Towards reproducible and FAIR preclinical research: modernizing metadata management

This presentation addresses the crisis of low experimental reproducibility, with one project finding only 21% of experiments were replicable. It proposes a "FAIR-by-design" solution to enhance data quality and sharing. This centers on the FAIR Guiding Principles (Findable, Accessible, Interoperable, Reusable). The framework is extended to FAIRRR, which integrates the 3Rs (Reduction, Replacement, Refinement) to directly improve animal welfare through better data management. The tool proposed is METADATAPP, a single platform designed to capture, validate, and link all metadata automatically during an experiment. This system uses ontologies (like HCMO) to create high-quality, standardized, and "AI-ready" data. The ultimate goal is a Local data, Global Metadata communication model, where a federated layer enables analysis and reuse of standardized metadata across multiple distributed labs.