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FAIR in action - a flexible framework to guide FAIRification.

Reilly, Dorothy, Welter, Danielle, Juty, Nick, Rocca-Serra, Philippe, Xu, Fuji, Henderson, David, Gu, Wei, Strubel, Jolanda, Giessmann, Robert, Emam, Ibrahim, Gadiya, Yojana, Abbassi-Daloii, Tooba, Grey, Alasdair, Courtot, Melanie, Gribbon, Philip, Ioannidis, Vassilios, Lynch, Nick, Boiten, Jan-Willem, Satagopam, Venkata, Goble, Carole, Sansone, Susanna-Assunta and Burdett, Tony (2023) FAIR in action - a flexible framework to guide FAIRification. Scientific data, 10 (1). p. 291. ISSN 2052-4463


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.

Item Type: Article
Date Deposited: 03 Jun 2023 00:46
Last Modified: 03 Jun 2023 00:46