Project Documentation

Project Documentation#

Documentation is the bridge between your code and its users (including future you). Even minimal documentation makes a project usable and maintainable. This section establishes the baseline requirements for research software documentation.

Why Document?#

Good documentation doesn’t require extensive effort. A well-structured minimal documentation already makes your project:

  • Usable by others (and yourself in six months)

  • Citable with clear authorship and licensing

  • Reproducible with explicit installation steps

The goal isn’t perfection but clarity. Documentation serves multiple audiences:

  • End users need to know how to install and run your software

  • Researchers need to understand your methodology and data sources

  • Potential contributors need to know how to modify and extend your work

  • Your future self needs to remember why you made certain design decisions

The core of minimal documentation is a well-structured README.md file. Combined with proper licensing and version control (covered in the Using Git in Academia course), this foundation ensures your research software can be understood and reused.

The README.md File#

The README.md serves as your project’s landing page. Its structure should be predictable and comprehensive.

Essential Components#

A robust README.md contains these essential sections:

Project Title and Description: Start with a clear project name and a concise “elevator pitch” explaining what the software does and why it exists.

Methodology: A high-level summary of the scientific or technical methods employed. Detailed algorithms and math should go in separate extended documentation.

Installation Instructions: A step-by-step setup guide that allows users to reproduce your environment instantly (e.g., using uv sync).

Usage: Code snippets or command-line examples demonstrating core functionality. Show the most common use cases first and include the expected output.

Data: Information on dataset locations, access protocols, and ownership. Remember to push deeper processing details to a dedicated data/README.md.

License: A reference to the project’s licensing terms. Following the Single Source of Truth (SSOT) principle, simply link to the LICENSE file rather than duplicating its legal content.

Citation: Either a copy-pasteable text block (e.g., BibTeX) or a reference to a provided CITATION.cff format file so researchers can easily credit your work.

Contributors, Acknowledgments & Contact: Acknowledgment of the development team, funding sources, a link to CONTRIBUTING.md, and clear channels for reaching the maintainers.

Consensus Standard

While no universally “perfect” README exists, consensus among developers (and the training data for Large Language Models) has converged on this exact structure. Following it makes your project immediately familiar to new users and easily parseable by AI coding assistants.

Using Templates#

Rather than writing a README.md and setting up a repository from scratch for each project, consider using a project template. This ensures consistency, completeness, and adherence to modern best practices across all your research outputs.

Tools like Cookiecutter or Copier can generate entirely new project structures with pre-populated README.md templates, CI/CD workflows, and environment configurations in seconds.

Many research groups maintain their own templates (such as the T4D Python Project Template) that include institution-specific information, standard uv configurations, and comply with local data management policies.

Project Title & Description#

The “Elevator Pitch”#

The top of your README.md is the most important real estate in your repository. Visitors will decide within seconds whether your project is relevant to them or if they should look elsewhere.

Start with a clear, prominent Project Title. Immediately below it, provide a 1-to-2 sentence description explaining exactly what the software does, who it is for, and what problem it solves. Avoid jargon where possible.

Visual Identity & Status Badges#

To make your project look professional and well-maintained, include visual cues right at the top:

  • Logo: A simple, recognizable logo helps users remember your project and builds trust.

  • Badges: Use status shields (e.g., from shields.io) to provide instant information about the project’s health and compatibility. Common badges include:

    • CI/CD Status: e.g., build: passing

    • Test Coverage: e.g., coverage: 95%

    • Environment: e.g., python: 3.11 | 3.12 | 3.13

    • Package Registry: e.g., PyPI: v1.0.2

Keep the README Concise

It is tempting to write an entire essay explaining the historical context of your research, the theoretical background, or the full architecture of your software right on the front page. Don’t. The description should be no longer than one or two paragraphs. Any extended descriptions, background literature, or deep architectural overviews must be moved to the extended documentation (e.g., docs/background.md or docs/architecture.md).

Installation & Usage & Methodology#

Clear, reproducible installation steps are the most critical part of your documentation.

The Golden Rule:

Use Standard Tools

The more non-standard your installation process, the harder it is for others to use your work. Each custom step you add is another potential failure point and another barrier to adoption.

For Python projects, this means adhering to official packaging standards. The pyproject.toml file is the modern standard for Python project configuration. Combined with a project manager like uv, installation reduces to a single, universal command.

Installation#

For Python projects following modern packaging standards, we recommend using uv, a fast Python package and project manager written in Rust.

A. Develop locally (Clone and Sync):

  1. Clone the repository:

    git clone https://github.com/<owner>/<repo-name>.git
cd repo-name

  2. Install python version, create environment and install project:

    uv sync

Running uv sync handles everything for you: it installs the required Python version, creates an isolated virtual environment (.venv), resolves your dependencies from the pyproject.toml, and installs them.

By default, it installs your package in “editable” (symlink) mode, meaning changes you make to the source code are immediately reflected without needing to reinstall.

B. Install the package directly from GitHub: If you only want to use the package and its dependencies without modifying the source code, you can install it directly into your active environment from the repository:

  1. Setup or activeate your virtual environment using python 3.13:

    uv venv --python 3.13

  2. Install the package directly from GitHub:

    uv pip install git+https://github.com/<owner>/<repo-name>.git

Reproducibility Benefit

This pattern works identically on Windows, macOS, and Linux. Users don’t need to understand your project’s internals, manually create virtual environments, or worry about pip versions — they just use standard uv commands regardless of complexity.

Usage#

Provide a copy-pasteable use case with expected output.

Examples:

For a package:
import numpy as np
from mypkgs.math import multiply_matrices

a = np.array([[1,2],[3,4]])
b = np.array([[4,3],[2,1]])
multiply_matrices(a,b)
# Out[5]: array([[8, 5], [20, 13]])
For scripts:
uv run python scripts/drafts/hello.py

Expected Output:

Results exported to /app/results/out.txt

uv run automatically detects the .venv and safely executes your code without requiring you to manually activate the environment.

(Alternatively, you can manually activate the environment using source .venv/bin/activate, and simply run python scripts/drafts/hello.py).

Extended Documentation

To keep the README.md concise, advanced scenarios should not be detailed directly here. Instead, refer to the extended documentation (e.g., under the docs/ folder) or provide direct links to advanced scripts.

Methodology#

The methodology section should provide a high-level overview of the scientific or computational approach taken in your project. Simply list the core algorithms, mathematical models, or computational workflows utilized (for example, “Uses a Convolutional Neural Network for image feature extraction and Runge-Kutta integration for system dynamics”).

This allows readers to quickly grasp the technical foundation of your work without getting bogged down in the math or implementation details right on the landing page.

Keep It High-Level

Do not include full derivations, extensive mathematical proofs, or deep architectural diagrams in the main README.md. Further explanations, complex formulas, and detailed methodological justifications must reside in the extended documentation (e.g., under docs/methodology.md or a similar dedicated file).

Data & Licensing & Citation#

Data#

The data section in your main documentation should be strictly limited to the essentials: where the data resides, how it can be accessed, and who owns it.

Crucially, if your repository provides data alongside code, you must specify the Data License. Software licenses (like MIT) do not properly cover datasets. Specify if the data is covered under a data-specific license like CC-BY-4.0 or ODbL.

Keeping this section concise ensures users immediately know if they can use the data and where to find it. Any further details—such as data provenance, processing pipelines, transformations, or structural descriptions—must reside in a dedicated README.md file under the data/ directory.

Restricted Data Sources

When data cannot be made public (due to privacy concerns, institutional policies, or proprietary restrictions), you must clearly state:

  • Ownership: The institution or entity that officially owns the data.

  • Access procedure: How authorized researchers can request access (e.g., via an institutional data committee or ethics board approval).

Transparency about restrictions is better than silence. This allows others to assess whether pursuing access is worthwhile for their research.

License#

Without a license, default copyright laws apply—usually “all rights reserved,” meaning no one can legally use your code. A proper LICENSE file acts as the legal contract between you and your users.

In most cases, a simple reference and link to the LICENSE file in the root of your repository is enough. You do not need to duplicate the license text or its standard conditions in your main documentation.

Mixed Licensing

If parts of the repository are differently licensed (for example, the core code is MIT, but you include third-party scripts under GPLv3, or your documentation is CC-BY), this information must be explicitly stated here! Provide a clear breakdown of which licenses apply to which directories or specific files.

Citation#

Provide a citation format directly in the README or link to a CITATION.cff file in your repository.

If you are using this repository for academic or published research, make it easy for others to credit your work:

Example:

@software{your_project_2026,
  author = {Your Name},
  title = {Your Project Name},
  year = {2026},
  publisher = {GitHub},
  url = {[https://github.com/your-username/your-repo](https://github.com/your-username/your-repo)}
}

Acknowledgments & Contributing & Contact#

A well-structured repository doesn’t just share code; it builds a community around it while giving proper credit to its creators and supporters.

Acknowledgments#

In academic and research software, giving credit is essential. Briefly acknowledge the main authors, the affiliated laboratories or institutions, and any funding agencies or grants that supported the work.

Example:

This project was developed by the [Your Lab Name] at [Your University]. This work was supported by the [Funding Agency] under Grant No. [123456].

Contributing#

Just as we moved advanced usage to the docs/ folder, we do not clutter the README with instructions on how to run tests, format code, or submit Pull Requests.

Simply state that contributions are welcome and provide a direct link to your CONTRIBUTING.md file.

Example Contribution Statement

We welcome contributions from the community! Whether you are fixing bugs, improving documentation, or proposing new features, please read our CONTRIBUTING.md for guidelines on our development workflow and code standards.

Contact Information#

Clearly define how users should get in touch with you, and set boundaries for different types of communication. This prevents your personal inbox from being flooded with basic troubleshooting questions.

  • For bugs, issues, and feature requests: Direct users to the repository’s Issue Tracker (e.g., “Please open a GitHub Issue”). This keeps problem-solving public and searchable for future users.

  • For academic inquiries, private collaboration, or security vulnerabilities: Provide an official contact email address.

Example:

For bug reports and feature requests, please open an issue on GitHub. For academic collaborations or private inquiries, please contact your.email@institution.edu.

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