Rigor

Rigor in data management means ensuring that the practices surrounding data collection, processing, and analysis uphold the standards of sound scientific methodology. Rigorous data management helps prevent:

• Accidental data corruption or loss.
• Silent errors in processing pipelines.
• Confusion between different versions or stages of data.
• Unintentional mixing of training and test data, or other methodological mistakes.

STAMPED principles promote rigor by enforcing structure and discipline: version control prevents silent overwrites, provenance tracking creates an auditable trail, automated pipelines reduce human error, and clear separation of raw and processed data guards against contamination. When the data management infrastructure itself enforces good practices, researchers are less likely to make mistakes that compromise their results.