Create professional data management plans for grant applications with our free DMP generator tool. No registration, no fees - just comprehensive data planning meeting NSF and NIH requirements.
What is a Data Management Plan?
A Data Management Plan (DMP) is a formal document outlining how research data will be handled during and after a project. Most funding agencies now require DMPs as part of grant applications. A comprehensive DMP addresses data collection, storage, security, sharing, and long-term preservation.
Why DMPs Matter
Funding Requirement - NSF, NIH, and most major funders mandate DMPs for all grant applications
Research Integrity - Systematic data management prevents loss, ensures reproducibility, and maintains research quality
Compliance - DMPs demonstrate compliance with privacy regulations, ethical standards, and institutional policies
Collaboration - Clear data management facilitates team coordination and data sharing with collaborators
NSF Data Management Requirements
The National Science Foundation requires DMPs for all proposals. NSF DMPs must address:
Data Types and Formats Describe data types generated (experimental, observational, computational, etc.) and file formats used. Specify standards followed for data formatting.
Data Storage and Backup Explain where data will be stored during the project, backup strategies, and disaster recovery plans. Address data security measures.
Data Sharing and Access Describe how data will be shared with the research community, access policies, and any restrictions on data sharing. Include timelines for sharing.
Data Preservation Detail long-term preservation plans beyond project completion. Specify repositories for data deposition and retention periods.
Roles and Responsibilities Identify who is responsible for data management activities. Clarify roles for data collection, quality control, backup, and archiving.
NIH Data Sharing Requirements
The National Institutes of Health requires data sharing plans for applications requesting $500,000+ in direct costs per year.
Data to be Shared Specify which data will be shared - final datasets, analytical tools, or other materials. Justify any data withholding.
Data Sharing Timeline State when data will be available for sharing, typically upon publication acceptance or within specified timeframes after collection.
Data Access Procedures Describe how researchers can access shared data. Include any approval processes, data use agreements, or access restrictions.
De-identification Methods For human subjects research, explain how data will be de-identified to protect participant privacy while maintaining research utility.
Eight Essential DMP Sections
1. Project Information
Document project title, principal investigator, institution, funding agency, and project duration. Provide context for data management decisions.
2. Data Description
Specify data types (quantitative, qualitative, images, video), file formats, estimated data volume, and collection methods. Be comprehensive and specific.
3. Metadata and Documentation
Explain metadata standards used, documentation practices, and how data will be organized for understandability. Good documentation enables reuse.
4. Storage and Backup
Detail storage locations (local servers, cloud services, institutional repositories), backup frequency, redundancy strategies, and disaster recovery procedures.
5. Security and Privacy
Describe security measures protecting sensitive data, access controls, encryption methods, and compliance with privacy regulations like HIPAA or FERPA.
6. Data Sharing Policies
Articulate sharing policies, restrictions, embargo periods, and mechanisms for data distribution. Balance openness with legitimate restrictions.
7. Long-term Preservation
Identify repositories for permanent data archiving, preservation strategies, file format migrations, and retention periods meeting disciplinary norms and funder requirements.
8. Ethical and Legal Considerations
Address IRB approvals, informed consent provisions, intellectual property rights, data ownership, and any legal restrictions on data use or sharing.
Common DMP Mistakes
Vague Descriptions - Generic statements don't satisfy reviewers. Be specific about data types, volumes, formats, and procedures.
Ignoring Sensitive Data - Failing to address privacy and security for human subjects data signals lack of ethical awareness.
Unrealistic Sharing Plans - Promising to share data you can't actually share creates compliance problems later.
Missing Cost Considerations - DMPs should address data management costs. Budget accordingly in your grant application.
Format Inconsistencies - Follow funder-specific templates and requirements exactly. Each agency has unique DMP expectations.
Using the DMP Generator
Step-by-Step Process
1. Enter Project Details - Input project title, PI name, institution, funding agency, and project duration
2. Complete Each Section - Work through all eight DMP sections systematically with detailed information
3. Address Funder Requirements - Ensure compliance with specific NSF, NIH, or other funder guidelines
4. Review and Refine - Check for completeness, specificity, and alignment with project scope
5. Export Your DMP - Generate formatted document ready for grant submission or institutional review
Data Management Best Practices
Plan Early - Develop your DMP during grant writing, not as an afterthought
Be Realistic - Promise only what you can actually deliver regarding data sharing and preservation
Budget Appropriately - Include data management costs in your grant budget
Consult Experts - Work with your institution's data librarians and research office
Update Regularly - Treat your DMP as a living document, updating as your project evolves
Transform Your Grant Applications
Create comprehensive, compliant data management plans that strengthen your grant proposals. Our free tool guides you through all required elements for successful DMP development.
Visit https://www.subthesis.com/tools/data-management-plan-generator - Generate your DMP now, completely free!