The original intent was that these principles would apply seamlessly to data and all scholarly digital objects, including research software 3, workflows 4, and even domain-specific custom digital objects 5. In order to maximize the impact of these investments and create best practices that lead to scientific discovery and innovation, a diverse set of stakeholders defined a set of findable, accessible, interoperable, and reusable (FAIR) principles in 2016 1, 2. The production, collection, and curation of data require painstaking planning and the use of sophisticated experimental and computational facilities. ![]() ![]() Scientific Data volume 10, Article number: 487 ( 2023) ![]() ![]() FAIR for AI: An interdisciplinary and international community building perspective
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