Bioimage management and analysis in Galaxy: Tools, workflows, training, and community practices.

Abstract

Image analysis in the life sciences is constrained by fragmented software ecosystems, heterogeneous data formats, and limited reproducibility. These barriers hinder the reuse of image analysis methods and the sustainability of tools. In this article, we describe how the Galaxy platform enables FAIR (Findable, Accessible, Interoperable, and Reusable) image analysis by providing an integrated environment for data access, workflow execution, provenance tracing, and training. We present Galaxy as a computational workbench that supports diverse image formats and integrates with public, institutional, and private repositories. We describe a reference structure for FAIR image analysis workflows and illustrate how this pattern supports reproducibility, interoperability, and reuse. We also describe community-driven training and sustainability practices that embed FAIR principles directly into executable tutorials and shared workflows. Together, these foundations position Galaxy as a reproducible, scalable, and community-maintained platform for FAIR image analysis across the life sciences and beyond.