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Research data management in academic institutions: A scoping review
Laure Perrier1, Erik Blondal2, A Patricia Ayala1
1Gerstein Science Information Centre, University of Toronto, Toronto, Ontario, Canada.
Plos One
|May 26, 2017
Summary
This scoping review reveals that most research data management studies focus on data sharing and use self-report methods. More empirical research is needed on data management interventions and early project phases.
Area of Science:
- Bibliometrics and Scientometrics
- Information Science
- Research Management
Background:
- Research data management (RDM) is crucial for academic institutions.
- Understanding the RDM literature landscape is essential for identifying research gaps.
Purpose of the Study:
- To describe the volume, topics, and methodological approaches of RDM research.
- To map the RDM literature within academic institutions.
Main Methods:
- A comprehensive scoping review was conducted.
- Forty literature databases were searched from inception to April 2016.
- Included all study types, extracting data on design, discipline, tools, and research data lifecycle phase.
Main Results:
- 301 articles and 10 companion reports were included.
- Most publications (85%) are from 2010 onwards, predominantly in the sciences (86%).
- Interviews, cross-sectional, and case studies (78%) were common methods, often focusing on data sharing (68%). Data quality emerged as a key theme.
Conclusions:
- The RDM literature predominantly uses self-report methods (interviews, surveys) and case studies.
- Few studies provide empirical evidence on RDM interventions or early-stage research project management.
- Improved RDM practices and data deposition are critical for research output quality.
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