OBJECTIVE: Biobanks and biomolecular resources are increasingly central to data-driven biomedical research, encompassing not only metadata but also granular, sample-related data from diverse sources such as healthcare systems, national registries, and research outputs. However, the lack of a standardised, machine-readable format for representing such data limits interoperability, data reuse and integration into clinical and research environments. While MIABIS provides a conceptual model for biobank data, its abstract nature and reliance on heterogeneous implementations create barriers to practical, scalable adoption. This study presents a pragmatic, operational implementation of MIABIS focused on enabling real-world exchange and integration of sample-level data. METHODS: We systematically evaluated established data exchange standards, comparing HL7 FHIR and OMOP CDM with respect to their suitability for structuring sample-related data in a semantically robust and machine-readable form. Based on this analysis, we developed a FHIR-based representation of MIABIS that supports complex biobank structures and enables integration with federated data infrastructures. Supporting tools, including a Python library and an implementation guide, were created to ensure usability across diverse research and clinical contexts. RESULTS: We created nine interoperable FHIR profiles covering core MIABIS entities, ensuring consistency with FHIR standards. To support adoption, we developed an open-source Python library that abstracts FHIR interactions and provides schema validation for MIABIS-compliant data. The library was integrated into an ETL tool in operation at Czech Node of BBMRI-ERIC, European Biobanking and Biomolecular Resources Research Infrastructure, to demonstrate usability with real-world sample-related data. Separately, we validated the representation of MIABIS entities at the organisational level by converting the data structures of BBMRI-ERIC Directory into FHIR, demonstrating compatibility with federated data infrastructures. CONCLUSION: This work delivers a machine-readable, interoperable implementation of MIABIS, enabling the exchange of both organisational and sample-level data across biobanks and health information systems. By integrating MIABIS with HL7 FHIR, we provide a host of reusable tools and mechanisms for further evolution of the data model. Combined, these benefits can help with the integration into clinical and research workflows, supporting data discoverability, reuse, and cross-institutional collaboration in biomedical research.

Bank of Biological Material Masaryk Memorial Cancer Institute Žlutý kopec 7 Brno 656 53 Czech Republic

Bank of Biological Material Masaryk Memorial Cancer Institute Žlutý kopec 7 Brno 656 53 Czech Republic; Faculty of Informatics Masaryk University Botanická 68a Brno 60200 Czech Republic

BBMRI ERIC Neue Stiftingtalstrasse 2 B 6 Graz 8010 Austria

Faculty of Informatics Masaryk University Botanická 68a Brno 60200 Czech Republic; Bank of Biological Material Masaryk Memorial Cancer Institute Žlutý kopec 7 Brno 656 53 Czech Republic; BBMRI ERIC Neue Stiftingtalstrasse 2 B 6 Graz 8010 Austria

German Cancer Consortium Partner site Charité Berlin a partnership between DKFZ and University Hospital Charité Germany Im Neuenheimer Feld 280 Heidelberg 69120 Germany; Charité Universitätsmedizin Berlin Charitéplatz 1 Berlin 10117 Germany

Institute of Computer Science Masaryk University Botanická 68a Brno 60200 Czech Republic; BBMRI ERIC Neue Stiftingtalstrasse 2 B 6 Graz 8010 Austria

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