Work

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

Year: 2025

Type: article

Abstract: 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.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.17105307

Authors: Jung, Jascha, Martini, Daniel, Schneider, Gabriel, Reinosch, Nils

Institutions: Kuratorium für Technik und Bauwesen in der Landwirtschaft, ZB MED - Information Centre for Life Sciences

Language: English

Topic: Research Data Management Practices

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

SDG: Zero hunger

Open Access status: green

Funder: Deutsche Forschungsgemeinschaft