Work

Increasing FAIRness of FAIRagro data through AI supported metadata enrichment

Year: 2025

Type: article

Abstract: 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.

Source: OpenAgrar

DOI: 10.5281/zenodo.17349977

Authors: Abdelmalak, Abanoub, Fluck, Juliane, Golz, Leonard, Husain, Murtuza, Meier, Kristin, Riegler, Heike, Schneider, Gabriel, Specka, Xenia, Svoboda, Nikolai, on behalf of the FAIRagro consortium

Institutions: ZB MED - Information Centre for Life Sciences, Julius Kühn-Institut, Leibniz Centre for Agricultural Landscape Research

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

Award: FAIRe Dateninfrastruktur für die Agrosystemforschung

OpenAlex ID: W7092616941

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