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Selection of data sets for FAIRification in drug discovery and development: Which, why, and how?

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Alharbi, Ebtisam | Gadiya, Yojana | Henderson, David | Zaliani, Andrea | Delfin-Rossaro, Alejandra | Cambon-Thomsen, Anne | Kohler, Manfred | Witt, Gesa | Welter, Danielle | Juty, Nick | Jay, Caroline | Engkvist, Ola | Goble, Carole | Reilly, Dorothy, S | Satagopam, Venkata | Ioannidis, Vassilios | Gu, Wei | Gribbon, Philip

Edité par CCSD ; Elsevier -

International audience. Despite the intuitive value of adopting the Findable, Accessible, Interoperable, and Reusable (FAIR) principles in both academic and industrial sectors, challenges exist in resourcing, balancing longversus short-term priorities, and achieving technical implementation. This situation is exacerbated by the unclear mechanisms by which costs and benefits can be assessed when decisions on FAIR are made. Scientific and research and development (R&D) leadership need reliable evidence of the potential benefits and information on effective implementation mechanisms and remediating strategies. In this article, we describe procedures for cost-benefit evaluation, and identify best-practice approaches to support the decision-making process involved in FAIR implementation.

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