Skip to main content

Currently Skimming:

2 Framing the Workshop
Pages 8-12

The Chapter Skim interface presents what we've algorithmically identified as the most significant single chunk of text within every page in the chapter.
Select key terms on the right to highlight them within pages of the chapter.

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)
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.
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.
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.
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.

This material may be derived from roughly machine-read images, and so is provided only to facilitate research.
More information on Chapter Skim is available.