FAIR in action - a flexible framework to guide FAIRification.

• DOI: 10.5281/zenodo.7702124
• Publication Date: 2023-05-19
• Authors: Welter, D; Juty, N; Rocca-Serra, P; Xu, F; Henderson, D; Gu, W; Strubel, J; Giessmann, RT; Emam, I; Gadiya, Y; Abbassi-Daloii, T; Alharbi, E; Gray, AJG; Courtot, M; Gribbon, P; Ioannidis, V; Reilly, DS; Lynch, N; Boiten, J-W; Satagopam, V; Goble, C; Sansone, S-A; Burdett, T
• Keywords: FAIR data; Science; FAIRplus; Q; Humans; COVID-19; Pandemics; Public-Private Sector Partnerships; Reproducibility of Results; Datasets as Topic; COVID-19; Reproducibility of Results; Datasets as Topic; Interoperability; Data publication and archiving; Public-Private Sector Partnerships; Data management; Article; FAIRification; 3. Good health; Research data; Data processing; Humans; IMI; Data integration; Innovative Medicines Initiative; Pandemics; FAIR

AbstractThe 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.