All the data for this dataset is provided from CARMA: Data from CARMA (www.carma.org) This dataset provides information about Power Plant emissions in North Korea. Power Plant emissions from all power plants in North Korea were obtained by CARMA for the past (2000 Annual Report), the present (2007 data), and the future. CARMA determine data presented for the future to reflect planned plant construction, expansion, and retirement. The dataset provides the name, company, parent company, city, state, zip, county, metro area, lat/lon, and plant id for each individual power plant. The dataset reports for the three time periods: Intensity: Pounds of CO2 emitted per megawatt-hour of electricity produced. Energy: Annual megawatt-hours of electricity produced. Carbon: Annual carbon dioxide (CO2) emissions. The units are short or U.S. tons. Multiply by 0.907 to get metric tons. Carbon Monitoring for Action (CARMA) is a massive database containing information on the carbon emissions of over 50,000 power plants and 4,000 power companies worldwide. Power generation accounts for 40% of all carbon emissions in the United States and about one-quarter of global emissions. CARMA is the first global inventory of a major, sector of the economy. The objective of CARMA.org is to equip individuals with the information they need to forge a cleaner, low-carbon future. By providing complete information for both clean and dirty power producers, CARMA hopes to influence the opinions and decisions of consumers, investors, shareholders, managers, workers, activists, and policymakers. CARMA builds on experience with public information disclosure techniques that have proven successful in reducing traditional pollutants. Please see carma.org for more information

The Gross Domestic Product (GDP) in South Korea was worth 1712.79 billion US dollars in 2023, according to official data from the World Bank. The GDP value of South Korea represents 1.62 percent of the world economy. This dataset provides - South Korea GDP - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The Health and Well-Being of Koreans (HAWK) was a cross-sectional survey of adults ages 18 and older in the United States who are of Korean descent supported by the National Institute on Aging. The goal of HAWK was to implement Web-based respondent driven sampling (Web-RDS) is an extension of RDS, which exploits existing social networks for recruiting research participants, specifically, of small and hard-to-sample groups. In the case of HAWK, the target participants were of granular racial/ethnic minority groups. A total of 772 participated between May 2022 and January 2023. The participants were asked about COVID-19, physical as well as mental health, life satisfaction, health behaviors, healthcare coverage and utilization as well as social relations and networks along with socio-demographic characteristics. The questions mostly come from established surveys, such as the American Community Survey and the California Health Interview Survey. The questionnaires were provided in English and Korean, and participants self-selected the language that they preferred to complete the survey in. The data is expected to provide a broad overview of Korean Americans through the last stage of the Covid-19 pandemic. To protect respondent privacy and data confidentiality, peer recruitment information as well as sensitive information (e.g., citizenship), such variables were excluded from the current file and will be included in a dataset available through ICPSR in a restricted format. Because HAWK uses RDS, which does not follow the conventions of probability sampling, users are advised to exercise caution in making population-level inference with results from the analysis of HAWK data.

The Gross Domestic Product (GDP) in North Korea was worth 18 billion US dollars in 2019, according to official data from the World Bank. The GDP value of North Korea represents 0.02 percent of the world economy. This dataset provides - North Korea GDP - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The percentage of market value attributable to intangible assets has increased exponentially from 32% in 1985 to 87% in 2015. This trend is expected to continue, making valuation of intangible assets vital for investors. (https://www.oceantomo.com/insights/ocean-tomo-releases-2015-annual-study-of-intangible-asset-market-value/) The Patent Valuation System estimates the profits generated by patents based on existing industry financial data and patent data, in order to minimize the subjective analysis involved. This method can be combined with any valuation method that requires an estimate of the expected returns generated by patents. Comparisons between the values generated by the patent valuation system and real-life values of actual patent transactions are carried out in order to gauge the system’s accuracy. Using PTR (price to technology ratio) ratio : The price-to-technology ratio (PTR) is the ratio for valuing a company that measures its current share price relative to its per-share technology value, where technology value of corporations is defined as the sum of value of patents they hold. PTR allows investors to make an investment decision in terms of technology value of corporations. PTR helps investors identify stocks that are overvalued or undervalued by comparing technology value to its stock price.

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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.

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