Evolving FAIR Image Analysis in Galaxy for Cross-domain and AI-ready Applications

The increasing adoption of image-based technologies across life sciences, environmental research, and related domains has increased the demand for interoperable, reproducible, and FAIR-compliant image analysis infrastructures. At ELIXIR BioHackathon Europe 2025, Project 9, “Evolving FAIR Image Analysis in Galaxy for Cross-domain and AI-ready Applications”, addressed these challenges by enhancing the Galaxy platform for bioimage analysis with a focus on semantic interoperability, content-based reproducibility validation, and user-centered onboarding tutorials.To advance semantic interoperability, we developed a curated vocabulary based on the EDAM Bioimaging ontology, which was applied to annotate tutorials on the Galaxy Training Network, improving discoverability and aligning with evolving community standards. For reproducibility and AI-readiness, we integrated the International Standard Content Code (ISCC) via the ISCC-SUM tool suite, enabling format-independent content-based validation, dataset deduplication, and assessment of data similarity for robust model training. Finally, usability improvements included a comprehensive onboarding tutorial for newcomers, enhanced integration with OMERO and BioImage Archive, and generally improved tool interoperability, including support for GeoJSON-based spatial annotations.Collectively, these developments establish a scalable, cross-domain image analysis framework within Galaxy, promoting FAIR-aligned practices while enabling reproducible and AI-ready workflows.