¹Berlin Institute of Health at Charité Universitätsmedizin Berlin, Berlin, Germany
²ZB MED – Information Centre for Life Sciences, Köln, Germany
³University Medicine Greifswald, Greifswald, Germany
⁴Heidelberg Institute for Theoretical Studies (HITS), Heidelberg, Germany

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Introduction: With the growing volume of data generated in clinical, epidemiological, and public health research, the application of the FAIR principles is essential to ensure that data can be found, exchanged, and reused across institutions and systems. However, metadata often lacks structural consistency and semantic standardization, impeding interoperability and secondary use. Metadata, in this context, refers to data about data – that is, structured descriptors that define key properties of studies, documents, questionnaires, and registries, such as design, objectives, population, data collection instruments and provenance. The German National Research Data Infrastructure for Personal Health Data (NFDI4Health) addresses this challenge by developing a modular Metadata Schema (MDS) that supports syntactic and semantic harmonization of metadata describing these research resources.

Methods: All elements of NFDI4Health MDS version 3.3.1 were systematically analyzed with regard to structure, terminology requirements, and compatibility with international standards. Standard terminologies such as SNOMED CT, LOINC, ICD, UCUM, and the NCI Thesaurus were used for semantic annotation. Where no suitable terms existed, local CodeSystems were defined. The schema was operationalized using HL7 FHIR R4, developed with the FHIR Shorthand (FSH) compiler SUSHI and validated with the gematik codfsh validator. Profiles were created for key resource types including ResearchStudy, Questionnaire, DocumentReference, Library, and Composition. All artifacts were published as a FHIR Implementation Guide via GitHub and Simplifier.

Results: The resulting Implementation Guide includes 19 FHIR profiles, 67 ValueSets, and 51 extensions. It provides a reusable framework for representing structured metadata across various study types. The Composition resource was adapted to reference other resources depending on the context. Conditional cardinalities were used to express logical dependencies between metadata elements. Use-case-specific modules such as nutritional epidemiology and chronic diseases were implemented within the Composition structure.

Discussion: The framework enables standardized, interoperable metadata representation for health research, but several limitations were identified. These include the extensive use of extensions, gaps in terminology coverage, and technical constraints of FHIR R4. The metadata schema aims to support interoperability by providing a structured, machine-readable foundation for health research descriptions. While EHDS alignment is not yet part of this release, it is planned for Phase II of the project.

Conclusion: The NFDI4Health FHIR-based metadata framework offers a scalable, standards-compliant approach to metadata representation in health research. The openly available Implementation Guide supports harmonization and fosters interoperability. Further refinement in collaboration with the NFDI4Health community and alignment with European initiatives are planned.

The authors declare that they have no competing interests.

The authors declare that an ethics committee vote is not required.

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