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Big Data in the Geosciences, University Corporation for Atmospheric Research (UCAR/NCAR), and the NCAR Wyoming Supercomputing Center (NWSC) Marla Meehl.

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Presentation on theme: "Big Data in the Geosciences, University Corporation for Atmospheric Research (UCAR/NCAR), and the NCAR Wyoming Supercomputing Center (NWSC) Marla Meehl."— Presentation transcript:

1 Big Data in the Geosciences, University Corporation for Atmospheric Research (UCAR/NCAR), and the NCAR Wyoming Supercomputing Center (NWSC) Marla Meehl NCAR/CISL Network Engineering & Telecommunications Section

2 Geosciences "Over the next decade, the geosciences community commits to developing a framework to understand and predict responses of the Earth as a system, from the space-atmosphere boundary to the core, including the influences of humans and ecosystems." Atmospheric, geospace, oceanic, biological, geological observed and modeled data Geoscientists are increasingly engaged in data intensive science and investigation, data management, and long-term storage and access issues including the full data life cycle.

3 EarthCube NSF is facilitating a community dialog with a goal of transforming the conduct of research in geosciences by supporting the development of a community-guided CI, an EarthCube, to integrate data and information for knowledge management across the geosciences. High Performance Computing Data Storage Networking Analysis

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5 Change the paradigm – just can’t move this much data – the network cannot be the only solution to this problem – integrated solution Minimize what moves Data Services Analysis computing capability closer to the computing “Distill” the data Minimize what save Specialized Code Save only what need

6 Change Paradigm Cont’d Better metadata Allows to build service Allows to find data Standard metadata enable standardized services Focus on the network between large data and data server providers Integrated data services across the network “Ecosystems of interoperable data services Network is coupler

7 Change Paradigm Cont’d “Engage the Community” User education on data services and practices Distributed data environment Integrate data efficiency into proposals Invest in people and time to develop data services, tools, standards, etc.

8 Thanks and Q & A Thank you Questions?


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