NIH Data Management and Sharing

NIH Data Management & Sharing (DMS) Policy, effective January 25, 2023, applies to all research, funded or conducted in whole or in part by NIH, that results in the generation of scientific data.

This includes all NIH-supported research regardless of funding level, including: extramural grants, extramural contracts, intramural research projects, and other funding agreements.

Scientific Data is defined as data commonly accepted in the scientific community as of sufficient quality to validate and replicate research findings, regardless of whether the data are used to support scholarly publications.

• Scientific data includes any data needed to validate and replicate research findings.
• Scientific data does not include laboratory notebooks, preliminary analyses, completed case report forms, drafts of scientific papers, plans for future research, peer reviews, communications with colleagues, or physical objects such as laboratory specimens.

The effective date for the DMS Policy is January 25, 2023. Specifically, the policy applies to:

• Competing grant applications that are submitted to NIH for January 25, 2023 and subsequent receipt dates.
• Proposals for contracts that are submitted to NIH on or after January 25, 2023.
• NIH Intramural Research Projects conducted on or after January 25, 2023.
• Other funding agreements (e.g., Other Transactions) that are executed on or after January 25, 2023, unless otherwise stipulated by NIH.

Other policies and expectations may apply (see NIH Institute and Center Data Sharing Policies).

The NIH does not specify data content, formatting, presentation, or transport mode. There are no standards or best practices. The method you choose may depend on several factors, including the sensitivity of your data, its size and complexity, and the volume of requests anticipated.

There are three methods for sharing data:

1. The PI may store the data where he likes and share it in any manner he chooses. For example, you can share your data on a website or in a journal.
2. Data Archive (open access database): for data that will get a high number of requests, possible frivolous requests, and data that needs technical assistance for researchers to use. Most of them charge a fee which you can include in your grant. You can find data archives at:
   1. NIH Data Sharing Repositories
   2. Registry of Research Data Repositories
   3. DigitalCommons@TMC (An online repository sponsored by The TMC Library and dedicated to serving as a scholarly resource for member institutions of the Texas Medical Center, check out the Getting Started Guide for more information)
3. Data Enclave (restricted access database): for data that cannot be distributed to the general public due to confidentiality concerns, third-party licensing agreements, or national security considerations. One example is the CDC’s National Center for Health Statistics’ Research Data Center

Regardless of the method used to share data, datasets will require documentation which gives information about the methodology and procedures used to collect the data, details about codes, definitions of variables, variable field locations, frequencies, etc. For more information see Data Standards and Common Data Elements Resource Guide.

Data would need to be shared when your work is published, or before your performance period ends, whichever comes first. In general, you should make your data accessible as soon as possible. You can also use relevant requirements and expectations such as data repository policies, award record retention requirements, or journal policies, to decide when to share your data sets.

NIH expects that researchers will take steps to maximize scientific data sharing, but may acknowledge in Plans that certain factors (i.e., ethical, legal, or technical) may necessitate limiting sharing to some extent.  Some of these to-be-expected limitations should be described in Data Management and Sharing Plans. Compelling rationale for limiting scientific data sharing should be provided and will be assessed by NIH. Potential examples of justifiable factors include:

• informed consent will not permit or will limit the scope or extent of sharing and future research use
• existing consent (e.g., for previously collected biospecimens) prohibits sharing or limits the scope or extent of sharing and future research use
• privacy or safety of research participants would be compromised or place them at greater risk of re-identification or suffering harm, and protective measures such as de-identification and Certificates of Confidentiality would be insufficient
• explicit federal, state, local, or Tribal law, regulation, or policy prohibits disclosure
• restrictions imposed by existing or anticipated agreements (e.g., with third party funders, with partners, with repositories, with Health Insurance Portability and Accountability Act (HIPAA) covered entities that provide Protected Health Information under a data use agreement, through licensing limitations attached to materials needed to conduct the research)
• datasets cannot practically be digitized with reasonable efforts

Examples of reasons that would generally not be justifiable factors limiting scientific data sharing include:

• data are considered to be too small
• data that researchers anticipate will not be widely used
• data are not thought to have a suitable repository

NIH respects and recognizes Tribal sovereignty and American Indian and Alaska Native (AI/AN) communities’ data sharing concerns, and NIH has proposed additional considerations when with working with Tribes in the draft supplemental information on “Responsible Management and Sharing of AI/AN Participant Data.”