FAIR in action - a flexible framework to guide FAIRification
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, Mélanie Courtot, Philip Gribbon, Vassilios Ioannidis, Dorothy Reilly, Nick Lynch, Jan‐Willem Boiten, Venkata Satagopam, Carole Goble, Susanna‐Assunta Sansone, Tony Burdett
Abstract
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.