The SPHN Metadata Catalog: A comprehensive tool for health data discovery and exploration based on FAIR principles

Overview

EFMI Medical Informatics Europe (MIE) is the flagship conference of the European Federation for Medical Informatics, bringing together clinicians, informaticians, and data engineers working on health data infrastructure, interoperability, and digital health across Europe. The 2025 edition was held in Glasgow, Scotland.

At this conference, I presented and demonstrated the SPHN Metadata Catalog alongside my colleague Dr. Harald Witte. Rather than a purely slide-based talk, the session included a live demonstration walking the audience through the SPHN Metadata Catalog - from the perspective of both a data manager submitting metadata and a researcher discovering and exploring datasets.

Demonstration

The demonstration covered four aspects of the SPHN Metadata Catalog:

• Design: An overview of the catalog's architecture and its design - why a FAIR Data Point was chosen as the foundation, how HealthDCAT-AP and the SPHN Metadata Catalog Schema define the metadata model, and how VoID statistics are incorporated to give researchers quantitative insight into dataset structure before requesting access.
• Metadata Submission Workflow: We walked through the process a data manager follows to describe a dataset in the catalog - from filling in structured metadata fields aligned with the SPHN Metadata Catalog Schema, through to validation and publication.
• FAIR Data Point UI: We demonstrated the catalog's main interface - built on the FDP reference implementation with SPHN-specific modifications - showing how researchers can browse the catalog, inspect dataset descriptions, view access conditions, and retrieve machine-readable metadata via the FDP API.
• SPHN Schema Scope: Finally, we demonstrated SPHN Schema Scope, the SPHN schema exploration interface, which allows users to navigate the SPHN and project-specific RDF Schema interactively - traversing class hierarchies, inspecting properties and their value sets, and understanding how clinical concepts are modeled. And also explore how datasets are structured based on the schema by exploring instance count and data densities. This component enables researchers to assess the semantic structure of a dataset before requesting access.