Healthcare innovation

NIH Data Management and Sharing Policy Going…

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NIH Data Management and Sharing Policy Going…

A National Institutes for Health (NIH) Data Management and Sharing (DMS) Policy that goes into effect on Jan. 25, 2023, expects researchers to maximize the appropriate sharing of scientific data, and consider legal, ethical, or technical issues that may limit the extent of data sharing and preservation.

Individuals receiving NIH funding to generate scientific data must comply with the DMS Policy. This compliance level differs from the 2003 DMS Policy: Previously, only awards totaling $500,000 per year or more had to comply with the policy.

In a recent blog post, Susan Gregurick, Ph.D., associate director for data science and director of the Office of Data Science Strategy, noted that “data sharing accelerates biomedical research discovery and innovation, enhances research rigor and reproducibility, provides accessibility to high-value datasets, and promotes data reuse for future research studies. Ultimately, sharing data speeds up the process of turning research results into knowledge, products, and procedures to improve human health.

Although all researchers are encouraged to share their scientific data, Gregurick’s blog notes that there are a few reasons for limiting data sharing:

  • The data submission is not consistent with applicable national, tribal, and state laws and regulations as well as relevant institutional policies
  • Data limitations on the research use of the data, as expressed in the informed consent documents
  • The identities of research participants would be disclosed to NIH-designated data repositories.
  • An Institutional Review Board (IRB), and/or Privacy Board, and/or equivalent body, as applicable, has reviewed the investigator’s proposal for data submission and will not assure that:
  • The protocol for the data is consistent with 45 CFR Part 46;
  • Data submission and subsequent data sharing for research purposes are not consistent with the informed consent of study participants from whom the data were obtained;
  • Consideration was not given to risks to individual participants and their families associated with data submitted to NIH-designated data repositories and subsequent sharing,
  • To the extent relevant and possible, consideration was not given to risks to groups or populations associated with submitting data to NIH-designated data repositories and subsequent sharing,
  • The investigator’s plan for de-identifying datasets is not consistent with the DHHS and NIH standards and expectations.

A research guide produced by the University of Michigan Library system states that NIH strongly encourages the use of established repositories to the extent possible for preserving and sharing scientific data. “Once a grant has been awarded, the DMS Plan becomes part of the award’s terms and conditions. NIH expects researchers and institutions to implement data management and sharing practices as described in their approved Plan. Compliance with the DMS Plan, including any updates, may be reviewed during regular reporting intervals,” the guide notes.

The guide states that a DMS Plan should include the following 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

The policy allows researchers to budget for data management and sharing activities, including:

  • Curating data
  • Developing supporting documentation
  • Formatting data according to accepted community standards, or for transmission to and storage at a selected repository for long-term preservation and access
  • De-identifying data
  • Preparing metadata to foster discoverability, interpretation, and reuse
  • Local data management considerations, such as unique and specialized information infrastructure necessary to provide local management and preservation (for example, before deposit into an established repository)
  • Preserving and sharing data through established repositories, such as data deposit fees

Gregurick’s blog post concludes by stating that data sharing is not new and has been a part of the fabric of NIH-funded research projects for decades. “However, if noncompliance becomes an issue for any NIH-funded research projects, the NIH Institutes, Centers, and Offices may add special terms and conditions or terminate award funding.”

One resource that is available to help researchers prepare DMS plans is the DMPTool, a free, open-source, online application that helps researchers create data management plans (DMPs). 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 data management best practices resources.

On its website, the organization explains that “the original DMPTool was a grassroots effort, beginning in 2011 with eight institutions partnering to provide in-kind contributions of personnel and development. The effort was in direct response to demands from funding agencies, such as the National Science Foundation and the National Institutes of Health, that researchers plan for managing their research data. As a result, the contributing institutions consolidated expertise and reduced costs in addressing data management needs by joining forces.

The original contributing institutions were: University of California Curation Center (UC3) at the California Digital Library, DataONE, Digital Curation Centre (DCC-UK), Smithsonian Institution University of California, Los Angeles Library, University of California, San Diego Libraries, University of Illinois, Urbana-Champaign Library and the University of Virginia Library. Given the success of the first version of the DMPTool, the founding partners obtained funding from the Alfred P. Sloan Foundation to create a second version of the tool, released in 2014.

 

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