FAIRification of computational models in biology

Computational models are essential for studying complex systems which, particularly in clinical settings, need to be quality-approved and transparent. To enhance the communication of a model's features and capabilities, we propose an adaptation of the Findability, Accessibility, Interoperabilit...

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Published inbioRxiv
Main Authors Balaur, Irina, Nickerson, David P, Welter, Danielle, Wodke, Judith A H, Ancien, Francois, Gebhardt, Tom, Grouès, Valentin, Hermjakob, Henning, König, Matthias, Radde, Nicole, Rougny, Adrien, Schneider, Reinhard, Malik-Sheriff, Rahuman S, Shiferaw, Kirubel Biruk, Stefan, Melanie, Satagopam, Venkata, Waltemath, Dagmar
Format Journal Article Paper
LanguageEnglish
Published United States Cold Spring Harbor Laboratory 24.03.2025
Edition1.1
Subjects
Systems Biology
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ISSN2692-8205
2692-8205
DOI10.1101/2025.03.21.644517

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Summary:Computational models are essential for studying complex systems which, particularly in clinical settings, need to be quality-approved and transparent. To enhance the communication of a model's features and capabilities, we propose an adaptation of the Findability, Accessibility, Interoperability and Reusability (FAIR) indicators published by the Research Data Alliance to assess models encoded in domain-specific standards, such as those established by COMBINE. The assessments guide FAIRification and add value to models.
Bibliography:ObjectType-Working Paper/Pre-Print-3
ObjectType-Article-1
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Competing Interest Statement: The authors have declared no competing interest.
ISSN:2692-8205
2692-8205
DOI:10.1101/2025.03.21.644517