[Strategies for Biobank Networks. Classification of Different Approaches for Locating Samples and an Outlook on the Future Within the BBMRI-ERIC]

Background: Medical research projects often require more biological material than can be supplied by a single biobank. For this reason, a multitude of strategies support locating potential research partners with matching material without requiring centralization of sample storage.

Objectives: Classification of different strategies for biobank networks, in particular for locating suitable samples. Description of an IT infrastructure combining these strategies.

Materials And Methods: Existing strategies can be classified according to three criteria: (a) granularity of sample data: coarse bank-level data (catalogue) vs. fine-granular sample-level data, (b) location of sample data: central (central search service) vs. decentral storage (federated search services), and (c) level of automation: automatic (query-based, federated search service) vs. semi-automatic (inquiry-based, decentral search). All mentioned search services require data integration. Metadata help to overcome semantic heterogeneity.

Results: The "Common Service IT" in BBMRI-ERIC (Biobanking and BioMolecular Resources Research Infrastructure) unites a catalogue, the decentral search and metadata in an integrated platform. As a result, researchers receive versatile tools to search suitable biomaterial, while biobanks retain a high degree of data sovereignty.

Conclusions: Despite their differences, the presented strategies for biobank networks do not rule each other out but can complement and even benefit from each other.