A Framework for Machine-Actionable FAIR Assessment as a Tool to Measure FAIRification Success

The FAIR principles (Findable, Accessible, Interoperable, Reusable) are central to building efficient digital infrastructures in agriculture, forestry, and food systems. Manual FAIR assessments, however, often lead to inconsistent results. Within the FAIRagro project, we present a framework for automated FAIR assessment that combines multiple evaluation tools. The assessment outcomes are represented in RDF using the Data Quality Vocabulary (DQV), providing a uniform and machine-readable expression of data quality. This approach supports consistent evaluation and enables ongoing improvement of FAIRification processes, contributing to FAIRagro’s mission of advancing FAIR data practices.