Operationalizing the FAIR principles at CGIAR
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This 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.
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training, data management, best practices, open science