Research Data Quality Studio
Profile, audit, clean, and document research data before quality problems affect analysis, reporting, or publication.
The StatsWithR Research Data Quality Studio is a browser-based data-quality application for researchers, statisticians, data managers, study coordinators, and analysts. Import a research dataset to evaluate its structure, variable types, missingness, categories, duplicate records, dates, unusual values, and compliance with study-specific quality rules.
The application combines automated profiling with a flexible rules system. Create checks for required values, acceptable ranges, allowed categories, unique identifiers, date relationships, conditional requirements, text patterns, variable types, cross-variable comparisons, text length, and potential outliers.
Findings are organized in a searchable issue register so that questionable records can be reviewed by variable, record, rule, severity, or resolution status. Supported cleaning tools help standardize missing values, categories, text, dates, variable types, and duplicate handling. Individual cells can also be reviewed and edited when a case-specific decision is required.
Every correction is documented in the change history. Undo and redo tools, reusable rule sets, saved sessions, quality reports, and multiple cleaned-data exports support a transparent and repeatable workflow from initial review through final analysis preparation.
Imported data are processed locally in your browser. Research data are not uploaded to a StatsWithR server by the application. Users should still work only on approved devices and follow all institutional requirements for confidential, regulated, or sensitive information.
Included Features
Variable-type, missingness, category, and distribution summaries
Duplicate-record and identifier review
Required-value and missing-data rules
Acceptable-range and allowed-category rules
Unique-key and duplicate checks
Date-format, date-range, and date-order checks
Pattern, text-length, and variable-type validation
Potential outlier identification
Cleaning and recoding tools
The StatsWithR research focused statistical software suite gives researchers browser-based applications for a variety of purposes including: publication-ready tables from raw statistical output, REDCap dictionary auditing, and research data quality management tools. The subscription includes all current paid applications, newly released paid applications, and ongoing software improvements.
Research Data Quality Studio supports professional data review but does not replace source-document verification, study protocols, institutional data governance, statistical judgment, regulatory validation, or subject-matter expertise. Users remain responsible for confirming that every correction is scientifically and operationally appropriate.