A New Framework for a Model-Based Data Science Computational Platform

  • Muna D
  • Huff E
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Astronomy produces extremely large data sets from ground-based telescopes, space missions, and simulation. The volume and complexity of these rich data sets require new approaches and advanced tools to understand the information contained therein. No one can load this data on their own computer, most cannot even keep it at their institution, and worse, no platform exists that allows one to evaluate their models across the whole of the data. Simply having an extremely large volume of data available in one place is not sufficient; one must be able to make valid, rigorous, scientific comparisons across very different data sets from very different instrumentation. We propose a framework to directly address this which has the following components: a model-based computational platform, streamlined access to large volumes of data, and an educational and social platform for both researchers and the public.

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  • Demitri Muna

  • Eric Huff

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