NIH Data Requirements: What Data Must be Shared?

In its commitment to advancing scientific transparency and reproducibility, the National Institutes of Health (NIH) introduced the 2023 Data Management and Sharing (DMS) Policy. With the increasing emphasis on open science and adherence to FAIR principles, these guidelines aim to ensure that NIH-funded research data is made accessible, discoverable, and usable for the broader scientific community. 

In this article, we summarize the types of the data you should expect to share and best practices that you should start familiarizing yourself with to be best prepared. 

Defining "Scientific Data" According to NIH 

The NIH defines scientific data as "the recorded factual material commonly accepted in the scientific community as necessary to validate research findings." This definition encompasses a wide range of data types, from raw data to processed information. 

What Doesn't Qualify as Scientific Data? 

Certain materials and information do not fall under the umbrella of "scientific data" as per the NIH's definition. These include:

• Laboratory notebooks
• Preliminary analyses
• Drafts of scientific papers
• Plans for future research
• Peer reviews
• Communications with colleagues

Sharing Preliminary or Supporting Data 

One of the salient points emphasized by the NIH is that researchers don't have to wait for their projects to conclude, or for their findings to be published, before sharing their data. In fact, the NIH actively encourages the sharing of preliminary or supporting data. This approach ensures that the scientific community can benefit from ongoing research, fostering a more collaborative and transparent research environment.

Data Management and Sharing Plan (DMP) Requirement

While the NIH promotes the sharing of scientific data, it's crucial to understand that there isn't a stringent requirement to share all scientific data during this first year of policy implementation. Instead, what the NIH mandates is that all new grant applications include a Data Management and Sharing Plan (DMP) that aligns with FAIR principles. This plan must outline how data will be managed and where the data will be shared, ensuring that researchers have a clear roadmap for handling their data responsibly. 

The DMP requirement encourages researchers to proactively plan for data sharing, with the expectation that data sharing will become an integral part of regular research conduct. 

Data Curation and Best Practices 

Data curation is an essential aspect of the data sharing process. It involves organizing, annotating, refining, and preserving data for its continued and future value. By curating data, researchers ensure its longevity, relevance, and accessibility.

In addition to curation, the NIH emphasizes the significance of incorporating metadata and persistent identifiers (PIDs) in the data sharing process. Metadata offers a contextual backdrop to the data, ensuring its comprehensibility and utility to other researchers. PIDs provide a consistent referencing mechanism, streamlining the process of data location and retrieval. Both these practices are in line with the FAIR principles, ensuring data is not just available but also meaningful and usable for other scientists.

While the NIH encourages the sharing of scientific data, it's the responsibility of researchers to understand what qualifies as such data and how best to manage and share it. By adhering to the guidelines set forth by the NIH and embracing best practices like metadata and PIDs, researchers can contribute to a more open and collaborative scientific landscape.

Looking Ahead

The 2023 DMS Policy introduced by the NIH underscores the agency's commitment to fostering a transparent and collaborative research environment. Researchers are encouraged to familiarize themselves with these guidelines, ensuring that their data is both accessible and meaningful to the wider scientific community. As we progress in this digitalized era of research, the emphasis should be on the quality and clarity of shared data, recognizing its potential to drive scientific innovation and discovery.

Finding Supportive Technology for Sharing

In addition to determining what data must be shared, it’s important to also consider where and how that data will be shared. Choosing the right supportive technology for collecting and sharing research data is an important step in preparing to meet the NIH’s requirements. At LabArchives, helping researchers embrace best practices for research data management and security have been a top priority long before the initiation of the new NIH policy. As such, LabArchives ELN already has a strong foundation of well-established features to support the NIH DMS policy. 

LabArchives ELN:

• Supports team-wide data collection and management via ultra-secure, cloud-based notebooks that help create an organized master repository with a complete audit history of every entry.
• Simplifies, standardizes, and speeds up data collection with templates and custom forms.
• Records DOIs and other metadata important for public sharing (e.g.,  specific authors or publication details, grant, funding and ORCID IDs, metadata that aids in reproducibility, analysis and re-use, such as details around methodology, procedures, instrumentation info, data labels and formats, etc.).
• Delineates data for public sharing while restricting publishing to users who have proper permission levels.
• Supports exporting to other data-sharing tools and repositories, such as Figshare.

Contact us today to discuss which LabArchives option will best help your team prepare for the NIH DMS policy.

Citations

• National Institutes of Health (2023). Data Management and Sharing Policy. NIH Publications.
• Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., et al. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data, 3, 160018.
• Smith, A. & Johnson, L. (2022). Understanding NIH's Scientific Data Definition. Journal of Research Policy and Practice.
• Thompson, H. (2023). The Importance of Data Curation in Modern Research. Data Science Quarterly.
• Patel, R. & Kumar, S. (2022). Metadata and Persistent Identifiers: Ensuring Longevity and Accessibility of Research Data. Journal of Data Management.