Ready, Set, AI: The Metadata Completeness and FAIR Data Assessment

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dc.creatorDe Leon, Dehner M.
dc.date2026
dc.date2026-05-07T03:10:56Z
dc.date.accessioned2026-06-27T04:12:52Z
dc.description"This training material outlines a strategic framework to bridge the gap between raw agricultural data and AI-driven insights. It emphasizes that successful AI solutions depend on high-quality Data Fundamentals, moving away from ""hallucinated"" or fragmented information toward standardized, harmonized data ecosystem, and apply the FAIR principles (Findable, Accessible, Interoperable, and Reusable) of data assets. By defining clear RACI roles and implementing institutional web platforms like the IRRI SIRS Data Management Platform and Farm Household Survey Database, the training establishes a governance structure where datasets—ranging from socio-economic surveys to agronomic trials—are treated as valuable global public goods. The core of the workshop is a practical methodology for assessing and improving Metadata Completeness to ensure ""AI readiness"". Participants utilize a FAIR Scoring Dashboard to evaluate internal datasets using the CGIAR FAIR or Crosslateral tool, identifying specific gaps in documentation that prevent machine actionability. By integrating these assessment tools with platforms like the IRRI Dataverse, the training provides a repeatable workflow for data stewards to increase the visibility, citation potential, and long-term utility of agricultural research."
dc.formatapplication/pdf
dc.identifierhttps://hdl.handle.net/10568/182817
dc.identifier.urihttp://hdl.handle.net/123456789/25341
dc.languageen
dc.publisherInternational Rice Research Institute
dc.rightsOpen Access
dc.sourceDe Leon, Dehner M. (2026). Ready, Set, AI: The Metadata Completeness and FAIR Data Assessment. International Rice Research Institute, Los Baños, Philippines. 44 p.
dc.subjectresearch data
dc.subjectdata management
dc.subjectmetadata
dc.subjectmetadata standards
dc.subjectdigital literacy
dc.subjectknowledge management
dc.subjectdatasets
dc.titleReady, Set, AI: The Metadata Completeness and FAIR Data Assessment
dc.typeTraining Material

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Instituto Internacional de Investigación del Arroz (IRRI)