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Advancing Standardization in Scientific Data Infrastructures: Key Insights from Sophia Antipolis

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This report summarizes the outcomes of the e-Infrastructure concertation meeting held in Sophia Antipolis on December 5-6, 2007. The session focused on identifying community-specific requirements for scientific data infrastructures, exploring the relationship with standardization, and outlining actionable next steps. Key objectives included enhancing research capacities, developing ICT-based infrastructures, and promoting effective collaboration among diverse user communities. Emphasis was placed on interoperability challenges and the importance of using and developing standards in scientific data management.

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Advancing Standardization in Scientific Data Infrastructures: Key Insights from Sophia Antipolis

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  1. 4th e-Infrastructure Concertation Sophia Antipolis 5-6 December 2007 Standardisation ande-Infrastructures DATA TRACK (Meeting Room IRIS 6 ) Chair : Krystyna Marek Rapporteur: Wolfram Horstmann

  2. Session Objectives • Find community specific requirements • Find relations with standardization • Find useful next steps

  3. Starting points • Scientific data infrastructures: new field in FP7 • Very different from networking and grids • Representing 15% non-IT disciplines from study sample • Programme Objectives • New projects reinforcing Research Capacities • Develop ICT-based infrastructures • Learning from communities • Standardization envisioned for later stages • Relation to council conclusions • Stress publications and data

  4. Participants • By call • Repository infrastructures • NMBD • DRIVER-II • EuroVO-DCA, EuroVO-AIDA • Genesi-DR • METAFOR • User communities • D4Science (Diligent) • Design studies • Scientific data infrastructures (2008) • Parse.insight, (Caspar) (preservation) • Observers • D-Grid (Knowledge Layer) • BELIEF: for reporting

  5. Self-perception • ‚Vertical‘ Communities • Complex, multidisciplinary disciplines with intreroperability challenges within the community • Specific, heterogeneous provenance of data • Users of standards (but also developers?)

  6. Metadata, Data and Formats • Legacy: data collected for many years • proprietary encodings (e.g. vendor-driven) • Bottom-up problem solving • instrument/methodology-driven • Wide range of data volumes (GB vs. PB) • Resources have complex life-cycle • Multidisciplinary: No common denominator • Formats differently applied across communities • Differentiation between metadata and data non always valid

  7. Focus on Interoperability • Not the same as standards • Problem oriented solutions • „Diversity of Formats not the main problem“ • e.g. language harmonization by converter • e.g. running models again cheaper than reformat • Standardization as a posteriori process • „As opposed to industry“ (?) • Usage vs. Preservation • Actual requirements vs. sustainability

  8. Common standards usage • Authentication and Authorization • Authenticity of Resources • Provenance information • Contextualize the creation situation • Preservation >> „But it‘s not our core business“

  9. A notion: ”Division of labour“ • Networks and GRIDs provide generic interop. • the research process is not immediately touched • Preservation not done by the researcher • Responsibility of data-centers and data producers • Curation / quality control • Collaboration with researchers needed • Research process is community-driven

  10. Expectations on data interoperability / standards • Access layer to a wide range of different resources needed • Not much horizontal data standardization • Only interface standardization • Virtualization of resources • Respecting (not developing) standards

  11. Standards-Use Red = proactive contributons

  12. Standards-Use Red = proactive contributons

  13. Next steps • Share lessons learnt in data-management • Simple forms of networking • Standards web-site • Functions / usage models • Forum • Mailing-list (?) • List of contact-persons • Bilateral discussions • Workshops • Consultancy for generic standards

  14. Conclusions • Research-process dominates data-management • Distance from generic technology standards (e.g. networking/grids) • Cross-consultancy demand is acknowledged • Heavy use of standards and even participation in standardization • Further knowledge exchange appreciated

  15. Data infrastructure basics

  16. Data infrastructure challenges

  17. Impact • Bioinformatics

  18. NMDB • Geophysical • Standards • Metadata (IVOC) • Interfaces (…)

  19. METAFOR • Climate Modelling • „Standard CIM definition“ • CIM=common information model • Metadata generation tools • International • ISO 19139 (Geographic MetaData XML) • ISO 19115 (geographic information and services ) • Modelling (emerging) • Gridspec, SensorML, NMM, CERA2 • Semantic Description • CF for netcdf

  20. EuroVO-AIDA • Astronomy • Research • Evolution of interoperability standards • Data access protocols and data models • Standardizing body (IVOA) • Semantics, data models, data access, query, grid, registry and WS

  21. Genesi-DR • Earth science • SOA/OAIS • ISO 19115 (geographic information and services ) • Inspire • GEOSS • GPOD

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