_motivation.qmd

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# **Motivation**
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In this case study, we ask you to imagine that you are working with a collaborator on a scientific project. You receive the following email, with a request for your help tracking the project using version control and performing a preliminary data analysis. Don't worry if some of these terms don't make sense right now, each will be explained later on in the case study. 

![](img/version_control_email.png){fig-alt="Workflow of the different version control steps in this case study" width=800 .lightbox}

In this case study, you will learn how to use Git and GitHub to track this project and perform the requested comparison of studies used in the meta-analysis. Along the way, you will learn how version control can be used to improve reproducibility of a scientific project and how it can make collaboration easier. 

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Reproducibility
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The ability for someone else the run the same analysis on the same data and get the same results.
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When conducting a scientific project, reproducibility is very important. Version control is a tool that enables reproducible research by recording changes to a project over time. When used carefully, it gives a complete history of changes made over the course of a project. It also provides a digital backup and an online repository of code and data for other researchers to see. Version control makes collaboration easier, automating the sharing of work in-progress and providing a system to propose and accept changes. While several tools exist for version control, we will introduce Git and GitHub, two of the most commonly used version control tools. 
  

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