FAIR in action - a flexible framework to guide FAIRification.
Danielle WelterNick JutyPhilippe Rocca-SerraFuqi XuDavid HendersonWei GuJolanda StrubelRobert T GiessmannIbrahim EmamYojana GadiyaTooba Abbassi-DaloiiEbtisam AlharbiAlasdair J G GrayMélanie CourtotPhilip GribbonVassilios IoannidisDorothy S ReillyNick LynchJan-Willem BoitenVenkata P SatagopamCarole GobleSusanna-Assunta SansoneTony BurdettPublished in: Scientific data (2023)
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.