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, Melanie Courtot, Philip Gribbon, Vassilios Ioannidis, Dorothy s. Reilly, Nick Lynch, Jan-Willem Boiten, Venkata SatagopamCarole Goble, Susanna-Assunta Sansone, Tony Burdett*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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.
Original languageEnglish
Article number291
Number of pages9
JournalScientific data
Volume10
Issue number1
DOIs
Publication statusPublished - 19 May 2023

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