The document discusses new rules from the National Institutes of Health (NIH) requiring funding applicants to include a Data Management and Sharing Plan (DMSP) beginning in January 2023. The new policy aims to improve data sharing and management practices for NIH-funded research. Under the new rules, applicants must describe how they will preserve, access, and share scientific data generated by their research. The DMSP must address six elements: data type, related tools/software, standards, data preservation/access timelines, access considerations, and oversight. The goal is to maximize research outcomes while supporting rigor and reproducibility.

1. NIH Grants
and Data: New
Rules Coming
in 2023
ERIN OWENS, PROFESSOR
SHSU NEWTON GRESHAM LIBRARY

2. Housekeeping Notes
▪ I will email the slides to all attendees after the session!
▪ Revisit details and links as needed
▪ Feel free to share with colleagues
▪ Download worksheet (shared in the chat) and brainstorm how the
new requirements relate to your own research data as we talk

3. What/Who Is Affected?
▪ NIH funding submissions on or after 25 Jan 2023
▪ Applies to all research, funded or conducted in whole or in part by
NIH, that results in the generation of "scientific data."
▪ "Scientific data" is defined as:
▪ "the recorded factual material 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."
▪ Does NOT apply to research projects not generating scientific data or
non-research projects (training, fellowships, conference grants,
construction, infrastructure, etc.)

4. What/Who Is Affected?
▪ Scientific data will vary depending on the project and the context
▪ Scientific data might include:
▪ Electrophysiological recordings and fMRI images
▪ Step activity from a wearable device
▪ Clinical trial data
▪ Genomic sequences
▪ Bench experiments with cells or animals
▪ But also think broader…
▪ Surveys, interviews, or observational notes
▪ Past medical records (“chart review”)
▪ Images

5. What/Who Is Affected?
▪ Scientific data do NOT include:
▪ Data not necessary for or of sufficient quality to validate and replicate
research findings
▪ Laboratory notebooks
▪ Preliminary analyses
▪ Completed case report forms
▪ Drafts of scientific papers
▪ Plans for future research
▪ Peer reviews
▪ Communications with colleagues
▪ Physical objects (e.g., laboratory specimens)

6. What Are the New Requirements?
▪ Funding applicants must include
a Data Management and Sharing
Plan (DMSP)
▪ Boils down to just two core steps:
▪ Submit a plan.
▪ Follow the plan!
▪ Challenge: Writing a plan that will
be approved AND can be
followed Image by Gerd Altmann from Pixabay

7. What is the DMSP?
▪ 2 pages (max) supplementing grant submission
▪ Not set in stone! Living document to be adjusted
during award negotiation and before research begins
▪ Covers 6 elements:
▪ Data type
▪ Related tools, software, and/or code
▪ Standards
▪ Data preservation, access, and associated timelines
▪ Access, distribution, or reuse considerations
▪ Oversight of data management and sharing; how
compliance will be supervised, by whom, how often
Plan
Elements
Data Type
Related tools,
Software
and/or Code
Standards
Data
Preservation,
Access and
Associated
Timelines
Access,
Distribution, or
Reuse
Considerations
Oversight of
Data
Management
and Sharing

8. What is the DMSP?
Even if
you do not intend to
or cannot
share your scientific data,
you must still
submit a DMSP!

9. Exploring the Elements: Data Type
▪ This could be called data and documentation types.
▪ Summarize the types and amount of data to be generated/used
▪ EX: 256-channel EEG data and fMRI images from 50 participants
▪ May include data modality (e.g., imaging, genomic, mobile, survey),
level of aggregation (e.g., individual, aggregated, summarized), and/or
the degree of data processing
▪ Describe which data will be preserved and shared – Does not
necessarily have to be everything
▪ Decide based on ethical, legal, and technical factors
▪ Describe the reasoning for these decisions

10. Exploring the Elements: Data Type
▪ Briefly list metadata or documentation that will be shared to
enable interpretation
▪ Study protocols
▪ Research instruments
▪ Technical metadata such as instrumentation settings
▪ Human-made documentation like README files
▪ In short: Summarize the data plus definitions, documentation, and
metadata you will collect and create.
▪ This is also where you can address aggregation and processing
levels.

11. Exploring the Elements: Related Tools
▪ Are specialized tools needed to access or manipulate data for
replication or reuse?
▪ If so, state names of the needed tools and software
▪ If applicable, specify how needed tools can be accessed
▪ If known, whether tools are likely to remain available for as long as the
data remain available
▪ Consider custom code developed in labs or teams
▪ In silico /dry bench / computational work: Will you include scripts
with your data sharing package?

12. Exploring the Elements: Standards
▪ Describe what standards, if any, will be applied to the scientific
data and associated metadata, such as…
▪ Data formats
▪ Data dictionaries
▪ Data identifiers
▪ Definitions
▪ Unique identifiers
▪ Other data documentation

13. Exploring the Elements: Standards
▪ In your discipline, do people talk about technical issues in file
formats and data collection? If so: Discuss your preference.
▪ Examples: BCL conversion to FASTQ format; BAM versus VCF files
▪ Applying MeSH (medical subject headings)
▪ While many scientific fields have developed and adopted common
data standards, others have not. In such cases, the Plan may
indicate that no consensus data standards exist.
▪ Boilerplate: “There is no consensus standard format for this kind of
data in the discipline.”

14. Exploring the Elements: Preservation, Access, and
Associated Timelines
▪ This is the actual “plan” part of the plan to provide access to
the data – May help to write this section first
▪ Give plans and timelines for data preservation and access
▪ Name of repository(ies) where data/metadata will be archived
▪ How data will be findable and identifiable
▪ Persistent unique identifier (DOI, HANDLE, PURL)
▪ Other standard indexing tools

15. Exploring the Elements: Preservation, Access, and
Associated Timelines
▪ When data will be made available to other users and for how
long
▪ NIH encourages data to be shared as soon as possible
▪ Data must be shared no later than publication of findings or end of
award period (whichever comes first)
▪ Identify any differences in timelines for different subsets
▪ NIH encourages data to remain available for as long as researchers
anticipate it being useful for the larger research community,
institutions, and/or the broader public
▪ In short: What can you get uploaded to a repository by Grant
Closeout that will be more than an article’s tables but is still
feasible with your work? Take a deep breath and jot that down.

16. Exploring the Elements: Access, Distribution or
Reuse Considerations
▪ Considerations really = “restrictions / limitations”
▪ 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

17. Exploring the Elements: Considerations
▪ Any potential limitations on subsequent data use should be
communicated to individuals/entities (for example, data repository
managers) who will preserve and share the data
▪ Some justifiable ethical, legal, and technical factors for limiting
sharing include:
▪ Informed consent will not permit or limits scope of sharing or use
▪ Privacy or safety of research participants would be compromised, and
available protections are insufficient
▪ Explicit federal, state, local, or Tribal law, regulation, or policy prohibits
disclosure
▪ Restrictions imposed by existing or anticipated agreements with other
parties

18. Exploring the Elements: Considerations
▪ Reasons NOT generally justifiable to limit sharing:
▪ Data are considered too small
▪ Researchers anticipate data will not be widely used
▪ Data are not thought to have a suitable repository
▪ See NIH’s Frequently Asked Questions for more examples of
justifiable reasons for limiting sharing of data

19. Exploring the Elements: Oversight
▪ Indicate how compliance with the DMS Plan will be monitored and
managed
▪ Who’s responsible? This might seem simple, but can become
complicated for big teams or multi-institutional work
▪ Postdoc or stats core? More detail may be a good idea

20. Reviewing Our Worksheets
▪ Did/can you write at least a sentence in each of the 6 sections?
▪ Does it address the most-basic-est basics of the points we discussed?
▪ Congrats! You can write a DMS Plan!
▪ Can you imagine downloading the data and changing it the way you
described?
▪ Can you also imagine writing the abstract and other metadata and
documentation that describes it?
▪ Can you upload to the repository you described, or find someone who can?
▪ You can create a basic “data package” to comply with a DMS Plan!
▪ Many researchers can start with a spreadsheet, an abstract, some MeSH
keywords, and an approved generalist repository

21. Data Management? Data Sharing? Both?
▪ NIH has stated that a primary goal for the new policy is cultural
change, aimed at challenging researchers to develop their projects
with the goal of data sharing in mind.
▪ Researchers are expected to share the results of their NIH-funded
studies throughout the life of the project to
▪ maximize research outcomes (taxpayer dollars)
▪ while supporting research rigor & reproducibility

22. Data Management? Data Sharing? Both?
▪ Focus is on SHARING
▪ Plan should discuss data management as it applies to sharing
▪ Not focused on data management details for security or safety
▪ Plan should align with security, but not focus on it
▪ Much less security detail needed than an IRB protocol or Data Safety
and Monitoring Plan* protocol
* Note that the Data Safety and Monitoring Plan abbreviates to DSMP. Confusion of
DSMP and DMSP is highly likely!

23. Planning & Budgeting for Costs
▪ Reasonable, allowable costs may be included in NIH budget
requests for:
• Curating data
• De-identifying data
• Preparing metadata to foster discoverability, interpretation, and reuse
• Developing supporting documentation
• Formatting data according to accepted community standards or for
transmission to and long-term storage at a specific repository
• Local data management considerations, such as unique and
specialized information infrastructure necessary
• Preserving and sharing data through established repositories, such as
data deposit fees

24. Planning & Budgeting for Costs
▪ All allowable costs submitted in budget requests must be incurred
during the performance period, even for scientific data and
metadata preserved and shared beyond the award period
▪ EXAMPLE:
▪ DMS plan proposes preserving and sharing scientific data for 10 years
in an established repository with a deposition fee
▪ Cost for the entire 10-year period must be paid before the end of the
period of performance

25. Planning & Budgeting for Costs
• Unallowable Costs - Budget requests must NOT include:
• Infrastructure costs that are included in institutional overhead (for
instance, Facilities and Administrative costs)
• Costs associated with the routine conduct of research, including costs
associated with collecting or gaining access to research data
• Costs that are double charged or inconsistently charged as both direct
and indirect costs
▪ See more details from the NIH on the steps to request funds for
data management and sharing

26. Selecting a Repository to Share Data
▪ Encourages use of existing, quality repositories
▪ Choose a domain-specific repository whenever possible
▪ Generalist repositories can fill the gap when needed
▪ Don't share the same data in multiple repositories

27. Selecting a Repository to Share Data
▪ Desirable Characteristics
▪ Unique Persistent Identifiers (e.g., DOI, HANDLE, PURL)
▪ Long-Term Sustainability
▪ Metadata
▪ Curation and Quality Assurance
▪ Free and Easy Access (i.e., free for use, not necessarily for deposit)
▪ Broad and Measured Reuse
▪ Clear Use Guidance
▪ Security and Integrity
▪ Confidentiality
▪ Common Format
▪ Provenance
▪ Retention Policy

28. Selecting a Repository to Share Data
▪ More Resources
▪ White House Report: Desirable Characteristics of Data Repositories
(2022)
▪ Filterable list of 70+ NIH Repositories
▪ Generalist repositories
▪ Nature's Data Repository Guidance
▪ Registry of Research Data Repositories

29. Additional Possible Expectations
▪ NIH may develop more granular data management and sharing
polices based on data types, participant type, and data sensitivity
▪ A specific funding opportunity or Institute/Center may already
have or may establish more specific expectations, such as…
▪ What data to share
▪ Relevant standards
▪ Repository
▪ View a list of NIH Institute or Center data sharing policies
▪ Investigators are encouraged to reach out to program officers with
questions about specific requirements

30. Submitting Your Plan
▪ New “Other Plan(s)” field
in PHS 398 form will
collect a single PDF
attachment
▪ Data Sharing Plans and
Genomic Data Sharing
Plans will no longer be
submitted to the
“Resource Sharing
Plan(s)” field

31. Assessment and Revision
▪ Peer reviewers will not comment on DMSP or factor into scoring
▪ …Unless sharing data is integral to the project design and specified in
the Funding Opportunity Announcement
▪ NIH program staff will assess DMS Plans
▪ All elements addressed
▪ Reasonable of responses
▪ May request additional details/revisions pre-award
▪ Ultimately applications selected for funding will only be funded if the
DMS Plan is complete and acceptable
▪ Revisions should be submitted during the award period if
circumstances affecting the plan change

32. Summing Up
Include sharing updates in annual progress reports
Share and manage data according to the plan
Publish findings
Collect data according to the plan
Submit application including the plan
Develop a thoughtful, specific, actionable plan you'll be able to follow

33. Additional Resources
▪ Data Management and Sharing Policy Overview (from NIH)
▪ Writing a Data Management & Sharing Plan (from NIH)
▪ NIH webinar recordings and slides about DMSP
I wish to gratefully acknowledge Nina Exner, Research Data Librarian, Virginia Commonwealth University, for her
presentation for data librarians: “Preparing Your Workshops on the New NIH Data Management and Sharing Plans”

34. Thank You!
Questions?
Erin Owens
Scholarly Communications Librarian
eowens@shsu.edu
Image by Alexa from Pixabay