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Opportunities from the Integration of Simulation Science and Data Science Proceedings of a Workshop (2018) / Chapter Skim
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3 Workshop Wrap-up Discussion
Pages 18-24

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From page 18...
... Using the data commons his team built for the National Cancer Institute, researchers can apply big query and analogous techniques with various clouds to perform gene by environment computations. It is a capability multiple research teams have found useful that was not difficult to build into the system, he said.
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From page 19...
... The cloud providers benefit when they find ways to monetize that data, while NOAA benefits because the arrangement vastly reduces the cost of storing and handling its data. Based on his experience in the nonprofit arena, Grossman said one incentive sometimes used to attract private sector partners is to offer companies early access to data that will eventually be made public.
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From page 20...
... On the other hand, Furlani suggested that some might justify charging overhead because, even if a service is "free" to an institution's researchers, it still requires support personnel to help them take advantage of the service. Gropp noted that the discussion points to the broader challenges involved in determining which costs should be direct versus overhead.
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From page 21...
... At the same time, there is a shared understanding of the challenges and opportunities in finding a way to support convergence in the broad sense of bringing data and computing together. The planning committee is pleased to see the many advances made since its 2016 report and believes that further discussions around convergence would foster additional progress.
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From page 23...
... Appendixes
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Key Terms

  • support personnel
  • planning committee
  • data commons
  • data science
  • computing facilities

  • william gropp
  • offer companies
  • sensor data
  • government data
  • data movements

  • data scientists
  • midscale data
  • unrelated data
  • supporting data
  • data flows

  • moving data
  • big data
  • larger data
  • data michela
  • data transfer

  • create larger
  • sector work
  • thomas furlani
  • simply purchase
  • procurement model

  • assess success
  • challenges involved
  • cloud providers
  • network backbones
  • ongoing costs


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