TY - JOUR
T1 - FAIR in action - a flexible framework to guide FAIRification
AU - Welter, Danielle
AU - Juty, Nick
AU - Rocca-Serra, Philippe
AU - Xu, Fuqi
AU - Henderson, David
AU - Gu, Wei
AU - Strubel, Jolanda
AU - Giessmann, Robert T.
AU - Emam, Ibrahim
AU - Gadiya, Yojana
AU - Abbassi-Daloii, Tooba
AU - Alharbi, Ebtisam
AU - Gray, Alasdair J. G.
AU - Courtot, Mélanie
AU - Gribbon, Philip
AU - Ioannidis, Vassilios
AU - Reilly, Dorothy S.
AU - Lynch, Nick
AU - Boiten, Jan-Willem
AU - Satagopam, Venkata
AU - Goble, Carole
AU - Sansone, Susanna-Assunta
AU - Burdett, Tony
N1 - Funding Information:
This manuscript describes a “FAIRification framework” designed to address this demand by supporting organisations and projects undertaking a FAIR transformation. Specifically, we describe a reproducible and sustainable process that can be used to improve the adoption of the FAIR principles by optimising the use of available resources and expanding organisational FAIR data management capabilities. This is achieved through focused prioritisation of needs, based on a thorough analysis of the unique and specific FAIR challenges of each specific project. This framework is one of the outcomes of the FAIRplus consortium ( https://fairplus-project.eu ), an international project with partners from academia and major pharmaceutical companies, funded by the Innovative Medicines Initiative (IMI, https://www.imi.europa.eu ), the largest private-public partnership program funding health research and innovation.
Publisher Copyright:
© 2023, The Author(s).
PY - 2023/5/19
Y1 - 2023/5/19
N2 - 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.
AB - 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.
KW - COVID-19
KW - Datasets as Topic
KW - Humans
KW - Pandemics
KW - Public-Private Sector Partnerships
KW - Reproducibility of Results
UR - https://www.scopus.com/pages/publications/85159717611
U2 - 10.1038/s41597-023-02167-2
DO - 10.1038/s41597-023-02167-2
M3 - Article
C2 - 37208349
SN - 2052-4463
VL - 10
JO - Scientific Data
JF - Scientific Data
M1 - 291
ER -