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Manage & Share Research Data

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

What is research data management (RDM)?

The goal of research data management (RDM) is to make it easy for researchers to find and access data for their research projects when they need it. Because we have high expectations about the integrity, validity, and reliability of research results, the ways that data are managed are very important in research. Managing data is a crucial part of the research process. RDM includes all activities performed while handling data generated or gathered as part of a research project. While good data management practices supports good research, poor data management practices can make data unusable and lead to the failure of a project. RDM includes decisions and practices 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?

Good data practices are context-specific

The specific strategies for managing data differ between fields of research, like research design, methods, and expectations for research integrity can vary. However, there are core elements which remain consistent across all types of research. Core elements of good practices for data management and sharing include the following:

  • Develop a Data Management and Sharing Plan (DMSP) that documents your obligations and plans
  • Know your obligations with respect to data management and sharing
    • Legal requirements - international, federal, state, local
    • Ethical obligations of your professional and/or research communities
    • Funder or sponsor
    • Institution(s)
    • Publisher or journal
  • Know what you want to do with the data
  • Use of data management and sharing plans to ensure consistency in practices such as file naming, storing data securely, data curation, data retention and disposal, etc.
  • Adopt and implement relevant data and metadata standards, such as the FAIR Guiding Principles and the CARE Principles for Indigenous Data Governance


Other good research practices that effect data practices (usually the responsibility of the Lead/Principal Investigator)

  • Set clear expectations for research personnel as documented in team/lab manuals, including designation of responsibilities
  • Maintain project/study documentation that is accessible by all project/study personnel
  • All research personnel take responsibility for the trustworthiness of the research
  • Determine the constraints of your technological resources and recognize when external expertise (beyond the research team) is necessary, particularly when related to cybersecurity, contracts or licenses, and data curation


Key Resources

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, whether data are managed well can effect 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 areas are facing these challenges, such as psychology, cancer research, cell biology and more, though the specific issues differ by the field of research. 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