Skip to content

Getting Started

Setting up a consistent and reproducible development environment is crucial for conducting reproducible research.

This guide provides an overview of the essential components needed for a robust research environment.

Overview

To establish a productive research environment, you'll need to set up three key components:

  1. Python Environment Management - Tools to manage Python installations, packages, and virtual environments
  2. Integrated Development Environment (IDE) - Software to write, debug, and manage your code effectively
  3. Version Control System - Tools to track changes, collaborate, and ensure reproducibility

Quick Start Guide

For detailed instructions on each component, please refer to the dedicated guides:

1. Python Environment Management

→ Python Management Guide

Learn how to set up and manage Python environments using: - Conda - Popular for scientific computing with pre-compiled packages - uv - Fast, modern Python package manager written in Rust - Poetry - Sophisticated dependency management and packaging tool

Choose the tool that best fits your workflow and project requirements.

2. IDE Setup

→ IDE Setup Guide

Configure your development environment with: - PyCharm - Full-featured Python IDE with advanced debugging and analysis - Visual Studio Code - Lightweight, extensible editor with excellent Python support - Jupyter - Interactive notebook environment for exploratory research

Each IDE offers unique advantages depending on your coding style and project needs.

3. Version Control

→ Version Control Guide

Master Git and GitHub for: - Tracking changes in your research code - Collaborating with team members - Ensuring reproducible research practices - Managing project history and releases

Version control is essential for maintaining research integrity and enabling collaboration.

For new researchers, we recommend this setup sequence:

  1. Start with Python Management: Choose and install one environment manager (Conda is often easiest for beginners)
  2. Set up your IDE: Install and configure either PyCharm or VS Code based on your preferences
  3. Initialize Version Control: Set up Git and create your first repository on GitHub
  4. Create your first project: Apply all three components in a simple research project

Integration Tips

These tools work best when used together: - Configure your IDE to recognize your Python environments - Set up your IDE's Git integration for seamless version control - Use environment files (environment.yml, pyproject.toml) that are tracked in version control - Establish consistent project structures across all your research projects

Best Practices

General Setup Tips

  1. Always use virtual environments to isolate project dependencies
  2. Pin dependency versions for reproducibility
  3. Document your environment setup in your project README
  4. Use lock files (poetry.lock, conda-lock) when available
  5. Keep development and production dependencies separate
  6. Regularly update dependencies while testing for compatibility

Reproducibility Tips

  • Include environment files (environment.yml, pyproject.toml, requirements.txt) in version control
  • Document the Python version and environment manager used
  • Consider using Docker for ultimate reproducibility
  • Test your environment setup on different machines

Additional Resources

Quick Reference

  • Cheat Sheets - Quick reference guides for Conda, Git, and GitHub

Reproducible Research

Development Best Practices

Community and Support

Next Steps

Once you've completed the basic setup:

  1. Practice with a sample project - Create a small research project to test your environment
  2. Explore advanced features - Dive deeper into the specific tools you've chosen
  3. Join the community - Participate in relevant online communities and forums
  4. Keep learning - Stay updated with new tools and best practices in research computing

Remember: The goal is to create a reproducible, collaborative, and efficient research environment that supports your scientific work.