Abstract

The dataset is a relational dataset of 8,000 households households, representing a sample of the population of an imaginary middle-income country. The dataset contains two data files: one with variables at the household level, the other one with variables at the individual level. It includes variables that are typically collected in population censuses (demography, education, occupation, dwelling characteristics, fertility, mortality, and migration) and in household surveys (household expenditure, anthropometric data for children, assets ownership). The data only includes ordinary households (no community households). The dataset was created using REaLTabFormer, a model that leverages deep learning methods. The dataset was created for the purpose of training and simulation and is not intended to be representative of any specific country.

The full-population dataset (with about 10 million individuals) is also distributed as open data.

Geographic coverage

The dataset is a synthetic dataset for an imaginary country. It was created to represent the population of this country by province (equivalent to admin1) and by urban/rural areas of residence.

Analysis unit

Household, Individual

Universe

The dataset is a fully-synthetic dataset representative of the resident population of ordinary households for an imaginary middle-income country.

Kind of data

ssd

Sampling procedure

The sample size was set to 8,000 households. The fixed number of households to be selected from each enumeration area was set to 25. In a first stage, the number of enumeration areas to be selected in each stratum was calculated, proportional to the size of each stratum (stratification by geo_1 and urban/rural). Then 25 households were randomly selected within each enumeration area. The R script used to draw the sample is provided as an external resource.

Mode of data collection

other

Research instrument

The dataset is a synthetic dataset. Although the variables it contains are variables typically collected from sample surveys or population censuses, no questionnaire is available for this dataset. A "fake" questionnaire was however created for the sample dataset extracted from this dataset, to be used as training material.

Cleaning operations

The synthetic data generation process included a set of "validators" (consistency checks, based on which synthetic observation were assessed and rejected/replaced when needed). Also, some post-processing was applied to the data to result in the distributed data files.

Response rate

This is a synthetic dataset; the "response rate" is 100%.

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.

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Synthetic Data Generation Market Size 2025-2029

The synthetic data generation market size is forecast to increase by USD 4.39 billion, at a CAGR of 61.1% between 2024 and 2029.

The market is experiencing significant growth, driven by the escalating demand for data privacy protection. With increasing concerns over data security and the potential risks associated with using real data, synthetic data is gaining traction as a viable alternative. Furthermore, the deployment of large language models is fueling market expansion, as these models can generate vast amounts of realistic and diverse data, reducing the reliance on real-world data sources. However, high costs associated with high-end generative models pose a challenge for market participants. These models require substantial computational resources and expertise to develop and implement effectively. Companies seeking to capitalize on market opportunities must navigate these challenges by investing in research and development to create more cost-effective solutions or partnering with specialists in the field. Overall, the market presents significant potential for innovation and growth, particularly in industries where data privacy is a priority and large language models can be effectively utilized.

What will be the Size of the Synthetic Data Generation Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
Request Free SampleThe market continues to evolve, driven by the increasing demand for data-driven insights across various sectors. Data processing is a crucial aspect of this market, with a focus on ensuring data integrity, privacy, and security. Data privacy-preserving techniques, such as data masking and anonymization, are essential in maintaining confidentiality while enabling data sharing. Real-time data processing and data simulation are key applications of synthetic data, enabling predictive modeling and data consistency. Data management and workflow automation are integral components of synthetic data platforms, with cloud computing and model deployment facilitating scalability and flexibility. Data governance frameworks and compliance regulations play a significant role in ensuring data quality and security. Deep learning models, variational autoencoders (VAEs), and neural networks are essential tools for model training and optimization, while API integration and batch data processing streamline the data pipeline. Machine learning models and data visualization provide valuable insights, while edge computing enables data processing at the source. Data augmentation and data transformation are essential techniques for enhancing the quality and quantity of synthetic data. Data warehousing and data analytics provide a centralized platform for managing and deriving insights from large datasets. Synthetic data generation continues to unfold, with ongoing research and development in areas such as federated learning, homomorphic encryption, statistical modeling, and software development. The market's dynamic nature reflects the evolving needs of businesses and the continuous advancements in data technology.

How is this Synthetic Data Generation Industry segmented?

The synthetic data generation industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. End-userHealthcare and life sciencesRetail and e-commerceTransportation and logisticsIT and telecommunicationBFSI and othersTypeAgent-based modellingDirect modellingApplicationAI and ML Model TrainingData privacySimulation and testingOthersProductTabular dataText dataImage and video dataOthersGeographyNorth AmericaUSCanadaMexicoEuropeFranceGermanyItalyUKAPACChinaIndiaJapanRest of World (ROW)

By End-user Insights

The healthcare and life sciences segment is estimated to witness significant growth during the forecast period.In the rapidly evolving data landscape, the market is gaining significant traction, particularly in the healthcare and life sciences sector. With a growing emphasis on data-driven decision-making and stringent data privacy regulations, synthetic data has emerged as a viable alternative to real data for various applications. This includes data processing, data preprocessing, data cleaning, data labeling, data augmentation, and predictive modeling, among others. Medical imaging data, such as MRI scans and X-rays, are essential for diagnosis and treatment planning. However, sharing real patient data for research purposes or training machine learning algorithms can pose significant privacy risks. Synthetic data generation addresses this challenge by producing realistic medical imaging data, ensuring data privacy while enabling research

These data are modelled using the OMOP Common Data Model v5.3.Correlated Data SourceNG tube vocabulariesGeneration RulesThe patient’s age should be between 18 and 100 at the moment of the visit.Ethnicity data is using 2021 census data in England and Wales (Census in England and Wales 2021) .Gender is equally distributed between Male and Female (50% each).Every person in the record has a link in procedure_occurrence with the concept “Checking the position of nasogastric tube using X-ray”2% of person records have a link in procedure_occurrence with the concept of “Plain chest X-ray”60% of visit_occurrence has visit concept “Inpatient Visit”, while 40% have “Emergency Room Visit”NotesVersion 0Generated by man-made rule/story generatorStructural correct, all tables linked with the relationshipWe used national ethnicity data to generate a realistic distribution (see below)2011 Race Census figure in England and WalesEthnic Group : Population(%)Asian or Asian British: Bangladeshi - 1.1Asian or Asian British: Chinese - 0.7Asian or Asian British: Indian - 3.1Asian or Asian British: Pakistani - 2.7Asian or Asian British: any other Asian background -1.6Black or African or Caribbean or Black British: African - 2.5Black or African or Caribbean or Black British: Caribbean - 1Black or African or Caribbean or Black British: other Black or African or Caribbean background - 0.5Mixed multiple ethnic groups: White and Asian - 0.8Mixed multiple ethnic groups: White and Black African - 0.4Mixed multiple ethnic groups: White and Black Caribbean - 0.9Mixed multiple ethnic groups: any other Mixed or multiple ethnic background - 0.8White: English or Welsh or Scottish or Northern Irish or British - 74.4White: Irish - 0.9White: Gypsy or Irish Traveller - 0.1White: any other White background - 6.4Other ethnic group: any other ethnic group - 1.6Other ethnic group: Arab - 0.6

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.

What Makes Our Data Unique?

Scale and Coverage: - A global dataset encompassing 800M B2B profiles from a wide array of industries and geographies. - Includes coverage across the Americas, Europe, Asia, and other key markets, ensuring worldwide representation.

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.

Primary Use Cases and Verticals

Natural Language Processing (NLP): Train models for named entity recognition (NER), text classification, sentiment analysis, and conversational AI. Ideal for chatbots, language models, and content categorization.

Predictive Analytics and Recommendation Systems: Enable personalized marketing campaigns by predicting buyer behavior. Build smarter recommendation engines for ecommerce and content platforms.

B2B Lead Generation and Market Insights: Create models that identify high-value leads using enriched company and contact information. Develop AI systems that track trends and provide strategic insights for businesses.

HR and Talent Acquisition AI: Optimize talent-matching algorithms using structured job descriptions and candidate profiles. Build AI-powered platforms for recruitment analytics.

How This Product Fits Into Xverum’s Broader Data Offering Xverum is a leading provider of structured, high-quality web datasets. While we specialize in B2B profiles and company data, we also offer complementary datasets tailored for specific verticals, including ecommerce product data, job listings, and customer reviews. The AI Training Data is a natural extension of our core capabilities, bridging the gap between structured data and machine learning workflows. By providing annotation-ready datasets, real-time API access, and customization options, we ensure our clients can seamlessly integrate our data into their AI development processes.

Why Choose Xverum? - Experience and Expertise: A trusted name in structured web data with a proven track record. - Flexibility: Datasets can be tailored for any AI/ML application. - Scalability: With 800M profiles and more being added, you’ll always have access to fresh, up-to-date data. - Compliance: We prioritize data ethics and security, ensuring all data adheres to GDPR and other legal frameworks.

Ready to supercharge your AI and ML projects? Explore Xverum’s AI Training Data to unlock the potential of 800M global B2B profiles. Whether you’re building a chatbot, predictive algorithm, or next-gen AI application, our data is here to help.

Contact us for sample datasets or to discuss your specific needs.

BackgroundClinical data is instrumental to medical research, machine learning (ML) model development, and advancing surgical care, but access is often constrained by privacy regulations and missing data. Synthetic data offers a promising solution to preserve privacy while enabling broader data access. Recent advances in large language models (LLMs) provide an opportunity to generate synthetic data with reduced reliance on domain expertise, computational resources, and pre-training.ObjectiveThis study aims to assess the feasibility of generating realistic tabular clinical data with OpenAI’s GPT-4o using zero-shot prompting, and evaluate the fidelity of LLM-generated data by comparing its statistical properties to the Vital Signs DataBase (VitalDB), a real-world open-source perioperative dataset.MethodsIn Phase 1, GPT-4o was prompted to generate a dataset with qualitative descriptions of 13 clinical parameters. The resultant data was assessed for general errors, plausibility of outputs, and cross-verification of related parameters. In Phase 2, GPT-4o was prompted to generate a dataset using descriptive statistics of the VitalDB dataset. Fidelity was assessed using two-sample t-tests, two-sample proportion tests, and 95% confidence interval (CI) overlap.ResultsIn Phase 1, GPT-4o generated a complete and structured dataset comprising 6,166 case files. The dataset was plausible in range and correctly calculated body mass index for all case files based on respective heights and weights. Statistical comparison between the LLM-generated datasets and VitalDB revealed that Phase 2 data achieved significant fidelity. Phase 2 data demonstrated statistical similarity in 12/13 (92.31%) parameters, whereby no statistically significant differences were observed in 6/6 (100.0%) categorical/binary and 6/7 (85.71%) continuous parameters. Overlap of 95% CIs were observed in 6/7 (85.71%) continuous parameters.ConclusionZero-shot prompting with GPT-4o can generate realistic tabular synthetic datasets, which can replicate key statistical properties of real-world perioperative data. This study highlights the potential of LLMs as a novel and accessible modality for synthetic data generation, which may address critical barriers in clinical data access and eliminate the need for technical expertise, extensive computational resources, and pre-training. Further research is warranted to enhance fidelity and investigate the use of LLMs to amplify and augment datasets, preserve multivariate relationships, and train robust ML models.

The Agency for Healthcare Research and Quality (AHRQ) created SyH-DR from eligibility and claims files for Medicare, Medicaid, and commercial insurance plans in calendar year 2016. SyH-DR contains data from a nationally representative sample of insured individuals for the 2016 calendar year. SyH-DR uses synthetic data elements at the claim level to resemble the marginal distribution of the original data elements. SyH-DR person-level data elements are not synthetic, but identifying information is aggregated or masked.

The Synthetic Data Generation Marketsize was valued at USD 288.5 USD Million in 2023 and is projected to reach USD 1920.28 USD Million by 2032, exhibiting a CAGR of 31.1 % during the forecast period.Synthetic data generation stands for the generation of fake datasets that resemble real datasets with reference to their data distribution and patterns. It refers to the process of creating synthetic data points utilizing algorithms or models instead of conducting observations or surveys. There is one of its core advantages: it can maintain the statistical characteristics of the original data and remove the privacy risk of using real data. Further, with synthetic data, there is no limitation to how much data can be created, and hence, it can be used for extensive testing and training of machine learning models, unlike the case with conventional data, which may be highly regulated or limited in availability. It also helps in the generation of datasets that are comprehensive and include many examples of specific situations or contexts that may occur in practice for improving the AI system’s performance. The use of SDG significantly shortens the process of the development cycle, requiring less time and effort for data collection as well as annotation. It basically allows researchers and developers to be highly efficient in their discovery and development in specific domains like healthcare, finance, etc. Key drivers for this market are: Growing Demand for Data Privacy and Security to Fuel Market Growth. Potential restraints include: Lack of Data Accuracy and Realism Hinders Market Growth. Notable trends are: Growing Implementation of Touch-based and Voice-based Infotainment Systems to Increase Adoption of Intelligent Cars.

OpenSeek-Reasoning-Data

OpenSeek [Github|Blog] Recent reseach has demonstrated that the reasoning ability of LLMs originates from the pre-training stage, activated by RL training. Massive raw corpus containing complex human reasoning process, but lack of generalized and effective synthesis method to extract these reasoning process.

  News

🔥🔥🔥[2025/02/25] We publish some math, code, and general knowledge domain reasoning data synthesized from the current pipeline.… See the full description on the dataset page: https://huggingface.co/datasets/BAAI/OpenSeek-Synthetic-Reasoning-Data-Examples.

Outline

This dataset is originally created for the Knowledge Graph Reasoning Challenge for Social Issues (KGRC4SI)

Video data that simulates daily life actions in a virtual space from Scenario Data.

Knowledge graphs, and transcriptions of the Video Data content ("who" did what "action" with what "object," when and where, and the resulting "state" or "position" of the object).

Knowledge Graph Embedding Data are created for reasoning based on machine learning

This data is open to the public as open data

Details

Videos

mp4 format

203 action scenarios

For each scenario, there is a character rear view (file name ending in 0), an indoor camera switching view (file name ending in 1), and a fixed camera view placed in each corner of the room (file name ending in 2-5). Also, for each action scenario, data was generated for a minimum of 1 to a maximum of 7 patterns with different room layouts (scenes). A total of 1,218 videos

Videos with slowly moving characters simulate the movements of elderly people.

Knowledge Graphs

RDF format

203 knowledge graphs corresponding to the videos

Includes schema and location supplement information

The schema is described below

SPARQL endpoints and query examples are available

Script Data

txt format

Data provided to VirtualHome2KG to generate videos and knowledge graphs

Includes the action title and a brief description in text format.

Embedding

Embedding Vectors in TransE, ComplEx, and RotatE. Created with DGL-KE (https://dglke.dgl.ai/doc/)

Embedding Vectors created with jRDF2vec (https://github.com/dwslab/jRDF2Vec).

Specification of Ontology

Please refer to the specification for descriptions of all classes, instances, and properties: https://aistairc.github.io/VirtualHome2KG/vh2kg_ontology.htm

Related Resources

KGRC4SI Final Presentations with automatic English subtitles (YouTube)

VirtualHome2KG (Software)

VirtualHome-AIST (Unity)

VirtualHome-AIST (Python API)

Visualization Tool (Software)

Script Editor (Software)

Summary of identified synthetic data use cases in health care and examples.

Objective: Biomechanical Machine Learning (ML) models, particularly deep-learning models, demonstrate the best performance when trained using extensive datasets. However, biomechanical data are frequently limited due to diverse challenges. Effective methods for augmenting data in developing ML models, specifically in the human posture domain, are scarce. Therefore, this study explored the feasibility of leveraging generative artificial intelligence (AI) to produce realistic synthetic posture data by utilizing three-dimensional posture data.Methods: Data were collected from 338 subjects through surface topography. A Variational Autoencoder (VAE) architecture was employed to generate and evaluate synthetic posture data, examining its distinguishability from real data by domain experts, ML classifiers, and Statistical Parametric Mapping (SPM). The benefits of incorporating augmented posture data into the learning process were exemplified by a deep autoencoder (AE) for automated feature representation.Results: Our findings highlight the challenge of differentiating synthetic data from real data for both experts and ML classifiers, underscoring the quality of synthetic data. This observation was also confirmed by SPM. By integrating synthetic data into AE training, the reconstruction error can be reduced compared to using only real data samples. Moreover, this study demonstrates the potential for reduced latent dimensions, while maintaining a reconstruction accuracy comparable to AEs trained exclusively on real data samples.Conclusion: This study emphasizes the prospects of harnessing generative AI to enhance ML tasks in the biomechanics domain.

Synthetic dataset:

generated.csv - synthetic datasets containing 41,185 clinical note samples spanning 219 ICD-10 codes.

Data field Description

idx Unique sample identifier.

ICD-10 The targeted ICD-10 code used for prior data sampling.

generation_model The model used for sample generation (GTP-3.5, GPT-4, LLaMA-7b, LLaMA-13b)

prompt Prompt used for sample generation.

prior Type of prior data used for sample generation.

example Bool variable for the presence or… See the full description on the dataset page: https://huggingface.co/datasets/Glebkaa/MedSyn-synthetic.

Ainnotate’s proprietary dataset generation methodology based on large scale generative modelling and Domain randomization provides data that is well balanced with consistent sampling, accommodating rare events, so that it can enable superior simulation and training of your models.

Ainnotate currently provides synthetic datasets in the following domains and use cases.

Internal Services - Visa application, Passport validation, License validation, Birth certificates Financial Services - Bank checks, Bank statements, Pay slips, Invoices, Tax forms, Insurance claims and Mortgage/Loan forms Healthcare - Medical Id cards

The dataset has been created by using the open-source code released by LNDS (Luxembourg National Data Service). It is meant to be an example of the dataset structure anyone can generate and personalize in terms of some fixed parameter, including the sample size. The file format is .csv, and the data are organized by individual profiles on the rows and their personal features on the columns. The information in the dataset has been generated based on the statistical information about the age-structure distribution, the number of populations over municipalities, the number of different nationalities present in Luxembourg, and salary statistics per municipality. The STATEC platform, the statistics portal of Luxembourg, is the public source we used to gather the real information that we ingested into our synthetic generation model. Other features like Date of birth, Social matricule, First name, Surname, Ethnicity, and physical attributes have been obtained by a logical relationship between variables without exploiting any additional real information. We are in compliance with the law in putting close to zero the risk of identifying a real person completely by chance.

Multiturn Multimodal

We want to generate synthetic data that able to understand position and relationship between multi-images and multi-audio, example as below, All notebooks at https://github.com/mesolitica/malaysian-dataset/tree/master/chatbot/multiturn-multimodal

  multi-images

synthetic-multi-images-relationship.jsonl, 100000 rows, 109MB. Images at https://huggingface.co/datasets/mesolitica/translated-LLaVA-Pretrain/tree/main

  Example data

{'filename':… See the full description on the dataset page: https://huggingface.co/datasets/mesolitica/synthetic-multiturn-multimodal.

Synthetic Data Generation Engine Market Outlook

According to our latest research, the global Synthetic Data Generation Engine market size reached USD 1.42 billion in 2024, reflecting a rapidly expanding sector driven by the escalating demand for advanced data solutions. The market is expected to achieve a robust CAGR of 37.8% from 2025 to 2033, propelling it to an estimated value of USD 21.8 billion by 2033. This exceptional growth is primarily fueled by the increasing need for high-quality, privacy-compliant datasets to train artificial intelligence and machine learning models in sectors such as healthcare, BFSI, and IT & telecommunications. As per our latest research, the proliferation of data-centric applications and stringent data privacy regulations are acting as significant catalysts for the adoption of synthetic data generation engines globally.

One of the key growth factors for the synthetic data generation engine market is the mounting emphasis on data privacy and compliance with regulations such as GDPR and CCPA. Organizations are under immense pressure to protect sensitive customer information while still deriving actionable insights from data. Synthetic data generation engines offer a compelling solution by creating artificial datasets that mimic real-world data without exposing personally identifiable information. This not only ensures compliance but also enables organizations to accelerate their AI and analytics initiatives without the constraints of data access or privacy risks. The rising awareness among enterprises about the benefits of synthetic data in mitigating data breaches and regulatory penalties is further propelling market expansion.

Another significant driver is the exponential growth in artificial intelligence and machine learning adoption across industries. Training robust and unbiased models requires vast and diverse datasets, which are often difficult to obtain due to privacy concerns, labeling costs, or data scarcity. Synthetic data generation engines address this challenge by providing scalable and customizable datasets for various applications, including machine learning model training, data augmentation, and fraud detection. The ability to generate balanced and representative data has become a critical enabler for organizations seeking to improve model accuracy, reduce bias, and accelerate time-to-market for AI solutions. This trend is particularly pronounced in sectors such as healthcare, automotive, and finance, where data diversity and privacy are paramount.

Furthermore, the increasing complexity of data types and the need for multi-modal data synthesis are shaping the evolution of the synthetic data generation engine market. With the proliferation of unstructured data in the form of images, videos, audio, and text, organizations are seeking advanced engines capable of generating synthetic data across multiple modalities. This capability enhances the versatility of synthetic data solutions, enabling their application in emerging use cases such as autonomous vehicle simulation, natural language processing, and biometric authentication. The integration of generative AI techniques, such as GANs and diffusion models, is further enhancing the realism and utility of synthetic datasets, expanding the addressable market for synthetic data generation engines.

From a regional perspective, North America continues to dominate the synthetic data generation engine market, accounting for the largest revenue share in 2024. The region's leadership is attributed to the strong presence of technology giants, early adoption of AI and machine learning, and stringent regulatory frameworks. Europe follows closely, driven by robust data privacy regulations and increasing investments in digital transformation. Meanwhile, the Asia Pacific region is emerging as the fastest-growing market, supported by expanding IT infrastructure, government-led AI initiatives, and a burgeoning startup ecosystem. Latin America and the Middle East & Africa are also witnessing gradual adoption, fueled by the growing recognition of synthetic data's potential to overcome data access and privacy challenges.

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

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.

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 Use
This 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}
}

Title: Rule-based Synthetic Data for Japanese GEC. Dataset Contents:This dataset contains two parallel corpora intended for the training and evaluating of models for the NLP (natural language processing) subtask of Japanese GEC (grammatical error correction). These are as follows:Synthetic Corpus - synthesized_data.tsv. This corpus file contains 2,179,130 parallel sentence pairs synthesized using the process described in [1]. Each line of the file consists of two sentences delimited by a tab. The first sentence is the erroneous sentence while the second is the corresponding correction.These paired sentences are derived from data scraped from the keyword-lookup site