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Manage & Share Research Data

Tips & tools to help student researchers manage research data & information with less stress


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Heather Coates
University Library Center for Digital Scholarship
UL 1115G

What is research data management (RDM)?

Research data management includes the activities performed while handling data generated or gathered as part of a research project. Managing data is a crucial part of the research process. While good data management supports good research, poor data management can make data unusable and lead to the failure of a project. Data management includes activities relating to:

  • how to generate or gather data
  • where to store it
  • how to document and describe it
  • how to process it
  • identifying your legal and ethical obligations for protecting and/or sharing it
  • choosing what data to archive and discard
  • where to share your data & how to license it
  • how to cite your data in reports and publications

What are good data practices?

Research & data integrity

Data is a key piece of the scholarly record. This means that the way you manage your research data has an impact on the accuracy and integrity of the research record (i.e., scholarly literature including journal articles, conference posters & presentations, abstracts, etc.). Since the scholarly literature is used to inform new areas for research, how well data are managed effects the potential for data curation, sharing, and reuse or secondary analysis. This is recognized by the Office of Research Integrity, the National Academies of Science, federal funding agencies requiring data management plans, and initiatives like FORCE11. Kenneth Pimple describes data management as “the neglected, but essential, twin to the ‘scientific method.’”

Source: Coates, H. (2014). Ensuring research integrity: The role of data management in current crises. College & Research Libraries News, 75(11), 598-601.

Reproducibility & Replicability

Data management is an important part of the conversations about challenges in reproducing and/or replicating published results. Many disciplines are facing these challenges, psychology, cancer research, cell biology, and more, though the details differ by field. Advancing our understanding of the world requires an accumulation of evidence from more than one study. Put simply, one study does not prove a theory. Many studies producing consistent data and results are necessary for a theory to be accepted as the most likely explanation.

Comparing, aggregating, and analyzing data across multiple studies requires that data are accessible, interoperable, defined, well-documented, and citable. One approach to describing and making these ideas practical are the FAIR Guiding Principles, which are: Findable, Accessible, Interoperable, and Reusable.

Open Data & Open Science