Research Project Template

This repository serves as a robust template for organizing and managing research projects, from data acquisition to manuscript preparation and presentation.

Getting Started

To begin a new project using this template, follow these steps:

1. Create a new repository from this template:

   • Click the "Use this template" button (a green button near the top right).

   • Choose "Create a new repository."

   • Select an owner (your GitHub username or an Organization's name), provide a repository name (e.g., my-awesome-research-project), and choose its visibility (public or private).

   • Click "Create repository from template."

2. Clone your new repository:

git clone https://github.com/your-username/your-new-repository-name.git
cd your-new-repository-name

3. Customize the README:

   • Update this top-level README.md with a concise project title, description, and any initial setup instructions specific to your project.

   • Populate the README.md files within each subdirectory with relevant details as your project progresses.

Directory Structure

Below is an overview of the repository layout:

.
├── 01_data/                # All project data
│   ├── raw/                # Original, immutable data (do not modify)
│   ├── external/           # Data from outside sources (e.g., public datasets)
│   ├── processed/          # Cleaned/transformed data (outputs from scripts)
│   └── README.md           # Data sources, descriptions, processing steps
├── 02_analysis/            # Analysis scripts and utilities
│   ├── 01_data_cleaning.R  # Example: data cleaning script (R)
│   ├── 02_exploratory_analysis.py # Example: exploratory analysis (Python)
│   ├── 03_modeling.R       # Example: modeling script (R)
│   ├── utils/              # Reusable functions or modules
│   └── README.md           # Analysis workflow and script purposes
├── 03_manuscript/          # Manuscript drafts, figures, documents
│   └── README.md           # Track manuscript progress, submission plans, authors
├── 04_presentation/        # Materials for talks, posters, slides
│   └── README.md           # Summarize presentations (title, date, format)
├── 05_misc/                # Supplementary files (proposals, notes, etc.)
│   └── README.md           # Describe contents
└── README.md               # Project overview and setup instructions

Good Practices

• Reproducibility:

  • Immutability of Raw Data: Never modify files in 01_data/raw/. Perform all cleaning and transformation programmatically or on copies, saving outputs to 01_data/processed/.
  • Environment Documentation: Record your computational environment (e.g., requirements.txt for Python, renv.lock for R) to enable others to replicate your setup.

• Version Control:

  • Commit changes frequently with clear, descriptive messages.
  • Use branches for new features or analyses.
  • Use .gitignore: Add files and directories to .gitignore to prevent tracking unnecessary or auto-generated content (e.g., intermediate results, logs, or large files). For example, you may choose not to track 01_data/processed/ since processed data should be reproducible by running scripts.

• Clear Documentation:

  • Keep all README.md files up to date.
  • Comment code to explain critical steps and assumptions.

• Sensitive Information:

  • Never commit sensitive data or API keys to the repository.

• Large Files:

  • Avoid committing very large files directly to Git. Use Git LFS, git-annex, or research-specific tools such as DataLad for managing large datasets or files, if appropriate for your workflow.