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NIH 2023 Data Management and Sharing Policy: Background

Rigor and Reproducibility

This policy is part of an effort by NIH to improve the rigor and reproducibility of science funded by the agency. The policy states that sharing scientific data accelerates biomedical research discovery, in part, by enabling validation of research results, providing accessibility to high-value datasets, and promoting data reuse for future research studies. NIH believes that data sharing can help expedite the translation of research.

Goals of Scientific Data Sharing:

  • Accelerate biomedical research discovery and translation of research
  • Enable validation of research results
  • Provide accessibility to high-value datasets
  • Promote reuse for future studies

Public Access Policy

Two important memos from the Office of Science an Technology Policy have signaled these shifts at the federal level toward making data more accessible. In 2013, the Holdren memo mandated that the published results of federally funded research be made freely available to the public within one year of publication and that researchers better account for and manage the digital data resulting from federally funded scientific research. If you’ve ever had to deposit an article in PMC, this memo was behind that Public Access Policy.

Now, just this summer, the Nelson memo from the OSTP was published, and will require all federal agencies to update their public access policies as soon as possible with the goal of making publicly funded publications and research available without an embargo or cost by 2025. This will include publications and data.  NIH has signaled that they expect their new Data Management and Sharing Policy to line up with this directive.

FAIR Data

NIH encourages researchers to follow FAIR (Findable, Accessible, Interoperable, and Reusable) data principles in their data management and sharing plans.

The FAIR principles facilitate data reuse in secondary research because they allow computers to process and analyze complex datasets with little or no human intervention.

More information about the FAIR data principles is available from GO FAIR and the NIH Strategic Plan for Data Science.