Institutional Strategies for the NIH Data Management andSharing Policy: Infrastructure, Policies, and Services
Association of Academic Health Science Libraries
Association of American Medical Colleges
Association of Research Libraries
September 2022

1BackgroundIn the fall of 2020, the National Institutes of Health (NIH) released its new policy fordata management and sharing that will go into effect in January 2023. This policy
applies to all NIH-funded research and requires investigators to submit data
management and sharing (DMS) plans.
As research data sharing has started to become an enforced requirement from funders
and publishers, many academic institutions, libraries, and individual researchers havedeveloped services, technology, and workflows to meet this requirement. As institutions
gear up to meet what will be a greater demand for support among researchers on theircampuses given the upcoming NIH DMS policy, identifying and sharing existing tactics
and expected strategic opportunities for academic institutions is critical to meeting thisdemand.
The Association of Academic Health Science Libraries (AAHSL), the AAMC (Associationof American Medical Colleges), and the Association of Research Libraries (ARL)
conducted a mixed methods research project to identify and share these existing orproposed innovations for other institutions to reuse, build upon, or otherwise leverage
to meet this upcoming NIH requirement.

About the Research ProjectIn the fall of 2021, representatives from AAHSL, the AAMC, and ARL collaboratively
drafted a survey to identify these tactics and activities from the AAHSL and ARL
membership. The AAMC also queried constituents separately about their plans toprepare for the policy. The survey asked the following questions:● How many full-time equivalent (FTE) positions does your library designate fordata management and sharing support?
● What processes, workflows, and services does your institution currently havein place that will be used to meet the requirements in the upcoming NIH data
management and sharing policy?
● What processes, workflows, and services are your institution developing orplanning to develop to meet the requirements in the upcoming NIH data
management and sharing policy?
● Is there anything else you want to share about your institution’s processes,
workflows, and services that specifically support biomedical and health researchdata generated by NIH-funded projects?
Once the survey was completed, the research team reached out to responding
institutions to conduct brief interviews about their local strategies for meeting theupcoming NIH policy. These interviews took place in spring and fall of 2022. The
following sections describe the key themes that emerged from the research.

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Five Areas of Institutional Support

Coordination andCommunication

Governance andCompliance

Services andProcesses

Training andEducation

Infrastructure3

Coordination and Communication
Addressing the institutional implications of the NIH DMS is not
solely the responsibility of the library or the researcher. Rather,coordination across campus stakeholders emerged as a key firststep in strategically building infrastructure and services to complywith the policy.

Campus-Wide Task Forces
Many institutions have formed or are currently forming campus-wide teams or task
forces to meet the NIH DMS requirements. These teams are composed ofrepresentatives from the following offices and units and often include components fromthe university as well as the medical school or health sciences school:● Office of the provost or dean.
● Office of research or sponsored projects.
● University libraries.
● Research compliance.
● Research computing.
● Campus information technology.
● NIH-funded faculty representatives.
The charge and activities of these teams vary depending on the institutional context but
may include some or all of the following:● Evaluating institutional readiness and expected impact.
● Developing strategic plans to address researcher needs, roles and responsibilities,
governance, outreach and engagement, services, and infrastructure.
● Creating workflows or service referral processes for support among institutional
stakeholders.
● Benchmarking with peer institutions.
In some cases, institutions have used these impending requirements as an opportunity
to establish working groups focused on broader research data management and sharingneeds, in which case they are evaluating requirements across different federal,nonprofit, and philanthropic funders as well as various publishers.

4Governance and Compliance
Several institutions had research data governance and compliancepractices and policies in place or were in the process ofestablishing formal documents. These documents included
institutional data policies as well as data strategy.

Resources
Institutional Research Data Policies
Briney K, Goben A, Zilinski L. Do you have an institutional data policy? a review of thecurrent landscape of library data services and institutional data policies. J Libr Sch
Commun. 2015;3(2):p.eP1232. https://doi.org/10.7710/2162-3309.1232
Research Data Strategy
Hosch BJ. Key elements of a data strategy. In: Powers K, ed. Data Strategy in Collegesand Universities: From Understanding to Implementation. Routledge; 2019. doi:
10.4324/9780429437564-2
Examples:• East Carolina University: Data Governance Policies and Standards.
• Indiana University School of Medicine Research Data Ownership and Retention
Policy.
• The Ohio State University: Research Data Policy.
• Virginia Commonwealth University: Research Data Ownership, Retention,Access, and Security.
• Yale University Research Data and Materials Policy.
Informed Consent and Sensitive Data
Clinical and translational research often produces data from human research
participants, subject to additional policies and regulations at the institutional, state,federal, and international level. Explicitly outlining these requirements for investigators
producing such data is a key component of compliance. Below are some links toexamples of institutional guidance or initiatives for managing and sharing sensitive
health data:••Brown University: Guidance and FAQs for Sharing Information with Data
Repositories.
Western Michigan University School of Medicine Virtual Data Warehouse.
5Services and Processes
Cross-campus services that involve multiple stakeholders are
critical activities for institutions. These services include
consultation and review services and training and education.

Consultations
The following types of in-depth consultations were indicated by institutions:● Data management plan (DMP) drafting.
● Human subjects and privacy.
● Security.
● Informed consent for research data sharing.
● Curation.
● Storage.

Training and Education

Training, education, and outreach is a core tactic for institutions tosupport the NIH DMS with researchers on their campuses. Training
has differed based on institutional context. Respondents indicatedthey would be focusing their training activities on graduate students,faculty, and library staff.
Methods of educating and engaging the community include:● Creating regularly scheduled brown bag events open to the university researchcommunity on specific aspects of the policy● Scheduling trainings and presentations at existing departmental meetings● Offering on-demand webinars for researchers through a campus resource portal● Incorporating research data management training into graduate student
coursework● Providing a centralized email for researchers to ask questions● Training the trainers: providing opportunities for library, IT, or research office
staff to gain knowledge about the policy, existing resources, and referral
information to share with researchers
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Infrastructure
Institutions have invested significant resources in building andmaintaining tools to help researchers meet funding agency
requirements for the sharing and management of research data.
Infrastructure and staffing varied significantly depending on theinstitution’s local context; libraries had on average one FTEemployee in research data services. Below are some of the various
tactics used by institutions.

Repository Tools
Hosting or providing access to a repository platform is a key component of
infrastructure for many institutions. These included:● Data catalog — a local repository of metadata about datasets produced on acampus.
● Hosted data repository — a hosted repository of datasets produced on acampus (examples included Dryad, Vivli, and Isilon).
● Local data repository — a locally operated repository of datasets produced ona campus.

Data Management Tools
Many institutions adopted data management and sharing tools to assist faculty withmanaging active data and developing funding-agency-compliant DMPs. These included:● Local data management planning application – some institutions aredeveloping their own institutional DMP tools that integrate more closely withexisting institutional applications● DMP repository – a number of institutions have created a repository or aclearinghouse of DMPs created by researchers on their campuses● DMPTool – many institutions indicated they would be highly customizing theCalifornia Digital Library’s DMPTool for the upcoming NIH Policy.
● ORCID – institutions have adopted the use of ORCID, a persistent digital
identifier that assists researchers with creating an ID to tie to their research data.
● Electronic Lab Notebook (ELN) software – institutions have subscriptions
to programs such as LabArchives, which allow investigators to organize, store,
and share data.

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Conclusion
The NIH data sharing policy becomes effective on Jan. 25, 2023. As the dateapproaches, we hope the recommendations here provide a useful starting point andreference for institutions as they prepare to meet the policy requirements. AAHSL, theAAMC, and ARL will continue to share information on policy implementation withconstituents through our listservs.
This document is available at aamc.org/nihdatasharing, which will be continuously
updated with links to new resources and services to support researchers in complying
with the NIH data sharing and management policy.

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