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BytesAgain β€Ί Skills β€Ί Data Management Plan Creator
πŸ¦€ ClawHub

Data Management Plan Creator

by @aipoch-ai

Automatically generate NIH 2023-compliant Data Management and Sharing Plan (DMSP) drafts following FAIR principles

Versionv0.1.1
Downloads602
#writing#legal#automation#productivity
TERMINAL
clawhub install data-management-plan-creator

πŸ“– About This Skill

name: data-management-plan-creator description: Automatically generate NIH 2023-compliant Data Management and Sharing Plan (DMSP) drafts following FAIR principles version: 1.0.0 category: Grant tags:
  • NIH
  • DMP
  • DMSP
  • FAIR
  • research-data
  • compliance
    • author: AIPOCH license: MIT status: Draft risk_level: Medium skill_type: Tool/Script owner: AIPOCH reviewer: '' last_updated: '2026-02-06'

    Data Management Plan (DMP) Creator

    Automatically generate draft Data Management and Sharing Plans (DMSP) compliant with NIH 2023 policy requirements and FAIR principles.

    Overview

    This Skill generates comprehensive Data Management and Sharing Plans (DMSP) that meet NIH's 2023 Final Policy for Data Management and Sharing. The output follows FAIR principles (Findable, Accessible, Interoperable, Reusable) to ensure research data is properly managed and shared.

    Requirements

  • Python 3.8+
  • No external dependencies required (uses standard library only)

    • Usage

    Command Line

    python scripts/main.py \
    --project-title "Your Research Project Title" \
    --pi-name "Principal Investigator Name" \
    --data-types "genomic,imaging,clinical" \
    --repository "GEO,Figshare" \
    --output dmsp_draft.md

    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

    | Parameter | Type | Default | Required | Description | |-----------|------|---------|----------|-------------| | --project-title | string | - | Yes | Title of the research project | | --pi-name | string | - | Yes | Name of the Principal Investigator | | --institution | string | - | Yes | Research institution or organization | | --data-types | string | - | Yes | Comma-separated list of data types (e.g., "genomic,imaging,clinical") | | --estimated-size | float | - | No | Estimated data size in GB | | --repository | string | - | Yes | Comma-separated list of target repositories | | --sharing-timeline | string | No later than the end of the award period | No | When data will be shared | | --access-restrictions | string | - | No | Any access restrictions (e.g., "controlled-access for sensitive data") | | --format-standards | string | - | No | Data format standards to be used | | --output | string | dmsp_[timestamp].md | No | Output file path | | --interactive | flag | - | No | Run in interactive mode |

    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

    | Risk Indicator | Assessment | Level | |----------------|------------|-------| | Code Execution | Python/R scripts executed locally | Medium | | Network Access | No external API calls | Low | | File System Access | Read input files, write output files | Medium | | Instruction Tampering | Standard prompt guidelines | Low | | Data Exposure | Output files saved to workspace | Low |

    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 β†’ Expected output 2. Edge Case: Invalid input β†’ Graceful error handling 3. Performance: Large dataset β†’ Acceptable processing time

    Lifecycle Status

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

    πŸ’‘ Examples

    Command Line

    python scripts/main.py \
    --project-title "Your Research Project Title" \
    --pi-name "Principal Investigator Name" \
    --data-types "genomic,imaging,clinical" \
    --repository "GEO,Figshare" \
    --output dmsp_draft.md

    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")

    βš™οΈ Configuration

    # Python dependencies
pip install -r requirements.txt