Folder structure

It is important to have a folder structure that provides a good overview of your project and that maintains all data and documentation of your project logically. Important aspects of a good project structure are:

1. Separate source and raw data from further processed data. Preferably make raw data read-only so they cannot be edited.
2. Separate different types of data, when necessary
3. Provide scripts or descriptions that were used to go from the raw data to all processed data > make sure your analyses from start to finish are reproducible!
4. Provide sufficient metadata about your project, e.g., preregistrations, study protocol, codebooks/data dictionaries, a data management plan, general information about the project, the ethics protocol, etc. Provide readme.txt files were necessary.
5. Provide all materials and instructions used to collect the data, e.g., tasks, questionnaires and instructions
6. Overall: use self-explanatory file and folder names or provide documentation explaining which files can be found where. Also keep in mind versioning (e.g., use the date in the filenames, the version or use a formal version control system like git)
7. Finally, use your folders consistently, e.g., place all data in the Data folder.

Example folder structure

Below, you can find an example folder structure of the project "YYYY_ProjectName". You are encouraged to use this or a similar format, but don't feel obliged!