What is the Synthetic Data Module?
The Synthetic Data Module generates privacy-safe "digital twins" of real datasets. You provide an Excel source dataset, and the module produces a brand-new synthetic dataset that preserves the original's statistical structure -- distributions, correlations, and effect sizes -- without containing a single one of your original data points.
The result is a dataset you can share, publish, and analyse freely -- for collaboration, teaching, machine-learning training, or methods development -- without exposing confidential or regulated information.
What "synthetic data" means here
Synthetic data mirrors the statistical patterns of a real dataset without reusing any actual records. Because no original observation survives into the output, the synthetic dataset can be distributed without the privacy, consent, and regulatory constraints that apply to the source data. The statistical relationships -- how variables are distributed, how they correlate, how large the effects between them are -- are reconstructed so that analyses reach the same conclusions they would on the original.
The four-step workflow
| Step | What happens |
|---|---|
| 1. Upload Your Data | Provide a raw dataset as an Excel .xlsx or .xls file |
| 2. Automatic Analysis | The module detects each variable's type, distribution, and relationships |
| 3. Configure Settings | Set target sample size, review correlations and effect sizes, fine-tune parameters |
| 4. Generate & Validate | The engine produces the synthetic dataset and a full validation report in seconds |
What the module preserves
| Capability | What it does |
|---|---|
| Privacy Protection | Synthetic dataset can be shared without exposing any original data point |
| Statistical Integrity | Correlation structures preserved using Cholesky decomposition |
| Effect Size Matching | Cohen's d, Odds Ratios, and Hazard Ratios are maintained |
| Distribution Fitting | Variable distributions (Normal, Uniform, Binomial, etc.) detected and replicated |
| Visual Validation | Love plots, correlation matrices, and SMD comparisons produced every run |
| Fast Processing | Optimised algorithms generate well over 100,000 rows in seconds |
Who the module is for
| Audience | Typical uses |
|---|---|
| Clinical Research | Power-analysis calculations, method development, IRB-compliant datasets, multi-site studies |
| Machine Learning | Training-data augmentation, class-imbalance handling, model validation, feature engineering |
| Education | Statistics coursework, workshop demonstrations, student exercises, publication examples |
How accurate is the generated data?
The module preserves the large majority of a dataset's statistical relationships, and every generated dataset is accompanied by a validation report showing standardised-mean-difference (SMD) analysis and quality scores, so you can verify the fidelity of each run.
Supported data
The module supports six variable types -- continuous, binary, categorical, count, time-to-event, and proportion -- and detects each type automatically. Datasets can range from a few hundred rows to well over 100,000 rows in a single run.
Data sources
The current workspace accepts a raw dataset in Excel .xlsx or .xls format. It does not currently expose summary-statistics, HTML-table, or manual-template input routes in the interface.
Privacy and data handling
The module is built privacy-first: uploads are encrypted, and the original data is used only to analyse its statistical structure -- it is not retained after processing. Because the synthetic output contains no original records, it sidesteps the confidentiality and regulatory constraints that govern the source data.
Access states
| State | What you see |
|---|---|
| Not signed in | Redirected to the sign-in page with a return link |
| Signed in, without All Access | The module remains locked; choose an All Access plan from the dashboard or sidebar |
| Signed in, with All Access | Full generation workflow |