The synthetic data generation market is booming, projected to reach $10 billion by 2033 with a 25% CAGR. Learn about key drivers, trends, and major players shaping this rapidly expanding sector, including AI model training, data privacy, and software testing solutions. Discover market analysis and forecasts for synthetic data generation.

Domain-Adaptive Data Synthesis for Large-Scale Supermarket Product Recognition

This repository contains the data synthesis pipeline and synthetic product recognition datasets proposed in [1].

Data Synthesis Pipeline:

We provide the Blender 3.1 project files and Python source code of our data synthesis pipeline pipeline.zip, accompanied by the FastCUT models used for synthetic-to-real domain translation models.zip. For the synthesis of new shelf images, a product assortment list and product images must be provided in the corresponding directories products/assortment/ and products/img/. The pipeline expects product images to follow the naming convention c.png, with c corresponding to a GTIN or generic class label (e.g., 9120050882171.png). The assortment list, assortment.csv, is expected to use the sample format [c, w, d, h], with c being the class label and w, d, and h being the packaging dimensions of the given product in mm (e.g., [4004218143128, 140, 70, 160]). The assortment list to use and the number of images to generate can be specified in generateImages.py (see comments). The rendering process is initiated by either executing load.py from within Blender or within a command-line terminal as a background process.

Datasets:

• SG3k - Synthetic GroZi-3.2k (SG3k) dataset, consisting of 10,000 synthetic shelf images with 851,801 instances of 3,234 GroZi-3.2k products. Instance-level bounding boxes and generic class labels are provided for all product instances.
• SG3kt - Domain-translated version of SGI3k, utilizing GroZi-3.2k as the target domain. Instance-level bounding boxes and generic class labels are provided for all product instances.
• SGI3k - Synthetic GroZi-3.2k (SG3k) dataset, consisting of 10,000 synthetic shelf images with 838,696 instances of 1,063 GroZi-3.2k products. Instance-level bounding boxes and generic class labels are provided for all product instances.
• SGI3kt - Domain-translated version of SGI3k, utilizing GroZi-3.2k as the target domain. Instance-level bounding boxes and generic class labels are provided for all product instances.
• SPS8k - Synthetic Product Shelves 8k (SPS8k) dataset, comprised of 16,224 synthetic shelf images with 1,981,967 instances of 8,112 supermarket products. Instance-level bounding boxes and GTIN class labels are provided for all product instances.
• SPS8kt - Domain-translated version of SPS8k, utilizing SKU110k as the target domain. Instance-level bounding boxes and GTIN class labels for all product instances.

Table 1: Dataset characteristics.

Sample Format

A sample consists of an RGB image (i.png) and an accompanying label file (i.txt), which contains the labels for all product instances present in the image. Labels use the YOLO format [c, x, y, w, h].

¹SG3k and SG3kt use generic pseudo-GTIN class labels, created by combining the GroZi-3.2k food product category number i (1-27) with the product image index j (j.jpg), following the convention i0000j (e.g., 13000097).

²SGI3k and SGI3kt use the generic GroZi-3.2k class labels from https://arxiv.org/abs/2003.06800.

Download and Use
This data may be used for non-commercial research purposes only. If you publish material based on this data, we request that you include a reference to our paper [1].

[1] Strohmayer, Julian, and Martin Kampel. "Domain-Adaptive Data Synthesis for Large-Scale Supermarket Product Recognition." International Conference on Computer Analysis of Images and Patterns. Cham: Springer Nature Switzerland, 2023.

BibTeX citation:

@inproceedings{strohmayer2023domain,
 title={Domain-Adaptive Data Synthesis for Large-Scale Supermarket Product Recognition},
 author={Strohmayer, Julian and Kampel, Martin},
 booktitle={International Conference on Computer Analysis of Images and Patterns},
 pages={239--250},
 year={2023},
 organization={Springer}
}

Data on businesses collected by statistical agencies are challenging to protect.Many businesses have unique characteristics, and distributions of employment,sales, and profits are highly skewed. Attackers wishing to conduct identificationattacks often have access to much more information than for any individual. Asa consequence, most disclosure avoidance mechanisms fail to strike an accept-able balance between usefulness and confidentiality protection. Detailed aggregatestatistics by geography or detailed industry classes are rare, public-use microdataon businesses are virtually inexistant, and access to confidential microdata can beburdensome. Synthetic microdata have been proposed as a secure mechanism topublish microdata, as part of a broader discussion of how to provide broader accessto such datasets to researchers. In this article, we document an experiment to cre-ate analytically valid synthetic data, using the exact same model and methods previ-ously employed for the United States, for data from two different countries: Canada(Longitudinal Employment Analysis Program (LEAP)) and Germany (EstablishmentHistory Panel (BHP)). We assess utility and protection, and provide an assessmentof the feasibility of extending such an approach in a cost-effective way to other data.

These datasets were made for my Tic Tac Toe neural network agent. Given a tictactoe board (flattened into a vector represented by a string) my implementations of the algorithms choose the optimal move. For something like minimax, this will be objectively the best move. Running the algorithms themselves can be sometimes time consuming whereas training a neural network agent to make the same moves without exploring options can create a less deterministic but faster agent. I limited my neural network approach but this dataset could easily be used to make better agents!!!

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.

Reliable machine learning and statistical analysis rely on diverse, well-distributed training data. However, real-world datasets are often limited in size and exhibit underrepresentation across key subpopulations, leading to biased predictions and reduced performance, particularly in supervised tasks such as classification. To address these challenges, we propose Conditional Data Synthesis Augmentation (CoDSA), a novel framework that leverages generative models, such as diffusion models, to synthesize high-fidelity data for improving model performance across multimodal domains, including tabular, textual, and image data. CoDSA generates synthetic samples that faithfully capture the conditional distributions of the original data, with a focus on under-sampled or high-interest regions. Through transfer learning, CoDSA fine-tunes pre-trained generative models to enhance the realism of synthetic data and increase sample density in sparse areas. This process preserves inter-modal relationships, mitigates data imbalance, improves domain adaptation, and boosts generalization. We also introduce a theoretical framework that quantifies the statistical accuracy improvements enabled by CoDSA as a function of synthetic sample volume and targeted region allocation, providing formal guarantees of its effectiveness. Extensive experiments demonstrate that CoDSA consistently outperforms non-adaptive augmentation strategies and state-of-the-art baselines in both supervised and unsupervised settings.

ydwen/new-synthesized-data-0 dataset hosted on Hugging Face and contributed by the HF Datasets community

ydwen/synthesized-data-1 dataset hosted on Hugging Face and contributed by the HF Datasets community

The Ecological Data Synthesis Tools is a spatially-explicit visualization tool that combines ecological resource layers into a single layer representing relative environmental sensitivity of dredging impacts to provide decision support. The tool incorporates multiple geospatial ecological data layers such as oyster reef habitat and submerged aquatic vegetation, and utilizes existing studies and data to scale the relative risk of each ecological resource to dredging and/or placement activities. The integrated impacts are then weighted across all layers providing an indication of the relative risk of negative project impacts on the environment. The tool was developed as a planning tool to assist Dredged Material Management Plans (DMMP) and preliminary Assessments (PA) project development teams to prioritize efforts and resources in areas of high environmental concern.

1. Specifications Format : 44.1 kHz/48 kHz, 16bit/24bit, uncompressed wav, mono channel.

Recording environment : professional recording studio.

Recording content : general narrative sentences, interrogative sentences, etc.

Speaker : native speaker

Annotation Feature : word transcription, part-of-speech, phoneme boundary, four-level accents, four-level prosodic boundary.

Device : Microphone

Language : American English, British English, Japanese, French, Dutch, Catonese, Canadian French,Australian English, Italian, New Zealand English, Spanish, Mexican Spanish

Application scenarios : speech synthesis

Accuracy rate: Word transcription: the sentences accuracy rate is not less than 99%. Part-of-speech annotation: the sentences accuracy rate is not less than 98%. Phoneme annotation: the sentences accuracy rate is not less than 98% (the error rate of voiced and swallowed phonemes is not included, because the labelling is more subjective). Accent annotation: the word accuracy rate is not less than 95%. Prosodic boundary annotation: the sentences accuracy rate is not less than 97% Phoneme boundary annotation: the phoneme accuracy rate is not less than 95% (the error range of boundary is within 5%)

1. About Nexdata Nexdata owns off-the-shelf PB-level Large Language Model(LLM) Data, 3 million hours of Audio Data and 800TB of Annotated Imagery Data. These ready-to-go AI & ML Training Data support instant delivery, quickly improve the accuracy of AI models. For more details, please visit us at https://www.nexdata.ai/datasets/tts?source=Datarade

This archived Paleoclimatology Study is available from the NOAA National Centers for Environmental Information (NCEI), under the World Data Service (WDS) for Paleoclimatology. The associated NCEI study type is Paleoceanography. The data include parameters of paleocean (reconstruction) with a geographic location of Global. The time period coverage is from 1950 to -50 in calendar years before present (BP). See metadata information for parameter and study location details. Please cite this study when using the data.

The synthetic predictor tables and fully synthetic neuroimaging data produced for the analysis of fully synthetic data in the current study are available as Research Data available from Mendeley Data. Ten fully synthetic datasets include synthetic gray matter images (nifti files) that were generated for analysis with simulated participant data (text files). An archive file predictor_tables.tar.gz contains ten fully synthetic predictor tables with information for 264 simulated subjects. Due to large file sizes, a separate archive was created for each set of synthetic gray matter image data: RBS001.tar.gz, …, RBS010.tar.gz. Regression analyses were performed for each synthetic dataset, then average statistic maps were made for each contrast, which were then smoothed (see accompanying paper for additional information).

The supplementary materials also include commented MATLAB and R code to implement the current neuroimaging data synthesis methods (SKexample.zip). The example data were selected from an earlier fMRI study (Kuchinsky et al., 2012) to demonstrate that the current approach can be used with other types of neuroimaging data. The example code can also be adapted to produce fully synthetic group-level datasets based on observed neuroimaging data from other sources. The zip archive includes a document with important information for performing the example analyses, and details that should be communicated with recipients of a synthetic neuroimaging dataset.

Kuchinsky, S.E., Vaden, K.I., Keren, N.I., Harris, K.C., Ahlstrom, J.B., Dubno, J.R., Eckert, M.A., 2012. Word intelligibility and age predict visual cortex activity during word listening. Cerebral Cortex 22, 1360–71. https://doi.org/10.1093/cercor/bhr211

Dataset

This dataset was created by Atanu Misra

Contents

Test Data Synthesis for Continuous Integration (CI) Market Outlook

According to our latest research, the Global Test Data Synthesis for Continuous Integration (CI) market size was valued at $1.42 billion in 2024 and is projected to reach $5.67 billion by 2033, expanding at a robust CAGR of 16.7% during the forecast period of 2025–2033. The primary driver fueling this market’s exponential growth is the accelerating adoption of DevOps and agile methodologies across enterprises, which demand rapid, reliable, and privacy-compliant test data generation to support continuous integration and delivery pipelines. This surge in demand for automated, scalable, and secure data synthesis solutions is transforming software testing paradigms, ensuring faster time-to-market and improved software quality while adhering to stringent data privacy regulations.

Regional Outlook

North America currently commands the largest share of the global Test Data Synthesis for CI market, accounting for over 38% of total revenue in 2024. This dominance is attributed to the region’s mature technology landscape, early adoption of DevOps and CI/CD practices, and the presence of leading software and cloud service providers. The United States, in particular, leads with its robust IT infrastructure, substantial investments in digital transformation, and strict data privacy laws such as CCPA and HIPAA, which necessitate advanced test data synthesis solutions. Moreover, North American enterprises are increasingly leveraging synthetic data to address compliance and security challenges, further cementing the region’s leadership in this market.

The Asia Pacific region is projected to be the fastest-growing market, with a remarkable CAGR of 20.5% from 2025 to 2033. This growth is propelled by rapid digitalization, burgeoning IT and telecom sectors, and the proliferation of cloud-native startups across countries like India, China, and Singapore. Organizations in this region are investing heavily in automation to enhance software delivery speed and quality, while government initiatives supporting digital infrastructure and data privacy are fostering widespread adoption of test data synthesis tools. The influx of foreign direct investments, coupled with a rising developer ecosystem, is further amplifying demand for scalable and cost-effective continuous integration solutions.

Emerging economies in Latin America and the Middle East & Africa are witnessing gradual adoption, though their market share remains comparatively modest at under 10% combined. Challenges such as limited skilled workforce, budgetary constraints, and inconsistent regulatory frameworks are slowing adoption rates. However, localized demand is steadily increasing as enterprises in these regions recognize the value of synthetic data in overcoming data privacy hurdles and modernizing legacy testing practices. Regional governments are also beginning to introduce data protection policies, which is expected to drive future market penetration and investment in test data synthesis for CI.

Report Scope

Veri Setleri Hakkında / About the Datasets

Bu dosya, çeşitli veri setlerinin özelliklerini ve kullanım alanlarını özetlemektedir. / This document summarizes the features and use cases of various datasets.

  anthracite-org/kalo-opus-instruct-22k-no-refusal

Açıklama / Description: Bu veri seti, çeşitli talimat ve yanıt çiftlerini içeren geniş bir koleksiyondur. Eğitim ve değerlendirme süreçlerinde kullanılmak üzere tasarlanmıştır. / This dataset contains a large collection… See the full description on the dataset page: https://huggingface.co/datasets/Kasimyildirim/Data-Synthesis-422K.

The booming voice synthesis data service market is projected to reach $8 billion by 2033, fueled by AI advancements and rising demand for multilingual voice assistants. Explore market trends, key players, and regional insights in this comprehensive analysis.

Movement Data Synthesis Dataset

  Dataset Summary

This dataset contains 106 examples of movement tracking data specifically designed for training Large Language Models to generate synthetic physiotherapy and rehabilitation movement data. The dataset focuses on left arm circular exercises performed in a clockwise direction, captured using MediaPipe pose estimation technology.

  Intended Use





  Primary Use Cases

Fine-tuning LLMs for synthetic movement data… See the full description on the dataset page: https://huggingface.co/datasets/lucasbrandao/movement-synthesis-dataset.

Dataset

This dataset was created by Minh Nguyen Dich Nhat

Released under Apache 2.0

Contents

Data published in Bradford, J., Shafiei, M., MacLeod, J. et al. Synthesis and characterization of WS2/graphene/SiC van der Waals heterostructures via WO3−x thin film sulfurization. Sci Rep 10,... QUT Research Data Respository Dataset Resource available for download

The ability to synthesize realistic data in a parametrizable way is valuable for a number of reasons, including privacy, missing data imputation, and evaluating the performance of statistical and computational methods. When the underlying data generating process is complex, data synthesis requires approaches that balance realism and simplicity. In this paper, we address the problem of synthesizing sequential categorical data of the type that is increasingly available from mobile applications and sensors that record participant status continuously over the course of multiple days and weeks. We propose the paired Markov Chain (paired-MC) method, a flexible framework that produces sequences that closely mimic real data while providing a straightforward mechanism for modifying characteristics of the synthesized sequences. We demonstrate the paired-MC method on two datasets, one reflecting daily human activity (time use) patterns collected via a smartphone application, and one encoding the intensities of physical activity measured by wearable accelerometers. In both settings, sequences synthesized by paired-MC better capture key characteristics of the real data than alternative approaches. Supplemental materials for this article are available online.