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World-Historical Dataverse : Research Challenges

World-Historical Dataverse : Research Challenges. Hassan A. Karimi Geoinformatics Laboratory School of Information Sciences University of Pittsburgh. Outline. Key findings of the SBE VISION 2020 (Rebuilding the MOSIAC) report World-Historical Dataverse and the SBE VISION 2020 report

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World-Historical Dataverse : Research Challenges

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  1. World-Historical Dataverse: Research Challenges Hassan A. Karimi Geoinformatics Laboratory School of Information Sciences University of Pittsburgh

  2. Outline • Key findings of the SBE VISION 2020 (Rebuilding the MOSIAC) report • World-Historical Dataverse and the SBE VISION 2020 report • World-Historical Dataverse Research Challenges

  3. SBE VISION 2020: Rebuilding the MOSIAC • Fostering Research in the Social, Behavioral, and Economic Sciences (SBE) at the National Science Foundation in the Next Decade • National Science Foundation, Directorate for Social, Behavioral and Economic Sciences (2011)

  4. SBE VISION 2020: Topics • “Important questions (the fundamental science questions)” • “Data and infrastructure (data-intensive science, methodologies, research centers, shared toolkits, and so on)” • “Capacity building (education and training of graduate students, faculty, and systems of prestige, promotion, and recognition)”

  5. SBE VISION 2020: What Is Collaborative Research? • “The notion of collaborative research teams is one way that data-intensive SBE research implies a shift away from the independent, single investigator/small team model of scientific research.”

  6. SBE VISION 2020: Next Generation SBE Research

  7. World-Historical Dataverse: Multidisciplinary Research • Ontologies • Conceptualizations of problems/scenarios within and across disciplines • Data • Collected and contributed by researchers from different disciplines • Public health, geology, economics, geography, anthropology,… • Models • Social sciences, information science, computer science,… • Analysts • Scientists, researchers, students,…

  8. World-Historical Dataverse: Data-Intensive Research

  9. World-Historical Dataverse: Collaborative Research

  10. Research Challenge: Data • Data Model • A unified space-time data model that integrates • Heterogeneous data sources • Structured and unstructured data • Data Analysis • Understanding data through visualization

  11. Research Challenge: Data-Intensive • Storage • Distributed data repository • Efficient processing • Supercomputers • Grids • Clouds • Visualization • Adaptive maps

  12. Research Challenge: Collaboration • A cyberinfrastructure platform that facilitates • Identification and connection of the analysts needed to solve a given a problem automatically • Resource sharing • Collaborative visualization • Collaborative problem solving

  13. Research Challenge: Analytics • Simulation of data- and compute-intensive problems/scenarios • Knowledge discovery through connection of data/scenarios across disciplines • Predication methodologies, models, and techniques

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