Chapter 2 Why This New DMS Policy

First, we will discuss the motivations behind the new data management and sharing policy. This policy is applicable for (most) grant proposals submitted to the NIH as of January 25, 2023.

We are on the first step of the pathway- understanding the motivations of the policy.

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

  • Transparency - Sharing data provides more clarity about how studies are performed. Many scientists also believe in an ethical responsibility to study participants (Bauchner, Golub, and Fontanarosa 2016).
  • 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.
  • Multi-modal work - Scientists can perform studies using multiple types of data when this data is easily accessible (Thessen 2021).
  • Efficiency and cost effectiveness - Some data are especially difficult or expensive to produce. Resources that might have been used to regenerate the data can be used elsewhere.
  • Researcher Inclusion - Data generation can be especially difficult for those at institutes with fewer resources. Publicly available data can therefore be used by these researchers to better enable their participation.
  • Impact - Papers that share their data in repositories appear to be cited more often based on the study by Colavizza et al. (2020) .
  • Collaboration opportunities - Available data can inspire other researchers to explore new directions or extend upon previous work. These researchers might 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.

Scientist examines the result of a plate assay, which is a test that allows scientists to count how many flu virus particles (virions) are in a mixture

2.1 Key terms

Data Management

The work involved with validating, organizing, protecting, maintaining, and processing scientific data to ensure the accessibility, reliability, and quality of the scientific data for use in research. All research data should be actively managed.

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. Some data carry limitations on how data sharing can be done and some meet criteria that make them exempt from data sharing.


Data that provide additional information intended to make scientific data interpretable and reusable. Metadata can include features like dates, independent sample and variable construction and description, methodology, data provenance, data transformations, any intermediate or descriptive observational variables.

Data Management and Sharing Plan

A plan describing an approach to data management, preservation, and sharing of scientific data and accompanying metadata.