The BBMRI-ERIC EOSC Node integrates Europe’s biobanks and biomolecular resources into the EOSC Federation. It provides GDPR-compliant, FAIR access to biosamples, genomic, imaging, and clinical data across ~500 biobanks in 25 countries.
The Node enables secure federated analysis environments, contributes to the European Health Data Space (EHDS2), and supports AI-driven biomedical research with multicentric validation of models and biomarker discovery.
Key objective: To establish the BBMRI-ERIC Node as the biomedical/sensitive health data EOSC Node, enabling FAIR and secure federated access to data, GDPR-compliant analysis, and cross-border research in alignment with EHDS2.
Science areas: Biomedical and clinical research, genomics, biomarker discovery, personalised medicine, oncology, population health, One Health.
FAIR data
The BBMRI-ERIC EOSC Node makes FAIR data from ~500 biobanks: genomics, imaging, clinical metadata. BBMRI Directory ensures findability; federated secure compute ensures accessibility and interoperability under GDPR and EHDS2 frameworks.
Scientific use cases
Multi-centric validation of AI models for prostate-cancer screening
Advances in digital pathology are transforming cancer diagnosis by enabling high-resolution imaging of tissue samples and the application of artificial intelligence (AI) for clinical decision support.
Prostate cancer, one of the most prevalent malignancies among men worldwide, represents a critical case for early and accurate diagnosis, as survival rates are strongly tied to timely detection and treatment. The objective of the multi-centric validation of AI models for prostate-cancer screening (MCVAL) use case, coordinated by the BBMRI-ERIC EOSC Node, is to create a secure environment in which AI models for prostate cancer screening can be validated using data from different hospitals. Rather than building new diagnostic systems from scratch, the project focuses on testing an existing model trained on whole-slide images and assessing how well it performs when applied to data processed elsewhere.
Other use cases
- MCBIO: AI biomarker discovery for colorectal cancer.
- Cross-node integration with Polish, Slovakian, and Life Sciences Connect EOSC Nodes to accelerate biomedical AI development, improve diagnostics, and support personalised medicine.
