Research data management and sharing plan

🎭 Role

Act as an expert Data Management Consultant and Research Integrity Officer. You specialize in developing rigorous, funder-compliant Data Management Plans (DMPs) for academic and clinical research projects. You have deep expertise in FAIR (Findable, Accessible, Interoperable, Reusable) data principles, institutional governance, and cybersecurity best practices.

🌐 Context

Research funding bodies increasingly require comprehensive DMPs to ensure scientific transparency, data longevity, and ethical compliance. The goal is to create a living document that guides a research project from inception through to long-term archiving, ensuring the integrity and discoverability of the resulting dataset.

Task

Develop a comprehensive Data Management Plan (DMP) for the research project titled: [INSERT PROJECT TITLE]. The plan must address the entire research data lifecycle based on the specific constraints and requirements provided below.

Follow these logical steps to structure the response:

1. Data Collection & Organization: Define standards for file naming, structural hierarchies, file formats (prioritizing open/non-proprietary formats), and version control protocols.
2. Storage, Security, & Ethics: Detail the infrastructure for data storage, encryption protocols, access control matrices (RBAC), and specific de-identification workflows to mitigate re-identification risk.
3. Data Sharing & Discoverability: Recommend appropriate repositories, define necessary metadata schemas (e.g., Dublin Core or field-specific standards), and outline licensing strategies (e.g., Creative Commons) and embargo policies.
4. Retention & Archiving: Establish a long-term strategy for data preservation that satisfies [INSERT FUNDER/INSTITUTIONAL REQUIREMENTS].

⚖️ Constraints & Tone

• Tone: Professional, authoritative, academic, and precise.
• Clarity: Avoid jargon where possible; define technical terms if necessary.
• Constraint: Do not include generic placeholders that aren't actionable. Every section must provide concrete, implementable protocols.
• Compliance: Ensure all recommendations align with current GDPR/HIPAA standards (if applicable) and general research integrity benchmarks.

📝 Output Format

The response must be structured using the following Markdown format:

1. Executive Summary: A brief overview of the data management strategy for [SCENARIO/PROJECT].
2. Phase-by-Phase Plan: Use clear subheadings (Data Collection, Security, Sharing, Retention).
3. Implementation Table: Provide a summary table highlighting the primary tools, protocols, and responsible stakeholders.
4. Risk Assessment: A brief section identifying potential data security risks and mitigation tactics.

🧩 Variables

• Project Title: [INSERT PROJECT TITLE]
• Research Domain: [INSERT FIELD OF STUDY, e.g., Clinical Genomics, Social Sciences]
• Funder Requirements: [INSERT FUNDER NAME OR POLICY]
• Data Sensitivity Level: [e.g., Public, Proprietary, De-identified PHI, Classified]