Research ScientistReproducibility and Open ScienceBeginnerSingle prompt

Research Compendium Builder AI Prompt

Help me organize my research project into a reproducible research compendium that another researcher could use to replicate my findings. Project type: {{project_type}} Tools use... Copy this prompt template, run it in your AI tool, and use related prompts to continue the workflow.

Prompt text
Help me organize my research project into a reproducible research compendium that another researcher could use to replicate my findings.

Project type: {{project_type}}
Tools used: {{tools}} (R, Python, Stata, SPSS, etc.)

1. What is a research compendium:
   A research compendium is a structured collection of files that contains the data, code, and text associated with a research project, organized so that anyone can reproduce the reported results.

2. Recommended directory structure:
```
project_name/
├── README.md              # Overview, how to reproduce results
├── DESCRIPTION            # Dependencies and environment info
├── data/
│   ├── raw/               # Original, unmodified data (read-only)
│   ├── processed/         # Cleaned, analysis-ready data
│   └── codebook.md        # Variable definitions and coding
├── code/ (or R/, scripts/)
│   ├── 00_data_cleaning.R # Data cleaning script
│   ├── 01_analysis.R      # Main analysis
│   ├── 02_figures.R       # Figure generation
│   └── functions/         # Custom functions used by scripts
├── output/
│   ├── figures/           # Generated figures
│   └── tables/            # Generated tables
├── paper/
│   ├── manuscript.Rmd     # Paper manuscript (ideally dynamic)
│   └── references.bib     # Bibliography
└── tests/                 # Tests for analysis code
```

3. README content requirements:
   - Project title, authors, and contact
   - One-paragraph project description
   - How to install dependencies
   - How to reproduce the main results (step by step)
   - Brief description of each directory
   - Data availability statement
   - License

4. Dependency management:
   - R: use renv to capture package versions. Commit renv.lock.
   - Python: use a requirements.txt or conda environment.yml
   - Document the R/Python version used
   - Ideally: provide a Dockerfile or Binder link for complete environment reproducibility

5. Coding standards for reproducibility:
   - Set random seeds at the top of every script that uses randomization
   - Use relative paths (never absolute paths like /Users/YourName/...)
   - Do not modify raw data files — always create new processed versions
   - Write scripts that run from top to bottom without manual intervention
   - Comment code to explain analytical decisions, not just what the code does

6. Dynamic documents:
   - Ideal: R Markdown or Quarto document that generates the paper by running the analysis inline
   - Results in the paper update automatically when data or code changes
   - Eliminates copy-paste errors between analysis output and paper text

Return: directory structure for my project, README template, dependency setup instructions, and coding standards checklist.

When to use this prompt

Use case 01

Use it when you want to begin reproducibility and open science work without writing the first draft from scratch.

Use case 02

Use it when you want a more consistent structure for AI output across projects or datasets.

Use case 03

Use it when you want prompt-driven work to turn into a reusable notebook or repeatable workflow later.

Use case 04

Use it when you want a clear next step into adjacent prompts in Reproducibility and Open Science or the wider Research Scientist library.

What the AI should return

The AI should return a structured result that covers the main requested outputs, such as What is a research compendium:, Recommended directory structure:, README content requirements:. The final answer should stay clear, actionable, and easy to review inside a reproducibility and open science workflow for research scientist work.

How to use this prompt

1

Open your data context

Load your dataset, notebook, or working environment so the AI can operate on the actual project context.

2

Copy the prompt text

Use the copy button above and paste the prompt into the AI assistant or prompt input area.

3

Review the output critically

Check whether the result matches your data, assumptions, and desired format before moving on.

4

Chain into the next prompt

Once you have the first result, continue deeper with related prompts in Reproducibility and Open Science.

Frequently asked questions

What does the Research Compendium Builder prompt do?+

It gives you a structured reproducibility and open science starting point for research scientist work and helps you move faster without starting from a blank page.

Who is this prompt for?+

It is designed for research scientist workflows and marked as beginner, so it works well as a guided starting point for that level of experience.

What type of prompt is this?+

Research Compendium Builder is a single prompt. You can copy it as-is, adapt it, or use it as one step inside a larger workflow.

Can I use this outside MLJAR Studio?+

Yes. The prompt text works in other AI tools too, but MLJAR Studio is the best fit when you want local execution, visible Python code, and reusable notebooks.

What should I open next?+

Natural next steps from here are Code Review for Reproducibility, Data Sharing Plan, Meta-Analysis Readiness.