title: [Strategies for biobank networks. Classification of different approaches for locating samples and an outlook on the future within the BBMRI-ERIC].
abstract: 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.
  
  
  OBJECTIVE
  Classification of different strategies for biobank networks, in particular for locating suitable samples. Description of an IT infrastructure combining these strategies.
  
  
  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.
authors.affiliation: Medizinische Informatik in der Translationalen Onkologie, Deutsches Krebsforschungszentrum, Im Neuenheimer Feld 280, 69120, Heidelberg, Deutschland. m.lablans@dkfz.de.
authors.forename: Martin
authors.initials: M
authors.lastname: Lablans
authors.affiliation: Institut für medizinische Biometrie, Epidemiologie und Informatik (IMBEI), Universitätsmedizin Mainz, 55101, Mainz, Deutschland.
authors.forename: Dennis
authors.initials: D
authors.lastname: Kadioglu
authors.affiliation: Lehrstuhl für Medizinische Informatik, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Deutschland.
authors.forename: Sebastian
authors.initials: S
authors.lastname: Mate
authors.affiliation: Lehrstuhl für Medizinische Informatik, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Deutschland.
authors.forename: Ines
authors.initials: I
authors.lastname: Leb
authors.affiliation: Lehrstuhl für Medizinische Informatik, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Deutschland.
authors.forename: Hans-Ulrich
authors.initials: HU
authors.lastname: Prokosch
authors.affiliation: Medizinische Informatik in der Translationalen Onkologie, Deutsches Krebsforschungszentrum, Im Neuenheimer Feld 280, 69120, Heidelberg, Deutschland.
authors.forename: Frank
authors.initials: F
authors.lastname: Ückert
mesh_terms: D018070:Biological Specimen Banks / Q000458:organization & administration*; D035843:Biomedical Research / Q000458:organization & administration*; D003628:Database Management Systems / Q000458:organization & administration*; D016208:Databases, Factual; D005060:Europe; D005544:Forecasting; D033181:Information Dissemination / Q000379:methods; D016247:Information Storage and Retrieval / Q000379:methods; D007373:Interinstitutional Relations; D017146:Models, Organizational; D012042:Registries; D013048:Specimen Handling / Q000379:methods
keywords: Data integration; Federation; Joint research; Registry; Research infrastructure