Sprecher
Beschreibung
The German National Research Data Infrastructure (NFDI) BioMed Interest Group unites five NFDI consortia - GHGA, NFDI4BIOIMAGE, NFDI4Health, NFDI4Immuno, and NFDI4Microbiota - to address the distinct technical, regulatory and ethical challenges of research data management (RDM) in biomedical science. These consortia represent diverse research communities, each with their own approach to defining knowledge and data organisation. This results in heterogeneity across metadata schemas, terminologies, perspectives, stakeholder groups and even legal frameworks, making comparison of data and interfacing between services difficult. Consequently, systematic mappings between metadata schemas is difficult, but such interoperability is essential to establish an ecosystem of linked resources.
To address this, the group held workshops in 2024 (DKFZ, Heidelberg) and 2025 (ZB Med, Cologne). The first established mutual understanding of current practices through deep-dive sessions on data models, infrastructure, and regulations; the second developed a metadata interoperability roadmap (Figure 1). This roadmap focuses on discovery-level metadata describing study characteristics rather than enforcing deep semantic alignment of all raw variables. A key opportunity lies in the European Health Data Space (EHDS) requirement for the HealthDCAT-AP specification. The group is developing a consortia-overarching metadata layer that aligns with NFDI Task-Force Metadata recommendations while incorporating HealthDCAT-AP and Health Level 7 Fast Healthcare Interoperability Resources (HL7 FHIR). This approach enables both interoperability across data infrastructures through harmonised general metadata and metadata flexibility for the different communities.
This strategy enables interoperability across infrastructures through harmonised discovery metadata while allowing individual communities to maintain the flexibility of their domain-specific schemas. By implementing this overarching layer, it provides a pathway to preserve disciplinary diversity while facilitating cross-repository data discovery and meta-analysis. Furthermore, this framework provides a viable model for a common data catalogue description suitable for the European Open Science Cloud (EOSC) without losing the granular detail required by individual research communities.