# Data Management Plan vs. NIH DMSP: What's the Difference? A [data management](/blog/guides/data-management-basics-principles-processes-and-best-practices) plan (DMP) is a general document describing how you will organize, store, document, and share the data from a research project. A NIH Data Management and Sharing Plan (DMSP) is a specific, required component of NIH grant applications that must follow a defined structure set by federal policy. The rule of thumb is simple: if you are applying for NIH funding, you write a DMSP that satisfies the NIH Data Management and Sharing (DMS) Policy. For any other funder, a journal, your institution, or your own lab, you write an ordinary data management plan shaped to whoever is asking for it. Many researchers treat these terms as interchangeable, and the confusion is understandable. The documents overlap in content, the vocabulary is similar, and NIH itself uses several names for the same plan. This article separates the two concepts, shows where they converge and diverge, and walks through concrete scenarios so you know exactly which document to produce during grant proposal planning. ## What Is a Data Management Plan? A data management plan is a short document, usually one to three pages, that describes how research data will be handled during a project and after it ends. The typical sections cover: 1. **Data description.** What data will be collected or generated, in what formats, and roughly how much. 2. **Documentation and metadata.** How the data will be described so that other people, including your future self, can understand and reuse them. 3. **Storage and backup.** Where the data will live during the project, how it will be protected, and how it will be preserved long term. 4. **Access and sharing.** Which parts of the data will be made public, where they will be deposited, and under what restrictions. 5. **Responsibilities.** Who on the team owns each data management task. 6. **Budget.** What data-related activities will cost and how they will be funded. No single authority dictates the content of a general DMP. Requirements come from whichever funder, journal, dissertation committee, or institution is requesting the plan. The National Science Foundation, for example, has asked for data management plans with proposals since 2011, and many universities require one for doctoral dissertations. Because there is no universal template, DMPs vary widely in length, depth, and formality. A two-page plan for a small pilot study and a fifteen-page plan for a multi-site clinical trial are both legitimately DMPs. The DMP is fundamentally a planning tool. Its value comes from forcing decisions early: what file formats you will use, how you will name files, where raw data will be backed up, and who will deposit the final datasets. Researchers who write one before a project starts tend to encounter fewer data crises later. ## What Is the NIH DMSP? ![A five step flow listing the six NIH DMSP elements from data types through budget, with the tools and software element flagged as lowest compliance.](/article-images/illustrations/data-management-plan-vs-nih-dmsp-dmsp-six-elements.webp) The NIH DMSP, which NIH officially calls a "Data Management and Sharing Plan" or "DMS Plan," is a document required under the NIH Policy for Data Management and Sharing, issued as NOT-OD-21-013 and effective for applications submitted on or after January 25, 2023 [1]. The policy states that NIH-funded research generating scientific data must include a plan describing how that data will be managed, preserved, and shared, and the plan must be submitted at the time of the application [2][1]. Unlike a general DMP, the NIH DMSP has a fixed structure. NIH supplemental guidance (NOT-OD-22-198) organizes the plan around six required elements [3]: 1. **Data types.** What scientific data will be generated and which data will be preserved and shared. 2. **Related tools, software, and informational materials.** Any software, pipelines, protocols, or documents needed to interpret the data. 3. **Standards.** The data formats, metadata standards, identifiers, and ontologies you will apply. 4. **Data preservation, access, and timelines.** Where data will be deposited, when it will become accessible, and for how long it will be retained. 5. **Responsibilities and oversight.** Who manages the plan and monitors compliance. 6. **Budget.** Costs for curating and sharing data, and how they are covered in the budget request. NIH did not mandate a strict fill-in template but released guidance on the elements to address, which led librarians and research administrators to build structured templates around that guidance [1][3]. Note that NIH uses several interchangeable names, including DMS Plan, Data Management and Sharing Plan, and DMSP. All refer to the same document. Researchers on NIH awards also sometimes encounter institute-specific variations, such as the template NIH's National Institute of Mental Health published under NOT-MH-23-100 for mental health research [3]. The DMSP is a compliance document as well as a planning tool. NIH program staff and advisory councils review submitted plans, and the Just-in-Time process after a score is awarded can generate reviewer comments about plan completeness [2]. A plan judged noncompliant can hold up an award. ## Data Management Plan vs. NIH DMSP: Side-by-Side Comparison | Criterion | General Data Management Plan (DMP) | NIH DMSP (DMS Plan) | |--|--|--| | Who requires it | Any funder, journal, institution, degree program, or the researcher personally | NIH, for applications producing scientific data | | Governing policy | Varies by requester, no single authority | NIH DMS Policy, NOT-OD-21-013, effective January 25, 2023 | | Structure | Flexible, funder-specific or freeform | Six required elements defined by NIH supplemental guidance | | Length and detail | Varies from a paragraph to many pages | Usually a few pages, but each required element must be addressed | | When it is submitted | Depends on the requester | With the grant application | | Who reviews it | Whatever body required it, often informally | NIH program staff and advisory council, with possible Just-in-Time queries | | Consequences of a weak plan | Reduced competitiveness, weak data practices | Potential compliance findings, award delays, required revisions | | Budget treatment | Optional section | Explicitly required element, allowable DMS costs must be requested | | Common tools | DMPTool, DMPonline, local templates | Structured NIH templates in DMPTool, institute-specific templates | The single most practical difference is accountability. A general DMP is aspirational and self-enforcing. The NIH DMSP is tied to a funding agreement, reviewed by the agency, and enforceable through the terms and conditions of the award. ## Why the Distinction Matters in Grant Proposal Planning ![A yes or no path that starts with whether the project is NIH funded and ends at writing a DMSP or a general data management plan.](/article-images/illustrations/data-management-plan-vs-nih-dmsp-which-plan.webp) The distinction matters because treating a DMSP like an ordinary DMP produces weak, noncompliant submissions. Evidence from an 18-month institutional review of NIH grant submissions found that unstructured DMSPs consistently underperformed structured ones, and structured templates outperformed non-structured approaches in producing policy-conformant plans [2]. In that same review, only 79.3 percent of submitted DMSPs addressed all NIH-required elements, with element-level compliance ranging from 98.9 percent for data types down to 82.7 percent for the tools and software element [2]. Researchers writing from scratch, without a structured template, were the ones most likely to miss required content. The distinction also matters for planning effort. In a survey of researchers at an academic medical center, 67 percent understood the DMS Policy but only 47 percent felt comfortable developing a plan, and confidence was lowest for identifying appropriate repositories (40 percent) and budgeting for data sharing activities (38 percent) [4]. Those two skills, repository selection and budget justification, are where NIH-specific knowledge is essential and where a generic DMP template offers little help. ## How the Two Documents Overlap A good NIH DMSP contains everything a strong general DMP contains: data descriptions, metadata decisions, storage plans, sharing commitments, and assigned responsibilities. The reverse is not true. A general DMP written for an NSF proposal or a thesis committee will almost certainly lack elements NIH requires, such as the explicit statement of which data will *not* be preserved and shared, the justification for those exclusions, and the specific timeline for making shared data accessible. The overlap is why many institutions now use one workflow with two outputs. You gather the same underlying information once, then format it either as a flexible narrative DMP or as a DMSP mapped to the six NIH elements. Tools built for this purpose carry separate templates for different funders. The DMPTool, a free online platform developed by a consortium of US research institutions and libraries, hosts a generic NIH-Default template built to comply with NOT-OD-21-013 and NOT-OD-22-198, plus institute-specific templates, and by October 2023 more than 5,000 DMS Plans had been written using its NIH templates [3]. ## When to Use a Data Management Plan Write a general data management plan in these situations: - **You are applying to a funder other than NIH** that requires one, such as the NSF or many private foundations. - **You are starting a thesis or dissertation** and your graduate program requires a plan. - **You are launching a new lab project** and want to establish file organization, naming conventions, and backup routines before data accumulates. - **A journal asks for a data availability statement** and you want a plan that makes meeting that commitment straightforward. - **You are writing a grant application to a funder with lighter requirements** and want the data narrative to be credible and specific. In all of these cases, the DMP's content is yours to shape. Match its depth to the audience. A funder that scores the plan needs rigor. A plan written for your own lab needs practicality more than polish. ## When You Need an NIH DMSP You need a DMSP whenever you submit an NIH application that will generate scientific data, and you should write it as part of the grant proposal planning process rather than as a last-minute attachment. The document must address each of the six required elements, state clearly what data will and will not be shared, name the repository or repositories where data will be deposited, and give realistic timelines. Two NIH-specific considerations deserve attention. First, budgeting. The DMS Policy makes data management and sharing costs allowable expenses, so your plan and your budget must agree. Survey data show budgeting for DMS activities is a weak spot for many researchers, with only 38 percent expressing confidence in that task [4]. Second, sensitive data. Human subjects data, genomic data, and other restricted categories require you to explain your sharing restrictions and controlled-access approach within the plan itself. Researchers managing sensitive data report particular uncertainty here [4]. The practical path to a strong DMSP is a structured template. In addition to the DMPTool NIH templates [3], specialized templates exist for specific research portfolios. For the NIH HEAL Initiative, which funds chronic pain and substance use research involving diverse data types with varying sharing restrictions, researchers adapted the generic NIH DMPTool template to incorporate HEAL-specific guidance on common data elements, metadata standardization, protecting sensitive information, and selecting HEAL-compliant repositories [5]. If your work sits in a portfolio with its own data sharing policy, look for a template built for it before you start writing. A cross-institutional working group of more than 30 librarians and research data professionals also produced freely available resources in response to the policy, including a glossary of NIH and data management terminology, example plans, checklists for researchers, and an interactive repository finder [6]. These resources reduce the guesswork, particularly for first-time NIH applicants. ## Worked Scenarios ### Scenario 1: R01 application to NCI You are a cancer biology investigator preparing an R01. Your institution requires a data management plan for every funded project anyway, but the NIH application specifically requires a DMSP. You use the NIH-Default template in DMPTool, which walks you through all six elements with guidance at each subsection [3]. In the data types element, you list your [single-cell RNA sequencing](/knowledge/bioinformatics/single-cell-rna-sequencing-from-bulk-to-resolution) datasets and imaging data, and you explicitly state that de-identified clinical metadata will be shared while certain identifiers will not. You name a domain repository, request data curation costs in the budget, and assign the laboratory manager responsibility for deposits. The document you hand your grants office is a DMSP, not a generic DMP. ### Scenario 2: NSF proposal for an ecological study You are writing an NSF proposal and the solicitation requires a data management plan of up to two pages. This is a general DMP. You describe your field measurements, the metadata standard you will use, the long-term ecological research repository where data will go, and your timeline. You do not need to follow NIH's six-element structure because NSF is not NIH, and its requirements differ. ### Scenario 3: Multi-source study in a special NIH portfolio Your team is applying for HEAL Initiative funding for a chronic pain study that combines survey data, electronic health record extracts, and wearable sensor data. A generic plan would strain to cover the varying access restrictions across these sources. You use the HEAL-specific DMPTool template, which embeds guidance on standardizing metadata, protecting sensitive information, and choosing HEAL-compliant repositories [5]. The structured format helps you show reviewers that complex, multisource data integration has been thought through. ### Scenario 4: Thesis project with no funder requirement You are a doctoral student starting a project on [gene expression](/blog/guides/gene-expression) in a model organism. Nobody requires a plan, but your adviser recommends one. You write a two-page general DMP covering folder structure, raw data backup to institutional storage, version control for analysis code, and an intention to deposit final data in a public repository at publication. No NIH elements are needed because no NIH application is involved. ## Common Mix-Ups **Assuming "DMSP" and "DMP" are the same document.** They overlap but differ in authority, structure, and review. The DMSP is a compliance instrument tied to a federal funding agreement [1]. **Believing the DMSP must be written on an official NIH form.** NIH provides guidance on required elements rather than a mandatory fill-in form, which is why institutional and library-built templates proliferated [1][3]. What is mandatory is addressing the required elements, not using one particular document format. **Treating the plan as a formality to write the night before submission.** The Just-in-Time process can surface reviewer comments about plan completeness after your score is awarded, meaning a thin plan can generate work at the worst possible moment in the award cycle [2]. **Assuming general familiarity with the policy is enough to write the plan.** Understanding the policy and being able to execute a plan are different skills. In the institutional survey cited above, two-thirds of researchers understood the policy but fewer than half felt comfortable writing the plan, and most wanted tailored templates and centralized consultation [4]. **Reusing a generic plan across funders.** A DMSP pasted into an NSF proposal, or an NSF-style plan submitted to NIH, will omit required content on one side or pad the other with irrelevant detail. **Confusing the plan with the data sharing itself.** The DMSP describes intentions and processes. It does not share any data. Deposits, metadata, and access controls happen during and after the award, following the plan. ## Common Mistakes and Limitations - **Vague data descriptions.** The strongest predictor of a weak DMSP is imprecise description of the data. In the institutional compliance review, the sub-element "data description" scored 98.9 percent while the sub-element covering how data will be generated reached only 49.3 percent, showing that researchers describe their data well but often under-explain the details of generation and provenance [2]. Write as though a stranger must reproduce your dataset from the plan alone. - **Skipping the tools and software element.** This element had the lowest compliance among the six required elements in the institutional review, at 82.7 percent [2]. List the software, code, and analytical tools needed to interpret your data, and say whether and how they will be shared. - **Ignoring structured templates.** Unstructured plans consistently underperformed structured ones in measured compliance [2]. Starting from a blank page is both slower and riskier than starting from a template with guidance at each subsection. - **Guessing at repository choice.** Repository selection requires checking that a repository accepts your data type, meets NIH's sharing expectations, and handles any access restrictions your data carry. Low confidence in this task is documented among researchers [4], and resources such as an interactive repository finder exist specifically to close that gap [6]. - **Leaving the budget out of sync with the plan.** If your plan promises curation, deposition, and documentation but your budget requests nothing for those activities, reviewers notice. Budget for the DMS activities your plan describes. - **Underestimating the ongoing burden.** Roughly two-thirds of surveyed researchers perceived executing their plans as burdensome [4]. Build realistic staff time into the plan and the budget rather than treating data sharing as a final-week task. - **Assuming one plan fits all institutes.** Some NIH institutes issue their own supplemental expectations, as the National Institute of Mental Health did with NOT-MH-23-100, and templates exist that reflect them [3]. Check whether your target institute has specific guidance. A final limitation: policies and template resources continue to evolve, and this article reflects the DMS Policy framework as documented in the cited literature. Always check the current NIH policy notices and your funding institute's guidance before submission, and consult your institution's research data office or library when a plan involves sensitive data or unusual data types. ## Frequently Asked Questions ### Is a DMSP the same thing as a data management plan? No. A DMSP is a specific [type of data](/blog/data-analysis/types-of-data-nominal-ordinal-interval-ratio) management plan required by the NIH Data Management and Sharing Policy, with six required elements and agency review. Every NIH DMSP is a data management plan, but most data management plans written for other funders or purposes are not DMSPs. ### Which NIH applications require a DMSP? Applications for NIH funding that will generate scientific data must include one under the DMS Policy effective January 25, 2023 [1]. Applications that do not generate scientific data can state why a plan is not needed, following NIH's guidance. ### Does NIH approve the DMSP before the award is made? The plan is submitted with the application and assessed for compliance, and reviewer comments can arise during the Just-in-Time period before an award is issued [2]. You should expect to revise the plan if program staff identify gaps. ### Can I reuse my DMSP from a previous grant? You can reuse sections that describe your standard data practices, but each application needs a plan tailored to the specific project's data types, repositories, timelines, and budget. Copying an old plan wholesale risks omitting elements the new project requires. ### Do I need a data management plan if I am not funded by NIH? Not automatically, but many other funders, journals, and institutions request one, and writing one is good practice regardless of requirement. The content and format depend on who is asking. ### What tools can help me write a DMSP? DMPTool hosts a generic NIH-Default template built to the DMS Policy and institute-specific templates such as one for NIMH, and more than 5,000 plans had been drafted with these NIH templates by October 2023 [3]. Specialized templates exist for portfolios like the HEAL Initiative [5], and community resources including example plans, checklists, and a repository finder are also available [6]. Institutional data librarians can provide consultation as well [4]. ## Sources 1. [Ten simple rules for maximizing the recommendations of the NIH data management and sharing plan.](https://pubmed.ncbi.nlm.nih.gov/35921268/) 2. [Structured Data Management and Sharing Plan (DMSP) templates outperformed non-structured ones in an institutional implementation of the NIH Data Management and Sharing (DMS) policy.](https://pubmed.ncbi.nlm.nih.gov/41445711/) 3. [The DMPTool NIH DMSP Templates Project.](https://pubmed.ncbi.nlm.nih.gov/39119154/) 4. [Supporting researcher compliance: assessing institutional readiness for the 2023 NIH Data Management and Sharing Policy.](https://pubmed.ncbi.nlm.nih.gov/42889533/) 5. [Navigating the new NIH Data Management Sharing plan: the NIH HEAL Initiative Data2Action DMPTool template.](https://pubmed.ncbi.nlm.nih.gov/40401004/) 6. [A Problem Shared Is a Community Created: Recommendations for Cross-Institutional Collaborations.](https://pubmed.ncbi.nlm.nih.gov/39239113/)