NIH Data Management & Sharing Policy Overview
NIH has issued the Data Management and Sharing (DMS) policy (effective January 25, 2023) to promote the sharing of scientific data. 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.
Under the DMS policy, NIH expects that investigators and institutions:
- Plan and budget for the managing and sharing of data, Planning & Budgeting for Data Management and Sharing | Data Sharing (nih.gov)
- Submit a DMS plan for review when applying for funding, Writing a Data Management & Sharing Plan
- Comply with the approved DMS plan. Manage and Share Data
Individual NIH Institutes, Centers, or Offices may have additional policies and expectations (see NIH Institute and Center Data Sharing Policies).
Submission & Review of DMS Plans
Applicants planning to generate scientific data will submit DMS Plans to NIH as part of the funding application or proposal. Note that plans are NOT part of scored review criteria unless specifically noted in the Funding Opportunity Announcement. NIH Program Staff oversee reviewing and approving Plans prior to award.
If the DMS Plan provided in the application cannot be approved based on the information provided, applicants will be notified that additional information is needed. This will occur through the Just-in-Time (JIT) process. Applicants will be expected to communicate with their Program Officer and/or Grants Management Specialist to resolve any issues that prevent the funding IC from approving the DMS Plan. If needed, applicants should submit a revised DMS Plan. Refer to NIH Grants Policy Statement Section 2.5.1 Just-in-Time Procedures for additional guidance.
Data Management and Sharing Plan Format
DSM Plans are recommended to be two pages or less in length. NIH has developed an optional DSM Plan format page that aligns with the recommended elements of a DMS Plan. NIH has provided sample DMS Plans. Important: Do not include hypertext. (e.g., hyperlinks and URLs) in the DMS plan attachment.
Elements to Include in a Data Management and Sharing Plan
As outlined in NIH Guide Notice Supplemental Policy Information: Elements of an NIH Data Management and Sharing Plan, DMS Plans should address the following recommended elements and are recommended to be two pages or less in length. As described in the Application Guide, the DMS Plan should be attached to the application as a PDF file.
- Data Type: Briefly describe the scientific data to be managed and shared.
- Related Tools, Software and/or Code: Indicate whether specialized tools are needed to access or manipulate shared scientific data to support replication or reuse, and name(s) of the needed tool(s) and software. If applicable, specify how needed tools can be accessed.
- Standards: Describe what standards, if any, will be applied to the scientific data and associated metadata (i.e., data formats, data dictionaries, data identifiers, definitions, unique identifiers, and other data documentation).
- Data Preservation, Access, and Associated Timelines: Give plans and timelines for data preservation and access.
- Access, Distribution, or Reuse Considerations: Describe any applicable factors affecting subsequent access, distribution, or reuse of scientific data.
- Oversight of Data Management and Sharing: Indicate how compliance with the DMS Plan will be monitored and managed, the frequency of oversight, and by whom (e.g., title, roles). This element refers to oversight by the funded institution, rather than by NIH. The DMS Policy does not create any expectations about who will be responsible for Plan oversight at the institution.
The following individual, XXXX, Principal Investigator of the project at XXXX Hospital will ultimately be responsible for data collection, management, storage, retention, and dissemination of project data, including updating and revising the Data Management and Sharing Plan when necessary, and will report on data sharing and compliance in the annual project progress reports. Research Project Coordinator in Dr. XXXX's lab, will also maintain the Data Management and Sharing Plan, and coordinate permissions with data repositories.
Sharing Scientific Data
Sharing scientific data accelerates biomedical research discovery, enhances research rigor and reproducibility, provides accessibility to high-value datasets, and promotes data reuse for future research studies. Under the NIH Data Management & Sharing Policy, investigators are empowered to choose the most appropriate methods for sharing scientific data. Learn more about methods for data sharing and selecting data repositories.
Post-Award Plan Revisions
Although investigators submit plans before research begins, plans may need to be updated or revised over the course of a project for a variety of reasons for example, if the type(s) of data generated change(s), a more appropriate data repository becomes available, or if the sharing timeline shifts. If any changes occur during the award or support period that affects how data is managed or shared, investigators should update the Plan to reflect the changes. It may be helpful to discuss potential changes with the Program Officer. In addition, the funding NIH ICO will need to approve the updated Plan. NIH staff will monitor compliance with approved DMS Plans during the annual RPPR process as well.
Provide updates on data management and sharing activities in annual progress reports.
For more information, contact: Jason T. Machan, Ph.D., Director, Lifespan Biostatistics, Epidemiology, Research Design, and Informatics (BERDI) firstname.lastname@example.org, 401-639-3942
More Research Resources
This list of resources below was curated by Sarah B. Andrea, PhD, MPH. Last revised November 2021.
Clinical researchers can now access and share professional development offerings and resources on the NIH-funded Development, Implementation, and AssessMent Of Novel Training in Domain-based competencies (DIAMOND) Portal. DIAMOND is a collaborative discovery learning space for clinical research professionals and other members of research study teams. Training and assessment items included in the DIAMOND collection are searchable by competency domain and provide information and links to offerings for study teams.
Guides for critically evaluating the quality of health studies
- NIH study quality assessment guides
- Effective Public Health Practice Project. (1998). Quality Assessment Tool For Quantitative Studies. Hamilton, ON: Effective Public Health Practice Project.
- Ryan R, Hill S, Prictor M, McKenzie J; Cochrane Consumers and Communication Review Group. Study Quality Guide. May 2013
- STROBE: Strengthening the reporting of observational studies in epidemiology **
** I also find these these checklists helpful when devising analytic plans, drafting manuscripts, and performing peer-review
Existing health datasets for secondary analyses
- Data Sets for Quantitative Research: Public Use Datasets Resources compiled by the University of Missouri
- Finding Data Sets for the Health and Natural Sciences Resources compiled by UNC
- NCHS Public-Use Data Files and Documentation
- Datasets and raw data List of publicly available health datasets compiled by UC Berkeley
- Open data repositories this is a crowd sourced list of publicly available health, medicine, and epidemiologic datasets free for use
- #epitwitter’s favorite public use datasets (and another thread here)
Data Management, Analysis & Visualization
- Universities across the US have Biostatistics Epidemiology & Research Design (BERD) programs that offer training to clinical researchers. Some archive lectures, slides, and workshop material for public use:
- Brown University Advanced CTR youtube channel, event recordings & webinars, and recent Biostatistics Core seminars
- Oregon Health & Science University archived seminars
- UCLA has an impressive collection of coding examples across statistical software that are typically well annotated.
Statistical Software: R
- Recordings from Oregon Health & Science University’s Biostatistics, Epidemiology, and Research Design (BERD) Program. Resources and exercises for the webinars below can be found here.
- “Getting started with R studio” September 24th, 2019 ; February 19th, 2020
- “Data wrangling in R” Part 1A (April 18th, 2019) ; Part 1B (April 18th, 2019) ; Part 2 (April 25th, 2019)
- “Reproducible reports with R markdown” September 26th, 2019
- “Data visualization with R & ggplot2” March 4th, 2020 ; May 20th, 2020
Statistical Software: Stata
Resources for writing up the manuscript
General manuscript composition
- Looking for an appropriate target journal for your manuscript and/or relevant articles to cite? You can paste your abstract – and even your full text – into Jane and click on ‘Find journals’, ‘Find authors’ or ‘Find Articles’. Jane will then compare your text to millions of documents in PubMed to find the best matching journals, authors or articles.
- Cummings P, Rivara FP, Koepsell TD. Writing Informative Abstracts for Journal Articles. Arch Pediatr Adolesc Med. 2004;158(11):1086–1088. doi:10.1001/archpedi.158.11.1086
- Mensh B, Kording K (2017) Ten simple rules for structuring papers. PLoS Comput Biol 13(9): e1005619. https://doi.org/10.1371/journal.pcbi.1005619
- Bem D. Writing the empirical journal article
- Lamott, Anne. Shitty First Drafts. Language Awareness: Readings for College Writers. Ed. by Paul Eschholz, Alfred Rosa, and Virginia Clark. 9th ed. Boston: Bedford/St. Martin’s, 2005: 93-96.
- Luby S, Southern D. The Pathway to Publishing: A Guide to Quantitative Writing in the Health Sciences. Last revised August 2017
- Hayes-Larson E, Kezios KL, Mooney SJ, Lovasi G. Who is in this study, anyway? Guidelines for a useful Table 1. J Clin Epidemiol. 2019;114:125-132. doi:10.1016/j.jclinepi.2019.06.011
- Westreich D, Greenland S. The table 2 fallacy: presenting and interpreting confounder and modifier coefficients. Am J Epidemiol. 2013;177(4):292-298. doi:10.1093/aje/kws412
- Cummings P, Rivara FP. Reporting Statistical Information in Medical Journal Articles. Arch Pediatr Adolesc Med. 2003;157(4):321–324. doi:10.1001/archpedi.157.4.321
- Rivara FP, Christakis DA, Cummings P. Duplicate Publication. Arch Pediatr Adolesc Med. 2004;158(9):926. doi:10.1001/archpedi.158.9.926
- Cummings P, Rivara FP. Spin and Boasting in Research Articles. Arch Pediatr Adolesc Med. 2012;166(12):1099–1100. doi:10.1001/archpediatrics.2012.1461
Discipline Specific Guides
- Rivara FP, Cummings P. Writing for Publication in Archives of Pediatrics & Adolescent Medicine. Arch Pediatr Adolesc Med. 2001;155(10):1090–1092. doi:10.1001/archpedi.155.10.1090
- Bennett DA, Brayne C, Feigin VL, et al. Development of the standards of reporting of neurological disorders (STROND) checklist: a guideline for the reporting of incidence and prevalence studies in neuroepidemiology. Eur J Epidemiol. 2015;30(7):569-576. doi:10.1007/s10654-015-0034-5
- Rothman KJ. Writing for epidemiology. Epidemiology. 1998;9(3):333-337. doi:10.1097/00001648-199805000-00019
- APA Publications and Communications Board Working Group on Journal Article Reporting Standards. Reporting standards for research in psychology: why do we need them? What might they be?. Am Psychol. 2008;63(9):839-851. doi:10.1037/0003-066X.63.9.839
- Vandenbroucke, Jan P.; von Elm, Erik; Altman, Douglas G.; Gøtzsche, Peter C.; Mulrow, Cynthia D.; Pocock, Stuart J.; Poole, Charles; Schlesselman, James J.; Egger, Matthias for the STROBE Initiative. Strengthening the Reporting of Observational Studies in Epidemiology (STROBE): Explanation and Elaboration, Epidemiology: November 2007 – Volume 18 – Issue 6 – p 805-835 doi: 10.1097/EDE.0b013e3181577511
- Cummings P, Rivara FP. Responding to Reviewers’ Comments on Submitted Articles. Arch Pediatr Adolesc Med. 2002;156(2):105–107. doi:10.1001/archpedi.156.2.105
- Althouse A. Reference collection to push back against “common statistical myths”
Allyship/Antiracism in Health Research
- In partnership with the OHSU BERD Core, Dr. Andrea has co-created and coordinates a seminar series titled “Antiracism in Data and Analysis”. Materials from previous sessions can be viewed here:
- Antiracism Resources for Epidemiologists & Public Health Researchers Crowdsourced list of resources on: critical histories of epidemiologic and public health research, research ethics and epidemiologic knowledge production, epidemiology for social justice, allyship/antiracism in epidemiologic research, subject matter, theoretical frameworks, methods, and data science & equity
- ‘Health equity tourists’: How white scholars are colonizing research on health disparities An important commentary highlighting the ways in which racism is even embedded in the way we approach researching health disparities.
- Race & Medicine A selection of articles from the New England Journal of Medicine on race and medicine, with implications for improving patient care, professional training, research, and public health
- “On Racism: A New Standard For Publishing On Racial Health Inequities, ” Health Affairs Blog, July 2, 2020. DOI: 10.1377/hblog20200630.939347
- Ward JB, Gartner DR, Keyes KM, Fliss MD, McClure ES, Robinson WR. How do we assess a racial disparity in health? Distribution, interaction, and interpretation in epidemiological studies. Ann Epidemiol. 2019;29:1-7. doi:10.1016/j.annepidem.2018.09.007
Miscellaneous Epidemiology & Public Health Resources
- Population Health Exchange (PHX): Update your population health skill set or deepen your understanding of the pressing public health issues of our time with these tools and resources.
- Columbia SPH keeps and updates great descriptions and additional resources regarding important and emerging population health techniques and the tensions that may arise in the selection and application of appropriate techniques.
- EpiToDate is an effort to curate, catalog, annotate and share useful, interesting and relevant resources in epidemiology and allied fields in a compact easy-to-read format.