Resources for GDR data submitters and curators, including training videos, step-by-step guides on data submission, and detailed documentation of the GDR. The Data Management and Submission Best Practices document also contains API access and metadata schema information for developers interested in harvesting GDR metadata for federation or inclusion in their local catalogs.

Following the protocol for the reporting of conversations with ChatGPT [1], this supportive document provides the full text of the conversations with ChatGPT that were used and analysed in the following paper: Spennemann, Dirk H. R. (2025). “Draw me a curator” Visual stereotyping of a profession by generative Ai.

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:

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

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

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.

Data-Centric AI Platforms Market Outlook

According to our latest research, the Global Data-Centric AI Platforms market size was valued at $4.3 billion in 2024 and is projected to reach $23.1 billion by 2033, expanding at a robust CAGR of 20.1% during the forecast period of 2024–2033. The primary driver behind this remarkable growth is the increasing need for high-quality, well-curated data to fuel artificial intelligence and machine learning applications across diverse industries. As organizations recognize that the quality of data is as critical as the sophistication of algorithms, there is a marked shift towards platforms that enable efficient data management, annotation, governance, and quality assurance. This paradigm shift is further accentuated by the rapid digital transformation initiatives, surging adoption of AI-driven analytics, and the proliferation of big data, all of which necessitate a robust foundation of reliable, labeled, and structured data for optimal AI outcomes.

Regional Outlook

North America currently dominates the Data-Centric AI Platforms market, accounting for the largest share of the global revenue. This region’s leadership is underpinned by a mature technology ecosystem, widespread adoption of AI across major verticals such as BFSI, healthcare, and IT & telecommunications, and a strong presence of leading market players. The United States, in particular, is a hub for AI innovation, with a high concentration of data-centric startups, research institutions, and established enterprises investing heavily in AI infrastructure. Government initiatives promoting AI research, coupled with stringent data governance regulations, further drive the adoption of data-centric AI platforms. As of 2024, North America contributed approximately 41% of the global market value, reflecting its advanced digital maturity and early adoption curve.

The Asia Pacific region is emerging as the fastest-growing market for Data-Centric AI Platforms, projected to record a remarkable CAGR of 24.5% between 2024 and 2033. This accelerated growth is fueled by rapid urbanization, digitalization efforts, and increasing investments in AI infrastructure by both governments and private enterprises. Countries like China, Japan, South Korea, and India are witnessing a surge in AI-driven projects, particularly in manufacturing, retail, and healthcare sectors. The region’s expanding data ecosystem, coupled with a growing pool of skilled AI professionals, is fostering the adoption of advanced data annotation, labeling, and quality management solutions. Furthermore, strategic initiatives such as China’s AI development plans and India’s Digital India mission are catalyzing the deployment of data-centric AI platforms, making Asia Pacific a key region to watch over the forecast period.

Latin America, the Middle East, and Africa are gradually gaining traction in the Data-Centric AI Platforms market, albeit at a slower pace compared to North America and Asia Pacific. These emerging economies face unique challenges such as limited AI expertise, infrastructural constraints, and inconsistent regulatory frameworks. However, localized demand for AI-driven solutions in sectors like banking, agriculture, and public safety is prompting incremental adoption. Governments in these regions are beginning to recognize the strategic importance of AI, leading to policy reforms and capacity-building initiatives. While the overall market share remains modest, the potential for growth is significant, particularly as digital literacy improves, investment in cloud infrastructure increases, and global vendors expand their geographic footprint into these untapped markets.

Report Scope

Artificial intelligence (AI) systems already greatly impact our lives — they increasingly shape what we see, believe, and do. Based on the steady advances in AI technology and the significant recent increases in investment, we should expect AI technology to become even more powerful and impactful in the following years and decades.

It is easy to underestimate how much the world can change within a lifetime, so it is worth taking seriously what those who work on AI expect for the future. Many AI experts believe there is a real chance that human-level artificial intelligence will be developed within the following decades, and some think it will exist much sooner.

How such powerful AI systems are built and used will be very important for the future of our world and our own lives. All technologies have positive and negative consequences, but with AI, the range of these consequences is extraordinarily large: the technology has immense potential for good. Still, it comes with significant downsides and high risks.

A technology that has such an enormous impact needs to be of central interest to people across our entire society. But currently, the question of how this technology will get developed and used is left to a small group of entrepreneurs and engineers.

With our publications on artificial intelligence, we want to help change this status quo and support a broader societal engagement.

On this page, you will find key insights, articles, and charts of AI-related metrics that let you monitor what is happening and where we might be heading. We hope that this work will be helpful for the growing and necessary public conversation on AI.

About the files: 1- The affiliation of the research team building a particular notable AI system was classified according to the following:— Academia: 100% of researchers affiliated with academia— Collaboration, Academia-majority: 71–99% affiliated with academia— Collaboration: 30–70% affiliated with academia— Collaboration, Industry-majority: 71–99% affiliated with industry— Industry: 100% of researchers affiliated with industry

2- The AI systems shown here were built using machine learning and deep learning methods. These involve complex mathematical calculations that require significant computational resources. Training these systems generally involves feeding large amounts of data through various layers and nodes and adjusting internal system parameters over numerous iterations to optimize the system’s performance.

3- Annually, the IFR publishes the World Robotics Report, which provides comprehensive insights into global trends concerning robot installations.

4- CAT, or Country Activity Tracker, is a research tool curated by CSET that offers a wealth of data about artificial intelligence (AI) globally. This data comes from a vast repository known as the Merged Academic Corpus (MAC), which contains details about more than 270 million academic articles worldwide. In CAT, only those articles that are related to AI are utilized.

5- Training computation, often measured in total FLOP (floating-point operations), refers to the total number of computer operations used to train an AI system. One FLOP is equivalent to one addition, subtraction, multiplication, or division of two decimal numbers, and one petaFLOP equals one quadrillion (10^15) FLOP.

6- The data for 1985–2019 comes from Chess.com, as detailed in this thread on Twitter. Their primary data source is the Swedish Computer Chess Association (SSDF). We manually extracted the data by watching the video, such that the chess engine with the highest ELO rating in a given year became our datapoint for that year. We were unable to find the data in any other format. The data after 2019 comes from SSDF: • 2020 datapoint • 2021 datapoint • 2022 datapoint

7- This dataset by the research group Epoch collates two existing datasets on GPU price-performance: • Median Group (2019). Feasibility of Training an AGI using Deep RL: A Very Rough Estimate. • Sun et al. (2019). Summarizing CPU and GPU Design Trends with Product Data. arXiv. The report by Epoch researchers Hobbhahn & Besiroglu (2022) describes their collation method, as well as their findings from statistically analyzing the trends in GPU price-performance.

8- The Advanced Semiconductor Supply Chain Dataset includes manually compiled, high-level information about the tools, materials, processes, countries, and firms involved in the production of advanced logic chips. The current version of the dataset reflects how researchers understood this supply chain in early 2021. It uses a wide variety of sources, such as corporate websites and disclosures, specialized market research, and industry group publications.

9- Reporting a time series of AI investments in nominal prices (i.e., without adjusting for inflation) means it makes little sense to compare observations across ...

Context:

In today's rapidly evolving technological landscape, artificial intelligence (AI) stands at the forefront of change, particularly in the professional sphere. This dataset, aptly named the "Job Threat Index," offers a deep dive into how AI is influencing a myriad of job roles across diverse domains.

Sources:

The data has been meticulously curated from a range of reputable job analytics platforms, AI impact studies, and organizational reports. Each entry has been verified to ensure accuracy and relevance to the ongoing AI advancements in the respective fields.

Inspiration:

The genesis of this dataset lies in the increasing discussions around AI's role in the job market. With concerns about AI replacing human jobs on one side and the potential for AI to create new roles on the other, there's a pressing need for clear, data-driven insights. The "Job Threat Index" seeks to bridge this knowledge gap, offering researchers, analysts, and enthusiasts a comprehensive view of where we stand and where we might be heading.

Intellizence is an award-winning AI platform focused on monitoring growth & sales, risk & distress signals in companies of interest. Intellizence helps customers to identify emerging business opportunities & risks and make timely strategic & tactical decisions.

Intellizence Company News Signals API delivers curated news signals about your interested public & private companies.

Customers / Clients - Monitor news related to sales & risk signals like M&A, CXO changes, cost-cutting, etc.

Competitors - Track competitive moves like product launches, partnerships, new clients acquisitions, etc.,

Portfolios - Monitor news related to growth & distress signals like business expansion, Joint Venture, sustainability initiatives, employee activism, etc

Suppliers - Monitor adverse news like supply chain disruption, factory fire, employee strike, etc.,

Partners - Track news related to major partnership announcements, product launches, etc.,

The API is designed for product & data teams. Stop spending time, effort & cost in searching for news about your interested companies.

Accelerate your product launches by doing a bold integration with Intellizence Company News Signals API. The API gives the flexibility to customize news signals for the companies & triggers relevant to you.

Intellizence News Signals are highly curated with a signal relevance of over 95%. The curation is done by a proprietary curation platform powered by advanced Natural Language Processing, Machine Learning & Deep Learning techniques and validated by human curators to ensure the signals are contextual and relevant.

Aggregated from thousands of business news sources in real-time Noise-filtered De-duplicated Contextually classified to ~80 sales & growth, risk & distress signals Delivered through Rest API

Synthetic Financial Transaction Data SAMPLE

This dataset is a sample of our comprehensive Synthetic Financial Transaction Data collection, specifically designed for AI/ML training and development. It contains key attributes like customer IDs, transaction dates, amounts, merchants, and categories, all generated synthetically to ensure realistic patterns without involving any real-world personal data. This sample dataset is ideal for exploratory analysis and model development in areas like fraud detection, transaction analysis, and financial forecasting.

The full version of the dataset contains 10 million rows of synthetic financial transactions, complete with detailed metadata for advanced AI/ML projects.

Key Columns in the Dataset:

• Customer_ID: A unique ID for each customer (integer).
• Date: Transaction date (datetime).
• Amount: Transaction amount in USD (float).
• Merchant: The merchant where the transaction occurred (string).
• Category: The category of the merchant (string).
• Transaction_Type: Specifies whether the transaction is a debit or credit (string).
• Transaction_ID: A unique identifier for each transaction (string).

The dataset was generated on October 8, 2024, ensuring the most up-to-date patterns and features for training AI/ML models.

Use Cases:

• Fraud Detection: Train models to identify fraudulent activities within financial transactions.
• Predictive Modeling: Build models to forecast transaction outcomes or financial trends.
• Pattern Recognition: Leverage the dataset for AI to identify hidden patterns in financial data.

Full Dataset Availability:

The full version of this dataset, containing 10 million synthetic transactions, is available for purchase. The full dataset includes more in-depth financial transaction data for large-scale AI/ML training.

To inquire about purchasing the full dataset, please send an email to:

syntheticdata@sellersift.com

Email Format:

Please ensure that your email contains the following details:

• Subject: Inquiry About Full Synthetic Financial Transactions Dataset Purchase
• Name: [Your Full Name]
• Organization: [Your Organization Name]
• Position: [Your Position/Role]
• Email: [Your Contact Email]
• Phone Number: [Your Contact Number]
• Use Case: [Describe your intended use of the dataset, e.g., fraud detection model training, financial trend forecasting, etc.]
• Expected Data Volume: [How many records you need or details about your requirements, if applicable]
• License Requirements: [Mention if there are any specific licensing requirements for your use case]

License:

This sample dataset is made available under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license. You are free to:

• Share: Copy and redistribute the material in any medium or format.
• Adapt: Remix, transform, and build upon the material.

Under the following terms:

• Attribution: You must give appropriate credit, provide a link to the license, and indicate if changes were made.
• NonCommercial: You may not use the material for commercial purposes.
• ShareAlike: If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.

For full license details, please visit: CC BY-NC-SA 4.0

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.

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.
• 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 markets. With continuous data updates, Success.ai ensures you’re always working with the freshest information.

Key Use Cases:

• Targeted Lead Generation: Build accurate lead lists by filtering data by company size, industry, or location. Target decision-makers in key industries to streamline your B2B sales outreach.
• Account-Based Marketing (ABM): Use B2B company data to personalize marketing campaigns, focusing on high-value accounts and improving conversion rates.
• Investment Research: Track company growth, funding rounds, and employee trends to identify investment opportunities or potential M&A targets.
• Market Research: Enrich your market intelligence initiatives by gain...

A curated collection of free datasets for AI learning, data analytics, and remote work research.

APISCRAPY's AI & ML training data is meticulously curated and labelled to ensure the best quality. Our training data comes from a variety of areas, including healthcare and banking, as well as e-commerce and natural language processing.

AI-Curated B2B Lead Engine Market Outlook

According to our latest research, the global AI-Curated B2B Lead Engine market size reached USD 2.45 billion in 2024, with robust momentum expected to continue over the next decade. The market is projected to grow at a CAGR of 19.8% from 2025 to 2033, resulting in a forecasted market value of USD 11.97 billion by 2033. This remarkable growth trajectory is primarily driven by the increasing adoption of artificial intelligence in sales and marketing automation, the growing demand for data-driven lead generation, and the need for scalable solutions that enhance the efficiency of B2B sales processes. As per our latest research, organizations worldwide are rapidly integrating AI-powered lead engines to gain a competitive edge and streamline their sales pipelines.

The surge in digital transformation across industries stands as a pivotal growth factor for the AI-Curated B2B Lead Engine market. Enterprises are seeking innovative ways to identify, qualify, and convert leads with greater precision and speed. AI-powered lead engines leverage advanced algorithms, machine learning, and natural language processing to analyze large datasets, predict buyer intent, and deliver highly targeted lead recommendations. This capability significantly reduces manual effort, eliminates guesswork, and empowers sales teams to focus on high-value prospects, thereby improving conversion rates and overall revenue generation. The growing emphasis on hyper-personalization and real-time engagement further accelerates the adoption of AI-curated solutions, especially among organizations with complex B2B sales cycles.

Another key driver fueling market expansion is the increasing integration of AI-curated lead engines with existing CRM and marketing automation platforms. Businesses are recognizing the value of seamless interoperability, which allows for the continuous enrichment of customer profiles, automated lead scoring, and dynamic segmentation. This integration not only improves lead management efficiency but also enhances the accuracy of sales forecasting and pipeline management. As organizations strive to optimize their marketing spend and maximize ROI, the demand for AI-powered lead generation tools that can deliver measurable results is witnessing exponential growth. Additionally, the proliferation of cloud-based deployment models is lowering the barriers to entry, enabling small and medium enterprises to harness sophisticated AI capabilities without significant upfront investment.

The rapid evolution of AI technologies, combined with the increasing availability of high-quality data, is unlocking new opportunities for innovation within the AI-Curated B2B Lead Engine market. Vendors are continuously enhancing their platforms with advanced features such as predictive analytics, conversational AI, and intent data analysis. These innovations are enabling more granular targeting, improved lead nurturing, and enhanced customer engagement across multiple channels. Furthermore, regulatory developments around data privacy and security are prompting solution providers to invest in robust compliance frameworks, thereby increasing customer trust and accelerating market adoption. The growing recognition of AI as a strategic enabler for sales and marketing transformation is expected to sustain high growth rates over the forecast period.

In the evolving landscape of B2B sales, Lead-to-Account Matching AI has emerged as a pivotal technology that enhances the precision and effectiveness of lead management strategies. By leveraging sophisticated algorithms, this AI-driven approach enables organizations to accurately match leads to the appropriate accounts, thereby streamlining the sales process and improving the alignment between sales and marketing teams. The integration of Lead-to-Account Matching AI not only reduces the time spent on manual lead qualification but also enhances the accuracy of lead scoring, ensuring that sales teams focus their efforts on high-value opportunities. As businesses increasingly prioritize data-driven decision-making, the adoption of this technology is set to transform traditional sales methodologies and drive significant improvements in conversion rates.

From a regional perspective, North America continues to dominate the global AI-Curated B2B Lead Engine market, accounting for the largest

Sales Prospecting AI Market Outlook

According to our latest research, the global Sales Prospecting AI market size is valued at USD 1.92 billion in 2024 and is expected to reach USD 15.84 billion by 2033, growing at an impressive CAGR of 26.1% during the forecast period. This robust growth is primarily driven by the increasing demand for automation in sales processes, the proliferation of data-driven decision-making, and the rapid adoption of artificial intelligence across industries seeking to optimize lead generation and improve sales outcomes.

One of the primary growth factors propelling the Sales Prospecting AI market is the urgent need for businesses to enhance the efficiency and effectiveness of their sales teams. Traditional sales prospecting methods are often time-consuming and yield inconsistent results, especially in highly competitive markets. AI-powered sales prospecting solutions leverage advanced algorithms and machine learning to automate repetitive tasks such as lead identification, qualification, and scoring. This automation allows sales professionals to focus on high-value activities, resulting in increased productivity and higher conversion rates. Furthermore, the integration of AI with CRM systems and marketing automation platforms enables organizations to create a seamless sales pipeline, reducing the time and effort required to move prospects through the funnel.

Another significant driver for the Sales Prospecting AI market is the exponential growth in customer data generated across multiple digital touchpoints. As businesses collect massive volumes of structured and unstructured data from websites, social media, email campaigns, and customer interactions, the need for sophisticated tools to analyze and extract actionable insights becomes paramount. Sales Prospecting AI leverages natural language processing (NLP), predictive analytics, and data mining techniques to segment customers, forecast sales, and personalize outreach strategies. This data-driven approach not only improves the accuracy of prospecting but also enhances customer experience by delivering highly relevant and timely communications, thereby driving higher engagement and loyalty.

The increasing adoption of cloud-based AI solutions is also a critical growth factor for the Sales Prospecting AI market. Cloud deployment offers scalability, flexibility, and cost-effectiveness, making it an attractive option for both large enterprises and small and medium-sized businesses (SMEs). Cloud-based AI platforms facilitate real-time data processing, remote accessibility, and seamless integration with other business applications. As more organizations embrace digital transformation initiatives, the demand for cloud-enabled Sales Prospecting AI tools is expected to surge, further accelerating market expansion. Additionally, advancements in AI technologies, such as deep learning and conversational AI, are continuously enhancing the capabilities of sales prospecting solutions, enabling businesses to stay ahead of the competition.

In the realm of sales prospecting, the emergence of the AI-Curated B2B Lead Engine is revolutionizing how businesses identify and engage potential clients. This innovative technology harnesses the power of artificial intelligence to sift through vast amounts of data, curating high-quality leads that are most likely to convert. By automating the lead generation process, the AI-Curated B2B Lead Engine not only saves time but also enhances the precision of prospecting efforts. This tool is particularly beneficial for businesses operating in competitive markets, where the ability to quickly identify and act on promising leads can make a significant difference in sales outcomes. As more companies recognize the value of AI in streamlining their sales processes, the adoption of such advanced lead engines is expected to grow, further driving the expansion of the Sales Prospecting AI market.

From a regional perspective, North America currently dominates the Sales Prospecting AI market, accounting for the largest revenue share in 2024. The region's leadership is attributed to the early adoption of AI technologies, the presence of major technology vendors, and a mature digital infrastructure. Europe follows closely, driven by stringent data privacy regulations and a strong focus on customer-centric sales strategies. Meanwhile, the Asia Pacific region is witnessing the fastest

Overview

The Social Media Post Dataset contains 60 entries of social media-style posts in 11 languages, covering trending topics like AI integration, remote work, digital transformation, DEI (Diversity, Equity, and Inclusion), sustainability, leadership, health, and global concerns. Designed for NLP research and AI-driven content generation, it provides both raw and enriched post versions to aid text analysis, sentiment classification, and engagement prediction.

Dataset Features

Use Cases

Natural Language Processing (NLP) – Training models for text classification, sentiment analysis, and language detection.
AI-Powered Content Generation – Enhancing post suggestions, engagement prediction, and language adaptability.
Social Media Insights – Understanding how different tones and languages affect engagement.
Multilingual AI Research – Developing models that handle diverse linguistic and cultural content.

Data Source & Collection

The dataset is synthetically generated based on real-world engagement trends from global platforms. It simulates diverse languages, tones, and topics, making it valuable for AI research, content analysis, and multilingual model training.

Developments in Artificial Intelligence (AI) have had an enormous impact on scientific research in recent years. Yet, relatively few robust methods have been reported in the field of structure-based drug discovery. To train AI models to abstract from structural data, highly curated and precise biomolecule-ligand interaction datasets are urgently needed. We present MISATO, a curated dataset of almost 20000 experimental structures of protein-ligand complexes, associated molecular dynamics traces, and electronic properties. Semi-empirical quantum mechanics was used to systematically refine protonation states of proteins and small molecule ligands. Molecular dynamics traces for protein-ligand complexes were obtained in explicit water. The dataset is made readily available to the scientific community via simple python data-loaders. AI baseline models are provided for dynamical and electronic properties. This highly curated dataset is expected to enable the next-generation of AI models for structure-based drug discovery. Our vision is to make MISATO the first step of a vibrant community project for the development of powerful AI-based drug discovery tools.

Overview This dataset provides comprehensive insights into the global popularity trends of Artificial Intelligence (AI) and Machine Learning (ML). The data has been meticulously gathered and curated to reflect the growing interest and adoption of these technologies across various regions and sectors.

Data Sources The dataset aggregates information from multiple sources, including:

Search engine query data Social media mentions and hashtags Research publication counts Online course enrolments Job postings

Safety Training Data Curation Market Outlook

According to our latest research, the global Safety Training Data Curation market size reached USD 1.32 billion in 2024, reflecting robust growth momentum. The market is projected to expand at a CAGR of 12.1% during the forecast period, reaching USD 3.38 billion by 2033. This remarkable growth is primarily driven by the escalating need for accurate and reliable data to power safety training programs across diverse industries, as organizations increasingly prioritize workplace safety and compliance in an evolving regulatory landscape.

One of the primary growth factors fueling the expansion of the Safety Training Data Curation market is the heightened emphasis on workplace safety regulations and compliance standards globally. As governments and industry bodies enforce stricter safety mandates, organizations are compelled to adopt advanced safety training solutions. The demand for curated, high-quality datasets is intensifying, as these datasets form the backbone of effective safety training modules, especially those leveraging artificial intelligence and machine learning. The rise in workplace accidents, coupled with the increasing complexity of industrial operations, further underscores the necessity for meticulously curated safety training data. Organizations are investing heavily in digital transformation initiatives, which include the integration of data-driven safety training programs to reduce incidents and improve overall workforce safety.

Another significant driver is the rapid digitalization of training environments and the adoption of immersive technologies such as virtual reality (VR) and augmented reality (AR) in safety training. These technologies require vast amounts of curated data to simulate real-world scenarios and deliver effective experiential learning. The proliferation of cloud-based platforms has also made it easier for organizations to access, manage, and update safety training data, thereby enhancing scalability and flexibility. Additionally, the increasing prevalence of remote and hybrid work models has necessitated the development of digital safety training programs, further boosting demand for curated data that can be seamlessly integrated into diverse training delivery modes. The growing awareness among enterprises about the tangible benefits of data-driven safety training, including reduced incident rates and improved compliance, is expected to sustain market growth over the coming years.

The market is also benefiting from the surge in investments by both public and private sectors in occupational health and safety (OHS) initiatives. Governments across regions are launching campaigns and providing incentives to promote workplace safety, which in turn is driving the adoption of advanced safety training solutions. The integration of artificial intelligence, big data analytics, and IoT technologies into safety training programs requires large volumes of high-quality, annotated data, further propelling the need for professional data curation services and software. However, the market faces challenges such as data privacy concerns, high initial costs, and the complexity of curating data across multiple languages and regulatory frameworks. Despite these hurdles, the market outlook remains positive, with continuous technological advancements and regulatory support expected to create new growth avenues.

From a regional perspective, North America currently dominates the Safety Training Data Curation market, owing to the presence of stringent regulatory standards, a mature industrial sector, and high adoption of advanced training technologies. Europe follows closely, driven by robust workplace safety regulations and increasing investments in digital transformation. The Asia Pacific region is anticipated to witness the highest CAGR during the forecast period, fueled by rapid industrialization, growing awareness of workplace safety, and expanding manufacturing and construction sectors. Latin America and the Middle East & Africa are also expected to register notable growth, supported by improving regulatory frameworks and increasing focus on occupational safety. The regional outlook indicates a broadening global footprint for safety training data curation solutions, with significant opportunities for market players to capitalize on emerging markets.

Component Analysis

The Component segment of the Safety Training Data Curation market is bifurca

Resources for Water DAMS data submitters and curators, including training videos, step-by-step guides on data submission, and detailed documentation of Water DAMS. The Data Management and Submission Best Practices document also contains API access and metadata schema information for developers interested in harvesting Water DAMS metadata for federation or inclusion in their local catalogs.

The curated fault experiment data set consists of tagged and fully described time series representing measured faults from the AFDD test building (ORNLs Flexible Research Platform [FRP]), including baseline performance and faulty performance. A total of 10 different faults are tested for 49 different faulted and unfaulted scenarios with various fault intensity levels.

Additional Contacts: Principal investigator: Matt Leach Matt.Leach@nrel.gov Experiments coordinator: Piljae Im imp1@ornl.gov Document preparation: Janghyun Kim Janghyun.Kim@nrel.gov

Golden Dataset Curation for LLMs Market Outlook

According to our latest research, the Global Golden Dataset Curation for LLMs market size was valued at $1.2 billion in 2024 and is projected to reach $8.7 billion by 2033, expanding at a CAGR of 24.8% during 2024–2033. This remarkable growth trajectory is primarily driven by the increasing demand for high-quality, bias-mitigated, and diverse datasets essential for training and evaluating large language models (LLMs) across industries. As generative AI applications proliferate, organizations are recognizing the strategic importance of curating "golden datasets"—carefully selected, annotated, and validated data collections that ensure robust model performance, regulatory compliance, and ethical AI outcomes. The accelerating adoption of AI-powered solutions in sectors such as healthcare, finance, and government, coupled with ongoing advances in data curation technologies, are further fueling the expansion of the Golden Dataset Curation for LLMs market globally.

Regional Outlook

North America currently commands the largest share of the Golden Dataset Curation for LLMs market, accounting for approximately 38% of the global revenue in 2024. This dominance is underpinned by the region’s mature artificial intelligence ecosystem, the presence of leading technology companies, and robust investments in R&D. The United States, in particular, boasts a high concentration of AI expertise, advanced data infrastructure, and a strong regulatory framework that supports ethical data curation. Furthermore, North America’s proactive adoption of generative AI across industries such as healthcare, BFSI, and government has spurred demand for meticulously curated datasets to drive innovation and ensure compliance with evolving data privacy standards. The region’s leadership in launching open-source initiatives and public-private partnerships for AI research further cements its preeminent position in the global market.

Asia Pacific is emerging as the fastest-growing region, projected to register a robust CAGR of 28.4% from 2024 to 2033. The region’s rapid market expansion is propelled by exponential growth in digital transformation initiatives, increasing AI investments, and supportive government policies aimed at fostering indigenous AI capabilities. Countries such as China, India, and South Korea are making significant strides in AI research, with a particular emphasis on local language and multimodal dataset curation to cater to diverse populations. The proliferation of startups and technology incubators, coupled with strategic collaborations between academia and industry, is accelerating the development and adoption of golden datasets. Additionally, the region’s burgeoning internet user base and mobile-first economies are generating vast volumes of data, providing fertile ground for dataset curation innovation.

Emerging economies in Latin America, the Middle East, and Africa are witnessing gradual but promising adoption of Golden Dataset Curation for LLMs. While market penetration remains lower compared to developed regions, localized demand for AI-driven solutions in sectors such as public health, education, and government services is spurring investment in dataset curation capabilities. However, challenges such as limited access to high-quality data, fragmented regulatory environments, and a shortage of specialized talent are impeding rapid growth. Despite these hurdles, targeted policy reforms, international collaborations, and capacity-building initiatives are laying the groundwork for future market expansion, particularly as governments recognize the strategic value of AI and data sovereignty.

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