As of January 25, 2023 NIH requires all researchers seeking grant funds that result in the generation of scientific data to:
- Submit a 2-page Data Management and Sharing plan outlining how their scientific data and accompanying documentation will be managed and shared
- As part of that plan, maximize the appropriate sharing of scientific data generated from NIH-funded or conducted research, with justified limitations or exceptions.
- NIH DMS Policy Homepage
- NIH Institutes and Centers Data Sharing Policies - note that some institutes like NIMH, NIAAA, and NIDDK have more specific plan requirements
Data Management and Sharing (DMS) Plan Templates and Samples
- UCSF Generic DMS Template
- UCSF Clinical Trial DMS Template
- UCSF Social Science DMS Template
- UCSF Basic Science DMS Template
- DMPtool - step by step data management planning tool with templates
- Sample Plans from NIH
- Sample UCSF Plans - example plans from UCSF researchers. Have a plan to share? Email Ariel at email@example.com
- Sample Plan for Vivli clinical trial repository
- UCSF Budget Guidance - how to plan for data management costs
Planning for Data Sharing
- UCSF Guide to Sharing De-Identified Data - learn how to incorporate data sharing into your research workflow
- UCSF Library guide to data management
Selecting a Data Repository
NIH recommends the use of well-established data repositories to share your data. To find the best repository for your research check out the UCSF Library guide to data repositories
Policy Presentations and Slides
- NIH DMS Policy Presentations from the UCSF Library
- Library and HRPP Presentation for UCSF Qualitative Researchers
How to get help
Questions about the new policy or need help selecting an appropriate data repository or metadata standard? Want someone to review your draft plan? Want someone to present on the policy to your team or department? Contact the UCSF Library's data management team.
For consulting on de-identification please visit UCSF CTSI Consultation Services
FAQs about the new DMS Policy
The NIH encourages data sharing in order to:
- Increase public access to federally funded research
- Enable transparency and reproducibility of research results
- Make data available for discovery and reuse
The policy applies to all NIH grants that generate scientific data including research projects, some career development awards (Ks), Small Business SBIR/STTR, and Research Centers.
It does not apply to grants for Training (T), Fellowships (F), Construction (C06), Conferences (R13), Resource (Gs), and Research-Related Infrastructure Programs (S06).
See the complete list of activity codes covered by the policy.
The final NIH Policy defines Scientific Data as: “The recorded factual material commonly accepted in the scientific community as of sufficient quality to validate and replicate research findings"
Researchers are not expected to share: data that are not necessary for or of sufficient quality to validate and replicate the research findings, 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.
Yes. 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. Foreseeable limitations should be described in DMS Plans. Per the supplemental information “Elements of an NIH Data Management Sharing Plan,” a 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
In these max two-page documents, researchers will describe their:
- Data type
- Related tools, software, and/or code
- Data preservation, access, and associated timelines
- Access, distribution, or reuse considerations
- Oversight of data management and sharing
No. NIH prefers that scientific data be shared and preserved through data repositories rather than kept by a researcher and provided upon request.
To select a data repository relevant to your research consider:
- Is there a specific NIH repository named in the funding announcement or mandated by your institute? If so, use that one
- Is there a data repository specific to your discipline or data type?
- If not, is there a generalist data repository you can use?
- For UCSF basic science researchers, consider Dryad data repository - free for UCSF
To learn more, check out the UCSF Library guide to data repositories
NIH encourages scientific data be shared as soon as possible, and no later than time of an associated publication or end of the performance period, whichever comes first.
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 refer to the adoption of the same (or similar) data management-related activities, conventions, or strategies by different researchers and across different projects.
Learn more about standards on this guide from the UCSF Library.
Allowable costs can include:
- data curation and developing documentation (formatting data, de-identifying data, preparing metadata, curating data for a data repository)
- data management considerations (unique and specialized information infrastructure necessary to provide local management and preservation before depositing in a repository)
- preserving data in data repositories (data deposit fees)
NIH program staff will assess the DMS plans but peer reviewers may comment on the proposed budget for data management and sharing.
NIH Program Staff will be monitoring compliance with the policy during the funding period. “Noncompliance with Plans may result in the NIH ICO adding special Terms and Conditions of Award or terminating the award. If award recipients are not compliant with Plans at the end of the award, noncompliance may be factored into future funding decisions.”
According to NIH researchers are not expected to share existing, shared primary data used to conduct secondary research. Researchers are, however, expected to maximize appropriate sharing of any new, derived data generated as a result of their research if at all possible. That said, one of the justifiable reasons not to share is "restrictions imposed by agreements" meaning that if you need to sign a strict DUA in order to access the data (for example CMS or other claims data) you are justified in not making the derived data public. In this case you would simply describe why data sharing is not possible in your DMS plan.
Here is an example NIH plan using secondary data.
- Review the NIH DMS Policy Presentations from the UCSF Library
- NIH has released 2 webinars about the policy. Watch the recordings
General FAQs about Data Sharing
UCSF researchers should follow the guidance for sharing de-identified data to incorporate data sharing into their research process.
Yes, UCSF researchers sharing de-identified human subjects data need to do the following (note that no approvals are needed for researchers working with non-human data):
- If you are sharing in a controlled access repository (recommended) work with UCSF Industry Contracts to evaluate and sign the repository's data sharing agreement.
- If you wish to share your data in an open repository or a controlled access repository not listed on the above list, email firstname.lastname@example.org with a short description of your data and the name of your chosen repository and the EIA Steering Committee will review and approve.
Visit this page on de-identification to learn about UCSF offices that can offer de-identification advice and see additional resources.
NIH strongly encourages researchers who work with sensitive topics and/or populations to address data sharing in the Informed Consent process. UCSF consent form templates include appropriate sample language.
Researchers should pay special attention to their de-identification process to ensure that all identifying information has been fully removed. CTSI’s data de-identification service can provide advice on de-identification and connect you with third party de-identification validation services.
Finally, UCSF recommends that researchers share their de-identified human subjects data in controlled access repositories that require data use agreements and research plans in order to access the data.
See more advice from NIH about protecting participant privacy.
While sharing data can lead to scooping, this is very rare. If you are worried about scooping you may want to hold off publishing your data until your associated publication is ready to be published. Some repositories also have a "private for peer review" option which allows you to make your data available to peer reviewers but not fully publish your data until the article has been accepted.