The global synthetic data tool market is projected to reach USD 10,394.0 million by 2033, exhibiting a CAGR of 34.8% during the forecast period. The growing adoption of AI and ML technologies, increasing demand for data privacy and security, and the rising need for data for training and testing machine learning models are the key factors driving market growth. Additionally, the availability of open-source synthetic data generation tools and the increasing adoption of cloud-based synthetic data platforms are further contributing to market growth. North America is expected to hold the largest market share during the forecast period due to the early adoption of AI and ML technologies and the presence of key vendors in the region. Europe is anticipated to witness significant growth due to increasing government initiatives to promote AI adoption and the growing data privacy concerns. The Asia Pacific region is projected to experience rapid growth due to government initiatives to develop AI capabilities and the increasing adoption of AI and ML technologies in various industries, namely healthcare, retail, and manufacturing.

This dataset contains all recorded and hand-annotated as well as all synthetically generated data as well as representative trained networks used for detection and tracking experiments in the replicAnt - generating annotated images of animals in complex environments using Unreal Engine manuscript. Unless stated otherwise, all 3D animal models used in the synthetically generated data have been generated with the open-source photgrammetry platform scAnt peerj.com/articles/11155/. All synthetic data has been generated with the associated replicAnt project available from https://github.com/evo-biomech/replicAnt.

Abstract:

Deep learning-based computer vision methods are transforming animal behavioural research. Transfer learning has enabled work in non-model species, but still requires hand-annotation of example footage, and is only performant in well-defined conditions. To overcome these limitations, we created replicAnt, a configurable pipeline implemented in Unreal Engine 5 and Python, designed to generate large and variable training datasets on consumer-grade hardware instead. replicAnt places 3D animal models into complex, procedurally generated environments, from which automatically annotated images can be exported. We demonstrate that synthetic data generated with replicAnt can significantly reduce the hand-annotation required to achieve benchmark performance in common applications such as animal detection, tracking, pose-estimation, and semantic segmentation; and that it increases the subject-specificity and domain-invariance of the trained networks, so conferring robustness. In some applications, replicAnt may even remove the need for hand-annotation altogether. It thus represents a significant step towards porting deep learning-based computer vision tools to the field.

Benchmark data

Two video datasets were curated to quantify detection performance; one in laboratory and one in field conditions. The laboratory dataset consists of top-down recordings of foraging trails of Atta vollenweideri (Forel 1893) leaf-cutter ants. The colony was collected in Uruguay in 2014, and housed in a climate chamber at 25°C and 60% humidity. A recording box was built from clear acrylic, and placed between the colony nest and a box external to the climate chamber, which functioned as feeding site. Bramble leaves were placed in the feeding area prior to each recording session, and ants had access to the recording area at will. The recorded area was 104 mm wide and 200 mm long. An OAK-D camera (OpenCV AI Kit: OAK-D, Luxonis Holding Corporation) was positioned centrally 195 mm above the ground. While keeping the camera position constant, lighting, exposure, and background conditions were varied to create recordings with variable appearance: The “base” case is an evenly lit and well exposed scene with scattered leaf fragments on an otherwise plain white backdrop. A “bright” and “dark” case are characterised by systematic over- or underexposure, respectively, which introduces motion blur, colour-clipped appendages, and extensive flickering and compression artefacts. In a separate well exposed recording, the clear acrylic backdrop was substituted with a printout of a highly textured forest ground to create a “noisy” case. Last, we decreased the camera distance to 100 mm at constant focal distance, effectively doubling the magnification, and yielding a “close” case, distinguished by out-of-focus workers. All recordings were captured at 25 frames per second (fps).

The field datasets consists of video recordings of Gnathamitermes sp. desert termites, filmed close to the nest entrance in the desert of Maricopa County, Arizona, using a Nikon D850 and a Nikkor 18-105 mm lens on a tripod at camera distances between 20 cm to 40 cm. All video recordings were well exposed, and captured at 23.976 fps.

Each video was trimmed to the first 1000 frames, and contains between 36 and 103 individuals. In total, 5000 and 1000 frames were hand-annotated for the laboratory- and field-dataset, respectively: each visible individual was assigned a constant size bounding box, with a centre coinciding approximately with the geometric centre of the thorax in top-down view. The size of the bounding boxes was chosen such that they were large enough to completely enclose the largest individuals, and was automatically adjusted near the image borders. A custom-written Blender Add-on aided hand-annotation: the Add-on is a semi-automated multi animal tracker, which leverages blender’s internal contrast-based motion tracker, but also include track refinement options, and CSV export functionality. Comprehensive documentation of this tool and Jupyter notebooks for track visualisation and benchmarking is provided on the replicAnt and BlenderMotionExport GitHub repositories.

Synthetic data generation

Two synthetic datasets, each with a population size of 100, were generated from 3D models of \textit{Atta vollenweideri} leaf-cutter ants. All 3D models were created with the scAnt photogrammetry workflow. A “group” population was based on three distinct 3D models of an ant minor (1.1 mg), a media (9.8 mg), and a major (50.1 mg) (see 10.5281/zenodo.7849059)). To approximately simulate the size distribution of A. vollenweideri colonies, these models make up 20%, 60%, and 20% of the simulated population, respectively. A 33% within-class scale variation, with default hue, contrast, and brightness subject material variation, was used. A “single” population was generated using the major model only, with 90% scale variation, but equal material variation settings.

A Gnathamitermes sp. synthetic dataset was generated from two hand-sculpted models; a worker and a soldier made up 80% and 20% of the simulated population of 100 individuals, respectively with default hue, contrast, and brightness subject material variation. Both 3D models were created in Blender v3.1, using reference photographs.

Each of the three synthetic datasets contains 10,000 images, rendered at a resolution of 1024 by 1024 px, using the default generator settings as documented in the Generator_example level file (see documentation on GitHub). To assess how the training dataset size affects performance, we trained networks on 100 (“small”), 1,000 (“medium”), and 10,000 (“large”) subsets of the “group” dataset. Generating 10,000 samples at the specified resolution took approximately 10 hours per dataset on a consumer-grade laptop (6 Core 4 GHz CPU, 16 GB RAM, RTX 2070 Super).

Additionally, five datasets which contain both real and synthetic images were curated. These “mixed” datasets combine image samples from the synthetic “group” dataset with image samples from the real “base” case. The ratio between real and synthetic images across the five datasets varied between 10/1 to 1/100.

Funding

This study received funding from Imperial College’s President’s PhD Scholarship (to Fabian Plum), and is part of a project that has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (Grant agreement No. 851705, to David Labonte). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

The data annotation and labeling tools market is experiencing robust growth, driven by the escalating demand for high-quality training data in the burgeoning fields of artificial intelligence (AI) and machine learning (ML). The market's expansion is fueled by the increasing adoption of AI across diverse sectors, including autonomous vehicles, healthcare, and finance. These industries require vast amounts of accurately labeled data to train their AI models, leading to a significant surge in the demand for efficient and scalable annotation tools. While precise market sizing for 2025 is unavailable, considering a conservative estimate and assuming a CAGR of 25% (a reasonable figure given industry growth), we can project a market value exceeding $2 billion in 2025, rising significantly over the forecast period (2025-2033). Key trends include the growing adoption of cloud-based solutions, increased automation in the annotation process through AI-assisted tools, and a heightened focus on data privacy and security. The rise of synthetic data generation is also beginning to impact the market, offering potential cost savings and improved data diversity. However, challenges remain. The high cost of skilled annotators, the need for continuous quality control, and the inherent complexities of labeling diverse data types (images, text, audio, video) pose significant restraints on market growth. While leading players like Labelbox, Scale AI, and SuperAnnotate dominate the market with advanced features and robust scalability, smaller companies and open-source tools continue to compete, often focusing on niche applications or offering cost-effective alternatives. The competitive landscape is dynamic, with continuous innovation and mergers and acquisitions shaping the future of this rapidly evolving market. Regional variations in adoption are also expected, with North America and Europe likely leading the market, followed by Asia-Pacific and other regions. This continuous evolution necessitates careful strategic planning and adaptation for businesses operating in or considering entry into this space.

The NIST BGP RPKI IO framework (BRIO) is a test tool only subset of the BGP-SRx Framework. It is an open source implementation and test platform that allows the synthetic generation of test data for emerging BGP security extensions such as RPKI Origin Validation and BGPSec Path Validation and ASPA validation. BRIO is designed in such that it allows the creation of stand alone testbeds, loaded with freely configurable scenarios to study secure BGP implementations. As a result, much functionality is provided.

AI in Generative Adversarial Networks (GANs) Market Outlook

According to our latest research, the global AI in Generative Adversarial Networks (GANs) market size reached USD 2.65 billion in 2024, reflecting robust growth driven by rapid advancements in deep learning and artificial intelligence. The market is expected to register a remarkable CAGR of 31.4% from 2025 to 2033, accelerating the adoption of GANs across diverse industries. By 2033, the market is forecasted to achieve a value of USD 32.78 billion, underscoring the transformative impact of GANs in areas such as image and video generation, data augmentation, and synthetic content creation. This trajectory is supported by the increasing demand for highly realistic synthetic data and the expansion of AI-driven applications across enterprise and consumer domains.

A primary growth factor for the AI in Generative Adversarial Networks market is the exponential increase in the availability and complexity of data that organizations must process. GANs, with their unique adversarial training methodology, have proven exceptionally effective for generating realistic synthetic data, which is crucial for industries like healthcare, automotive, and finance where data privacy and scarcity are significant concerns. The ability of GANs to create high-fidelity images, videos, and even text has enabled organizations to enhance their AI models, improve data diversity, and reduce bias, thereby accelerating the adoption of AI-driven solutions. Furthermore, the integration of GANs with cloud-based platforms and the proliferation of open-source GAN frameworks have democratized access to this technology, enabling both large enterprises and SMEs to harness its potential for innovative applications.

Another significant driver for the AI in Generative Adversarial Networks market is the surge in demand for advanced content creation tools in media, entertainment, and marketing. GANs have revolutionized the way digital content is produced by enabling hyper-realistic image and video synthesis, deepfake generation, and automated design. This has not only streamlined creative workflows but also opened new avenues for personalized content, virtual influencers, and immersive experiences in gaming and advertising. The rapid evolution of GAN architectures, such as StyleGAN and CycleGAN, has further enhanced the quality and scalability of generative models, making them indispensable for enterprises seeking to differentiate their digital offerings and engage customers more effectively in a highly competitive landscape.

The ongoing advancements in hardware acceleration and AI infrastructure have also played a pivotal role in propelling the AI in Generative Adversarial Networks market forward. The availability of powerful GPUs, TPUs, and AI-specific chips has significantly reduced the training time and computational costs associated with GANs, making them more accessible for real-time and large-scale applications. Additionally, the growing ecosystem of AI services and consulting has enabled organizations to overcome technical barriers, optimize GAN deployments, and ensure compliance with evolving regulatory standards. As investment in AI research continues to surge, the GANs market is poised for sustained innovation and broader adoption across sectors such as healthcare diagnostics, autonomous vehicles, financial modeling, and beyond.

From a regional perspective, North America continues to dominate the AI in Generative Adversarial Networks market, accounting for the largest share in 2024, driven by its robust R&D ecosystem, strong presence of leading technology companies, and early adoption of AI technologies. Europe follows closely, with significant investments in AI research and regulatory initiatives promoting ethical AI development. The Asia Pacific region is emerging as a high-growth market, fueled by rapid digital transformation, expanding AI talent pool, and increasing government support for AI innovation. Latin America and the Middle East & Africa are also witnessing steady growth, albeit from a smaller base, as enterprises in these regions begin to explore the potential of GANs for industry-specific applications.

Component Analysis

The AI in Generative Adversarial Networks market is segmented by component into software, hardware, and services, each playing a vital role in the ecosystem’s development and adoption. Software solutions constitute the largest share of the market in 2024, reflecting the growing demand for ad

Augmented Texas 7000-bus synthetic grid Augmented version of the synthetic Texas 7k dataset published by Texas A&M University. The system has been populated with high-resolution distributed photovoltaic (PV) generation, comprising 4,499 PV plants of varying sizes with associated time series for 1 year of operation. This high-resolution dataset was produced following publicly available data and it is free of CEII. Details on the procedure followed to generate the PV dataset can be found in the Open COG Grid Project Year 1 Report (Chapter 6). The technical data of the system is provided using the (open) CTM specification for easy accessibility from Python without additional packages (data can be loaded as a dictionary). The time series for demand and PV production are provided as a HDF5 file, also loadable with standard open-source tools. We additionally provide example scripts for parsing the data in Python. Prepared by LLNL under Contract DE-AC52-07NA27344. LLNL control number: LLNL-DATA-2001833.

Bitext - Customer Service Tagged Training Dataset for LLM-based Virtual Assistants

  Overview

This hybrid synthetic dataset is designed to be used to fine-tune Large Language Models such as GPT, Mistral and OpenELM, and has been generated using our NLP/NLG technology and our automated Data Labeling (DAL) tools. The goal is to demonstrate how Verticalization/Domain Adaptation for the Customer Support sector can be easily achieved using our two-step approach to LLM… See the full description on the dataset page: https://huggingface.co/datasets/bitext/Bitext-customer-support-llm-chatbot-training-dataset.

Privacy-Preserving Synthetic Voice Market Outlook

According to our latest research, the global Privacy-Preserving Synthetic Voice market size reached USD 1.28 billion in 2024, supported by a robust surge in privacy-centric AI applications across industries. The market is expected to grow at a remarkable CAGR of 26.1% from 2025 to 2033, projecting a value of USD 10.82 billion by 2033. This exponential growth is primarily driven by the rising demand for secure, AI-enabled voice solutions that safeguard sensitive user data while enabling seamless human-computer interaction. As privacy regulations tighten and digital transformation accelerates, organizations are increasingly prioritizing privacy-preserving technologies in their voice-based solutions to foster trust and compliance.

One of the key growth factors for the Privacy-Preserving Synthetic Voice market is the global escalation of privacy concerns and regulatory frameworks such as GDPR, CCPA, and emerging data protection laws in Asia Pacific. These regulations mandate stringent data handling and consent protocols, compelling enterprises to adopt synthetic voice technologies that integrate privacy-preserving mechanisms by design. The demand is further amplified by high-profile data breaches and growing consumer awareness regarding the misuse of biometric and voice data. As organizations strive to maintain customer trust and avoid legal repercussions, privacy-preserving synthetic voice solutions offer a strategic advantage by anonymizing, encrypting, and securely processing voice data, thus enabling compliance and competitive differentiation.

Another significant driver is the proliferation of voice-enabled applications across diverse sectors such as healthcare, finance, and customer service. In these sensitive domains, the ability to generate lifelike synthetic voices without compromising user privacy is paramount. Healthcare providers, for instance, are leveraging privacy-preserving voice synthesis for telehealth, patient engagement, and accessibility services, ensuring that patient information remains confidential. Similarly, financial institutions are deploying these solutions in customer support and authentication processes to prevent identity theft and fraud. The integration of advanced AI models, federated learning, and edge computing further enhances the privacy and performance of synthetic voice systems, fueling their adoption across both B2B and B2C markets.

Technological advancements and the democratization of AI development tools are also accelerating market growth. The emergence of open-source frameworks, cloud-based AI services, and hardware accelerators has lowered the barriers to entry, enabling startups and established players alike to innovate rapidly. This has led to the creation of highly customizable and scalable privacy-preserving synthetic voice solutions tailored to specific industry needs. The convergence of natural language processing, deep learning, and secure multi-party computation is enabling more accurate, expressive, and context-aware synthetic voices while maintaining robust privacy safeguards. As a result, enterprises are able to deploy voice interfaces in high-stakes environments such as legal, government, and education, further expanding the addressable market.

From a regional perspective, North America currently dominates the Privacy-Preserving Synthetic Voice market, accounting for over 38% of the global revenue in 2024, followed by Europe and Asia Pacific. The strong presence of leading AI technology providers, early adoption of privacy regulations, and a mature digital ecosystem in these regions have fostered rapid market expansion. Asia Pacific is expected to witness the fastest growth, with a projected CAGR of 29.2% during the forecast period, driven by increasing investments in AI research, a burgeoning digital economy, and evolving regulatory landscapes. Meanwhile, Latin America and the Middle East & Africa are gradually emerging as promising markets, spurred by digital transformation initiatives and rising awareness of data privacy.