Data quality in biobanks: The Federated Data Quality Framework

Register here

Pre-analytical sample quality is an established topic in biobanking – the previous BBMRI-ERIC Academy webinar “High-Quality Samples and What About the Data?” raised the question that ultimately determines the usability of any collection: the quality of the data describing those samples. Data quality is frequently the bottleneck in discovery, sharing and reuse.

This follow-up webinar introduces the Federated Data Quality Framework (FDQF), a tool developed by BBMRI-ERIC and BBMRI.cz teams that aims to assess biobank data quality continuously, comparably, and without centralising the data. The approach is federated: checks run locally at each node, and only aggregated quality indicators are passed to the central level. Neither the data nor individual donor records leave the institution — essential for GDPR compliance and for trust between nodes.

The webinar covers the principles and architecture of FDQF, the catalogue of checks and data quality dimensions (completeness, consistency, validity, uniqueness, timeliness), the form of the outputs — quality indicators, dashboards and comparison of collections over time — and practical experience from deployment in the BBMRI.czenvironment. It will also address what node onboarding involves and how to embed data quality into routine biobank operations.

Speaker: Radovan Tomášik — developer of the FDQF tool, BBMRI-ERIC HQ/BBMRI.cz

Host: Sten Hanke — BBMRI-ERIC HQ

After watching the webinar, participants will have acquired the following knowledge:

• how federated data quality assessment works and why it matters
• which quality dimensions can be monitored and how to interpret the indicators
• what node onboarding involves and its operational requirements