Good data management practices aren’t just for grants. They are a gift to your future self:
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 ResearchDataMgmt@uoregon.edu to schedule a consultation.
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.
In order to promote open access to research data, many funding agencies require research data produced as part of a funded project to be made publicly available. Many agencies have instituted requirements for data sharing and formal data management plan, including, but not limited to:
Templates and Examples
Below are examples of data management and sharing plans that are helpful as guides for your own data management and sharing plan. When developing your own plan or choosing an existing template, consider your own project needs.
Compiled from researchers, institutions, libraries, and workgroups who shared their data management plans online from 2012-2022 to help researchers comply with the new 2023 NIH policy. Includes multiple funders and will not be updated after publishing.
Provided by NIH as examples of how a DMS Plan could be completed in different contexts. Reflects additional expectations established by NIH or specific NIH Institutes, Centers, or Offices that go beyond the 2023 policy.
created using the DMPTool service and shared publicly by their owners. Not vetted for quality, completeness, or adherence to funder guidelines.
We can provide you with fast, free, and confidential feedback on your draft Plans. Send us your draft Plan, a link to the funding announcement, and the deadline to ResearchDataMgmt@uoregon.edu. If you’ve created your Plan in the DMPTool, then select the “Request Feedback” option on the last page of your template to contact us for a review.