Dataset used in the article entitled 'Synthetic Datasets Generator for Testing Information Visualization and Machine Learning Techniques and Tools'. These datasets can be used to test several characteristics in machine learning and data processing algorithms.

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.

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 2024-2028

The synthetic data generation market size is forecast to increase by USD 2.88 billion at a CAGR of 60.02% between 2023 and 2028.

The global synthetic data generation market is expanding steadily, driven by the growing need for privacy-compliant data solutions and advancements in AI technology. Key factors include the increasing demand for data to train machine learning models, particularly in industries like healthcare services and finance where privacy regulations are strict and the use of predictive analytics is critical, and the use of generative AI and machine learning algorithms, which create high-quality synthetic datasets that mimic real-world data without compromising security.
This report provides a detailed analysis of the global synthetic data generation market, covering market size, growth forecasts, and key segments such as agent-based modeling and data synthesis. It offers practical insights for business strategy, technology adoption, and compliance planning. A significant trend highlighted is the rise of synthetic data in AI training, enabling faster and more ethical development of models. One major challenge addressed is the difficulty in ensuring data quality, as poorly generated synthetic data can lead to inaccurate outcomes.
For businesses aiming to stay competitive in a data-driven global landscape, this report delivers essential data and strategies to leverage synthetic data trends and address quality challenges, ensuring they remain leaders in innovation while meeting regulatory demands

What will be the Size of the Market During the Forecast Period?

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Synthetic data generation offers a more time-efficient solution compared to traditional methods of data collection and labeling, making it an attractive option for businesses looking to accelerate their AI and machine learning projects. The market represents a promising opportunity for organizations seeking to overcome the challenges of data scarcity and privacy concerns while maintaining data diversity and improving the efficiency of their artificial intelligence and machine learning initiatives. By leveraging this technology, technology decision-makers can drive innovation and gain a competitive edge in their respective industries.

Market Segmentation

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

End-user

  Healthcare and life sciences
  Retail and e-commerce
  Transportation and logistics
  IT and telecommunication
  BFSI and others


Type

  Agent-based modelling
  Direct modelling


Data

  Tabular Data
  Text Data
  Image & Video Data
  Others


Offering Band

  Fully Synthetic Data
  Partially Synthetic Data
  Hybrid Synthetic Data


Application

  Data Protection
  Data Sharing
  Predictive Analytics
  Natural Language Processing
  Computer Vision Algorithms
  Others


Geography

  North America

    US
    Canada
    Mexico


  Europe

    Germany
    UK
    France
    Italy


  APAC

    China
    Japan
    India


  Middle East and Africa



  South America

By End-user Insights

The healthcare and life sciences segment is estimated to witness significant growth during the forecast period. In the thriving healthcare and life sciences sector, synthetic data generation is gaining significant traction as a cost-effective and time-efficient alternative to utilizing real-world data. This market segment's rapid expansion is driven by the increasing demand for data-driven insights and the importance of safeguarding sensitive information. One noteworthy application of synthetic data generation is in the realm of computer vision, specifically with geospatial imagery and medical imaging.

For instance, in healthcare, synthetic data can be generated to replicate medical imaging, such as MRI scans and X-rays, for research and machine learning model development without compromising patient privacy. Similarly, in the field of physical security, synthetic data can be employed to enhance autonomous vehicle simulation, ensuring optimal performance and safety without the need for real-world data. By generating artificial datasets, organizations can diversify their data sources and improve the overall quality and accuracy of their machine learning models.

Get a glance at the share of various segments. Request Free Sample

The healthcare and life sciences segment was valued at USD 12.60 million in 2018 and showed a gradual increase during the forecast period.

Regional Insights

North America is estimated to contribute 36% to the growth of the global market during the forecast period. Technavio's analysts have elaborately explained the regional trends and drivers that shape the m

The global synthetic data market size is projected to grow from USD 0.4 billion in the current year to USD 19.22 billion by 2035, representing a CAGR of 42.14%, during the forecast period till 2035

The Synthetic Data Generation Market size is expected to reach a valuation of USD 36.09 Billion in 2033 growing at a CAGR of 39.45%. The research report classifies market by share, trend, demand and based on segmentation by Data Type, Modeling Type, Offering, Application, End Use and Regional Outlook.

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 synthetic data generation market is projected to be worth US$ 300 million in 2024. The market is anticipated to reach US$ 13.0 billion by 2034. The market is further expected to surge at a CAGR of 45.9% during the forecast period 2024 to 2034.

Country-wise Insights

Category-wise Insights

Report Scope

The synthetic data generation market size is projected to grow from USD 307.42 million to USD 18.23 billion, witnessing a CAGR of over 36.9% during the forecast period, between 2025 and 2037. North America region is attributed to hold the largest revenue share of about 33% by 2037 due to the increasing technological advancements in the region.

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.

Synthetic Data Generation Market Size

As per the latest insights from Market.us, the Global Synthetic Data Generation Market is set to reach USD 6,637.98 million by 2034, expanding at a CAGR of 35.7% from 2025 to 2034. The market, valued at USD 313.50 million in 2024, is witnessing rapid growth due to rising demand for high-quality, privacy-compliant, and AI-driven data solutions.

North America dominated in 2024, securing over 35% of the market, with revenues surpassing USD 109.7 million. The region’s leadership is fueled by strong investments in artificial intelligence, machine learning, and data security across industries such as healthcare, finance, and autonomous systems. With increasing reliance on synthetic data to enhance AI model training and reduce data privacy risks, the market is poised for significant expansion in the coming years.

Explore the top companies and key players in the Synthetic Data Generation (SDG) Market with our detailed report. Get insights on key players, market strategies and learn about their market positions and contributions to the industry.

Synthetic datasets (training/validation) for end-to-end Relation Extraction of relationships between Organisms and Natural-Products. The datasets are provided for reproducibility purposes, but, can also be used to train new models. As in the corresponding article, 3 subtypes of synthetic datasets are provided:

Diversity-synt: The seed literature references used in the generation process correspond to the top-500 extracted items per biological kingdoms using the GME-sampler. Random-synt: 5 datasets of equivalent sizes as Diversity-synt, but using randomly sampled seed literature references. Extended-synt: A merge of Diversity-synt and the 5 Random-synt datasets. All datasets were produced with Vicuna-13b-v1.3. Like the model, the produced synthetic data are also submitted to the License of the model used for generation, see the original LLaMA model card. LLaMA is licensed under the LLaMA License, Copyright (c) Meta Platforms, Inc. All Rights Reserved.

The Synthea generated data is provided here as a 1,000 person (1k), 100,000 person (100k), and 2,800,000 persom (2.8m) data sets in the OMOP Common Data Model format. SyntheaTM is a synthetic patient generator that models the medical history of synthetic patients. Our mission is to output high-quality synthetic, realistic but not real, patient data and associated health records covering every aspect of healthcare. The resulting data is free from cost, privacy, and security restrictions. It can be used without restriction for a variety of secondary uses in academia, research, industry, and government (although a citation would be appreciated). You can read our first academic paper here: https://doi.org/10.1093/jamia/ocx079

Parameter Settings of Synthetic Data Generation.

shreyabalaji23/synthetic-data-generation-project dataset hosted on Hugging Face and contributed by the HF Datasets community

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.

Organizations can license synthetic, structured data generated by Syntegra from electronic health record systems of community hospitals across the United States, reaching beyond just claims and Rx data.

The synthetic data provides a detailed picture of the patient's journey throughout their hospital stay, including patient demographic information and payer type, as well as rich data not found in any other sources. Examples of this data include: drugs given (timing and dosing), patient location (e.g., ICU, floor, ER), lab results (timing by day and hour), physician roles (e.g., surgeon, attending), medications given, and vital signs. The participating community hospitals with bed sizes ranging from 25 to 532 provide unique visibility and assessment of variation in care outside of large academic medical centers and healthcare networks.

Our synthetic data engine is trained on a broadly representative dataset made up of deep clinical information of approximately 6 million unique patient records and 18 million encounters over 5 years of history. Notably, synthetic data generation allows for the creation of any number of records needed to power your project.

EHR data is available in the following formats: — Cleaned, analytics-ready (a layer of clean and normalized concepts in Tuva Health’s standard relational data model format — FHIR USCDI (labs, medications, vitals, encounters, patients, etc.)

The synthetic data maintains full statistical accuracy, yet does not contain any actual patients, thus removing any patient privacy liability risk. Privacy is preserved in a way that goes beyond HIPAA or GDPR compliance. Our industry-leading metrics prove that both privacy and fidelity are fully maintained.

— Generate the data needed for product development, testing, demo, or other needs — Access data at a scalable price point — Build your desired population, both in size and demographics — Scale up and down to fit specific needs, increasing efficiency and affordability

Syntegra's synthetic data engine also has the ability to augment the original data: — Expand population sizes, rare cohorts, or outcomes of interest — Address algorithmic fairness by correcting bias or introducing intentional bias — Conditionally generate data to inform scenario planning — Impute missing value to minimize gaps in the data

The publication describes the generation of the synthetic PV time series data set for real locations in the US, including satellite irradiance, rainfall, and a number of physical challenges introduced within the data.

Analyze the market segmentation of the Synthetic Data Generation (SDG) industry. Gain insights into market share distribution with a detailed breakdown of key segments and their growth.