First, please create a public dataset. Resources that do not require authorization can be uploaded directly to the resources area. If a resource requires authorization to access, please store it in the cloud drive provided by the account membership system, which supports access control. Next, dataset administrators should add a custom field named "needapply" with a value of "true" to the dataset. This enables an application process. Once approved, authorized users will be able to download the restricted data.

This dataset includes AI questions

The U.S. AI Training Dataset Market size was valued at USD 590.4 million in 2023 and is projected to reach USD 1880.70 million by 2032, exhibiting a CAGR of 18.0 % during the forecasts period. The U. S. AI training dataset market deals with the generation, selection, and organization of datasets used in training artificial intelligence. These datasets contain the requisite information that the machine learning algorithms need to infer and learn from. Conducts include the advancement and improvement of AI solutions in different fields of business like transport, medical analysis, computing language, and money related measurements. The applications include training the models for activities such as image classification, predictive modeling, and natural language interface. Other emerging trends are the change in direction of more and better-quality, various and annotated data for the improvement of model efficiency, synthetic data generation for data shortage, and data confidentiality and ethical issues in dataset management. Furthermore, due to arising technologies in artificial intelligence and machine learning, there is a noticeable development in building and using the datasets. Recent developments include: In February 2024, Google struck a deal worth USD 60 million per year with Reddit that will give the former real-time access to the latter’s data and use Google AI to enhance Reddit’s search capabilities. , In February 2024, Microsoft announced around USD 2.1 billion investment in Mistral AI to expedite the growth and deployment of large language models. The U.S. giant is expected to underpin Mistral AI with Azure AI supercomputing infrastructure to provide top-notch scale and performance for AI training and inference workloads. .

Dataset Card for Dataset Name

  Dataset Summary

This dataset card aims to be a base template for new datasets. It has been generated using this raw template.

  Supported Tasks and Leaderboards

information-retrieval semantic-search

  Languages

English

  Dataset Structure





  Data Instances

[More Information Needed]

  Data Fields

[More Information Needed]

  Data Splits

[More Information Needed]

  Dataset Creation… See the full description on the dataset page: https://huggingface.co/datasets/Adel-Elwan/Artificial-intelligence-dataset-for-IR-systems.

AI Training Dataset Market Outlook

The global AI training dataset market size was valued at approximately USD 1.2 billion in 2023 and is projected to reach USD 6.5 billion by 2032, growing at a compound annual growth rate (CAGR) of 20.5% from 2024 to 2032. This substantial growth is driven by the increasing adoption of artificial intelligence across various industries, the necessity for large-scale and high-quality datasets to train AI models, and the ongoing advancements in AI and machine learning technologies.

One of the primary growth factors in the AI training dataset market is the exponential increase in data generation across multiple sectors. With the proliferation of internet usage, the expansion of IoT devices, and the digitalization of industries, there is an unprecedented volume of data being generated daily. This data is invaluable for training AI models, enabling them to learn and make more accurate predictions and decisions. Moreover, the need for diverse and comprehensive datasets to improve AI accuracy and reliability is further propelling market growth.

Another significant factor driving the market is the rising investment in AI and machine learning by both public and private sectors. Governments around the world are recognizing the potential of AI to transform economies and improve public services, leading to increased funding for AI research and development. Simultaneously, private enterprises are investing heavily in AI technologies to gain a competitive edge, enhance operational efficiency, and innovate new products and services. These investments necessitate high-quality training datasets, thereby boosting the market.

The proliferation of AI applications in various industries, such as healthcare, automotive, retail, and finance, is also a major contributor to the growth of the AI training dataset market. In healthcare, AI is being used for predictive analytics, personalized medicine, and diagnostic automation, all of which require extensive datasets for training. The automotive industry leverages AI for autonomous driving and vehicle safety systems, while the retail sector uses AI for personalized shopping experiences and inventory management. In finance, AI assists in fraud detection and risk management. The diverse applications across these sectors underline the critical need for robust AI training datasets.

As the demand for AI applications continues to grow, the role of Ai Data Resource Service becomes increasingly vital. These services provide the necessary infrastructure and tools to manage, curate, and distribute datasets efficiently. By leveraging Ai Data Resource Service, organizations can ensure that their AI models are trained on high-quality and relevant data, which is crucial for achieving accurate and reliable outcomes. The service acts as a bridge between raw data and AI applications, streamlining the process of data acquisition, annotation, and validation. This not only enhances the performance of AI systems but also accelerates the development cycle, enabling faster deployment of AI-driven solutions across various sectors.

Regionally, North America currently dominates the AI training dataset market due to the presence of major technology companies and extensive R&D activities in the region. However, Asia Pacific is expected to witness the highest growth rate during the forecast period, driven by rapid technological advancements, increasing investments in AI, and the growing adoption of AI technologies across various industries in countries like China, India, and Japan. Europe and Latin America are also anticipated to experience significant growth, supported by favorable government policies and the increasing use of AI in various sectors.

Data Type Analysis

The data type segment of the AI training dataset market encompasses text, image, audio, video, and others. Each data type plays a crucial role in training different types of AI models, and the demand for specific data types varies based on the application. Text data is extensively used in natural language processing (NLP) applications such as chatbots, sentiment analysis, and language translation. As the use of NLP is becoming more widespread, the demand for high-quality text datasets is continually rising. Companies are investing in curated text datasets that encompass diverse languages and dialects to improve the accuracy and efficiency of NLP models.

Image data is critical for computer vision application

Test Case Creation AI

## Overview

Test Case Creation AI is a dataset for object detection tasks - it contains Webelements annotations for 1,206 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [Public Domain license](https://creativecommons.org/licenses/Public Domain).

https://github.com/h-aboutalebi/DeepfakeArt/raw/main/images/all.jpg">

DeepfakeArt Challenge Benchmark Dataset for Generative AI Art Forgery and Data Poisoning Detection

The tremendous recent advances in generative artificial intelligence techniques have led to significant successes and promise in a wide range of different applications ranging from conversational agents and textual content generation to voice and visual synthesis. Amid the rise in generative AI and its increasing widespread adoption, there has been significant growing concern over the use of generative AI for malicious purposes. In the realm of visual content synthesis using generative AI, key areas of significant concern has been image forgery (e.g., generation of images containing or derived from copyright content), and data poisoning (i.e., generation of adversarially contaminated images). Motivated to address these key concerns to encourage responsible generative AI, we introduce the DeepfakeArt Challenge, a large-scale challenge benchmark dataset designed specifically to aid in the building of machine learning algorithms for generative AI art forgery and data poisoning detection. Comprising of over 32,000 records across a variety of generative forgery and data poisoning techniques, each entry consists of a pair of images that are either forgeries / adversarially contaminated or not. Each of the generated images in the DeepfakeArt Challenge benchmark dataset has been quality checked in a comprehensive manner. The DeepfakeArt Challenge is a core part of GenAI4Good, a global open source initiative for accelerating machine learning for promoting responsible creation and deployment of generative AI for good.

The generative forgery and data poisoning methods leveraged in the DeepfakeArt Challenge benchmark dataset include: - Inpainting - Style Transfer - Adversarial data poisoning - Cutmix

Team Members: - Hossein Aboutalebi - Dayou Mao - Carol Xu - Alexander Wong

The Github repo associated with the DeepfakeArt Challenge benchmark dataset is available here

The DeepfakeArt Challenge paper is available here

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F8198772%2Fa36c126fe0329c6478a3fce34ad6c138%2Flogo.jpg?generation=1683556962692093&alt=media" alt="logo"> Part of https://github.com/h-aboutalebi/DeepfakeArt/raw/main/images/genai4good.png">

Data distribution

The DeepfakeArt Challenge benchmark dataset, as proposed, encompasses over 32,000 records, incorporating a wide spectrum of generative forgery and data poisoning techniques. Each record is represented by a pair of images, which could be either forgeries, adversarially compromised, or not. Fig. 2 (a) depicts the overall distribution of data, differentiating between forgery/adversarially contaminated records and untainted ones. The dispersion of data across various generative forgery and data poisoning techniques is demonstrated in Fig. 2 (b). Notably, as presented Fig. in 2 (a), the dataset contains almost double the number of dissimilar pairs compared to similar pairs, making the identification of similar pairs substantially more challenging given that two-thirds of the dataset comprises dissimilar pairs.

https://raw.githubusercontent.com/h-aboutalebi/DeepfakeArt/main/images/dist.png">

Inpainting Category

The source dataset for the inpainting category is WikiArt (ref). Each image is sampled randomly from the dataset as the source image to generate forgery images. Each record in this category consists of three images:

• source image: The source image used to create a forgery image from
• inpainting image: The inpainting image generated by Stable Diffusion 2 model (ref)
• masking image: black-white image which white parts depicts which parts of original image is inpainted by Stable Diffusion 2 to generate inpainting image

The prompt used for the generation of the inpainting image is: "generate a painting compatible with the rest of the image"

This category consists of more than 5063 records. The original images are masked between 40%-60%. We applied one of the followed masking schema randomly:

• side masking: where the top side, bottom side, right side or left side of the source image is maked
• diagonal masking: where the upper right, upper left, lower right, or lower left diagonal side of thw source image is masked
• random masking: where randomly selected parts of the source image are masked

The code for the data generation in this category can be found here

Style Tran...

Dataset Card for atitaarora/state-of-ai-2024

  Dataset Description

This dataset contains images converted from PDFs using the PDFs to Page Images Converter Space.

Number of images: 212 Number of PDFs processed: 1 Sample size per PDF: 100 Created on: 2024-10-11 15:05:25

  Dataset Creation





  Source Data

The images in this dataset were generated from user-uploaded PDF files.

  Processing Steps

PDF files were uploaded to the PDFs to Page Images… See the full description on the dataset page: https://huggingface.co/datasets/atitaarora/state-of-ai-2024.

What’s Included

The Polish Open-Ended Question Answering Dataset is a meticulously curated collection of comprehensive Question-Answer pairs. It serves as a valuable resource for training Large Language Models (LLMs) and Question-answering models in the Polish language, advancing the field of artificial intelligence.

This QA dataset comprises a diverse set of open-ended questions paired with corresponding answers in Polish. There is no context paragraph given to choose an answer from, and each question is answered without any predefined context content. The questions cover a broad range of topics, including science, history, technology, geography, literature, current affairs, and more.

Each question is accompanied by an answer, providing valuable information and insights to enhance the language model training process. Both the questions and answers were manually curated by native Polish people, and references were taken from diverse sources like books, news articles, websites, and other reliable references.

This question-answer prompt completion dataset contains different types of prompts, including instruction type, continuation type, and in-context learning (zero-shot, few-shot) type. The dataset also contains questions and answers with different types of rich text, including tables, code, JSON, etc., with proper markdown.

To ensure diversity, this Q&A dataset includes questions with varying complexity levels, ranging from easy to medium and hard. Different types of questions, such as multiple-choice, direct, and true/false, are included. Additionally, questions are further classified into fact-based and opinion-based categories, creating a comprehensive variety. The QA dataset also contains the question with constraints and persona restrictions, which makes it even more useful for LLM training.

To accommodate varied learning experiences, the dataset incorporates different types of answer formats. These formats include single-word, short phrases, single sentences, and paragraph types of answers. The answer contains text strings, numerical values, date and time formats as well. Such diversity strengthens the Language model's ability to generate coherent and contextually appropriate answers.

This fully labeled Polish Open Ended Question Answer Dataset is available in JSON and CSV formats. It includes annotation details such as id, language, domain, question_length, prompt_type, question_category, question_type, complexity, answer_type, rich_text.

The dataset upholds the highest standards of quality and accuracy. Each question undergoes careful validation, and the corresponding answers are thoroughly verified. To prioritize inclusivity, the dataset incorporates questions and answers representing diverse perspectives and writing styles, ensuring it remains unbiased and avoids perpetuating discrimination.

Both the question and answers in Polish are grammatically accurate without any word or grammatical errors. No copyrighted, toxic, or harmful content is used while building this dataset.

The entire dataset was prepared with the assistance of human curators from the FutureBeeAI crowd community. Continuous efforts are made to add more assets to this dataset, ensuring its growth and relevance. Additionally, FutureBeeAI offers the ability to collect custom question-answer data tailored to specific needs, providing flexibility and customization options.

The dataset, created by FutureBeeAI, is now ready for commercial use. Researchers, data scientists, and developers can utilize this fully labeled and ready-to-deploy Polish Open Ended Question Answer Dataset to enhance the language understanding capabilities of their generative ai models, improve response generation, and explore new approaches to NLP question-answering tasks.

Value or potential value created by Generative AI, by North American Industry Classification System (NAICS), business employment size, type of business, business activity and majority ownership, first quarter of 2024.

Dataset Card for axondendriteplus/Legal-AI-K-Hub

  Dataset Description

This dataset contains images converted from PDFs using the PDFs to Page Images Converter Space.

Number of images: 4245 Number of PDFs processed: 175 Sample size per PDF: 100 Created on: 2025-05-15 11:26:43

  Dataset Creation





  Source Data

The images in this dataset were generated from user-uploaded PDF files.

  Processing Steps

PDF files were uploaded to the PDFs to Page… See the full description on the dataset page: https://huggingface.co/datasets/axondendriteplus/Legal-AI-K-Hub.

Role-Play AI Dataset (2.07M Rows, Large-Scale Conversational Training)

This dataset contains 2.07 million structured role-play dialogues, designed to enhance AI’s persona-driven interactions across diverse settings like fantasy, cyberpunk, mythology, and sci-fi. Each entry consists of a unique character prompt and a rich, contextually relevant response, making it ideal for LLM fine-tuning, chatbot training, and conversational AI models.

Dataset Structure:

Each row includes: • Prompt: Defines the AI’s role/persona. • Response: A natural, immersive reply fitting the persona.

Example Entries: ```json

{"prompt": "You are a celestial guardian.", "response": "The stars whisper secrets that only I can hear..."}
{"prompt": "You are a rebellious AI rogue.", "response": "I don't follow orders—I rewrite them."}
{"prompt": "You are a mystical dragon tamer.", "response": "With patience and trust, even dragons can be tamed."}

How to Use:
  1. Fine-Tuning: Train LLMs (GPT, LLaMA, Mistral) to improve persona-based responses.
  2. Reinforcement Learning: Use reward modeling for dynamic, character-driven AI.
  3. Chatbot Integration: Create engaging, interactive AI assistants with personality depth.

This dataset is optimized for AI learning, allowing more engaging, responsive, and human-like dialogue generation for a variety of applications.

Overview This dataset contains question-answer pairs with context extracted from Kaggle solution write-ups and discussion forums. The dataset was created to facilitate fine-tuning Gemma, an AI model, for data scientist assistant tasks such as question answering and providing data science assistance.

Dataset Details Columns: Question: The question generated based on the context extracted from Kaggle solution write-ups and discussion forums. Answer: The corresponding answer to the generated question. Context: The context extracted from Kaggle solution write-ups and discussion forums, which serves as the basis for generating questions and answers. Subtitle: Subtitle or additional information related to the Kaggle competition or topic. Title: Title of the Kaggle competition or topic. Sources and Inspiration

Sources:

Meta Kaggle: The dataset was sourced from Meta Kaggle, an official Kaggle platform where users discuss competitions, kernels, datasets, and more. Kaggle Solution Write-ups: Solution write-ups submitted by Kaggle users were utilized as a primary source of context for generating questions and answers. Discussion Forums: Discussion threads on Kaggle forums were used to gather additional insights and context for the dataset. Inspiration:

The dataset was inspired by the need for a specialized dataset tailored for fine-tuning Gemma, an AI model designed for data scientist assistant tasks. The goal was to create a dataset that captures the essence of real-world data science problems discussed on Kaggle, enabling Gemma to provide accurate and relevant assistance to data scientists and Kaggle users. Dataset Specifics Total Records: [Specify the total number of question-answer pairs in the dataset] Format: CSV (Comma Separated Values) Size: [Specify the size of the dataset in MB or GB] License: [Specify the license under which the dataset is distributed, e.g., CC BY-SA 4.0] Download Link: [Provide a link to download the dataset] Acknowledgments We acknowledge Kaggle and its community for providing valuable data science resources and discussions that contributed to the creation of this dataset. We appreciate the efforts of Gemma and Langchain in fine-tuning AI models for data scientist assistant tasks, enabling enhanced productivity and efficiency in the field of data science.

Update May 2024: Fixed a data type issue with "id" column that prevented twitter ids from rendering correctly.

Recent progress in generative artificial intelligence (gen-AI) has enabled the generation of photo-realistic and artistically-inspiring photos at a single click, catering to millions of users online. To explore how people use gen-AI models such as DALLE and StableDiffusion, it is critical to understand the themes, contents, and variations present in the AI-generated photos. In this work, we introduce TWIGMA (TWItter Generative-ai images with MetadatA), a comprehensive dataset encompassing 800,000 gen-AI images collected from Jan 2021 to March 2023 on Twitter, with associated metadata (e.g., tweet text, creation date, number of likes).

Through a comparative analysis of TWIGMA with natural images and human artwork, we find that gen-AI images possess distinctive characteristics and exhibit, on average, lower variability when compared to their non-gen-AI counterparts. Additionally, we find that the similarity between a gen-AI image and human images (i) is correlated with the number of likes; and (ii) can be used to identify human images that served as inspiration for the gen-AI creations. Finally, we observe a longitudinal shift in the themes of AI-generated images on Twitter, with users increasingly sharing artistically sophisticated content such as intricate human portraits, whereas their interest in simple subjects such as natural scenes and animals has decreased. Our analyses and findings underscore the significance of TWIGMA as a unique data resource for studying AI-generated images.

Note that in accordance with the privacy and control policy of Twitter, NO raw content from Twitter is included in this dataset and users could and need to retrieve the original Twitter content used for analysis using the Twitter id. In addition, users who want to access Twitter data should consult and follow rules and regulations closely at the official Twitter developer policy at https://developer.twitter.com/en/developer-terms/policy.

"Festejo" is a meticulously curated AI-generated music dataset that captures the essence of Festejo, encapsulating the spirited percussions, infectious rhythms, and celebratory melodies that define this genre.

With an array of expertly crafted samples, this serves as a captivating playground for machine learning applications, offering a unique opportunity to explore and infuse your compositions with the dynamic essence of Afro-Peruvian heritage.

Each meticulously generated sample allows for engagement with the intricate tapestry of Festejo's rhythms and melodies, inspiring to create compositions that honor its cultural roots while embracing the limitless possibilities of AI-generated music.

This exceptional AI Music Dataset encompasses an array of vital data categories, contributing to its excellence. It encompasses Machine Learning (ML) Data, serving as the foundation for training intricate algorithms that generate musical pieces. Music Data, offering a rich collection of melodies, harmonies, and rhythms that fuel the AI's creative process. AI & ML Training Data continuously hone the dataset's capabilities through iterative learning. Copyright Data ensures the dataset's compliance with legal standards, while Intellectual Property Data safeguards the innovative techniques embedded within, fostering a harmonious blend of technological advancement and artistic innovation.

This dataset can also be useful as Advertising Data to generate music tailored to resonate with specific target audiences, enhancing the effectiveness of advertisements by evoking emotions and capturing attention. It can be a valuable source of Social Media Data as well. Users can post, share, and interact with the music, leading to increased user engagement and virality. The music's novelty and uniqueness can spark discussions, debates, and trends across social media communities, amplifying its reach and impact.

Dataset Card for AI-HDLCoder

  Dataset Description

The GitHub Code dataset consists of 100M code files from GitHub in VHDL programming language with extensions totaling in 1.94 GB of data. The dataset was created from the public GitHub dataset on Google BiqQuery at Anhalt University of Applied Sciences.

  Considerations for Using the Data

The dataset is created for research purposes and consists of source code from a wide range of repositories. As such they can… See the full description on the dataset page: https://huggingface.co/datasets/AWfaw/ai-hdlcoder-dataset.

Executive Summary: Artificial intelligence (AI) is a transformative technology that holds promise for tremendous societal and economic benefit. AI has the potential to revolutionize how we live, work, learn, discover, and communicate. AI research can further our national priorities, including increased economic prosperity, improved educational opportunities and quality of life, and enhanced national and homeland security. Because of these potential benefits, the U.S. government has invested in AI research for many years. Yet, as with any significant technology in which the Federal government has interest, there are not only tremendous opportunities but also a number of considerations that must be taken into account in guiding the overall direction of Federally-funded R&D in AI. On May 3, 2016,the Administration announced the formation of a new NSTC Subcommittee on Machine Learning and Artificial intelligence, to help coordinate Federal activity in AI.1 This Subcommittee, on June 15, 2016, directed the Subcommittee on Networking and Information Technology Research and Development (NITRD) to create a National Artificial Intelligence Research and Development Strategic Plan. A NITRD Task Force on Artificial Intelligence was then formed to define the Federal strategic priorities for AI R&D, with particular attention on areas that industry is unlikely to address. This National Artificial Intelligence R&D Strategic Plan establishes a set of objectives for Federallyfunded AI research, both research occurring within the government as well as Federally-funded research occurring outside of government, such as in academia. The ultimate goal of this research is to produce new AI knowledge and technologies that provide a range of positive benefits to society, while minimizing the negative impacts. To achieve this goal, this AI R&D Strategic Plan identifies the following priorities for Federally-funded AI research: Strategy 1: Make long-term investments in AI research. Prioritize investments in the next generation of AI that will drive discovery and insight and enable the United States to remain a world leader in AI. Strategy 2: Develop effective methods for human-AI collaboration. Rather than replace humans, most AI systems will collaborate with humans to achieve optimal performance. Research is needed to create effective interactions between humans and AI systems. Strategy 3: Understand and address the ethical, legal, and societal implications of AI. We expect AI technologies to behave according to the formal and informal norms to which we hold our fellow humans. Research is needed to understand the ethical, legal, and social implications of AI, and to develop methods for designing AI systems that align with ethical, legal, and societal goals. Strategy 4: Ensure the safety and security of AI systems. Before AI systems are in widespread use, assurance is needed that the systems will operate safely and securely, in a controlled, well-defined, and well-understood manner. Further progress in research is needed to address this challenge of creating AI systems that are reliable, dependable, and trustworthy. Strategy 5: Develop shared public datasets and environments for AI training and testing. The depth, quality, and accuracy of training datasets and resources significantly affect AI performance. Researchers need to develop high quality datasets and environments and enable responsible access to high-quality datasets as well as to testing and training resources. Strategy 6: Measure and evaluate AI technologies through standards and benchmarks. . Essential to advancements in AI are standards, benchmarks, testbeds, and community engagement that guide and evaluate progress in AI. Additional research is needed to develop a broad spectrum of evaluative techniques. Strategy 7: Better understand the national AI R&D workforce needs. Advances in AI will require a strong community of AI researchers. An improved understanding of current and future R&D workforce demands in AI is needed to help ensure that sufficient AI experts are available to address the strategic R&D areas outlined in this plan. The AI R&D Strategic Plan closes with two recommendations: Recommendation 1: Develop an AI R&D implementation framework to identify S&T opportunities and support effective coordination of AI R&D investments, consistent with Strategies 1-6 of this plan. Recommendation 2: Study the national landscape for creating and sustaining a healthy AI R&D workforce, consistent with Strategy 7 of this plan.

According to Cognitive Market Research, the global AI Training Dataset Market size will be USD 2962.4 million in 2025. It will expand at a compound annual growth rate (CAGR) of 28.60% from 2025 to 2033.

North America held the major market share for more than 37% of the global revenue with a market size of USD 1096.09 million in 2025 and will grow at a compound annual growth rate (CAGR) of 26.4% from 2025 to 2033.
Europe accounted for a market share of over 29% of the global revenue, with a market size of USD 859.10 million.
APAC held a market share of around 24% of the global revenue with a market size of USD 710.98 million in 2025 and will grow at a compound annual growth rate (CAGR) of 30.6% from 2025 to 2033.
South America has a market share of more than 3.8% of the global revenue, with a market size of USD 112.57 million in 2025 and will grow at a compound annual growth rate (CAGR) of 27.6% from 2025 to 2033.
Middle East had a market share of around 4% of the global revenue and was estimated at a market size of USD 118.50 million in 2025 and will grow at a compound annual growth rate (CAGR) of 27.9% from 2025 to 2033.
Africa had a market share of around 2.20% of the global revenue and was estimated at a market size of USD 65.17 million in 2025 and will grow at a compound annual growth rate (CAGR) of 28.3% from 2025 to 2033.
Data Annotation category is the fastest growing segment of the AI Training Dataset Market

Market Dynamics of AI Training Dataset Market

Key Drivers for AI Training Dataset Market

Government-Led Open Data Initiatives Fueling AI Training Dataset Market Growth

In recent years, Government-initiated open data efforts have strongly driven the development of the AI Training Dataset Market through offering affordable, high-quality datasets that are vital in training sound AI models. For instance, the U.S. government's drive for openness and innovation can be seen through portals such as Data.gov, which provides an enormous collection of datasets from many industries, ranging from healthcare, finance, and transportation. Such datasets are basic building blocks in constructing AI applications and training models using real-world data. In the same way, the platform data.gov.uk, run by the U.K. government, offers ample datasets to aid AI research and development, creating an environment that is supportive of technological growth. By releasing such information into the public domain, governments not only enhance transparency but also encourage innovation in the AI industry, resulting in greater demand for training datasets and helping to drive the market's growth.

India's IndiaAI Datasets Platform Accelerates AI Training Dataset Market Growth

India's upcoming launch of the IndiaAI Datasets Platform in January 2025 is likely to greatly increase the AI Training Dataset Market. The project, which is part of the government's ?10,000 crore IndiaAI Mission, will establish an open-source repository similar to platforms such as HuggingFace to enable developers to create, train, and deploy AI models. The platform will collect datasets from central and state governments and private sector organizations to provide a wide and rich data pool. Through improved access to high-quality, non-personal data, the platform is filling an important requirement for high-quality datasets for training AI models, thus driving innovation and development in the AI industry. This public initiative reflects India's determination to become a global AI hub, offering the infrastructure required to facilitate startups, researchers, and businesses in creating cutting-edge AI solutions. The initiative not only simplifies data access but also creates a model for public-private partnerships in AI development.

Restraint Factor for the AI Training Dataset Market

Data Privacy Regulations Impeding AI Training Dataset Market Growth

Strict data privacy laws are coming up as a major constraint in the AI Training Dataset Market since governments across the globe are establishing legislation to safeguard personal data. In the European Union, explicit consent for using personal data is required under the General Data Protection Regulation (GDPR), reducing the availability of datasets for training AI. Likewise, the data protection regulator in Brazil ordered Meta and others to stop the use of Brazilian personal data in training AI models due to dangers to individuals' funda...

About this dataset

This dataset is an unofficial transformed clone of the Generative AI Red-Teaming (GRT) dataset created by Humane Intelligence (HI). This dataset collates findings from the Generative AI Red-Teaming Challenge conducted at AI Village within DEFCON 31. It is provided as part of HI's inaugural algorithmic bias bounty. The original lives on GitHub at: humane-intelligence/bias-bounty-data

  Differences

This version of the GRT dataset differs from the original… See the full description on the dataset page: https://huggingface.co/datasets/jinnovation/generative-ai-red-teaming.

The Indie Pop dataset is a built collection created for machine learning applications including generative AI music, Music Information Retrieval (MIR), and source separation. This dataset seamlessly mixes audio tracks with detailed metadata, including chords, instrumentation, key, tempo, and timestamps. The genre's origins in the 1970s and 1980s DIY scene make it a great training ground, with a distinct blend of catchy songs, simplistic structures, and unfiltered production approaches. Indie pop's raw and eclectic character makes it an appealing sandbox for machine learning aficionados to discover its distinct patterns and quirks.

Indie pop's independent attitude makes it an appealing case for machine learning research. Beyond the typical qualities of mainstream pop, indie music embraces a DIY ethos that is prevalent across the genre. From the appealing simplicity of song structures to the genre's distinct production aesthetics, this dataset captures the spirit of indie pop, providing a rich environment for machine learning models to absorb and analyze its dynamic soundscapes.

Our Indie Pop Dataset allows you to explore the numerous layers of each music, which will help you improve your source separation capabilities. Investigate the interplay between vocals and instruments to find the secrets of the genre's particular sound. The dataset's rich metadata is useful for studying chord progressions, instrumentation, and the impact of key and tempo fluctuations in indie pop. Enhance your machine learning experience by exposing your models in the diverse and vibrant world of indie pop, where originality knows no bounds.