The Generations study is a five-year study designed to examine health and well-being across three generations of lesbians, gay men, and bisexuals (LGB). The study explored identity, stress, health outcomes, and health care and services utilization among LGBs in three generations of adults who came of age during different historical contexts. This collection includes baseline, wave 1, and wave 2 data collected as part of the Generations study. The study aimed to assess whether younger cohorts of LGBs differed from older cohorts in how they viewed their LGB identity and experienced stress related to prejudice and everyday forms of discrimination, as well as whether patterns of resilience differed between different LGB cohorts. Additionally, the study sought to examine how differences in stress experience affected mental health and well-being, including depressive and anxiety symptoms, substance and alcohol use, suicide ideation and behavior, and how younger LGBs utilized LGB-oriented social and health services, relative to older cohorts. In wave 2, respondents were re-interviewed approximately one year after completion of the baseline (wave 1) survey. Only respondents who participated in the original sample of participants were surveyed at wave 2 (i.e., the enhancement oversample was not included in the longitudinal design of this study). In wave 3, respondents were re-interviewed approximately one year after the completion of the wave 2 survey. Demographic variables collected as part of this study include questions related to age, education, race, ethnicity, sexual identity, gender identity, income, employment, and religiosity.

Test Data Generation Tools Market Outlook

The global market size for Test Data Generation Tools was valued at USD 800 million in 2023 and is projected to reach USD 2.2 billion by 2032, growing at a CAGR of 12.1% during the forecast period. The surge in the adoption of agile and DevOps practices, along with the increasing complexity of software applications, is driving the growth of this market.

One of the primary growth factors for the Test Data Generation Tools market is the increasing need for high-quality test data in software development. As businesses shift towards more agile and DevOps methodologies, the demand for automated and efficient test data generation solutions has surged. These tools help in reducing the time required for test data creation, thereby accelerating the overall software development lifecycle. Additionally, the rise in digital transformation across various industries has necessitated the need for robust testing frameworks, further propelling the market growth.

The proliferation of big data and the growing emphasis on data privacy and security are also significant contributors to market expansion. With the introduction of stringent regulations like GDPR and CCPA, organizations are compelled to ensure that their test data is compliant with these laws. Test Data Generation Tools that offer features like data masking and data subsetting are increasingly being adopted to address these compliance requirements. Furthermore, the increasing instances of data breaches have underscored the importance of using synthetic data for testing purposes, thereby driving the demand for these tools.

Another critical growth factor is the technological advancements in artificial intelligence and machine learning. These technologies have revolutionized the field of test data generation by enabling the creation of more realistic and comprehensive test data sets. Machine learning algorithms can analyze large datasets to generate synthetic data that closely mimics real-world data, thus enhancing the effectiveness of software testing. This aspect has made AI and ML-powered test data generation tools highly sought after in the market.

Regional outlook for the Test Data Generation Tools market shows promising growth across various regions. North America is expected to hold the largest market share due to the early adoption of advanced technologies and the presence of major software companies. Europe is also anticipated to witness significant growth owing to strict regulatory requirements and increased focus on data security. The Asia Pacific region is projected to grow at the highest CAGR, driven by rapid industrialization and the growing IT sector in countries like India and China.

Synthetic Data Generation has emerged as a pivotal component in the realm of test data generation tools. This process involves creating artificial data that closely resembles real-world data, without compromising on privacy or security. The ability to generate synthetic data is particularly beneficial in scenarios where access to real data is restricted due to privacy concerns or regulatory constraints. By leveraging synthetic data, organizations can perform comprehensive testing without the risk of exposing sensitive information. This not only ensures compliance with data protection regulations but also enhances the overall quality and reliability of software applications. As the demand for privacy-compliant testing solutions grows, synthetic data generation is becoming an indispensable tool in the software development lifecycle.

Component Analysis

The Test Data Generation Tools market is segmented into software and services. The software segment is expected to dominate the market throughout the forecast period. This dominance can be attributed to the increasing adoption of automated testing tools and the growing need for robust test data management solutions. Software tools offer a wide range of functionalities, including data profiling, data masking, and data subsetting, which are essential for effective software testing. The continuous advancements in software capabilities also contribute to the growth of this segment.

In contrast, the services segment, although smaller in market share, is expected to grow at a substantial rate. Services include consulting, implementation, and support services, which are crucial for the successful deployment and management of test data generation tools. The increasing complexity of IT inf

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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

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 Outloo...

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.

A 2022 survey of adults in the United States found that over 50 percent of them expected the companies to handle their collected data securely, and only that did not make them have a better opinion of a company. When it came to different generations, Gen Z was the less concerned group, with 31 percent of respondents not knowing or having no opinion regarding this. On the other hand, baby boomers were more interested in their data's safety, with 75 percent stating that keeping their data secure is their basic expectation from the companies.

The synthetic data generation market is projected to be worth USD 0.3 billion in 2024. The market is anticipated to reach USD 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 Generations of Talent Study sought to examine several dimensions of quality of employment as experienced by today's multigenerational workforces. The primary goal was to explore how country-related factors and age-related factors affect employees' perceptions of quality of employment. Information was gathered from employees working in 11 different countries including the United States, United Kingdom, China, India, Spain, Brazil, Japan, Mexico, the Netherlands, South Africa, and Botswana. The industry sectors included technology, pharmaceuticals, consulting, energy, and finance. Demographic variables included gender, birth year, race/ethnicity, education, marital status, number of children, hourly wage, salary, and household income.

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

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 Generation Engine Market Outlook

According to our latest research, the global synthetic data generation engine market size reached USD 1.48 billion in 2024. The market is experiencing robust expansion, driven by the increasing demand for privacy-compliant data and advanced analytics solutions. The market is projected to grow at a remarkable CAGR of 35.6% from 2025 to 2033, reaching an estimated USD 18.67 billion by the end of the forecast period. This rapid growth is primarily propelled by the adoption of artificial intelligence (AI) and machine learning (ML) across various industry verticals, along with the escalating need for high-quality, diverse datasets that do not compromise sensitive information.

One of the primary growth factors fueling the synthetic data generation engine market is the heightened focus on data privacy and regulatory compliance. With stringent regulations such as GDPR, CCPA, and HIPAA being enforced globally, organizations are increasingly seeking solutions that enable them to generate and utilize data without exposing real customer information. Synthetic data generation engines provide a powerful means to create realistic, anonymized datasets that retain the statistical properties of original data, thus supporting robust analytics and model development while ensuring compliance with data protection laws. This capability is especially critical for sectors like healthcare, banking, and government, where data sensitivity is paramount.

Another significant driver is the surging adoption of AI and ML models across industries, which require vast volumes of diverse and representative data for training and validation. Traditional data collection methods often fall short due to limitations in data availability, quality, or privacy concerns. Synthetic data generation engines address these challenges by enabling the creation of customized datasets tailored for specific use cases, including rare-event modeling, edge-case scenario testing, and data augmentation. This not only accelerates innovation but also reduces the time and cost associated with data acquisition and labeling, making it a strategic asset for organizations seeking to maintain a competitive edge in AI-driven markets.

Moreover, the increasing integration of synthetic data generation engines into enterprise IT ecosystems is being catalyzed by advancements in cloud computing and scalable software architectures. Cloud-based deployment models are making these solutions more accessible and cost-effective for organizations of all sizes, from startups to large enterprises. The flexibility to generate, store, and manage synthetic datasets in the cloud enhances collaboration, speeds up development cycles, and supports global operations. As a result, cloud adoption is expected to further accelerate market growth, particularly among businesses undergoing digital transformation and seeking to leverage synthetic data for innovation and compliance.

Regionally, North America currently dominates the synthetic data generation engine market, accounting for the largest revenue share in 2024, followed closely by Europe and the Asia Pacific. North America's leadership is attributed to the presence of major technology providers, robust regulatory frameworks, and a high level of AI adoption across industries. Europe is experiencing rapid growth due to strong data privacy regulations and a thriving technology ecosystem, while Asia Pacific is emerging as a lucrative market, driven by digitalization initiatives and increasing investments in AI and analytics. The regional outlook suggests that market expansion will be broad-based, with significant opportunities for vendors and stakeholders across all major geographies.

Component Analysis

The component segment of the synthetic data generation engine market is bifurcated into software and services, each playing a vital role in the overall ecosystem. Software solutions form the backbone of this market, providing the core algorithms and platforms that enable the generation, management, and deployment of synthetic datasets. These platforms are continually evolving, integrating advanced techniques such as generative adversarial networks (GANs), variational autoencoders, and other deep learning models to produce highly realistic and diverse synthetic data. The software segment is anticipated to maintain its dominance throughout the forecast period, as organizations increasingly invest in proprietary and commercial tools to address their un

The performance of statistical methods is frequently evaluated by means of simulation studies. In case of network meta-analysis of binary data, however, available data- generating models are restricted to either inclusion of two-armed trials or the fixed-effect model. Based on data-generation in the pairwise case, we propose a framework for the simulation of random-effect network meta-analyses including multi-arm trials with binary outcome. The only of the common data-generating models which is directly applicable to a random-effects network setting uses strongly restrictive assumptions. To overcome these limitations, we modify this approach and derive a related simulation procedure using odds ratios as effect measure. The performance of this procedure is evaluated with synthetic data and in an empirical example.

The global Synthetic Data Generation Market in terms of revenue was estimated to be worth USD 208.02 million in 2024 and exhibiting a CAGR of 34.91% by 2034

Full edition for scientific use. The GGS (Generations & Gender Programme) is an international panel survey. It provides insight on family compositions, fertility behaviour, life trajectories and gender roles. The data set contains Austrian data from the first wave of the second round of data collection, conducted between 2022 and 2023. Other than the international data set available under https://www.ggp-i.org/ (DOI: https://doi.org/10.17026/dans-z5z-xn8g) it contains Austrian specific variables and additional levels for some individual questions.

The global synthetic data generation market size was valued at over USD 307.42 million in 2024 and is expected to grow at a CAGR of more than 36.9%, surpassing USD 18.24 billion by 2037. The tabular data segment is anticipated to hold a 50% share, due to its role in overcoming privacy issues in synthetic data generation.

Synthetic Data Generation For Ocean Environment With Raycast

## Overview

Synthetic Data Generation For Ocean Environment With Raycast is a dataset for object detection tasks - it contains Human Boat annotations for 6,299 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

The Distributed Generation Market Demand (dGen) model simulates customer adoption of distributed energy resources (DERs) for residential, commercial, and industrial entities in the United States or other countries through 2050. The dGen model can be used for identifying the sectors, locations, and customers for whom adopting DERs would have a high economic value, for generating forecasts as an input to estimate distribution hosting capacity analysis, integrated resource planning, and load forecasting, and for understanding the economic or policy conditions in which DER adoption becomes viable, and for illustrating sensitivity to market and policy changes such as retail electricity rate structures, net energy metering, and technology costs.

A dataset of a survey of intergenerational relations among 2,044 adult members of some 300 three- (and later four-) generation California families: grandparents (then in their sixties), middle-aged parents (then in their early forties), grandchildren (then aged 16 to 26), and later the great-grandchildren as they turn age 16, and further surveys in 1985, 1988, 1991, 1994, 1997 and 2001. This first fully-elaborated generation-sequential design makes it possible to compare sets of parents and adult-children at the same age across different historical periods and addresses the following objectives: # To track life-course trajectories of family intergenerational solidarity and conflict over three decades of adulthood, and across successive generations of family members; # To identify how intergenerational solidarity, and conflict influence the well-being of family members throughout the adult life course and across successive generations; # To chart the effects of socio-historical change on families, intergenerational relationships, and individual life-course development during the past three decades; # To examine women''s roles and relationships in multigenerational families over 30 years of rapid change in the social trajectories of women''s lives. These data can extend understanding of the complex interplay among macro-social change, family functioning, and individual well-being over the adult life-course and across successive generations. Data Availability: Data from 1971-1997 are available through ICPSR as Study number 4076. * Dates of Study: 1971-2001 * Study Features: Longitudinal * Sample Size: ** 345 Three-generational families ** 2,044 Adults (1971 baseline) Link: * ICPSR: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/04076

Synthetic Data Software Market Outlook

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

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

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

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

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

Component Analysis

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

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

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

Motivation

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

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

Collection

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

Preprocessing

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

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

Recommended Uses

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

Known Limitations/Biases

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

Feedback

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

Further Documentation and Resources

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