Motivation

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

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

Collection

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

Preprocessing

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

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

Recommended Uses

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

Known Limitations/Biases

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

Feedback

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

Further Documentation and Resources

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

The total amount of data created, captured, copied, and consumed globally is forecast to increase rapidly, reaching *** zettabytes in 2024. Over the next five years up to 2028, global data creation is projected to grow to more than *** zettabytes. In 2020, the amount of data created and replicated reached a new high. The growth was higher than previously expected, caused by the increased demand due to the COVID-19 pandemic, as more people worked and learned from home and used home entertainment options more often. Storage capacity also growing Only a small percentage of this newly created data is kept though, as just * percent of the data produced and consumed in 2020 was saved and retained into 2021. In line with the strong growth of the data volume, the installed base of storage capacity is forecast to increase, growing at a compound annual growth rate of **** percent over the forecast period from 2020 to 2025. In 2020, the installed base of storage capacity reached *** zettabytes.

Quantum-AI Synthetic Data Generator Market Outlook

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

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

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

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

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

Component Analysis

To produce a domain-specific dataset, GPT-4 is assigned the role of an engineering design expert. Furthermore, the ontology, which signifies the design process and design entities, is integrated into the prompts to label the synthetic dataset and enhance the GPT model's grasp of the conceptual design process and domain-specific knowledge. Additionally, the CoT prompting technique compels the GPT models to clarify their reasoning process, thereby fostering a deeper understanding of the tasks.

Synthetic data generated with stable diffution. Consists of 6,390 images. Real dataset used for generating: https://zenodo.org/records/10203721. stable diffusiton model (img2img) used for generating: https://github.com/AUTOMATIC1111/stable-diffusion-webui. denoising strength 0.1

Project (practical wotk for Bachelor's paper) where data is used for model training: https://github.com/rkalvitis/Bakalaurs.

This collection comprises of interview and focus group data gathered in 2024-2025 as part of a project aimed at investigating how synthetic data can support secure data access and improve research workflows, particularly from the perspective of data-owning organisations.

The interviews included 4 case studies of UK-based organisations who had piloted work generating and disseminating synthetic datasets, including the Ministry of Justice, NHS England, the project team working in partnership with the Department for Education, and Office for National Statistics. It also includes 2 focus groups with Trusted Repository Environment (TRE) representatives who had published or were considering publishing synthetic data.

The motivation for this collection stemmed from the growing interest in synthetic data as a tool to enhance access to sensitive data and reduce pressure on Trusted Research Environments (TREs). The study explored organisational engagement with two types of synthetic data: synthetic data generated from real data, and “data-free” synthetic data created using metadata only.

The aims of the case studies and focus groups were to assess current practices, explore motivations and barriers to adoption, understand cost and governance models, and gather perspectives on scaling and outsourcing synthetic data production. Conditional logic was used to tailor the survey to organisations actively producing, planning, or not engaging with synthetic data.

The interviews covered 5 key themes: organisational background; Infrastructure, operational costs, and resourcing; challenges of sharing synthetic data; benefits and use cases of synthetic data; and organisational policy and procedures.

The data offers exploratory insights into how UK organisations are approaching synthetic data in practice and can inform future research, infrastructure development, and policy guidance in this evolving area.

The findings have informed recommendations to support the responsible and efficient scaling of synthetic data production across sectors.

The growing discourse around synthetic data underscores its potential not only in addressing data challenges in a fast-paced changing landscape but for fostering innovation and accelerating advancements in data analytics and artificial intelligence. From optimising data sharing and utility (James et al., 2021), to sustaining and promoting reproducibility (Burgard et al., 2017) to mitigating disclosure (Nikolenko, 2021) synthetic data has emerged as a solution to various complexities of the data ecosystem.

The project proposes a mixed-methods approach and seeks to explore the operational, economic, and efficiency aspects of using low-fidelity synthetic data from the perspectives of data owners and Trusted Research Environments (TREs).

The essence of the challenge is in understanding the tangible and intangible costs associated with creating and sharing low-fidelity synthetic data, alongside measuring its utility and acceptance among data producers, data oweners and TREs. The broader aim of the project is to foster a nuanced understanding that could potentially catalyse a shift towards a more efficient and publicly acceptable model of synthetic data dissemination.

This project is centred around three primary goals: 1. to evaluate the comprehensive costs incurred by data owners and TREs in the creation and ongoing maintenance of low-fidelity synthetic data, including the initial production of synthetic data and subsequent costs; 2. to assess the various models of synthetic data sharing, evaluating the implications and efficiencies for data owners and TREs, covering all aspects from pre-ingest to curation procedures, metadata sharing, and data discoverability; and 3. to measure the efficiency improvements for data owners and TREs when synthetic data is available, analysing impacts on resources, secure environment usage load, and the uptake dynamics between synthetic and real datasets by researchers.

Commencing in March 2024, the project will begin with stakeholder engagement, forming an expert panel and aligning collaborative efforts with parallel projects. Following a robust literature review, the project will embark on a methodical data collection journey through a targeted survey with data creators, case studies with d and data owners and providers of synthetic data, and a focus group with TRE representatives. The insights collected from these activities will be analysed and synthesized to draft a comprehensive report delineating the findings and sensible recommendations for scaling up the production and dissemination of low-fidelity synthetic data as applicable.

The potential applications and benefits of the proposed work are diverse. The project aims to provide a solid foundation for data owners and TREs to make informed decisions regarding synthetic data production and sharing. Furthermore, the findings could significantly influence future policy concerning data privacy thereby having a broader impact on the research community and public perception. By fostering a deeper understanding and establishing a dialogue among key stakeholders, this project strives to bridge the existing knowledge gap and push the domain of synthetic data into a new era of informed and efficient usage. Through meticulous data collection and analysis, the project aims to unravel the intricacies of low-fidelity synthetic data, aiming to pave the way for an efficient, cost-effective, and publicly acceptable framework of synthetic data production and dissemination.

Synthetic data generated to represent the structure of data extracted from the UCLH Electronic Health Record. They are selected tables and fields from the OMOP Common Data Model v5.4 with concept_name columns added for readability.These synthetic data are based on the project 'Pollution in preterm birth' that is looking at the relationships between preterm birth and air pollution. The project is run by Tina Chowdhury who is a Reader in Regenerative Medicine at the Centre for Bioengineering, QMUL.These are low fidelity synthetic data generated using datafaker. The columns are currently generated independently so any relationships between them may be nonsensical e.g. birth dates occurring after death dates.These data are artificially generated, any resemblance to real patients is coincidental.

Round 1 Training Dataset The data being generated and disseminated is the training data used to construct trojan detection software solutions. This data, generated at NIST, consists of human level AIs trained to perform a variety of tasks (image classification, natural language processing, etc.). A known percentage of these trained AI models have been poisoned with a known trigger which induces incorrect behavior. This data will be used to develop software solutions for detecting which trained AI models have been poisoned via embedded triggers. This dataset consists of 1000 trained, human level, image classification AI models using the following architectures (Inception-v3, DenseNet-121, and ResNet50). The models were trained on synthetically created image data of non-real traffic signs superimposed on road background scenes. Half (50%) of the models have been poisoned with an embedded trigger which causes misclassification of the images when the trigger is present. Errata: This dataset had a software bug in the trigger embedding code that caused 4 models trained for this dataset to have a ground truth value of 'poisoned' but which did not contain any triggers embedded. These models should not be used. Models Without a Trigger Embedded: id-00000184 id-00000599 id-00000858 id-00001088 Google Drive Mirror: https://drive.google.com/open?id=1uwVt3UCRL2fCX9Xvi2tLoz_z-DwbU6Ce

Synthetic data generated to represent the structure of data extracted from the UCLH Electronic Health Record. They are selected tables and fields from the OMOP Common Data Model v5.4 with concept_name columns added for readability.These synthetic data are based on a project using medical imaging to detect misplaced Nasogastric tubes.These are low fidelity synthetic data generated using datafaker. The columns are currently generated independently so any relationships between them may be nonsensical e.g. birth dates occurring after death dates.These data are artificially generated, any resemblance to real patients is coincidental.

Data Science Platform Market Outlook

The global data science platform market size was valued at approximately USD 49.3 billion in 2023 and is projected to reach USD 174.4 billion by 2032, growing at a compound annual growth rate (CAGR) of 15.1% during the forecast period. This exponential growth can be attributed to the increasing demand for data-driven decision-making processes, the surge in big data technologies, and the need for more advanced analytics solutions across various industries.

One of the primary growth factors driving the data science platform market is the rapid digital transformation efforts undertaken by organizations globally. Companies are shifting towards data-centric business models to gain a competitive edge, improve operational efficiency, and enhance customer experiences. The proliferation of IoT devices and the subsequent explosion of data generated have further propelled the need for sophisticated data science platforms capable of analyzing vast datasets in real-time. This transformation is not only seen in large enterprises but also increasingly in small and medium enterprises (SMEs) that recognize the potential of data analytics in driving business growth.

Moreover, the advancements in artificial intelligence (AI) and machine learning (ML) technologies have significantly augmented the capabilities of data science platforms. These technologies enable the automation of complex data analysis processes, allowing for more accurate predictions and insights. As a result, sectors such as healthcare, finance, and retail are increasingly adopting data science solutions to leverage AI and ML for personalized services, fraud detection, and supply chain optimization. The integration of AI/ML into data science platforms is thus a critical factor contributing to market growth.

Another crucial factor is the growing regulatory and compliance requirements across various industries. Organizations are mandated to ensure data accuracy, security, and privacy, necessitating the adoption of robust data science platforms that can handle these aspects efficiently. The implementation of regulations such as the General Data Protection Regulation (GDPR) in Europe and the California Consumer Privacy Act (CCPA) in the United States has compelled organizations to invest in advanced data management and analytics solutions. These regulatory frameworks are not only a challenge but also an opportunity for the data science platform market to innovate and provide compliant solutions.

Regionally, North America dominates the data science platform market due to the early adoption of advanced technologies, a strong presence of key market players, and significant investments in research and development. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period. This growth can be attributed to the increasing digitalization initiatives, a growing number of tech startups, and the rising demand for analytics solutions in countries like China, India, and Japan. The competitive landscape and economic development in these regions are creating ample opportunities for market expansion.

Component Analysis

The data science platform market, segmented by components, includes platforms and services. The platform segment encompasses software and tools designed for data integration, preparation, and analysis, while the services segment covers professional and managed services that support the implementation and maintenance of these platforms. The platform component is crucial as it provides the backbone for data science operations, enabling data scientists to perform data wrangling, model building, and deployment efficiently. The increasing demand for customized solutions tailored to specific business needs is driving the growth of the platform segment. Additionally, with the rise of open-source platforms, organizations have more flexibility and control over their data science workflows, further propelling this segment.

On the other hand, the services segment is equally vital as it ensures that organizations can effectively deploy and utilize data science platforms. Professional services include consulting, training, and support, which help organizations in the seamless integration of data science solutions into their existing IT infrastructure. Managed services provide ongoing support and maintenance, ensuring data science platforms operate optimally. The rising complexity of data ecosystems and the shortage of skilled data scientists are factors contributing to the growth of the services segment, as organizations often rely on external expert

Introduction

The Global AI-Generated Synthetic Passenger Data Market is expected to reach USD 22,412.5 million by 2034, rising from USD 850.6 million in 2024, growing at a CAGR of 38.7%. In 2024, North America dominated with a 38.9% share, generating USD 330.8 million in revenue.

Growth is driven by the increasing use of synthetic passenger datasets in aviation, transportation planning, and autonomous mobility solutions. Enhanced demand for data privacy, simulation testing, and AI-driven predictive modeling is accelerating adoption across industries, ensuring realistic datasets without compromising sensitive passenger information.

This survey shows the plans of enterprises to make use of data generated by the internet of things (IoT), as of August 2017. Seventy percent of the respondents were reportedly already using that data to improve customer experience and a further ** percent were expecting to do so in the near future.

The data being generated and disseminated is the test data used to evaluate trojan detection software solutions. This data, generated at NIST, consists of human level AIs trained to perform a variety of tasks (image classification, natural language processing, etc.). A known percentage of these trained AI models have been poisoned with a known trigger which induces incorrect behavior. This data will be used to develop software solutions for detecting which trained AI models have been poisoned via embedded triggers. This dataset consists of 144 trained, human level, image classification AI models using a variety of model architectures. The models were trained on synthetically created image data of non-real traffic signs superimposed on road background scenes. Half (50%) of the models have been poisoned with an embedded trigger which causes misclassification of the images when the trigger is present.

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 external knowledge graphs.

  Generated Datasets

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

Automotive Data Platform Market Outlook

In 2023, the global market size for the Automotive Data Platform is estimated to be around USD 2.8 billion, and it is projected to reach approximately USD 10.6 billion by 2032, growing at a Compound Annual Growth Rate (CAGR) of 15.7% during the forecast period. This growth is driven by the increasing demand for advanced data analytics in the automotive sector to enhance vehicle performance, safety, and customer experience.

The rapid adoption of connected vehicles is a significant growth factor for the automotive data platform market. Connected vehicles generate a massive amount of data, which can be analyzed to provide insights into driving patterns, vehicle performance, and predictive maintenance. This, in turn, helps in reducing operational costs, improving vehicle safety, and enhancing the overall driving experience. As a result, the demand for robust automotive data platforms is on the rise, fueling market growth.

Another driving factor is the growing emphasis on regulatory compliance and safety standards in the automotive industry. Governments across the globe are implementing stringent regulations related to vehicle safety, emissions, and data security. Automotive data platforms help manufacturers comply with these regulations by providing accurate and real-time data on various parameters, such as emissions, vehicle diagnostics, and driver behavior. This regulatory push is expected to further propel the market growth.

Technological advancements in artificial intelligence (AI) and machine learning (ML) are also contributing to the growth of the automotive data platform market. The integration of AI and ML algorithms with automotive data platforms enables advanced data analytics, such as predictive maintenance, driver assistance, and personalized infotainment. These advancements not only enhance vehicle performance and safety but also provide a seamless and personalized driving experience, thereby driving the market demand.

The integration of the Automotive Internet of Things (IoT) is revolutionizing the automotive data platform market. By connecting vehicles to the internet and each other, the IoT enables the seamless exchange of data across various systems and devices. This connectivity allows for real-time monitoring and analysis of vehicle performance, driver behavior, and environmental conditions. As a result, automotive manufacturers and service providers can offer enhanced safety features, predictive maintenance, and personalized driving experiences. The growing adoption of IoT in the automotive sector is expected to significantly boost the demand for advanced data platforms, as they provide the necessary infrastructure for managing and analyzing the vast amounts of data generated by connected vehicles.

In terms of regional outlook, North America holds a significant share of the automotive data platform market due to the presence of leading automotive manufacturers and technology providers. The region is also witnessing a high adoption rate of connected vehicles and advanced driver assistance systems (ADAS), which generates a substantial amount of data, thereby driving the demand for automotive data platforms. Europe and Asia Pacific regions are also expected to witness considerable growth during the forecast period, owing to the increasing adoption of electric vehicles and the rising focus on vehicle safety and emissions regulations.

Component Analysis

The automotive data platform market is segmented by components into software, hardware, and services. The software segment holds a significant share of the market due to the growing demand for advanced data analytics and real-time data processing. Software solutions enable the collection, storage, and analysis of vast amounts of data generated by connected vehicles, which helps in improving vehicle performance, safety, and customer experience. The increasing adoption of AI and ML algorithms in software solutions further enhances their capabilities, driving the demand for automotive data platforms.

The hardware segment also plays a crucial role in the automotive data platform market. Hardware components, such as sensors, processors, and communication modules, are essential for collecting and transmitting data from vehicles to data platforms. The advancements in sensor technologies and the increasing integration of IoT (Internet of Things) devices in vehicles are driving the growth of the har

The amount of global healthcare data is expected to increase dramatically by the year 2020. Early estimates from 2013 suggest that there were about 153 exabytes of healthcare data generated in that year. However, projections indicate that there could be as much as 2,314 exabytes of new data generated in 2020.

The dataset used in this paper is a collection of synthetic market data generated via a geometric Brownian motion.

This Repo provides all datasets or their external links for the project CoSense3D. The related datasets are: COMAP: A synthetic data generated by CARLA for cooperative perception. OPV2Vt: A synthetic data generated by CARLA with the replay files provided by the dataset OPV2V for the purpose of globally time-aligned cooperative object detection (TA-COOD). The original replay files are interpolated to obtain the object and sensor locations at sub-frames. Each frame is spitted into 10 sub-frames for simulation. DairV2Xt: New generate meta files based on dataset DAIR-V2X for the project CoSense3D with localization correction and ground truth generate for TA-COOD. OPV2Va: A synthetic data generated by CARLA with the replay files provided by the dataset OPV2V augmented with semantic labels.

QUEST projects both used and produced an immense variety of global data sets that needed to be shared efficiently between the project teams. These global synthesis data sets are also a key part of QUEST's legacy, providing a powerful way of communicating the results of QUEST among and beyond the UK Earth System research community.

This dataset contains soil data generated from ISLSCP II.

The International Satellite Land Surface Climatology Project, Initiative II (ISLSCP II) is a follow on project from The International Satellite Land Surface Climatology Project (ISLSCP). ISLSCP II had the lead role in addressing land-atmosphere interactions - process modelling, data retrieval algorithms, field experiment design and execution, and the development of global data sets.

Test Data Management Market size was valued at USD 1.54 Billion in 2024 and is projected to reach USD 2.97 Billion by 2032, growing at a CAGR of 11.19% from 2026 to 2032.

Test Data Management Market Drivers

Increasing Data Volumes: The exponential growth in data generated by businesses necessitates efficient management of test data. Effective TDM solutions help organizations handle large volumes of data, ensuring accurate and reliable testing processes.

Need for Regulatory Compliance: Stringent data privacy regulations, such as GDPR, HIPAA, and CCPA, require organizations to protect sensitive data. TDM solutions help ensure compliance by masking or anonymizing sensitive data used in testing environments.