Data Management Plan Template: A Practical Guide for Researchers
A data management plan (DMP) is a formal document that describes how research data will be collected, organized, stored, documented, preserved, and shared throughout a project lifecycle. For researchers applying to funders such as the National Institutes of Health (NIH) or the National Science Foundation (NSF), a DMP is a required component of most grant applications. This guide provides a fillable DMP template with section-by-section explanations, funder-specific checklists, and practical guidance for implementation. The template is designed for students, researchers, life-science professionals, and informed general readers who need to write a compliant and useful data management plan.
At a Glance: DMP Template Overview
The table below summarizes the core sections of a data management plan, what each section requires, and the typical funder expectations.
| DMP Section | What to Document | Funder Relevance |
|---|---|---|
| Data Description | Types and formats of data generated or used | Required by NIH and NSF |
| Data Collection and Organization | Methods for generating, organizing, and naming data | Required by NIH and NSF |
| Data Storage and Security | Backup procedures, access controls, and data protection | Required by NIH and NSF |
| Documentation and Metadata | Standards for describing data so others can understand it | Required by NIH and NSF |
| Data Sharing and Access | Plans for making data available to other researchers | Required by NIH and NSF |
| Data Preservation and Archiving | Long-term storage and repository selection | Required by NIH and NSF |
| Roles and Responsibilities | Who manages data at each project stage | Required by NIH and NSF |
| Budget and Resources | Costs associated with data management activities | Required by NIH and NSF |
Understanding Data Management Plans and Their Purpose
A data management plan serves as a roadmap for handling research data from project start to completion and beyond. The primary purpose is to explain how data will be governed according to the regulations and policies of all relevant stakeholders, including the funding agency, the host institution, and any collaborating organizations. For studies involving human participants, the plan must describe processes for protecting personal information, especially when working with vulnerable populations.
The framework of most data management plans consists of describing the collection, organization, use, storage, contextualization, preservation, sharing, and access of research data and samples. A well-constructed plan also identifies responsible parties for establishing, implementing, and managing the data management strategy. Importantly, the plan serves to highlight potential problems with data collection, sharing, and preservation before they become critical issues during the research project.
Different funders have different requirements, and the nature of the study influences what a plan must address. The NIH Data Management and Sharing Policy, effective January 2023, requires submission of a Data Management and Sharing Plan with funding applications. The policy recognizes the NIH role as a steward of United States biomedical research and seeks to enhance that stewardship through systematic recommendations for preserving and sharing research data generated by funded projects.
Core Principles for Writing an Effective DMP
Clarity and Specificity
A data management plan must be specific enough that a reviewer can understand exactly what data will be produced and how it will be handled. Ambiguity in communicating key study parameters limits the utility of research in decision-making. Clear communication about data provenance, design, analysis, and implementation facilitates reproducibility, replication in independent data, and assessment of potential sources of bias.
When writing each section, describe the actual data types, file formats, naming conventions, and storage systems you will use. Avoid generic statements that could apply to any project. For example, instead of stating that data will be stored securely, specify the institutional server, the backup frequency, and who has access.
Alignment with Funder Requirements
The NIH DMS Policy requires six key elements in a Data Management and Sharing Plan: data type, related tools and software, data standards, data preservation and access timeline, access and distribution, and oversight of data management and sharing. The NSF requires a supplementary document of no more than two pages describing how the project will conform to NSF policy on dissemination and sharing of research results.
Structured templates demonstrate greater alignment with funder policy than unstructured approaches. In an institutional implementation study, structured DMP templates consistently outperformed unstructured ones in producing policy-conformant plans. Researchers should use structured templates to enhance the quality and consistency of their data management and sharing plans.
FAIR Data Principles
Data should follow the Findable, Accessible, Interoperable, and Reusable principles. This means data should have persistent identifiers, be described with rich metadata, use standardized vocabularies, and include clear usage licenses. Incorporating FAIR principles into a data management plan helps ensure that data can be discovered and used by other researchers after the project ends.
The Fillable DMP Template
The following template provides a structured format for writing a data management plan. Each section includes prompts and explanations to guide your responses. Copy this template into your own document and fill in the bracketed information.
Section 1: Data Description
Purpose: Describe the types and formats of data that will be generated or used during the project.
Template Prompts:
- What types of data will this project produce? Examples include quantitative measurements, qualitative interview transcripts, images, genomic sequences, survey responses, or simulation outputs.
- What file formats will be used for each data type? Specify versions where relevant.
- What is the estimated volume of data?
- Will the project reuse existing data from other sources? If so, describe the sources and any restrictions on use.
- Will the project generate new data through experiments, observations, or computational methods?
Guidance: Be specific about data types. For example, a study might generate clinical measurements, laboratory results, molecular data, and demographic information from participants. Each data type may require different handling procedures. The plan should describe the data types generated or used in the research project, as this information helps reviewers understand the scope of data management activities.
Section 2: Data Collection and Organization
Purpose: Explain how data will be collected, organized, and named during the project.
Template Prompts:
- What methods will be used to collect or generate data?
- How will files be named and organized within project folders?
- What version control procedures will be used?
- How will data quality be assessed during collection?
- Will data collection procedures be documented in a study protocol or standard operating procedure?
Guidance: Consistent file naming and folder organization prevent confusion and data loss. Establish naming conventions at the project start and document them in the plan. Version control is particularly important for collaborative projects where multiple researchers may edit shared files. Consider using version control software or establishing clear procedures for tracking file versions.
Section 3: Data Storage and Security
Purpose: Describe how data will be stored, backed up, and protected during the active phase of the project.
Template Prompts:
- Where will data be stored during the project? Specify institutional servers, cloud storage, or local devices.
- What backup procedures will be in place? How often will backups occur?
- Who will have access to the data? How will access be controlled?
- What security measures will protect sensitive data?
- How will data be encrypted during storage and transmission?
Guidance: Storage and security requirements depend on the sensitivity of the data. Research involving human participants requires additional protections for personal information. The plan should describe the governance of clinical, biochemical, laboratory, molecular, and other sources of data according to the regulations and policies of all relevant stakeholders. For sensitive data, describe the security measures that will protect participant privacy and confidentiality.
Section 4: Documentation and Metadata
Purpose: Describe how data will be documented so that others can understand and use it.
Template Prompts:
- What metadata standards will be used to describe the data?
- What documentation will accompany the data? Examples include codebooks, data dictionaries, readme files, and study protocols.
- How will documentation be maintained and updated during the project?
- Will documentation include information about data collection methods, variable definitions, and processing steps?
Guidance: Metadata standards vary by discipline. Some fields have established standards, while others rely on general documentation practices. The plan should identify the standards that will be used and describe how they will be applied. Standardizing metadata helps ensure that data can be discovered and understood by other researchers.
Section 5: Data Sharing and Access
Purpose: Explain how data will be shared with other researchers and the broader community.
Template Prompts:
- What data will be shared and what data will be excluded from sharing?
- When will data become available to other researchers?
- What repository or platform will be used for sharing?
- What access restrictions or conditions will apply?
- How will data be licensed for reuse?
Guidance: Data sharing plans must balance the benefits of open access with the need to protect sensitive information and respect participant consent. For human subjects research, informed consent documents should explain data sharing plans, limitations, and procedures. The plan should describe how data will be de-identified before sharing and what conditions will govern access to sensitive data.
Section 6: Data Preservation and Archiving
Purpose: Describe how data will be preserved for the long term after the project ends.
Template Prompts:
- What data will be preserved for the long term?
- What repository will be used for archiving?
- How long will data be preserved?
- What file formats are suitable for long-term preservation?
- How will the repository ensure data integrity over time?
Guidance: Selecting an appropriate repository is a key decision in the data management plan. Repositories may be disciplinary, institutional, or general purpose. The plan should identify the repository and explain why it was selected. Consider whether the repository provides persistent identifiers, versioning, and long-term preservation services.
Section 7: Roles and Responsibilities
Purpose: Identify who will be responsible for data management activities at each stage of the project.
Template Prompts:
- Who is responsible for data collection and documentation?
- Who manages data storage and backup?
- Who oversees data sharing and preservation?
- What happens to data management responsibilities when team members leave the project?
- Who is responsible for ensuring compliance with funder and institutional policies?
Guidance: Clear assignment of responsibilities prevents gaps in data management. The plan should identify responsible parties for the establishment, implementation, and overall management of the data management strategy. For collaborative projects, specify how responsibilities are divided among institutions and team members.
Section 8: Budget and Resources
Purpose: Describe the resources needed to implement the data management plan.
Template Prompts:
- What personnel time is allocated to data management activities?
- What costs are associated with data storage, repositories, and preservation?
- What equipment or software is needed?
- Are there costs for data preparation and de-identification?
- How will ongoing data management costs be covered after the project ends?
Guidance: Data management activities require resources, including personnel time, storage space, and repository fees. The plan should identify these costs and explain how they will be covered. Some funders allow data management costs to be included in the grant budget, while others require separate funding arrangements.
Funder-Specific Checklists
NIH Data Management and Sharing Plan Checklist
The NIH DMS Policy requires six elements in every Data Management and Sharing Plan. Use this checklist to ensure your plan addresses each requirement.
| Required Element | What to Include | Checklist |
|---|---|---|
| Data Type | Types and formats of scientific data generated | Describe all data types and formats |
| Related Tools and Software | Software, code, and tools needed to access and use data | List all relevant tools and software |
| Data Standards | Standards for data and metadata | Identify applicable standards |
| Data Preservation and Access Timeline | How long and where data will be preserved | Specify repository and duration |
| Access and Distribution | How data will be shared and any restrictions | Describe sharing methods and conditions |
| Oversight of Data Management and Sharing | Who is responsible for compliance | Name responsible parties |
The NIH provides supplemental guidance on elements to consider when developing a plan. While no strict template is required, structured templates have demonstrated greater alignment with NIH policy. The DMPTool NIH DMSP Templates Project provides templates with curated guidance at the point of need, breaking out each plan section and subsection with related guidance and examples.
NSF Data Management Plan Checklist
The NSF requires a supplementary document of no more than two pages describing how the project will conform to NSF policy on dissemination and sharing of research results. Use this checklist to ensure your plan addresses NSF expectations.
| Required Element | What to Include | Checklist |
|---|---|---|
| Data Types | Types of data produced by the project | Describe all data types |
| Data Format | Formats for data and metadata | Specify file formats |
| Data Access | How data will be shared and accessed | Describe sharing methods |
| Data Reuse | How data can be reused by others | Explain reuse conditions |
| Data Preservation | Long-term storage and archiving | Identify repository and duration |
Practical Implementation Steps
Step 1: Review Funder Requirements
Before writing your plan, review the specific requirements of the funding agency. The NIH DMS Policy and NSF data management requirements differ in structure and emphasis. Check the funding opportunity announcement for any additional requirements or guidance.
Step 2: Inventory Your Data
Make a list of all data types your project will generate or use. Include data from experiments, observations, surveys, simulations, and any existing datasets you will reuse. For each data type, note the format, estimated volume, and any sensitivity or access restrictions.
Step 3: Draft Each Section
Work through the template section by section, filling in the prompts with specific information about your project. Be concrete about the systems, standards, and procedures you will use. Avoid vague language that could apply to any project.
Step 4: Consult Institutional Resources
Many institutions have data librarians, research data managers, or core facilities that can provide guidance on data management planning. Shared research resources provide access to advanced technologies, equipment, and expert personnel who can help with data management challenges. Consult these resources early in the planning process.
Step 5: Review and Revise
Review your draft plan for completeness and clarity. Check that every required element is addressed and that the plan is specific enough to guide actual data management activities. Ask colleagues or data management professionals to review the plan and provide feedback.
Step 6: Update the Plan Throughout the Project
A data management plan is not a static document. Update the plan as the project evolves, data types change, or new requirements emerge. The plan should reflect the actual data management practices of the project.
Records and Measurements for DMP Compliance
Tracking Data Management Activities
Maintain records of data management activities to demonstrate compliance with the plan. Useful records include:
- Data collection logs documenting when and how data were collected
- File naming and version control logs
- Backup verification records
- Metadata documentation updates
- Data sharing and access logs
- Repository deposit records
Measuring Plan Effectiveness
Evaluate whether the data management plan is working as intended. Indicators of effective data management include:
- All data files are named consistently and organized logically
- Backups occur on schedule and are verified
- Metadata is complete and accurate
- Data can be located and retrieved quickly
- Data sharing proceeds according to the timeline in the plan
- No data loss or corruption incidents occur
Reviewing Compliance
Periodically review the plan against funder requirements and institutional policies. Check that all required elements are addressed and that the plan reflects current practices. The NIH DMS Policy requires that plans address all required elements, and structured templates have demonstrated greater alignment with policy requirements.
Common Failure Patterns in Data Management Plans
Vague or Generic Language
Many plans fail because they use generic language that does not describe the specific data management practices of the project. Statements like "data will be stored securely" or "data will be shared in a repository" do not provide enough information for reviewers to assess the adequacy of the plan.
Correction: Describe specific storage systems, backup procedures, repository names, and access conditions.
Missing Required Elements
A notable percentage of submitted plans omit one or more required elements. In an institutional implementation study, 20.7 percent of plans omitted at least one required element. Common omissions include tools and software, data generation methods, and oversight responsibilities.
Correction: Use a structured template and checklist to ensure all required elements are addressed.
Inadequate Description of Data Types
Reviewers often differ on questions pertaining to the data types generated or used in a research project. Plans that do not clearly describe data types make it difficult for reviewers to assess the adequacy of data management procedures.
Correction: List all data types explicitly and describe their formats, volumes, and sensitivity.
Unrealistic Timelines
Some plans describe data sharing timelines that are not feasible given the nature of the data or the project timeline. For example, plans may promise immediate data sharing when data require de-identification or embargo periods.
Correction: Describe realistic timelines that account for data processing, de-identification, and repository deposit procedures.
Insufficient Budget Planning
Data management activities require resources, but many plans do not adequately address costs. Repository fees, personnel time, and data preparation costs can be significant.
Correction: Identify all data management costs and explain how they will be covered.
Limitations and Professional Escalation Criteria
When to Seek Professional Help
Some data management situations require professional assistance. Escalate to data management professionals, institutional research offices, or legal counsel when:
- The project involves sensitive data with complex access restrictions
- Data sharing agreements or material transfer agreements are required
- The project involves multiple institutions with different data policies
- The data include protected health information or other regulated data types
- The project involves international data transfer
- The funder has specific requirements that are unclear or conflicting
Recognizing the Limits of Templates
Templates provide a useful starting point, but they cannot address every situation. Different forms of data management plans exist, and requirements may vary due to funder guidelines and the nature of the study under consideration. Adapt the template to your specific project and consult professional guidance when needed.
When to Revise the Plan
Revise the data management plan when:
- The project scope changes significantly
- New data types are added or existing data types are eliminated
- Storage or sharing arrangements change
- Institutional or funder policies are updated
- Data management problems are identified
Welfare and Safety Context for Research Data
Protecting Human Participant Data
For research involving human participants, data management plans must describe how personal information will be protected. This includes de-identification procedures, access controls, and secure storage. Informed consent documents should explain data sharing plans, limitations, and procedures so participants understand how their data will be used and shared.
Handling Sensitive Data
Some research data require additional protections due to their sensitive nature. This may include data related to health conditions, genetic information, or other categories of personal data. The plan should describe the specific security measures that will protect sensitive data and the conditions governing access.
Data Security Incidents
The plan should describe procedures for responding to data security incidents, including data breaches or unauthorized access. Identify who should be notified and what steps will be taken to mitigate harm.
Frequently Asked Questions
What is the difference between a data management plan and a data sharing plan?
A data management plan is a broader document that covers all aspects of data handling, including collection, organization, storage, documentation, preservation, and sharing. A data sharing plan focuses specifically on how data will be made available to other researchers. The NIH DMS Policy uses the term Data Management and Sharing Plan to encompass both aspects.
How long should a data management plan be?
The length depends on the funder and the complexity of the project. The NSF requires a supplementary document of no more than two pages. The NIH does not specify a length limit, but plans should be concise while addressing all required elements. A typical NIH plan is two to five pages.
When should I write the data management plan?
Write the data management plan during the grant application process, as it is a required component of most funding applications. Start early to allow time for consultation with data management professionals and revision based on feedback.
Can I use the same data management plan for multiple funders?
You can use the same plan as a starting point, but you must adapt it to meet the specific requirements of each funder. Different funders have different requirements and expectations, and a plan that works for one funder may not satisfy another.
What happens if I do not submit a data management plan?
Most funders require a data management plan as part of the grant application. Applications without a required plan may be returned without review or considered incomplete. The NIH DMS Policy requires submission of a Data Management and Sharing Plan with funding applications.
How do I choose a data repository?
Consider disciplinary repositories that are well established in your field, institutional repositories, and general-purpose repositories. The plan should identify the repository and explain why it was selected. Consider factors such as persistent identifiers, preservation services, access controls, and costs.
What should I do if my data management plan needs to change during the project?
Update the plan to reflect the changes and document the reasons for the changes. Some funders may require notification of significant changes to the data management plan. Consult the funder guidance for specific requirements.
How do I handle data from human participants in a data management plan?
Describe the protections for participant data, including de-identification procedures, access controls, and secure storage. Explain how informed consent addresses data sharing and what conditions will govern access to sensitive data. The plan should describe the governance of clinical, biochemical, laboratory, molecular, and other sources of data according to the regulations and policies of all relevant stakeholders.
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References and Further Reading
- Research Data Framework. National Institute of Standards and Technology.
- EQUATOR Network. EQUATOR Network.
- Experimental Design Assistant. NC3Rs.
- NCBI Literature Resources. National Center for Biotechnology Information.
- PubMed. National Library of Medicine.
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- Prophylaxis, clinical management, and monitoring of datopotamab deruxtecan-associated oral mucositis/stomatitis.. The oncologist, 2025.
- HARmonized Protocol Template to Enhance Reproducibility of hypothesis evaluating real-world evidence studies on treatment effects: A good practices report of a joint ISPE/ISPOR task force.. Pharmacoepidemiology and drug safety, 2023.
- Navigating the new NIH Data Management Sharing plan: the NIH HEAL Initiative Data2Action DMPTool template.. JAMIA open, 2025.
- Ten simple rules for maximizing the recommendations of the NIH data management and sharing plan.. PLoS computational biology, 2022.
- The DMPTool NIH DMSP Templates Project.. Journal of the Medical Library Association : JMLA, 2024.
- 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.. Journal of clinical and translational science, 2025.
- A community-informed recruitment plan template to increase recruitment of racial and ethnic groups historically excluded and underrepresented in clinical research.. Contemporary clinical trials, 2023.
- Regional and National Shared Research Resources: Challenges and Opportunities§.. 2025.
- Educational Intervention to Improve Sexual Health and Quality of Life in Survivors of Breast and Gynecological Cancer: Protocol for a Mixed Methods Feasibility Study.. 2026.
- An Antarctic ecosystem value index to quantify ecological value across trophic levels and over time.. 2026.
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- Preparing for the New NIH Data Management and Sharing Policy. Hypothesis, 2023.
- Plan Overview A Data Management Plan created using DMPTool DMP ID: https://doi.org/10.48321/D1GP87 Title: Exploration and Validation of SHIPi Approaches in Models of Alzheimer’s Disease. 2023.
- Plan Overview A Data Management Plan created using DMPTool Title: IRF3 and macrophage polarization during experimental periodontitis. 2023.
- Informed Consent for Secondary Research under the New NIH Data Sharing Policy. Journal of Law, Medicine & Ethics, 2021.
- PARTHENOS Data Management Plan template for Open Research in Archaeology. Proceedings of the 2018 3rd Digital Heritage International Congress Digital Heritage 2018 Held Jointly with the 2018 24th International Conference on Virtual Systems and Multimedia Vsmm 2018, 2018.
- Road to a Chemistry-Specific Data Management Plan. Data Science Journal, 2024.
This article is educational and does not replace institutional policy, professional advice, or applicable safety and regulatory requirements.