Research Data Management

At the Institute for Biomedical Informatics, our Research Data Management (RDM) team plays a key role in enabling the effective, sustainable, and FAIR (Findable, Accessible, Interoperable, Reusable) handling of medical and biomedical research data. We work closely with researchers, clinicians, and data stewards to ensure that data is collected, documented, and structured in a way that supports reproducible science and downstream reuse.

Our expertise spans clinical data standards (such as HL7 FHIR and SNOMED CT), metadata management, privacy-compliant data sharing, and harmonization of heterogeneous data sources across institutional and national infrastructures.

As part of our ongoing innovation efforts, the RDM team is currently exploring the potential of large language models (LLMs) to support and enhance research data management workflows. This includes investigating how LLMs can assist with data documentation, metadata enrichment, semantic annotation, automated mapping to clinical terminologies, and improving the discoverability and usability of research data. Our goal is to evaluate and integrate these tools in ways that are both technically robust and aligned with ethical and regulatory standards in the biomedical domain.

Through this work, we aim to make research data not only well-managed but also truly useful for researchers, data scientists, and ultimately, for patients.