Writing a data management (and sharing) plan

Many research funders require a data management plan with a grant proposal. The guide below describes the major areas that researchers should consider in preparing a data management plan.

This guide is not specific to any particular funder, discipline, or type of data, and prospective PIs should always review the specific proposal request documents and requirements of the funder.

DMSP Review

We offer free and confidential consultations and feedback on draft plans. We will respond to requests within 2-5 days, and require one business week to provide meaningful feedback and answer your questions. Please plan accordingly, especially during busy grant submission seasons.

1. Data types and sources

What types of data, samples, physical collections, code, software, curriculum materials and other materials will be produced in the course of the project?

• including a brief description of each type of data to be generated (e.g. experimental, qualitative, raw, processed).
• how much data you anticipate will be generated over the course of the project.
• which data you will share and at what stage (raw, processed, reduced, or analyzed).
• why the data you will share will be of interest to a broader community and how your plan will maximize potential for reuse.
• if you are using data from other sources. If so, provide a brief description, including content, source, and any conditions required for obtaining and using that data. If you will combine existing data with your own, describe the relationship between the data sets.

2. Formats and standards

What standards will be used for your files and metadata?

• formats of data files created over the course of the project, and approximate volume of data.
  • Select non-proprietary file formats for sharing and archiving to maximize the potential for reuse and longevity, and describe the plans for conversion to those formats, if necessary.
  • Identify community metadata standards used. Indicate if no applicable standards exist and describe what additional documentation you will provide to make the data understandable and usable by others (e.g. readme file).

  Learn More:

  • File management
  • File formats
  • Metadata and describing data
  • Readme metadata
  • Tabular data

  3. Roles and responsibilities

  What are the roles and who has responsibilities for managing data?

  • who will have primary responsibility for implementing the data management plan?
    • If multiple institutions are involved, funding agencies typically task the lead PI with executing the DMP.

    4. Dissemination methods

    What are the methods for sharing data and metadata during and after the award period?

    • how the data sets will be stored (if secure storage and/or restricted access are required) and backed up during the course of the project.
      • Describe hardware, storage environment, and local or external services to be used.
      • Include the costs for these services in proposal budget, if applicable.
      • Some funders and publishers require an identifier (e.g.DOI, Handle) for persistent access to the dataset.

      5. Policies for public access, data sharing, and re-use

      How will you meet funder requirements to provide public access to your data while protecting privacy, confidentiality, security and intellectual property rights?

      • conditions for reuse of the data by others including any licenses that will be applied.
      • whether data acquired from another source will be shared, and under what conditions.
      • Privacy and Security
        • how the data will be managed to protect privacy (e.g. measures taken to anonymize data, disposition of data including personally identifiable information).
        • legal and ethical requirements that may preclude sharing of any of your data. If so, explain the circumstances that prevent you from sharing data.
        • if your research is subject to oversight by the Institutional Review Board. Refer to applicable requirements and describe how your data management practices will ensure compliance.
        • copyright protection and whether it extends to your data. Some standard licensing options (Creative Commons, Open Data Commons) exist. Many metadata standards accommodate rights or usage statements where conditions for reuse may be expressed.
        • funding agencies (including the NSF) recognize that commercialization potential may delay or preclude data sharing, and exempt trade secrets and commercial information from the data sharing requirement.

        6. Preservation

        How will you preserve the integrity of your data over time?

        Note, some options for public access, sharing and re-use noted in earlier sections may also address longer term preservation.

        • any departmental, institutional, or programmatic policies on data retention, how they influence your plan, and how you will adhere to the policies.
        • how long data will be retained (PDF) or preserved and why.
          • Some data may only be retained for the lifetime of the project, some may be retained for the project plus a specified number of years, and some may be worth the effort of long-term preservation (several years to decades).
          • Consider what data are needed to validate the research, what data directly support publications based on the research, and what data have the greatest potential for reuse.

          Other considerations

          Some funders and/or RFPs require that additional data management areas be addressed in a DMP.

          These may include:

          • Rationale
          • Monitoring and reporting
          • Specific assurance of having resources to carry out the plans
          • Data processing workflow (e.g. how you plan to get data from point of collection to point of access)
          • Data quality assurance or quality control measures
          • For sensitive data, the security measures and any formal standards that will be used (e.g. biological agent permitting)

          Learn More

          

          • Funder Data Requirements
          • Data Management Guidelines from the California Digital Library

          Online tool for creating data management plans, with templates for many funding agencies.

          Creative Commons License: This work is licensed under a Creative Commons Attribution 4.0 International License