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Refining the Concept of Scientific Inference When Working with Big Data Proceedings of a Workshop (2017) / Chapter Skim
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2 Framing the Workshop
Pages 8-12

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From page 8...
... said the overarching goals of the workshop were to characterize the barriers that prevent one from drawing reliable inferences from big data and to identify significant research opportunities that could propel multiple fields forward. PERSPECTIVES FROM STAKEHOLDERS Michelle Dunn, National Institutes of Health Nandini Kannan, National Science Foundation Chaitan Baru, National Science Foundation Michelle Dunn and Nandini Kannan delivered a joint presentation describing the shared interests and ongoing work between the National Institutes of Health (NIH)
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From page 9...
... Dunn mentioned several NIH programs to improve training and education for all levels, with a focus on graduate and postgraduate researchers -- for example, the National Institute of General Medical Sciences Biostatistics Training Grant Program.1 The BD2K initiative funds biomedical data science training as well as open educational resources and short courses that improve understanding in the broader research community. Kannan described NSF's focus on the training and education of the next generation of science, technology, engineering, and mathematics researchers and educators.
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From page 10...
... For example, NSF is seeking to create the infra­ structure and institutions that will facilitate hosting and sharing large data sets with the research community, thereby reducing barriers to analysis and allowing easier replication of studies. Regarding education, Baru pointed to the proliferation of master's-level programs but suggested that principles-based undergraduate cur ricula and doctoral programs are required for data science to become a true disci pline.
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From page 11...
... He emphasized the difference between confirmatory data analysis to answer a targeted question and exploratory analyses to generate hypotheses. He used the example of comparative effectiveness research based on EHRs to call attention to challenges related to missing data and selection bias, confounding bias, choice of covariates to adjust for these biases, and generalizability.
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From page 12...
... Daniels concluded by stating that because existing statistical tools are in many cases inadequate for supporting inference from big data, this workshop was designed to demonstrate the state of the art today and point to critical research opportunities over the next 10 years.
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Key Terms

  • data science
  • biomedical data
  • massive data
  • data research
  • diverse data

  • missing data
  • effectiveness research
  • comparative effectiveness
  • michelle dunn
  • training grant

  • biostatistics training
  • chaitan baru
  • causal effect
  • nandini kannan
  • grant program

  • health records
  • major big
  • nsf big
  • introduce data
  • observational data

  • biomedical big
  • data themes
  • confirmatory data
  • data revolution
  • monitoring data

  • using big
  • ing data
  • computing initiative
  • move biomedical
  • significant research


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