This jupyter book is FAIR because:¶
It’s Findable. The source code is publicly available on GitHub under a stable URL, and the book is rendered as a public website. All input datasets are drawn from catalogued, citeable repositories (Copernicus CDS, GHS, Eurostat/NUTS).
It’s Accessible. The book requires no local software installation: notebooks can be executed directly in the browser, for example via Binder. Input data are either bundled with the repository or freely downloadable from open data portals. The book itself is published under an open-access policy.
It’s Interoperable. All data are stored in open, community-standard formats — NetCDF for gridded climate fields and CSV for derived tabular outputs. Region geometries use the official NUTS shapefile provided by Eurostat. The analysis relies exclusively on well-documented, open-source Python libraries.
It’s Reusable. The code is released under the Apache-2.0 licence, allowing others to freely use, modify, and redistribute it. How-to guides document each processing step in a reproducible way, and the explanation section provides the conceptual background needed to adapt the workflow to other hazards or regions.
- Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J. J., & others. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data, 3, 160018. 10.1038/sdata.2016.18