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Data Management: Data Management Plans

Connecting the UR Community to data management resources.

What is a Data Management Plan?

What is a Data Management Plan?

A data management plan is a document, usually included in a grant or other funding application, that describes the data that will be created during the course of the project, how it will be managed throughout the course of the project, how it will be archived after the project is over, and how it will be made available to other researchers and the public.

Elements of a Data Management Plan

Elements of a Data Management Plan

The particular requirements of a data management plan vary among funding agencies, so it is best to always consult the agency. However, there are a few common attributes:

  • A description of the type(s) of data to be produced
  • Methods of how the data will be collected and who will be responsible for data management
  • Standards you will use to describe your data (metadata standards)
  • Backup and storage procedures
  • Provisions for long-term archiving and preservation
  • Access policies and provisions for secondary uses: will it be available to others? How?
  • Any protection or security measures taken to protect participant confidentiality 
  • Expected costs for data management and preservation

For sample language to use in your plan, see NCSU's Elements of a Data Management Plan.

Funder Requirements

Many government agencies and private organizations that fund research are now requiring data management plans as part of their grant applications.

Journal Requirements

Many academic journals have also adopted data sharing/archiving policies. There are several lists below of such journals, but you can also check with the editor of a journal (or its website) to find out if it has a data policy.

What do they mean by "data"?

A Few Definitions of "Research Data"

  • United States Code of Federal Regulations: According to the Code of Federal Regulations, research data is, "... defined as the recorded factual material commonly accepted in the scientific community as necessary to validate research findings, but not any of the following: Preliminary analyses, drafts of scientific papers, plans for future research, peer reviews, or communications with colleagues."
  • University of Oregon's Research Data Management page provides some examples of items that you might consider part of your "research data":
    • Documents (text, Word), spreadsheets
    • Laboratory notebooks, field notebooks, diaries
    • Questionnaires, transcripts, codebooks
    • Audiotapes, videotapes
    • Photographs, films
    • Protein or genetic sequences
    • Spectra
    • Test responses
    • Slides, artifacts, specimens, samples
    • Collection of digital objects acquired and generated during the process of research
    • Database contents (video, audio, text, images)
    • Models, algorithms, scripts
    • Contents of an application (input, output, logfiles for analysis software, simulation software, schemas)
    • Methodologies and workflows
    • Standard operating procedures and protocols