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Planning for Long-Term Use of Biomedical Data Proceedings of a Workshop (2020) / Chapter Skim
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4 Tools and Practices for Risk Management, Data Preservation, and Accessing Decisions
Pages 29-37

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From page 29...
... will also embrace data science and explained that members of the biomedical community can both teach these disciplines and learn from them moving forward. He discussed the various stakeholders in data supply chains: Funders contribute to the development of resources, publishers provide resources to both readers and authors, the National Institutes of Health (NIH)
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From page 30...
... It remains to be seen whether these approaches are reliable and sustainable, Bourne explained. He also pointed out that PubMed is now including and supporting data because data sets that are aggregated are more useful than a single data set.
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From page 31...
... Postsecondary institutions can thus relieve the burden from federal funders and begin to maintain more useful research output through a combination of (1) internal resources (i.e., if reference data sets and quality data can be used year after year by incoming students, they could be supported by tuition funds)
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From page 32...
... When students begin to use those data and credit the use to those researchers, a new wave of data sharing could begin within a postsecondary institution. Nuno Bandeira, University of California, San Diego, explained that as postsecondary institutions begin to embrace data, metrics will be needed to assess the value of data sets and database interaction.
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From page 33...
... Google Cloud, which contains many high-level data sets (e.g., population health, Centers for Disease Control data, Centers for Medicaid and Medicare Services data, Google Data Set Search) , provides another avenue for data sharing.
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From page 34...
... He noted that because preeclampsia screening was inadequate, his team sought to develop a more precise diagnostic tool for the potentially lethal condition. The team searched NCBI's Gene Expression Omnibus and EBI's ArrayExpress, found dozens of experiments with hundreds of samples, looked for commonalities and repeating patterns, and conducted tests.
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From page 35...
... Butte replied that researchers already pay for high-value data sets, but it is not a model that they appreciate. Alexa McCray, Harvard Medical School, pointed out that if there is a fee for the use of "highvalue" data sets, free data will no longer be available and it will be impossible to aggregate across multiple data sets.
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From page 36...
... SOURCE: Atul Butte, University of California, San Francisco, presentation to the workshop, July 11, 2019. Images available courtesy of CC-BY attribution license for K
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From page 37...
... TOOLS AND PRACTICES FOR RISK MANAGEMENT 37 oject: From the control groups curated experiments The 10,000 Immunome Project: From the control groups of 242 manually curated experiments Kelly Zalocusky Sanchita Bhattacharya Kelly Zalocusky @ImmPortDB Cell Reports bit.ly/10kimmunome Sanchita Bhattacharya http://10kimmunomes.org/ FIGURE 4.1 continued. @ImmPortDB Cell Reports bit.ly/10kimmunome http://10kimmunomes.org/
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Key Terms

  • data science
  • data sharing
  • data supply
  • own data
  • share data

  • postsecondary institution
  • atul butte
  • accessed october
  • big data
  • embrace data

  • expression omnibus
  • asked butte
  • cell reports
  • management plans
  • supply chain

  • trauma surgeon
  • cytometry data
  • protein data
  • enforcing data
  • enforce data

  • data bank
  • university data
  • excellent data
  • google data
  • training data

  • quality data
  • collecting data
  • free data
  • data ownership
  • data policies


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