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@@ -38,7 +38,7 @@ The following summarizes the key features and design principles of Py maidr:
 
 6. Reactivity: maidr supports widely adopted reactive and interactive computing including Jupyter Notebooks, Jupyter Labs, Google Colab, Streamlit dashboard, and Shiny dashboard. maidr also supports interactive computing inside code editors, such as Visual Studio Code.
 
-7. Reproducibility: maidr supports the generation of accessible data visualizations as part of the reproducible data science workflow with Quarto scientific publishing system. You can easily create accessible data representations within your reproducible reports, website blogs, slides, e-books, dashboards, and more.
+7. Reproducibility: maidr supports the generation of accessible data visualizations as part of the reproducible data science workflow with [Quarto scientific publishing system](https://quarto.org/). You can easily create accessible data representations within your reproducible reports, website blogs, slides, e-books, dashboards, and more.
 
 8. Scalability: maidr supports a wide range of data visualization types, including bar plots, histograms, line plots, box plots, heatmaps, scatter plots, and more. maidr is designed to be extensible to support new visualization types. [Multi-figure and multi-layer visualizations are underway to support complex data visualizations.]