Welter, DanielleDanielleWelterJuty, Nick S.Nick S.JutyRocca-Serra, PhilippePhilippeRocca-SerraXu, FuqiFuqiXuHenderson, David A.David A.HendersonGu, WeiWeiGuStrubel, JolandaJolandaStrubelGiessmann, Robert T.Robert T.GiessmannEmam, Ibrahim I.Ibrahim I.EmamGadiya, YojanaYojanaGadiyaAbbassi-Daloii, ToobaToobaAbbassi-DaloiiAlharbi, EbtisamEbtisamAlharbiGray, Alasdair J.G.Alasdair J.G.GrayCourtot, MelanieMelanieCourtotGribbon, PhilipPhilipGribbonIoannidis, VassiliosVassiliosIoannidisReilly, Dorothy S.Dorothy S.ReillyLynch, NickNickLynchBoiten, Jan-WillemJan-WillemBoitenSatagopam, Venkata P.Venkata P.SatagopamGoble, Carole AnneCarole AnneGobleSansone, Susanna-AssuntaSusanna-AssuntaSansoneBurdett, TonyTonyBurdett2023-08-162023-08-162023https://publica.fraunhofer.de/handle/publica/44825810.1038/s41597-023-02167-22-s2.0-8515971761137208349The 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.enjournal article