Responding to the NIH Data Management and Sharing Policy

This page provides guidance on how to complete a Data Management and Sharing Plan (DMSP) for an NIH application. 

DMSPs should be added to an application as a PDF document. An optional Data Management and Sharing Plan format page has been provided by NIH to assist applicants in preparing their DMSPs. While use of this format page is recommended, plans generated using other approaches will be accepted. Plans created in DMPTool can be exported in PDF format.

Once an application is funded, the contents of the plan will become part of the terms and conditions of the award. This means it is essential that the practices and strategies described in the plan are followed and the plan is kept up to date.

Note: Different institutes, centers, offices, or calls for proposals may have requirements in addition to those outlined on this page. Use the "Which Policies Apply to My Research" page and this list of policies from NIH institutes and centers to determine which policies apply to you and your work.

For more information or to schedule a consultation related to creating, revising, or implementing such a plan, contact the Stanford DMP Service.

In general, a DMSP should reflect the proposed approach to data management and sharing-related activities at the time it is prepared. If data-related practices or strategies change during the award period, the plan should be updated to reflect these changes.

For some programs and data types, NIH ICOs have developed specific data sharing expectations (e.g., scientific data to share, relevant standards, repository selection, timelines) that apply and should be reflected in a DMSP. When no additional NIH and/or NIH ICO data sharing expectations apply, researchers should propose their own approaches to data management and sharing in their plans.

NIH encourages data management and sharing practices to be consistent with the FAIR (Findable, Accessible, Interoperable, and Reusable) data principles and reflective of practices within specific research communities. For more information about FAIR, see the "How to Share Data" tab.

A successful DMSP will contain the following elements:

1. Data Type

A brief description of the scientific data to be managed, preserved, and shared, including:

• A general summary of the type(s) and estimated amount of scientific data to be generated and/or used in the research. 
• A description of which scientific data from the project will be preserved and shared.
• A brief listing of the metadata, other relevant data, and any associated documentation (e.g., study protocols and data collection instruments) that will be made accessible to facilitate interpretation of the scientific data.

What this means:

Research projects vary widely in the types of data they produce. In this section, researchers should describe the categories, amounts, and degree of processing applied to their data. If not all data produced in the course of your project will be shared, they should describe the factors, such as ethical or legal considerations, that affect the degree of data sharing.  They should also provide a list of the metadata and documentation or protocols that will be made available.

2. Related Tools, Software and/or Code

An indication of whether specialized tools are needed to access or manipulate shared scientific data to support replication or reuse, and name(s) of the needed tool(s) and software.

• If applicable, it should be specified how the required tools can be accessed and, if known, whether such tools are likely to remain available for as long as the scientific data remain available.

What this means:

In this section, researchers should describe if particular or specialized tools are needed to work with their data and, if necessary, how such tools can be accessed. It is possible that the data associated with a single project will exist across multiple formats and/or will require multiple software tools to be accessed, manipulated, replicated, and reused. 

Please note: Hyperlinks or URLs should not be inserted into your data management and sharing plan!

3. Standards

An indication of which standards will be applied to the scientific data and associated metadata (i.e., data formats, data dictionaries, data identifiers, definitions, unique identifiers, and other data documentation).

What this means:

A standard specifies how exactly data and related materials should be stored, organized, and described. In the context of research data, the term typically refers to the use of specific and well-defined formats, schemas, vocabularies, and ontologies in the description and organization of data. However, for researchers within a community where more formal standards have not been well established, it can also be interpreted more broadly to mean the adoption of the same (or similar) data management-related activities or strategies by different researchers and across different projects.

It is likely that data generated in a given research project will employ multiple formal standards or a mix of formal standards and other data management strategies. 

4. Data Preservation, Access, and Associated Timelines

An outline of plans and timelines for data preservation and access, including

• The name of the repository(ies) where scientific data and metadata arising from the project will be archived.
• How the scientific data will be findable and identifiable (i.e., via a persistent unique identifier or other standard indexing tools).
• When the scientific data will be made available to other users (i.e., the larger research community, institutions, and/or the broader public) and for how long.

What this means:

There are various ways in which to disseminate, preserve, and make scientific data discoverable. In this section, you should describe where and when the scientific data associated with your research will be made available. The primary way to satisfy this requirement is to put your scientific data into a repository, which will support preservation of that data and provide long term access.

5. Access, Distribution, or Reuse Considerations

NIH expects that in drafting their olans, researchers will maximize the appropriate sharing of scientific data generated from NIH-funded or conducted research, consistent with privacy, security, informed consent, and proprietary issues. Therefore, this section of the plan should outline any applicable factors affecting subsequent access, distribution, or reuse of scientific data related to:

• Informed consent 
• Privacy and confidentiality protections 
• Whether access to scientific data derived from humans will be controlled
• Any restrictions imposed by federal, Tribal, or state laws, regulations, or policies, or existing or anticipated agreements.
• Any other considerations that may limit the extent of data sharing.

What this means:

Certain kinds of data, especially human subjects data, require extra preparation before they can be shared to ensure participant privacy. In this section you will describe your approach to preparing human subjects data for sharing and note any additional restrictions or policies that will impact access to your data. If you are working with human subjects you should also describe how you will address data management and sharing in your informed consent process. You will also need to describe your methods for ensuring privacy and confidentiality, including how you will de-identify your data. If you have decided that a controlled access repository (where researchers must apply to access data) is a better fit for your data than an open repository, you should describe the repository's access procedures.

6. Oversight of Data Management and Sharing

A description of how compliance with the DMSP will be monitored and managed, including information about the frequency of oversight, and by whom (e.g., titles, roles).

What this means:

In this section, researchers should list who is responsible for data management and sharing-related activities implemented throughout the course of the project. 

Sample Language:

“Developing, executing, and monitoring this Data Management and Sharing Plan will be the responsibility of the project’s Principal Investigator. The plan will be implemented and managed by professional staff working under the direction of the PI. Practices and procedures related to data management and data sharing will be outlined and reviewed with all project personnel and will also be discussed as part of regular team meetings. The PI will also be responsible for ensuring this plan remains up-to-date and will communicate with the appropriate NIH program officer(s) to ensure that any revisions are approved.”

Does your DMSP contain the following elements?

The following checklist is designed to help you evaluate your DMSP to make sure it contains all of the elements recommended by NIH. 

NOTE: Individual institutes, centers, offices, or calls for proposals may include s items not on this checklist (e.g. deposit to a specific repository). 

Though not required, writing your Data Management and Sharing Plan in DMPtool can help ensure you are writing an effective plan and can simplify the compliance process once your proposal is funded.

DMPTool is free to use and accessible using your Stanford credentials.

DMPTool can be used to create data management and sharing plans for a wide variety of funders, including NIH. Once you log in, type in the name of your funder to access the appropriate template.

If you choose the "No funder associated with this plan or my funder is not listed" option, you'll be brought to a very lightweight Stanford University-specific template.

Your data management and sharing plan can be updated over time. To help you keep track of your data and other materials, update with links to published datasets.