Ready, Set, AI: The Metadata Completeness and FAIR Data Assessment
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International Rice Research Institute
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"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."
Palabras clave
research data, data management, metadata, metadata standards, digital literacy, knowledge management, datasets