),a("a",{attrs:{href:"https://julialang.org/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Julia language"),a("OutboundLink")],1),e._v(". I quickly became a fan, and started looking for ways to include it in my workflow. Thanks to the collaboration enabled by the Frictionless Data Tool Fund, I will now be able to focus on this goal and start connecting the dots more quickly. More bridges need to be built to help open data users use Julia’s computing environment, and Julia users could use sturdier access to open data.")]),e._v(" "),a("p",[e._v("There are two high level use cases which I think are particularly interesting when it comes to Frictionless Data: strongly typed and easy to validate dataset schema leading to a “light” version of semantic interoperability, helping data analysts, developers, even automated agents, to see at a glance how compatible datasets might be. Take a look at "),a("RouterLink",{attrs:{to:"/blog/2016/11/15/dataship/"}},[e._v("dataship")]),e._v(", "),a("RouterLink",{attrs:{to:"/blog/2016/11/15/open-power-system-data/"}},[e._v("open power system data")]),e._v(" and other case studies at "),a("RouterLink",{attrs:{to:"/"}},[e._v("Frictionlessdata.io")]),e._v(" for examples. The other is the pipelines approach which, as a "),a("a",{attrs:{href:"https://en.wikipedia.org/wiki/Pipeline_(Unix)"}},[e._v(" feature of Unix")]),e._v(" and "),a("a",{attrs:{href:"https://docs.microsoft.com/en-us/powershell/scripting/learn/understanding-the-powershell-pipeline?view=powershell-7",target:"_blank",rel:"noopener noreferrer"}},[e._v("other OS"),a("OutboundLink")],1),e._v(" is the basis for an incredibly powerful system building tool, now laying the foundation of a rich and reliable world of "),a("a",{attrs:{href:"http://datahub.io/blog/core-data-essential-datasets-for-data-wranglers-and-data-scientists",target:"_blank",rel:"noopener noreferrer"}},[e._v("shared data"),a("OutboundLink")],1),e._v(".")],1),e._v(" "),a("p",[e._v("At a more practical level, I have been using Data Packages to publish data for "),a("a",{attrs:{href:"http://hack.opendata.ch",target:"_blank",rel:"noopener noreferrer"}},[e._v("hackathons"),a("OutboundLink")],1),e._v(", School of Data "),a("a",{attrs:{href:"http://schoolofdata.ch",target:"_blank",rel:"noopener noreferrer"}},[e._v("workshops"),a("OutboundLink")],1),e._v(" and other activities in my Open Knowledge chapter, and regularly explaining the concepts and training people to use Frictionless Data tools in the Open Data module I teach at the "),a("a",{attrs:{href:"https://www.bfh.ch/en/home.html",target:"_blank",rel:"noopener noreferrer"}},[e._v("Bern University of Applied Sciences"),a("OutboundLink")],1),e._v(". I have built support for them into "),a("a",{attrs:{href:"http://datalets.ch/dribdat",target:"_blank",rel:"noopener noreferrer"}},[e._v("Dribdat"),a("OutboundLink")],1),e._v(", a tool we use for connecting the dots between people, code and data.")]),e._v(" "),a("p",[e._v("Over the years, I have made small contributions to OKI’s codebases on projects like "),a("a",{attrs:{href:"https://ckan.org/",target:"_blank",rel:"noopener noreferrer"}},[e._v("CKAN"),a("OutboundLink")],1),e._v(". Contributing to the Frictionless Data project clears the way to the frontlines of development: putting better tools in users’ hands, committing directly to the needs of the community, setting an elevated expectation of responsibility and quality. That said, I am a novice in Julia. But my initial ambition is modest: make a working set of tools, produce a stable "),a("a",{attrs:{href:"https://blog.okfn.org/2017/09/05/frictionless-data-v1-0/",target:"_blank",rel:"noopener noreferrer"}},[e._v("v1.0 specification"),a("OutboundLink")],1),e._v(" release. Run tests, get reviewed, interact with the community, and iterate. This project will be a learning process, and my intention is to widen the goalposts as much as I can for others to follow.")]),e._v(" "),a("p",[e._v("The Julia language also needs to be better known, so I will start threads on the "),a("a",{attrs:{href:"https://discuss.okfn.org/u/loleg",target:"_blank",rel:"noopener noreferrer"}},[e._v("OKI forums"),a("OutboundLink")],1),e._v(", at the "),a("a",{attrs:{href:"https://schoolofdata.org/",target:"_blank",rel:"noopener noreferrer"}},[e._v("School of Data"),a("OutboundLink")],1),e._v(", in technical and academic circles. I am likewise really looking forward to representing Frictionless Data in the diverse and wide-ranging "),a("a",{attrs:{href:"https://julialang.org/community/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Julia community"),a("OutboundLink")],1),e._v(", sharing whatever questions and needs arise both ways. The specifications, libraries and tools will help to preserve key information on widely used datasets, foster a more in-depth technical discussion between everyone involved in data sharing, and open the door to more critical feedback loops between creators, publishers and users of open data.")]),e._v(" "),a("p",[e._v("I will be developing the "),a("a",{attrs:{href:"https://github.com/loleg/datapackage-jl",target:"_blank",rel:"noopener noreferrer"}},[e._v("datapackage-jl"),a("OutboundLink")],1),e._v(" and "),a("a",{attrs:{href:"https://github.com/loleg/tableschema-jl",target:"_blank",rel:"noopener noreferrer"}},[e._v("tableschema-jl"),a("OutboundLink")],1),e._v(" libraries on GitHub, and you can follow me on "),a("a",{attrs:{href:"http://github.com/loleg/",target:"_blank",rel:"noopener noreferrer"}},[e._v("GitHub"),a("OutboundLink")],1),e._v(" to see how this develops and read stories about putting Frictionless Data libraries to use. Please feel free to "),a("a",{attrs:{href:"http://datalets.ch/",target:"_blank",rel:"noopener noreferrer"}},[e._v("write me a note"),a("OutboundLink")],1),e._v(", send in your use case, respond to anything I’m working on or writing about, share a tricky dataset or any other kind of challenge - and "),a("a",{attrs:{href:"https://gitter.im/frictionlessdata/chat",target:"_blank",rel:"noopener noreferrer"}},[e._v("let’s chat"),a("OutboundLink")],1),e._v("!")])])}),[],!1,null,null,null);t.default=n.exports}}]); \ No newline at end of file diff --git a/assets/js/83.74731cae.js b/assets/js/83.c4c2156e.js similarity index 99% rename from assets/js/83.74731cae.js rename to assets/js/83.c4c2156e.js index c85abd79b..004b8b66f 100644 --- a/assets/js/83.74731cae.js +++ b/assets/js/83.c4c2156e.js @@ -1 +1 @@ -(window.webpackJsonp=window.webpackJsonp||[]).push([[83],{603:function(e,t,o){"use strict";o.r(t);var a=o(29),r=Object(a.a)({},(function(){var e=this,t=e.$createElement,o=e._self._c||t;return o("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[o("p",[o("em",[e._v("This grantee profile features Ori Hoch for our series of Frictionless Data Tool Fund posts, written to shine a light on Frictionless Data’s Tool Fund grantees, their work and to let our technical community know how they can get involved.")])]),e._v(" "),o("p",[e._v("My name is Ori Hoch, I am 35 years old, living in Israel and married with 2 children. I recently took my family to Midburn - the Israeli regional Burning Man event where I juggled some fire clubs in the main burn ceremony. Through the Tool Fund, I am working on implementing the PHP libraries for Frictionless Data. I am also working on several other open source data projects: "),o("a",{attrs:{href:"https://github.com/hasadna/Open-Knesset",target:"_blank",rel:"noopener noreferrer"}},[e._v("Open Knesset"),o("OutboundLink")],1),e._v(", "),o("a",{attrs:{href:"https://github.com/OpenBudget/budgetkey-data-pipelines",target:"_blank",rel:"noopener noreferrer"}},[e._v("Open Budget"),o("OutboundLink")],1),e._v(", "),o("a",{attrs:{href:"https://github.com/beit-hatfutsot",target:"_blank",rel:"noopener noreferrer"}},[e._v("Beit Hatfutsot"),o("OutboundLink")],1),e._v(" - all projects are open source and fully transparent - both the code and the development process - which I think is a great way to work. I’m also very interested in community and teamwork - how to get a group of people working together on a common goal, a hard task in normal scenarios which grows even more complex when dealing with volunteers / open source contributors. Of course, besides all the philosophical ideals I’m also a hard-core technologist who loves diving into complex problems, finding and implementing the right solution.")]),e._v(" "),o("p",[e._v("I first heard about the Frictionless Data ecosystem from my activity in "),o("a",{attrs:{href:"http://www.hasadna.org.il/en/",target:"_blank",rel:"noopener noreferrer"}},[e._v("The Public Knowledge Workshop"),o("OutboundLink")],1),e._v(" where I worked with Adam Kariv and Paul Walsh. I have a lot of experience working with data, and know many of the common problems and pitfalls. One of the major obstacles is interoperability between different data sources. Having the core Frictionless Data libraries available in different languages will allow for easier interoperability and integrations between sources.")]),e._v(" "),o("p",[e._v("At "),o("a",{attrs:{href:"http://www.bh.org.il/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Beit Hatfutsot"),o("OutboundLink")],1),e._v(" (The Museum of The Jewish People), we aggregate data from many sources, including some data from PHP frameworks such as MediaWiki / Wordpress. At the moment we ask developers of the external data sources to create a datapackage for us, based on a given schema. Frictionless Data libraries for PHP will make this much easier for people to do, and will have a huge effect in reducing errors.")]),e._v(" "),o("p",[e._v("In addition to interoperability, the Frictionless Data specifications and software are based on the combined experience of many individuals working on a variety of data projects. Anyone using the libraries and tools will benefit from these experiences and will avoid problems and pitfalls which other people encountered in the past.")]),e._v(" "),o("p",[e._v("I welcome PHP enthusiasts to join in the development effort of the "),o("a",{attrs:{href:"https://github.com/frictionlessdata/tableschema-php",target:"_blank",rel:"noopener noreferrer"}},[e._v("tableschema"),o("OutboundLink")],1),e._v(" and "),o("a",{attrs:{href:"https://github.com/frictionlessdata/datapackage-php",target:"_blank",rel:"noopener noreferrer"}},[e._v("datapackage"),o("OutboundLink")],1),e._v(" libraries which I am currently working on. Both repositories follow standard GitHub development flow using Issues, Pull Requests, Releases et al. Check the README and CONTRIBUTING files in the repositories above for more details and reach out to me or the rest of the Frictionless Data developer community on "),o("a",{attrs:{href:"https://gitter.im/frictionlessdata/chat",target:"_blank",rel:"noopener noreferrer"}},[e._v("the active Gitter channel"),o("OutboundLink")],1),e._v(".")]),e._v(" "),o("p",[e._v("I would also love to have PHP developers use the core libraries to write some more high-level tools. For example - consider an organization which has some data in their Wordpress / Drupal installation which they would like to publish or use with Frictionless Data compatible tools. Without a compatible plugin for their framework it would require them to either write some custom code or create the datapackage manually - both options are time consuming and error prone. 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Allowing the developer to focus on the plugin logic.")]),e._v(" "),o("p",[e._v("Additional possibilities for leveraging the PHP libraries:")]),e._v(" "),o("ul",[o("li",[e._v("Import plugins - for loading datapackages into a data store")]),e._v(" "),o("li",[e._v("visualization tools to allow people to view and analyze data packages from PHP based code")]),e._v(" "),o("li",[e._v("Integration of existing Frictionless Data to be available for use from PHP, for example the "),o("a",{attrs:{href:"https://github.com/frictionlessdata/datapackage-pipelines",target:"_blank",rel:"noopener noreferrer"}},[e._v("datapackage-pipelines"),o("OutboundLink")],1),e._v(" framework")])]),e._v(" "),o("p",[e._v("Finally, I would like to thank Open Knowledge International and The Sloan foundation for the opportunity to work on the forefront of the open data eco-system. I think that the tools we are developing now will have tremendous effects on how we manage and use data in the future and we have not yet seen all the possible benefits and outcomes from this work.")])])}),[],!1,null,null,null);t.default=r.exports}}]); \ No newline at end of file +(window.webpackJsonp=window.webpackJsonp||[]).push([[83],{601:function(e,t,o){"use strict";o.r(t);var a=o(29),r=Object(a.a)({},(function(){var e=this,t=e.$createElement,o=e._self._c||t;return o("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[o("p",[o("em",[e._v("This grantee profile features Ori Hoch for our series of Frictionless Data Tool Fund posts, written to shine a light on Frictionless Data’s Tool Fund grantees, their work and to let our technical community know how they can get involved.")])]),e._v(" "),o("p",[e._v("My name is Ori Hoch, I am 35 years old, living in Israel and married with 2 children. I recently took my family to Midburn - the Israeli regional Burning Man event where I juggled some fire clubs in the main burn ceremony. Through the Tool Fund, I am working on implementing the PHP libraries for Frictionless Data. I am also working on several other open source data projects: "),o("a",{attrs:{href:"https://github.com/hasadna/Open-Knesset",target:"_blank",rel:"noopener noreferrer"}},[e._v("Open Knesset"),o("OutboundLink")],1),e._v(", "),o("a",{attrs:{href:"https://github.com/OpenBudget/budgetkey-data-pipelines",target:"_blank",rel:"noopener noreferrer"}},[e._v("Open Budget"),o("OutboundLink")],1),e._v(", "),o("a",{attrs:{href:"https://github.com/beit-hatfutsot",target:"_blank",rel:"noopener noreferrer"}},[e._v("Beit Hatfutsot"),o("OutboundLink")],1),e._v(" - all projects are open source and fully transparent - both the code and the development process - which I think is a great way to work. I’m also very interested in community and teamwork - how to get a group of people working together on a common goal, a hard task in normal scenarios which grows even more complex when dealing with volunteers / open source contributors. Of course, besides all the philosophical ideals I’m also a hard-core technologist who loves diving into complex problems, finding and implementing the right solution.")]),e._v(" "),o("p",[e._v("I first heard about the Frictionless Data ecosystem from my activity in "),o("a",{attrs:{href:"http://www.hasadna.org.il/en/",target:"_blank",rel:"noopener noreferrer"}},[e._v("The Public Knowledge Workshop"),o("OutboundLink")],1),e._v(" where I worked with Adam Kariv and Paul Walsh. I have a lot of experience working with data, and know many of the common problems and pitfalls. One of the major obstacles is interoperability between different data sources. Having the core Frictionless Data libraries available in different languages will allow for easier interoperability and integrations between sources.")]),e._v(" "),o("p",[e._v("At "),o("a",{attrs:{href:"http://www.bh.org.il/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Beit Hatfutsot"),o("OutboundLink")],1),e._v(" (The Museum of The Jewish People), we aggregate data from many sources, including some data from PHP frameworks such as MediaWiki / Wordpress. At the moment we ask developers of the external data sources to create a datapackage for us, based on a given schema. Frictionless Data libraries for PHP will make this much easier for people to do, and will have a huge effect in reducing errors.")]),e._v(" "),o("p",[e._v("In addition to interoperability, the Frictionless Data specifications and software are based on the combined experience of many individuals working on a variety of data projects. 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Therefore, make sure you read the "),t("a",{attrs:{href:"#key-tips"}},[e._v("Key Tips")]),e._v(" section below.")]),e._v(" "),t("h2",{attrs:{id:"key-tips"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#key-tips"}},[e._v("#")]),e._v(" Key Tips")]),e._v(" "),t("p",[e._v("However you publish your Data Package there are a few key points to keep in"),t("br"),e._v("\nmind:")]),e._v(" "),t("ul",[t("li",[t("p",[e._v("All the files in the Data Package should be accessible online")])]),e._v(" "),t("li",[t("p",[e._v("The structure of your Data Package should be preserved. Specifically the paths between your "),t("code",[e._v("datapackage.json")]),e._v(" and the data files must be preserved. For example, if your Data Package directory looked like this on disk:")]),e._v(" "),t("div",{staticClass:"language- extra-class"},[t("pre",[t("code",[e._v("datapackage.json\ndata.csv\nsomedir/other-data.csv\n")])])]),t("p",[e._v("then online it should look like:")]),e._v(" "),t("div",{staticClass:"language- extra-class"},[t("pre",[t("code",[e._v("http://your.website.com/mydatapackage/datapackage.json\nhttp://your.website.com/mydatapackage/data.csv\nhttp://your.website.com/mydatapackage/somedir/other-data.csv\n")])])]),t("p",[e._v("This can be a problem with services like e.g. Google Drive where files in a given folder don’t have a web address that relates to that folder. The reason we need to preserve relative paths is that when using the Data Package client software will compute the full path from the location of the "),t("code",[e._v("datapackage.json")]),e._v(" itself plus the relative path for the file give in the "),t("code",[e._v("datapackage.json")]),e._v(" resources section.")])])]),e._v(" "),t("p",[e._v("Recommended reading: Find out how to use Frictionless Data software to improve your data publishing workflow in our new and comprehensive "),t("a",{attrs:{href:"/tag/field-guide"}},[e._v("Frictionless Data Field Guide")]),e._v(".")])])}),[],!1,null,null,null);a.default=r.exports}}]); \ No newline at end of file diff --git a/assets/js/88.151550eb.js b/assets/js/88.35b64325.js similarity index 98% rename from assets/js/88.151550eb.js rename to assets/js/88.35b64325.js index 8ef3f1e78..71437fb85 100644 --- a/assets/js/88.151550eb.js +++ b/assets/js/88.35b64325.js @@ -1 +1 @@ -(window.webpackJsonp=window.webpackJsonp||[]).push([[88],{612:function(e,t,a){"use strict";a.r(t);var o=a(29),n=Object(o.a)({},(function(){var e=this,t=e.$createElement,a=e._self._c||t;return a("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[a("p",[e._v("This grantee profile features Stephan Max for our series of Frictionless Data Tool Fund posts, written to shine a light on Frictionless Data’s Tool Fund grantees, their work and to let our technical community know how they can get involved.")]),e._v(" "),a("h3",{attrs:{id:"meet-stephan-max"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#meet-stephan-max"}},[e._v("#")]),e._v(" Meet Stephan Max")]),e._v(" "),a("p",[e._v("Hi, my name is Stephan Max and I am a computer scientist based in Cologne, Germany. I’ve been in the industry for over 10 years now and worked for all kinds of companies, ranging from own startup (crowd-funded online journalism), over big corporate (IBM), to established African business data startup (Asoko Insight). I am now a filter engineer at eyeo trying to make the web a fair, open, and safe place for everybody.")]),e._v(" "),a("p",[e._v("I love working with kids and teenagers, cooking, and doing music—I just recently started drum lessons!")]),e._v(" "),a("h3",{attrs:{id:"how-did-you-first-hear-about-frictionless-data"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#how-did-you-first-hear-about-frictionless-data"}},[e._v("#")]),e._v(" How did you first hear about Frictionless Data?")]),e._v(" "),a("p",[e._v("I’ve been following the work of the Open Knowledge Foundation for a while now and contributed to the German branch as a mentor for the teenage hackathon weekends project “Jugend Hackt” (Youth Hacks). I first heard about the Frictionless Data program when the OKF announced funding by the Sloan Foundation in 2018. After watching Serah Njambi Rono’s talk on Youtube ("),a("a",{attrs:{href:"https://www.youtube.com/watch?v=3Ranx9Jz0Ro",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://www.youtube.com/watch?v=3Ranx9Jz0Ro"),a("OutboundLink")],1),e._v(") and reading about the Reproducible Research Tool Fund on Twitter, I knew I wanted to contribute.")]),e._v(" "),a("h3",{attrs:{id:"why-did-you-apply-for-a-tool-fund-grant"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#why-did-you-apply-for-a-tool-fund-grant"}},[e._v("#")]),e._v(" Why did you apply for a Tool Fund grant?")]),e._v(" "),a("p",[e._v("I first heard about the concepts and challenges around Reproducible Research when taking the MOOC “Data Science” from Johns Hopkins University on Coursera. Since I had my fair share of work inside proprietary data formats and tools, I was happy to see that there are people out there making serious efforts to remedy the loss of attribution and data manipulation steps. After browsing through OKF’s Frictionless Data website, I was even happier that there are actual tools, libraries, and standards already available. Applying for the tool fund and contributing my own humble idea was a no-brainer for me.")]),e._v(" "),a("h3",{attrs:{id:"what-specific-issues-are-you-looking-to-address-with-the-tool-fund"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#what-specific-issues-are-you-looking-to-address-with-the-tool-fund"}},[e._v("#")]),e._v(" What specific issues are you looking to address with the Tool Fund?")]),e._v(" "),a("p",[e._v("My goal is to add a Data Package import/export add-on to Google Sheets. I understand that a lot of data wrangling is still done in Sheets, Excel, and files being swapped around. A lot of information is lost that way. Where did the data initially come from? How was it manipulated, cleaned, or otherwise altered? How can we feed spreadsheets back into a Reproducible Research pipeline? I think Data Packages is a brilliant format to model and preserve exactly that information. While I do not want to lure people away from the tools they are already familiar with, I think we can bridge the gap between Google Sheets and Frictionless Data by making Data Packages a first-class citizen.")]),e._v(" "),a("h3",{attrs:{id:"how-can-the-open-data-open-source-community-engage-with-the-work-you-are-doing-around-frictionless-data-google-sheets-add-on"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#how-can-the-open-data-open-source-community-engage-with-the-work-you-are-doing-around-frictionless-data-google-sheets-add-on"}},[e._v("#")]),e._v(" How can the open data, open source, community engage with the work you are doing around Frictionless Data Google Sheets add-on?")]),e._v(" "),a("p",[e._v("I think open source and data is a unique and wonderful opportunity to get access to the “wisdom of the crowd” and ensure that software and information is and remains accessible to everyone. In the first few weeks I will focus on getting a first prototype and sufficient documentation up, so you can all play with the Data Package import/export add-on as soon as possible. After that, I invite you to take a look at our Github repository ("),a("a",{attrs:{href:"https://github.com/frictionlessdata/googlesheets-datapackage-tools",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://github.com/frictionlessdata/googlesheets-datapackage-tools"),a("OutboundLink")],1),e._v("), play around with the tool, and contribute. Raising an issue, opening a pull request, improving the documentation, giving feedback on the user experience—everything counts! I am so stoked to be part of this Frictionless Data journey and can’t wait to see what we will accomplish. Thank you very much in advance!")])])}),[],!1,null,null,null);t.default=n.exports}}]); \ No newline at end of file +(window.webpackJsonp=window.webpackJsonp||[]).push([[88],{610:function(e,t,a){"use strict";a.r(t);var o=a(29),n=Object(o.a)({},(function(){var e=this,t=e.$createElement,a=e._self._c||t;return a("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[a("p",[e._v("This grantee profile features Stephan Max for our series of Frictionless Data Tool Fund posts, written to shine a light on Frictionless Data’s Tool Fund grantees, their work and to let our technical community know how they can get involved.")]),e._v(" "),a("h3",{attrs:{id:"meet-stephan-max"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#meet-stephan-max"}},[e._v("#")]),e._v(" Meet Stephan Max")]),e._v(" "),a("p",[e._v("Hi, my name is Stephan Max and I am a computer scientist based in Cologne, Germany. I’ve been in the industry for over 10 years now and worked for all kinds of companies, ranging from own startup (crowd-funded online journalism), over big corporate (IBM), to established African business data startup (Asoko Insight). I am now a filter engineer at eyeo trying to make the web a fair, open, and safe place for everybody.")]),e._v(" "),a("p",[e._v("I love working with kids and teenagers, cooking, and doing music—I just recently started drum lessons!")]),e._v(" "),a("h3",{attrs:{id:"how-did-you-first-hear-about-frictionless-data"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#how-did-you-first-hear-about-frictionless-data"}},[e._v("#")]),e._v(" How did you first hear about Frictionless Data?")]),e._v(" "),a("p",[e._v("I’ve been following the work of the Open Knowledge Foundation for a while now and contributed to the German branch as a mentor for the teenage hackathon weekends project “Jugend Hackt” (Youth Hacks). I first heard about the Frictionless Data program when the OKF announced funding by the Sloan Foundation in 2018. After watching Serah Njambi Rono’s talk on Youtube ("),a("a",{attrs:{href:"https://www.youtube.com/watch?v=3Ranx9Jz0Ro",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://www.youtube.com/watch?v=3Ranx9Jz0Ro"),a("OutboundLink")],1),e._v(") and reading about the Reproducible Research Tool Fund on Twitter, I knew I wanted to contribute.")]),e._v(" "),a("h3",{attrs:{id:"why-did-you-apply-for-a-tool-fund-grant"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#why-did-you-apply-for-a-tool-fund-grant"}},[e._v("#")]),e._v(" Why did you apply for a Tool Fund grant?")]),e._v(" "),a("p",[e._v("I first heard about the concepts and challenges around Reproducible Research when taking the MOOC “Data Science” from Johns Hopkins University on Coursera. Since I had my fair share of work inside proprietary data formats and tools, I was happy to see that there are people out there making serious efforts to remedy the loss of attribution and data manipulation steps. After browsing through OKF’s Frictionless Data website, I was even happier that there are actual tools, libraries, and standards already available. Applying for the tool fund and contributing my own humble idea was a no-brainer for me.")]),e._v(" "),a("h3",{attrs:{id:"what-specific-issues-are-you-looking-to-address-with-the-tool-fund"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#what-specific-issues-are-you-looking-to-address-with-the-tool-fund"}},[e._v("#")]),e._v(" What specific issues are you looking to address with the Tool Fund?")]),e._v(" "),a("p",[e._v("My goal is to add a Data Package import/export add-on to Google Sheets. I understand that a lot of data wrangling is still done in Sheets, Excel, and files being swapped around. A lot of information is lost that way. Where did the data initially come from? How was it manipulated, cleaned, or otherwise altered? How can we feed spreadsheets back into a Reproducible Research pipeline? I think Data Packages is a brilliant format to model and preserve exactly that information. While I do not want to lure people away from the tools they are already familiar with, I think we can bridge the gap between Google Sheets and Frictionless Data by making Data Packages a first-class citizen.")]),e._v(" "),a("h3",{attrs:{id:"how-can-the-open-data-open-source-community-engage-with-the-work-you-are-doing-around-frictionless-data-google-sheets-add-on"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#how-can-the-open-data-open-source-community-engage-with-the-work-you-are-doing-around-frictionless-data-google-sheets-add-on"}},[e._v("#")]),e._v(" How can the open data, open source, community engage with the work you are doing around Frictionless Data Google Sheets add-on?")]),e._v(" "),a("p",[e._v("I think open source and data is a unique and wonderful opportunity to get access to the “wisdom of the crowd” and ensure that software and information is and remains accessible to everyone. In the first few weeks I will focus on getting a first prototype and sufficient documentation up, so you can all play with the Data Package import/export add-on as soon as possible. After that, I invite you to take a look at our Github repository ("),a("a",{attrs:{href:"https://github.com/frictionlessdata/googlesheets-datapackage-tools",target:"_blank",rel:"noopener noreferrer"}},[e._v("https://github.com/frictionlessdata/googlesheets-datapackage-tools"),a("OutboundLink")],1),e._v("), play around with the tool, and contribute. Raising an issue, opening a pull request, improving the documentation, giving feedback on the user experience—everything counts! I am so stoked to be part of this Frictionless Data journey and can’t wait to see what we will accomplish. Thank you very much in advance!")])])}),[],!1,null,null,null);t.default=n.exports}}]); \ No newline at end of file diff --git a/assets/js/90.9fdb036a.js b/assets/js/90.89ae865f.js similarity index 98% rename from assets/js/90.9fdb036a.js rename to assets/js/90.89ae865f.js index f50df6119..6f234b376 100644 --- a/assets/js/90.9fdb036a.js +++ b/assets/js/90.89ae865f.js @@ -1 +1 @@ -(window.webpackJsonp=window.webpackJsonp||[]).push([[90],{614:function(e,t,a){"use strict";a.r(t);var o=a(29),n=Object(o.a)({},(function(){var e=this,t=e.$createElement,a=e._self._c||t;return a("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[a("p",[e._v("This grantee profile features Greg Bloom & Shelby Switzer for our series of Frictionless Data Tool Fund posts, written to shine a light on Frictionless Data’s Tool Fund grantees, their work and to let our technical community know how they can get involved.")]),e._v(" "),a("h3",{attrs:{id:"meet-greg-shelby-and-open-referral"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#meet-greg-shelby-and-open-referral"}},[e._v("#")]),e._v(" Meet Greg, Shelby, and Open Referral")]),e._v(" "),a("p",[e._v("Shelby Switzer and Greg Bloom work with "),a("a",{attrs:{href:"https://openreferral.org/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Open Referral"),a("OutboundLink")],1),e._v(", which develops data standards and open source tools for health, human, and social services. For the Tool Fund, they will be building out datapackage support for all their interfaces, from the open source tools that transform and validate human services data to the Human Services API Specification. Greg is the founder of the Open Referral Initiative, and has experience in nonprofit communications, cooperative development, and community organizing. Shelby is a long-time civic tech contributor, and will be the lead developer on this project.")]),e._v(" "),a("blockquote",[a("p",[e._v("I got my start in tech through civic tech and open data. After a variety of software development and API product management roles in my career, including most recently leading the API and integrations team at a healthcare technology company, I’ve returned to my roots to write about and contribute to open source, community-focused tech projects full-time. - Shelby")])]),e._v(" "),a("p",[e._v("Open Referral develops data standards and open platforms that make it easy to share and find information about community resources – i.e. the health, human, and social services available to people in need. The Open Referral Initiative is developing the Human Services Data Toolkit – a suite of open source data management tools that facilitate transformation, validation, and publication of standardized data about health, human, and social services. By leveraging the JSON datapackage specification across each of these components, we can provide a comprehensive approach to frictionless data management of information about any kind of community resources provisioned by governments, charity, and civic institutions.")]),e._v(" "),a("h3",{attrs:{id:"shelby-how-did-you-hear-about-frictionless-data"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#shelby-how-did-you-hear-about-frictionless-data"}},[e._v("#")]),e._v(" Shelby, how did you hear about Frictionless Data?")]),e._v(" "),a("p",[e._v("I think I heard about Frictionless Data first over the past year or two just through working with Open Referral. I was doing research on what tools already existed out there for data munging and CSV processing, to help inform my own with open data and specifically diverse sets of community resource data. First impressions? I thought it was awesome, and wanted to explore more to figure out how to incorporate some of FD’s specs and tools into my own pipelines.")]),e._v(" "),a("h3",{attrs:{id:"what-specific-issues-are-you-looking-to-address-with-the-tool-fund-grant"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#what-specific-issues-are-you-looking-to-address-with-the-tool-fund-grant"}},[e._v("#")]),e._v(" What specific issues are you looking to address with the Tool Fund grant?")]),e._v(" "),a("p",[e._v("I’m definitely excited about building out datapackage support for all our interfaces, from the open source tools that transform and validate human services data to the Human Services API Specification. This will help us plug-and-play tools much more efficiently to build pipelines customized to each deployment. A lot of our work is in Ruby, JavaScript, and PHP, so I think this will be an opportunity to help contribute some tools in those languages to the Frictionless Data ecosystem, for example a Ruby library for generating datapackages given an input directory or a library for generating a SQL Server database from a datapackage. We want to do more with our existing data pipeline tools, especially to link them together using the datapackage spec as a common exchange format. We’re also about to use some of these tools in specific projects in the US validating and federating community resource data sets, and we hoped that applying for a tool grant might help us have the runway to iterate on tool improvements based on what we learn from these deployments.")]),e._v(" "),a("h3",{attrs:{id:"how-can-the-open-data-open-source-community-engage-with-the-work-you-are-doing-around-frictionless-data-and-the-human-data-services-toolkit"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#how-can-the-open-data-open-source-community-engage-with-the-work-you-are-doing-around-frictionless-data-and-the-human-data-services-toolkit"}},[e._v("#")]),e._v(" How can the open data, open source, community engage with the work you are doing around Frictionless Data and the Human Data Services Toolkit?")])])}),[],!1,null,null,null);t.default=n.exports}}]); \ No newline at end of file +(window.webpackJsonp=window.webpackJsonp||[]).push([[90],{612:function(e,t,a){"use strict";a.r(t);var o=a(29),n=Object(o.a)({},(function(){var e=this,t=e.$createElement,a=e._self._c||t;return a("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[a("p",[e._v("This grantee profile features Greg Bloom & Shelby Switzer for our series of Frictionless Data Tool Fund posts, written to shine a light on Frictionless Data’s Tool Fund grantees, their work and to let our technical community know how they can get involved.")]),e._v(" "),a("h3",{attrs:{id:"meet-greg-shelby-and-open-referral"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#meet-greg-shelby-and-open-referral"}},[e._v("#")]),e._v(" Meet Greg, Shelby, and Open Referral")]),e._v(" "),a("p",[e._v("Shelby Switzer and Greg Bloom work with "),a("a",{attrs:{href:"https://openreferral.org/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Open Referral"),a("OutboundLink")],1),e._v(", which develops data standards and open source tools for health, human, and social services. For the Tool Fund, they will be building out datapackage support for all their interfaces, from the open source tools that transform and validate human services data to the Human Services API Specification. Greg is the founder of the Open Referral Initiative, and has experience in nonprofit communications, cooperative development, and community organizing. Shelby is a long-time civic tech contributor, and will be the lead developer on this project.")]),e._v(" "),a("blockquote",[a("p",[e._v("I got my start in tech through civic tech and open data. After a variety of software development and API product management roles in my career, including most recently leading the API and integrations team at a healthcare technology company, I’ve returned to my roots to write about and contribute to open source, community-focused tech projects full-time. - Shelby")])]),e._v(" "),a("p",[e._v("Open Referral develops data standards and open platforms that make it easy to share and find information about community resources – i.e. the health, human, and social services available to people in need. The Open Referral Initiative is developing the Human Services Data Toolkit – a suite of open source data management tools that facilitate transformation, validation, and publication of standardized data about health, human, and social services. By leveraging the JSON datapackage specification across each of these components, we can provide a comprehensive approach to frictionless data management of information about any kind of community resources provisioned by governments, charity, and civic institutions.")]),e._v(" "),a("h3",{attrs:{id:"shelby-how-did-you-hear-about-frictionless-data"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#shelby-how-did-you-hear-about-frictionless-data"}},[e._v("#")]),e._v(" Shelby, how did you hear about Frictionless Data?")]),e._v(" "),a("p",[e._v("I think I heard about Frictionless Data first over the past year or two just through working with Open Referral. I was doing research on what tools already existed out there for data munging and CSV processing, to help inform my own with open data and specifically diverse sets of community resource data. First impressions? I thought it was awesome, and wanted to explore more to figure out how to incorporate some of FD’s specs and tools into my own pipelines.")]),e._v(" "),a("h3",{attrs:{id:"what-specific-issues-are-you-looking-to-address-with-the-tool-fund-grant"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#what-specific-issues-are-you-looking-to-address-with-the-tool-fund-grant"}},[e._v("#")]),e._v(" What specific issues are you looking to address with the Tool Fund grant?")]),e._v(" "),a("p",[e._v("I’m definitely excited about building out datapackage support for all our interfaces, from the open source tools that transform and validate human services data to the Human Services API Specification. This will help us plug-and-play tools much more efficiently to build pipelines customized to each deployment. A lot of our work is in Ruby, JavaScript, and PHP, so I think this will be an opportunity to help contribute some tools in those languages to the Frictionless Data ecosystem, for example a Ruby library for generating datapackages given an input directory or a library for generating a SQL Server database from a datapackage. We want to do more with our existing data pipeline tools, especially to link them together using the datapackage spec as a common exchange format. We’re also about to use some of these tools in specific projects in the US validating and federating community resource data sets, and we hoped that applying for a tool grant might help us have the runway to iterate on tool improvements based on what we learn from these deployments.")]),e._v(" "),a("h3",{attrs:{id:"how-can-the-open-data-open-source-community-engage-with-the-work-you-are-doing-around-frictionless-data-and-the-human-data-services-toolkit"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#how-can-the-open-data-open-source-community-engage-with-the-work-you-are-doing-around-frictionless-data-and-the-human-data-services-toolkit"}},[e._v("#")]),e._v(" How can the open data, open source, community engage with the work you are doing around Frictionless Data and the Human Data Services Toolkit?")])])}),[],!1,null,null,null);t.default=n.exports}}]); \ No newline at end of file diff --git a/assets/js/91.260e264e.js b/assets/js/91.34e2a6c6.js similarity index 98% rename from assets/js/91.260e264e.js rename to assets/js/91.34e2a6c6.js index 5c125eeaa..da31be232 100644 --- a/assets/js/91.260e264e.js +++ b/assets/js/91.34e2a6c6.js @@ -1 +1 @@ -(window.webpackJsonp=window.webpackJsonp||[]).push([[91],{615:function(e,t,r){"use strict";r.r(t);var o=r(29),a=Object(o.a)({},(function(){var e=this,t=e.$createElement,r=e._self._c||t;return r("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[r("p",[e._v("As part of our commitment to opening up scientific knowledge, we recently launched the "),r("a",{attrs:{href:"https://fellows.frictionlessdata.io/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Frictionless Data for Reproducible Research Fellows Programme"),r("OutboundLink")],1),e._v(", which will run from mid-September until June 2020.")]),e._v(" "),r("p",[e._v("We received over 200 impressive applications for the Programme, and are very excited to introduce the four selected Fellows:")]),e._v(" "),r("p",[r("strong",[e._v("Monica Granados")]),e._v(", a Mitacs Canadian Science Policy Fellow;"),r("br"),e._v(" "),r("strong",[e._v("Selene Yang")]),e._v(", a graduate student researcher at the National University of La Plata, Argentina;"),r("br"),e._v(" "),r("strong",[e._v("Daniel Ouso")]),e._v(", a postgraduate researcher at the International Centre of Insect Physiology and Ecology;"),r("br"),e._v(" "),r("strong",[e._v("Lily Zhao")]),e._v(", a graduate student researcher at the University of California, Santa Barbara.")]),e._v(" "),r("p",[e._v("Next month, the Fellows will be writing blogs to further introduce themselves to the Frictionless Data community, so stay tuned to learn more about these impressive researchers.")]),e._v(" "),r("p",[e._v("The Programme will train early career researchers to become champions of the Frictionless Data tools and approaches in their field. 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- +

On our last community call was on March 21st, we continued discussing the Frictionless Standards update with community members. As a reminder, thanks to the generous support of NLnet (opens new window), the Frictionless core team, together with a working group composed of members of the community, is now working on a v2 of the Frictionless Standards. Read the announcement blog (opens new window) to know more about it.

# Working group discussion

We are very excited we are getting closer to the first Data Package v2 draft release! We closed almost 30 issues already, and only have a few pending items to address for the draft release (opens new window), mainly to do with Table Dialect (see issue #344 (opens new window), #697 (opens new window), #681 (opens new window)). During the call we discussed with the working group how to split the last remaining items in order to release the v2 draft already by the beginning of April.

For a full overview of the items that the working group tackled on the call, you can have a look at our live tracker on GitHub (opens new window), or watch the recording linked here below.

A few issues were moved out of the draft release to be rather included in the final release (opens new window), which is planned for June. One example is versioning, which was discussed in the last call (opens new window). As versioning is a brand new element, it will require more time to be tackled. We are currently working on a solid proposal to discuss with the working group next month.

# Join us in April!

Next community call is on April 25th, join us to hear all the exciting news about the Frictionless standards (opens new window) update!

Do you have something you would like to present to the community at one of the upcoming calls? Let us know via this form (opens new window), or come and tell us on our community chat on Slack (opens new window)(also accessible via a Matrix bridge (opens new window) if you prefer to use an open protocol).

You can sign up for the call already here (opens new window). Do you want to share something with the community? Let us know when you sign up.

# Call Recording

Here is the recording of the full call:

# Thank you

On a final note, we would like to thank all community members that joined the call and that keep all these discussions alive, and particularly to those who are putting a lot of thinking and hard work into the specs working group. Without you, all of this would not be possible.

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  • Developer Guide
    July 9, 2018 by Frictionless Data -

  • Using Data Packages in Clojure +

    This guide introduces you to the Frictionless Data tool stack and how you can contribute to it. Update note (2021): this blog is out of date. Please see the contributing guide for updated information.

    +

  • Using Data Packages in Clojure
    May 7, 2018 by Matt Thompson
  • Matrix
    • - + diff --git a/blog/page/14/index.html b/blog/page/14/index.html index 99bec566e..f5b5c91e0 100644 --- a/blog/page/14/index.html +++ b/blog/page/14/index.html @@ -30,8 +30,8 @@ - - + +
  • Using Data Packages in R
    February 14, 2018 by Kleanthis Koupidis

    A guide on how to create datapackage with R

  • Using Data Packages in R +

    A guide on how to use datapackage with R

  • Creating Data Packages in R
    February 14, 2018 by Kleanthis Koupidis

    A guide on how to use datapackage with R

  • Data Management for TEDDINET +

    A guide on how to create datapackage with R

  • Data Management for TEDDINET
    December 19, 2017 by Julian Padget (DM4T), Dan Fowler (OKI), Evgeny Kariv (OKI), Paul Walsh (OKI), Jo Barratt (OKI)
  • Matrix
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  • Center for Data Science and Public Policy, Workforce Data Initiative
    August 15, 2017 by Frictionless Data

    Investigating the applicability of the Data Package concept to facilitate data reuse in the field of Computational Neuroscience.

  • Collections as Data Facets - Carnegie Museum of Art Collection Data +

    Investigating the applicability of the Data Package concept to facilitate data reuse in the field of Computational Neuroscience.

  • Causa Natura - Pescando Datos
    - August 9, 2017 by Dan Fowler -

    Use of Frictionless Data specifications in the release of Carnegie Museum of Arts’ Collection Data for public access & creative use



    • - + diff --git a/blog/page/17/index.html b/blog/page/17/index.html index 12625e38c..bf140efc6 100644 --- a/blog/page/17/index.html +++ b/blog/page/17/index.html @@ -30,8 +30,8 @@ - - + +
  • Publish Your Data Package Online
    August 29, 2016 by Frictionless Data -

  • Creating Data Packages in Python +

  • Publish Any Kind of Data as a Data Package
    July 21, 2016 by Frictionless Data -

    A guide on how to create datapackages in Python

  • Publish Any Kind of Data as a Data Package +

    A guide on how to publish any kind of data as datapackages

  • Creating Data Packages in Python
    July 21, 2016 by Frictionless Data -

    A guide on how to publish any kind of data as datapackages

  • Publish Tabular Data as a Data Package +

    A guide on how to create datapackages in Python

  • Publish Tabular Data as a Data Package
    July 21, 2016 by Frictionless Data

    A guide on how to publish tabular datapackages

  • Publishing Geospatial Data as a Data Package @@ -152,6 +152,6 @@
  • Matrix
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    - + diff --git a/feed.atom b/feed.atom index 1d39d6e93..cec704a44 100644 --- a/feed.atom +++ b/feed.atom @@ -2,7 +2,7 @@ https://frictionlessdata.io Frictionless Data - 2024-04-03T10:25:04.234Z + 2024-05-13T15:07:52.590Z https://github.com/webmasterish/vuepress-plugin-feed diff --git a/hackathon/README(pt-br).html b/hackathon/README(pt-br).html index 26f24b10c..6cd13f1f6 100644 --- a/hackathon/README(pt-br).html +++ b/hackathon/README(pt-br).html @@ -30,8 +30,8 @@ - - + +
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    Lily Zhao

    Lily Zhao

    Work
    Work icons created by Freepik - Flaticon
    Reproducible Research Fellow 2019-2020
    City
    World icons created by Freepik - Flaticon
    USA

    # Code Contributors

    Frictionless Data is possible due to our awesome contributor community. You can click on the pictures below to see code contributions in detail. This is only a subset of all the people working on the project - please take a look on our Github Organization (opens new window) to view more. Are you interested in contributing? Check out our Contributing page to get started.

    project

    website

    specs

    datahub.io

    frictionless-py

    frictionless-js

    frictionless-r

    datapackage-py

    tableschema-py

    datapackage-js

    tableschema-js

    datapackage-rb

    tableschema-rb

    datapackage-php

    tableschema-php

    datapackage-java

    tableschema-java

    datapackage-go

    tableschema-go

    datapackage-r

    tableschema-r

    datapackage-swift

    tableschema-swift

    datapackage-jl

    tableschema-jl

    datapackage-clj

    tableschema-clj

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