Understanding the NIH Data Management & Sharing Policy
Anna Simonson, MA, MLIS, PhD�Digital Scholarship & Research Services (DSRS)�University of South Dakota, University Libraries
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About this webinar
I. Survey results
II. Scope, goals, and requirements of the policy
III. Basics of writing a Data Management �& Sharing (DMS) Plan
IV. Q & A
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NIH DEFINITIONS
�What is data management?
The process of validating, organizing, protecting, maintaining, and processing scientific data to ensure the accessibility, reliability, and quality of the scientific data for its users.
What is data sharing?
The act of making scientific data available for use by others (e.g., the larger research community, institutions, the broader public), for example, via an established repository.
I. �Survey results
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Survey results��Thoughts on data sharing
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�It is important for open science.�
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It’s important to build support for research and engagement with communities we do research with.
I believe it is important as long as it is possible within the context of the particular project. It is important as a level of peer review and also extending knowledge.
It is important to disseminate data in a timely manner for public good.
It is important in terms of ensuring reproducibility of experiments and their findings.
It has not been important to me in the past.
Survey results��The meaning of good data stewardship
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Being able to have the data ready at hand for your own purposes and for others to be able to look at.��
Storage, dissemination and protection.�
It means to remember that human subjects derived data came from people and that their data must be protected.
Data gets to the people who need it most, that data is not just hoarded by PIs with great resources and many grants.
Saving data in such a way that it can be mined at a later date, realizing that the technology that exists now may differ later. Learning best practices to ensure data security as well as longevity.
It is crucial for further data analysis and also helpful for others to explore your data or to reproduce the findings.
At this time, it only means following the rules stipulated in the applicable IRB.
Survey results��Concerns about data management and sharing in general
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The time investment involved on fulfilling this obligation.�
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IRB/Privacy.��
If there is personnel time built in to adequately do this well.
Protection of IP.
Data sharing policies need to recognize the complexity of working with marginalized populations or those that are sharing traumatic experiences and whether they are willing to share their data.
Time commitment. Data are useless unless they are clean and a system is followed. This will mean that extra steps of work will be required.
Survey results��Concerns about the new NIH �DMS policy
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The time investment required.
Do not know enough about it.�
No concerns so far.�
Red tape. Coordinating IRB/DUA/etc. Also, persistence of the data. How will IT and others ensure that data will be retained on the servers for 15 years.
It will become way more burdensome to publish data in general - not just data that are gathered with NIH support. Everyone follows the NIH's lead.
If requirements will honor sovereign nations' laws.
Data-Sharing Behaviour:��“Reasons [for not sharing] included a lack of informed consent or ethics approval to share; misplaced data; and that others had moved on from the project.”
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Source: Watson, C. (21 June 2022). May researchers say they’ll share data---but don’t. Nature News. https://www.nature.com/articles/d41586-022-01692-1
II. �Scope, goals, & requirements
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Policy Effective Dates
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Extramural
Contracts & Intramural
Other funding agreements (e.g. Other Transactions)
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2016�RFI: Strategies on Data Management, Sharing and Citation
2018�RFI: Proposed Provisions for a Draft Policy
2019�RFC: Draft Policy and Guidance
2020�Final Policy Released
2023�Policy Effective
*RFI: Request for Information
*RFC: Request for Comment
Policy Development through
Consistent Community Engagement
NIH:�Developing a culture of data sharing
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2003
Data Sharing Policy
2015
Genomic Data Sharing Policy
2016
FAIR standards: Findable, Accessible, Interoperable, and Reusable
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“By integrating data sharing into the routine conduct of a project, the NIH aims to shift the biomedical research culture into an era in which data sharing is the rule rather than the exception.”
Jorgenson, L. A., Wolinetz, C. D., & Collins, F. S. (2021). Incentivizing a New Culture of Data Stewardship: The NIH Policy for Data Management and Sharing. JAMA, 326(22): 2259-2260.
Goals of the Policy:
A New Culture �of Data Stewardship
The 2023 Data Management and Sharing Policy
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Scope of the policy
The NIH DMS Policy applies to research funded or conducted in whole or in part by NIH that results in the generation of scientific data.
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What is scientific data?
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.
The DMS policy does NOT apply to:
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Laboratory notebooks
Preliminary analyses
Completed case report forms
Drafts of scientific papers
Plans for future research
Peer reviews
Communications with colleagues
Physical objects (i.e., laboratory specimens)
Activities subject to the NIH DMS policy
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The DMS Policy applies to all research that generates scientific data, including:
The DMS Policy does not apply to research and other activities that do not generate scientific data, including:
Policy Requirements
Submission
Compliance
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Per the policy, does all data need to be shared?
No. The policy does not require data sharing. It requires the submission of a Data Management & Sharing Plan. However, the NIH expects researchers to “maximize the appropriate sharing of scientific data.” Researchers must determine what constitutes scientific data for their research, including both what will and will not be shared, and why.
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Justifiable reasons for limiting the sharing of data
�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 working with Tribes and AI/AN communities.
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Best practices for protecting privacy when sharing human research participant data
1. De-identify to the greatest extent while maintaining scientific utility; use Common Rule AND HIPAA Privacy Rule Standards.
2. Use agreements for transferring data.
3. Understand applicable legal protections and limitations on disclosure.
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When and where should I share my data?
At the time of an associated publication
By the end of the performance period
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OR
An established repository
WHEN
WHERE
Whichever one comes first
To request funds toward DMS costs, investigators should include:�
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Budgeting for Data Management and Sharing
III. �Basics of the DMS plan: writing, submission, �& review
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Submission & Review
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Submit DMS Plans and budget requests as part of the funding application or proposal.
Peer Review will not see or review DMS Plans, but will consider any related budget items.
NIH program staff will review the DMS Plan for acceptability and may request modifications prior to award as appropriate.�
Plans must be approved by the funding institute prior to award.
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The Data Management
and Sharing Plan
6 Elements of the DMS Plan
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Element 1: �Data Type
“Spatial Coordinates: Capture locations for animals will be recorded via GPS in Universal Transverse Mercator (UTM) coordinates and saved as .csv files.”
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Briefly describe the scientific data to be managed and shared
Note that the GDS Policy expects certain types of data to be shared that may not be covered by the DMS Policy’s definition of “scientific data”.
Element 2: related tools, software, and/or code
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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.
Element 3: �Standards
“Whenever possible, our data will conform to international standards. For example, dates will be encoded using the ISO8601 standard (e.g. 2016-08-05).”
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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).
Element 4: �Data preservation, access, and associated timelines
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Give plans and timelines for data preservation and access, including:�
Element 5: �Access, distribution, �or reuse considerations
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Describe any applicable factors affecting subsequent access, distribution, or reuse of scientific data related to:�
Element 6: Oversight of data management
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“The postdoctoral associate will be responsible for periodic checks on adherence to the data management plan, consulting with PI to resolve issues. Should PI be unable to complete this primary role for any reason, co-PI will step into this role. We will ensure that all co-PIs have full access to this plan and cloud-based data and metadata so that if such a transition must occur, it will be seamless.”
Indicate how compliance with the DMS Plan will be monitored and managed.
Recap
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WHO: Anyone using NIH funds to conduct research that generates scientific data.
WHAT: Creating a DMS plan for 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.
WHERE: An established repository
WHEN: As soon as possible, and no later than the time of an associated publication, or the end of the award/support period, whichever comes first.
WHY: To promote open science
Need help putting your DMS Plan together?
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��CONTACT INFORMATION�
�Anna Simonson, Digital Scholarship & Research Services Librarian
Anna.Simonson@usd.edu�ID Weeks 131A�Wegner Library 150
Marc Guilford, Human Subjects Director�Marc.Guilford@usd.edu�107 Robert L. Slagle Hall�605-658-3767
Melinda Robinson, Director of Sponsored Programs�Melinda.Robinson@usd.edu�105 Robert L. Slagle Hall�605-658-3765�