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Data: Data Management Plans

What is a Data Management Plan?

A Data Management Plan (DMP), also called a Data Management and Sharing Plan (DMSP), 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 (i.e., stored, organized, documented, and preserved), 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

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
  • Plans and provisions for sharing, dissemination, and secondary uses of the data and other research products (including submission to an established data repository, if applicable)
  • Protection or security measures taken to protect participant confidentiality
  • Expected costs for data management and preservation

A Data Management Plan will:

  • Define the steps you need to manage your data
  • Strengthen your research ethics application
  • Ensure you follow funder and journal requirements
  • Clarify what you need for longer-term data management
  • Give your data a longer lifespan and strengthen your research process

NSF Dear Colleague Letter (2019): Effective Practices for Data

NSF Dear Colleague Letter (2019): Effective Practices for Data

Describes and encourages two effective practices for managing research data: the use of persistent identifiers (PIDs) for data and machine-readable data management plans.

Persistent Identifiers (PIDs) for Data

  • Globally unique, resolvable, persistent IDs for research data make the data more findable and accessible, enable citation, and permit linking to data from within publications and other kinds of research presentations.

  • Digital Object Identifiers (DOIs) offer a common example of a persistent ID. Global information trackers (such as Scholix) use persistent IDs in publications or citations to facilitate greater information sharing about data and related materials.

  • A benefit of a persistent ID for research data is that the dataset can be cited in a researcher's NSF biographical sketch, as previously noted, as well as in the "results of prior research" section of future grant proposals. Use of persistent IDs confer other long-term benefits as well. For example, information about a dataset can be findable even though the dataset itself is no longer accessible.

  • In a publication reference to a dataset, the citation to the dataset should appear in the body of the article with a corresponding reference in the reference list.

  • Researchers can obtain persistent IDs from their home institutions, repositories or data services in which the data or software are to reside, or other sources.

Machine-readable Data Management Plans

  • When written effectively, DMPs clarify how researchers will effectively disseminate and share research results, data, and associated materials. However, DMPs can also contain complex and/or ambiguous terms that produce uncertainty about the benefits of data management activities. Such ambiguity can produce situations where the DMP does not adequately explain what data will be created or where the data will be deposited.

  • For this reason, NSF encourages the use of DMP tools, such as EZDMP or the DMPTool, to create machine-readable DMPs. The DMP specifies how data will be produced, prepared, curated, and stored. A machine-readable document allows a computer program to interpret the DMP, such as to prepare a data repository for an eventual deposit of a large or complicated dataset.

  • A machine-readable DMP, moreover, can be a living document that is modified as the project evolves with documentation of essential attributes of each modification. 

  • A benefit of DMP tools for researchers is that they can generate both a PDF version of the DMP that is suitable for inclusion in a grant proposal and a machine-readable version suitable for sharing with an intended recipient data repository or the researcher's home institution.

Data Management Plan Content Checklist

DMP content checklist:

  1. What data will you collect or create?
  2. How will the data be collected or created?
  3. How will the data be organized?
  4. What documentation and metadata will accompany the data?
  5. For research involving human participants, how will you manage any ethical issues?
  6. How will you manage copyright and intellectual rights issues?
  7. How will the data be stored and backed up during the research project?
  8. How will you manage data access and data security?
  9. Which data should be retained, shared, and/or preserved?
  10. What is the long-term preservation plan for the dataset?
  11. How will you share the data?
  12. Are any restrictions on data sharing required?
  13. Who will be responsible for data management?
  14. What resources will you need to implement the DMP?

Source: Research Data Management and Sharing course ( Accessed August 2019.

Funder and Journal DMP Requirements

Funder requirements

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

Journals 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.

Data Management: Tools and Resources







ezDMP (for NSF grant applications):