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Research Data Management

This guide will assist researchers in planning for the various stages of managing their research data and in preparing data management plans required with funding proposals.

What is data?

At a broad level, data are items of recorded information considered collectively for reference or analysis.

Data can occur in a variety of formats that include, but are not limited to,

  • notebooks
  • survey responses
  • software and code
  • measurements from laboratory or field equipment (such as IR spectra or hygrothermograph charts)
  • geospacial information
  • images (such as photographs, films, scans, or autoradiograms)
  • audio recordings
  • physical samples 

Data can be defined in a variety of ways, depending the discipline and the context. When it comes to making decisions about managing your research data, you may wish to consult the definitions used by your funder or the West Virginia University Research Office.

Examples of different defintions of data from U.S. Goverment funders


"The recorded factual material commonly accepted in the scientific community as of sufficient quality to validate and replicate research findings, regardless of whether the data are used to support scholarly publications.”


"data will be determined by the community of interest through the process of peer review and program management. This may include, but is not limited to: data, publications, samples, physical collections, software and models."


“Data” is defined as materials generated or collected during the course of conducting research. Examples of humanities data could include citations, software code, algorithms, digital tools, documentation, databases, geospatial coordinates (for example, from archaeological digs), reports, and articles. Excluded, however, are things such as preliminary analyses, drafts of papers, plans for future research, peer-review assessments, communications with colleagues, materials that must remain confidential until they are published, and information whose release would result in an invasion of personal privacy (for example, information that could be used to identify a particular person who wasone of the subjects of a research study).”

NASA Earth Science:

For NASA's Earth Science Program and according to NASA's Earth Science Data & Information Policy, the term 'data' includes observation data, metadata, products, information, algorithms, including scientific source code, documentation, models, images, and research results.

What is Research Data Management?

Research data management (RDM) is a term that describes the organization, storage, preservation, and sharing of data collected and used in a research project. Although most often associated with the sciences, RDM can be a valuable tool for all disciplines. 

RDM involves the everyday management of research data during the lifetime of a research project (for example, using consistent file naming conventions). It also involves decisions about how data will be preserved and shared after the project is completed (for example, depositing the data in a repository for long-term archiving and access). No matter what your area of study, you will amass a significant amount of data over the course of conducting your research.

There are a host of reasons why research data management is important:

  • Data, like journal articles and books, is a scholarly product.
  • Data (especially digital data) is fragile and easily lost.
  • There are growing research data requirements imposed by funders and publishers.
  • Research data management saves time and resources in the long run.
  • Good management helps to prevent errors and increases the quality of your analyses.
  • Well-managed and accessible data allows others to validate and replicate findings.
  • Research data management facilitates sharing of research data and, when shared, data can lead to valuable discoveries by others outside of the original research team.

Data Managment Planning

Perhaps the most important step in managing your research data is planning. We will explore data management plans in-depth in this guide. Some general this you will want to consider:

  • Institutional and funding agency's expectations and policies
  • Whether you collect new data or reuse existing data
  • The kind of data collected and its format
  • The quantity of data collected
  • Whether versions of the data need to be tracked
  • Storage of active data and backup policy and implementation
  • Storage and archiving options and requirements
  • Organizing and describing or labeling the data
  • Data access and sharing
  • Privacy, consent, intellectual property, and security issues
  • Roles and responsibilities for data management on your research team
  • Budgeting for data management