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Introduction IsoFoodTrack is a robust and extensible database developed by the Jožef Stefan Institute for managing isotopic and elemental composition data across food commodities. It enables structured storage, integration and retrieval of data enriched with metadata such as geographical parameters, farming practices, processing methods and analytical protocols. This information is essential for traceability, authenticity verification and fraud detection. Methodologies This Use Case will enhance IsoFoodTrack with new food types, analytical techniques and metadata requirements. It will expand the data schema, standardize data entry – make them FAIR, support interoperability through formats and APIs, and enable statistical and machine learning analysis for origin and production-pattern assessment. Output The main output will be an interoperable version of the IsoFoodTrack database, supporting newly integrated food commodities, analytical methods and customizable metadata fields. The deliverable will be a database design and functionalities report, developed as open as possible. Impact The upgraded platform will support food provenance (origin), safety, traceability, authenticity verification and food fraud detection. Isoscapes will help assess geographical origin and environmental influences on food composition. A user-friendly web interface will support efficient querying, visualization and export, while the open-access model will promote transparency, institutional collaboration and policy support. What I can offer Expertise in isotopic and elemental food data, IsoFood ontology, metadata structuring, isoscape generation, food authenticity, traceability and fraud detection. The Use Case also offers an existing platform that can be expanded and connected with external research infrastructures. What I need Support is needed for integrating new food commodities, expanding analytical methods, updating data structures, harmonizing metadata, developing APIs and ensuring interoperability. Collaboration is also needed for data sharing, pilot-study data, literature data integration, machine learning development and long-term platform uptake.
Source: Annual AgData Use Case Summit 2026 booklet.