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NIH Data Management and Sharing Plans

This guide will present three options for engaging with content on how to write Data Management and Sharing Plans for NIH grants. Basic, Intermediate, and Advanced options will be available which will cover all necessary elements for writing the plan and

NIH Data Management and Sharing Policy

NIH grants submitted on or after January 25, 2023 must follow the updated Data Management & Sharing PolicyIndividual NIH institutes, Centers, or Offices may have additional policies and expectations (see NIH Institute and Center Data Sharing Policies).

Under the DMS Policy, NIH expects that investigators and institutions:

  • Plan and budget for the managing and sharing of data 

  • Submit a DMS plan for review when applying for funding (DMS Plans are recommended to be 2 pages or less)

  • Comply with the approved DMS plan

Elements of a DMSP

Data Type

Describe the types and estimated amount of scientific data that will be managed, preserved, and shared. 

  • A general summary of the types and estimated amount of scientific data to be generated and/or used in the research.
  • Describe data in general terms, type, and amount/size (50 JPEG images, 50 MBs) This can be an estimate.
  • Descriptions may indicate the data modality (imaging, genome, etc.), level of aggregation (individual, aggregated, summarized) and/or the degree of data processing that has occurred (how raw or processed the data will be).

Related Tools, Software, and/or Code

Indicate if specialized tools are necessary to access or manipulate the shared data. Provide the name(s) of the tools and how they can be accessed (are they open source, purchased, etc.?)


State what common data standards, if any, will be applied to the scientific data and associated metadata from the project. Provide the name(s) and describe how these data standards will be applied to the scientific data generated by the project.

While many scientific fields have common data standards, others do not. In such cases, the plan may indicate that no consensus of data standards exists for the data that will be collected, preserved, and shared. 

Data Preservation, Access, and Associated Timelines

Plans and timelines for data preservation and access, including:

  • Provide the name(s) of the repository where the scientific data and metadata from the project will be archived. 
  • Describe how it will be findable and identifiable, i.e. via a persistent unique identifier or other standard indexing tools. 
  • Describe when and how long will the data be made available to others. In general, scientific data should be made available as soon as practicable, and no later than time of an associated publication or end of the performance period, whichever comes first.
  • Identify any differences in timelines for different subsets of scientific data to be shared.

Researchers are encouraged to consider relevant requirements and expectations (e.g. data repository policies, award record retention requirements, journal policies) as guidance for the minimum time frame scientific data should be made available.

NIH encourages researchers to make scientific data available for as long as they anticipate it being useful for the larger research community, institutions, and/or the broader public.

Access, Distribution, or Reuse Considerations

NIH expects researchers to maximize appropriate sharing of scientific data. In this section, describe any applicable factors affecting subsequent access, distribution, or reuse of scientific data related to:

  • Privacy and confidentiality (If generating scientific data from humans, describe how privacy and confidentiality will be protected e.g. through de-identification or other protective measures.)
  • Informed consent
  • Whether access to scientific data derived from humans will be controlled (e.g. made available by a data repository only after approval)
  • Any restrictions imposed by federal, Tribal, or state laws, regulations, or policies, or existing or anticipated agreements (e.g. with third-party funders, HIPAA, covered entities that provide Protected Health Information under a data use agreement, etc.)
  • Any other considerations that may limit the extent of data sharing

Oversight of Data Management and Sharing

Describe how compliance with the plan will be monitored and managed and by whom (usually PI, Co-PI, or collaborators). 

Note: This should not be the Office of Research Services. This should be personnel within your grant.


DMPTool Logo

DMPTool is a free, open source, online resource that helps researchers create data management plans that comply with funder requirements. It also has direct links to funder websites, help text for answering questions, and data management best practices resources. See the DMPTool Quick Start Guide and FAQ for a general overview of DMPTool. 


To use DMPTool, sign-in/sign-up with your institutional email address and eRaider credentials. 


On "My Dashboard," create a new plan and select the NIH-Default template provided by National Institutes of Health for writing your DMSP. 


Data Preservation, Access and Sharing

Awardees are expected to carry out data management and sharing as outlined in their approved DMSP. This includes the act of making scientific data generated by the project available publicly, for example, via an established repository. 

Note that NIH encourages scientific data to be shared as soon as possible and no later than the time of an associated publication or the end of the grant period, whichever comes first. 

Choosing a Repository

  • NIH does not require sharing data in any specific repository (although some initiatives and funding opportunities may have individual requirements). In general, NIH encourages researchers to select the repository that is most appropriate for their data type and discipline.
  • NIH Repositories for Sharing Scientific Data provides a non-exhaustive list of NIH-supported repositories. 
  • is a global registry of research data repositories for many different academic disciplines. 
  • In addition, Texas Tech provides institutional access to the Texas Tech University Dataverse and Dryad repositories at no cost.
    • Texas Tech University Dataverse

      • An open-source repository sponsored by the Texas Digital Library. 
      • By default, published data is assigned a CC0 license, so that others may freely access and build upon the work. Researchers can alter this license and create custom terms of use for their data if appropriate.
      • There is no size limit for datasets submitted to Dataverse. However, individual files have a limit of 4GB. 
      • DOIs are issued for each uploaded dataset. 
      • Allows researchers to submit their data and work on it in an "unpublished" state until ready for making the dataset available publicly.
      • To access the Texas Tech University Dataverse, login with your eraider credentials. 
    • Dryad
      • An open-source general-purpose repository. 
      • Submitted data is curated to ensure quality and accessibility. All data is published exclusively under a CC0 license
      • DOIs are issued for each uploaded dataset. 
      • Up to 1TB of storage per dataset and individual files up to 100GB. There is no limit for storage space per researcher. 
      • Dryad also works directly with many publishers, allowing for linking datasets to related publications. 
      • To access Dryad, login with your ORCID or create one on the site.