Imagine you’ve cooked a delicious, elaborate dish for a party. It’s a hit; everyone loves it and keeps asking for the recipe. You’re thrilled, but there’s a problem: you never wrote the recipe down. You made it on the go, threw in ingredients by memory, adjusted the spices on the fly, and used techniques you didn’t bother to note. Now, months later, someone asks you to recreate the dish. You can try, but you’re left guessing the amounts, the process, and even the ingredients.

The same thing happens with code. Without proper documentation, trying to revisit, understand, or build upon your work becomes a frustrating game of trial and error.

As code has become increasingly popular in the research domain, it serves as the backbone for innovation and problem-solving. From modeling climate change to simulating neural networks, code drives progress. However, if you’re attempting to recreate a study published by another researcher years ago or to improve on an old research strategy and upgrade it with a new approach, you must be able to execute and comprehend the old code to replicate it. What if the code is written on the fly by the prior programmer? You may be unable to duplicate the prior programmer’s train of thought and may not completely grasp the code.

An approach that may lessen the impact of these worries is documenting the code. If you want to record your technique and make sure you don’t make any mistakes along the way, you can find various tips for documentation in the paragraphs that follow.

Why Code Documentation Matters

When research code is poorly documented, it becomes nearly impossible for others to understand, reuse, or replicate. Clear documentation serves several purposes:

• Reproducibility: Your documentation ensures that others can verify your findings by running your code exactly as you intended.

• Collaboration: Research is often collaborative, and good documentation helps team members work together efficiently.

• Longevity: A well-documented project can remain usable and relevant long after you’ve moved on.

It is important to remember that your code’s impact depends on how accessible, understandable, and replicable it is by other scholars.

Essentials of Good Code Documentation

The following is a step-by-step tutorial to help you better manage and document your code.

1. Start with a Clear README

   A ‘README’ file is the first thing that a user should encounter when they come across your code. The file should provide a brief overview of the process involved in your code. The user should have a clear line of sight to the process that you are describing. Some common things that you should include in the ‘README’ file are:

   • A brief project description.

   • Dependencies and installation instructions.

   • Code snippets that you used from a reference.

   • Citations for relevant research papers.

2. Document the Codebase

   Provide explanations of your code structure, especially for larger projects. Break your documentation into these to improve the accessibility of the code:

   • Modules: Explain each module’s purpose.

   • Functions: Use docstrings to describe what each function does, its inputs, and its outputs.

   • Classes: Detail how classes are structured and how they should be used.

   Tools like Sphinx or Javadoc can help generate professional-grade documentation for your projects.

3. Inline Comments

   Inline comments clarify tricky logic or calculations in the code. They are like little sticky notes on a puzzle, explaining why the programmer has chosen a certain way of logic and how these pieces fit together. They make the bigger picture easier to understand. Keep them concise and relevant, and avoid over-commenting on obvious lines.

4. Use Consistent Naming Conventions Throughout the Code

   When you’re documenting your programming project, you’ll regularly label and name files and reference them. However, for large projects, it is possible that you will use one sort of notation in some areas and another in others. One method to keep things simple for yourself and others is to use a basic and consistent naming standard that is easy to replicate and read.

   Consider structuring a library with each book labeled differently—some with titles, others with random numbers, and a few with colors or symbols. There is no consistency, making it difficult to find a certain book. The same is true for naming conventions in code. Using a consistent standard, such as ISO 8601 for dates or meaningful names for variables, guarantees that everything is simple to find, understand, and work with—for both you and anybody else who uses your code.

   For subsidiary files, you should create a uniform structure that makes them easier to find. Consider giving facts like the project name along with the name or initials of the individual who worked on the file, the date the file was modified, as well as the version number of the project on which you are working. These things allow for effortless reproducibility and seamless collaboration.

Common Mistakes to Avoid

• Too Much Detail: Avoid overwhelming readers with overly technical jargon. Aim for clarity and simplicity.

• Neglecting Reproducibility: Make sure to include all dependencies, installation instructions, and datasets required to run your code.

• Ignoring Feedback: Collaborators or peers might point out unclear sections in your documentation. Act on their suggestions to improve clarity.

In today’s fast-paced research environment, writing excellent code documentation is no longer optional—it’s essential. Whether you’re sharing a cutting-edge neural network for predictive modeling or simulation scripts for climate forecasting, well-documented code has the power to leave a lasting impact.

Research codes need constant sharing and easy accessibility as a multitude of researchers work on a project. The best way to share any code is the static way, that guarantees long-term preservation, and will be findable and citable along with other project outputs. The best way to do this is through sharing the code along with the data in a dedicated data repository. But where can you find a dedicated repository for well-documented research codes?

Meet ScholarsColab

A dedicated hub for researchers to share their code, collaborate with other researchers, and monetize their codes. By uploading your projects to ScholarsColab, you contribute to a growing community of researchers dedicated to collaboration and innovation. ScholarsColab provides a wide array of research codes in the domain of Machine Learning and Deep Learning.

Explore ScholarsColab today and be part of the movement toward more open and reproducible research!

abhishek gupta

ScholarsColab.com is an innovative and first of its kind platform created by Vidhilekha Soft Solutions Pvt Ltd, a Startup recognized by the Department For Promotion Of Industry And Internal Trade, Ministry of Commerce and Industry, Government of India recognised innovative research startup.