Planning for Long-Term Use of Biomedical Data Proceedings of a Workshop (2020) / Chapter Skim
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6 Reflections and Next Steps
6 Reflections and Next Steps
Pages 50-58
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From page 50... ...
to participate in the final panel discussion of the workshop. She asked the researchers to reflect on the following questions, based on the information that was shared over the course of the workshop: • What are your needs, and what could you use to reduce the costs of sharing, preserving, and providing access to data over the data life cycle?
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During the past 20 years, Williams has been building families of genetically diverse animals that can be used to compute correlation coefficients. Such work relies on multiplicity -- some data should be available forever, and thus "life cycle" is the wrong phrase to use to describe data.
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From page 52... ...
She concluded by suggesting that researchers aim for conducting translucent research instead of transparent research, especially when working with clinical data. Nuno Bandeira, University of California, San Diego, said that a discussion about data preservation should include the costs of data reutilization: If data are not going to be reused, why pay to store them?
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From page 53... ...
Williams suggested developing a funding mechanism that would enable the interoperability of research efforts, and Levenstein mentioned an organization of repositories in the social sciences and statistical communities called Data-PASS.1 She added that the Research Data Alliance has also tried to create a community. Patricia Flatley Brennan, NLM, explained that NLM would like to increase the efficiency of spending and decrease waste rather than simply cut costs.
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. Expertise in comput ing and information science can lessen barriers to data access, help maintain safety, increase data quality, and decrease costs (Wendy Nilsen, National Science Foundation)
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For example, while some publishers are currently requiring that data be depos ited into a repository in order to publish the results, it is unclear if these repositories will be reliable or sustainable. Academic approaches toward data also need to change to ensure that they can train data professionals, use academic data to improve pro ductivity, improve data infrastructure, bolster academic libraries as they transition from data preservationists to data analysts, and update institutional data policies (Bourne)
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. Sus tained infrastructure investments could help advance scientific discovery (Tourassi)
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Melissa Cragin, San Diego Supercomputer Center, described four different odels m to support research data services, including the unfunded linked facilitator model, the research unit fee-for-service model, the all campus coordination model, and the institutional commitment model. Each has its own benefits, challenges, and limitations.
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From page 58... ...
In closing the workshop, Martone emphasized that communities are ready to use the wealth of existing tools and expertise available to think seriously about data management. However, funding mechanisms to create platforms to connect expertise and allow people to share experiences are still needed.
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