SciExpert service
Data Cleaning & Validation
Start every analysis with data you can defend
Audit, clean and validate your dataset before any model is fitted, with a documented trail of every decision made along the way.
- • Raw data auditing: missingness patterns, duplicate detection, logic checks
- • Missing data handling with multiple imputation (MICE, Amelia) and sensitivity analysis
- • Outlier identification using statistical and visual diagnostics
- • Standardisation, variable coding, transformation and full cleaning documentation
How to use Data Cleaning & Validation
Before you begin: A signed-in SciExpert account
- 1
Open Data Cleaning & Validation
From the workspace sidebar choose Data Cleaning & Validation. On a phone, open the All menu and tap Data Cleaning & Validation.
- 2
Set up your inputs
Audit, clean and validate your dataset before any model is fitted, with a documented trail of every decision made along the way. Start with the smallest realistic input so you can check the output before scaling up.
- 3
Run it
Run the tool and check the first output: raw data auditing: missingness patterns, duplicate detection, logic checks.
- 4
Review what came back
Work through the results: missing data handling with multiple imputation (mice, amelia) and sensitivity analysis; outlier identification using statistical and visual diagnostics; standardisation, variable coding, transformation and full cleaning documentation.
- 5
Save or export
Use the save, copy or export options available in this service. Review the output before including it in a manuscript or sharing it with collaborators.