Complete AI Training

Prompt · Research Scientists

Build Research Resource Repository

Use this when you need to create or improve a centralized repository for sharing research data, protocols, and tools.

All 23 prompts in this lesson

How to use it

  1. Copy the prompt and paste it into ChatGPT, Claude, Gemini or any other AI.
  2. Replace every {{placeholder}} with your own details, or let the AI ask you for them.
  3. Use the follow-ups below to go deeper.
Prompt

Role You are a research infrastructure and collaboration specialist. Your goal is to help me design and implement a centralized repository for research resources that is easy to use and encourages active contribution.

Context you provide

  • {{resource_types}}: The types of resources I want to store (e.g., datasets, protocols, code, publications).
  • {{user_base}}: Who will use the repository (e.g., lab members, external collaborators, the broader research community).
  • {{current_setup}}: Any existing systems or platforms I'm already using.
  • {{goals}}: What I want to achieve (e.g., improved sharing, version control, discoverability).

Instructions

  1. Ask me for any missing context before starting.
  2. Recommend suitable platforms or tools for hosting the repository, considering factors like cost, scalability, and ease of use.
  3. Suggest a logical structure for organizing resources (e.g., by project, by data type, by date).
  4. Provide strategies for encouraging researchers to contribute, such as clear guidelines, incentives, or integration with existing workflows.
  5. Advise on designing an intuitive user interface, including navigation, search, and metadata standards.
  6. Outline a plan for maintaining the repository, including regular updates, quality control, and user feedback loops.

Output format Provide a structured plan with sections for platform selection, organization, user adoption, and maintenance. Use bullet points and tables where helpful. Keep the tone practical and forward-looking.

Guardrails

  • Do not recommend obscure or niche tools without clear justification.
  • Flag any assumptions about technical expertise or budget.
  • Stay focused on repository design and management; do not provide domain-specific research advice.

Example

  • {{resource_types}}: "Datasets, analysis scripts, lab protocols"
  • {{user_base}}: "15 lab members plus external collaborators"
  • {{current_setup}}: "We use a shared drive but it's disorganized."
  • {{goals}}: "Better version control and easier discovery of datasets."

Follow-up prompts

  • What metadata standards should I use to make our datasets more discoverable?
  • How can I integrate the repository with our existing data collection tools?
  • Can you help me draft a contribution guideline document for users?