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SEA Currents November 18th, 2019
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Upcoming Research Data Management Webinar: If You Share It, Will They Come? Quantifying and Characterizing Reuse of Biomedical Research Data

Posted by on September 4th, 2019 Posted in: Continuing Education, Data Science, Research Data Management


Date: Wednesday, October 2nd

Time: 2:00PM – 3:00PM ET

Presenter: Lisa Federer, PhD, MLIS is the Data Science and Open Science Librarian at the National Library of Medicine (NLM), focusing on developing efforts to support workforce development and enhance capacity in the biomedical research and library communities for data science and open science. Prior to joining NLM, Lisa spent five years as the Research Data Informationist at the National Institutes of Health Library, where she developed and ran the Library’s Data Services Program. She holds a PhD in information studies from the University of Maryland and an MLIS from the University of California-Los Angeles, as well as graduate certificates in data science and data visualization. Her research focuses on quantifying and characterizing biomedical data reuse and development of meaningful scholarly metrics for shared data.

Description: Since the mid-2000s, new data sharing mandates have led to an increase in the amount of research data available for reuse. Reuse of data benefits the scientific community and the public by potentially speeding scientific discovery and increasing the return on investment of publicly funded research. However, despite the potential benefits of reuse and the increasing availability of data, research on the impact of data reuse is so far sparse. This talk will provide a deeper understanding of the impacts of shared biomedical research data by answering the question “what happens with datasets once they are shared?”

Specifically, this talk will demonstrate that data are often reused in very different contexts than for which they were originally collected, as well as explore how patterns of reuse differ between dataset types. This talk also considers patterns of data reuse over time and the topics of the most highly reused datasets to determine whether it is possible to predict which datasets will go on to be highly reused over time. Finally, career stage and geographic location of data reusers provide an understanding of who benefits from shared research data. These findings have implications for several stakeholders, including researchers who share data and those who reuse it, funders and institutions developing policies to reward and incentivize data sharing, and repositories and data curators who must make choices about which datasets to curate and preserve

Registration: Registration is free and can be accessed through the NNLM class instance.

For additional information, please contact Kiri Burcat.

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