Increasing FAIRness of FAIRagro data through AI supported metadata enrichment
Background: High quality metadata is essential for FAIR1 data.Method: A training corpus has been generated to enable AI-based extraction of crop and soil metadata from FAIRagro Research Data Infrastructures (RDIs).Outlook: Enhanced findability through integration into the FAIRagro Search Hub2 and RDIs and support researchers in creating high quality metadata during data publication.
Institutions
- Julius Kühn-Institut (DE)
- ZB MED - Information Centre for Life Sciences (DE)
- Leibniz Centre for Agricultural Landscape Research (DE)
Publication Details
- Journal
- Zenodo (CERN European Organization for Nuclear Research)
- Published
- 2025-10-14
- DOI
- https://doi.org/10.5281/zenodo.17349977
- Primary Topic
- Research Data Management Practices
- Type
- article
- Field-Weighted Citation Impact
- 0.00