The National Institutes of Health (NIH) has issued a Final NIH Policy for Data Management and Sharing to promote the sharing of scientific data. Starting on January 25, 2023, NIH requires researchers to submit a plan outlining how scientific data from their research will be managed and shared. The policy also establishes the expectation for maximizing the appropriate sharing of scientific data generated from NIH-funded or conducted research, with justified limitations or exceptions.
Requirements of the policy
Submission of Data Management & Sharing Plan.
Compliance with the approved Data Management & Sharing Plan
Scope: All NIH-supported research generating scientific data.
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 policy 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.
Recommended Elements of a Data Management and Sharing Plan:
Data type: Describe the scientific data to be managed and shared
Related tools, software, and/or code: Indicate 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, specify how needed tools can be accessed.
Standards: Describe what standards, if any, 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).
Preservation, access, and associated timelines: Give 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 and for how long. Identify any differences in timelines for different subsets of scientific data to be shared.
Access, distribution, and reuse considerations: Describe any applicable factors affecting subsequent access, distribution, or reuse of scientific data related to:
- Informed consent
- Privacy and confidentiality protections consistent with applicable federal, Tribal, state, and local laws, regulations, and policies
- 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. Any potential limitations on subsequent data use should be communicated to the individuals or entities (for example, data repository managers) that will preserve and share the scientific data.
6. Oversight of data management and sharing: Indicate who is responsible for which roles in managing your data and monitoring compliance with the DMSP. DataONE maintains a list of possible roles and responsibilities for an ideal DMSP here. However, every research group is different and might not need people for each role.
Timelines: When to share data: No later than the time of an associated publication or end of award (for unpublished data), whichever comes first
UC Merced Resources for developing a Data Mangement Plan
Request a Research Data Management Consultation (with IT Research Computing and the Library) to discuss the hardware, software, and data management needs for an upcoming research project.
Request a DSMP Consultation (with the Library) to discuss and review the Data Management Plan for your grant.
The DMPTool is a free, open-source, online application that helps researchers create data management plans. The DMPTool provides a click-through wizard for creating a DMP that complies with funder requirements. It also has direct links to funder websites, help text for answering questions, and resources for best practices surrounding data management. Sign into the DMPTool using your UCM Net ID to start creating your own plans based on requirements from funding agencies.
NIH has provided supplemental resources to help the research community prepare for the new policy. These include:
UCSD Sample NSF Data Management Plans
Includes numerous sample Data Management Plans across a range of disciplines
Guidelines for Effective Data Management (ICPSR)
Includes a framework, and links to example plans
NSF ENG example from University of Michigan
Provides examples of good and bad language used in an ENG data management plan
Examples of Data Management Plans