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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 and development. Moreover

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.

The advent of Quantum-AI Synthetic Data Generator is revolutionizing the landscape of synthetic data creation. By harnessing the power of quantum computing and artificial intelligence, this technology is capable of producing highly complex and realistic datasets at unprecedented speeds. This innovation is particularly beneficial for industries that require vast amounts of data for AI model training, such as finance and healthcare. The Quantum-AI Synthetic Data Generator not only enhances the quality and diversity of synthetic data but also significantly reduces the time and cost associated with data generation. As organizations strive to stay ahead in the competitive AI landscape, the integration of quantum computing into synthetic data generation is poised to become a game-changer, offering new levels of efficiency and accuracy.

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 AmericaÂ’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

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%.

This dataset contains synthetic and real images, with their labels, for Computer Vision in robotic surgery. It is part of ongoing research on sim-to-real applications in surgical robotics. The dataset will be updated with further details and references once the related work is published. For further information see the repository on GitHub: https://github.com/PietroLeoncini/Surgical-Synthetic-Data-Generation-and-Segmentation

The global Synthetic Data Solution market is experiencing robust growth, projected to reach an estimated market size of approximately $1,500 million by 2025, with a Compound Annual Growth Rate (CAGR) of around 25% from 2019 to 2033. This significant expansion is primarily propelled by the increasing demand for privacy-preserving data generation, especially within sensitive sectors like financial services and healthcare, where regulations around data privacy are stringent. The retail industry is also a key driver, leveraging synthetic data for enhanced customer analytics, personalized marketing, and fraud detection without compromising consumer privacy. Furthermore, the burgeoning adoption of AI and machine learning across various industries necessitates vast amounts of high-quality training data, a need that synthetic data effectively addresses by overcoming limitations of real-world data scarcity and bias. The shift towards cloud-based solutions is also accelerating market penetration, offering scalability, flexibility, and cost-effectiveness for businesses of all sizes. Despite the promising growth trajectory, the market faces certain restraints. The complexity and cost associated with developing sophisticated synthetic data generation models, alongside concerns regarding the potential for bias inherited from the underlying real data, pose challenges. Ensuring the statistical fidelity and representativeness of synthetic data to real-world scenarios remains a critical area of focus for solution providers. However, ongoing advancements in generative adversarial networks (GANs) and other AI techniques are continuously improving the quality and realism of synthetic data. Geographically, North America currently leads the market due to its early adoption of AI technologies and strong regulatory frameworks promoting data privacy. Asia Pacific is emerging as a high-growth region, fueled by rapid digital transformation and increasing investments in AI research and development by countries like China and India. The market is characterized by intense competition among established tech giants and innovative startups, driving continuous innovation in synthetic data generation methodologies and applications. This in-depth report offers a panoramic view of the global Synthetic Data Solution market, providing a meticulous analysis of its current landscape, historical trajectory, and future potential. With a study period spanning from 2019 to 2033, and a base year of 2025, the report leverages comprehensive data from the historical period (2019-2024) to project a robust growth trajectory through the forecast period (2025-2033). The estimated market size for 2025 is projected to be in the hundreds of millions of US dollars, with significant expansion anticipated in the coming years.

Dataset Description

Overview: This dataset contains three distinct fake datasets generated using the Faker and Mimesis libraries. These libraries are commonly used for generating realistic-looking synthetic data for testing, prototyping, and data science projects. The datasets were created to simulate real-world scenarios while ensuring no sensitive or private information is included.

Data Generation Process: The data creation process is documented in the accompanying notebook, Creating_simple_Sintetic_data.ipynb. This notebook showcases the step-by-step procedure for generating synthetic datasets with customizable structures and fields using the Faker and Mimesis libraries.

File Contents:

Datasets: CSV files containing the three synthetic datasets. Notebook: Creating_simple_Sintetic_data.ipynb detailing the data generation process and the code used to create these datasets.

Synthetic Data Platform Market Outlook

According to our latest research, the global synthetic data platform market size reached USD 1.45 billion in 2024, reflecting robust momentum driven by the rising demand for high-quality, privacy-compliant data. With a remarkable compound annual growth rate (CAGR) of 34.2% projected through 2033, the market is expected to surge to USD 19.51 billion by 2033. This tremendous growth trajectory is primarily fueled by the increasing adoption of artificial intelligence (AI) and machine learning (ML) technologies across various industries, alongside heightened concerns regarding data privacy and regulatory compliance.

The growth of the synthetic data platform market is underpinned by several key factors. First and foremost, as organizations intensify their digital transformation efforts, the demand for large, diverse, and high-quality datasets has soared. However, real-world data is often constrained by privacy regulations such as GDPR and CCPA, as well as limitations in data accessibility and quality. Synthetic data platforms address these challenges by generating artificial datasets that mimic real-world data distributions without exposing sensitive information, thus enabling organizations to innovate rapidly while mitigating compliance risks. The ability to generate tailored datasets for specific use cases, such as model training or testing, further amplifies the value proposition of synthetic data platforms in todayÂ’s data-driven landscape.

Another significant growth driver is the rapid proliferation of AI and ML applications across sectors such as healthcare, finance, retail, and automotive. These technologies rely on vast amounts of labeled data for training robust and unbiased models. However, acquiring such data can be costly, time-consuming, or even impractical due to privacy concerns or data scarcity. Synthetic data platforms empower organizations to overcome these barriers by producing scalable, diverse, and balanced datasets that enhance model accuracy and generalizability. This capability is particularly crucial for industries like healthcare and finance, where the ethical and legal implications of using real-world data are profound. As a result, synthetic data is becoming an indispensable tool for accelerating AI adoption and innovation.

Moreover, the evolution of data privacy regulations worldwide is compelling organizations to rethink their data management strategies. With stricter compliance requirements and increasing public scrutiny over data usage, businesses are seeking robust solutions to ensure data privacy without compromising analytical capabilities. Synthetic data platforms offer a compelling answer by enabling privacy-preserving data sharing, testing, and analytics. This not only supports regulatory compliance but also fosters collaboration and innovation across organizational boundaries. The convergence of regulatory pressures, technological advancements, and the strategic imperative for data-driven decision-making is expected to sustain the momentum of the synthetic data platform market well into the next decade.

Regionally, North America continues to dominate the synthetic data platform market, accounting for the largest revenue share in 2024. This leadership is attributed to the presence of major technology companies, early adoption of AI and ML, and a strong regulatory framework supporting data privacy. Europe follows closely, driven by stringent data protection laws and a growing emphasis on ethical AI. The Asia Pacific region is emerging as a high-growth market, propelled by rapid digitalization, expanding AI investments, and increasing awareness of data privacy issues. Latin America and the Middle East & Africa are also witnessing steady growth, albeit from a smaller base, as organizations in these regions begin to recognize the strategic value of synthetic data in driving digital innovation and regulatory compliance.

In the realm of cybersecurity, Synthetic Data for Security is gaining traction as a pivotal tool for enhancing threat detection and mitigation strategies. By generating artificial datasets that mimic potential security threats, organizations can train and test their security systems more effectively without exposing real data to risk. This approach allows for the simulation of various attack scenar

The dataset is a synthetic mental health dataset designed for use in predictive analytics, machine learning models, and research purposes. The dataset contains simulated patient information related to mental health conditions, symptoms, therapies, and other factors affecting mental well-being. Given the sensitivity of real-world mental health data, synthetic datasets provide a safe alternative for research and development without risking the privacy of individuals.

This dataset aims to provide a foundation for developing mental health applications that predict conditions, suggest therapies, and assess factors like stress and mood levels. It's intended to enhance the understanding of patient conditions in clinical or research settings, supporting AI-driven therapeutic solutions.

The features in this dataset are inspired by real-world factors commonly considered in mental health diagnostics and treatment. For instance:

Symptoms: Reflects psychological or physical symptoms patients may report during clinical sessions.

Therapy History: Considers the impact of previous treatments on current conditions.

Mood and Stress Levels: Important mental health markers that help in evaluating a patient's state of well-being.

By using synthetic data, this dataset allows for the development and testing of AI models without the ethical concerns tied to real patient data. The dataset could be used for:

• Predictive analytics in mental health apps.
• Training chatbots or virtual assistants to provide real-time therapy recommendations.
• Educational purposes, where students and researchers can explore mental health prediction models.

Synthetic Data Generation for Vision Market Outlook

As per our latest research, the global Synthetic Data Generation for Vision market size in 2024 stands at USD 0.95 billion, demonstrating remarkable momentum across diverse industries seeking scalable data solutions. The market is expected to expand at a robust CAGR of 34.7% from 2025 to 2033, reaching a forecasted value of USD 12.5 billion by 2033. This exponential growth is primarily fueled by the urgent need for high-quality, diverse, and privacy-compliant datasets to train and validate computer vision models, particularly as AI adoption accelerates in sectors such as autonomous vehicles, healthcare, and security. The surge in demand for synthetic data is further propelled by advancements in generative AI, which enable the creation of hyper-realistic images, videos, and 3D data, overcoming the limitations of traditional data collection and annotation methods.

One of the key growth factors driving the Synthetic Data Generation for Vision market is the escalating complexity and scale of computer vision applications. As industries increasingly deploy AI-powered solutions for tasks such as object detection, facial recognition, and scene understanding, the need for vast, annotated datasets has become a critical bottleneck. Real-world data acquisition is not only expensive and time-consuming but also fraught with privacy concerns and regulatory hurdles, especially in sensitive domains like healthcare and surveillance. Synthetic data generation addresses these challenges by providing customizable, scalable, and bias-mitigated datasets, accelerating model development cycles and reducing dependency on real-world data. The integration of advanced generative models, including GANs and diffusion models, has significantly enhanced the realism and utility of synthetic data, making it a preferred choice for both established enterprises and innovative startups.

Another significant driver is the growing emphasis on data privacy and regulatory compliance. With stringent data protection laws such as GDPR and CCPA in place, organizations are under mounting pressure to safeguard personal information and minimize the risks associated with sharing or processing real-world data. Synthetic data offers a compelling solution by enabling the creation of fully anonymized datasets that retain the statistical properties and utility of original data without exposing sensitive information. This capability is particularly valuable in sectors like healthcare, where patient confidentiality is paramount, and in automotive, where real-world driving data may contain personally identifiable information. By leveraging synthetic data, organizations can unlock new opportunities for research, testing, and collaboration while maintaining regulatory compliance and ethical standards.

The regional outlook for the Synthetic Data Generation for Vision market reveals dynamic growth trajectories across key geographies. North America currently leads the market, driven by a robust ecosystem of AI innovators, early technology adopters, and substantial investments in autonomous systems and smart infrastructure. Europe follows closely, benefiting from strong regulatory frameworks and a thriving research community focused on privacy-preserving AI. The Asia Pacific region is emerging as a high-growth market, propelled by rapid digitalization, government support for AI initiatives, and the burgeoning adoption of computer vision in sectors like manufacturing, retail, and mobility. Meanwhile, Latin America and the Middle East & Africa are witnessing increasing adoption, albeit at a more gradual pace, as local industries recognize the advantages of synthetic data for scaling AI-driven vision solutions.

Component Analysis

The Synthetic Data Generation for Vision market is segmented by component into Software and Services, each playing a pivotal role in the ecosystem. The software segment dominates the market, accounting for a substantial share of global revenues in 2024. This dominance is attributed to the proliferation of advanc

Synthetic Data for Computer Vision Market Outlook

According to our latest research, the synthetic data for computer vision market size reached USD 410 million globally in 2024, with a robust year-on-year growth rate. The market is expected to expand at a CAGR of 32.7% from 2025 to 2033, propelling the industry to a forecasted value of USD 4.62 billion by the end of 2033. This remarkable growth is primarily driven by the escalating demand for high-quality, annotated datasets to train computer vision models, coupled with the increasing adoption of AI and machine learning across diverse sectors. As per our comprehensive analysis, advancements in synthetic data generation technologies and the urgent need to overcome data privacy challenges are pivotal factors accelerating market expansion.

The synthetic data for computer vision market is witnessing exponential growth due to several compelling factors. One of the most significant drivers is the growing complexity of computer vision applications, which require massive volumes of accurately labeled and diverse data. Traditional data collection methods are often time-consuming, expensive, and fraught with privacy concerns, especially in sensitive sectors such as healthcare and security. Synthetic data offers a scalable and cost-effective alternative, enabling organizations to generate vast datasets with customizable attributes, thus facilitating the training of robust and unbiased computer vision models. Additionally, the rise of autonomous vehicles, advanced robotics, and smart surveillance systems is fueling the demand for synthetic data, as these applications necessitate highly accurate and versatile datasets for real-world deployment.

Another key growth factor is the rapid evolution of generative AI and simulation technologies, which have significantly enhanced the quality and realism of synthetic data. Innovations in 3D modeling, photorealistic rendering, and deep learning-based data augmentation have enabled the creation of synthetic datasets that closely mimic real-world scenarios. This technological progress not only improves model performance but also accelerates development cycles, allowing enterprises to bring AI-powered solutions to market faster. Furthermore, synthetic data helps address the issue of data bias by enabling the generation of balanced datasets, which is crucial for ensuring fairness and accuracy in computer vision applications. The growing regulatory scrutiny around data privacy and the implementation of stringent data protection laws globally are further encouraging the shift towards synthetic data solutions.

The expanding ecosystem of AI and machine learning startups, coupled with increasing investments from venture capitalists and large technology firms, is also propelling the synthetic data for computer vision market forward. Organizations across industries are recognizing the strategic value of synthetic data in accelerating innovation while minimizing operational risks associated with real-world data collection. The proliferation of cloud-based synthetic data generation platforms has democratized access to advanced tools, enabling small and medium enterprises to leverage synthetic data for their AI initiatives. As a result, the market is experiencing widespread adoption across automotive, healthcare, retail, robotics, and other sectors, each with unique requirements and use cases for synthetic data.

From a regional perspective, North America currently leads the synthetic data for computer vision market, driven by the presence of major technology companies, robust research and development activities, and early adoption of AI technologies. Europe follows closely, with strong regulatory frameworks and a focus on ethical AI development. The Asia Pacific region is emerging as a high-growth market, fueled by rapid digitalization, increasing investments in AI infrastructure, and a burgeoning ecosystem of AI startups. Latin America and the Middle East & Africa are also witnessing growing interest, particularly in sectors such as security, agriculture, and retail, as organizations seek to harness the benefits of synthetic data to overcome local data collection challenges and accelerate digital transformation.

Component Analysis

The synthetic data for computer vision market is segmented by component into software and services, each playing a crucial role in the ecosystem. The software segment encompasses a wide range of synthetic data ge

Dataset Card for synthetic-data-generation-with-llama3-405B

This dataset has been created with distilabel.

  Dataset Summary

This dataset contains a pipeline.yaml which can be used to reproduce the pipeline that generated it in distilabel using the distilabel CLI: distilabel pipeline run --config "https://huggingface.co/datasets/lukmanaj/synthetic-data-generation-with-llama3-405B/raw/main/pipeline.yaml"

or explore the configuration: distilabel pipeline info… See the full description on the dataset page: https://huggingface.co/datasets/lukmanaj/synthetic-data-generation-with-llama3-405B.

This repository stores synthetic datasets derived from the database of the UK Biobank (UKB) cohort.

The datasets were generated for illustrative purposes, in particular for reproducing specific analyses on the health risks associated with long-term exposure to air pollution using the UKB cohort. The code used to create the synthetic datasets is available and documented in a related GitHub repo, with details provided in the section below. These datasets can be freely used for code testing and for illustrating other examples of analyses on the UKB cohort.

The synthetic data have been used so far in two analyses described in related peer-reviewed publications, which also provide information about the original data sources:

• Vanoli J, et al. Long-term associations between time-varying exposure to ambient PM2.5 and mortality: an analysis of the UK Biobank. Epidemiology. 2025;36(1):1-10. DOI: 10.1097/EDE.0000000000001796 [freely available here, with code provided in this GitHub repo]
• Vanoli J, et al. Confounding issues in air pollution epidemiology: an empirical assessment with the UK Biobank cohort. International Journal of Epidemiology. 2025;54(5):dyaf163. DOI: 10.1093/ije/dyaf163 [freely available here, with code provided in this GitHub repo]

Note: while the synthetic versions of the datasets resemble the real ones in several aspects, the users should be aware that these data are fake and must not be used for testing and making inferences on specific research hypotheses. Even more importantly, these data cannot be considered a reliable description of the original UKB data, and they must not be presented as such.

The work was supported by the Medical Research Council-UK (Grant ID: MR/Y003330/1).

Content

The series of synthetic datasets (stored in two versions with csv and RDS formats) are the following:

• synthbdcohortinfo: basic cohort information regarding the follow-up period and birth/death dates for 502,360 participants.
• synthbdbasevar: baseline variables, mostly collected at recruitment.
• synthpmdata: annual average exposure to PM_2.5 for each participant reconstructed using their residential history.
• synthoutdeath: death records that occurred during the follow-up with date and ICD-10 code.

In addition, this repository provides these additional files:

• codebook: a pdf file with a codebook for the variables of the various datasets, including references to the fields of the original UKB database.
• asscentre: a csv file with information on the assessment centres used for recruitment of the UKB participants, including code, names, and location (as northing/easting coordinates of the British National Grid).
• Countries_December_2022_GB_BUC: a zip file including the shapefile defining the boundaries of the countries in Great Britain (England, Wales, and Scotland), used for mapping purposes [source].

Generation of the synthetic data

The datasets resemble the real data used in the analysis, and they were generated using the R package synthpop (www.synthpop.org.uk). The generation process involves two steps, namely the synthesis of the main data (cohort info, baseline variables, annual PM_2.5 exposure) and then the sampling of death events. The R scripts for performing the data synthesis are provided in the GitHub repo (subfolder Rcode/synthcode).

The first part merges all the data, including the annual PM_2.5 levels, into a single wide-format dataset (with a row for each subject), generates a synthetic version, adds fake IDs, and then extracts (and reshapes) the single datasets. In the second part, a Cox proportional hazard model is fitted on the original data to estimate risks associated with various predictors (including the main exposure represented by PM_2.5), and then these relationships are used to simulate death events in each year. Details on the modelling aspects are provided in the article.

This process guarantees that the synthetic data do not hold specific information about the original records, thus preserving confidentiality. At the same time, the multivariate distribution and correlation across variables, as well as the mortality risks, resemble those of the original data, so the results of descriptive and inferential analyses are similar to those in the original assessments. However, as noted above, the data are used only for illustrative purposes, and they must not be used to test other research hypotheses.

Synthetic datasets generated for the paper "Real Enough to Matter? Implications of Synthetic Data for Reproducible
Learning Analytics" submitted to the LAK'26 conference.

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.

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

Synthetic Data Generation for Robotics Market Outlook

As per our latest research, the global synthetic data generation for robotics market size reached USD 1.42 billion in 2024, demonstrating robust momentum driven by the increasing adoption of robotics across industries. The market is forecasted to grow at a compound annual growth rate (CAGR) of 38.2% from 2025 to 2033, reaching an estimated USD 23.62 billion by 2033. This remarkable growth is fueled by the surging demand for high-quality training datasets to power advanced robotics algorithms and the rapid evolution of artificial intelligence and machine learning technologies.

The primary growth factor for the synthetic data generation for robotics market is the exponential increase in the deployment of robotics systems in diverse sectors such as automotive, healthcare, manufacturing, and logistics. As robotics applications become more complex, there is a pressing need for vast quantities of labeled data to train machine learning models effectively. However, acquiring and labeling real-world data is often costly, time-consuming, and sometimes impractical due to privacy or safety constraints. Synthetic data generation offers a scalable, cost-effective, and flexible alternative by creating realistic datasets that mimic real-world conditions, thus accelerating innovation in robotics and reducing time-to-market for new solutions.

Another significant driver is the advancement of simulation technologies and the integration of synthetic data with digital twin platforms. Robotics developers are increasingly leveraging sophisticated simulation environments to generate synthetic sensor, image, and video data, which can be tailored to cover rare or hazardous scenarios that are difficult to capture in real life. This capability is particularly crucial for applications such as autonomous vehicles and drones, where exhaustive testing in all possible conditions is essential for safety and regulatory compliance. The growing sophistication of synthetic data generation tools, which now offer high fidelity and customizable outputs, is further expanding their adoption across the robotics ecosystem.

Additionally, the market is benefiting from favorable regulatory trends and the growing emphasis on ethical AI development. With increasing concerns around data privacy and the use of sensitive information, synthetic data provides a privacy-preserving solution that enables robust AI model training without exposing real-world identities or confidential business data. Regulatory bodies in North America and Europe are encouraging the use of synthetic data to support transparency, reproducibility, and compliance. This regulatory tailwind, combined with the rising awareness among enterprises about the strategic importance of synthetic data, is expected to sustain the market’s high growth trajectory in the coming years.

From a regional perspective, North America currently dominates the synthetic data generation for robotics market, accounting for the largest share in 2024, followed closely by Europe and Asia Pacific. The strong presence of leading robotics manufacturers, AI startups, and technology giants in these regions, coupled with significant investments in research and development, underpins their leadership. Asia Pacific is anticipated to witness the fastest growth over the forecast period, propelled by rapid industrialization, increasing adoption of automation, and supportive government initiatives in countries such as China, Japan, and South Korea. Meanwhile, emerging markets in Latin America and the Middle East & Africa are beginning to recognize the potential of synthetic data to drive robotics innovation, albeit from a smaller base.

Component Analysis

The synthetic data generation for robotics market is segmented by component into software and services, each playing a vital role in the ecosystem. The software segment currently holds the largest market share, driven by the widespread adoption of advanced synthetic data generation platforms and simulation tools. These software solutions enable robotics developers to create, manipulate, and validate synthetic datasets across various modalities, including image, sensor, and video data. The increasing sophistication of these platforms, which now offer features such as scenario customization, domain randomization, and seamless integration with robotics development environments, is a key factor fueling segment growth. Software providers are also focusing on enhancing the scalability and us

Synthetic Data for Banking Market Outlook

According to our latest research, the synthetic data for banking market size reached USD 583.2 million globally in 2024, driven by the accelerating adoption of artificial intelligence and machine learning in the financial sector. The market is expected to grow at a robust CAGR of 35.7% from 2025 to 2033, projecting a value of approximately USD 7,083.9 million by 2033. This exponential growth is primarily fueled by the increasing need for high-quality, privacy-compliant data to enhance analytics, risk management, and fraud detection capabilities in banking, as per our comprehensive industry analysis.

The rapid evolution of digital banking and financial technologies has created a pressing demand for innovative solutions to address data scarcity and privacy concerns. Traditional banking data, while rich in insights, is often limited by stringent regulatory requirements and privacy laws such as GDPR and CCPA. Synthetic data emerges as a transformative solution, enabling banks to generate realistic, anonymized datasets that facilitate advanced analytics and AI model training without compromising customer confidentiality. The ability to simulate diverse scenarios and rare events using synthetic data is particularly valuable for risk modeling, stress testing, and fraud detection, where real-world data may be insufficient or too sensitive to use. The convergence of regulatory compliance, technological advancement, and the quest for operational agility is thus propelling the synthetic data for banking market forward at an unprecedented pace.

Another key growth factor is the rising sophistication of cyber threats and financial crimes, which necessitates robust fraud detection and prevention systems. Synthetic data plays a crucial role in augmenting these systems by providing vast, varied, and balanced datasets for training machine learning algorithms. Unlike traditional data, synthetic datasets can be engineered to include rare or emerging fraud patterns, enabling banks to proactively identify and mitigate risks. This capability not only enhances the accuracy of fraud detection models but also reduces bias and improves generalization. Furthermore, the integration of synthetic data with advanced analytics tools and cloud-based platforms allows financial institutions to scale their data science initiatives rapidly, driving innovation in customer analytics, credit scoring, and personalized financial services.

The shift towards cloud computing and the adoption of open banking frameworks are also significant drivers for the synthetic data for banking market. Cloud-based synthetic data solutions offer unparalleled scalability, flexibility, and cost-efficiency, making them attractive to banks of all sizes. As financial institutions increasingly collaborate with fintechs and third-party providers, the need for secure, shareable, and compliant data becomes paramount. Synthetic data addresses these challenges by enabling safe data sharing and collaborative model development without exposing real customer information. This not only accelerates digital transformation but also fosters an ecosystem of innovation, where banks can experiment with new products and services in a risk-free environment. The synergy between cloud adoption, data privacy, and open banking is thus creating fertile ground for the widespread adoption of synthetic data technologies in the banking sector.

As the demand for data-driven solutions continues to grow, Synthetic Data as a Service (SDaaS) is emerging as a pivotal offering in the banking sector. This service model allows financial institutions to access synthetic data on-demand, without the need for extensive in-house data generation capabilities. By leveraging SDaaS, banks can quickly obtain high-quality, privacy-compliant datasets tailored to their specific needs, whether for model training, compliance testing, or customer analytics. This flexibility is particularly beneficial for banks with limited data science resources or those seeking to accelerate their AI initiatives. The ability to scale synthetic data usage dynamically aligns with the agile and digital-first strategies that many banks are adopting, enabling them to innovate rapidly while maintaining compliance with stringent data privacy regulations.

From a regional perspe

The Synthetic Data Generation Market is estimated to reach USD 6,637.9 Mn By 2034, Riding on a Strong 35.9% CAGR during forecast period.

Overview This dataset contains 1,000 synthetic financial transactions, mimicking real-world spending behaviors across various expense categories. It is ideal for machine learning, data analysis, and financial modeling tasks such as expense classification, anomaly detection, and trend analysis.

Dataset Features Transaction_ID: Unique identifier for each transaction (e.g., TX0001).

Date: Transaction date (randomly generated within the past year).

Amount: Transaction value (ranging from $5 to $150, following a uniform distribution).

Description: Short description of the transaction.

Merchant: Business or service provider where the transaction occurred.

Category: High-level expense category (e.g., Food & Beverage, Bills, Healthcare).

Categories & Merchants Food & Beverage: Starbucks, McDonald's, Subway, Dunkin

Bills: Local Utility, Internet Provider, Mobile Carrier

Entertainment: AMC Theatres, Netflix, Spotify

Transportation: Uber, Lyft, Local Transit

Groceries: Walmart, Target, Costco

Healthcare: CVS Pharmacy, Walgreens, Local Clinic

Use Cases ✅ Financial Analysis: Understand spending patterns across different categories. ✅ Anomaly Detection: Identify potential fraud by analyzing transaction amounts. ✅ Time-Series Analysis: Study spending behavior trends over time. ✅ Classification & Clustering: Build models to categorize transactions automatically. ✅ Synthetic Data Research: Use it as a benchmark dataset for developing synthetic data generation techniques.

Limitations This dataset is fully synthetic and does not reflect real financial data.

Spending patterns are generated using random sampling, without real-world statistical distributions.

Does not include user profiles, locations, or payment methods.

We developed an Australianised version of Synthea. Synthea is a synthetic data generation software that uses publicly available population aggregate statistics such as demographics, disease prevalence and incidence rates, and health reports. Synthea generates data based on manually curated models of clinical workflows and disease progression that cover a patient’s entire life and does not use real patient data; guaranteeing a completely synthetic dataset. We generated 117,258 synthetic patients from Queensland.