> For the complete documentation index, see [llms.txt](https://data-champions.renew-platforms.dk/renew-data-champions/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://data-champions.renew-platforms.dk/renew-data-champions/informational-guides/research-planning/directory-structure.md).

# Directory Structure

## Organizing Your Research Data: 

### Folder, File Structure, and Naming Conventions:

#### Tips and Recommendations

A data management workflow streamlines research data and processes to ensure understandability and reproducibility for those unfamiliar with the project. To achieve this objective, team members need clear, concise guidelines and tasks, responsibility for their work, and a means to share progress and feedback. Consequently, the four essential components of an effective data management workflow are:

1. Consistent file organization and naming conventions facilitate easy navigation and comprehension of folder and file contents.
2. Code and data cleaning protocols and upload procedures enable team members to understand, verify, and collaborate on each other's work.
3. Transparent data management roles within the group to ensure file security, adherence to established procedures, and regulatory compliance.
4. A research group wiki that serves as a central hub for team members to share vital lab documents, such as lab notebooks, project updates, and published research data.

Once you initiate data creation, collection, or manipulation, it can quickly become disorganized. To save time and avoid errors in the long run, you and your colleagues must establish a consistent approach to naming and structuring files and folders. Incorporating documentation (or 'metadata') adds context to your data, enabling you and others to understand better and utilize it in the short, medium, and long term.

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