University Libraries - Research Data Services - NIH Data Management & Sharing Plans

Policy & Guidelines  |  Checklist  |  Example Plans  |  Budgeting for Data

NIH Policy and Guidelines

NIH provides an overview of the policy that went into effect in 2023.

In short, the policy:

• Applies to all awards that generate scientific data, regardless of the funding amount or mechanism.
• Requires a data management and sharing plan for all proposals.
• Requires that data must be shared at the time of associated publications, or by the end of the grant performance period, whichever comes first.
• Strongly encourages researchers to use repositories for sharing data.
• Institutes, Centers, and Offices may have more specific requirements than the broader NIH policy, so review the Funding Opportunity Announcement (FOA) that you are applying to.

Guides

1. Start with this NIH page: Writing a Data Management & Sharing Plan
2. Use the optional NIH template (Word document), or the outline on the NIH website
3. See also the checklist further below on this page.

Assistance and Training

We’ve assisted many UI researchers with their data management and sharing plans. Contact us via email for assistance, or to schedule a presentation for your team or department.

Example Plans

If you’ve never created a data management and sharing plan, a template or examples can provide a framework that guides you through many of the details that are typically in a plan.

Use with care

Not every plan that is shared is a good plan. Double check that any text from other plans that you use is appropriate and relevant to your data, and is what you are prepared to comply with.

If in doubt, don’t include it! If approved by NIH, your plan is a term and condition of your award.

We can review your plans and assist with writing plans. Contact us

Sample Plans

A number of NIH Institutes and Centers have provided sample plans.

• These may reflect additional requirements and expectations by certain Institutes and Centers that go beyond the general NIH Policy.
• On this page, scroll down to “Sample Plans” (about half way down the page)

Other Examples

Whenever you find examples from other sources, review them carefully. Not every plan that is shared by someone is a good plan.

The College of Medicine’s Scientific Editing and Research Communication Core (SERCC) has created a set of examples.

DMPTool provides templates for creating NIH data management and sharing plans , based on the NIH guidance. Example text is also provided for those who have never created a plan before. Get started with DMPTool (guide).

Checklist

1. Data Types | 2. Related Tools | 3. Standards | 4. Preservation & Access | 5. Restrictions | 6. Oversight

This checklist addresses the 6 required elements which can be found on the Writing a Data Management and Sharing Plan page , in the NIH DMS Policy , and in an optional format worksheet (docx), all from NIH.
*There are some slight differences in formatting and layout between these sources.

• Check the Funding Opportunity Announcement (FOAs) or Request for Applications you are applying to for more specific requirements than the general NIH policy.
• Similarly, Institutes, Centers, and Offices (ICOs) may have more specific data policies .
• Keep track of plan elements that can be included in the grant budget.
  • See the section below on budgeting for data sharing.

Links below take you to more guidance and examples from this website and NIH.

1. Data Type

Summarize the type(s) of scientific data you expect to collect or generate that will be necessary to validate your findings.

• List and describe the types of data that will be created or used as part of the study, including:
  • Types of data, file formats, size, and number of files (estimate quantities as necessary).
  • Describe which data from the project will be preserved and the reasoning.
  • Describe aggregation, de-identification, or processing/cleaning that will be done prior to sharing.
  • Describe the source(s) of any secondary data or previously collected data to be used in the project.
• Describe the metadata and other documentation that will be shared with your data to facilitate interpretation, such as:
  • Readme files
  • Minimum Information about an Experiment (see also 3. Standards, below)
  • Data dictionaries
  • Codebooks

For data subject to the GDS Policy: Data types expected to be shared under the GDS Policy should be described in this element. Note that the GDS Policy expects certain types of data to be shared that may not be covered by the DMS Policy’s definition of “scientific data”. For more information on the data types to be shared under the GDS Policy, consult Data Submission and Release Expectations .

2. Related Tools, Software, and/or Code

Identify hardware, software/code or other tools necessary to access or manipulate the shared data.

• State whether or not specialized software, code/scripts, or hardware are needed.
• For each tool that is necessary, list:
  • Software version number and operating system, if applicable.
  • Describe how the tool can be accessed (e.g., open source and freely available, generally available for a fee in the marketplace, or available only from the research team or some other source). For instance, see sharing code.
  • How long they will be available (if known).

3. Standards

List the standards that will be used for sharing the data and metadata. These may range from open file formats for data, to standards or commonly accepted practices for the structure and organization of documentation (metadata).

Describe and state whether or not there are standards or commonly accepted practices for your field that are applicable:

• For data, standards such as:
  • Data file formats that enable reuse, preservation and access
  • Common data elements — CDEs, or other systematic data collection tools and instruments
  • NCI Genomic Data Standards
• For metadata:
  • Standard terminologies (e.g., from thesauri, taxonomies, ontologies)
  • Minimum information standards (e.g., MIAME , MINSEQE )
  • Chemical substance identifiers
  • Research resource identifiers
  • Methods, protocols
• For more examples and ideas, see these registries of standards:
  • NCBO BioPortal
  • FAIRSharing such as the Minimum Information for Biological and Biomedical Experiments collection
• If there are no established standards, how will you facilitate access and reuse of the data and metadata?

4. Data Preservation, Access, and Associated Timelines

Provide details and timelines for sharing and preserving data for long term usability.

• Name the repository(ies) where data will be archived:
  1. Does the Funding Opportunity specify a repository?
  2. Does the Institute or Center specify certain repository(ies)?

Selecting a repository: see the NIH Guide , NIH Domain-Specific Repositories , and our guide.

If no domain repository exists, we strongly recommend using Iowa Research Online (IRO) over other generalist repositories (more reasons why you should use IRO).

• Specify which type of unique identifier is used by the repository (DOI, handle, ID number, accession number) (NOTE: an identifier is not required at time of DMS plan submission).
• If the repository(ies) has/have particular metadata standards (not usually described), list in the standards section.
• Revisit your data list from section 1 and
  • Describe when each of the types of data from Section 1 will be made available (portions of the data may be released at different times). Either:
    • Data will be made available when the work is published or the award/support period ends (whichever comes first)
      – OR –
    • Data will be made available earlier
• State the minimum number of years data will be available, based on repository policies.

For data subject to the GDS Policy: For human genomic data: Investigators are expected to submit data to a repository acceptable under the Genomic Data Sharing Policy. See Where to Submit Genomic Data . Human genomic data is expected to be shared according to NIH’s Data Submission and Release Expectations , but no later than the end of the performance period, whichever comes first. For non-human genomic data: Investigators may submit data to any widely used repository. Non-human genomic data is expected to be shared as soon as possible, but no later than the time of an associated publication, or end of the performance period, whichever is first.

5. Access, Distribution, or Reuse Considerations

Describe how sharing will be maximized while respecting restrictions.

• Is there a license you will use (check with the repository for options) to enable or restrict reuse and redistribution? For instance, in IRO we recommend ODC-BY v 4.0 for data.
• Describe any considerations that may affect the extent of data sharing:
  • Legal (e.g., licenses, intellectual property)
  • Technical considerations or limitations
  • Ethical (e.g., protecting human subjects)
• Rather than refraining from sharing altogether, consider whether data can be shared through a controlled access repository, or via a data use agreement. If there are intellectual property concerns, consider if an embargo (no access for a specified amount of time) would suffice.
• If you have human subject data, describe how you will protect the privacy, rights, and confidentiality of study participants (de-identification, etc.).

Expectations for human genomic data subject to the GDS Policy: Informed Consent Expectations: For research involving the generation of large-scale human genomic data from cell lines or clinical specimens that were created or collected AFTER the effective date of the GDS Policy (January 25, 2015): NIH expects that informed consent for future research use and broad data sharing will have been obtained. This expectation applies to de-identified cell lines or clinical specimens regardless of whether the data meet technical and/or legal definitions of de-identified (i.e. the research does not meet the definition of “human subjects research” under the Common Rule). For research involving the generation of large-scale human genomic data from cell lines or clinical specimens that were created or collected BEFORE the effective date of the GDS Policy: There may or may not have been consent for research use and broad data sharing. NIH will accept data derived from de-identified cell lines or clinical specimens lacking consent for research use that were created or collected before the effective date of this Policy. Institutional Certifications and Data Sharing Limitation Expectations: DMS Plans should address limitations on sharing by anticipating sharing according to the criteria of the Institutional Certification . In cases where it is anticipated that Institutional Certification criteria cannot be met (i.e., data cannot be shared as expected by the GDS Policy), investigators should state the institutional Certification criteria in their DMS Plan, explaining why the element cannot be met, and indicating what data, if any, can be shared and how to enable sharing to the maximal extent possible (for example, sharing data in a summary format). In some instances, the funding NIH ICO may need to determine whether to grant an exception to the data submission expectation under the GDS Policy. Genomic Summary Results: Investigators conducting research subject to the GDS Policy should indicate in their DMS Plan if a study should be designated as “sensitive” for the purposes of access to Genomic Summary Results (GSR), as described in NOT-OD-19-023 .

6. Oversight of Data Management and Sharing

Identify who will be responsible for plan compliance and oversight.

• List names and titles/roles of everyone who will be responsible for monitoring compliance with the data management plan and updating it as needed.
• State how often compliance with the data management plan will be verified (e.g., every ___ months, on the first of each month, etc.).

This checklist is based on one created by the NIH DMSP Guidance for Data Support Services Working Group , with modifications and additional links by Brian Westra. Updated November 2024.

Budgeting for Data Management & Sharing

Investigators can include costs for data sharing in the proposal budget, and NIH provides guidance, as well as answers to some Frequently Asked Questions about budgeting for data sharing.

Watch this video for a quick summary on budgeting for data management in NIH grants:

Video by FASEB (Federation of American Societies for Experimental Biology).