Research data cleaning is the documented process of checking variable definitions, missing values, duplicates, range violations, coding consistency, outliers, derived variables, exclusions, and reproducibility before analysis. A checklist helps make decisions traceable instead of silently changing the evidence.
Research Data Cleaning Checklist applies this concept to its defined inputs and workflow. It creates a transparent readiness summary before analysis while keeping notes in the browser.
The helper structures status lines and returns readiness score, issue list, and action checklist so decisions and omissions can be reviewed explicitly. Input structure, labels, missing values, and selected options must match the intended workflow. It supports preparation and independent checking rather than replacing the governing software, standard, evidence, or professional review. It is a documentation checklist, not automatic data cleaning.