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 Satagopam; Carole Goble; Susanna-Assunta Sansone; Tony Burdett

doi:10.1038/s41597-023-02167-2

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

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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 language	English
Article number	291
Journal	Scientific Data
Volume	10
DOIs	https://doi.org/10.1038/s41597-023-02167-2
Publication status	Published - 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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Welter, D., Juty, N., Rocca-Serra, P., Xu, F., Henderson, D., Gu, W., Strubel, J., Giessmann, R. T., Emam, I., Gadiya, Y., Abbassi-Daloii, T., Alharbi, E., Gray, A. J. G., Courtot, M., Gribbon, P., Ioannidis, V., Reilly, D. S., Lynch, N., Boiten, J.-W., ... Burdett, T. (2023). FAIR in action - a flexible framework to guide FAIRification. Scientific Data, 10, Article 291. https://doi.org/10.1038/s41597-023-02167-2

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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.",

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author = "Danielle Welter and Nick Juty and Philippe Rocca-Serra and Fuqi Xu and David Henderson and Wei Gu and Jolanda Strubel and Giessmann, {Robert T.} and Ibrahim Emam and Yojana Gadiya and Tooba Abbassi-Daloii and Ebtisam Alharbi and Gray, {Alasdair J. G.} and M{\'e}lanie Courtot and Philip Gribbon and Vassilios Ioannidis and Reilly, {Dorothy S.} and Nick Lynch and Jan-Willem Boiten and Venkata Satagopam and Carole Goble and Susanna-Assunta Sansone and Tony Burdett",

note = "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: {\textcopyright} 2023, The Author(s).",

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doi = "10.1038/s41597-023-02167-2",

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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 2023, 'FAIR in action - a flexible framework to guide FAIRification', Scientific Data, vol. 10, 291. https://doi.org/10.1038/s41597-023-02167-2

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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).

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KW - COVID-19

KW - Datasets as Topic

KW - Humans

KW - Pandemics

KW - Public-Private Sector Partnerships

KW - Reproducibility of Results

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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 -

FAIR in action - a flexible framework to guide FAIRification

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