Discover Data Portal


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  • Friday: Cyberinfrastructure and Science Education Science is a team sport, played on grids that span the globe. The practice of science has been profoundly altered by technologies that enable global sharing of ideas, data, images, and even investigation techniques. How have those changes been infused into the way we teach science in pre-college classrooms? What additional changes can we expect to see as cyberinfrastructure changes the way we teach as well as the content that we endeavor to share with students?
  • Discover Data Portal

    1. 1. Jeff Sale and Diane Baxter San Diego Supercomputer Center University of California, San Diego
    2. 2. Data: Evidence to unravel the mysteries of our Universe
    3. 3. Data provide answers to our children’s single most persistent question: <ul><li>How do you know that? </li></ul>
    4. 4. Data Come From Every Field . . . Life Sciences Astronomy Physics Modeling and Simulation Data Management and Mining GAMESS Geosciences
    5. 5. And are shared around the world. Open Science Grid: Physics-driven Grid infrastructure NEES: Earthquake Engineering Grid SDSC PRAGMA: Pacific Rim Grid Middleware Consortium TeraGrid: National Research Resource Grid GEON: Geosciences Grid BIRN: Biomedical Informatics Grid
    6. 6. Life Sciences Disciplinary Databases Users Portals, Domain Specific APIs provide access to data Middleware federates data across disciplinary vocabularies Organisms Organs Cells Atoms Biopolymers Organelles Cell Biology Anatomy Physiology Proteomics Medicinal Chemistry Genomics
    7. 7. How much data are we producing*? 1 human brain at the micron level = 1 PetaByte 1 novel = 1 MegaByte iPod Shuffle (up to 120 songs) = 512 MegaBytes Printed materials in the Library of Congress = 10 TeraBytes SDSC HPSS tape archive = 25 PetaBytes and growing All worldwide information in one year = 2 ExaBytes 1 Low Resolution Photo = 100 KiloBytes * Rough/average estimates 1 DVD = 9.4 GigaBytes Kilo 10 3 Mega 10 6 Giga 10 9 Tera 10 12 Peta 10 15 Exa 10 18
    8. 8. Computational tools are essential to comprehend that much data! <ul><li>Integrate vast data collections from a wide variety of collection points </li></ul><ul><li>Visualize empirical results </li></ul><ul><li>Create mathematical models based on complex, interconnected data </li></ul>
    9. 9. Computational Models <ul><li>Extend beyond data to: </li></ul><ul><ul><li>Predict </li></ul></ul><ul><ul><li>Ask “what if” questions </li></ul></ul><ul><ul><li>Evaluate alternate hypotheses </li></ul></ul><ul><ul><li>Visualize vast, complex data collections </li></ul></ul><ul><ul><li>Manipulate multiple variables </li></ul></ul>
    10. 10. Why is Data Literacy So Essential? <ul><ul><li>Data = the foundation of science </li></ul></ul><ul><ul><li>Data shared can solve problems </li></ul></ul><ul><ul><li>Data can bridge and connect fields, ideas and people </li></ul></ul><ul><ul><li>Computation, the “third leg” of research, depends upon data </li></ul></ul>
    11. 11. From atomic interaction data that form a model of a molecular dynamics
    12. 12. To light emission data from long-dead stars that explain the origins of the Universe
    13. 13. Teaching about data means teaching the “language and currency” of science. <ul><li>A Window to Data </li></ul><ul><li>The Discover Data Education Portal </li></ul>