FAIR in action - a flexible framework to guide FAIRification

• Main Authors: Abbassi-Daloii, T. (Author), Alharbi, E. (Author), Boiten, J.-W (Author), Burdett, T. (Author), Courtot, M. (Author), Emam, I. (Author), Gadiya, Y. (Author), Giessmann, R.T (Author), Goble, C. (Author), Gray, A.J.G (Author), Gribbon, P. (Author), Gu, W. (Author), Henderson, D. (Author), Ioannidis, V. (Author), Juty, N. (Author), Lynch, N. (Author), Reilly, D.S (Author), Rocca-Serra, P. (Author), Sansone, S.-A (Author), Satagopam, V. (Author), Strubel, J. (Author), Welter, D. (Author), Xu, F. (Author)
• Format: Article
• Language: English
• Published: NLM (Medline) 2023
• Subjects: Agnostic; article; FAIR principles; fairness; human; public-private partnership; reproducibility
• Online Access: View Fulltext in Publisher; View in Scopus

 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. © 2023. The Author(s).