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The Global Scene

This vision aims to create a scientific e-infrastructure that enables seamless access, use, re-use, and trust of data across different domains. Researchers can collaborate, share, and combine data while protecting its integrity and provenance, increasing productivity, and addressing grand challenges such as climate change. The vision also includes the development of environmental research infrastructures for deep Earth, land, sea, and atmosphere, facilitating interoperability of data and methods.

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The Global Scene

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  1. The Global Scene Wouter Los University of Amsterdam The Netherlands

  2. Courtesy: psfk.com

  3. Data sustainabilityData qualityOrphan data The data desert

  4. A cottage industry in the desert Interdisciplinary challenges Data generator User Data generator User Data generators User Data generators User Support services Tool Tool Tool Tool Data storage Data storage Data storage Data storage Data infrastructure

  5. A collaborative Data Infrastructure Trust & Curation Data Generators Users User functionalities, data capture and transfer, virtual research environments Community Support Services Data discovery & navigation, workflow generation, annotation, interpretability Persistant storage, identification, authencity, workflow execution Common Data Services

  6. Climatology Climatology Biology Biology Astronomy Astronomy Chemistry History History Common Science Data Infrastructure Physical chemistry Bio-chemistry Crystallography Earth Science Ground Truth Earth Observation Earth Science Earth Science Chemistry Aggregation in broad disciplines Researcher 2 Researcher 1 Scientific World Community Support Services Data Services Separate disciplines Non Scientific World

  7. Vision 2030 high-level experts group on Scientific Data “Our vision is a scientific e-Infrastructure that supports seamless access, use, re-use and trust of data. In a sense, the physical and technical infrastructure becomes invisible and the data themselves become the infrastructure – a valuable asset, on which science, technology, the economy and society can advance.” High-Level Group on Scientific Data “Riding the Wave: how Europe can gain from the raising tide of scientific data”

  8. US Report of the Blue Ribbon Task Force on Sustainable Digital Preservation and Access

  9. Global collaboratories With a proper scientific e-Infrastructure, researchers in different domains can collaborate on the same data set, finding new insights. They can share the data across the globe, protecting its integrity and checking its provenance. They can use, re-use and combine data, increasing productivity. They can engage in whole new forms of scientific inquiry and treat information at a scale we are only beginning to see. … and help us solving today’s Grand Challenges such as climate change and energy supply.

  10. The laboratory of environmental research infrastructures Deep Earth, land and sea, the atmosphere Living and dead environments

  11. Distributed measurements and monitoring • observatories, sensors, radars, human eyes . . . • physical, chemical and biological parameters Laboratories and experimental facilities • in fixed monitoring stations • on research vehicles, ships, floats and buoys • from aircraft and satellites A variety of data • complex and sometimes fuzzy • heterogeneous and distributed • primary and processed data Analytical and modelling platforms • data exchange and integration • high performance computing and Grid services • e-Laboratories

  12. ESFRI Projects for Env. Sciences IAGOS-ERI EURO-ARGO SIOS Status 2009 EUFAR-COPAL AURORA BOREALIS LIFEWATCH EISCAT-3D EPOS EMSO ICOS

  13. ENVRICommon operations of Environmental Research Infrastructures

  14. Facilitating interoperability of data and methods • Experiments • Controlled parameters • Long-term monitoring • 20+ years for a many parameters • In different systems • Modelling • Covering environmental complexity • Simulations • Understanding parameter changes • Scenarios

  15. Data from various origins indifferent spatial and temporal scales

  16. Example: Oceans and CO2 sequestration The role of fytoplankton

  17. A single vizualized result. But we would like to see thousands of these. Andrew D. Barton et al, Patterns of Diversity in Marine Phytoplankton, Science online 25 Feb 2010

  18. User create their own collaborative virtual laboratories or services, sharing data and models with others, while controlling access. Composition allows for making preferred work flows or clouds. E-Infrastructure integrates resources and provides grid computing power. Resources: data and software A community driven cyber infrastructure Research Infrastructure for Biodiversity and Ecosystem Research

  19. Requirements to study complexity in collaborative projects • Access to interoperable data and workflows • Capabilities to (re) use workflows: support to manage a (analytical and modelling) toolbox for different research communities • Options for fast computation of the effect of changes in parameter data • Virtual collaborative environments, allowing scientists to do experiments in silica • Visualisation of (intermediate) results with 4D resolution • Export to publications

  20. Thank you for your attention w.los@uva.nl

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