Data Management Plan Creator

Interactive Mode

python scripts/main.py --interactive

As a Module

from scripts.main import DMSPCreator

creator = DMSPCreator(
    project_title="Cancer Genomics Study",
    pi_name="Dr. Jane Smith",
    institution="National Cancer Institute",
    data_types=["genomic sequencing", "clinical metadata"],
    estimated_size_gb=500,
    repositories=["dbGaP", "GEO"],
    sharing_timeline="6 months after study completion"
)

dmsp = creator.generate_plan()
creator.save_to_file("dmsp_output.md")

Parameters

NIH DMSP Required Elements

The generated plan addresses all six required elements per NIH policy:

1. Data Type - Types and estimated amount of scientific data
2. Related Tools, Software and/or Code - Tools needed to access/manipulate data
3. Standards - Standards for data/metadata to be applied
4. Data Preservation, Access, and Associated Timelines - Repository selection and sharing timeline
5. Access, Distribution, or Reuse Considerations - Factors affecting subsequent access
6. Oversight of Data Management and Sharing - Plans for compliance monitoring

FAIR Principles Implementation

Findable

• Persistent identifiers (DOIs)
• Rich metadata with standard vocabularies
• Registration in searchable repositories

Accessible

• Standardized communication protocols
• Metadata available even if data is no longer available
• Access procedures clearly documented

Interoperable

• Standard data formats
• Standard terminologies and vocabularies
• Qualified references to other data

Reusable

• Detailed provenance information
• Clear usage licenses
• Domain-relevant community standards

Example Output

The generated DMSP includes:

• Executive summary
• NIH-compliant section headers
• Specific language for data type descriptions
• FAIR-aligned metadata standards
• Repository recommendations
• Timeline for data sharing
• Access control procedures
• Roles and responsibilities

References

• NIH Data Management and Sharing Policy
• NIH DMSP Template
• FAIR Principles

License

MIT License - See project root for details.

Risk Assessment

Security Checklist

• No hardcoded credentials or API keys
• No unauthorized file system access (../)
• Output does not expose sensitive information
• Prompt injection protections in place
• Input file paths validated (no ../ traversal)
• Output directory restricted to workspace
• Script execution in sandboxed environment
• Error messages sanitized (no stack traces exposed)
• Dependencies audited

Prerequisites

# Python dependencies
pip install -r requirements.txt

Evaluation Criteria

Success Metrics

• Successfully executes main functionality
• Output meets quality standards
• Handles edge cases gracefully
• Performance is acceptable

Test Cases

1. Basic Functionality: Standard input → Expected output
2. Edge Case: Invalid input → Graceful error handling
3. Performance: Large dataset → Acceptable processing time

Lifecycle Status

• Current Stage: Draft
• Next Review Date: 2026-03-06
• Known Issues: None
• Planned Improvements:
  • Performance optimization
  • Additional feature support