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

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)
44 Downloads (Pure)

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
JournalScientific Data
Volume10
DOIs
Publication statusPublished - 19 May 2023

Keywords

  • COVID-19
  • Datasets as Topic
  • Humans
  • Pandemics
  • Public-Private Sector Partnerships
  • Reproducibility of Results

ASJC Scopus subject areas

  • Information Systems
  • Education
  • Library and Information Sciences
  • Statistics and Probability
  • Computer Science Applications
  • Statistics, Probability and Uncertainty

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