Abstract:

In recent years there has been an increased interest in Artificial Intelligence for IT Operations (AIOps). This field utilizes monitoring data from IT systems, big data platforms, and machine learning to automate various operations and maintenance (O&M) tasks for distributed systems. The major contributions have been materialized in the form of novel algorithms. Typically, researchers took the challenge of exploring one specific type of observability data sources, such as application logs, metrics, and distributed traces, to create new algorithms. Nonetheless, due to the low signal-to-noise ratio of monitoring data, there is a consensus that only the analysis of multi-source monitoring data will enable the development of useful algorithms that have better performance.
Unfortunately, existing datasets usually contain only a single source of data, often logs or metrics. This limits the possibilities for greater advances in AIOps research. Thus, we generated high-quality multi-source data composed of distributed traces, application logs, and metrics from a complex distributed system. This paper provides detailed descriptions of the experiment, statistics of the data, and identifies how such data can be analyzed to support O&M tasks such as anomaly detection, root cause analysis, and remediation.

General Information:

This repository contains the simple scripts for data statistics, and link to the multi-source distributed system dataset.

You may find details of this dataset from the original paper:

Sasho Nedelkoski, Ajay Kumar Mandapati, Jasmin Bogatinovski, Soeren Becker, Jorge Cardoso, Odej Kao, "Multi-Source Distributed System Data for AI-powered Analytics". [link very soon]

If you use the data, implementation, or any details of the paper, please cite!

The multi-source/multimodal dataset is composed of distributed traces, application logs, and metrics produced from running a complex distributed system (Openstack). In addition, we also provide the workload and fault scripts together with the Rally report which can serve as ground truth (all at the Zenodo link below). We provide two datasets, which differ on how the workload is executed. The openstack_multimodal_sequential_actions is generated via executing workload of sequential user requests. The openstack_multimodal_concurrent_actions is generated via executing workload of concurrent user requests.

The difference of the concurrent dataset is that:

Due to the heavy load on the control node, the metric data for wally113 (control node) is not representative and we excluded it.

Three rally actions are executed in parallel: boot_and_delete, create_and_delete_networks, create_and_delete_image, whereas for the sequential there were 5 actions executed.

The raw logs in both datasets contain the same files. If the user wants the logs filetered by time with respect to the two datasets, should refer to the timestamps at the metrics (they provide the time window). In addition, we suggest to use the provided aggregated time ranged logs for both datasets in CSV format.

Important: The logs and the metrics are synchronized with respect time and they are both recorded on CEST (central european standard time). The traces are on UTC (Coordinated Universal Time -2 hours). They should be synchronized if the user develops multimodal methods.

Our GitHub repository can be found at: https://github.com/SashoNedelkoski/multi-source-observability-dataset/

Medical AI Research Foundations is a repository of open-source medical foundation models. With this collection of non-diagnostic models, APIs, and resources like code and data, researchers and developers can accelerate their medical AI research. This is a clear unmet need as currently there is no central resource today that developers and researchers can leverage to build medical AI and as such, this has slowed down both research and translation efforts. Our goal is to democratize access to foundational medical AI models, and help researchers and medical AI developers rapidly build new solutions. To this end, we open-sourced REMEDIS code-base and we are currently hosting REMEDIS models for chest x-ray and pathology. We expect to add more models and resources for training medical foundation models such as datasets and benchmarks in the future. We also welcome the medical AI research community to contribute to this.

The dataset comprises responses to 116 questions, with contributions from both human and AI sources. The data is organized into a single folder called "AI classifier dataset," containing 100 Excel files and one JSON list file named "dataset.jsonl." Each Excel file contains three attributes: "Question", "Human", and "AI" except one file, 457c895.xlsx, which has columns "Question", "Answer," and "AI or Human."The JSON file includes four attributes for each entry: an ID, the original question, the answer, and Is_it_AI. In total, the JSON list file contains 4,231 rows of data. The source code folder contains the website design code for the question distribution and data collection website.

Saudi Arabia had the highest score for government strategy of AI in 2024 or 100. Following closely behind was the United States with 83.

Generative AI Tools, Models and Resources is a curated dataset designed to provide an accessible, organized collection of resources in the field of generative artificial intelligence. The dataset is derived from the Awesome Generative AI list (https://github.com/steven2358/awesome-generative-ai) and is available in both CSV and JSON formats. Each resource includes the following fields: Name, URL, description, tags, category, and subcategory.

This dataset is curated by Steven Van Vaerenbergh, a lecturer and researcher in machine learning and mathematics education at the University of Cantabria, Spain. The aim is to provide a practical and well-organized resource for the scientific community. The inclusion criteria reflect a combination of community input and the curator's judgment, making this a selected, rather than exhaustive, collection.

Potential use cases include academic research, teaching, and industry applications for identifying generative AI tools and trends. This repository will be periodically updated, with version history tracked via Zenodo.

License: CC BY 4.0. Proper attribution is required for use of this dataset.

In 2024, the market size change in the 'Machine Learning' segment of the artificial intelligence market worldwide was modeled to stand at 44.66 percent. Between 2021 and 2024, the market size change dropped by 99.08 percentage points. The market size change is expected to drop by 15.3 percentage points between 2024 and 2031, showing a continuous downward movement throughout the period.Further information about the methodology, more market segments, and metrics can be found on the dedicated Market Insights page on Machine Learning.

United States Consumer List Database with full contact information, including; Addresses, Telephone Numbers, Email Address and Location as well as hundreds of available consumer behavior/buying activity/lifestyle/interest attributes. Attribute categories include; Income, Net Worth, Home Ownership, Vehicle Ownership, Loan and Mortgage, Credit Usage, Buying Activities, Donor History and Lifestyle Interests/Hobbies. Please contact us for a full list of available attributes, list counts, and pricing.

Success.ai offers a comprehensive, enterprise-ready B2B leads data solution, ideal for businesses seeking access to over 150 million verified employee profiles and 170 million work emails. Our data empowers organizations across industries to target key decision-makers, optimize recruitment, and fuel B2B marketing efforts. Whether you're looking for UK B2B data, B2B marketing data, or global B2B contact data, Success.ai provides the insights you need with pinpoint accuracy.

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Global Coverage with Real-Time Accuracy: Success.ai’s dataset spans a wide range of industries such as technology, finance, healthcare, and manufacturing. With continuous real-time updates, your team can rely on the most accurate data available: 150M+ Employee Profiles: Access professional profiles worldwide with insights including full name, job title, seniority, and industry. 170M Verified Work Emails: Reach decision-makers directly with verified work emails, available across industries and geographies, including Singapore and UK B2B data. GDPR-Compliant: Our data is fully compliant with GDPR and other global privacy regulations, ensuring safe and legal use of B2B marketing data.

Key Data Points for Every Employee Profile: Every profile in Success.ai’s database includes over 20 critical data points, providing the information needed to power B2B sales and marketing campaigns: Full Name, Job Title, Company, Work Email, Location, Phone Number, LinkedIn Profile, Experience, Education, Technographic Data, Languages, Certifications, Industry, Publications & Awards.

Use Cases Across Industries: Success.ai’s B2B data solution is incredibly versatile and can support various enterprise use cases, including: B2B Marketing Campaigns: Reach high-value professionals in industries such as technology, finance, and healthcare. Enterprise Sales Outreach: Build targeted B2B contact lists to improve sales efforts and increase conversions. Talent Acquisition: Accelerate hiring by sourcing top talent with accurate and updated employee data, filtered by job title, industry, and location. Market Research: Gain insights into employment trends and company profiles to enrich market research. CRM Data Enrichment: Ensure your CRM stays accurate by integrating updated B2B contact data. Event Targeting: Create lists for webinars, conferences, and product launches by targeting professionals in key industries.

Use Cases for Success.ai's Contact Data - Targeted B2B Marketing: Create precise campaigns by targeting key professionals in industries like tech and finance. - Sales Outreach: Build focused sales lists of decision-makers and C-suite executives for faster deal cycles. - Recruiting Top Talent: Easily find and hire qualified professionals with updated employee profiles. - CRM Enrichment: Keep your CRM current with verified, accurate employee data. - Event Targeting: Create attendee lists for events by targeting relevant professionals in key sectors. - Market Research: Gain insights into employment trends and company profiles for better business decisions. - Executive Search: Source senior executives and leaders for headhunting and recruitment. - Partnership Building: Find the right companies and key people to develop strategic partnerships.

Why Choose Success.ai’s Employee Data? Success.ai is the top choice for enterprises looking for comprehensive and affordable B2B data solutions. Here’s why: Unmatched Accuracy: Our AI-powered validation process ensures 99% accuracy across all data points, resulting in higher engagement and fewer bounces. Global Scale: With 150M+ employee profiles and 170M veri...

In a 2024 survey, around 84 percent of businesses/corporations expressed their positive impressions of artificial intelligence in their work. In contrast, 36 percent of government organizations highlighted their negative outlook on AI within their scope of work.

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.

ST - DHS Public Access Database: Consistent with the 2013 OSTP Memorandum and the 2022 update, “Increasing Access to the Results of Federally Funded Scientific Research,” directed all agencies with greater than $100 million in R&D expenditures each year to prepare a plan for improving the public’s access to the results of federally funded research, specifically peer-reviewed scholarly publications and digital data. In response to the memorandum, DHS developed a DHS Public Access Plan, and intends to make available to the public digitally formatted scientific data that support the conclusions in peer-reviewed scholarly publications that are the results of DHS R&D funding. This data repository site with a customized DHS Storefront allows DHS to post releasable scientific digital data from peer-reviewed publications resulting from DHS-funded research. The data repository is configured to allow DHS users (and publishers acting on behalf of these users) to deposit data sets into the repository, making them available to the general public.

AI Prompt Repository Market Outlook

According to our latest research, the AI Prompt Repository market size reached USD 1.2 billion globally in 2024, with a robust year-on-year growth driven by rising adoption across content creation and enterprise automation. The market is projected to grow at a CAGR of 28.4% from 2025 to 2033, reaching an estimated USD 11.1 billion by the end of the forecast period. This exponential surge is fueled by the increasing demand for AI-driven productivity tools, the proliferation of generative AI applications, and the growing need for scalable, high-quality prompt management solutions across diverse industries.

One of the primary growth factors for the AI Prompt Repository market is the rapid expansion of generative AI technologies and their integration into mainstream business processes. Organizations in sectors such as marketing, education, and research are leveraging AI prompt repositories to streamline content generation, automate customer support, and enhance creativity. The ability to store, manage, and curate high-quality prompts enables enterprises to scale their AI initiatives efficiently, reduce time-to-market for new products and services, and maintain consistency in output. Furthermore, the shift towards digital transformation and the emphasis on operational efficiency have accelerated the adoption of AI prompt repositories, as businesses seek to harness the full potential of AI-driven automation.

Another significant driver is the surge in demand from individual creators and small businesses, who are increasingly turning to AI prompt repositories to enhance their creative workflows. As generative AI becomes more accessible, content creators, educators, and freelancers are utilizing these platforms to generate compelling content, design innovative marketing campaigns, and develop personalized educational materials. The democratization of AI tools has lowered entry barriers, enabling a broader user base to benefit from advanced prompt engineering and repository capabilities. This trend is further amplified by the proliferation of cloud-based solutions, which offer scalability, affordability, and ease of access, making AI prompt repositories an attractive option for both large enterprises and individual users.

The AI Prompt Repository market is also experiencing growth due to the increasing emphasis on data-driven decision-making and the need for reliable, high-quality training data for AI models. Enterprises are recognizing the value of curated prompt libraries in improving the accuracy, relevance, and ethical compliance of AI outputs. As regulatory scrutiny around AI-generated content intensifies, organizations are investing in robust prompt management solutions to ensure transparency, traceability, and accountability. This focus on governance and compliance is driving the adoption of advanced AI prompt repositories that offer version control, audit trails, and customizable access controls, further fueling market expansion.

Regionally, North America continues to dominate the AI Prompt Repository market, accounting for the largest revenue share in 2024, followed by Europe and Asia Pacific. The United States leads in terms of technological innovation, early adoption of AI-driven solutions, and the presence of major industry players. However, Asia Pacific is poised for the fastest growth over the forecast period, driven by rapid digitalization, increasing investments in AI research, and the emergence of a vibrant startup ecosystem. Europe is also witnessing significant traction, particularly in sectors such as education and marketing, where AI prompt repositories are being leveraged to enhance productivity and creativity. Latin America and the Middle East & Africa are gradually catching up, supported by growing awareness and government initiatives to promote AI adoption.

Component Analysis

The Component segment of the AI Prompt Repository market is bifurcated into Platform and Services, each playing a critical role in shaping the overall market landscape. Platforms form the backbone of the market, providing robust infrastructure for storing, organizing, and retrieving AI prompts. These platforms are designed with advanced features such as search optimization, categorization, prompt versioning, and integration with various generative AI models. The increasing sophistication of platforms, coupled with user-friendly interfaces and customizable workflows, has made them

Clinical Trial Data Repository Market Outlook

The global clinical trial data repository market size was estimated to be approximately $1.8 billion in 2023 and is projected to grow at a compound annual growth rate (CAGR) of 9.5% to reach around $4.1 billion by 2032. The primary growth factors include the increasing volume and complexity of clinical trials, rising need for efficient data management systems, and stringent regulatory requirements for data accuracy and integrity. The advent of advanced technologies such as artificial intelligence and big data analytics further drives market expansion by enhancing data processing capabilities and providing actionable insights.

The growth of the clinical trial data repository market is significantly influenced by the increasing number of clinical trials being conducted globally. With the rise in chronic diseases, the need for innovative treatments and therapies has surged, leading to an upsurge in clinical trials. This increase in clinical trials necessitates robust data management systems to handle vast amounts of data generated, thereby propelling the demand for clinical trial data repositories. Moreover, the complexity of modern clinical trials, which often involve multiple sites and diverse patient populations, further amplifies the need for sophisticated data management solutions.

Another critical driver for the market is the stringent regulatory landscape governing clinical trial data. Regulatory bodies such as the FDA, EMA, and other local authorities mandate rigorous data management standards to ensure data integrity, accuracy, and accessibility. These regulations necessitate the adoption of advanced data repository systems that can comply with regulatory requirements, thereby fueling market growth. Additionally, regulatory frameworks are becoming increasingly stringent, prompting pharmaceutical and biotechnology companies to invest in state-of-the-art data management systems to avoid compliance issues and potential financial penalties.

Technological advancements play a pivotal role in the market's growth. The integration of artificial intelligence, machine learning, and big data analytics into data repository systems enhances data processing and analysis capabilities. These technologies enable real-time data monitoring, predictive analytics, and improved decision-making, thereby improving the efficiency of clinical trials. Furthermore, the shift towards cloud-based solutions offers scalability, flexibility, and cost-effectiveness, making advanced data management systems accessible to even small and medium-sized enterprises.

Regionally, North America dominates the clinical trial data repository market owing to its robust healthcare infrastructure, high R&D investments, and presence of major pharmaceutical and biotechnology companies. Europe follows closely due to stringent regulatory standards and a strong focus on clinical research. The Asia Pacific region is expected to witness the highest growth rate during the forecast period due to increasing clinical trial activities, growing healthcare expenditure, and the rising adoption of advanced technologies. Latin America and the Middle East & Africa are also likely to experience growth, albeit at a slower pace, driven by improving healthcare systems and increasing focus on clinical research.

Component Analysis

The clinical trial data repository market is segmented by components into software and services. The software segment is anticipated to hold a significant share of the market due to the essential role software plays in data management. Advanced software solutions offer capabilities such as data storage, management, retrieval, and analysis, which are critical for effective clinical trial management. The integration of AI and machine learning algorithms into these software systems further enhances their efficiency by enabling predictive analytics and real-time monitoring, thus driving the software segment's growth.

Software solutions in clinical trial data repositories also offer interoperability, enabling seamless integration with other clinical trial management systems (CTMS) and electronic data capture (EDC) systems. This interoperability is crucial for ensuring data consistency and accuracy across different platforms, thereby enhancing overall data management. Additionally, the increasing adoption of cloud-based software solutions provides scalability, cost-effectiveness, and remote acce

• Data Sources The contact details of your targeted healthcare professionals are compiled from highly credible resources like,  Trade shows  Websites  Medical seminars  Medical conferences  Healthcare directory  Medical records  Government records  Surveys etc.

• Information We Offer  First Name  Last Name  Email Address  SIC Code  Phone Number  NAICS Code  Fax Number  Postal Address  Web Addresses

• Customization Based on below Selects  Job Title  License Type  Years of Experience  Specialty  Licensure State  School/college  Department  Geography  And more!

• What Medical Device Industry Data from MedicoReach? With a higher emphasis on extensive research, our proficient team of data scientists has contributed to the development of a highly reputable marketing list of medical device industries collected from trustworthy sources of information to help reach the prospect at the right moment. Medical Device Industry Data from MedicoReach consists of marketing list of medical device manufacturers, suppliers, and distributors.

Our Medical Device Industry Data has been integrated with fresh and updated to assist you generate more business leads and maximum responses. With our well-crafted and competitive marketing list, you may trump your competitors in the quest to obtain more conversions. To assist and serve you in an exceptional way, we also permit you to customize the list as per your specific preferences and business requirements.

Our medical device industry marketing list is updated and verified in a systematic process to ensure maximum accuracy and high deliverability ratio. With excellent coverage across USA, UK, Canada, Europe, Asia, North America, and Australia, MedicoReach makes your service available to a greater number of medical device manufacturers, suppliers, and distributors who are eagerly waiting to hear from you.

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AI in Healthcare - Quick Overview Statistics

Artificial Intelligence in healthcare refers to the use of advanced computer algorithms and machine learning techniques to analyze data in the healthcare sector to provide better healthcare services.

AI helps healthcare providers make more accurate and real-time diagnoses, personalize treatment plans, and improve patient safety by identifying health risks earlier.

Types of AI Applications in Healthcare Statistics

• Medical imaging analysis
• Natural language processing (NLP)
• Disease prediction and risk assessment
• Virtual Assistants and Chabot’s
• Drug discovery and development
• Robot-assisted surgery
• Patient engagement
• Diagnosis and treatment
• Machine learning

This text file (Reference_List_V1.txt) lists references that describe relevant characteristics for reservoir thermal energy storage (RTES) research in the United States. References are grouped by corresponding city, including: Albuquerque, New Mexico; Charleston, South Carolina; Chicago, Illinois; Decatur, Illinois; Lansing, Michigan; Memphis, Tennessee; Phoenix, Arizona; and Portland, Oregon. The document includes hyphenated lines and headers to distinguish city-specific subsections. Internet links are provided for each reference in the event that the reference was accessible online (as of January 28, 2021).

This index compiles empirical data on AI and big data surveillance use for 179 countries around the world between 2012 and 2022— although the bulk of the sources stem from between 2017 and 2022. The index does not distinguish between legitimate and illegitimate uses of AI and big data surveillance. Rather, the purpose of the research is to show how new surveillance capabilities are transforming governments’ ability to monitor and track individuals or groups. Last updated February 2022.

This index addresses three primary questions: Which countries have documented AI and big data public surveillance capabilities? What types of AI and big data public surveillance technologies are governments deploying? And which companies are involved in supplying this technology?

The index measures AI and big data public surveillance systems deployed by state authorities, such as safe cities, social media monitoring, or facial recognition cameras. It does not assess the use of surveillance in private spaces (such as privately-owned businesses in malls or hospitals), nor does it evaluate private uses of this technology (e.g., facial recognition integrated in personal devices). It also does not include AI and big data surveillance used in Automated Border Control systems that are commonly found in airport entry/exit terminals. Finally, the index includes a list of frequently mentioned companies – by country – which source material indicates provide AI and big data surveillance tools and services.

All reference source material used to build the index has been compiled into an open Zotero library, available at https://www.zotero.org/groups/2347403/global_ai_surveillance/items. The index includes detailed information for seventy-seven countries where open source analysis indicates that governments have acquired AI and big data public surveillance capabilities. The index breaks down AI and big data public surveillance tools into the following categories: smart city/safe city, public facial recognition systems, smart policing, and social media surveillance.

The findings indicate that at least seventy-seven out of 179 countries are actively using AI and big data technology for public surveillance purposes:

• Smart city/safe city platforms: fifty-five countries • Public facial recognition systems: sixty-eight countries • Smart policing: sixty-one countries • Social media surveillance: thirty-six countries

Using various data sources from the RBHU Budapest Campus Building BP201, BP205, (BP106), we created this data repository containing the following types of data:

Time-Series Data from Sensors: This includes temperature, humidity, air quality, pressure, flow, energy consumption, valve and damper positions, pump and fan status, control system outputs, switches and relays status, enthalpy, operation counters, setpoints, control values, alarm, and fault indicators.

EdSight is an education data portal that integrates information from over 30 different sources – some reported by districts and others from external sources. The portal can be accessed here: http://edsight.ct.gov/. Information is available on key performance measures that make up the Next Generation Accountability System, as well as dozens of other topics, including school finance, special education, staffing levels and school enrollment.

AI has become a necessary tool used by many businesses for increased efficiency and reducing human error. In a 2024 survey, 42 percent of respondents from different professions stated that in the next five years AI and GenAI will have transformational impact, while 36 percent indicated high impact.