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Research Data Management

Create a Data Management Plan

**UO Libraries now has DMP Tool, which offers funder-specific guidance on writing your Data Management Plan. Click here to learn how to register.

Good data management practices aren’t just for grants. They are a gift to your future self:

  • Keep better track of workflows, code, and dataset versions to avoid mix-ups
  • Automate backups to avoid disaster
  • Use software to collaborate more effectively
  • Publish data, code, and workflows => get more citations
  • Know where your data is in 5 years, so you can re-use or refer to it in your next study

Resources and guidelines for data management are constantly changing. If you would like support and advice specific to your field or project, write us at to schedule a consultation.

Data management plans usually include the following:

1.    Describe the data that your research will generate/collect.
Data may originate from observations, experiments, or references; it may be derived from other sources, transformed, or the result of a simulation. Data also includes your code and workflow documentation.

Describe your data file formats. Whenever possible, use non-proprietary formats or convert your data to open, shareable formats when archiving data.
2.    Describe how you will document your data, including metadata standards and tools.
Descriptions of your data (or metadata) can help you and others locate, understand, and interpret your data. It is useful during the research process, and is also a critical component of systems for publicizing and sharing data with others.

You should describe the applicable standards for metadata content and format that you will follow, including the procedures and tools/software you will use to capture and edit the metadata.
3.    Describe how the data will be organized, stored and protected during the research project.
Describe storage methods and backup procedures for the data, including physical and cyber resources and facilities (hard-disk space, backup server, repository). For sensitive data, describe how you will protect privacy and confidentiality. Also consider security, intellectual property, and other rights.
4.    How will the data be shared with others, during and/or after the project?
The National Science Foundation (NSF) and other agencies now require you to make your data public whenever possible. Doing so increases the visibility of your work, and creates opportunities to build citations for published datasets.

Describe what you will do to provide access to the data. Unless the data includes sensitive information, this usually involves publishing the data to a public repository. Describe any restrictions on who may access the data and under what conditions and a timeline for providing access.
5.    Where and how will the data be archived/preserved for long-term access?
Describe your plans for preserving data in accessible form. Plans should include a timeline proposing how long the data are to be preserved, outlining any changes in access anticipated during the preservation timeline, and documenting the resources and capabilities (e.g., equipment, connections, systems, expertise) needed to meet the preservation goals. Where data will be preserved beyond the duration of direct project funding, a description of other funding sources or institutional commitments necessary to achieve the long-term preservation and access goals should be provided.

Frequently, there is an overlap between preservation and data sharing (#4 above), because deposit of data in many repositories entails preservation, and provides open access to the data sets. Keep in mind, however, that you also may want to preserve some unpublished/unshared data beyond the grant funding cycle, due to confidentiality or other concerns.

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