Exploring FAIR Scientific Datasets with Python#
This book is your guide to discovering, accessing, and visualising fascinating scientific datasets using Python. Each chapter introduces a new dataset and provides step-by-step instructions to help you access and plot the data.
Basic familiarity with Python is recommended, but don’t worry if you’re new to it—each chapter includes detailed explanations to help you follow along.
Why This Book?#
Increasingly, vast amounts of FAIR (Findable, Accessible, Interoperable, Reusable) data are being published openly, waiting to be explored and used. These datasets are not just for scientists—they’re available to anyone, from researchers seeking to complement their own work with high-quality data, to curious members of the public eager to uncover new insights. This book aims to:
Help you discover FAIR datasets.
Teach you how to access and analyse these datasets programmatically using Python.
Show you how to plot these data.
How to Use This Book#
Each chapter is independent, so you can jump to the datasets that interest you most.
Contents#
Useful links#
My other relevant tutorials#
A introduction to working with NetCDF files using Python:
For visualising the data#
Map projections: https://scitools.org.uk/cartopy/docs/v0.15/crs/projections.html
Matplotlib built-in colourmaps: https://matplotlib.org/stable/users/explain/colors/colormaps.html
Colourmaps for oceanography from
cmocean: https://matplotlib.org/cmocean/
Conventions#
The following conventions are commonly used in NetCDF file. A NetCDF file by itself is not neccessarily FAIR-compliant. These conventions provide rules and recommendations on which metadata terms should be included in a file and how a file should be structured, thereby making the file machine-readable:
Climate & Forecast (CF) conventions: https://cfconventions.org/
Attribute Convention for Data Discovery (ACDD): https://wiki.esipfed.org/Attribute_Convention_for_Data_Discovery_1-3