FAIR Cookbook

The FAIR Coobook overview

Content and learning outcomes

Learn how to FAIRify a number of exemplar datasets, putting the FAIR principles in practices; learn about levels and indicators of FAIRness; the maturity model, the technologies and tools available to assess and improve FAIRness; learn about the skills required, as well as the challenges.

Getting started

To get started, you may be interested in the following links. Here are a few links of interest:


Here's a brief rundown of how to create your own Jupyter Book using this site. For a more complete guide, see the Jupyter Book guide.

  • Fork the FAIR Book jupyter-book template repo
  • Update or Replace the files in content/ with your own notebooks and markdown files.
  • Update or create the Table of Contents yaml file by editing _data/toc.yaml.
  • Generate the Jekyll markdown for your notebooks by running jupyter-book build docs
  • Push your changes to a branch on your GitHub fork (or wherever you host your site)!
  • Send a Pull Requests to the remote GitHub repository


FAIR Cookbook is produced thanks to Jupyter Book, project originally created by Sam Lau and Chris Holdgraf with support of the UC Berkeley Data Science Education Program and the Berkeley Institute for Data Science.