Write a data management plan

A data management plan (DMP) will help you manage your data, meet funder requirements, and help others use your data if shared.

The DMPTool is a web-based tool that helps you construct data management plans using templates that address specific funder requirements. From within this tool, you can save your plans, access MIT-specific information & resources, and request a review of your DMP by a member of our Data Management Services team. To access the MIT-customized DMPTool, choose “Massachusetts Institute of Technology” as your institution to log in via Touchstone.

Alternatively, you can use the questions below and any specific data management requirements from your funding agency to write your data management plan. Additional resources for creating plans are also provided below. To request a review contact data-management@mit.edu.

1.  Project, experiment, and data description
   • What’s the purpose of the research?
   • What is the data? How and in what format will the data be collected? Is it numerical data, image data, text sequences, or modeling data?
   • How much data will be generated for this research?
   • How long will the data be collected and how often will it change?
   • Are you using data that someone else produced? If so, where is it from?
   • Who is responsible for managing the data? Who will ensure that the data management plan is carried out?
2. Documentation, organization, and storage
   • What documentation will you be creating in order to make the data understandable by other researchers?
   • Are you using metadata that is standard to your field? How will the metadata be managed and stored?
   • What file formats will be used? Do these formats conform to an open standard and/or are they proprietary?
   • Are you using a file format that is standard to your field? If not, how will you document the alternative you are using?
   • What directory and file naming convention will be used?
   • What are your local storage and backup procedures? Will this data require secure storage?
   • What tools or software are required to read or view the data?
3. Access, sharing, and re-use
   • Who has the right to manage this data? Is it the responsibility of the PI, student, lab, MIT, or funding agency?
   • What data will be shared, when, and how?
   • Does sharing the data raise privacy, ethical, or confidentiality concerns?  Do you have a plan to protect or anonymize data, if needed?
   • Who holds intellectual property rights for the data and other information created by the project? Will any copyrighted or licensed material be used? Do you have permission to use/disseminate this material?
   • Are there any patent- or technology-licensing-related restrictions on data sharing associated with this grant? The Technology Licensing Office (TLO) can provide this information.
   • Will this research be published in a journal that requires the underlying data to accompany articles?
   • Will there be any embargoes on the data?
   • Will you permit re-use, redistribution, or the creation of new tools, services, data sets, or products (derivatives)? Will commercial use be allowed?
4. Archiving
   • How will you be archiving the data? Will you be storing it in an archive or repository for long-term access? If not, how will you preserve access to the data?
   • Is a discipline-specific repository available? If not, consider depositing your data into a generalist data repository. Email us at data-management@mit.edu if you’re interested in discussing repository options for your data.
   • How will you prepare data for preservation or data sharing? Will the data need to be anonymized or converted to more stable file formats?
   • Are software or tools needed to use the data? Will these be archived?
   • How long should the data be retained? 3-5 years, 10 years, or forever?

Additional resources for creating plans

• Managing your data – Project Start & End Checklists (MIT Data Management Services): Checklist (PDF) with detailed resources to help researchers set up and maintain robust data management practices for the full life of a project.
• ezDMP: a free web-based tool for creating DMPs specific to a subset of NSF funding requirements.
• Guidelines for Effective Data Management Plans and Data Management Plan Resources and Examples (ICPSR): Framework for creating a plan and links to examples of data management plans in various scientific disciplines
• Example Plans (University of Minnesota)
• Assessing your DMP
  • NSF (by the DART project): assessment rubric and guidance
  • NIH (by FASEB)
  • Request a review from MIT Data Management Services.

See other guides to data management for additional guidance on managing data and select information related to particular formats or disciplines.