Synthetic Data Software Market Outlook

The global synthetic data software market size was valued at approximately USD 1.2 billion in 2023 and is projected to reach USD 7.5 billion by 2032, growing at a compound annual growth rate (CAGR) of 22.4% during the forecast period. The growth of this market can be attributed to the increasing demand for data privacy and security, advancements in artificial intelligence (AI) and machine learning (ML), and the rising need for high-quality data to train AI models.

One of the primary growth factors for the synthetic data software market is the escalating concern over data privacy and governance. With the rise of stringent data protection regulations like GDPR in Europe and CCPA in California, organizations are increasingly seeking alternatives to real data that can still provide meaningful insights without compromising privacy. Synthetic data software offers a solution by generating artificial data that mimics real-world data distributions, thereby mitigating privacy risks while still allowing for robust data analysis and model training.

Another significant driver of market growth is the rapid advancement in AI and ML technologies. These technologies require vast amounts of data to train models effectively. Traditional data collection methods often fall short in terms of volume, variety, and veracity. Synthetic data software addresses these limitations by creating scalable, diverse, and accurate datasets, enabling more effective and efficient model training. As AI and ML applications continue to expand across various industries, the demand for synthetic data software is expected to surge.

The increasing application of synthetic data software across diverse sectors such as healthcare, finance, automotive, and retail also acts as a catalyst for market growth. In healthcare, synthetic data can be used to simulate patient records for research without violating patient privacy laws. In finance, it can help in creating realistic datasets for fraud detection and risk assessment without exposing sensitive financial information. Similarly, in automotive, synthetic data is crucial for training autonomous driving systems by simulating various driving scenarios.

From a regional perspective, North America holds the largest market share due to its early adoption of advanced technologies and the presence of key market players. Europe follows closely, driven by stringent data protection regulations and a strong focus on privacy. The Asia Pacific region is expected to witness the highest growth rate owing to the rapid digital transformation, increasing investments in AI and ML, and a burgeoning tech-savvy population. Latin America and the Middle East & Africa are also anticipated to experience steady growth, supported by emerging technological ecosystems and increasing awareness of data privacy.

Component Analysis

When examining the synthetic data software market by component, it is essential to consider both software and services. The software segment dominates the market as it encompasses the actual tools and platforms that generate synthetic data. These tools leverage advanced algorithms and statistical methods to produce artificial datasets that closely resemble real-world data. The demand for such software is growing rapidly as organizations across various sectors seek to enhance their data capabilities without compromising on security and privacy.

On the other hand, the services segment includes consulting, implementation, and support services that help organizations integrate synthetic data software into their existing systems. As the market matures, the services segment is expected to grow significantly. This growth can be attributed to the increasing complexity of synthetic data generation and the need for specialized expertise to optimize its use. Service providers offer valuable insights and best practices, ensuring that organizations maximize the benefits of synthetic data while minimizing risks.

The interplay between software and services is crucial for the holistic growth of the synthetic data software market. While software provides the necessary tools for data generation, services ensure that these tools are effectively implemented and utilized. Together, they create a comprehensive solution that addresses the diverse needs of organizations, from initial setup to ongoing maintenance and support. As more organizations recognize the value of synthetic data, the demand for both software and services is expected to rise, driving overall market growth.

Motivation

This dataset was created to pilot techniques for creating synthetic data from datasets containing sensitive and protected information in the local government context. Synthetic data generation replaces actual data with representative data generated from statistical models; this preserves the key data properties that allow insights to be drawn from the data while protecting the privacy of the people included in the data. We invite you to read the Understanding Synthetic Data white paper for a concise introduction to synthetic data.

This effort was a collaboration of the Urban Institute, Allegheny County’s Department of Human Services (DHS) and CountyStat, and the University of Pittsburgh’s Western Pennsylvania Regional Data Center.

Collection

The source data for this project consisted of 1) month-by-month records of services included in Allegheny County's data warehouse and 2) demographic data about the individuals who received the services. As the County’s data warehouse combines this service and client data, this data is referred to as “Integrated Services data”. Read more about the data warehouse and the kinds of services it includes here.

Preprocessing

Synthetic data are typically generated from probability distributions or models identified as being representative of the confidential data. For this dataset, a model of the Integrated Services data was used to generate multiple versions of the synthetic dataset. These different candidate datasets were evaluated to select for publication the dataset version that best balances utility and privacy. For high-level information about this evaluation, see the Synthetic Data User Guide.

For more information about the creation of the synthetic version of this data, see the technical brief for this project, which discusses the technical decision making and modeling process in more detail.

Recommended Uses

This disaggregated synthetic data allows for many analyses that are not possible with aggregate data (summary statistics). Broadly, this synthetic version of this data could be analyzed to better understand the usage of human services by people in Allegheny County, including the interplay in the usage of multiple services and demographic information about clients.

Known Limitations/Biases

Some amount of deviation from the original data is inherent to the synthetic data generation process. Specific examples of limitations (including undercounts and overcounts for the usage of different services) are given in the Synthetic Data User Guide and the technical report describing this dataset's creation.

Feedback

Please reach out to this dataset's data steward (listed below) to let us know how you are using this data and if you found it to be helpful. Please also provide any feedback on how to make this dataset more applicable to your work, any suggestions of future synthetic datasets, or any additional information that would make this more useful. Also, please copy wprdc@pitt.edu on any such feedback (as the WPRDC always loves to hear about how people use the data that they publish and how the data could be improved).

Further Documentation and Resources

1) A high-level overview of synthetic data generation as a method for protecting privacy can be found in the Understanding Synthetic Data white paper.
2) The Synthetic Data User Guide provides high-level information to help users understand the motivation, evaluation process, and limitations of the synthetic version of Allegheny County DHS's Human Services data published here.
3) Generating a Fully Synthetic Human Services Dataset: A Technical Report on Synthesis and Evaluation Methodologies describes the full technical methodology used for generating the synthetic data, evaluating the various options, and selecting the final candidate for publication.
4) The WPRDC also hosts the Allegheny County Human Services Community Profiles dataset, which provides annual updates on human-services usage, aggregated by neighborhood/municipality. That data can be explored using the County's Human Services Community Profile web site.

The synthetic data generation market is experiencing explosive growth, driven by the increasing need for high-quality data in various applications, including AI/ML model training, data privacy compliance, and software testing. The market, currently estimated at $2 billion in 2025, is projected to experience a Compound Annual Growth Rate (CAGR) of 25% from 2025 to 2033, reaching an estimated $10 billion by 2033. This significant expansion is fueled by several key factors. Firstly, the rising adoption of artificial intelligence and machine learning across industries demands large, high-quality datasets, often unavailable due to privacy concerns or data scarcity. Synthetic data provides a solution by generating realistic, privacy-preserving datasets that mirror real-world data without compromising sensitive information. Secondly, stringent data privacy regulations like GDPR and CCPA are compelling organizations to explore alternative data solutions, making synthetic data a crucial tool for compliance. Finally, the advancements in generative AI models and algorithms are improving the quality and realism of synthetic data, expanding its applicability in various domains. Major players like Microsoft, Google, and AWS are actively investing in this space, driving further market expansion. The market segmentation reveals a diverse landscape with numerous specialized solutions. While large technology firms dominate the broader market, smaller, more agile companies are making significant inroads with specialized offerings focused on specific industry needs or data types. The geographical distribution is expected to be skewed towards North America and Europe initially, given the high concentration of technology companies and early adoption of advanced data technologies. However, growing awareness and increasing data needs in other regions are expected to drive substantial market growth in Asia-Pacific and other emerging markets in the coming years. The competitive landscape is characterized by a mix of established players and innovative startups, leading to continuous innovation and expansion of market applications. This dynamic environment indicates sustained growth in the foreseeable future, driven by an increasing recognition of synthetic data's potential to address critical data challenges across industries.

Synthetic Data Generation Market Outlook

According to our latest research, the global synthetic data generation market size reached USD 1.6 billion in 2024, demonstrating robust expansion driven by increasing demand for high-quality, privacy-preserving datasets. The market is projected to grow at a CAGR of 38.2% over the forecast period, reaching USD 19.2 billion by 2033. This remarkable growth trajectory is fueled by the growing adoption of artificial intelligence (AI) and machine learning (ML) technologies across industries, coupled with stringent data privacy regulations that necessitate innovative data solutions. As per our latest research, organizations worldwide are increasingly leveraging synthetic data to address data scarcity, enhance AI model training, and ensure compliance with evolving privacy standards.

One of the primary growth factors for the synthetic data generation market is the rising emphasis on data privacy and regulatory compliance. With the implementation of stringent data protection laws such as the General Data Protection Regulation (GDPR) in Europe and the California Consumer Privacy Act (CCPA) in the United States, enterprises are under immense pressure to safeguard sensitive information. Synthetic data offers a compelling solution by enabling organizations to generate artificial datasets that mirror the statistical properties of real data without exposing personally identifiable information. This not only facilitates regulatory compliance but also empowers organizations to innovate without the risk of data breaches or privacy violations. As businesses increasingly recognize the value of privacy-preserving data, the demand for advanced synthetic data generation solutions is set to surge.

Another significant driver is the exponential growth in AI and ML adoption across various sectors, including healthcare, finance, automotive, and retail. High-quality, diverse, and unbiased data is the cornerstone of effective AI model development. However, acquiring such data is often challenging due to privacy concerns, limited availability, or high acquisition costs. Synthetic data generation bridges this gap by providing scalable, customizable datasets tailored to specific use cases, thereby accelerating AI training and reducing dependency on real-world data. Organizations are leveraging synthetic data to enhance algorithm performance, mitigate data bias, and simulate rare events, which are otherwise difficult to capture in real datasets. This capability is particularly valuable in sectors like autonomous vehicles, where training models on rare but critical scenarios is essential for safety and reliability.

Furthermore, the growing complexity of data types—ranging from tabular and image data to text, audio, and video—has amplified the need for versatile synthetic data generation tools. Enterprises are increasingly seeking solutions that can generate multi-modal synthetic datasets to support diverse applications such as fraud detection, product testing, and quality assurance. The flexibility offered by synthetic data generation platforms enables organizations to simulate a wide array of scenarios, test software systems, and validate AI models in controlled environments. This not only enhances operational efficiency but also drives innovation by enabling rapid prototyping and experimentation. As the digital ecosystem continues to evolve, the ability to generate synthetic data across various formats will be a critical differentiator for businesses striving to maintain a competitive edge.

Regionally, North America leads the synthetic data generation market, accounting for the largest revenue share in 2024, followed closely by Europe and Asia Pacific. The dominance of North America can be attributed to the strong presence of technology giants, advanced research institutions, and a favorable regulatory environment that encourages AI innovation. Europe is witnessing rapid growth due to proactive data privacy regulations and increasing investments in digital transformation initiatives. Meanwhile, Asia Pacific is emerging as a high-growth region, driven by the proliferation of digital technologies and rising adoption of AI-powered solutions across industries. Latin America and the Middle East & Africa are also expected to experience steady growth, supported by government-led digitalization programs and expanding IT infrastructure.

The emergence of <a href="https://growthmarketreports.com/report/synthe

Synthetic Data Market Outlook

According to our latest research, the synthetic data market size reached USD 1.52 billion in 2024, reflecting robust growth driven by increasing demand for privacy-preserving data and the acceleration of AI and machine learning initiatives across industries. The market is projected to expand at a compelling CAGR of 34.7% from 2025 to 2033, with the forecasted market size expected to reach USD 21.4 billion by 2033. Key growth factors include the rising necessity for high-quality, diverse, and privacy-compliant datasets, the proliferation of AI-driven applications, and stringent data protection regulations worldwide.

The primary growth driver for the synthetic data market is the escalating need for advanced data privacy and compliance. Organizations across sectors such as healthcare, BFSI, and government are under increasing pressure to comply with regulations like GDPR, HIPAA, and CCPA. Synthetic data offers a viable solution by enabling the creation of realistic yet anonymized datasets, thus mitigating the risk of data breaches and privacy violations. This capability is especially crucial for industries handling sensitive personal and financial information, where traditional data anonymization techniques often fall short. As regulatory scrutiny intensifies, the adoption of synthetic data solutions is set to expand rapidly, ensuring organizations can leverage data-driven innovation without compromising on privacy or compliance.

Another significant factor propelling the synthetic data market is the surge in AI and machine learning deployment across enterprises. AI models require vast, diverse, and high-quality datasets for effective training and validation. However, real-world data is often scarce, incomplete, or biased, limiting the performance of these models. Synthetic data addresses these challenges by generating tailored datasets that represent a wide range of scenarios and edge cases. This not only enhances the accuracy and robustness of AI systems but also accelerates the development cycle by reducing dependencies on real data collection and labeling. As the demand for intelligent automation and predictive analytics grows, synthetic data is emerging as a foundational enabler for next-generation AI applications.

In addition to privacy and AI training, synthetic data is gaining traction in test data management and fraud detection. Enterprises are increasingly leveraging synthetic datasets to simulate complex business environments, test software systems, and identify vulnerabilities in a controlled manner. In fraud detection, synthetic data allows organizations to model and anticipate new fraudulent behaviors without exposing sensitive customer data. This versatility is driving adoption across diverse verticals, from automotive and manufacturing to retail and telecommunications. As digital transformation initiatives intensify and the need for robust data testing environments grows, the synthetic data market is poised for sustained expansion.

Regionally, North America dominates the synthetic data market, accounting for the largest share in 2024, followed closely by Europe and Asia Pacific. The strong presence of technology giants, a mature AI ecosystem, and early regulatory adoption are key factors supporting North America’s leadership. Meanwhile, Asia Pacific is witnessing the fastest growth, driven by rapid digitalization, expanding AI investments, and increasing awareness of data privacy. Europe continues to see steady adoption, particularly in sectors like healthcare and finance where data protection regulations are stringent. Latin America and the Middle East & Africa are also emerging as promising markets, albeit at a nascent stage, as organizations in these regions begin to recognize the value of synthetic data for digital innovation and compliance.

Component Analysis

The synthetic data market is segmented by component into software and services. The software segment currently holds the largest market

Xverum’s AI & ML Training Data provides one of the most extensive datasets available for AI and machine learning applications, featuring 800M B2B profiles with 100+ attributes. This dataset is designed to enable AI developers, data scientists, and businesses to train robust and accurate ML models. From natural language processing (NLP) to predictive analytics, our data empowers a wide range of industries and use cases with unparalleled scale, depth, and quality.

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Rich Attributes for Training Models: - Over 100 fields of detailed information, including company details, job roles, geographic data, industry categories, past experiences, and behavioral insights. - Tailored for training models in NLP, recommendation systems, and predictive algorithms.

Compliance and Quality: - Fully GDPR and CCPA compliant, providing secure and ethically sourced data. - Extensive data cleaning and validation processes ensure reliability and accuracy.

Annotation-Ready: - Pre-structured and formatted datasets that are easily ingestible into AI workflows. - Ideal for supervised learning with tagging options such as entities, sentiment, or categories.

How Is the Data Sourced? - Publicly available information gathered through advanced, GDPR-compliant web aggregation techniques. - Proprietary enrichment pipelines that validate, clean, and structure raw data into high-quality datasets. This approach ensures we deliver comprehensive, up-to-date, and actionable data for machine learning training.

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The use of synthetic data is recognized as a crucial step in the development of neural network-based Artificial Intelligence (AI) systems. While the methods for generating synthetic data for AI applications in other domains have a role in certain biomedical AI systems, primarily related to image processing, there is a critical gap in the generation of time series data for AI tasks where it is necessary to know how the system works. This is most pronounced in the ability to generate synthetic multi-dimensional molecular time series data (subsequently referred to as synthetic mediator trajectories or SMTs); this is the type of data that underpins research into biomarkers and mediator signatures for forecasting various diseases and is an essential component of the drug development pipeline. We argue the insufficiency of statistical and data-centric machine learning (ML) means of generating this type of synthetic data is due to a combination of factors: perpetual data sparsity due to the Curse of Dimensionality, the inapplicability of the Central Limit Theorem in terms of making assumptions about the statistical distributions of this type of data, and the inability to use ab initio simulations due to the state of perpetual epistemic incompleteness in cellular/molecular biology. Alternatively, we present a rationale for using complex multi-scale mechanism-based simulation models, constructed and operated on to account for perpetual epistemic incompleteness and the need to provide maximal expansiveness in concordance with the Maximal Entropy Principle. These procedures provide for the generation of SMT that minimizes the known shortcomings associated with neural network AI systems, namely overfitting and lack of generalizability. The generation of synthetic data that accounts for the identified factors of multi-dimensional time series data is an essential capability for the development of mediator-biomarker based AI forecasting systems, and therapeutic control development and optimization.

Domain-Adaptive Data Synthesis for Large-Scale Supermarket Product Recognition

This repository contains the data synthesis pipeline and synthetic product recognition datasets proposed in [1].

Data Synthesis Pipeline:

We provide the Blender 3.1 project files and Python source code of our data synthesis pipeline pipeline.zip, accompanied by the FastCUT models used for synthetic-to-real domain translation models.zip. For the synthesis of new shelf images, a product assortment list and product images must be provided in the corresponding directories products/assortment/ and products/img/. The pipeline expects product images to follow the naming convention c.png, with c corresponding to a GTIN or generic class label (e.g., 9120050882171.png). The assortment list, assortment.csv, is expected to use the sample format [c, w, d, h], with c being the class label and w, d, and h being the packaging dimensions of the given product in mm (e.g., [4004218143128, 140, 70, 160]). The assortment list to use and the number of images to generate can be specified in generateImages.py (see comments). The rendering process is initiated by either executing load.py from within Blender or within a command-line terminal as a background process.

Datasets:

SG3k - Synthetic GroZi-3.2k (SG3k) dataset, consisting of 10,000 synthetic shelf images with 851,801 instances of 3,234 GroZi-3.2k products. Instance-level bounding boxes and generic class labels are provided for all product instances.

SG3kt - Domain-translated version of SGI3k, utilizing GroZi-3.2k as the target domain. Instance-level bounding boxes and generic class labels are provided for all product instances.

SGI3k - Synthetic GroZi-3.2k (SG3k) dataset, consisting of 10,000 synthetic shelf images with 838,696 instances of 1,063 GroZi-3.2k products. Instance-level bounding boxes and generic class labels are provided for all product instances.

SGI3kt - Domain-translated version of SGI3k, utilizing GroZi-3.2k as the target domain. Instance-level bounding boxes and generic class labels are provided for all product instances.

SPS8k - Synthetic Product Shelves 8k (SPS8k) dataset, comprised of 16,224 synthetic shelf images with 1,981,967 instances of 8,112 supermarket products. Instance-level bounding boxes and GTIN class labels are provided for all product instances.

SPS8kt - Domain-translated version of SPS8k, utilizing SKU110k as the target domain. Instance-level bounding boxes and GTIN class labels for all product instances.

Table 1: Dataset characteristics.

Dataset

images

products

instances

labels
translation

SG3k 10,000 3,234 851,801 bounding box & generic class¹ none

SG3kt 10,000 3,234 851,801 bounding box & generic class¹ GroZi-3.2k

SGI3k 10,000 1,063 838,696 bounding box & generic class² none

SGI3kt 10,000 1,063 838,696 bounding box & generic class² GroZi-3.2k

SPS8k 16,224 8,112 1,981,967 bounding box & GTIN none

SPS8kt 16,224 8,112 1,981,967 bounding box & GTIN SKU110k

Sample Format

A sample consists of an RGB image (i.png) and an accompanying label file (i.txt), which contains the labels for all product instances present in the image. Labels use the YOLO format [c, x, y, w, h].

¹SG3k and SG3kt use generic pseudo-GTIN class labels, created by combining the GroZi-3.2k food product category number i (1-27) with the product image index j (j.jpg), following the convention i0000j (e.g., 13000097).

²SGI3k and SGI3kt use the generic GroZi-3.2k class labels from https://arxiv.org/abs/2003.06800.

Download and UseThis data may be used for non-commercial research purposes only. If you publish material based on this data, we request that you include a reference to our paper [1].

[1] Strohmayer, Julian, and Martin Kampel. "Domain-Adaptive Data Synthesis for Large-Scale Supermarket Product Recognition." International Conference on Computer Analysis of Images and Patterns. Cham: Springer Nature Switzerland, 2023.

BibTeX citation:

@inproceedings{strohmayer2023domain, title={Domain-Adaptive Data Synthesis for Large-Scale Supermarket Product Recognition}, author={Strohmayer, Julian and Kampel, Martin}, booktitle={International Conference on Computer Analysis of Images and Patterns}, pages={239--250}, year={2023}, organization={Springer} }

These are simulated data without any identifying information or informative birth-level covariates. We also standardize the pollution exposures on each week by subtracting off the median exposure amount on a given week and dividing by the interquartile range (IQR) (as in the actual application to the true NC birth records data). The dataset that we provide includes weekly average pregnancy exposures that have already been standardized in this way while the medians and IQRs are not given. This further protects identifiability of the spatial locations used in the analysis. This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: File format: R workspace file; “Simulated_Dataset.RData”. Metadata (including data dictionary) • y: Vector of binary responses (1: adverse outcome, 0: control) • x: Matrix of covariates; one row for each simulated individual • z: Matrix of standardized pollution exposures • n: Number of simulated individuals • m: Number of exposure time periods (e.g., weeks of pregnancy) • p: Number of columns in the covariate design matrix • alpha_true: Vector of “true” critical window locations/magnitudes (i.e., the ground truth that we want to estimate) Code Abstract We provide R statistical software code (“CWVS_LMC.txt”) to fit the linear model of coregionalization (LMC) version of the Critical Window Variable Selection (CWVS) method developed in the manuscript. We also provide R code (“Results_Summary.txt”) to summarize/plot the estimated critical windows and posterior marginal inclusion probabilities. Description “CWVS_LMC.txt”: This code is delivered to the user in the form of a .txt file that contains R statistical software code. Once the “Simulated_Dataset.RData” workspace has been loaded into R, the text in the file can be used to identify/estimate critical windows of susceptibility and posterior marginal inclusion probabilities. “Results_Summary.txt”: This code is also delivered to the user in the form of a .txt file that contains R statistical software code. Once the “CWVS_LMC.txt” code is applied to the simulated dataset and the program has completed, this code can be used to summarize and plot the identified/estimated critical windows and posterior marginal inclusion probabilities (similar to the plots shown in the manuscript). Optional Information (complete as necessary) Required R packages: • For running “CWVS_LMC.txt”: • msm: Sampling from the truncated normal distribution • mnormt: Sampling from the multivariate normal distribution • BayesLogit: Sampling from the Polya-Gamma distribution • For running “Results_Summary.txt”: • plotrix: Plotting the posterior means and credible intervals Instructions for Use Reproducibility (Mandatory) What can be reproduced: The data and code can be used to identify/estimate critical windows from one of the actual simulated datasets generated under setting E4 from the presented simulation study. How to use the information: • Load the “Simulated_Dataset.RData” workspace • Run the code contained in “CWVS_LMC.txt” • Once the “CWVS_LMC.txt” code is complete, run “Results_Summary.txt”. Format: Below is the replication procedure for the attached data set for the portion of the analyses using a simulated data set: Data The data used in the application section of the manuscript consist of geocoded birth records from the North Carolina State Center for Health Statistics, 2005-2008. In the simulation study section of the manuscript, we simulate synthetic data that closely match some of the key features of the birth certificate data while maintaining confidentiality of any actual pregnant women. Availability Due to the highly sensitive and identifying information contained in the birth certificate data (including latitude/longitude and address of residence at delivery), we are unable to make the data from the application section publically available. However, we will make one of the simulated datasets available for any reader interested in applying the method to realistic simulated birth records data. This will also allow the user to become familiar with the required inputs of the model, how the data should be structured, and what type of output is obtained. While we cannot provide the application data here, access to the North Carolina birth records can be requested through the North Carolina State Center for Health Statistics, and requires an appropriate data use agreement. Description Permissions: These are simulated data without any identifying information or informative birth-level covariates. We also standardize the pollution exposures on each week by subtracting off the median exposure amount on a given week and dividing by the interquartile range (IQR) (as in the actual application to the true NC birth records data). The dataset that we provide includes weekly average pregnancy exposures that have already been standardized in this way while the medians and IQRs are not given. This further protects identifiability of the spatial locations used in the analysis. This dataset is associated with the following publication: Warren, J., W. Kong, T. Luben, and H. Chang. Critical Window Variable Selection: Estimating the Impact of Air Pollution on Very Preterm Birth. Biostatistics. Oxford University Press, OXFORD, UK, 1-30, (2019).

Data Description

We release the synthetic data generated using the method described in the paper Knowledge-Infused Prompting: Assessing and Advancing Clinical Text Data Generation with Large Language Models (ACL 2024 Findings). The external knowledge we use is based on LLM-generated topics and writing styles.

  Generated Datasets

The original train/validation/test data, and the generated synthetic training data are listed as follows. For each dataset, we generate 5000… See the full description on the dataset page: https://huggingface.co/datasets/ritaranx/clinical-synthetic-text-llm.

This dataset was generated employing a technique of randomly crossing out words from the IAM database, utilizing several types of strokes. The ratio of cross-out words to regular words in handwritten documents can vary greatly depending on the document and context. However, typically, the number of cross-out words is small compared with regular words. To ensure a realistic ratio of regular to cross-out words in our synthetic database, 30% of samples from the IAM training set were selected. First, the bounding box of each word in a line was detected. The bounding box covers the core area of the word. Then, at random, a word is crossed out within the core area. Each line contains a randomly struck-out word at a different position. The annotation of these struck-out words was replaced with the symbol #.

The folder has:
s-s0 images
Syn-trainset
Syn-validset
Syn_IAM_testset
The transcription files are in the format of
Filename, threshold label of handwritten line
s-s0-0,157 A # to stop Mr. Gaitskell from

Cite the below work if you have used this dataset:
"A deep learning approach to handwritten text recognition in the presence of struck-out text"
https://ieeexplore.ieee.org/document/8961024

Overview

This Dataset Contains Synthetic Images of Paper and Plastic Cups. The ImageClassesCombined folder contains annotated images of all classes combined. The annotations are in the COCO format. There is also a sample test_image.jpg but you could also use your own or split the data if you prefer. Foreground images are taken from free stock image sites like unsplash.com, pexels.com, and pixabay.com. Cover Photo Designed by brgfx / Freepik

Inspiration

I want to create a dataset that could be used for image classification in different settings. The dataset can be used to train a CNN model for image detection and segmentation tasks in domains like agriculture, recycling, and many more.

AI-Generated Synthetic Tabular Dataset Market Outlook

According to our latest research, the AI-Generated Synthetic Tabular Dataset market size reached USD 1.12 billion globally in 2024, with a robust CAGR of 34.7% expected during the forecast period. By 2033, the market is forecasted to reach an impressive USD 15.32 billion. This remarkable growth is primarily attributed to the increasing demand for privacy-preserving data solutions, the surge in AI-driven analytics, and the critical need for high-quality, diverse datasets across industries. The proliferation of regulations around data privacy and the rapid digital transformation of sectors such as healthcare, finance, and retail are further fueling market expansion as organizations seek innovative ways to leverage data without compromising compliance or security.

One of the key growth factors for the AI-Generated Synthetic Tabular Dataset market is the escalating importance of data privacy and compliance with global regulations such as GDPR, HIPAA, and CCPA. As organizations collect and process vast amounts of sensitive information, the risk of data breaches and misuse grows. Synthetic tabular datasets, generated using advanced AI algorithms, offer a viable solution by mimicking real-world data patterns without exposing actual personal or confidential information. This not only ensures regulatory compliance but also enables organizations to continue their data-driven innovation, analytics, and AI model training without legal or ethical hindrances. The ability to generate high-fidelity, statistically accurate synthetic data is transforming data governance strategies across industries.

Another significant driver is the exponential growth of AI and machine learning applications that demand large, diverse, and high-quality datasets. In many cases, access to real data is limited due to privacy, security, or proprietary concerns. AI-generated synthetic tabular datasets bridge this gap by providing scalable, customizable data that closely mirrors real-world scenarios. This accelerates the development and deployment of AI models in sectors like healthcare, where patient data is highly sensitive, or in finance, where transaction records are strictly regulated. The synthetic data market is also benefiting from advancements in generative AI techniques, such as GANs (Generative Adversarial Networks) and VAEs (Variational Autoencoders), which have significantly improved the realism and utility of synthetic tabular data.

A third major growth factor is the increasing adoption of cloud computing and the integration of synthetic data generation tools into enterprise data pipelines. Cloud-based synthetic data platforms offer scalability, flexibility, and ease of integration with existing data management and analytics systems. Enterprises are leveraging these platforms to enhance data availability for testing, training, and validation of AI models, particularly in environments where access to production data is restricted. The shift towards cloud-native architectures is also enabling real-time synthetic data generation and consumption, further driving the adoption of AI-generated synthetic tabular datasets across various business functions.

From a regional perspective, North America currently dominates the AI-Generated Synthetic Tabular Dataset market, accounting for the largest share in 2024. This leadership is driven by the presence of major technology companies, strong investments in AI research, and stringent data privacy regulations. Europe follows closely, with significant growth fueled by the enforcement of GDPR and increasing awareness of data privacy solutions. The Asia Pacific region is emerging as a high-growth market, propelled by rapid digitalization, expanding AI ecosystems, and government initiatives promoting data innovation. Latin America and the Middle East & Africa are also witnessing steady adoption, albeit at a slower pace, as organizations in these regions recognize the value of synthetic data in overcoming data access and privacy challenges.

Component Analysis

The AI-Generated Synthetic Tabular Dataset market by component is segmented into software and services, with each playing a pivotal role in shaping the industry landscape. Software solutions comprise platforms and tools that automate the generation of synthetic tabular data using advanced AI algorithms. These platforms are increasingly being adopted by enterprises seeking

AI Training Data | Annotated Checkout Flows for Retail, Restaurant, and Marketplace Websites Overview

Unlock the next generation of agentic commerce and automated shopping experiences with this comprehensive dataset of meticulously annotated checkout flows, sourced directly from leading retail, restaurant, and marketplace websites. Designed for developers, researchers, and AI labs building large language models (LLMs) and agentic systems capable of online purchasing, this dataset captures the real-world complexity of digital transactions—from cart initiation to final payment.

Key Features

Breadth of Coverage: Over 10,000 unique checkout journeys across hundreds of top e-commerce, food delivery, and service platforms, including but not limited to Walmart, Target, Kroger, Whole Foods, Uber Eats, Instacart, Shopify-powered sites, and more.

Actionable Annotation: Every flow is broken down into granular, step-by-step actions, complete with timestamped events, UI context, form field details, validation logic, and response feedback. Each step includes:

Page state (URL, DOM snapshot, and metadata)

User actions (clicks, taps, text input, dropdown selection, checkbox/radio interactions)

System responses (AJAX calls, error/success messages, cart/price updates)

Authentication and account linking steps where applicable

Payment entry (card, wallet, alternative methods)

Order review and confirmation

Multi-Vertical, Real-World Data: Flows sourced from a wide variety of verticals and real consumer environments, not just demo stores or test accounts. Includes complex cases such as multi-item carts, promo codes, loyalty integration, and split payments.

Structured for Machine Learning: Delivered in standard formats (JSONL, CSV, or your preferred schema), with every event mapped to action types, page features, and expected outcomes. Optional HAR files and raw network request logs provide an extra layer of technical fidelity for action modeling and RLHF pipelines.

Rich Context for LLMs and Agents: Every annotation includes both human-readable and model-consumable descriptions:

“What the user did” (natural language)

“What the system did in response”

“What a successful action should look like”

Error/edge case coverage (invalid forms, OOS, address/payment errors)

Privacy-Safe & Compliant: All flows are depersonalized and scrubbed of PII. Sensitive fields (like credit card numbers, user addresses, and login credentials) are replaced with realistic but synthetic data, ensuring compliance with privacy regulations.

Each flow tracks the user journey from cart to payment to confirmation, including:

Adding/removing items

Applying coupons or promo codes

Selecting shipping/delivery options

Account creation, login, or guest checkout

Inputting payment details (card, wallet, Buy Now Pay Later)

Handling validation errors or OOS scenarios

Order review and final placement

Confirmation page capture (including order summary details)

Why This Dataset?

Building LLMs, agentic shopping bots, or e-commerce automation tools demands more than just page screenshots or API logs. You need deeply contextualized, action-oriented data that reflects how real users interact with the complex, ever-changing UIs of digital commerce. Our dataset uniquely captures:

The full intent-action-outcome loop

Dynamic UI changes, modals, validation, and error handling

Nuances of cart modification, bundle pricing, delivery constraints, and multi-vendor checkouts

Mobile vs. desktop variations

Diverse merchant tech stacks (custom, Shopify, Magento, BigCommerce, native apps, etc.)

Use Cases

LLM Fine-Tuning: Teach models to reason through step-by-step transaction flows, infer next-best-actions, and generate robust, context-sensitive prompts for real-world ordering.

Agentic Shopping Bots: Train agents to navigate web/mobile checkouts autonomously, handle edge cases, and complete real purchases on behalf of users.

Action Model & RLHF Training: Provide reinforcement learning pipelines with ground truth “what happens if I do X?” data across hundreds of real merchants.

UI/UX Research & Synthetic User Studies: Identify friction points, bottlenecks, and drop-offs in modern checkout design by replaying flows and testing interventions.

Automated QA & Regression Testing: Use realistic flows as test cases for new features or third-party integrations.

What’s Included

10,000+ annotated checkout flows (retail, restaurant, marketplace)

Step-by-step event logs with metadata, DOM, and network context

Natural language explanations for each step and transition

All flows are depersonalized and privacy-compliant

Example scripts for ingesting, parsing, and analyzing the dataset

Flexible licensing for research or commercial use

Sample Categories Covered

Grocery delivery (Instacart, Walmart, Kroger, Target, etc.)

Restaurant takeout/delivery (Ub...

Artificial Intelligence-based image generation has recently seen remarkable advancements, largely driven by deep learning techniques, such as Generative Adversarial Networks (GANs). With the influx and development of generative models, so too have biometric re-identification models and presentation attack detection models seen a surge in discriminative performance. However, despite the impressive photo-realism of generated samples and the additive value to the data augmentation pipeline, the role and usage of machine learning models has received intense scrutiny and criticism, especially in the context of biometrics, often being labeled as untrustworthy. Problems that have garnered attention in modern machine learning include: humans' and machines' shared inability to verify the authenticity of (biometric) data, the inadvertent leaking of private biometric data through the image synthesis process, and racial bias in facial recognition algorithms. Given the arrival of these unwanted side effects, public trust has been shaken in the blind use and ubiquity of machine learning.

However, in tandem with the advancement of generative AI, there are research efforts to re-establish trust in generative and discriminative machine learning models. Explainability methods based on aggregate model salience maps can elucidate the inner workings of a detection model, establishing trust in a post hoc manner. The CYBORG training strategy, originally proposed by Boyd, attempts to actively build trust into discriminative models by incorporating human salience into the training process.

In doing so, CYBORG-trained machine learning models behave more similar to human annotators and generalize well to unseen types of synthetic data. Work in this dissertation also attempts to renew trust in generative models by training generative models on synthetic data in order to avoid identity leakage in models trained on authentic data. In this way, the privacy of individuals whose biometric data was seen during training is not compromised through the image synthesis procedure. Future development of privacy-aware image generation techniques will hopefully achieve the same degree of biometric utility in generative models with added guarantees of trustworthiness.

This is official synthetic dataset used to train GLiNER multi-task model. The dataset is a list of dictionaries consisting a tokenized text with named entity recognition (NER) information. Each item represents of two main components:

'tokenized_text': A list of individual words and punctuation marks from the original text, split into tokens.

'ner': A list of lists containing named entity recognition information. Each inner list has three elements:

Start index of the named entity in the… See the full description on the dataset page: https://huggingface.co/datasets/knowledgator/GLINER-multi-task-synthetic-data.

Context

• We would like to have good open source speech recognition
• Commercial companies try to solve a hard problem: map arbitrary, open-ended speech to text and identify meaning
• The easier problem should be: detect a predefined sequence of sounds and map it to a predefined action.
• Lets tackle the simplest problem first: Classifying single, short words (commands)
• Audio training data is difficult to obtain.

Approaches

• The parent project (spoken verbs) created synthetic speech datasets using text-to-speech programs. The focus there is on single-syllable verbs (commands).
• The Speech Commands dataset (by Pete Warden, see the TensorFlow Speech Recognition Challenge) asked volunteers to pronounce a small set of words: (yes, no, up, down, left, right, on, off, stop, go, and 0-9).
• This data set provides synthetic counterparts to this real world dataset.

Open questions

One can use these two datasets in various ways. Here are some things I am interested in seeing answered:

1. What is it in an audio sample that makes it "sound similar"? Our ears can easily classify both synthetic and real speech, but for algorithms this is still hard. Extending the real dataset with the synthetic data yields a larger training sample and more diversity.
2. How well does an algorithm trained on one data set perform on the other? (transfer learning) If it works poorly, the algorithm probably has not found the key to audio similarity.
3. Are synthetic data sufficient for classifying real datasets? If this is the case, the implications are huge. You would not need to ask thousands of volunteers for hours of time. Instead, you could easily create arbitrary synthetic datasets for your target words.

A interesting challenge (idea for competition) would be to train on this data set and evaluate on the real dataset.

Synthetic data creation

Here I describe how the synthetic audio samples were created. Code is available at https://github.com/JohannesBuchner/spoken-command-recognition, in the "tensorflow-speech-words" folder.

1. The list of words is in "inputwords". "marvin" was changed to "marvel", because "marvin" does not have a pronounciation coding yet.
2. Pronounciations were taken from the British English Example Pronciation dictionary (BEEP, http://svr-www.eng.cam.ac.uk/comp.speech/Section1/Lexical/beep.html ). The phonemes were translated for the next step with a translation table (see compile.py for details). This creates the file "words". There are multiple pronounciations and stresses for each word.
3. A text-to-speech program (espeak) was used to pronounce these words (see generatetfspeech.sh for details). The pronounciation, stress, pitch, speed and speaker were varied. This gives >1000 clean examples for each word.
4. Noise samples were obtained. Noise samples (airport babble car exhibition restaurant street subway train) come from AURORA (https://www.ee.columbia.edu/~dpwe/sounds/noise/), and additional noise samples were synthetically created (ocean white brown pink). (see ../generatenoise.sh for details)
5. Noise and speech were mixed. The speech volume and offset were varied. The noise source, volume was also varied. See addnoise.py for details. addnoise2.py is the same, but with lower speech volume and higher noise volume. All audio files are one second (1s) long and are in wav format (16 bit, mono, 16000 Hz).
6. Finally, the data was compressed into an archive and uploaded to kaggle.

Acknowledgements

This work built upon

• Pronounciation dictionary: BEEP: http://svr-www.eng.cam.ac.uk/comp.speech/Section1/Lexical/beep.html
• Noise samples: AURORA: https://www.ee.columbia.edu/~dpwe/sounds/noise/
• eSPEAK: http://espeak.sourceforge.net/ and mbrola voices http://www.tcts.fpms.ac.be/synthesis/mbrola/mbrcopybin.html

Please provide appropriate citations to the above when using this work.

To cite the resulting dataset, you can use:

APA-style citation: "Buchner J. Synthetic Speech Commands: A public dataset for single-word speech recognition, 2017. Available from https://www.kaggle.com/jbuchner/synthetic-speech-commands-dataset/".

BibTeX @article{speechcommands, title={Synthetic Speech Commands: A public dataset for single-word speech recognition.}, author={Buchner, Johannes}, journal={Dataset available from https://www.kaggle.com/jbuchner/synthetic-speech-commands-dataset/}, year={2017} }

Thanks to everyone trying to improve open source voice detection and speech recognition.

Links

• https://www.kaggle.com/jbuchner/spokenverbs
• https://www.kaggle.com/c/tensorflow-speech-recognition-challenge
• https://github.com/JohannesBuchner/spoken-command-recognition/

NADA (Not-A-Database) is an easy-to-use geometric shape data generator that allows users to define non-uniform multivariate parameter distributions to test novel methodologies. The full open-source package is provided at GIT:NA_DAtabase. See Technical Report for details on how to use the provided package.

This database includes 3 repositories:

• NADA_Dis: Is the model able to correctly characterize/Disentangle a complex latent space?
  The repository contains 3x100,000 synthetic black and white images to test the ability of the models to correctly define a proper latent space (e.g., autoencoders) and disentangle it. The first 100,000 images contain 4 shapes and uniform parameter space distributions, while the other images have a more complex underlying distribution (truncated Gaussian and correlated marginal variables).
  
  
• NADA_OOD: Does the model identify Out-Of-Distribution images?
  The repository contains 100,000 training images (4 different shapes with 3 possible colors located in the upper left corner of the canvas) and 6x100,000 increasingly different sets of images (changing the color class balance, reducing the radius of the shape, moving the shape to the lower left corner) providing increasingly challenging out-of-distribution images.
  This can help to test not only the capability of a model, but also methods that produce reliability estimates and should correctly classify OOD elements as "unreliable" as they are far from the original distributions.
  
  
• NADA_AlEp: Does the model distinguish between different types (Aleatoric/Epistemic) of uncertainties?
  The repository contains 5x100,000 images with different type of noise/uncertainties:
  
  • NADA_AlEp_0_Clean: Dataset clean of noise to use as a possible training set.
  • NADA_AlEp_1_White_Noise: Epistemic white noise dataset. Each image is perturbed with an amount of white noise randomly sampled from 0% to 90%.
  • NADA_AlEp_2_Deformation: Dataset with Epistemic deformation noise. Each image is deformed by a randomly amount uniformly sampled between 0% and 90%. 0% corresponds to the original image, while 100% is a full deformation to the circumscribing circle.
  • NADA_AlEp_3_Label: Dataset with label noise. Formally, 20% of Triangles of a given color are missclassified as a Square with a random color (among Blue, Orange, and Brown) and viceversa (Squares to Triangles). Label noise introduces \textit{Aleatoric Uncertainty} because it is inherent in the data and cannot be reduced.
  • NADA_AlEp_4_Combined: Combined dataset with all previous sources of uncertainty.

Each image can be used for classification (shape/color) or regression (radius/area) tasks.

All datasets can be modified and adapted to the user's research question using the included open source data generator.

Differential Privacy Data Synthesizer Market Outlook

According to our latest research, the global Differential Privacy Data Synthesizer market size reached USD 1.07 billion in 2024, reflecting growing demand for privacy-preserving data solutions across multiple sectors. The market is poised to expand robustly at a CAGR of 28.3% from 2025 to 2033, with the total market value projected to reach USD 8.84 billion by 2033. This remarkable growth is primarily driven by the increasing need for compliance with stringent data privacy regulations, advancements in artificial intelligence and machine learning, and the growing reliance on synthetic data for analytics and innovation without compromising sensitive information.

One of the primary growth factors for the Differential Privacy Data Synthesizer market is the escalating regulatory scrutiny surrounding data privacy worldwide. With regulations such as GDPR in Europe, CCPA in California, and similar frameworks emerging globally, organizations are under immense pressure to ensure that personal and sensitive data is protected at every stage of processing and analysis. Differential privacy offers a mathematically robust approach to anonymizing data, making it possible to extract valuable insights while significantly reducing the risk of re-identification. As more enterprises seek to harness data-driven strategies without falling foul of privacy laws, the adoption of differential privacy data synthesizers is accelerating, especially in highly regulated industries such as healthcare, finance, and government.

Another significant driver of market expansion is the rapid evolution of artificial intelligence and machine learning applications. Training AI models on real-world data often raises privacy concerns, particularly when datasets contain personally identifiable information. By generating synthetic datasets that maintain the statistical properties of the original data but do not contain actual personal details, differential privacy data synthesizers enable organizations to innovate freely. This capability is especially crucial for sectors such as healthcare and finance, where data sensitivity is paramount. Additionally, the growing use of cloud-based solutions and the proliferation of big data analytics are further propelling demand for scalable, secure, and privacy-preserving data solutions, reinforcing the market’s upward trajectory.

The increasing awareness of the value of synthetic data for testing, development, and analytics is also fueling the adoption of differential privacy data synthesizers. Organizations are recognizing that synthetic data can help eliminate data silos, support collaborative research, and enable safe data sharing with partners, vendors, or third-party analytics providers. This is particularly valuable for industries like retail and IT, where customer data is both a strategic asset and a potential liability. The ability to create high-fidelity synthetic datasets that reflect real-world patterns without exposing sensitive information is becoming a competitive differentiator. As a result, vendors are investing heavily in R&D to enhance the accuracy, scalability, and ease of integration of their differential privacy data synthesizer platforms.

From a regional perspective, North America currently dominates the Differential Privacy Data Synthesizer market, accounting for over 38% of the global share in 2024, driven by early adoption among technology leaders, strong regulatory frameworks, and significant investments in privacy-enhancing technologies. Europe follows closely, benefiting from stringent privacy regulations and a mature digital infrastructure. The Asia Pacific region is witnessing the fastest growth, with a CAGR projected at 31.2% through 2033, fueled by rapid digital transformation, expanding IT and telecommunications sectors, and increasing awareness of data privacy risks. Latin America and the Middle East & Africa are also emerging as promising markets, albeit from a smaller base, as local governments ramp up data protection initiatives and enterprises modernize their data management practices.

Component Analysis

The Component segment of the Differential Privacy Data Synthesizer market is bifurcated into Software and Services, each playing a pivotal role in driving market growth. The software segment, which comprises standalone platforms, integrated

Quantum-AI Synthetic Data Generator Market Outlook

According to our latest research, the global Quantum-AI Synthetic Data Generator market size reached USD 1.82 billion in 2024, reflecting a robust expansion driven by technological advancements and increasing adoption across multiple industries. The market is projected to grow at a CAGR of 32.7% from 2025 to 2033, reaching a forecasted market size of USD 21.69 billion by 2033. This growth trajectory is primarily fueled by the rising demand for high-quality synthetic data to train artificial intelligence models, address data privacy concerns, and accelerate digital transformation initiatives across sectors such as healthcare, finance, and retail.

One of the most significant growth factors for the Quantum-AI Synthetic Data Generator market is the escalating need for vast, diverse, and privacy-compliant datasets to train advanced AI and machine learning models. As organizations increasingly recognize the limitations and risks associated with using real-world data, particularly regarding data privacy regulations like GDPR and CCPA, the adoption of synthetic data generation technologies has surged. Quantum computing, when integrated with artificial intelligence, enables the rapid and efficient creation of highly realistic synthetic datasets that closely mimic real-world data distributions while ensuring complete anonymity. This capability is proving invaluable for sectors like healthcare and finance, where data sensitivity is paramount and regulatory compliance is non-negotiable. As a result, organizations are investing heavily in Quantum-AI synthetic data solutions to enhance model accuracy, reduce bias, and streamline data sharing without compromising privacy.

Another key driver propelling the market is the growing complexity and volume of data generated by emerging technologies such as IoT, autonomous vehicles, and smart devices. Traditional data collection methods are often insufficient to keep pace with the data requirements of modern AI applications, leading to gaps in data availability and quality. Quantum-AI Synthetic Data Generators address these challenges by producing large-scale, high-fidelity synthetic datasets on demand, enabling organizations to simulate rare events, test edge cases, and improve model robustness. Additionally, the capability to generate structured, semi-structured, and unstructured data allows businesses to meet the specific needs of diverse applications, ranging from fraud detection in banking to predictive maintenance in manufacturing. This versatility is further accelerating market adoption, as enterprises seek to future-proof their AI initiatives and gain a competitive edge.

The integration of Quantum-AI Synthetic Data Generators into cloud-based platforms and enterprise IT ecosystems is also catalyzing market growth. Cloud deployment models offer scalability, flexibility, and cost-effectiveness, making synthetic data generation accessible to organizations of all sizes, including small and medium enterprises. Furthermore, the proliferation of AI-driven analytics in sectors such as retail, e-commerce, and telecommunications is creating new opportunities for synthetic data applications, from enhancing customer experience to optimizing supply chain operations. As vendors continue to innovate and expand their service offerings, the market is expected to witness sustained growth, with new entrants and established players alike vying for market share through strategic partnerships, product launches, and investments in R&D.

From a regional perspective, North America currently dominates the Quantum-AI Synthetic Data Generator market, accounting for over 38% of the global revenue in 2024, followed by Europe and Asia Pacific. The strong presence of leading technology companies, robust investment in AI research, and favorable regulatory environment contribute to North America's leadership position. Europe is also witnessing significant growth, driven by stringent data privacy regulations and increasing adoption of AI across industries. Meanwhile, the Asia Pacific region is emerging as a high-growth market, fueled by rapid digitalization, expanding IT infrastructure, and government initiatives promoting AI innovation. As regional markets continue to evolve, strategic collaborations and cross-border partnerships are expected to play a pivotal role in shaping the global landscape of the Quantum-AI Synthetic Data Generator market.

Component Analysis