Planning for Long-Term Use of Biomedical Data Proceedings of a Workshop (2020) / Chapter Skim
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5 Lifetime Data Costs
5 Lifetime Data Costs
Pages 38-49
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From page 38... ...
Questions remain about how to fund data sharing and preservation -- if researchers can demonstrate that science advances as a direct result of sharing data via particular methods and platforms, an increase in long-term funding from the federal government and foundations could be justified. PANEL DISCUSSION: INCENTIVES, MECHANISMS, AND PRACTICES FOR IMPROVED AWARENESS OF COST CONSEQUENCES IN DATA DECISIONS Lars Vilhuber, Cornell University, Moderator John Chodacki, University of California Curation Center, California Digital Library Melissa Cragin, San Diego Supercomputer Center Wendy Nilsen, National Science Foundation Lucy Ofiesh, Center for Open Science Panel moderator Lars Vilhuber, Cornell University, noted that in July 2019, the American Economic Association updated its data and code 38
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From page 39... ...
Although the campus community thinks carefully about how to set up computational environments and support computational research, his team recognized that the preservation policies, as well as plans to make data accessible and reusable over time, were merely ad hoc processes. He described a pilot at CDL, with the entire University of California system, to address some of these data preservation issues.
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, which can be used to help model data preservation costs and understand those costs throughout the entire research life cycle. DMPTool,1 a platform with 43 templates for 17 U.S.
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SOURCE: John Chodacki, University of California Curation Center, California Digital Library, presentation to the workshop, July 12, 2019. • What processes are being used, and are there tools available?
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From page 42... ...
SOURCE: Adapted from Melissa Cragin, San Diego Supercomputer Center, presentation to the workshop, July 12, 2019. Cragin commented that Cornell University's library has developed an open-source web-based tool, Data Storage Finder, which can be customized for individual campuses.
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From page 43... ...
With the explosion of data, Nilsen noted the value of posing questions to data scientists and informaticians about what kind of data they need in order to move forward. She described a recent infrastructure initiative from NSF's Computer and Information Science and Engineering directorate to better understand the needs of the research community, including data that are usable, accessible, inexpensive, and machine-readable.
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Image available courtesy of CC-BY attribution license. in 2019, that number climbed to nearly 12 million.
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From page 45... ...
COS is also piloting programs related to registered reports, in which the researcher, journal, and funder are working together at the study design phase as well as engaging in peer review after both the study design phase and the report writing phase. This cooperative approach privileges rigorous scientific research and removes pressure for research ers to produce results that are "publishable." It also ensures that funders work in partnership with both the researcher and the journal throughout the research life cycle.
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From page 46... ...
If data are not shared, she cautioned, communities might miss opportunities to use data from other communities. Maryann Martone, University of California, San Diego, asked Cragin whether the level of information technology support provided to research laboratories drives data management.
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Sarah Nusser, Iowa State University, said that postsecondary institutions should be supporting preservation efforts regardless of faculty interest. Chodacki added that discussing how to forecast data preservation costs from a funder perspective is challenging, especially for cases in which the funder cannot allocate money for data preservation.
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From page 48... ...
Another topic that emerged during the group's discussion was the need to align individual and institutional practices with expectations from federal agencies to develop economies of scale. The group also discussed cost reduction incentives embedded in new initiatives, such as NIH's Science and Technology Research Infrastructure for Discovery, Experimentation, and Sustainability.
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From page 49... ...
Manski shared a series of comments made by group members. One participant said that data scientists should be engaged at the start of the research process to assist with dissemination and data sharing.
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