**Data management plans** have evolved from administrative checkboxes to legally binding commitments. NIH's 2023 policy shift makes your **NIH data management and sharing plan template** a contract—not a formality. Reviewers now score DMPs alongside scientific merit.

This **DMP tool** addresses three critical challenges:

• Funder-Specific Requirements - NIH requires 7 elements; NSF requires 2 sections; Horizon Europe demands 6 questions. Templates don't transfer.
• FAIR Compliance - **FAIR data** principles (Findable, Accessible, Interoperable, Reusable) determine whether your data meets open science standards.
• Integration with Research Design - Your **data analysis plan**, DMP, and methodology sections must align. Inconsistencies trigger reviewer concerns.

For comprehensive data management guidance, see our post on The Data Management Disaster.

Step 1: Select Your Funder - Choose NIH, NSF, Horizon Europe, or generate plans for multiple funders simultaneously. Each triggers funder-specific question sets.

Step 2: Define Data Types - Specify whether you'll generate new data, reuse existing datasets, or both. Identify data types (genomic, clinical, imaging, etc.) to trigger appropriate compliance requirements.

Step 3: Address Compliance - Answer questions about human subjects, controlled access requirements, GDPR compliance (for EU funders), and sensitive data categories.

Step 4: Describe Management - Define standards, formats, storage volumes, repository selection, and timeline for data sharing. System validates against funder requirements.

Step 5: Assign Oversight - Designate who manages data stewardship. NIH requires explicit responsibility assignment.

Step 6: Generate & Export - Receive formatted DMP text with section headings, compliance badges, **FAIR data** score (0-100), and copy-paste ready narrative paragraph.

This workflow integrates with proposal development. For methodology alignment, consult The Credibility Paradox on writing methods sections.

NIH's 2023 policy established seven required elements for **NIH data management and sharing plan templates**:

• Data Type - Describe scientific data to be generated, including data modality and estimated volume
• Related Tools, Software, and/or Code - List analysis tools needed for data interpretation
• Standards - Identify community standards for data and metadata (formats, ontologies)
• Data Preservation, Access, and Timelines - Specify repository, access mechanism, and release schedule
• Access, Distribution, or Reuse Considerations - Address informed consent, privacy, proprietary data
• Oversight - Name individual(s) responsible for data management implementation

The wizard auto-generates compliant text addressing each element. For NIH-specific proposal strategies, see The NIH R01 Decoded.

**NSF data management plans** require two sections: (1) data types, standards, and access policies; (2) provisions for reuse, redistribution, and derivative products. Unlike NIH, NSF doesn't mandate specific repositories but expects justification of choices.

Key considerations for NSF DMPs:

• Domain-Specific Standards - Reference discipline norms (e.g., GenBank for genomics, CSD for crystallography)
• Long-Term Accessibility - 10-year minimum preservation expectation, though not explicitly stated
• Intellectual Property - Address patents, copyrights if applicable to your data type
• Cost Justification - Budget narrative should reference DMP commitments for data curation

This tool generates NSF-compliant text. For directorate-specific nuances, see The NSF Code.

The wizard calculates a **FAIR data** score (0-100) evaluating your plan against Findable, Accessible, Interoperable, and Reusable criteria:

• Findable (F) - Persistent identifiers (DOIs), rich metadata, searchable repositories
• Accessible (A) - Open access when possible, clear retrieval protocols, authentication when necessary
• Interoperable (I) - Community standards, documented formats, linked metadata
• Reusable (R) - Clear licenses, provenance documentation, domain-relevant attributes

Scores above 70 indicate strong FAIR alignment. Below 50 suggests insufficient detail for reviewers. The tool highlights specific improvements to increase your score.