Instructors: Eli Holmes (NWFSC), Sunny Hospital (CoastWatch), Matt Grossi (SEFSC), Emily Markowitz (AFSC), Songzhi Liu (CoastWatch), Dan Pendleton (NEFSC) / NOAA Fisheries Open Science.
Earth science data is increasingly available from the cloud and due to the size of many of these dataset (Tb and Pb in some cases), data workflows are transitioning to workflows that involve programmatic access with either cloud-native analysis or server-side processing . In this workshop, participants will take part in hands-on tutorials on working with earth data in the cloud with Python and/or R using a JupyterHub (cloud computing platform) provisioned for geospatial analysis. Participants will learn the basics of searching cloud resources via SpatioTemporal Asset Catalogs (STAC) and NASA Earth Data via Common Metadata Repository (CMR). Participants will go through tutorials to learn how to incorporate earth data into their science projects via cloud-native and server-side workflows. Participants will also be exposed to the Python and R suite of geospatial packages for gridded and other spatial data.
- Learn how to discover and use oceanographic remote-sensing data in NASA Earth Data
- Familiarize participants with using remote-sensing data in R and Python with code.
- Obtain hands-on experience in using remote-sensing data for two science applications.
- Learn by doing and running through examples yourself.
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We will have short introductions and then will work through tutorials together. You are encouraged to adapt the code to create output and examples for your own data and areas of interest.
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All tutorials and examples are developed openly and will be publicly available during and following the event. Participants will strengthen their practice of open science, using open source code and collaborating on their projects with course peers.
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