Sure! I'd be happy to provide you with an introduction to machine learning basics for beginners. Machine learning is a subfield of artificial intelligence (AI) that focuses on enabling computers to learn and make predictions or decisions without being explicitly programmed. Here are some key concepts and terms to help you get started:

1. Supervised Learning: In supervised learning, the machine learning algorithm learns from labeled training data. The training data consists of input examples and their corresponding correct output or target values. The algorithm learns to generalize from this data and make predictions or classify new, unseen examples.

2. Unsupervised Learning: Unsupervised learning involves learning patterns and relationships from unlabeled data. Unlike supervised learning, there are no target values provided. Instead, the algorithm aims to discover inherent structures or clusters in the data.

3. Training Data and Test Data: Machine learning models require a dataset to learn from. The dataset is typically split into two parts: the training data and the test data. The model learns from the training data, and the test data is used to evaluate its performance and generalization ability.

4. Features and Labels: In supervised learning, the input examples are often represented by features or attributes. For example, in a spam email classification task, features might include the presence of certain keywords or the length of the email. The corresponding output or target values are called labels, indicating the class or category to which the example belongs (e.g., spam or not spam).

5. Model Evaluation Metrics: To assess the performance of a machine learning model, various evaluation metrics are used. Common metrics include accuracy (the proportion of correctly predicted examples), precision (the proportion of true positives among all positive predictions), recall (the proportion of true positives predicted correctly), and F1 score (a combination of precision and recall).

6. Overfitting and Underfitting: Overfitting occurs when a model becomes too complex and learns to memorize the training data instead of generalizing well to unseen examples. On the other hand, underfitting happens when a model is too simple and fails to capture the underlying patterns in the data. Balancing the complexity of the model is crucial to achieve good generalization.

7. Feature Engineering: Feature engineering involves selecting or creating relevant features that can help improve the performance of a machine learning model. It often requires domain knowledge and creativity to transform raw data into a suitable representation that captures the important information.

8. Bias and Variance Trade-off: The bias-variance trade-off is a fundamental concept in machine learning. Bias refers to the errors introduced by the model's assumptions and simplifications, while variance refers to the model's sensitivity to small fluctuations in the training data. Reducing bias may increase variance and vice versa. Finding the right balance is important for building a well-performing model.

9. Supervised Learning Algorithms: There are various supervised learning algorithms, including linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), and neural networks. Each algorithm has its own strengths, weaknesses, and specific use cases.

10. Unsupervised Learning Algorithms: Unsupervised learning algorithms include clustering algorithms like k-means clustering and hierarchical clustering, dimensionality reduction techniques like principal component analysis (PCA) and t-SNE, and anomaly detection algorithms, among others.

These concepts provide a starting point for understanding the basics of machine learning. As you delve deeper, you can explore more advanced topics such as deep learning, reinforcement learning, and natural language processing. Remember to practice hands-on with real-world datasets to gain practical experience and further refine your skills.

HelpSteer: AI Alignment Dataset

Real-World Helpfulness Annotated for AI Alignment

By Huggingface Hub [source]

About this dataset

HelpSteer is an Open-Source dataset designed to empower AI Alignment through the support of fair, team-oriented annotation. The dataset provides 37,120 samples each containing a prompt and response along with five human-annotated attributes ranging between 0 and 4; with higher results indicating better quality. Using cutting-edge methods in machine learning and natural language processing in combination with the annotation of data experts, HelpSteer strives to create a set of standardized values that can be used to measure alignment between human and machine interactions. With comprehensive datasets providing responses rated for correctness, coherence, complexity, helpfulness and verbosity, HelpSteer sets out to assist organizations in fostering reliable AI models which ensure more accurate results thereby leading towards improved user experience at all levels

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How to use the dataset

How to Use HelpSteer: An Open-Source AI Alignment Dataset

HelpSteer is an open-source dataset designed to help researchers create models with AI Alignment. The dataset consists of 37,120 different samples each containing a prompt, a response and five human-annotated attributes used to measure these responses. This guide will give you a step-by-step introduction on how to leverage HelpSteer for your own projects.

Step 1 - Choosing the Data File

Helpsteer contains two data files – one for training and one for validation. To start exploring the dataset, first select the file you would like to use by downloading both train.csv and validation.csv from the Kaggle page linked above or getting them from the Google Drive repository attached here: [link]. All the samples in each file consist of 7 columns with information about a single response: prompt (given), response (submitted), helpfulness, correctness, coherence, complexity and verbosity; all sporting values between 0 and 4 where higher means better in respective category.

## Step 2—Exploratory Data Analysis (EDA) Once you have your file loaded into your workspace or favorite software environment (e.g suggested libraries like Pandas/Numpy or even Microsoft Excel), it’s time explore it further by running some basic EDA commands that summarize each feature's distribution within our data set as well as note potential trends or points of interests throughout it - e.g what are some traits that are polarizing these responses more? Are there any outliers that might signal something interesting happening? Plotting these results often provides great insights into pattern recognition across datasets which can be used later on during modeling phase also known as “Feature Engineering”

## Step 3—Data Preprocessing After your interpretation of raw data while doing EDA should form some hypotheses around what features matter most when trying to estimate attribute scores of unknown responses accurately so proceeding with preprocessing such as cleaning up missing entries or handling outliers accordingly becomes highly recommended before starting any modelling efforts with this data set - kindly refer also back at Kaggle page description section if unsure about specific attributes domain ranges allowed values explicitly for extra confidence during this step because having correct numerical suggestions ready can make modelling workload lighter later on while building predictive models . It’s important not rushing over this stage otherwise poor results may occur later when aiming high accuracy too quickly upon model deployment due low quality

Research Ideas

• Designating and measuring conversational AI engagement goals: Researchers can utilize the HelpSteer dataset to design evaluation metrics for AI engagement systems.
• Identifying conversational trends: By analyzing the annotations and data in HelpSteer, organizations can gain insights into what makes conversations more helpful, cohesive, complex or consistent across datasets or audiences.
• Training Virtual Assistants: Train artificial intelligence algorithms on this dataset to develop virtual assistants that respond effectively to customer queries with helpful answers

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

**License: [CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication](https://creativecommons.org/pu...

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AI Training Dataset Market Size 2025-2029

The ai training dataset market size is valued to increase by USD 7.33 billion, at a CAGR of 29% from 2024 to 2029. Proliferation and increasing complexity of foundational AI models will drive the ai training dataset market.

Market Insights

North America dominated the market and accounted for a 36% growth during the 2025-2029.
By Service Type - Text segment was valued at USD 742.60 billion in 2023
By Deployment - On-premises segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 479.81 million 
Market Future Opportunities 2024: USD 7334.90 million
CAGR from 2024 to 2029 : 29%

Market Summary

The market is experiencing significant growth as businesses increasingly rely on artificial intelligence (AI) to optimize operations, enhance customer experiences, and drive innovation. The proliferation and increasing complexity of foundational AI models necessitate large, high-quality datasets for effective training and improvement. This shift from data quantity to data quality and curation is a key trend in the market. Navigating data privacy, security, and copyright complexities, however, poses a significant challenge. Businesses must ensure that their datasets are ethically sourced, anonymized, and securely stored to mitigate risks and maintain compliance. For instance, in the supply chain optimization sector, companies use AI models to predict demand, optimize inventory levels, and improve logistics. Access to accurate and up-to-date training datasets is essential for these applications to function efficiently and effectively. Despite these challenges, the benefits of AI and the need for high-quality training datasets continue to drive market growth. The potential applications of AI are vast and varied, from healthcare and finance to manufacturing and transportation. As businesses continue to explore the possibilities of AI, the demand for curated, reliable, and secure training datasets will only increase.

What will be the size of the AI Training Dataset Market during the forecast period?

Get Key Insights on Market Forecast (PDF) Request Free SampleThe market continues to evolve, with businesses increasingly recognizing the importance of high-quality datasets for developing and refining artificial intelligence models. According to recent studies, the use of AI in various industries is projected to grow by over 40% in the next five years, creating a significant demand for training datasets. This trend is particularly relevant for boardrooms, as companies grapple with compliance requirements, budgeting decisions, and product strategy. Moreover, the importance of data labeling, feature selection, and imbalanced data handling in model performance cannot be overstated. For instance, a mislabeled dataset can lead to biased and inaccurate models, potentially resulting in costly errors. Similarly, effective feature selection algorithms can significantly improve model accuracy and reduce computational resources. Despite these challenges, advances in model compression methods, dataset scalability, and data lineage tracking are helping to address some of the most pressing issues in the market. For example, model compression techniques can reduce the size of models, making them more efficient and easier to deploy. Similarly, data lineage tracking can help ensure data consistency and improve model interpretability. In conclusion, the market is a critical component of the broader AI ecosystem, with significant implications for businesses across industries. By focusing on data quality, effective labeling, and advanced techniques for handling imbalanced data and improving model performance, organizations can stay ahead of the curve and unlock the full potential of AI.

Unpacking the AI Training Dataset Market Landscape

In the realm of artificial intelligence (AI), the significance of high-quality training datasets is indisputable. Businesses harnessing AI technologies invest substantially in acquiring and managing these datasets to ensure model robustness and accuracy. According to recent studies, up to 80% of machine learning projects fail due to insufficient or poor-quality data. Conversely, organizations that effectively manage their training data experience an average ROI improvement of 15% through cost reduction and enhanced model performance.

Distributed computing systems and high-performance computing facilitate the processing of vast datasets, enabling businesses to train models at scale. Data security protocols and privacy preservation techniques are crucial to protect sensitive information within these datasets. Reinforcement learning models and supervised learning models each have their unique applications, with the former demonstrating a 30% faster convergence rate in certain use cases.

Data annot

This dataset features over 80,000 high-quality images of construction sites sourced from photographers worldwide. Built to support AI and machine learning applications, it delivers richly annotated and visually diverse imagery capturing real-world construction environments, machinery, and processes.

Key Features: 1. Comprehensive Metadata: the dataset includes full EXIF data such as aperture, ISO, shutter speed, and focal length. Each image is annotated with construction phase, equipment types, safety indicators, and human activity context—making it ideal for object detection, site monitoring, and workflow analysis. Popularity metrics based on performance on our proprietary platform are also included.

1. Unique Sourcing Capabilities: images are collected through a proprietary gamified platform, with competitions focused on industrial, construction, and labor themes. Custom datasets can be generated within 72 hours to target specific scenarios, such as building types, stages (excavation, framing, finishing), regions, or safety compliance visuals.

2. Global Diversity: sourced from contributors in over 100 countries, the dataset reflects a wide range of construction practices, materials, climates, and regulatory environments. It includes residential, commercial, industrial, and infrastructure projects from both urban and rural areas.

3. High-Quality Imagery: includes a mix of wide-angle site overviews, close-ups of tools and equipment, drone shots, and candid human activity. Resolution varies from standard to ultra-high-definition, supporting both macro and contextual analysis.

4. Popularity Scores: each image is assigned a popularity score based on its performance in GuruShots competitions. These scores provide insight into visual clarity, engagement value, and human interest—useful for safety-focused or user-facing AI models.

5. AI-Ready Design: this dataset is structured for training models in real-time object detection (e.g., helmets, machinery), construction progress tracking, material identification, and safety compliance. It’s compatible with standard ML frameworks used in construction tech.

6. Licensing & Compliance: fully compliant with privacy, labor, and workplace imagery regulations. Licensing is transparent and ready for commercial or research deployment.

Use Cases: 1. Training AI for safety compliance monitoring and PPE detection. 2. Powering progress tracking and material usage analysis tools. 3. Supporting site mapping, autonomous machinery, and smart construction platforms. 4. Enhancing augmented reality overlays and digital twin models for construction planning.

This dataset provides a comprehensive, real-world foundation for AI innovation in construction technology, safety, and operational efficiency. Custom datasets are available on request. Contact us to learn more!

The dataset consists of 100k JPG images (50k real and 50k- fake) at 224x224 resolution pre-processed and merged by the following links:

• Flickr-Faces-HQ Dataset (FFHQ)
• GenAI Faces

This dataset is designed to support research at the intersection of computer vision and generative models. By combining high-quality real face images from the Flickr-Faces-HQ (FFHQ) dataset with AI-generated counterparts, this dataset provides a robust foundation for multiple advanced applications:

GAN Training. With its high resolution and rich visual diversity, the dataset is ideal for training Generative Adversarial Networks (GANs), enabling models to learn realistic facial features across a wide range of demographics and conditions.

Synthetic Content Detection. The inclusion of both real and generated images makes the dataset particularly suitable for developing and benchmarking algorithms aimed at detecting AI-generated content, a critical task in the age of deepfakes.

Model Generalization Testing. The variety and complexity of the data offer a reliable benchmark for evaluating how well machine learning models generalize to unseen examples, contributing to the development of more robust and adaptable systems.

Our most comprehensive database of AI models, containing over 800 models that are state of the art, highly cited, or otherwise historically notable. It tracks key factors driving machine learning progress and includes over 300 training compute estimates.

"Collection of 100,000 high-quality video clips across diverse real-world domains, designed to accelerate the training and optimization of computer vision and multimodal AI models."

Overview This dataset contains 100,000 proprietary and partner-produced video clips filmed in 4K/6K with cinema-grade RED cameras. Each clip is commercially cleared with full releases, structured metadata, and available in RAW or MOV/MP4 formats. The collection spans a wide variety of domains — people and lifestyle, healthcare and medical, food and cooking, office and business, sports and fitness, nature and landscapes, education, and more. This breadth ensures robust training data for computer vision, multimodal, and machine learning projects.

The data set All 100,000 videos have been reviewed for quality and compliance. The dataset is optimized for AI model training, supporting use cases from face and activity recognition to scene understanding and generative AI. Custom datasets can also be produced on demand, enabling clients to close data gaps with tailored, high-quality content.

About M-ART M-ART is a leading provider of cinematic-grade datasets for AI training. With extensive expertise in large-scale content production and curation, M-ART delivers both ready-to-use video datasets and fully customized collections. All data is proprietary, rights-cleared, and designed to help global AI leaders accelerate research, development, and deployment of next-generation models.

Dataset Overview This dataset is designed for Urdu text classification

AI Training Data | Annotated Checkout Flows for Retail, Restaurant, and Marketplace Websites Overview

Unlock the next generation of agentic commerce and automated shopping experiences with this comprehensive dataset of meticulously annotated checkout flows, sourced directly from leading retail, restaurant, and marketplace websites. Designed for developers, researchers, and AI labs building large language models (LLMs) and agentic systems capable of online purchasing, this dataset captures the real-world complexity of digital transactions—from cart initiation to final payment.

Key Features

Breadth of Coverage: Over 10,000 unique checkout journeys across hundreds of top e-commerce, food delivery, and service platforms, including but not limited to Walmart, Target, Kroger, Whole Foods, Uber Eats, Instacart, Shopify-powered sites, and more.

Actionable Annotation: Every flow is broken down into granular, step-by-step actions, complete with timestamped events, UI context, form field details, validation logic, and response feedback. Each step includes:

Page state (URL, DOM snapshot, and metadata)

User actions (clicks, taps, text input, dropdown selection, checkbox/radio interactions)

System responses (AJAX calls, error/success messages, cart/price updates)

Authentication and account linking steps where applicable

Payment entry (card, wallet, alternative methods)

Order review and confirmation

Multi-Vertical, Real-World Data: Flows sourced from a wide variety of verticals and real consumer environments, not just demo stores or test accounts. Includes complex cases such as multi-item carts, promo codes, loyalty integration, and split payments.

Structured for Machine Learning: Delivered in standard formats (JSONL, CSV, or your preferred schema), with every event mapped to action types, page features, and expected outcomes. Optional HAR files and raw network request logs provide an extra layer of technical fidelity for action modeling and RLHF pipelines.

Rich Context for LLMs and Agents: Every annotation includes both human-readable and model-consumable descriptions:

“What the user did” (natural language)

“What the system did in response”

“What a successful action should look like”

Error/edge case coverage (invalid forms, OOS, address/payment errors)

Privacy-Safe & Compliant: All flows are depersonalized and scrubbed of PII. Sensitive fields (like credit card numbers, user addresses, and login credentials) are replaced with realistic but synthetic data, ensuring compliance with privacy regulations.

Each flow tracks the user journey from cart to payment to confirmation, including:

Adding/removing items

Applying coupons or promo codes

Selecting shipping/delivery options

Account creation, login, or guest checkout

Inputting payment details (card, wallet, Buy Now Pay Later)

Handling validation errors or OOS scenarios

Order review and final placement

Confirmation page capture (including order summary details)

Why This Dataset?

Building LLMs, agentic shopping bots, or e-commerce automation tools demands more than just page screenshots or API logs. You need deeply contextualized, action-oriented data that reflects how real users interact with the complex, ever-changing UIs of digital commerce. Our dataset uniquely captures:

The full intent-action-outcome loop

Dynamic UI changes, modals, validation, and error handling

Nuances of cart modification, bundle pricing, delivery constraints, and multi-vendor checkouts

Mobile vs. desktop variations

Diverse merchant tech stacks (custom, Shopify, Magento, BigCommerce, native apps, etc.)

Use Cases

LLM Fine-Tuning: Teach models to reason through step-by-step transaction flows, infer next-best-actions, and generate robust, context-sensitive prompts for real-world ordering.

Agentic Shopping Bots: Train agents to navigate web/mobile checkouts autonomously, handle edge cases, and complete real purchases on behalf of users.

Action Model & RLHF Training: Provide reinforcement learning pipelines with ground truth “what happens if I do X?” data across hundreds of real merchants.

UI/UX Research & Synthetic User Studies: Identify friction points, bottlenecks, and drop-offs in modern checkout design by replaying flows and testing interventions.

Automated QA & Regression Testing: Use realistic flows as test cases for new features or third-party integrations.

What’s Included

10,000+ annotated checkout flows (retail, restaurant, marketplace)

Step-by-step event logs with metadata, DOM, and network context

Natural language explanations for each step and transition

All flows are depersonalized and privacy-compliant

Example scripts for ingesting, parsing, and analyzing the dataset

Flexible licensing for research or commercial use

Sample Categories Covered

Grocery delivery (Instacart, Walmart, Kroger, Target, etc.)

Restaurant takeout/delivery (Ub...

LLM Dataset - Prompts and Generated Texts

The dataset contains prompts and texts generated by the Large Language Models (LLMs) in 32 different languages. The prompts are short sentences or phrases for the model to generate text. The texts generated by the LLM are responses to these prompts and can vary in length and complexity.

Researchers and developers can use this dataset to train and fine-tune their own language models for multilingual applications. The dataset provides a rich and diverse collection of outputs from the model, demonstrating its ability to generate coherent and contextually relevant text in multiple languages.

👉 Legally sourced datasets and carefully structured for AI training and model development. Explore samples from our dataset - Full dataset

Models used for text generation:

• GPT-3.5,
• GPT-4

Languages in the dataset:

Arabic, Azerbaijani, Catalan, Chinese, Czech, Danish, German, Greek, English, Esperanto, Spanish, Persian, Finnish, French, Irish, Hindi, Hungarian, Indonesian, Italian, Japanese, Korean, Malayalam, Maratham, Netherlands, Polish, Portuguese, Portuguese (Brazil), Slovak, Swedish, Thai, Turkish, Ukrainian

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2Ff60c93f09ec82a765aa39678e4aa0a58%2Fsnapedit_1709731090855.jpeg?generation=1709738798916444&alt=media" alt="">

🧩 This is just an example of the data. Leave a request here to learn more

Content

CSV File includes the following data: - from_language: language the prompt is made in, - model: type of the model (GPT-3.5, GPT-4 and Uncensored GPT Version), - time: time when the answer was generated, - text: user prompt, - response: response generated by the model

🚀 You can learn more about our high-quality unique datasets here

keywords: dataset, machine learning, natural language processing, artificial intelligence, deep learning, neural networks, text generation, language models, openai, gpt-3, data science, predictive modeling, sentiment analysis, keyword extraction, text classification, sequence-to-sequence models, attention mechanisms, transformer architecture, word embeddings, glove embeddings, chatbots, question answering, language understanding, text mining, information retrieval, data preprocessing, feature engineering, explainable ai, model deployment

BackgroundClinical data is instrumental to medical research, machine learning (ML) model development, and advancing surgical care, but access is often constrained by privacy regulations and missing data. Synthetic data offers a promising solution to preserve privacy while enabling broader data access. Recent advances in large language models (LLMs) provide an opportunity to generate synthetic data with reduced reliance on domain expertise, computational resources, and pre-training.ObjectiveThis study aims to assess the feasibility of generating realistic tabular clinical data with OpenAI’s GPT-4o using zero-shot prompting, and evaluate the fidelity of LLM-generated data by comparing its statistical properties to the Vital Signs DataBase (VitalDB), a real-world open-source perioperative dataset.MethodsIn Phase 1, GPT-4o was prompted to generate a dataset with qualitative descriptions of 13 clinical parameters. The resultant data was assessed for general errors, plausibility of outputs, and cross-verification of related parameters. In Phase 2, GPT-4o was prompted to generate a dataset using descriptive statistics of the VitalDB dataset. Fidelity was assessed using two-sample t-tests, two-sample proportion tests, and 95% confidence interval (CI) overlap.ResultsIn Phase 1, GPT-4o generated a complete and structured dataset comprising 6,166 case files. The dataset was plausible in range and correctly calculated body mass index for all case files based on respective heights and weights. Statistical comparison between the LLM-generated datasets and VitalDB revealed that Phase 2 data achieved significant fidelity. Phase 2 data demonstrated statistical similarity in 12/13 (92.31%) parameters, whereby no statistically significant differences were observed in 6/6 (100.0%) categorical/binary and 6/7 (85.71%) continuous parameters. Overlap of 95% CIs were observed in 6/7 (85.71%) continuous parameters.ConclusionZero-shot prompting with GPT-4o can generate realistic tabular synthetic datasets, which can replicate key statistical properties of real-world perioperative data. This study highlights the potential of LLMs as a novel and accessible modality for synthetic data generation, which may address critical barriers in clinical data access and eliminate the need for technical expertise, extensive computational resources, and pre-training. Further research is warranted to enhance fidelity and investigate the use of LLMs to amplify and augment datasets, preserve multivariate relationships, and train robust ML models.

This dataset includes three distinct subsets of text:Open Access Academic Articles: A collection of 100 open-access articles from various academic journals focused on mental health and psychiatry published between 2016-2018. The articles are selected from reputable journals including JAMA, The Lancet Psychiatry, WPJ, and AM J Psy.ChatGPT-Generated Texts: Discussion section samples generated by ChatGPT (GPT-4 model, version as of August 3, 2023, OpenAI) that are designed to imitate the style and content of academic articles in the field of mental health and psychiatry.Claude-Generated Texts: Discussion section samples generated by Claude (Version 2, Anthropic) with the aim of imitating academic articles in the same field.Additionally, the dataset contains the results of tests performed using ZeroGPT and Originality.AI to evaluate the AI texts vs the academic articles for the percentage of texts identified as being AI-generated.Please cite this dataset if you make use of it in your research.

The Large-Scale AI Models database documents over 200 models trained with more than 10²³ floating point operations, at the leading edge of scale and capabilities.

This dataset supports a meta-analytic structural equation modelling (MASEM) study investigating the factors influencing students’ behavioural intention to use educational AI (EAI) technologies. The research integrates constructs from the Technology Acceptance Model (TAM), Theory of Planned Behaviour (TPB), and Artificial Intelligence Literacy (AIL), aiming to resolve inconsistencies in previous studies and improve theoretical understanding of EAI technology adoption.

Research Hypotheses The study hypothesized that: Students’ behavioural intention (INT) to use EAI technologies is influenced by perceived usefulness (PU), perceived ease of use (PEU), attitude (ATT), subjective norm (SN), and perceived behavioural control (PBC), as described in TAM and TPB. AI literacy (AIL) directly and indirectly predicts PU, PEU, ATT, and INT. These relationships are moderated by contextual factors such as academic level (K–12 vs. higher education) and regional economic development (developed vs. developing countries).

What the Data Shows The meta-analytic dataset comprises 166 empirical studies involving over 69,000 participants. It includes pairwise Pearson correlations among seven constructs (PU, PEU, ATT, SN, PBC, INT, AIL) and is used to compute a pooled correlation matrix. This matrix was then used to test three models via MASEM: A baseline TAM-TPB model, An internal-extended model with additional TPB internal paths, An AIL-integrated extended model. The AIL-integrated model achieved the best fit (CFI = 0.997, RMSEA = 0.053) and explained 62.3% of the variance in behavioural intention.

Notable Findings AI literacy (AIL) is the strongest predictor of intention to use EAI technologies (Total Effect = 0.408). PU, ATT, and SN also significantly influence intention. The effect of PEU on intention is fully mediated by PU and ATT. Moderation analysis showed that the relationships differ between developed and developing countries and between K–12 and higher education populations.

How the Data Can Be Interpreted and Used The dataset includes bivariate correlations between variables, publication metadata, sample sizes, coding information, and reliability values (e.g., CR scores). Suitable for replication of MASEM procedures, moderation analysis, and meta-regression. Researchers may use it to test additional theoretical models or assess the influence of new moderators (e.g., AI tool type). Educators and policymakers can leverage insights from the meta-analytic results to inform AI literacy training and technology adoption strategies.

AI Dataset Search Platform Market Outlook

According to our latest research, the global AI Dataset Search Platform market size is valued at USD 1.18 billion in 2024, with a robust year-over-year expansion driven by the escalating demand for high-quality datasets to fuel artificial intelligence and machine learning initiatives across industries. The market is expected to grow at a CAGR of 22.6% from 2025 to 2033, reaching an estimated USD 9.62 billion by 2033. This exponential growth is primarily attributed to the increasing recognition of data as a strategic asset, the proliferation of AI applications across sectors, and the need for efficient, scalable, and secure platforms to discover, curate, and manage diverse datasets.

One of the primary growth factors propelling the AI Dataset Search Platform market is the exponential surge in AI adoption across both public and private sectors. Businesses and institutions are increasingly leveraging AI to gain competitive advantages, enhance operational efficiencies, and deliver personalized experiences. However, the effectiveness of AI models is fundamentally reliant on the quality and diversity of training datasets. As organizations strive to accelerate their AI initiatives, the need for platforms that can efficiently search, aggregate, and validate datasets from disparate sources has become paramount. This has led to a significant uptick in investments in AI dataset search platforms, as they enable faster data discovery, reduce development cycles, and ensure compliance with data governance standards.

Another key driver for the market is the growing complexity and volume of data generated from emerging technologies such as IoT, edge computing, and connected devices. The sheer scale and heterogeneity of data sources necessitate advanced search platforms equipped with intelligent indexing, semantic search, and metadata management capabilities. These platforms not only facilitate the identification of relevant datasets but also support data annotation, labeling, and preprocessing, which are critical for building robust AI models. Furthermore, the integration of AI-powered search algorithms within these platforms enhances the accuracy and relevance of search results, thereby improving the overall efficiency of data scientists and AI practitioners.

Additionally, regulatory pressures and the increasing emphasis on ethical AI have underscored the importance of transparent and auditable data sourcing. Organizations are compelled to demonstrate the provenance and integrity of the datasets used in their AI models to mitigate risks related to bias, privacy, and compliance. AI dataset search platforms address these challenges by providing traceability, version control, and access management features, ensuring that only authorized and compliant datasets are utilized. This not only reduces legal and reputational risks but also fosters trust among stakeholders, further accelerating market adoption.

From a regional perspective, North America dominates the AI Dataset Search Platform market in 2024, accounting for over 38% of the global revenue. This leadership is driven by the presence of major technology providers, a mature AI ecosystem, and substantial investments in research and development. Europe follows closely, benefiting from stringent data privacy regulations and strong government support for AI innovation. The Asia Pacific region is experiencing the fastest growth, propelled by rapid digital transformation, expanding AI research communities, and increasing government initiatives to foster AI adoption. Latin America and the Middle East & Africa are also witnessing steady growth, albeit from a smaller base, as organizations in these regions gradually embrace AI-driven solutions.

Component Analysis

The AI Dataset Search Platform market by component is segmented into platforms and services, each playing a pivotal role in the ecosystem. The platform segment encompasses the core software infrastructure that enables users to search, index, curate, and manage datasets. This segmen

Abstract

In the last years, neural networks have evolved from laboratory environments to the state-of-the-art for many real-world problems. Our hypothesis is that neural network models (i.e., their weights and biases) evolve on unique, smooth trajectories in weight space during training. Following, a population of such neural network models (refereed to as “model zoo”) would form topological structures in weight space. We think that the geometry, curvature and smoothness of these structures contain information about the state of training and can be reveal latent properties of individual models. With such zoos, one could investigate novel approaches for (i) model analysis, (ii) discover unknown learning dynamics, (iii) learn rich representations of such populations, or (iv) exploit the model zoos for generative modelling of neural network weights and biases. Unfortunately, the lack of standardized model zoos and available benchmarks significantly increases the friction for further research about populations of neural networks. With this work, we publish a novel dataset of model zoos containing systematically generated and diverse populations of neural network models for further research. In total the proposed model zoo dataset is based on six image datasets, consist of 24 model zoos with varying hyperparameter combinations are generated and includes 47’360 unique neural network models resulting in over 2’415’360 collected model states. Additionally, to the model zoo data we provide an in-depth analysis of the zoos and provide benchmarks for multiple downstream tasks as mentioned before.

Dataset

This dataset is part of a larger collection of model zoos and contains the zoos trained on the labelled samples from MNIST. All zoos with extensive information and code can be found at www.modelzoos.cc.

This repository contains two types of files: the raw model zoos as collections of models (file names beginning with "mnist_"), as well as preprocessed model zoos wrapped in a custom pytorch dataset class (filenames beginning with "dataset"). Zoos are trained in three configurations varying the seed only (seed), varying hyperparameters with fixed seeds (hyp_fix) or varying hyperparameters with random seeds (hyp_rand). The index_dict.json files contain information on how to read the vectorized models.

For more information on the zoos and code to access and use the zoos, please see www.modelzoos.cc.

This web-based validation system has been designed to perform visual validation of automated multi-class segmentation of concrete samples from scanning electron microscopy (SEM) images. The goal is to segment automatically SEM images into no-damage and damage sub-classes, where the damage sub-classes consist of paste damage, aggregate damage, and air voids. While the no-damage sub-classes are not included in the goal, they provide context for assigning damage sub-classes. The motivation behind this web validation system is to prepare a large number of pixel-level multi-class annotated microscopy images for training artificial intelligence (AI) based segmentation models (U-Net and SegNet models). While the purpose of the AI models is to predict accurately four damage labels, such as, paste damage, aggregate damage, air voids, and no-damage, our goal is to assert trust in such predictions (a) by using contextual labels and (b) by enabling visual validations of predicted damage labels.

Use Getty Images content to build or enhance your machine learning or artificial intelligence capabilities.

With nearly 30 years of visual expertise, Getty Images is the world’s foremost visual expert. Focused on identifying cultural shifts, spearheading trends and powering the creative economy Getty Images can provide you with the data you need to train your models. This sample Dataset includes 3,750 images from 15 categories including: Abstracts & Backgrounds, Built Environments… See the full description on the dataset page: https://huggingface.co/datasets/GettyImages/Getty-Images-Sample-Dataset.

Ransaka/ai-vs-human-generated-dataset-sample dataset hosted on Hugging Face and contributed by the HF Datasets community

AI Wit Training Dataset

This dataset contains witty comeback and humor training data for fine-tuning language models.

  Dataset Structure

Each sample contains:

messages: List of user/assistant conversation source: Data source (e.g., "reddit_jokes") style: Response style (e.g., "humorous", "witty")

  Usage

This dataset is designed for fine-tuning conversational AI models to generate witty, humorous responses to offensive or provocative inputs.

  Example

{… See the full description on the dataset page: https://huggingface.co/datasets/artificialreply/ai-wit-training-data.