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; Melanie 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
, Melanie Courtot
, Philip Gribbon
, Vassilios Ioannidis
, Dorothy s. Reilly
, Nick Lynch
, Jan-Willem Boiten
, Venkata Satagopam

Carole Goble, Susanna-Assunta Sansone, Tony Burdett^*

^*Corresponding author for this work

NUTRIM - Metabolic Health

Research output: Contribution to journal › Article › Academic › peer-review

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
Number of pages	9
Journal	Scientific data
Volume	10
Issue number	1
DOIs	https://doi.org/10.1038/s41597-023-02167-2
Publication status	Published - 19 May 2023

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https://www.nature.com/articles/s41597-023-02167-2

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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(1), Article 291. https://doi.org/10.1038/s41597-023-02167-2

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title = "FAIR in action - a flexible framework to guide FAIRification",

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.",

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 Melanie 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",

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month = may,

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

language = "English",

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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, no. 1, 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, Melanie

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

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.

U2 - 10.1038/s41597-023-02167-2

DO - 10.1038/s41597-023-02167-2

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SN - 2052-4463

VL - 10

JO - Scientific data

JF - Scientific data

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M1 - 291

ER -

FAIR in action - a flexible framework to guide FAIRification

Abstract

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