Xverum’s AI & ML Training Data provides one of the most extensive datasets available for AI and machine learning applications, featuring 800M B2B profiles with 100+ attributes. This dataset is designed to enable AI developers, data scientists, and businesses to train robust and accurate ML models. From natural language processing (NLP) to predictive analytics, our data empowers a wide range of industries and use cases with unparalleled scale, depth, and quality.

What Makes Our Data Unique?

Scale and Coverage: - A global dataset encompassing 800M B2B profiles from a wide array of industries and geographies. - Includes coverage across the Americas, Europe, Asia, and other key markets, ensuring worldwide representation.

Rich Attributes for Training Models: - Over 100 fields of detailed information, including company details, job roles, geographic data, industry categories, past experiences, and behavioral insights. - Tailored for training models in NLP, recommendation systems, and predictive algorithms.

Compliance and Quality: - Fully GDPR and CCPA compliant, providing secure and ethically sourced data. - Extensive data cleaning and validation processes ensure reliability and accuracy.

Annotation-Ready: - Pre-structured and formatted datasets that are easily ingestible into AI workflows. - Ideal for supervised learning with tagging options such as entities, sentiment, or categories.

How Is the Data Sourced? - Publicly available information gathered through advanced, GDPR-compliant web aggregation techniques. - Proprietary enrichment pipelines that validate, clean, and structure raw data into high-quality datasets. This approach ensures we deliver comprehensive, up-to-date, and actionable data for machine learning training.

Primary Use Cases and Verticals

Natural Language Processing (NLP): Train models for named entity recognition (NER), text classification, sentiment analysis, and conversational AI. Ideal for chatbots, language models, and content categorization.

Predictive Analytics and Recommendation Systems: Enable personalized marketing campaigns by predicting buyer behavior. Build smarter recommendation engines for ecommerce and content platforms.

B2B Lead Generation and Market Insights: Create models that identify high-value leads using enriched company and contact information. Develop AI systems that track trends and provide strategic insights for businesses.

HR and Talent Acquisition AI: Optimize talent-matching algorithms using structured job descriptions and candidate profiles. Build AI-powered platforms for recruitment analytics.

How This Product Fits Into Xverum’s Broader Data Offering Xverum is a leading provider of structured, high-quality web datasets. While we specialize in B2B profiles and company data, we also offer complementary datasets tailored for specific verticals, including ecommerce product data, job listings, and customer reviews. The AI Training Data is a natural extension of our core capabilities, bridging the gap between structured data and machine learning workflows. By providing annotation-ready datasets, real-time API access, and customization options, we ensure our clients can seamlessly integrate our data into their AI development processes.

Why Choose Xverum? - Experience and Expertise: A trusted name in structured web data with a proven track record. - Flexibility: Datasets can be tailored for any AI/ML application. - Scalability: With 800M profiles and more being added, you’ll always have access to fresh, up-to-date data. - Compliance: We prioritize data ethics and security, ensuring all data adheres to GDPR and other legal frameworks.

Ready to supercharge your AI and ML projects? Explore Xverum’s AI Training Data to unlock the potential of 800M global B2B profiles. Whether you’re building a chatbot, predictive algorithm, or next-gen AI application, our data is here to help.

Contact us for sample datasets or to discuss your specific needs.

Extreme weather events, including fires, heatwaves, and droughts, have significant impacts on earth, environmental, and energy systems. Mechanistic and predictive understanding, as well as probabilistic risk assessment of these extreme weather events, are crucial for detecting, planning for, and responding to these extremes. Records of extreme weather events provide an important data source for understanding present and future extremes, but the existing data needs preprocessing before it can be used for analysis. Moreover, there are many nonstandard metrics defining the levels of severity or impacts of extremes. In this study, we compile a comprehensive benchmark data inventory of extreme weather events, including fires, heatwaves, and droughts. The dataset covers the period from 2001 to 2020 with a daily temporal resolution and a spatial resolution of 0.5°×0.5° (~55km×55km) over the continental United States (CONUS), and a spatial resolution of 1km × 1km over the Pacific Northwest (PNW) region, together with the co-located and relevant meteorological variables. By exploring and summarizing the spatial and temporal patterns of these extremes in various forms of marginal, conditional, and joint probability distributions, we gain a better understanding of the characteristics of climate extremes. The resulting AI/ML-ready data products can be readily applied to ML-based research, fostering and encouraging AI/ML research in the field of extreme weather. This study can contribute significantly to the advancement of extreme weather research, aiding researchers, policymakers, and practitioners in developing improved preparedness and response strategies to protect communities and ecosystems from the adverse impacts of extreme weather events. Usage Notes We presented a long term (2001-2020) and comprehensive data inventory of historical extreme events with daily temporal resolution covering the separate spatial extents of CONUS (0.5°×0.5°) and PNW(1km×1km) for various applications and studies. The dataset with 0.5°×0.5° resolution for CONUS can be used to help build more accurate climate models for the entire CONUS, which can help in understanding long-term climate trends, including changes in the frequency and intensity of extreme events, predicting future extreme events as well as understanding the implications of extreme events on society and the environment. The data can also be applied for risk accessment of the extremes. For example, ML/AI models can be developed to predict wildfire risk or forecast HWs by analyzing historical weather data, and past fires or heateave , allowing for early warnings and risk mitigation strategies. Using this dataset, AI-driven risk assessment models can also be built to identify vulnerable energy and utilities infrastructure, imrpove grid resilience and suggest adaptations to withstand extreme weather events. The high-resolution 1km×1km dataset ove PNW are advantageous for real-time, localized and detailed applications. It can enhance the accuracy of early warning systems for extreme weather events, helping authorities and communities prepare for and respond to disasters more effectively. For example, ML models can be developed to provide localized HW predictions for specific neighborhoods or cities, enabling residents and local emergency services to take targeted actions; the assessment of drought severity in specific communities or watersheds within the PNW can help local authorities manage water resources more effectively.

Description:

The dataset ‘DMSP Particle Precipitation AI-ready Data’ accompanies the manuscript “Next generation particle precipitation: Mesoscale prediction through machine learning (a case study and framework for progress)” submitted to AGU Space Weather Journal and used to produce new machine learning models of particle precipitation from the magnetosphere to the ionosphere. Note that we have attempted to make these data ready to be used in artificial intelligence/machine learning explorations following a community definition of ‘AI-ready’ provided at https://github.com/rmcgranaghan/data_science_tools_and_resources/wiki/Curated-Reference%7CChallenge-Data-Sets

The purpose of publishing these data is two-fold:

1. To allow reuse of the data that led to the manuscript and extension, rather than reinvention, of the research produced there; and

2. To be an ‘AI-ready’ challenge data set to which the artificial intelligence/machine learning community can apply novel methods.

These data were compiled, curated, and explored by: Ryan McGranaghan, Enrico Camporeale, Kristina Lynch, Jack Ziegler, Téo Bloch, Mathew Owens, Jesper Gjerloev, Spencer Hatch, Binzheng Zhang, and Susan Skone

Pipeline for creation:

The steps to create the data were (Note that we do not provide intermediate datasets):

1. Access NASA-provided DMSP data at https://cdaweb.gsfc.nasa.gov/pub/data/dmsp/

2. Read CDF files for given satellite (e.g., F-16)

3. Collect the following variables at one-second cadence: SC_AACGM_LAT, SC_AACGM_LTIME, ELE_TOTAL_ENERGY_FLUX, ELE_TOTAL_ENERGY_FLUX_STD, ELE_AVG_ENERGY, ELE_AVG_ENERGY_STD, ID_SC

4. Sub-sample the variables to one-minute cadence and eliminate any rows for which ELE_TOTAL_ENERGY_FLUX is NaN

5. Combine all individual satellites into single yearly files

6. For each yearly file, use nasaomnireader to obtain solar wind and geomagnetic index data programmatically and timehist2 to calculate the time histories of each parameter. Collate with the DMSP observations and remove rows for which any solar wind or geomagnetic index data are missing.

7. For each row, calculate cyclical time variables (e.g., local time -> sin(LT) and cos(LT))

8. Merge all years

How to use:

The Github repository https://github.com/rmcgranaghan/precipNet is provided to detail the use of these data and to provide Jupyter notebooks to facilitate getting started. The code is implemented in Python 3 and is licensed under the GNU General Public License v3.0

Citation:

For anyone using these data, please cite each of the following papers:

• McGranaghan, R. M., Ziegler, J., Bloch, T., Hatch, S., Camporeale, E., Lynch, K., et al. (2021). Toward a next generation particle precipitation model: Mesoscale prediction through machine learning (a case study and framework for progress). Space Weather, 19, e2020SW002684. https://doi.org/10.1029/2020SW002684

• McGranaghan, R. (2019), Eight lessons I learned leading a scientific “design sprint”, Eos, 100, https://doi.org/10.1029/2019EO136427. Published on 11 November 2019.

For questions or comments please contact Ryan McGranaghan (ryan.mcgranaghan@gmail.com)

The study examines variables to assess teachers' preparedness for integrating AI into South African schools. The dataset on the Excel sheet consists of 42 columns. The first ten columns comprise demographic variables such as Gender, Years of Teaching Experience (TE), Age Group, Specialisation (SPE), School Type (ST), School Location (SL), School Description (SD), Level of Technology Usage for Teaching and Learning (LTUTL), Undergone Training/Workshop/Seminar on AI Integration into Teaching and Learning Before (TRAIN), and if Yes, Have You Used Any AI Tools to Teach Before (TEACHAI). Columns 11 to 42 contain constructs measuring teachers' preparedness for integrating AI into the school system. These variables are measured on a scale of 1 = strongly disagree to 6 = strongly agree.

AI Ethics (AE): This variable captures teachers' perspectives on incorporating discussions about AI ethics into the curriculum.

Attitude Towards Using AI (AT): This variable reflects teachers' beliefs about the benefits of using AI in their teaching practices. It includes their expectations of having a positive experience with AI, improving their teaching experience, and enhancing their participation in critical discussions through AI applications.

Technology Integration (TI): This variable measures teachers' comfort in integrating AI tools and technologies into lesson plans. It also assesses their belief that AI enhances the learning experience for students, their proactive efforts to learn about new AI tools, and the importance they place on technology integration for effective AI education.

Social Influence (SI): This variable examines the impact of colleagues, administrative support, peer discussions, and parental expectations on teachers' preparedness to incorporate AI into their teaching practices.

Technological Pedagogical Content Knowledge (TPACK): This variable assesses teachers' ability to use technology to facilitate AI learning. It includes their capability to select appropriate technology for teaching specific AI content, and bring real-life examples into lessons.

AI Professional Development (AIPD): This variable evaluates the impact of professional development training on teachers' ability to teach AI effectively. It includes the adequacy of these programs, teachers' proactive pursuit of further professional development opportunities, and schools' provision of such opportunities.

AI Teaching Preparedness (AITP): This variable measures teachers' feelings of preparedness to teach AI. It includes their belief that their teaching methods are engaging, their confidence in adapting AI content for different student needs, and their proactive efforts to improve their teaching skills for AI education.

Perceived Self-Efficacy to Teaching AI (PSE): This variable captures teachers' confidence in their ability to teach AI concepts, address challenges in teaching AI, and create innovative AI-related teaching materials.

This dataset features over 25,000,000 high-quality general-purpose images sourced from photographers worldwide. Designed to support a wide range of AI and machine learning applications, it offers a richly diverse and extensively annotated collection of everyday visual content.

Key Features: 1. Comprehensive Metadata: the dataset includes full EXIF data, detailing camera settings such as aperture, ISO, shutter speed, and focal length. Additionally, each image is pre-annotated with object and scene detection metadata, making it ideal for tasks like classification, detection, and segmentation. Popularity metrics, derived from engagement on our proprietary platform, are also included.

2.Unique Sourcing Capabilities: the images are collected through a proprietary gamified platform for photographers. Competitions spanning various themes ensure a steady influx of diverse, high-quality submissions. Custom datasets can be sourced on-demand within 72 hours, allowing for specific requirements—such as themes, subjects, or scenarios—to be met efficiently.

1. Global Diversity: photographs have been sourced from contributors in over 100 countries, covering a wide range of human experiences, cultures, environments, and activities. The dataset includes images of people, nature, objects, animals, urban and rural life, and more—captured across different times of day, seasons, and lighting conditions.

2. High-Quality Imagery: the dataset includes images with resolutions ranging from standard to high-definition to meet the needs of various projects. Both professional and amateur photography styles are represented, offering a balance of realism and creativity across visual domains.

3. Popularity Scores: each image is assigned a popularity score based on its performance in GuruShots competitions. This unique metric reflects how well the image resonates with a global audience, offering an additional layer of insight for AI models focused on aesthetics, engagement, or content curation.

4. AI-Ready Design: this dataset is optimized for AI applications, making it ideal for training models in general image recognition, multi-label classification, content filtering, and scene understanding. It integrates easily with leading machine learning frameworks and pipelines.

5. Licensing & Compliance: the dataset complies fully with data privacy regulations and offers transparent licensing for both commercial and academic use.

Use Cases: 1. Training AI models for general-purpose image classification and tagging. 2. Enhancing content moderation and visual search systems. 3. Building foundational datasets for large-scale vision-language models. 4. Supporting research in computer vision, multimodal AI, and generative modeling.

This dataset offers a comprehensive, diverse, and high-quality resource for training AI and ML models across a wide array of domains. Customizations are available to suit specific project needs. Contact us to learn more!

Success.ai’s Company Data Solutions provide businesses with powerful, enterprise-ready B2B company datasets, enabling you to unlock insights on over 28 million verified company profiles. Our solution is ideal for organizations seeking accurate and detailed B2B contact data, whether you’re targeting large enterprises, mid-sized businesses, or small business contact data.

Success.ai offers B2B marketing data across industries and geographies, tailored to fit your specific business needs. With our white-glove service, you’ll receive curated, ready-to-use company datasets without the hassle of managing data platforms yourself. Whether you’re looking for UK B2B data or global datasets, Success.ai ensures a seamless experience with the most accurate and up-to-date information in the market.

API Features:

• Real-Time Data Access: Our APIs ensure you can integrate and access the latest company data directly into your systems, providing real-time updates and seamless data flow.
• Scalable Integration: Designed to handle high-volume requests efficiently, our APIs can support extensive data operations, perfect for businesses of all sizes.
• Customizable Data Retrieval: Tailor your data queries to match specific needs, selecting data points that align with your business goals for more targeted insights.

Why Choose Success.ai’s Company Data Solution? At Success.ai, we prioritize quality and relevancy. Every company profile is AI-validated for a 99% accuracy rate and manually reviewed to ensure you're accessing actionable and GDPR-compliant data. Our price match guarantee ensures you receive the best deal on the market, while our white-glove service provides personalized assistance in sourcing and delivering the data you need.

Why Choose Success.ai?

• Best Price Guarantee: We offer industry-leading pricing and beat any competitor.
• Global Reach: Access over 28 million verified company profiles across 195 countries.
• Comprehensive Data: Over 15 data points, including company size, industry, funding, and technologies used.
• Accurate & Verified: AI-validated with a 99% accuracy rate, ensuring high-quality data.
• API Access: Our robust APIs and customizable data solutions provide the flexibility and scalability needed to adapt to changing market conditions and business needs.
• Real-Time Updates: Stay ahead with continuously updated company information.
• Ethically Sourced Data: Our B2B data is compliant with global privacy laws, ensuring responsible use.
• Dedicated Service: Receive personalized, curated data without the hassle of managing platforms.
• Tailored Solutions: Custom datasets are built to fit your unique business needs and industries.

Our database spans 195 countries and covers 28 million public and private company profiles, with detailed insights into each company’s structure, size, funding history, and key technologies. We provide B2B company data for businesses of all sizes, from small business contact data to large corporations, with extensive coverage in regions such as North America, Europe, Asia-Pacific, and Latin America.

Comprehensive Data Points: Success.ai delivers in-depth information on each company, with over 15 data points, including:

Company Name: Get the full legal name of the company. LinkedIn URL: Direct link to the company's LinkedIn profile. Company Domain: Website URL for more detailed research. Company Description: Overview of the company’s services and products. Company Location: Geographic location down to the city, state, and country. Company Industry: The sector or industry the company operates in. Employee Count: Number of employees to help identify company size. Technologies Used: Insights into key technologies employed by the company, valuable for tech-based outreach. Funding Information: Track total funding and the most recent funding dates for investment opportunities. Maximize Your Sales Potential: With Success.ai’s B2B contact data and company datasets, sales teams can build tailored lists of target accounts, identify decision-makers, and access real-time company intelligence. Our curated datasets ensure you’re always focused on high-value leads—those who are most likely to convert into clients. Whether you’re conducting account-based marketing (ABM), expanding your sales pipeline, or looking to improve your lead generation strategies, Success.ai offers the resources you need to scale your business efficiently.

Tailored for Your Industry: Success.ai serves multiple industries, including technology, healthcare, finance, manufacturing, and more. Our B2B marketing data solutions are particularly valuable for businesses looking to reach professionals in key sectors. You’ll also have access to small business contact data, perfect for reaching new markets or uncovering high-growth startups.

From UK B2B data to contacts across Europe and Asia, our datasets provide global coverage to expand your business reach and identify new...

Description This dataset is the June 2025 Data Release of Cell Maps for Artificial Intelligence (CM4AI; CM4AI.org), the Functional Genomics Grand Challenge in the NIH Bridge2AI program. This Beta release includes perturb-seq data in undifferentiated KOLF2.1J iPSCs; SEC-MS data in undifferentiated KOLF2.1J iPSCs, iPSC-derived NPCs, neurons, cardiomyocytes, and treated and untreated MDA-MB468 breast cancer cells; and IF images in MDA-MB-468 breast cancer cells in the presence and absence of chemotherapy (vorinostat and paclitaxel). External Data Links Access external data resources related to this dataset: Sequence Read Archive (SRA) Data: NCBI BioProject Mass Spectrometry Data (Human iPSCs): MassIVE Repository Mass Spectrometry Data (Human Cancer Cells): MassIVE Repository Data Governance & Ethics Human Subjects: No De-identified Samples: Yes FDA Regulated: No Data Governance Committee: Jillian Parker (jillianparker@health.ucsd.edu) Ethical Review: Vardit Ravitsky (ravitskyv@thehastingscenter.org) and Jean-Christophe Belisle-Pipon (jean-christophe_belisle-pipon@sfu.ca) Completeness These data are not yet in completed final form: Some datasets are under temporary pre-publication embargo Protein-protein interaction (SEC-MS), protein localization (IF imaging), and CRISPRi perturbSeq data interrogate sets of proteins which incompletely overlap Computed cell maps not included in this release Maintenance Plan Dataset will be regularly updated and augmented through the end of the project in November 2026 Updates on a quarterly basis Long term preservation in the University of Virginia Dataverse, supported by committed institutional funds Intended Use This dataset is intended for: AI-ready datasets to support research in functional genomics AI model training Cellular process analysis Cell architectural changes and interactions in presence of specific disease processes, treatment conditions, or genetic perturbations Limitations Researchers should be aware of inherent limitations: This is an interim release Does not contain predicted cell maps, which will be added in future releases The current release is most suitable for bioinformatics analysis of the individual datasets Requires domain expertise for meaningful analysis Prohibited Uses These laboratory data are not to be used in clinical decision-making or in any context involving patient care without appropriate regulatory oversight and approval Potential Sources of Bias Users should be aware of potential biases: Data in this release was derived from commercially available de-identified human cell lines Does not represent all biological variants which may be seen in the population at large

Surveillance data play a vital role in estimating the burden of diseases, pathogens, exposures, behaviors, and susceptibility in populations, providing insights that can inform the design of policies and targeted public health interventions. The use of Health and Demographic Surveillance System (HDSS) collected from the Kilifi region of Kenya, has led to the collection of massive amounts of data on the demographics and health events of different populations. This has necessitated the adoption of tools and techniques to enhance data analysis to derive insights that will improve the accuracy and efficiency of decision-making. Machine Learning (ML) and artificial intelligence (AI) based techniques are promising for extracting insights from HDSS data, given their ability to capture complex relationships and interactions in data. However, broad utilization of HDSS datasets using AI/ML is currently challenging as most of these datasets are not AI-ready due to factors that include, but are not limited to, regulatory concerns around privacy and confidentiality, heterogeneity in data laws across countries limiting the accessibility of data, and a lack of sufficient datasets for training AI/ML models. Synthetic data generation offers a potential strategy to enhance accessibility of datasets by creating synthetic datasets that uphold privacy and confidentiality, suitable for training AI/ML models and can also augment existing AI datasets used to train the AI/ML models. These synthetic datasets, generated from two rounds of separate data collection periods, represent a version of the real data while retaining the relationships inherent in the data. For more information please visit The Aga Khan University Website.

This dataset features over 5,500,000 high-quality images of animals sourced from photographers around the globe. Created to support AI and machine learning applications, it offers a richly diverse and precisely annotated collection of wildlife, domestic, and exotic animal imagery.

Key Features: 1. Comprehensive Metadata: the dataset includes full EXIF data such as aperture, ISO, shutter speed, and focal length. Each image is pre-annotated with species information, behavior tags, and scene metadata, making it ideal for image classification, detection, and animal behavior modeling. Popularity metrics based on platform engagement are also included.

1. Unique Sourcing Capabilities: the images are gathered through a proprietary gamified platform that hosts competitions on animal photography. This approach ensures a stream of fresh, high-quality content. On-demand custom datasets can be delivered within 72 hours for specific species, habitats, or behavioral contexts.

2. Global Diversity: photographers from over 100 countries contribute to the dataset, capturing animals in a variety of ecosystems—forests, savannas, oceans, mountains, farms, and homes. It includes pets, wildlife, livestock, birds, marine life, and insects across a wide spectrum of climates and regions.

3. High-Quality Imagery: the dataset spans from standard to ultra-high-resolution images, suitable for close-up analysis of physical features or environmental interactions. A balance of candid, professional, and artistic photography styles ensures training value for real-world and creative AI tasks.

4. Popularity Scores: each image carries a popularity score from its performance in GuruShots competitions. This can be used to train AI models on visual appeal, species preference, or public interest trends.

5. AI-Ready Design: optimized for use in training models in species classification, object detection, wildlife monitoring, animal facial recognition, and habitat analysis. It integrates seamlessly with major ML frameworks and annotation tools.

6. Licensing & Compliance: all data complies with global data and wildlife imagery licensing regulations. Licenses are clear and flexible for commercial, nonprofit, and academic use.

Use Cases: 1. Training AI for wildlife identification and biodiversity monitoring. 2. Powering pet recognition, breed classification, and animal health AI tools. 3. Supporting AR/VR education tools and natural history simulations. 4. Enhancing environmental conservation and ecological research models.

This dataset offers a rich, high-quality resource for training AI and ML systems in zoology, conservation, agriculture, and consumer tech. Custom dataset requests are welcomed. Contact us to learn more!

AI Modelplace Market Outlook

According to our latest research, the AI Modelplace market size reached USD 1.32 billion globally in 2024, demonstrating robust momentum with a CAGR of 29.7% projected through the forecast period. By 2033, the market is anticipated to attain a value of USD 12.13 billion, driven by surging demand for accessible AI solutions, the proliferation of AI-powered applications across industries, and the rapid evolution of model deployment platforms. This exceptional growth trajectory is primarily fueled by the increasing adoption of AI model marketplaces that streamline the procurement, customization, and integration of artificial intelligence models for diverse business needs.

One of the primary growth factors propelling the AI Modelplace market is the widespread democratization of artificial intelligence technologies. As organizations across sectors seek to harness the power of AI, there is a growing need for platforms that provide ready-to-use, customizable, and scalable AI models. AI modelplaces bridge the gap between AI developers and end-users by offering a centralized repository where pre-trained, open-source, and custom models can be accessed and deployed with ease. This accessibility significantly reduces the barriers to AI adoption, allowing even small and medium enterprises (SMEs) to leverage sophisticated machine learning capabilities without the need for extensive in-house expertise. The convenience of plug-and-play models, combined with robust support services, is enabling a broader spectrum of companies to innovate and accelerate their digital transformation journeys.

Another significant driver is the expanding array of applications for AI models across industries such as healthcare, finance, retail, manufacturing, and media. In healthcare, for instance, AI modelplaces enable rapid deployment of models for diagnostics, predictive analytics, and personalized medicine. Financial institutions are leveraging these platforms to implement fraud detection, risk assessment, and algorithmic trading solutions. Similarly, retailers are utilizing AI models for demand forecasting, customer personalization, and inventory optimization. The cross-industry applicability of AI modelplaces, coupled with the growing volume of data and advancements in computational power, is catalyzing market growth. These platforms not only expedite the innovation cycle but also ensure compliance, scalability, and security, which are critical factors for enterprise adoption.

Furthermore, the evolution of cloud computing and the shift towards cloud-native architectures are bolstering the AI Modelplace market. Cloud-based deployment modes offer unparalleled flexibility, scalability, and cost-efficiency, enabling organizations to access a vast library of AI models on demand. This trend is particularly prominent among enterprises with distributed operations and remote workforces, as cloud-based modelplaces facilitate seamless integration into existing workflows. Additionally, the emergence of hybrid and multi-cloud strategies is fostering interoperability and reducing vendor lock-in, making it easier for businesses to experiment with and adopt AI models from various sources. As cloud infrastructure continues to mature, it is expected to further accelerate the adoption and monetization of AI modelplaces worldwide.

From a regional perspective, North America currently leads the AI Modelplace market, accounting for the largest share due to its advanced technological ecosystem, high concentration of AI startups, and significant investments in research and development. Europe follows closely, with robust regulatory frameworks and innovation-driven economies supporting market expansion. The Asia Pacific region is witnessing the fastest growth, propelled by rapid digitalization, government initiatives, and a burgeoning tech-savvy population. Latin America and the Middle East & Africa are also emerging as promising markets, driven by increasing awareness and the gradual adoption of AI technologies. Regional dynamics are influenced by factors such as digital infrastructure, regulatory landscapes, and the availability of skilled talent, all of which shape the competitive positioning of market participants.

Dimensions is the largest database of research insight in the world. It represents the most comprehensive collection of linked data related to the global research and innovation ecosystem available in a single platform. Because Dimensions maps the entire research lifecycle, you can follow academic and industry research from early stage funding, through to output and on to social and economic impact. Businesses, governments, universities, investors, funders and researchers around the world use Dimensions to inform their research strategy and make evidence-based decisions on the R&D and innovation landscape. With Dimensions on Google BigQuery, you can seamlessly combine Dimensions data with your own private and external datasets; integrate with Business Intelligence and data visualization tools; and analyze billions of data points in seconds to create the actionable insights your organization needs. Examples of usage: Competitive intelligence Horizon-scanning & emerging trends Innovation landscape mapping Academic & industry partnerships and collaboration networks Key Opinion Leader (KOL) identification Recruitment & talent Performance & benchmarking Tracking funding dollar flows and citation patterns Literature gap analysis Marketing and communication strategy Social and economic impact of research About the data: Dimensions is updated daily and constantly growing. It contains over 112m linked research publications, 1.3bn+ citations, 5.6m+ grants worth $1.7trillion+ in funding, 41m+ patents, 600k+ clinical trials, 100k+ organizations, 65m+ disambiguated researchers and more. The data is normalized, linked, and ready for analysis. Dimensions is available as a subscription offering. For more information, please visit www.dimensions.ai/bigquery and a member of our team will be in touch shortly. If you would like to try our data for free, please select "try sample" to see our openly available Covid-19 data.En savoir plus

Secure AI Model Deployment Platforms Market Outlook

According to our latest research, the global Secure AI Model Deployment Platforms market size reached USD 2.85 billion in 2024, driven by the rising adoption of artificial intelligence across regulated industries and the increasing demand for robust security frameworks to protect sensitive models and data. The market is expected to grow at a CAGR of 26.1% during the forecast period, reaching approximately USD 22.64 billion by 2033. The primary growth factor is the heightened awareness of cyber threats targeting AI models, coupled with stringent data privacy regulations pushing enterprises to invest in secure AI deployment solutions.

The growth of the Secure AI Model Deployment Platforms market is being propelled by several key factors. One of the most significant drivers is the exponential increase in AI adoption across industries such as healthcare, finance, and government, where data sensitivity and compliance are paramount. As organizations deploy more AI models in production environments, the potential attack surface expands, making security a top priority. Enterprises are now seeking platforms that offer end-to-end encryption, secure model lifecycle management, and continuous monitoring to safeguard both proprietary models and the data they process. Furthermore, the rise in adversarial attacks, model theft, and data poisoning incidents has underscored the necessity of deploying AI models on platforms with advanced security capabilities. This trend is further amplified by the growing complexity of AI models and the need for secure collaboration among distributed teams.

Another crucial growth factor is the evolving regulatory landscape. Governments and regulatory bodies worldwide are introducing stricter guidelines for AI usage, particularly in sectors dealing with personal or sensitive information. Regulations such as the General Data Protection Regulation (GDPR) in Europe, the California Consumer Privacy Act (CCPA), and emerging AI-specific frameworks require organizations to implement robust security controls throughout the AI model lifecycle. Secure AI model deployment platforms help organizations achieve compliance by providing features like audit trails, access control, and automated compliance reporting. As a result, compliance-driven investments are significantly boosting market demand, especially among large enterprises and public sector organizations that face substantial regulatory scrutiny.

The increasing sophistication of cyber threats targeting AI infrastructure is also fueling market growth. Attackers are leveraging advanced techniques to exploit vulnerabilities in AI models, such as model inversion, membership inference, and adversarial attacks, which can lead to data breaches, intellectual property theft, and compromised decision-making. In response, platform vendors are integrating cutting-edge security features, including federated learning, differential privacy, and zero-trust architectures, to mitigate these risks. This ongoing innovation cycle is attracting both established enterprises and innovative startups to invest in secure AI deployment solutions, further accelerating market expansion.

From a regional perspective, North America holds the largest market share due to its mature AI ecosystem, high concentration of technology companies, and proactive regulatory environment. Europe follows closely, driven by stringent data protection laws and strong government initiatives promoting AI security. The Asia Pacific region is witnessing the fastest growth, fueled by rapid digital transformation, increasing investments in AI research, and the emergence of new regulatory frameworks. Latin America and the Middle East & Africa are gradually catching up, with growing awareness and adoption of secure AI deployment platforms, particularly in financial services and government sectors. Overall, regional dynamics are shaped by a combination of regulatory readiness, technological maturity, and industry-specific adoption patterns.

Component Analysis

The Secure AI Mo

Artificial Intelligence in Modern Warfare Market Outlook

According to our latest research, the global artificial intelligence in modern warfare market size reached USD 12.8 billion in 2024, driven by rapid technological advancements and increased defense spending worldwide. The market is expected to grow at a robust CAGR of 14.1% during the forecast period, reaching a projected value of USD 38.7 billion by 2033. The principal growth factor is the escalating adoption of AI-powered systems for enhanced situational awareness, decision-making, and operational efficiency across defense and security domains.

The primary driver propelling the artificial intelligence in modern warfare market is the increasing necessity for real-time data processing and actionable intelligence on the battlefield. Modern military operations demand rapid analysis of vast data streams from various sensors, satellites, and surveillance systems. AI technologies such as machine learning, computer vision, and natural language processing are being integrated into military platforms to automate threat detection, optimize mission planning, and reduce human error. This automation not only accelerates response times but also enables defense forces to operate with greater precision and effectiveness. Governments across the globe are investing heavily in AI-driven defense projects, recognizing the strategic advantage these technologies offer in both conventional and asymmetric warfare scenarios.

Another significant factor fueling market growth is the rising threat landscape, including cyber warfare, unmanned systems, and hybrid warfare tactics. Modern adversaries are leveraging sophisticated technologies, necessitating equally advanced countermeasures. AI-based cybersecurity solutions are becoming essential for protecting critical defense infrastructure from increasingly complex cyber threats. Additionally, AI is revolutionizing logistics and transportation within military operations, optimizing supply chains, predictive maintenance, and resource allocation. The integration of AI in simulation and training platforms is also enhancing preparedness by providing realistic, data-driven training environments for soldiers and commanders, thereby improving mission readiness and reducing training costs.

Furthermore, the proliferation of autonomous systems such as drones, robotic vehicles, and unmanned underwater vehicles is transforming the dynamics of modern warfare. AI is at the core of these autonomous platforms, enabling them to operate independently or in coordination with human operators. This shift towards man-machine teaming is not only enhancing operational capabilities but also minimizing risks to human life in high-threat environments. The growing collaboration between defense agencies and private technology firms is accelerating innovation, leading to the rapid deployment of AI solutions across land, air, naval, and space platforms. As international tensions and security concerns rise, the demand for AI-driven defense technologies is expected to surge, further propelling market expansion.

Regionally, North America dominates the artificial intelligence in modern warfare market, accounting for the largest revenue share in 2024, thanks to substantial investments by the United States Department of Defense and its allies. Europe follows closely, with countries like the United Kingdom, France, and Germany prioritizing AI integration into their military modernization programs. The Asia Pacific region is emerging as a high-growth market, fueled by escalating defense budgets in China, India, and Japan, as well as rising geopolitical tensions in the region. Meanwhile, the Middle East & Africa and Latin America are witnessing gradual adoption, primarily driven by security modernization initiatives and counter-terrorism efforts.

Technology Analysis

The artificial intelligence in modern warfare market is segmented by technology, encompassing machine learning, natural language processing (NLP), computer vision, robotics, and other emerging technologies. Machine learning remains the cor

According to Cognitive Market Research, the global Data Preparation Tools market size will be USD XX million in 2025. It will expand at a compound annual growth rate (CAGR) of XX% from 2025 to 2031.

North America held the major market share for more than XX% of the global revenue with a market size of USD XX million in 2025 and will grow at a CAGR of XX% from 2025 to 2031. Europe accounted for a market share of over XX% of the global revenue with a market size of USD XX million in 2025 and will grow at a CAGR of XX% from 2025 to 2031. Asia Pacific held a market share of around XX% of the global revenue with a market size of USD XX million in 2025 and will grow at a CAGR of XX% from 2025 to 2031. Latin America had a market share of more than XX% of the global revenue with a market size of USD XX million in 2025 and will grow at a CAGR of XX% from 2025 to 2031. Middle East and Africa had a market share of around XX% of the global revenue and was estimated at a market size of USD XX million in 2025 and will grow at a CAGR of XX% from 2025 to 2031. KEY DRIVERS

Increasing Volume of Data and Growing Adoption of Business Intelligence (BI) and Analytics Driving the Data Preparation Tools Market

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"Undoing Babel: AI, English, and the New Linguistic Infrastructure of Global Law"

This dataset accompanies the paper Undoing Babel: AI, English, and the New Linguistic Infrastructure of Global Law, which investigates the relationship between English language proficiency, colonial linguistic heritage, and a country’s readiness for AI governance. The core finding is that English proficiency—instrumented using colonial linguistic history—significantly predicts a country’s score on the 2024 Government AI Readiness Index (GAIRI). This suggests that English has become a global infrastructural language underpinning digital governance capacity.

The econometric strategy uses Two-Stage Least Squares (2SLS) and Generalized Method of Moments (GMM-IV) estimation via the linearmodels Python package. Colonial language variables are used as instruments for English proficiency to address potential endogeneity. The Hansen J-test confirms instrument validity (p = 0.21). The analysis is fully reproducible and all Python scripts, datasets, and regression outputs are included.

Files included:

• 2sls.py: Main estimation script (2SLS & GMM-IV models).

• ai.py, lgdp.py, orig.py: Supporting scripts for interaction effects and variable prep.

• README.md: Detailed project overview, variable definitions, and methodological notes.

• EF_EPI_2024_Ranking_with_Puerto_Rico.xlsx: English Proficiency Index data.

• 2024-GAIRI-data.xlsx: Government AI Readiness Index data.

• GDP_2023.xlsx: National GDP data (normalized and lagged).

• EEFR_All_States_and_Puerto_Rico.xlsx: U.S. state-level data (not used in global regressions).

Sample size: N = 98 countries
Software: Python (pandas, statsmodels, linearmodels)
License: Creative Commons Attribution 4.0 International (CC BY 4.0)
DOI: 10.5281/zenodo.15635672

This repository entry contains information from a research study that looks at how Generative Artificial Intelligence (AI), particularly Generative Pre-trained Transformer (GPT) models, helps senior supply chain executives think more flexibly during challenging situations. The study combines scenario-based experiments with 200 senior managers and qualitative interviews with 25 executives, providing comprehensive quantitative and qualitative insights.Research Objectives Empirically examine the impact of generative AI on cognitive agility in supply chains. Assess how cognitive agility influences decision accuracy, adaptability, and response time. Identify implementation challenges, barriers, and prerequisites for strategic integration of generative AI within organisational contexts. Methodological Overview Design: Mixed methods (Quantitative experimentation, qualitative semi-structured interviews) Sample: Quantitative: 200 senior supply chain managers Qualitative: 25 supply chain executives (purposively sampled for depth of insights) Tools Used: Scenario-based experiments using GPT-based generative AI tools NVivo 12 software for thematic qualitative analysis SmartPLS (PLS-SEM), Bayesian network modelling for quantitative analysis Data Collected Quantitative Data: Decision accuracy, response time, adaptability (Likert-scale assessments) Pre- and post-experiment psychometric scales measuring perceived cognitive agility, decision-making confidence, and perceived usefulness of generative AI Qualitative Data: Semi-structured interview transcripts Thematic categories: cognitive capabilities enhancement, implementation challenges, and strategic alignment factors Direct participant quotes providing granular, context-specific insights Key Findings Quantitative Outcomes: Generative AI significantly enhances cognitive agility (β = 0.64, p < 0.001). Improved cognitive agility positively affects decision accuracy (β = 0.52), adaptability (β = 0.48), and reduces response times (β = -0.41), all significant at p < 0.001. Qualitative Outcomes (Thematic): 1. Enhanced Cognitive Capabilities: Real-time analytics, improved responsiveness, creative problem-solving, and strategic foresight. 2. Implementation Challenges: Technological integration issues, legacy system constraints, data privacy and ethical compliance concerns, and human capital limitations such as skill gaps and resistance. 3. Strategic Alignment and Readiness: The importance of executive leadership commitment, an agile organisational culture, strategic alignment, and dedicated resources for effective AI adoption.

Globally available, ON-DEMAND noise pollution maps generated from real-world measurements (our sample dataset) and AI interpolation. Unlike any other available noise-level data sets! GIS-ready, high-resolution visuals for real estate platforms, government dashboards, and smart city applications.

Introduction

This Australian English Call Center Speech Dataset for the Retail and E-commerce industry is purpose-built to accelerate the development of speech recognition, spoken language understanding, and conversational AI systems tailored for English speakers. Featuring over 40 hours of real-world, unscripted audio, it provides authentic human-to-human customer service conversations vital for training robust ASR models.

Curated by FutureBeeAI, this dataset empowers voice AI developers, data scientists, and language model researchers to build high-accuracy, production-ready models across retail-focused use cases.

Speech Data

The dataset contains 40 hours of dual-channel call center recordings between native Australian English speakers. Captured in realistic scenarios, these conversations span diverse retail topics from product inquiries to order cancellations, providing a wide context range for model training and testing.

Topic Diversity

This speech corpus includes both inbound and outbound calls with varied conversational outcomes like positive, negative, and neutral, ensuring real-world scenario coverage.

Such variety enhances your model’s ability to generalize across retail-specific voice interactions.

Transcription

All audio files are accompanied by manually curated, time-coded verbatim transcriptions in JSON format.

These transcriptions are production-ready, making model training faster and more accurate.

Metadata

Rich metadata is available for each participant and conversation:

This granularity supports advanced analytics, dialect filtering, and fine-tuned model evaluation.

Usage and Applications

This dataset is ideal for a range of voice AI and NLP applications:

As of 2023, artificial intelligence (AI) has shown to improve work performance for both lower-skilled and higher-skilled workers. While the improvement gained from the use of AI was higher for lower-skilled workers with a performance score of 6.06, higher-skilled workers continued to perform better with and without the technology.

1. Overview This dataset is a collection of high view traffic images in multiple scenes, backgrounds and lighting conditions that are ready to use for optimizing the accuracy of computer vision models. All of the contents is sourced from PIXTA's stock library of 100M+ Asian-featured images and videos. PIXTA is the largest platform of visual materials in the Asia Pacific region offering fully-managed services, high quality contents and data, and powerful tools for businesses & organisations to enable their creative and machine learning projects.

2. Use case This dataset is used for AI solutions training & testing in various cases: Traffic monitoring, Traffic camera system, Vehicle flow estimation,... Each data set is supported by both AI and human review process to ensure labelling consistency and accuracy. Contact us for more custom datasets.

3. About PIXTA PIXTASTOCK is the largest Asian-featured stock platform providing data, contents, tools and services since 2005. PIXTA experiences 15 years of integrating advanced AI technology in managing, curating, processing over 100M visual materials and serving global leading brands for their creative and data demands. Visit us at https://www.pixta.ai/ for more details.