FAIR in action - a flexible framework to guide FAIRification

The COVID-19 pandemic has highlighted the need for FAIR (Findable, Accessible, Interoperable, and Reusable) data more than any other scientific challenge to date. We developed a flexible, multi-level, domain-agnostic FAIRification framework, providing practical guidance to improve the FAIRness for both existing and future clinical and molecular datasets. We validated the framework in collaboration with several major public-private partnership projects, demonstrating and delivering improvements across all aspects of FAIR and across a variety of datasets and their contexts. We therefore managed to establish the reproducibility and far-reaching applicability of our approach to FAIRification tasks.

Danielle Welter, Nick Juty, Philippe Rocca-Serra, Fuqi Xu, David Henderson, Wei Gu, Jolanda Strubel, Robert T. Giessmann, Ibrahim Emam, Yojana Gadiya, Tooba Abbassi-Daloii, Ebtisam Alharbi, Alasdair J. G. Gray, Melanie Courtot, Philip Gribbon, Vassilios Ioannidis, Dorothy S. Reilly, Nick Lynch, Jan-Willem Boiten, Venkata Satagopam, Carole Goble, Susanna-Assunta Sansone, Tony Burdett