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%.

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.

Dataset

This dataset was created by Takashi Someya

Released under Apache 2.0

Contents

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

AI-Generated Synthetic Tabular Dataset Market Outlook

According to our latest research, the AI-Generated Synthetic Tabular Dataset market size reached USD 1.42 billion in 2024 globally, reflecting the rapid adoption of artificial intelligence-driven data generation solutions across numerous industries. The market is expected to expand at a robust CAGR of 34.7% from 2025 to 2033, reaching a forecasted value of USD 19.17 billion by 2033. This exceptional growth is primarily driven by the increasing need for high-quality, privacy-preserving datasets for analytics, model training, and regulatory compliance, particularly in sectors with stringent data privacy requirements.

One of the principal growth factors propelling the AI-Generated Synthetic Tabular Dataset market is the escalating demand for data-driven innovation amidst tightening data privacy regulations. Organizations across healthcare, finance, and government sectors are facing mounting challenges in accessing and sharing real-world data due to GDPR, HIPAA, and other global privacy laws. Synthetic data, generated by advanced AI algorithms, offers a solution by mimicking the statistical properties of real datasets without exposing sensitive information. This enables organizations to accelerate AI and machine learning development, conduct robust analytics, and facilitate collaborative research without risking data breaches or non-compliance. The growing sophistication of generative models, such as GANs and VAEs, has further increased confidence in the utility and realism of synthetic tabular data, fueling adoption across both large enterprises and research institutions.

Another significant driver is the surge in digital transformation initiatives and the proliferation of AI and machine learning applications across industries. As businesses strive to leverage predictive analytics, automation, and intelligent decision-making, the need for large, diverse, and high-quality datasets has become paramount. However, real-world data is often siloed, incomplete, or inaccessible due to privacy concerns. AI-generated synthetic tabular datasets bridge this gap by providing scalable, customizable, and bias-mitigated data for model training and validation. This not only accelerates AI deployment but also enhances model robustness and generalizability. The flexibility of synthetic data generation platforms, which can simulate rare events and edge cases, is particularly valuable in sectors like finance and healthcare, where such scenarios are underrepresented in real datasets but critical for risk assessment and decision support.

The rapid evolution of the AI-Generated Synthetic Tabular Dataset market is also underpinned by technological advancements and growing investments in AI infrastructure. The availability of cloud-based synthetic data generation platforms, coupled with advancements in natural language processing and tabular data modeling, has democratized access to synthetic datasets for organizations of all sizes. Strategic partnerships between technology providers, research institutions, and regulatory bodies are fostering innovation and establishing best practices for synthetic data quality, utility, and governance. Furthermore, the integration of synthetic data solutions with existing data management and analytics ecosystems is streamlining workflows and reducing barriers to adoption, thereby accelerating market growth.

Regionally, North America dominates the AI-Generated Synthetic Tabular Dataset market, accounting for the largest share in 2024 due to the presence of leading AI technology firms, strong regulatory frameworks, and early adoption across industries. Europe follows closely, driven by stringent data protection laws and a vibrant research ecosystem. The Asia Pacific region is emerging as a high-growth market, fueled by rapid digitalization, government initiatives, and increasing investments in AI research and development. Latin America and the Middle East & Africa are also witnessing growing interest, particularly in sectors like finance and government, though market maturity varies across countries. The regional landscape is expected to evolve dynamically as regulatory harmonization, cross-border data collaboration, and technological advancements continue to shape market trajectories globally.

🧑‍💼 Employee Performance and Salary Dataset

This synthetic dataset simulates employee information in a medium-sized organization, designed specifically for data preprocessing and exploratory data analysis (EDA) tasks in Data Mining and Machine Learning labs.

It includes over 1,000 employee records with realistic variations in age, gender, department, experience, performance score, and salary — along with missing values, duplicates, and outliers to mimic real-world data quality issues.

📊 Columns Description

🧠 Example Lab Tasks

• Identify and impute missing values using mean or median.
• Detect and remove duplicate employee records.
• Detect outliers in Salary using IQR or Z-score.
• Normalize Salary and Performance_Score using Min-Max scaling.
• Encode categorical columns (Gender, Department) for model training.
• Ideal for Regression

🎯 Possible Regression Targets (Dependent Variables)

Salary → Predict salary based on experience, performance, department, and age. Performance_Score → Predict employee performance based on age, experience, and department.

🧩 Example Regression Problem

Predict the employee's salary based on their experience, performance score, and department.

🧠 Sample Features:

X = ['Age', 'Experience_Years', 'Performance_Score', 'Department', 'Gender'] y = ['Salary']

You can apply:

• Linear Regression
• Ridge/Lasso Regression
• Random Forest Regressor
• XGBoost Regressor
• SVR (Support Vector Regression)
• and evaluate with metrics like:

R², MAE, MSE, RMSE, and residual plots.

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 Data for Khmer Word Detection

This dataset contains 10,000 synthetic images and corresponding bounding box labels for training object detection models to detect Khmer words.

The dataset is generated using a custom tool designed to create diverse and realistic training data for computer vision tasks, especially where real annotated data is scarce.

✨ Highlights

• 100,000 images (.png) with random backgrounds and styles.
• Bounding boxes provided in YOLO (.txt) and Pascal VOC (.xml) formats.
• 50+ real background images + unlimited random background colors.
• 250+ different Khmer fonts.
• Randomized effects: brightness, contrast, blur, color jitter, and more.
• Wide variety of text sizes, positions, and layouts.

📂 Folder Structure

/
├── synthetic_images/   # Synthetic images (.png)
├── synthetic_labels/   # YOLO format labels (.txt)
├── synthetic_xml_labels/ # Pascal VOC format labels (.xml)

Each image has corresponding .txt and .xml files with the same filename.

📏 Annotation Formats

• YOLO Format (.txt):
  Each line represents a word, with format: class_id center_x center_y width height All values are normalized between 0 and 1.
  Example: 0 0.235 0.051 0.144 0.081

• Pascal VOC Format (.xml):
  Standard XML structure containing image metadata and bounding box coordinates (absolute pixel values).
  Example: ```xml

🖼️ Image Samples

Each image contains random Khmer words placed naturally over backgrounds, with different font styles, sizes, and visual effects.
The dataset was carefully generated to simulate real-world challenges like:

• Different lighting conditions
• Different text sizes
• Motion blur and color variations

🧠 Use Cases

• Train YOLOv5, YOLOv8, EfficientDet, and other object detection models.
• Fine-tune OCR (Optical Character Recognition) systems for Khmer language.
• Research on low-resource language computer vision tasks.
• Data augmentation for scene text detection.

⚙️ How It Was Generated

1. A random real-world background or random color is chosen.
2. Random Khmer words are selected from a large cleaned text file.
3. Words are rendered with random font, size, color, spacing, and position.
4. Image effects like motion blur and color jitter are randomly applied.
5. Bounding boxes are automatically generated for each word.

🧹 Data Cleaning

• Words were sourced from a cleaned Khmer corpus to avoid duplicates and garbage data.
• Fonts were tested to make sure they render Khmer characters properly.

📢 Important Notes

• This dataset is synthetic. While it simulates real-world conditions, it may not fully replace real-world labeled data for final model evaluation.
• All labels assume one class only (i.e., "word" = class_id 0).

❤️ Credits

• Khmer text data collected from open-source projects such as khmer-text-data.
• Khmer fonts collected from free and open repositories like Khmer Fonts Project.

📈 Future Updates

We plan to release:

• Datasets with rotated bounding boxes for detecting skewed text.
• More realistic mixing of real-world backgrounds and synthetic text.
• Advanced distortions (e.g., handwriting-like simulation).

Stay tuned!

📜 License

This project is licensed under MIT license.

Please credit the original authors when using this data and provide a link to this dataset.

✉️ Contact

If you have any questions or want to collaborate, feel free to reach out:

• 📧 Email: veasnaec@gmail.com
• 🌐 GitHub: Chanveasna ENG

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

Snapshot img

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

Description: This dataset contains 5 sample PDF Electronic Health Records (EHRs), generated as part of a synthetic healthcare data project. The purpose of this dataset is to assist with sales distribution, offering potential users and stakeholders a glimpse of how synthetic EHRs can look and function. These records have been crafted to mimic realistic admission data while ensuring privacy and compliance with all data protection regulations.

Key Features: 1. Synthetic Data: Entirely artificial data created for testing and demonstration purposes. 1. PDF Format: Records are presented in PDF format, commonly used in healthcare systems. 1. Diverse Use Cases: Useful for evaluating tools related to data parsing, machine learning in healthcare, or EHR management systems. 1. Rich Admission Details: Includes admission-related data that highlights the capabilities of synthetic EHR generation.

Potential Use Cases:

• Demonstrating EHR-related tools or services.
• Benchmarking data parsing models for PDF health records.
• Showcasing synthetic healthcare data in sales or marketing efforts.

Feel free to use this dataset for non-commercial testing and demonstration purposes. Feedback and suggestions for improvements are always welcome!

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.

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

Synthetic Evaluation Data Generation Market Outlook

According to our latest research, the synthetic evaluation data generation market size reached USD 1.4 billion globally in 2024, reflecting robust growth driven by the increasing need for high-quality, privacy-compliant data in AI and machine learning applications. The market demonstrated a remarkable CAGR of 32.8% from 2025 to 2033. By the end of 2033, the synthetic evaluation data generation market is forecasted to attain a value of USD 17.7 billion. This surge is primarily attributed to the escalating adoption of AI-driven solutions across industries, stringent data privacy regulations, and the critical demand for diverse, scalable, and bias-free datasets for model training and validation.

One of the primary growth factors propelling the synthetic evaluation data generation market is the rapid acceleration of artificial intelligence and machine learning deployments across various sectors such as healthcare, finance, automotive, and retail. As organizations strive to enhance the accuracy and reliability of their AI models, the need for diverse and unbiased datasets has become paramount. However, accessing large volumes of real-world data is often hindered by privacy concerns, data scarcity, and regulatory constraints. Synthetic data generation bridges this gap by enabling the creation of realistic, scalable, and customizable datasets that mimic real-world scenarios without exposing sensitive information. This capability not only accelerates the development and validation of AI systems but also ensures compliance with data protection regulations such as GDPR and HIPAA, making it an indispensable tool for modern enterprises.

Another significant driver for the synthetic evaluation data generation market is the growing emphasis on data privacy and security. With increasing incidents of data breaches and the rising cost of non-compliance, organizations are actively seeking solutions that allow them to leverage data for training and testing AI models without compromising confidentiality. Synthetic data generation provides a viable alternative by producing datasets that retain the statistical properties and utility of original data while eliminating direct identifiers and sensitive attributes. This allows companies to innovate rapidly, collaborate more openly, and share data across borders without legal impediments. Furthermore, the use of synthetic data supports advanced use cases such as adversarial testing, rare event simulation, and stress testing, further expanding its applicability across verticals.

The synthetic evaluation data generation market is also experiencing growth due to advancements in generative AI technologies, including Generative Adversarial Networks (GANs) and large language models. These technologies have significantly improved the fidelity, diversity, and utility of synthetic datasets, making them nearly indistinguishable from real data in many applications. The ability to generate synthetic text, images, audio, video, and tabular data has opened new avenues for innovation in model training, testing, and validation. Additionally, the integration of synthetic data generation tools into cloud-based platforms and machine learning pipelines has simplified adoption for organizations of all sizes, further accelerating market growth.

From a regional perspective, North America continues to dominate the synthetic evaluation data generation market, accounting for the largest share in 2024. This is largely due to the presence of leading technology vendors, early adoption of AI technologies, and a strong focus on data privacy and regulatory compliance. Europe follows closely, driven by stringent data protection laws and increased investment in AI research and development. The Asia Pacific region is expected to witness the fastest growth during the forecast period, fueled by rapid digital transformation, expanding AI ecosystems, and increasing government initiatives to promote data-driven innovation. Latin America and the Middle East & Africa are also emerging as promising markets, albeit at a slower pace, as organizations in these regions begin to recognize the value of synthetic data for AI and analytics applications.

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

This dataset contains 10,000 synthetic patient records representing a scaled-down US Medicare population. The records were generated by Synthea ( https://github.com/synthetichealth/synthea ) and are completely synthetic and contain no real patient data. This data is presented free of cost and free of restrictions. Each record is stored as one file in HL7 FHIR R4 ( https://www.hl7.org/fhir/ ) containing one Bundle, in JSON. For more information on how this specific population was created, or to generate your own at any scale, see: https://github.com/synthetichealth/populations/tree/master/medicare

High Level Description

This dataset uses Gemma 7B-IT to generate synthetic dataset for the LLM Prompt Recovery competition.

Contributors

Please go upvote these other datasets as my work is not possible without them

• thedrcat's dataset - LLM Prompt Recovery Data
• TBD

First Dataset - 1000 Examples From @thedrcat

Update 1 - February 29, 2024

The only file presently found in this dataset is gemma1000_7b.csv which uses the dataset created by @thedrcat found here: https://www.kaggle.com/datasets/thedrcat/llm-prompt-recovery-data?select=gemma1000.csv

The file below is the file Darek created with two additional columns appended. The first is the output of Gemma 7B-IT (raw based on the instructions below)(vs. 2B-IT that Darek used) and the second is the output with the 'Sure... blah blah

' sentence removed.

I generated things using the following setup:

# I used a vLLM server to host Gemma 7B on paperspace (A100)

# Step 1 - Install vLLM
>>> pip install vllm

# Step 2 - Authenticate HuggingFace CLI (for model weights)
>>> huggingface-cli login --token

OpenSeek-Reasoning-Data

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

  News

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

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