Totally Science Gitlab A Beginner’s Guide for Scientists

In the era of digital research, every scientist’s toolkit should include a robust version control system. But with a plethora of platforms out there, how do you choose the right one for your scientific needs? Look no further—GitLab offers an empowering set of tools tailor-made for researchers, from version-controlled code and data to project management that can truly organize and streamline your work.

In this comprehensive guide, we take a deep dive into how scientists can leverage GitLab, turning it from a development-centric platform into an ally for groundbreaking research. Whether you’re a biologist, physicist, or data scientist, GitLab can elevate your work, enhance collaboration, and foster a more open and transparent approach to science.

What is GitLab and Why Should Scientists Use It?

At its core, GitLab is a web-based DevOps lifecycle tool that provides a Git repository manager providing wiki, issue-tracking and CI/CD pipeline features. It offers a complete platform for collaborating on code and data, tracking progress, and streamlining the development process. Its open-source nature and comprehensive features make it appealing to scientists for a variety of reasons.

• Version Control: GitLab’s version control system allows you to keep track of changes in your code and data over time, so you can go back to any version whenever you need.
• Project Management: With GitLab, you can organize your project into milestones, with issues and merge requests that belong to a specific milestone.
• Continuous Integration: This enables you to automatically test and build your code, ensuring that it always meets the high standards and is always ready for review and production.
• Collaboration: Through its merge request feature, you can ask for reviews from colleagues, assign tasks, and manage approvals—all within the platform.
• Open Science and Reproducibility: GitLab promotes open science by making your work publicly accessible, trackable, and reproducible.

Now, let’s explore how you, as a scientist, can take advantage of these GitLab features.

Setting Up Your Science Project on GitLab

The beauty of GitLab lies in its versatility. Whether you’re working on statistical models, simulation software, or research papers, GitLab can accommodate your project. The first step is setting up your project effectively.

Creating a New Project

• Login to your GitLab account and click on ‘New Project’.
• Choose a visibility level. If your research can be open – and the best science usually is – select ‘Public’.
• Set up a README file that includes metadata about your project such as title, authors, and a brief description of the research.

Defining Milestones and Issues

Milestones and issues are key to organizing your work. Here’s how to get started:

• Create a milestone for each significant stage of your research, such as data collection, analysis, and publication.
• Under each milestone, create issues that represent the tasks to be completed. Each issue should be a specific, achievable goal that can be crossed off when completed.

Branching Strategy for Research

The branching strategy you use in GitLab can significantly impact your project’s cohesion and workflow. For most scientific projects, a ‘feature-branch’ workflow is recommended:

• Start with your ‘main’ or ‘master’ branch, which represents the current state of your project.
• Create a new branch for each new feature or experiment you’re working on. This isolates your work from the main codebase and keeps things clean.
• When a feature is complete, merge the branch back into ‘main’.

Organizing and Storing Your Data in GitLab

Your data is the lifeblood of your research, and GitLab can help you keep it safe and organized.

Large File Storage (LFS) and Data Storage

• Git is not ideal for large files. Use Git LFS to manage large files within your repositories without bloating your repository size.
• For extremely large datasets, GitLab also integrates with data storage solutions like DVC to version and control access to your data without storing it within the repository itself.

Data Organization Principles

• Organize your data logically, with directories and filenames that are clear and meaningful.
• Consider using Git-annex or similar tools to manage data within the repository, especially when multiple people are working on the same project.

Implementing Continuous Integration and Deployment

Continuous Integration and Deployment (CI/CD) can keep your research on track by automating testing and building processes.

Writing Tests for Your Research Code

• Develop a suite of tests for your code to ensure that it behaves as expected.
• Automate these tests with CI so that every change is tested before being merged into the main project.

CI Pipelines for Scientific Research

• Set up a CI pipeline in GitLab to run these tests and analyses automatically whenever changes are made to the repository.
• Automate repetitive tasks such as re-running all simulations with different parameters to ensure the reliability of your results.

Collaboration and Openness

Science thrives on collaboration and transparency. GitLab’s collaboration features make it easier to work with other researchers and share your findings with the world.

Code Review and Merge Requests

• Before merging your work into the main branch, create a merge request to ask for feedback from your peers.
• Use GitLab’s built-in tools to review others’ code and provide feedback, fostering a culture of collaborative improvement.

Sharing and Citations

• Once your work is ready to be shared, consider making it public.
• Use GitLab’s features to generate a DOI for your work, making it easy for others to cite your research.

Public Repositories and Published Work

• Publish your repo as a public project if it aligns with the ethical clearance and licenses for your research.
• GitLab provides a platform to host all your work, make it discoverable, and display the evolution and history of your research, which is essential for reproducibility and open science.

Optimizing for Reproducibility and Research Integrity

Reproducibility is a hallmark of good science. By following best practices and utilizing GitLab effectively, you can ensure that your work is as reproducible as possible.

Documentation and Context

• Leverage GitLab’s wiki pages or Issues to provide comprehensive documentation for your project, detailing every step and decision made during the research.

Versioning Data and Code

• Always version your data and code. GitLab’s version control system allows you to track and document these changes over time.
• Use Git tags and annotated commits to highlight significant points in your research project’s history.

Advanced Use Cases for GitLab in Science

Beyond the basics, there are several ways GitLab can cater to more complex scientific projects and workflows.

Data Distributions and Archives

• Use GitLab to distribute datasets with a record of changes and contributions.
• Set up an archive project that locks down a snapshot of your complete research environment at a specific point in time. It’s like creating a snapshot in time for your research, ensuring the tools and data are always available and consistent for future reproducibility.

Integrating with Jupyter Notebooks and RStudio

• Integrate Jupyter Notebooks or RStudio projects directly into your GitLab repository, allowing for a seamless transition between development and version control.
• You can version your Jupyter Notebooks using Jupytext or nbdime, making them easily trackable in GitLab’s familiar interface.

Troubleshooting in Scientific Projects

Finally, every researcher encounters problems. GitLab provides tools and resources to help you troubleshoot effectively.

Issue Tracking

• Use GitLab’s issue tracker to keep on top of problems. Encourage your team to:
• Label issues according to type or severity.
• Link issues to merge requests, fostering a direct connection between problems and their solutions.
• Set due dates and milestones to prioritize and organize.

Support and Community

• GitLab has a vibrant community and extensive documentation. If you encounter a problem, chances are someone has asked about it before.
• Consider subscribing to GitLab’s support services for extra peace of mind, ensuring that help is just a click away.

Conclusion: GitLab as the Linchpin of Modern Scientific Endeavors

In the fast-paced world of research, tools like GitLab are no longer optional—they are essential. By learning and utilizing the powerful features that GitLab offers, scientists can enhance their collaboration, organization, and openness.

GitLab is more than a version control tool; it’s a platform that can transform the way scientists work. From the initial setup of your project to the final publication, GitLab can support every step of your scientific journey, ensuring that your work is robust, reproducible, and ready to push the boundaries of knowledge.

For scientists just getting started with GitLab, take your time to familiarize yourself with these features. Incorporating them into your workflow may take some adjustments, but the benefits are worth it—both for your research and the scientific community at large. Happy coding, and may your discoveries be as vast as the commits in your repository!