# NIH Data Management and Sharing Plan Template By the end of this article you will be able to complete a [data management](/blog/guides/data-management-basics-principles-processes-and-best-practices) and sharing plan NIH template from start to finish. You will know what goes in each required element, how to word each section, how to pick a repository, how to set a realistic sharing timeline, and how to fold the whole exercise into the rest of your grant proposal planning. You do not need any special software. You need four things on hand before you start: your funding opportunity announcement (FOA) number, a description of the data your project will generate, a short list of candidate repositories, and your institution's policies on human subjects data and intellectual property. ## What the NIH DMS Plan Is and Why It Exists The National Institutes of Health (NIH) requires most grant applications that generate scientific data to include a Data Management and Sharing (DMS) Plan. The plan describes what data the project will produce, where those data will be stored, how other researchers will get access, and when sharing will happen. The requirement is formal. NIH cannot make awards until the applicant's DMS Plan has been reviewed and an approved plan is in place [1]. The plan is not an afterthought. It becomes a term and condition of the award. NIH expects investigators to follow it, and deviations require notification and justification. In practice, the plan shapes decisions you would otherwise make late in a project, such as which file formats to use, how to name files, where to deposit data, and who pays for storage. A DMS Plan also carries weight with reviewers. Reviewers assess whether the plan is appropriate, and NIH takes that assessment into account when making funding decisions. A vague plan reads as a sign that the data side of the project has not been thought through. ## Why a Template Matters Writing a DMS Plan from a blank page is hard, and the evidence says so. A survey of 155 researchers at an academic medical center, with 55 respondents, found that 67 percent understood the DMS Policy but only 47 percent felt comfortable actually developing a plan. Confidence dropped further on specific tasks. Just 40 percent felt confident identifying an appropriate repository and only 38 percent felt confident budgeting for data management and sharing activities. Sixty-eight percent viewed executing the plan as burdensome [2]. The same survey found a clear appetite for templates. Eighty-seven percent of respondents requested tailored, discipline-specific templates, while only 29 percent had used DMPTool.org, a free web-based tool that walks researchers through plan creation [2]. That gap is the reason a good NIH data management and sharing plan template is worth your time. A template converts an open-ended writing task into a structured fill-in exercise. There is a broader lesson here about templates in general. When a hospital dental service introduced four standardized proforma templates for documentation at key stages of a patient pathway, the team observed better compliance with local procedures, more consistent documentation, and stronger data collection [3]. The same mechanism applies to grant documents. A template enforces completeness, and completeness is exactly what NIH reviewers look for. ## The Data Management and Sharing Plan NIH Template Format NIH does not mandate a rigid form. It specifies the elements a plan must address, and most templates in circulation follow that structure. The standard data management and sharing plan NIH template format has six elements. Some templates split them differently, but the content requirements are the same. | Template element | What you must state | Typical length in a strong plan | |--|--|--| | 1. Data types | What data you will generate, how much, in what formats | 1 to 3 paragraphs | | 2. Related tools and software | Any software, code, or curricula needed to use the data | 1 short paragraph | | 3. Standards | Data format, metadata schema, and identifiers | 1 to 2 paragraphs | | 4. Preservation, access, and timelines | Repository choice, when data go live, how long they are kept | 2 to 4 paragraphs | | 5. Protection, rights, and confidentiality | Privacy protections, consent language, intellectual property | 2 to 3 paragraphs | | 6. Budget and compliance | DMS costs in the budget justification and how you will handle deviations | 1 to 2 paragraphs | Two formatting rules apply across all elements. First, the DMS Plan is written in narrative form, not as a table or spreadsheet. Second, the plan does not count against the application page limit. It is submitted as part of Other Project Information in the application package. ## Where to Find a Free Data Management and Sharing Plan NIH Template You do not need to buy anything. Several free options cover the same NIH elements, and you should pick the one closest to your situation. 1. **DMPTool.org.** A free, web-based planning tool with NIH DMS Policy templates built in. Your institution may be a partner organization, which lets you save plans to an institutional account and route them to a local data librarian for review. Survey data show that many researchers have not yet tried it, even though it directly addresses the template need they report [2]. 2. **NIH sample DMS Plans.** NIH publishes sample plans and a plan format page describing each required element. These are the closest thing to an official data management and sharing plan NIH template example, and they are written at the right level of detail. 3. **Institution-specific templates.** Many academic libraries and research offices publish their own NIH DMS templates with local language for security, consent, and repository access. Ask your grants and contracts office before you draft anything. 4. **Discipline-specific resources.** Some NIH institutes curate lists of repositories and matching plan language for their research communities. NIAID, for example, maintains a compiled list of data resources for infectious, allergic, and immune-mediated disease data specifically to help researchers prepare DMS Plans [1]. Whichever template you start from, the finished document must contain the six NIH elements. A template is scaffolding, not a substitute for content. ## Step-by-Step Walkthrough ![Six-step flow for drafting an NIH DMS Plan, from gathering inputs through repository choice, protection, budget, and final self-check.](/article-images/illustrations/data-management-and-sharing-plan-nih-template-dms-plan-steps.webp) The following walkthrough uses a hypothetical R01 project in microbiome research to show how each section gets filled in. Work through the same steps with your own project details. ### Step 1: Gather your inputs Before writing, collect four things. - Your FOA number and the NIH institute funding it, because institute-specific sharing expectations may apply. - A data inventory. List every data type the project will produce, whether that is sequencing reads, imaging, survey responses, or physiological measurements. - Two or three candidate repositories that accept your data type. - Your institutional review board (IRB) consent language, if human subjects are involved, because the consent wording limits what you can promise in the plan. ### Step 2: Draft the data types element State what data you will generate, the estimated volume, and the file formats. Be concrete. A weak version says "genomic data will be generated." A strong version says how many samples, what platform, what file type, and roughly how much storage the data will occupy. Here is a worked example paragraph you can adapt inside a free data management and sharing plan NIH template. ``` Data types. This project will generate [shotgun metagenomic sequencing](/blog/guides/shotgun-metagenomic-sequencing) data from 300 fecal samples collected longitudinally over 3 years (approximately 2 TB as FASTQ files), along with deidentified participant metadata recorded in a REDCap database (age band, sex, collection date, antibiotic exposure). Derived outputs include taxonomic abundance tables (CSV) and assembled metagenome-assembled genomes (FASTA). No individual-level identifiers will be deposited. Source code for all analyses will be written in Python 3.11 and R 4.3 and will be shared with the data. ``` Notice what this paragraph does. It names file formats a repository can accept, gives a volume estimate in terabytes, flags that identifiers are excluded, and states that analysis code ships with the data. Reviewers can picture the dataset from this paragraph alone. ### Step 3: Draft the standards element NIH expects you to name the standards that make your data usable by others. This means three things: the file format, the metadata standard, and any [persistent identifiers](/knowledge/bioinformatics/persistent-identifiers-in-bioinformatics-selection-criteria-and-implementation-workflow). For sequencing data, community standards include uncompressed FASTQ for reads and standard metadata checklists offered by sequence read archives. For imaging, the Digital Imaging and Communications in Medicine (DICOM) format is the standard clinical container. For [tabular data](/blog/research-skills/tabular-data-what-it-is-and-how-to-analyze-it), a plain CSV with a data dictionary is often the most shareable choice. A practical test is this question. If a stranger downloaded your data in five years, could they tell what each column means and reproduce your analysis? If not, your standards section needs work. Large collaborative projects handle this with explicit shared documentation, such as the project-specific codebooks used in systematic reviews to code dozens of intervention features across many trials in a consistent way [4]. A codebook is a data management instrument, and a one-paragraph promise to create one strengthens a DMS Plan. ### Step 4: Choose a repository and draft the preservation element ![Timeline showing a July 1 2026 award start, paper 1 data deposited in year 3, and all data deposited by June 30 2030.](/article-images/illustrations/data-management-and-sharing-plan-nih-template-sharing-anchors.webp) Repository choice is where researchers report the least confidence, and it is also the element reviewers scrutinize hardest [2]. Start with NIH-endorsed repositories for your data type. If none fits, a community-accepted repository is next. A generalist repository is the fallback, and NIH expects you to justify why no domain repository was suitable. The NIAID landscape review illustrates the scale of the choice. Researchers there identified 303 data resources relevant to infectious, allergic, and immune-mediated disease research, of which 58 focused specifically on infectious and immune disease data. Most were grouped as general infectious disease and pathogen resources (29 of 58, or 50 percent) and respiratory pathogen resources (10 of 58, or 17 percent). Scientific content was dominated by omics data (37 of 58, or 64 percent) and clinical data (21 of 58, or 36 percent) [1]. The lesson is that for most biomedical data types, a domain-specific destination exists, and finding it early makes the rest of the plan easier. Your preservation element should state four things. 1. The named repository and why it fits your data type. 2. When data will be made available. Under the NIH DMS Policy, data underlying a scientific article should be made available no later than the time of initial online publication of that article, and all data should be shared no later than the end of the award. Set your dates against these two anchors. 3. How long the data will be preserved. Many domain repositories retain data indefinitely, which you can state directly. 4. Any access controls, such as controlled access for human genomic data. A timeline calculation makes this concrete. Suppose your award starts July 1, 2026, runs for 4 years, and you plan two papers. Your DMS Plan should promise that the data underlying paper 1 (published, say, in year 3) are deposited at the time that article goes online, and that all remaining project data are deposited by June 30, 2030, the end of the award. ### Step 5: Draft the protection and rights element If your project involves human subjects, this section carries real weight. Describe privacy protections, consent provisions, and any restrictions on use. Be honest about what the consent form permits. If your consent language does not support open sharing of genetic data, the plan must say so and describe controlled access instead. Participant attitudes matter here, and the evidence is encouraging for well-governed sharing. In interviews with 20 adolescents and young adults with congenital heart disease, participants expressed willingness to share their genetic data for research when appropriate consent and privacy protections were in place. They were motivated by altruism and a desire to help others with the same condition, but they wanted to stay included in decisions because of concerns about confidentiality and misuse [5]. Written into a DMS Plan, this translates to three commitments: describe exactly what consent covered, name the controlled-access mechanism, and state who approves secondary use. Also address intellectual property if your project produces materials with commercial potential, and cite any institutional policies that govern data release. ### Step 6: Draft the budget and compliance element The DMS Policy requires costs for data management and sharing to appear in your budget justification. Eligible costs include repository deposit fees, personnel time for data curation and documentation, and specialized storage. Personnel effort for curating and preparing data is the line most applicants forget. Write one paragraph naming these costs and point reviewers to the budget justification where the dollar figures live. The compliance element is short. State that you will follow the approved plan, that any significant changes will be discussed with your NIH program official, and that deviations will be documented and justified. That is all NIH asks for here. ### Step 7: Assemble, review, and check Assemble the sections into your template and run this checklist against the draft. ``` DMS Plan self-check [x] Every data type listed with volume and format [x] Software needed to use the data is named and versioned [x] Metadata standard named for each data type [x] Repository named with a one-sentence justification [x] Sharing dates anchored to publication and award end [x] Preservation duration stated [x] Consent and privacy protections described, not just referenced [x] DMS costs listed and matched to the budget justification [x] Deviation-handling paragraph included ``` Then have someone outside the project read it. A data librarian or a colleague in a different subfield can usually spot a vague data description in two minutes. If your institution offers a data management consultation, use it. The operational gaps identified in the survey literature, including centralized consultation and budget guidance, are exactly the services many institutional libraries built after the policy took effect [2]. ## Fitting the Plan into Grant Proposal Planning A DMS Plan written in isolation tends to be generic. A plan written as part of grant proposal planning tends to be fundable. Build it into your writing sequence with the following order of operations. 1. **Write the data types element while drafting the research strategy.** The methods section and the data types element describe the same experiments, so draft them together and the wording stays consistent. 2. **Contact candidate repositories early.** Some repositories require registration, project accounts, or metadata manifests prepared before deposit. Lead time matters. 3. **Check the FOA for institute-specific expectations.** Some institutes name preferred repositories or additional data policies in the FOA text. 4. **Budget realistically.** Use the budget element to justify line items in the actual grant budget, not the other way around. 5. **Collect feedback in a cycle.** If you are taking a research [grant writing](/knowledge/molecular-biology/grant-writing) course, bring your DMS Plan draft to the review sessions. Plan language benefits from the same peer critique as aims pages. Researchers who treat the plan as a compliance chore tend to produce exactly the documents reviewers flag. Those who treat it as a project design exercise end up with cleaner data pipelines regardless of the funding outcome. ## Common Mistakes and Limitations These are the errors that appear most often in real drafts, and each has a direct fix. **Vague data descriptions.** Writing "omics data" or "clinical data" without formats and volumes. The fix is a table, kept privately, listing each data type, its format, and its estimated size, which then feeds the plan's narrative. **Missing budget.** Surveys consistently show low confidence in budgeting for DMS activities, with only 38 percent of surveyed researchers feeling confident in that task [2]. The fix is to name the cost categories (repository fees, curation effort, storage) even if the dollar estimates are rough at application time. **Wrong or unexplained repository.** Naming a generalist repository without justifying why no domain repository fits, or naming a repository that does not accept your data type. The fix is to consult curated resource lists for your field, such as the NIAID-compiled inventory of infectious and immune disease data resources [1], and to confirm submission requirements on the repository website. **Overpromising openness.** Promising unrestricted sharing when the consent form supports only controlled access. This creates an award compliance problem later. The fix is to read the actual consent language before writing the protection element. Participant perspectives on data sharing are well documented, and they consistently condition willingness on consent and privacy safeguards [5]. **No timeline anchors.** Writing "data will be shared in a timely manner." The fix is to name the two policy anchors, publication of each article and the end of the award, and attach dates to each. **Ignoring software and code.** The DMS Plan must address related tools and software needed to use the data. Data without the analysis code are far less reusable. **Template substitution without adaptation.** Borrowing a colleague's plan and changing the project name. NIH reviewers see recycled language often, and it signals a plan written for compliance rather than for the actual project. One limitation applies to the exercise as a whole. The plan is a document, not a guarantee. Real data volumes, consent outcomes, and repository capabilities can change during a project, and the compliance element exists precisely so you can document and justify those changes rather than silently abandoning them. ## Checking the Result Before You Submit Run three checks against your final draft. **Element check.** Every one of the six NIH elements is present with a substantive answer. If an element is one sentence long, ask whether a reviewer could act on it. **Consistency check.** Data types named in the plan match the methods described in the research strategy. Repository names match the budget justification. Sharing dates match the project period. **Specificity check.** Read each paragraph and count the named things: file formats, metadata standards, repository names, dates. A strong plan names many. A weak plan gestures at categories. If any check fails, revise before submission rather than after review. A rejected plan delays the award. A revised plan costs an afternoon. ## Frequently Asked Questions ### Is a free data management and sharing plan NIH template available? Yes. DMPTool.org offers free NIH DMS Policy templates with guided prompts, and NIH publishes sample plans and element descriptions you can copy structurally. Many institutional libraries also publish free local templates. ### What is the required data management and sharing plan NIH template format? NIH requires six elements addressed in narrative form: data types, related tools and software, standards, preservation and access with timelines, protection and rights, and budget with compliance handling. The plan does not count against page limits. ### Does the DMS Plan count toward the NIH page limit? No. The DMS Plan is submitted in addition to the application's page limits, so you have room to be specific without cutting your research strategy. ### When do data have to be shared under the NIH policy? Data underlying a scientific article should be made available no later than the article's initial online publication. All project data should be shared no later than the end of the award, whichever comes first for each dataset. ### Can I restrict access to my data? Yes. Controlled access is appropriate when data include human subjects information, genetic data, or other sensitive content. The plan must describe the restriction, the access mechanism, and what the consent permitted. ### What if I cannot follow my approved plan? Notify your NIH program official and document the deviation with a justification. The compliance element of your plan exists to cover exactly this situation, and handled changes are manageable. ### What is DMPTool and should I use it? DMPTool is a free web-based tool that provides NIH-aligned plan templates and, at partner institutions, routes drafts to local data experts for review. Only about a third of surveyed researchers had used it, so trying it early gives you an advantage many applicants skip. ### Does the DMS Plan affect my grant budget? Yes, indirectly and directly. The plan must include a budget element, and the costs it names must appear in the budget justification, so writing the plan before finalizing your budget is the efficient order. ## Further Reading - [Linking Healthcare Quality and Knowledge Management in Traditional and Ethnomedicine-Oriented Hospitals: A Scoping Review and Implementation Framework.](https://pubmed.ncbi.nlm.nih.gov/42668891/) ## Sources 1. [Infectious, Allergic, and Immune-Mediated Disease Data Resources: a Landscape Overview and Subset Assessment.](https://pubmed.ncbi.nlm.nih.gov/41273456/) 2. [Supporting researcher compliance: assessing institutional readiness for the 2023 NIH Data Management and Sharing Policy.](https://pubmed.ncbi.nlm.nih.gov/42889533/) 3. [Standardised proformas for the management of hypodontia patients: A new standard for clinical documentation.](https://pubmed.ncbi.nlm.nih.gov/42503722/) 4. [Unpacking weight management interventions measuring eating disorder risk in adults: coding of components of interventions in a systematic review.](https://pubmed.ncbi.nlm.nih.gov/42310783/) 5. [Attitudes toward clinical genetic testing and data sharing among adolescents and young adults with congenital heart disease.](https://pubmed.ncbi.nlm.nih.gov/42462639/)