[new project]


When working on a new project, it’s efficient to kick-start your work with a well-documented “repository template” that sets up your workflow, and provides you with some minimal documentation so that team members can quickly contribute to your work.

In this tutorial, we walk you through the steps of setting up a project on your computer. Throughout, you’ll use one of our project templates on GitHub.

1. Assess project requirements

Before we dive right into the nitty gritty details, here are a couple of things to consider when you set up new projects.

  • Will you collaborate with others on writing code?

    If you do, using a version control system like Git is a must-have, as you need to be able to work on a project simultaneously, without running the risk of overwriting each other’s work.

  • What’s the technical proficiency of your team members?

    It’s not uncommon that your team members may be unfamiliar with tools like Git, make, or the terminal. First, that’s not a problem at all. You can stay in charge of the main workflow, and integrate others' work as it is being updated (e.g., on Dropbox). However, it’s way better to help team members develop the skills to use Git and automation, just to name a few.

  • What minimum security levels do you have to ensure? Can you make your code public?

    Are you working on a data consultancy project for a Fortune 500 client? Have you signed a NDA for the data that requires you to treat it with a great sense of responsibility? If so, then you better make sure that you have configured your systems securely (e.g., private Github repositories, 2-factor authentication, etc.).

  • How will you manage your data?

    Can the raw data be managed in a cloud storage service like Dropbox or Google Drive, or does the sheer amount of data requires us to look for alternatives (e.g., database or object storage)?

  • How long will it take to run the workflow?

    While importing a dataset and running a bunch of regression models typically happens with a matter of seconds, you may encounter circumstances in which you need to factor in the run time. For example, if you throttle API calls, experiment with a variety of hyperparameters, run a process repeatedly (e.g., web scraping). In these cases, the hardware of your machine may not suffice nor do you want to keep your machine running all day long. Creating a virtual instance (e.g., EC2) adjusted to your specific needs can overcome these hurdles.

Together, these considerations can guide your decision making in terms of (a) code versioning, (b) raw data storage, and (c) computation (local vs remote).

2. Set up computing environment

Configure your software environment. The minimum requirements typically are

  • programming languages (e.g., Python, R)
  • version control system (e.g., Git/GitHub)
  • automation tools (e.g., make).

Head over to the software section section Tilburg Science Hub to view the installation guides.

3. Setup the repository

Never worked with Git/GitHub before? Then follow our onboarding for Git/GitHub first.

  1. Initialize a new Github repository and clone it to your local machine. Based on your project requirements, consider whether you need a public or private repository.

  2. Create a directory structure that suits your project’s goals. For example this one.

├── README.md
├── data
├── gen
│   ├── analysis
|   ├── data-preparation
|   └── paper
└── src
    ├── analysis
    ├── data-preparation
    └── paper
  1. Update the README.md file (e.g., add a project description and title)
  2. Commit your changes and push them to Github.
  3. Create a gitignore file (touch .gitignore) that skips the data and gen folder (after all, your code in the src folder should download the data and store the intermediate results in gen).
  4. Visit your repository on Github and check whether it works as expected. Please keep in mind that only folders that contain files will be visible (i.e., empty folders are ignored by Git). Once you start working on your project and create new files these folders will automatically become visible.
  5. Invite your team members to contribute to the repository!

4. Setup the data environment

  1. For small files and projects with limited duration, put your data file in Google Drive or Dropbox and generate a public sharing link. For more long-term projects, it’s better to use object storage (e.g., such as the one at S3).

  2. Create a script in the src folder that downloads the data from your cloud storage (or external website) and stores it in the data folder.

5. Automate your pipeline

  • Create a makefile that handles the end-to-end process (e.g., download data, preprocess data, estimate linear model, generate regression table and plot).
  • Start automating your project early on - even if it’s just downloading the data and producing a little overview of the data. Expand your makefile while you’re working on the project.

Next steps

Think you’re done? No way! This is just the start of your reproducible research project. So take some time to go through our suggestions on how to continue your work on the project.

  • Inspect raw data after data delivery
  • Prepare data for analysis
  • Pull in changes from GitHub (and push your own changes to the remote)
  • Create issues, and assign team members to these issues
  • Work on your repository’s readme - the first thing users of your repository will view when visiting it on GitHub