## Documentation

Effective documentation is a cornerstone of high-quality, responsible research. It ensures that your data, methods, and findings are not only understandable and accessible to others, but also reproducible, transparent, and sustainable over time. Good documentation allows your research to be verified, built upon, or reused, whether by collaborators, other researchers, or even your future self.

Documentation goes beyond simply recording your results. It encompasses detailed descriptions of your research design, how data were collected, processed, and analysed, as well as the rationale behind key decisions made throughout your project. It includes notes on software used, code written, data cleaning steps taken, and any transformations or assumptions applied. Without this contextual information, data can quickly lose its meaning and value.

#### Why Documentation Matters

Documentation is important because it helps make your research process clear and transparent, building trust in your results. It also allows others, and your future self, to reproduce your work and verify the findings. Good documentation ensures that your data and methods can be reused in a future project or by other researchers. Additionally, it supports continuity in long-term and collaborative projects by keeping important knowledge available, even if team members change or the project is revisited after an extended period.

#### What to Document

Depending on your discipline and methodology, documentation my include several key components.

- Project-level documentation covers the purpose, objectives, team members, funding sources, ethical approvals, timelines, and overall context of your research.

- Data-level documentation involved details such as file naming conventions, variable definitions, units of measurement, formats, data sources, and quality control processes.

- Methodological documentation describes sampling strategies, data collection protocols, instruments or tools used, software settings, codebooks, or interview guides.

- Analytical documentation includes scripts or code with detailed comments, version histories, statistical methods used, rationale for methodological choices, and interpretation notes.

- Finally, standardised metadata consists of structured descriptive information that helps others understand and locate your data,

#### Best Practices for Creating Documentation

- Start early and update regularly: Don’t wait until the end of your project to begin documenting. Make it part of your workflow.

- Be clear and consistent: Use standardized terminology, consistent formats, and clear language.

- Use tools wisely: Consider using electronic lab notebooks, version control systems (e.g., Git), DMPonline, or templates provided by the VU.

- Include links between data and documentation: Ensure users can easily locate relevant documentation for any dataset or file.

- Consider your audience: Write your documentation so that someone outside your project can understand it, even if they are not within your discipline.