Abstract
Objective and Setting: As universities and libraries grapple with data management and “big data,” the need for data management solutions across disciplines is particularly relevant in clinical and translational science (CTS) research, which is designed to traverse disciplinary and institutional boundaries. At the University of Florida Health Science Center Library, a team of librarians undertook an assessment of the research data management needs of CTS researchers, including an online assessment and follow-up one-on-one interviews. Design and Methods: The 20-question online assessment was distributed to all investigators affiliated with UF’s Clinical and Translational Science Institute (CTSI) and 59 investigators responded. Follow-up in-depth interviews were conducted with nine faculty and staff members. Results: Results indicate that UF’s CTS researchers have diverse data management needs that are often specific to their discipline or current research project and span the data lifecycle. A common theme in responses was the need for consistent data management training, particularly for graduate students; this led to localized training within the Health Science Center and CTSI, as well as campus-wide training. Another campus-wide outcome was the creation of an action-oriented Data Management/Curation Task Force, led by the libraries and with participation from Research Computing and the Office of Research. Conclusions: Initiating conversations with affected stakeholders and campus leadership about best practices in data management and implications for institutional policy shows the library’s proactive leadership and furthers our goal to provide concrete guidance to our users in this area.
Highlights
What is your role on the research project
What is the size of your research team
Data management service to outsource some of the work
Summary
8. What type of data do you generate? Please check all that apply. ● Numerical data, e.g. GIS annotated ocean temperatures ● Text, e.g. historical records and literature ● Still Images ● Audio files ● Video files ● Medical data, e.g. patient health information ● Biochemical data e.g. raw and processed “omic” data ● Tabulated data, e.g. survey results ● Other: 9. What format(s) are your data in? (file extension, etc.) (Open-ended) 10. How is your data labeled or annotated? Please check all that apply. ● Automatically, through data collection tool ● Manually, by a member of my research team ● Referentially, with an associated codebook ● My data is not annotated.
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