Research Data Management on Autopilot with RSpace and Galaxy

<p>Research data management systems often exist in isolation from computational platforms, creating fragmentation in the scientific record and hindering reproducibility and reusability. To address this challenge, we present an innovative integration between RSpace, a research data management platform with an electronic laboratory notebook (ELN) and sample man-agement system at its core, and the Galaxy Project, a user-friendly computational workflow platform. This collaboration aims to create a cohesive infrastructure that enables robust track-ing of primary research data through computational processing to final results, thereby en-hancing research reproducibility, data provenance, and compliance with data management requirements. Through tools like RSpace researchers provide critical experimental context to primary data. Furthermore, RSpace connects and shares data with a wide variety of research and RDM tools. By providing easy access to workflows in Galaxy, researchers can seamlessly transfer their data with all relevant contextual information to computational workflows. The Galaxy plat-form then takes care of reporting processing steps and transformations back to RSpace, en-suring complete provenance using RO-Crates. Thereby, a robust scientific record that at any point in the research date lifecycle provides access to the full history of data from primary collection through analysis to interpretation is accomplished. Our technical approach leverages multiple integration strategies: API-based connectivity ena-bles secure and direct communication between platforms; implementation of an RSpace pyfilesystem facilitates standardized data access; and adoption of BCO and RO-crate as a shared packaging format ensures interoperability and alignment with emerging standards in research data management. This integration, currently in development, exemplifies "RDM in Action" by transforming ab-stract principles of data management into practical, researcher-centered tools that support the scientific process. By connecting experimental documentation directly with computational resources, we reduce barriers to adoption of best practices in research data management. Our presentation will include a demonstration of the current integration, discussion of technical challenges and solutions, and a roadmap for future development. We invite feedback from the research data infrastructure community to inform ongoing refinement of this collaborative solution.</p>