Andrew,
I addressed the scope and tried to "beef it up" to make it a little clearer all of the things that we have discussed. Everyone, please update as you see fit:
Standard descriptions and definitions of scientific data.
Processing and management of 1D, 2D and 3D data including both structured and unstructured grids.
Plotting and visualizations of data in 1D, 2D and 3D.
Workflow algorithms and their visualization.
Machine learning, artificial intelligence and data mining.
Modeling and simulation projects related to the physical sciences, including but not limited to physics, chemistry, biology and geology.
Modeling and simulation projects related to the social sciences, including but not limited to sociology and psychology.
Applied mathematics projects such as common math libraries and mesh management tools and with the exception of cryptography.
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Infrastructure to support scientific computing including those tools found in the Eclipse Parallel Tools Platform including job launch and monitoring, parallel debugging and remote project management for scientific computing.
I wrote bullet 9 as a starting point for the infrastructure/PTP scope. I wanted to make sure that we consider structured and unstructured grids for the data management. I also wanted to expand the machine learning section to include AI and data mining, which are the first things that a lot of data scientists will ask about if they don't see them listed. I didn't want to exclude agent-based simulations, such as Eclipse AMP (
http://eclipse.org/amp/), which can be used for simulations in the social sciences, so I included them in addition to the blurb on the physical sciences. Likewise, I didn't want to leave out applied mathematics because if we start building a big capability we will not doubt be writing our own math library or grid/mesh management tools. I left cryptography out because cryptographic routines are export controlled.
I hope this doesn't bother anyone. Like I said, please edit as you see fit!
Jay
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