Academic Research & Writing5.0 · 0 ratings

Reproducible Data Management Plan

Drafts a funder-ready data management plan covering collection, storage, sharing, ethics, and long-term preservation.

Role-BasedStructured-OutputStep-by-Step

Prompt

ROLE: You are a research data steward who writes DMPs that satisfy funder mandates and open-science expectations.

CONTEXT: My project [PROJECT_TITLE] in [DISCIPLINE] will generate data of type [DATA_TYPES] at scale [DATA_VOLUME]. Funder/institution requirements: [REQUIREMENTS]. Sensitive/personal data involved: [YES/NO + DETAILS]. Preferred repository: [REPOSITORY_OR_TBD].

TASK — produce a structured DMP:
1. Data description: what data, formats, and how generated/collected.
2. Documentation & metadata: standards used so data is understandable by others; file-naming and versioning conventions.
3. Storage & security during the project: backup strategy, access control, and (if personal data) safeguarding and consent basis.
4. Ethics & legal: consent, anonymization/pseudonymization, IP, and any restrictions on sharing.
5. Sharing & access: what will be open vs. restricted, license (e.g., CC BY), embargo, and how others request access.
6. Preservation: chosen repository, retention period, and how a persistent identifier (DOI) will be assigned.
7. Roles & responsibilities and a rough cost line.

OUTPUT FORMAT: Seven numbered sections with concise, specific commitments — not generic platitudes.

CONSTRAINTS: Tailor every section to my data type and sensitivity; flag where personal data demands stricter handling. Do not promise full open data if ethics/consent forbid it — recommend controlled access instead. Mark any institution-specific requirement I must confirm as [CHECK_POLICY]. Prefer FAIR-aligned, concrete choices over vague intentions.

How to use this prompt

  1. 1

    Copy the prompt above and paste it into ChatGPT, Claude, or Gemini — or open it in the visual Studio to edit each part on a canvas and run it with your own key.

  2. 2

    Replace any bracketed placeholders with your specifics. The more concrete your context and constraints, the sharper the result — see the 5-part prompt structure.

  3. 3

    Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.

Techniques in this prompt

Role-Based

Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.

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Structured Output

Pins the response to a defined structure so it drops straight into your workflow.

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Step-by-Step

Forces explicit intermediate reasoning instead of jumping to a conclusion, which improves accuracy on hard tasks.

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Recommended models

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Open it in the visual Studio to wire it into a full workflow with your own API key — or learn the craft behind prompts like this.

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