Poster P289 presented at the annual meeting of the German Society of Nephrology (DGfN 2026), category Interdisziplinäre Nephrologie, on behalf of the NFDI4Immuno consortium. Abstract Many kidney related diseases - including lupus nephritis, IgA nephropathy, ANCA-associated vasculitis, and transplant rejection - are driven by undesired immune system function. Understanding the underlying processes requires access to high-quality, reusable data across diverse assay types. NFDI4Immuno, a consortium within the Nationale Forschungsdateninfrastruktur (NFDI), addresses this need by building a federated data hub that implements FAIR principles (Findable, Accessible, Interoperable, Reusable) for immunological data types directly relevant to nephrology, including AIRR-seq, cytometry, immunopeptidomics, microscopy, and immune receptor reactivity data. The metadata model for NFDI4Immuno is built on the framework laid out in the Ontology for Biomedical Investigations (OBI) and implements a study/subject/sample hierarchy that captures the full data provenance chain - from study design through sample collection to data generation. Domain-specific standards such as MiAIRR and MiFlowCyt are integrated within this structure, with harmonisation across assay types where possible. The metadata schema is module and can be independently extended with new assays and data types as they become available. Since human subject data is central to immunological research, the model is designed with GDPR compliance in mind, guiding which metadata can be collected and made discoverable. Interoperability is a core design goal. NFDI4Immuno collaborates closely with complementary NFDI consortia - including GHGA, NFDI4Health, NFDI4Microbiota, and NFDI4BIOIMAGE - and maps to external resources such as the AIRR Data Commons. Compliance with DCAT-AP and HealthDCAT-AP/FHIR standards enables integration into the European Open Science Cloud (EOSC) and the emerging European Health Data Space (EHDS), broadening data discovery across institutional and national boundaries. For the nephrology community, this infrastructure will offer standardised, machine-readable access to immunological datasets that can support translational research, biomarker discovery, and the development of AI-driven analysis tools - ultimately contributing to improved diagnostics and outcomes in immune-mediated kidney diseases. We invite nephrology researchers to engage with NFDI4Immuno through concrete use cases and data in our pilot-user programme to help shape this infrastructure for the needs of the community. NFDI4Immuno is funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), project number 501875662.
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