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EUDAT

EUDAT. PIDs in EUDAT Webinar , 15 Februari 2013. Mark van de Sanden EUDAT WP leader SARA, The Netherlands. Collaborative Data Infrastructure -A framework for the future ? -. User functionalities, data capture & transfer, virtual research environments. Data Generators. Users.

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EUDAT

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  1. EUDAT PIDs in EUDAT Webinar, 15 Februari 2013 Mark van de Sanden EUDAT WP leader SARA, The Netherlands

  2. Collaborative Data Infrastructure -A frameworkforthefuture? - User functionalities, data capture & transfer, virtual research environments Data Generators Users Trust Data discovery & navigation, workflow generation, annotation, interpretability Community Support Services Data Curation Persistent storage, identification, authenticity, workflow execution, mining Common Data Services

  3. Consortium

  4. Five research communities on Board • EPOS: European Plate Observatory System • CLARIN: Common Language Resources and Technology Infrastructure • ENES: Service for Climate Modelling in Europe • LifeWatch: Biodoversity Data and Observatories • VPH: The Virtual Physiological Human • All share common challenges: • Reference models and architectures • Persistent data identifiers • Metadata management • Distributed data sources • Data interoperability

  5. Communities ↔ Data Centers Requirements, servicecases Technology appraisal & matching Service provision

  6. First EUDAT Services Metadata Catalogue AAI Network of trust among authentication and authorization actors Aggregated EUDAT metadata domain. Data inventory Data Staging Safe Replication Simple Store Dynamic replication to HPC workspace for processing Data curation and access optimization Researcher data store (simple upload, share and access)

  7. Metadata First EUDAT Services Data PID Metadata Catalogue AAI Network of trust among authentication and authorization actors Aggregated EUDAT metadata domain. Data inventory Data Staging Safe Replication Simple Store Dynamic replication to HPC workspace for processing Data curation and access optimization Researcher data store (simple upload, share and access)

  8. Scientific Data Pyramid

  9. Long tail of data These type of services targets small research groups, homeless and citizen scientists Register data that it can be referenced

  10. Data Life Cycle Data Stage-Out EUDAT enrichment processing reduction analysis domain of globally referable data temporary data referabledata publication global registration citable acquisition generation description registration data preservation Data Stage-In Safe Replication EPIC PID

  11. Use Case: CLARIN – Safe Replication EPIC PID registry

  12. EPIC Consortium Make data reference able and findable Provide a sustainable service for storing and maintaining large volumes of PIDs Consists of 4 partners: GWDG, DKRZ, CSC and SARA Service basedon the Handle service withanEPICextensionfor easy management Handle service provides replication of PIDs and global search and acrossPID domain (Handle, DataCite and EPIC)

  13. EPIC Service Findhdl:11100/00335742-6026-11e2-9724-e41f13eb41b2 EPIC stores more then 5.5M DO EPOS communities registered >500k DO in the last few months Data User Data Manager Manage11100/xxx-……..-xxxx

  14. Questions

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