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Fix misleading about open source solutions with commercial license #5

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Expand Up @@ -14,28 +14,30 @@ Inspired by [Awesome Synthetic Data](https://github.com/gretelai/awesome-synthet

# Open source tools

+ [Copulas](https://github.com/sdv-dev/Copulas): a Python library for modeling multivariate distributions and sampling from them using copula functions.
+ [CTGAN](https://github.com/sdv-dev/CTGAN): SDV’s collection of deep learning-based synthetic data generators for single table data.
+ [DataGene](https://github.com/firmai/datagene): a tool to train, test, and validate datasets, detect and compare dataset similarity between real and synthetic datasets.
+ [DoppelGANger](https://github.com/fjxmlzn/DoppelGANger): a synthetic data generation framework based on generative adversarial networks (GANs).
+ [DP_WGAN-UCLANESL](https://github.com/nesl/nist_differential_privacy_synthetic_data_challenge): this solution trains a Wasserstein generative adversarial network (w-GAN) that is trained on the real private dataset.
+ [DPSyn](https://github.com/usnistgov/PrivacyEngCollabSpace/tree/master/tools/de-identification/Differential-Privacy-Synthetic-Data-Challenge-Algorithms/DPSyn): an algorithm for synthesizing microdata while satisfying differential privacy.
+ [Faker](https://github.com/joke2k/faker): a Python package that generates fake data (Note: this tool does not generate synthetic data but offers dummy data).
+ [Generative adversarial nets for synthetic time series data](https://github.com/stefan-jansen/synthetic-data-for-finance): a repository that shows how to create synthetic time-series data using generative adversarial networks (GANs).
+ [Gretel.ai](https://gretel.ai/): commercial synthetic data vendor that offers open source functionality.
+ [mirrorGen](https://github.com/DataResponsibly/MirrorDataGenerator): a python tool that generates synthetic data based on user-specified causal relations among features in the data.
+ [Plait.py](https://github.com/plaitpy/plaitpy): a program for generating fake data from composable yaml templates.
+ [Pydbgen](https://github.com/tirthajyoti/pydbgen): a Python package that generates a random database table based on the user's choice of data types.
+ [Smart noise synthesizer](https://smartnoise.org/): a differentially private open source synthesizer for tabular data.
+ [Synner](https://github.com/huda-lab/synner): an open source tool to generate real-looking synthetic data by visually specifying the properties of the dataset.
+ [Synth](https://www.getsynth.com/): an open source data-as-code tool that provides a simple CLI workflow for generating consistent data in a scalable way.
+ [Synthea](https://synthetichealth.github.io/synthea/): an open source synthetic patient generator that models the medical history of synthetic patients.
+ [Synthetic data vault (SDV)](https://sdv.dev/): one of the first open source synthetic data solutions, SDV provides tools for generating synthetic data for tabular, relational, and time series data.
+ [TGAN](https://github.com/sdv-dev/TGAN): generative adversarial training for generating synthetic tabular data.
+ [Tofu](https://github.com/spiros/tofu): a Python library for generating synthetic UK Biobank data.
+ [Twinify](https://github.com/DPBayes/twinify): a software package for privacy-preserving generation of a synthetic twin to a given sensitive data set.
+ [YData](https://github.com/ydataai/ydata-synthetic): synthetic structured data generator by YData, a commercial vendor.

# Open source with commercial license

+ [Gretel.ai](https://gretel.ai/): commercial synthetic data vendor that offers open source functionality.
+ [Synthetic data vault (SDV)](https://sdv.dev/): one of the first open source synthetic data solutions, SDV provides tools for generating synthetic data for tabular, relational, and time series data.
+ [Copulas](https://github.com/sdv-dev/Copulas): a Python library for modeling multivariate distributions and sampling from them using copula functions.
+ [CTGAN](https://github.com/sdv-dev/CTGAN): SDV’s collection of deep learning-based synthetic data generators for single table data.
+ [TGAN](https://github.com/sdv-dev/TGAN): generative adversarial training for generating synthetic tabular data.

# Commercial solutions

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