Skip to Main Content

Research Data

Data Management Plans

data management plan (DMP) is a short (2 pages max) but critical part of your grant application which outlines how you will collect, organize, manage, store, secure, backup, preserve, and share your data.

The particular requirements of a DMP will vary among funding agencies, so it is best to always consult the agency's resources for their specific needs. Most DMPs will ask you to provide some information about:

  • Types of data
  • Formats and standards,
  • Roles and responsibilities 
  • Dissemination of results
  • Data sharing, public access and reuse 
  • Privacy, confidentiality, security, intellectual property rights
  • Archiving data, samples, and other research products, and for on-going access to these products through their lifecycle of usefulness to research and education.


We recommend using DMPTool to draft your data management plans.

DMPTool is an open source tool maintained by the California Digital Library that provides step-by-step guidance and information specific to many granting agencies and their directorates. DMPTool also contain ready to use templates for data management plans according to the funder (including NSF, NIH, NEH, DOE, and IMLS) and allows users to make their plans publicly available here:

They provide a "Getting Started" guide to the platform here:

Project Description

Provide information such as name of applicant, project number, funding program, version of DMP.


Data Description

What data will be collected or produced?

  • Give details on the kind of data: for example numeric (databases, spreadsheets), textual (documents), image, audio, video, and/or mixed media.
  • What data formats will be used (for example pdf, csv, txt or rdf)?
  • Justify the use of certain formats. For example, decisions may be based on staff expertise within the host organization, a preference for open formats, standards accepted by data repositories, widespread usage within the research community, or on the software or equipment that will be used.
  • Give preference to open and standard formats as they facilitate sharing and long-term re-use of data.
  • Give details on the volumes (they can be expressed in storage space required (bytes), and/or in numbers of objects or files).

How will new data be collected or produced and/or how will existing data be re-used?

  • Are there special tools or software needed to create / process / visualise the data?
  • Will existing data be used? If so, from where.


Documentation, Metadata & Data Quality

What metadata and documentation will accompany the data?

  • How will metadata be captured, created and managed?
  • Indicate which metadata standards (for example DDI, TEI, EML, MARC, CMDI) will be used.
  • Use community metadata standards where these are in place.
  • Indicate how the data will be organized during the project, mentioning for example conventions, version control, and folder structures. Consistent, well-ordered research data will be easier to find, understand, and re-use.
  • What other documentation and contextual information will be available in order to help others understand the data? e.g. information on the methodology used to collect the data, analytical and procedural information, definitions of variables, units of measurement

What data quality control measures will be used?

  • Explain how the consistency and quality of data collection will be controlled and documented. This may include processes such as calibration, repeated samples or measurements, standardized data capture, data entry validation, peer review of data, or representation with controlled vocabularies.


Storage, Backup & Security

How will data and metadata be stored and backed up during the research?

  • Describe where the data will be stored and backed up during research activities and how often the backup will be performed.
  • It is recommended to store at least at three copies of your data.

How will data security and protection of sensitive data be taken care of during the research?

  • Explain who will have access to the data during the research and how access to data is controlled, especially in collaborative partnerships or where your data is sensitive, for example containing personal data, politically sensitive information, or trade secrets.
  • Consider encrypting your data.


Legal & Ethical Requirements

If personal data are processed, how will compliance with legislation on personal data and on security be ensured?

  • Ensure that when dealing with personal data, data protection laws (for example GDPR) are complied with:
    • Gain informed consent for preservation and/or sharing of personal data.
    • Consider anonymization of personal data for preservation and/or sharing (truly anonymous data are no longer considered personal data).
    • Explain whether there is a managed access procedure in place for authorized users of personal data.

How will other legal issues, such as intellectual property rights and ownership, be managed?

  • Explain who will be the owner of the data, meaning who will have the rights to control access. Consider the use of data access and re-use licenses.
  • Make sure to cover these matters of rights to control access to data for multi-partner projects and multiple data owners in the consortium agreement.
  • Indicate whether there are any restrictions on the re-use of third-party data.


Data Sharing & Long-term Preservation

How and when will data be shared? Are there possible restrictions to data sharing or embargo reasons?

  • Explain how the data will be discoverable and shared (for example by deposit in a trustworthy data repository, use of a secure data service, requests handled directly, or use of another mechanism).
  • Outline the plan for data preservation and give information on how long the data will be retained.
  • If data can’t be shared this should be justified. Explain what action will be taken to overcome or to minimize restrictions.
  • Indicate whether potential users need specific tools to access and (re-)use the data. Consider the sustainability of software needed for accessing the data
  • Persistent identifiers should be applied so that data can be reliably and efficiently located and referred to. Persistent identifiers also help to track citations and re-use. Typically, a trustworthy, long-term repository will provide a persistent identifier.


Data Management Responsibilities & Resources

  • Who will be responsible for data management?
  • How often will your plan be reviewed and updated?
  • What resources (for example financial and time) will be dedicated to data management and ensuring that data will be FAIR (Findable, Accessible, Interoperable, Re-usable)?

Funder Guidelines for Data Management Plans

DMP Policies of various funders

Agencies DMP Required?
Alfred P. Sloan Foundation Yes: "How will your data and code be shared, annotated, cited, and archived? What else will you do to make your findings reproducible by other researchers?" for general projects, and for those generating "information products," there is another section that is a fuller DMP. 
Defense Advanced Research Projects Agency (DARPA) No
Department of Energy (DOE)

Yes: a DMP for all stages of the digital data lifecycle, including capture, analysis, sharing, and preservation. The DOE’s main focus is on sharing and preservation of digital research data.

There are additional requirements for the following programs:

Gordon and Betty Moore Foundation Yes: "As part of the foundation grant development process, potential grantees are required to develop a Data Management and Sharing Plan with their foundation grant team. All data used in or developed in whole or in part by foundation-funded projects (and that can be shared in a manner consistent with applicable laws) will be made widely available and freely shared as soon as possible. If data used in foundation-funded projects are owned by an additional party other than the grantee, we do not require it to be released, but the grantee will use its best efforts to encourage the data owners to make it openly and freely available."
Institute for Museum and Library Services (IMLS) Yes: Specifically for projects that develop digital products.
National Aeronautics and Space Administration (NASA) Yes: NASA “promotes the full and open sharing of all data with the research and applications communities, private industry, academia, and the general public.” 
National Endowment for the Humanities (NEH) Yes: "The DMP should clearly articulate how sharing of primary data is to be implemented. It should outline the rights and obligations of all parties with respect to their roles and responsibilities in the management and retention of research data. It should also consider changes to roles and responsibilities that will occur if a project director or co-project director leaves the institution or project. Any costs stemming from the management of data should be explained in the budget narrative."
National Institute for Health (NIH) Yes: The NIH expects applicants to submit a plan for how they will manage and share their data and allows applicants to include certain costs associated with data management and sharing in their budget. See the NIH Scientific Data Sharing page, New Resources Available on Protecting Participant Privacy When Sharing Scientific Data,
 and the 2023 Data Management & Sharing Policy Frequently Asked Questions (FAQs).


National Oceanic and Atmospheric Administration (NOAA) Yes: The Federal Ocean Data Policy requires that appropriate oceanic data and related information collected under federal sponsorship be submitted to and archived by designated national data centers. In compliance with this directive, NOAA requires that all grant recipients submit a Data Sharing policy for their project: “all NOAA grantees must share data produced under NOAA grants and cooperative agreements in a timely fashion, except where limited by law, regulation, policy, or security requirements.”
National Science Foundation (NSF) Yes: requires a DMP for all full proposals submitted or due on or after January 1 8, 2011.