Data Organization

Data Organization is the foundational instrumentation level. It encompasses the conventions, naming schemes, and directory structures that bring order to research data without requiring any specialized software.

Examples include:

• Consistent directory hierarchies that separate raw data, processed data, code, and documentation.
• File naming conventions that encode meaningful metadata (subject IDs, dates, conditions).
• README files and data dictionaries that describe the contents and structure of a dataset.
• Separation of inputs and outputs to clarify data flow.

These practices are universally applicable. Every researcher can adopt them immediately, and they form the necessary foundation upon which higher instrumentation levels (tools, workflows, patterns) are built. Even when using sophisticated tooling, a clear organizational scheme remains essential.