NIH Data Sharing

Updated: July 3, 2023

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What you should know about the new 2023 NIH Data Management and Sharing Policy

Starting January 25th, 2023, the NIH implemented a new policy that requires new grant applications or renewals that generate scientific data to include a detailed data management and sharing plan. The NIH expects all shareable data to be made available, whether it is associated with a publication or not. The goals of this new policy are to establish the expectation for the management and sharing of scientific data generated from NIH-funded or conducted research, and to emphasize the importance of good data management practices.

You can learn more about “FAIR” (Findable, Accessible, Interoperable, Reusable) data sharing principles at the FAIRSharing site which has a catalogue of data preservation, management and sharing policies form international funding agencies, regulators and journals and a lot of other information about data standards.

Data Science Lab Supports

The Data Science Lab has generated a number of different a la carte resources to support the creation of Data Sharing and Management plans from self-paced guides to templated text to long term management of all your plans. Our goal is to create supports that work for many different types of studies, datasets and investigators to simplify creating these plans.

In response to this new, expanded scope of data sharing requirements as well as from other funding agencies, the Data Science Lab has begun customizing for Fred Hutch investigators. You can learn more about this free tool on our dmptool page.

NIH Data Sharing Guide

The Data Science Lab has also created a guide you can find here that walks you through the process of complying with the new 2023 NIH Data Sharing Policy.

If you take this course and want to give us feedback or would like to learn more about it, you can share your thoughts in the FH Data Slack in the #ask-dasl channel) or you can file an issue on the course’s GitHub repository or email us at

Data House Calls

Fred Hutch staff can always schedule a “Study Planning, Data Sharing and Grants” Data House Call with the Data Science Lab for a quick consultation about your specific project.

Other DaSL Resources

We have a set of template materials developed in partnership with Fred Hutch Shared Resources and other groups on campus and investigators that can be a valuable resource when first writing a data sharing plan.


Why is the NIH doing this? There are several reasons why sharing data can be beneficial to the scientific community.

  • Supports transparency - Sharing data provides more clarity about how studies are performed. Many scientists also believe in an ethical responsibility to study participants.
  • Encourages reproducibility and rigor - Having the data accessible, allows others to try to reproduce study findings. This can further enable studies that may replicate or validate the initial findings with different data.
  • Supports multi-modal work - When more data of various types are easily available it makes it easier for scientist to perform studies with multiple types of data.
  • More efficient and cost effective - Some data are especially difficult or expensive to produce.
  • Supports Researcher Inclusion - Data generation can be especially difficult for those at institutes with less resources. Publicly available data can therefore be used by these researchers to better enable their participation.
  • Increased impact - Papers that share their data in repositories appear to be cited more.
  • Increased collaboration opportunity - Having data available can encourage other researchers to expand the research in a new direction or reach out to collaborate.
  • Data Citations - Due to the importance of data generation and sharing to the NIH, data will now be seen as research product that demonstrates a contribution to the scientific community.

What does this mean for me?

The major requirement of the policy is that all grant proposals (submitted after January 25th, 2023) for mechanisms that require compliance, must include a plan for how they will manage and share their data.

For certain grant mechanisms for projects that do not generate data, compliance with the policy is not required. For certain types of data, sharing is not possible, and a justification will be required instead.

Does the Policy Apply?

What grant mechanisms require compliance with the DMS policy?

The DMS Policy applies to all research that generates scientific data, including:

  • Research Projects (R)
  • Some Career Development Awards (K)
  • Small Business SBIR/STTR
  • Research Centers

The DMS Policy does not apply to research and other activities that do not generate scientific data, including:

  • Training (T)
  • Fellowships (F)
  • Construction (C06)
  • Conference Grants (R13)
  • Resource (G)
  • Research-Related Infrastructure Programs (e.g., S06)

To determine if your research requires compliance with other policies that may influence how you share your data, take this quiz.

Does my research generate scientific data?

The NIH Data Management and Sharing (DMS) Policy applies to all NIH-supported research generating scientific data. But what is considered “scientific data”?

Scientific data are the “recorded factual material of sufficient quality to validate and replicate research findings, regardless of whether the data are used to support scholarly publications”. This can include any of the following if they are applicable to your study:

  • unpublished results
  • null results
  • results used to publish papers

You are not expected to share:

  • lab notebooks
  • preliminary analyses
  • case report forms
  • drafts of scientific papers
  • plans for future research
  • peer reviews
  • communications with colleagues
  • physical objects (such as biospecimens)

Essential Components of a DMS Plan

We outline these essential components in more depth in our DaSL developed resources described below, however there are 6 elements that will be required in your data management plan.

  1. Data type - describe what data (amount and type) will be generated over the course of funding and what data will or will not be shared
  2. Tools, software, and code - describe in what tools you intend to use to manage and analyze the data (note code is not required to be shared)
  3. Standards - describe any standards that you might need to use for your data and metadata to make them usable by others or be contributed to a repository
  4. Data preservation, access, timelines - describe where the data will be made available and when
  5. Access, distribution, reuse considerations - describe how you have carefully considered any reasons that might limit sharing
  6. Oversight - describe who will manage compliance of the DMS plan (likely a member of your group)

Updated: July 3, 2023

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