Operationalizing the FAIR principles at CGIAR

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dc.creatorLaporte, Marie-Angélique
dc.creatorPaul, Edie
dc.date2025-12-20
dc.date2026-02-09T09:51:24Z
dc.date2026-02-09T09:51:24Z
dc.date.accessioned2026-06-27T13:30:32Z
dc.descriptionThis training framed FAIR as a practical approach to making CGIAR data reusable at scale, for both humans and machines. It clarified where FAIR applies and how to interpret “FAIR enough” in a CGIAR context. Through CGIAR-specific examples, common failure modes, and role-based responsibilities, the training showed that FAIR is not a one-off compliance task but a design choice embedded across the data lifecycle.
dc.formatapplication/pdf
dc.identifierhttps://hdl.handle.net/10568/181318
dc.identifier.urihttp://hdl.handle.net/123456789/61605
dc.languageen
dc.rightsOpen Access
dc.sourceLaporte, M-A.; Paul, E. (2025) Operationalizing the FAIR principles at CGIAR. [Training material] 47 sl.
dc.subjecttraining
dc.subjectdata management
dc.subjectbest practices
dc.subjectopen science
dc.titleOperationalizing the FAIR principles at CGIAR
dc.typeTraining Material

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