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Workflow Setup Overview

Welcome to the Workflow Setup overview page! This page will guide you through the key steps and tools needed to set up an effective Open Science workflow. Whether you're a researcher working with quantitative methods or looking to improve your data management practices, this workflow is designed to help you stay organized, reproducible, and collaborative.

What You'll Learn​

The setup of a successful research workflow involves mastering the following tools and techniques:

  1. Version Control with Git and GitHub:
    Learn how to manage and track changes to your code, collaborate effectively, and ensure your work is reproducible using Git and GitHub.

  2. Reproducible Research:
    Understand how to set up and use tools like R and Python to ensure that your research can be reproduced by others — an essential part of Open Science.

  3. Collaborative Workflows:
    Master collaboration methods that allow you to work seamlessly with teammates and other researchers on shared projects.

  4. Data Management & Documentation:
    Learn how to properly store and document your research data so others can access and understand it easily.

Video Playlist: Getting Started with Your Workflow​

Note: This playlist is an ongoing series, so be sure to check back for new content as we continue to release videos on relevant topics for researchers.

Key Steps to Follow​

  1. Set Up Your GitHub Repository:
    Begin by creating a GitHub repository to store and manage your project. The tutorial will guide you step-by-step on how to create a repository, push your first file, and begin using version control.

  2. Learn Git Basics:
    Familiarize yourself with the basic Git commands like git clone, git commit, git push, and git pull. These commands will become your primary tools for managing changes to your project.

  3. Use GitHub for Collaboration:
    Once you’ve set up your repository, invite collaborators and start using GitHub to manage contributions, issues, and pull requests.

  4. Set Up Your Programming Environment:
    Depending on the tools you use (like R, Python, or Jupyter Notebooks), this tutorial will show you how to create reproducible scripts and ensure others can easily run your work.

  5. Document Your Workflow:
    A crucial step in Open Science is to make sure your work is well-documented. This means providing clear instructions, setting up README files, and ensuring your project can be easily understood by others.

This page will serve as your starting point for mastering Open Science workflows. With each video and step, you'll build a more efficient, transparent, and reproducible research process.

Good luck with your workflow setup!