This dataset, QASPER: NLP Questions and Evidence, is an exceptional collection of over 5,000 questions and answers focused on Natural Language Processing (NLP) papers. It has been crowdsourced from experienced NLP practitioners, with each question meticulously crafted based solely on the titles and abstracts of the respective papers. The answers provided are expertly enriched with evidence taken directly from the full text of each paper. QASPER features structured fields including 'qas' for questions and answers, 'evidence' for supporting information, paper titles, abstracts, figures and tables, and full text. This makes it a valuable resource for researchers aiming to understand how practitioners interpret NLP topics and to validate solutions for problems found in existing literature. The dataset contains 5,049 questions spanning 1,585 distinct papers.

Columns

• title: The title of the paper. (String)
• abstract: A summary of the paper. (String)
• full_text: The full text of the paper. (String)
• qas: Questions and answers about the paper. (Object)
• figures_and_tables: Figures and tables from the paper. (Object)
• id: Unique identifier for the paper.

Distribution

The QASPER dataset comprises 5,049 questions across 1,585 papers. It is distributed across five files in .csv format, with one additional .json file for figures and tables. These include two test datasets (test.csv and validation.csv), two train datasets (train-v2-0_lessons_only_.csv and trainv2-0_unsplit.csv), and a figures dataset (figures_and_tables_.json). Each CSV file contains distinct datasets with columns dedicated to titles, abstracts, full texts, and Q&A fields, along with evidence for each paper mentioned in the respective rows.

Usage

This dataset is ideal for various applications, including: * Developing AI models to automatically generate questions and answers from paper titles and abstracts. * Enhancing machine learning algorithms by combining answers with evidence to discover relationships between papers. * Creating online forums for NLP practitioners, using dataset questions to spark discussion within the community. * Conducting basic descriptive statistics or advanced predictive analytics, such as logistic regression or naive Bayes models. * Summarising basic crosstabs between any two variables, like titles and abstracts. * Correlating title lengths with the number of words in their corresponding abstracts to identify patterns. * Utilising text mining technologies like topic modelling, machine learning techniques, or automated processes to summarise underlying patterns. * Filtering terms relevant to specific research hypotheses and processing them via web crawlers, search engines, or document similarity algorithms.

Coverage

The dataset has a GLOBAL region scope. It focuses on papers within the field of Natural Language Processing. The questions and answers are crowdsourced from experienced NLP practitioners. The dataset was listed on 22/06/2025.

License

CC0

Who Can Use It

This dataset is highly suitable for: * Researchers seeking insights into how NLP practitioners interpret complex topics. * Those requiring effective validation for developing clear-cut solutions to problems encountered in existing NLP literature. * NLP practitioners looking for a resource to stimulate discussions within their community. * Data scientists and analysts interested in exploring NLP datasets through descriptive statistics or advanced predictive analytics. * Developers and researchers working with text mining, machine learning techniques, or automated text processing.

Dataset Name Suggestions

• NLP Expert QA Dataset
• QASPER: NLP Paper Questions and Evidence
• Academic NLP Q&A Corpus
• Natural Language Processing Research Questions

Attributes

Original Data Source: QASPER: NLP Questions and Evidence

The Data Processing and Hosting Services market, exhibiting a Compound Annual Growth Rate (CAGR) of 4.20%, presents a significant opportunity for growth. While the exact market size in millions is not specified, considering the substantial involvement of major players like Amazon Web Services, IBM, and Salesforce, coupled with the pervasive adoption of cloud computing and big data analytics across diverse sectors, a 2025 market size exceeding $500 billion is a reasonable estimate. This robust growth is driven by several key factors. The increasing reliance on cloud-based solutions by both large enterprises and SMEs reflects a shift towards greater scalability, flexibility, and cost-effectiveness. Furthermore, the exponential growth of data necessitates advanced data processing capabilities, fueling demand for data mining, cleansing, and management services. The burgeoning adoption of AI and machine learning further enhances this need, as these technologies require robust data infrastructure and sophisticated processing techniques. Specific industry segments like IT & Telecommunications, BFSI (Banking, Financial Services, and Insurance), and Retail are major consumers, demanding reliable and secure hosting solutions and data processing capabilities to manage their critical operations and customer data. However, challenges remain, including the ongoing threat of cyberattacks and data breaches, necessitating robust security measures and compliance with evolving data privacy regulations. Competition among existing players is intense, driving innovation and price wars, which can impact profitability for some market participants. The forecast period of 2025-2033 indicates a continued upward trajectory for the market, largely fueled by expanding digitalization efforts globally. The Asia Pacific region is projected to be a significant contributor to this growth, driven by increasing internet penetration and a burgeoning technological landscape. While North America and Europe maintain substantial market share, the faster growth rate anticipated in Asia Pacific and other emerging markets signifies an evolving global market dynamic. Continued advancements in technologies such as edge computing, serverless architecture, and improved data analytics techniques will further drive market expansion and shape the competitive landscape. The segmentation within the market (by organization size, service offering, and end-user industry) presents diverse investment opportunities for businesses catering to specific needs and technological advancements within these niches. Recent developments include: December 2022 - TetraScience, the Scientific Data Cloud company, announced that Gubbs, a lab optimization, and validation software leader, joined the Tetra Partner Network to increase and enhance data processing throughput with the Tetra Scientific Data Cloud., November 2022 - Kinsta, a hosting provider that provides managed WordPress hosting powered by Google Cloud Platform, announced the launch of Application Hosting and Database Hosting. It is adding these two hosting services to its Managed WordPress product ushers in a new era for Kinsta as a Cloud Platform, enabling developers and businesses to run powerful applications, databases, websites, and services more flexibly than ever.. Key drivers for this market are: Growing Adoption of Cloud Computing to Accomplish Economies of Scale, Rising Demand for Outsourcing Data Processing Services. Potential restraints include: Growing Adoption of Cloud Computing to Accomplish Economies of Scale, Rising Demand for Outsourcing Data Processing Services. Notable trends are: Web Hosting is Gaining Traction Due to Emergence of Cloud-based Platform.

This dataset provides essential information for entries related to question answering tasks using AI models. It is designed to offer valuable insights for researchers and practitioners, enabling them to effectively train and rigorously evaluate their machine learning models. The dataset serves as a valuable resource for building and assessing question-answering systems. It is available free of charge.

Columns

• instruction: Contains the specific instructions given to a model to generate a response.
• responses: Includes the responses generated by the model based on the given instructions.
• next_response: Provides the subsequent response from the model, following a previous response, which facilitates a conversational interaction.
• answer: Lists the correct answer for each question presented in the instruction, acting as a reference for assessing the model's accuracy.
• is_human_response: A boolean column that indicates whether a particular response was created by a human or by a machine learning model, helping to differentiate between the two. Out of nearly 19,300 entries, 254 are human-generated responses, while 18,974 were generated by models.

Distribution

The data files are typically in CSV format, with a dedicated train.csv file for training data and a test.csv file for testing purposes. The training file contains a large number of examples. Specific dates are not included within this dataset description, focusing solely on providing accurate and informative details about its content and purpose. Specific numbers for rows or records are not detailed in the available information.

Usage

This dataset is ideal for a variety of applications and use cases: * Training and Testing: Utilise train.csv to train question-answering models or algorithms, and test.csv to evaluate their performance on unseen questions. * Machine Learning Model Creation: Develop machine learning models specifically for question-answering by leveraging the instructional components, including instructions, responses, next responses, and human-generated answers, along with their is_human_response labels. * Model Performance Evaluation: Assess model performance by comparing predicted responses with actual human-generated answers from the test.csv file. * Data Augmentation: Expand existing data by paraphrasing instructions or generating alternative responses within similar contexts. * Conversational Agents: Build conversational agents or chatbots by utilising the instruction-response pairs for training. * Language Understanding: Train models to understand language and generate responses based on instructions and previous responses. * Educational Materials: Develop interactive quizzes or study guides, with models providing instant feedback to students. * Information Retrieval Systems: Create systems that help users find specific answers from large datasets. * Customer Support: Train customer support chatbots to provide quick and accurate responses to inquiries. * Language Generation Research: Develop novel algorithms for generating coherent responses in question-answering scenarios. * Automatic Summarisation Systems: Train systems to generate concise summaries by understanding main content through question answering. * Dialogue Systems Evaluation: Use the instruction-response pairs as a benchmark for evaluating dialogue system performance. * NLP Algorithm Benchmarking: Establish baselines against which other NLP tools and methods can be measured.

Coverage

The dataset's geographic scope is global. There is no specific time range or demographic scope noted within the available details, as specific dates are not included.

License

CC0

Who Can Use It

This dataset is highly suitable for: * Researchers and Practitioners: To gain insights into question answering tasks using AI models. * Developers: To train models, create chatbots, and build conversational agents. * Students: For developing educational materials and enhancing their learning experience through interactive tools. * Individuals and teams working on Natural Language Processing (NLP) projects. * Those creating information retrieval systems or customer support solutions. * Experts in natural language generation (NLG) and automatic summarisation systems. * Anyone involved in the evaluation of dialogue systems and machine learning model training.

Dataset Name Suggestions

• AI Question Answering Data
• Conversational AI Training Data
• NLP Question-Answering Dataset
• Model Evaluation QA Data
• Dialogue Response Dataset

Attributes

Original Data Source: Question-Answering Training and Testing Data

In a large network of computers, wireless sensors, or mobile devices, each of the components (hence, peers) has some data about the global status of the system. Many of the functions of the system, such as routing decisions, search strategies, data cleansing, and the assignment of mutual trust, depend on the global status. Therefore, it is essential that the system be able to detect, and react to, changes in its global status. Computing global predicates in such systems is usually very costly. Mainly because of their scale, and in some cases (e.g., sensor networks) also because of the high cost of communication. The cost further increases when the data changes rapidly (due to state changes, node failure, etc.) and computation has to follow these changes. In this paper we describe a two step approach for dealing with these costs. First, we describe a highly efficient local algorithm which detect when the L2 norm of the average data surpasses a threshold. Then, we use this algorithm as a feedback loop for the monitoring of complex predicates on the data – such as the data’s k-means clustering. The efficiency of the L2 algorithm guarantees that so long as the clustering results represent the data (i.e., the data is stationary) few resources are required. When the data undergoes an epoch change – a change in the underlying distribution – and the model no longer represents it, the feedback loop indicates this and the model is rebuilt. Furthermore, the existence of a feedback loop allows using approximate and “best-effort ” methods for constructing the model; if an ill-fit model is built the feedback loop would indicate so, and the model would be rebuilt.

Modern technologies such as the Internet of Things (IoT) play a key role in Smart Manufacturing and Business Process Management (BPM). In particular, process mining benefits from enriched event logs that incorporate physical sensor data. This dataset presents an IoT-enriched XES event log recorded in a physical smart factory environment. It builds upon the previously published dataset “An IoT-Enriched Event Log for Process Mining in Smart Factories” (available on Zenodo) and follows the DataStream XES extension. In this modified version, three types of common Data Quality Issues (DQIs) - missing sensor values, missing sensors, and time shifts - have been artificially injected into the sensor data. These issues reflect realistic challenges in industrial IoT data processing and are valuable for developing and testing robust data cleaning and analysis methods.

By comparing the original (clean) dataset with this modified version, researchers can systematically evaluate DQI detection, handling, and solving techniques under controlled conditions. Further details are provided for each of three DQI types in the subfolders in a csv changelog.

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Data Warehousing Market Size 2025-2029

The data warehousing market size is forecast to increase by USD 32.3 billion, at a CAGR of 14% between 2024 and 2029.

The market is experiencing significant shifts as businesses increasingly adopt cloud-based solutions and advanced storage technologies reshape the competitive landscape. The transition from on-premises to Software-as-a-Service (SaaS) models offers businesses greater flexibility, scalability, and cost savings. Simultaneously, the emergence of advanced storage technologies, such as columnar databases and in-memory storage, enables faster data processing and analysis, enhancing business intelligence capabilities. However, the market faces challenges as well. Data privacy and security risks continue to pose a significant threat, with the increasing volume and complexity of data requiring robust security measures. Ensuring data confidentiality, integrity, and availability is crucial for businesses to maintain customer trust and comply with regulatory requirements. Companies must invest in advanced security solutions and adopt best practices to mitigate these risks effectively.

What will be the Size of the Data Warehousing Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
Request Free SampleThe market continues to evolve, driven by the ever-increasing volume, variety, and velocity of data. ETL processes play a crucial role in data integration, transforming data from various sources into a consistent format for analysis. On-premise data warehousing and cloud data warehousing solutions offer different advantages, with the former providing greater control and the latter offering flexibility and scalability. Data lakes and data warehouses complement each other, with data lakes serving as a source for raw data and data warehouses providing structured data for analysis. Data warehouse optimization is a continuous process, with data stewardship, data transformation, and data modeling essential for maintaining data quality and ensuring compliance. Data mining and analytics extract valuable insights from data, while data visualization makes complex data understandable. Data security, encryption, and data governance frameworks are essential for protecting sensitive data. Data warehousing services and consulting offer expertise in implementing and optimizing data platforms. Data integration, masking, and federation enable seamless data access, while data audit and lineage ensure data accuracy and traceability. Data management solutions provide a comprehensive approach to managing data, from data cleansing to monetization. Data warehousing modernization and migration offer opportunities for improving performance and scalability. Business intelligence and data-driven decision making rely on the insights gained from data warehousing. Hybrid data warehousing offers a flexible approach to data management, combining the benefits of on-premise and cloud solutions. Metadata management and data catalogs facilitate efficient data access and management.

How is this Data Warehousing Industry segmented?

The data warehousing industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. DeploymentOn-premisesHybridCloud-basedTypeStructured and semi-structured dataUnstructured dataEnd-userBFSIHealthcareRetail and e-commerceOthersGeographyNorth AmericaUSCanadaEuropeFranceGermanyItalyUKAPACChinaIndiaJapanSouth KoreaRest of World (ROW).

By Deployment Insights

The on-premises segment is estimated to witness significant growth during the forecast period.In the dynamic the market, on-premise data warehousing solutions continue to be a preferred choice for businesses seeking end-to-end control and enhanced security. These solutions, installed and managed on the user's server, offer benefits such as workflow streamlining, speed, and robust data governance. The high cost of implementation and upgradation, coupled with the need for IT specialists, are factors contributing to the segment's popularity. Data security is a primary concern, with the complete ownership and management of servers ensuring that business data remains secure. ETL processes play a crucial role in data warehousing, facilitating data transformation, integration, and loading. Data modeling and mining are essential components, enabling businesses to derive valuable insights from their data. Data stewardship ensures data compliance and accuracy, while optimization techniques enhance performance. Data lake, a large storage repository, offers a flexible and cost-effective approach to managing diverse data types. Data warehousing consulting services help businesses navi

The MRO (Maintenance, Repair, and Operations) Data Cleansing and Enrichment Service market is experiencing robust growth, driven by the increasing need for accurate and reliable data across various industries. The digital transformation sweeping manufacturing, oil & gas, and transportation sectors is creating a surge in data volume, but much of this data is fragmented, incomplete, or inconsistent. This necessitates sophisticated data cleansing and enrichment solutions to improve operational efficiency, predictive maintenance capabilities, and informed decision-making. The market's expansion is fueled by the adoption of Industry 4.0 technologies, including IoT sensors and connected devices, generating massive datasets requiring rigorous cleaning and enrichment processes. Furthermore, regulatory compliance pressures and the need for improved supply chain visibility are contributing to strong market demand. We estimate the 2025 market size to be $2.5 billion, with a Compound Annual Growth Rate (CAGR) of 15% projected through 2033. This growth is primarily driven by the Chemical, Oil & Gas, and Pharmaceutical industries' increasing reliance on data-driven insights for optimizing operations and reducing downtime. Significant regional variations exist, with North America and Europe currently holding the largest market shares, but rapid growth is anticipated in the Asia-Pacific region due to the increasing industrialization and digitalization initiatives underway. The market segmentation by application reveals a diverse landscape. The Chemical and Oil & Gas industries are early adopters, followed closely by Pharmaceuticals, leveraging data cleansing and enrichment to improve safety, comply with regulations, and optimize asset management. The Mining and Transportation sectors are also rapidly adopting these services to enhance operational efficiency and predictive maintenance. Within the types of services offered, data cleansing represents a larger share currently, focusing on identifying and removing inconsistencies and inaccuracies. However, data enrichment, which involves augmenting existing data with external sources to improve its completeness and context, is experiencing accelerated growth due to its capacity to unlock deeper insights. While several established players operate in the market, such as Enventure, Sphera, and OptimizeMRO, the landscape is also characterized by numerous smaller, specialized service providers, indicative of a competitive and dynamic market structure. The presence of regional players further suggests opportunities for both consolidation and expansion in the coming years.

Purpose:The Integrated Support Environment (ISE) Laboratory serves the fleet, in-service engineers, logisticians and program management offices by automatically and periodically providing key decision makers with the big picture tools and actionable metrics needed for informed decision making within the realm of Support Equipment (SE) and Aircraft Launch and Recovery Equipment (ALRE) system improvements.Function:The ISE Laboratory at the Naval Air Warfare Center Aircraft Division, Lakehurst, NJ correlates cross-competency data to provide meaningful metrics. The lab provides a distributed data system that achieves the lab's mission of providing actionable metrics by combining multiple data sources and leveraging automated data feeds for near real-time situational awareness across all phases of a program including design, development, test and operational deployment all within a single system interface.Capabilities:The ISE Lab utilizes corporate toolsets to provide business intelligence to Naval Aviation Enterprise (NAE) leadership. The ISE Lab provides pertinent metrics to the fleet, engineers, logisticians and program management users on demand. The lab also utilizes specialized software to provide a thorough analysis of the data being collected, which allows for data mining, data cleansing, processing and modeling to identify and visualize trends. Moreover, the lab has defined and implemented streamlined processes for collecting data, performing data mining techniques and providing pertinent data metrics, via reports or dashboards, to decision makers.

The QA4MRE dataset offers a compelling collection of passages with associated questions and answers, serving as a foundational resource for researchers. This dataset has been instrumental in various research projects, including the CLEF 2011, 2012, and 2013 Shared Tasks. It provides training datasets for the main track, such as the 2011 German language training data, and includes documents for pilot studies related to Alzheimer's disease and entrance exams. This expansive dataset enables exploration into new possibilities and findings, acting as a rich source of information for diverse fields.

Columns

The dataset contains several key columns to facilitate question answering and reading comprehension research:

• topic_id: An identifier for the topic.
• topic_name: The name of the topic that the passage represents.
• test_id: An identifier for the test.
• document_id: An identifier for the document.
• document_str: The text of the passages or articles.
• question_id: An identifier for the question.
• question_str: The questions presented within the dataset.
• answer_options: The options provided for answering a question.
• correct_answer_id: An identifier for the correct answer.
• correct_answer_str: The optimal choice or solution given for a question.

Distribution

Data files are typically provided in CSV format. The dataset includes various versions of training and development data, encompassing passages with accompanying questions and answers. Specific numbers for total rows or records are not explicitly available, however, there are details regarding unique values and label counts for certain ranges within the training data, such as for the German Main Track 2011.

Usage

This dataset is ideal for a multitude of applications:

• Automated Question Answering Systems: Develop systems capable of engaging in conversations, potentially serving as teaching assistants for exam preparation or virtual assistants for customer service.
• Summarisation Tools: Create tools specifically for the dataset to extract key information from passages and generate concise summaries with confidence scores.
• Medical Research: Utilise natural language processing techniques to analyse questions related to Alzheimer's disease, building machine learning models to predict patient responses and aid early diagnosis.
• Academic and Research Projects: A go-to source for shared tasks and research, such as the CLEF Shared Tasks on reading comprehension.

Coverage

The dataset has a global regional coverage. It includes data from the CLEF 2011, 2012, and 2013 Shared Tasks, with specific training data available for the German language main track in 2011. It also encompasses documents for pilot studies related to Alzheimer's disease and entrance exams, indicating its application in specific demographic and educational contexts.

License

CC0

Who Can Use It

This dataset is intended for a wide array of users, including:

• Researchers: Seeking to explore creative approaches and solutions in natural language processing and machine learning.
• Developers: Creating automated question answering systems, summarisation tools, or other AI-powered applications.
• Educators and Students: For developing teaching assistants or studying for exams using automated systems.
• Healthcare Professionals/Researchers: Interested in leveraging NLP for insights into conditions like Alzheimer's disease.

Dataset Name Suggestions

• QA4MRE Reading Comprehension Q&A Dataset
• German Reading Comprehension Training Data
• CLEF Shared Tasks Question Answering Dataset
• Alzheimer's Disease & Entrance Exam Q&A
• Multilingual Question Answering Dataset

Attributes

Original Data Source: QA4MRE (Reading Comprehension Q&A)

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US Deep Learning Market Size 2025-2029

The deep learning market size in US is forecast to increase by USD 5.02 billion at a CAGR of 30.1% between 2024 and 2029.

The deep learning market is experiencing robust growth, driven by the increasing adoption of artificial intelligence (AI) in various industries for advanced solutioning. This trend is fueled by the availability of vast amounts of data, which is a key requirement for deep learning algorithms to function effectively. Industry-specific solutions are gaining traction, as businesses seek to leverage deep learning for specific use cases such as image and speech recognition, fraud detection, and predictive maintenance. Alongside, intuitive data visualization tools are simplifying complex neural network outputs, helping stakeholders understand and validate insights. 


However, challenges remain, including the need for powerful computing resources, data privacy concerns, and the high cost of implementing and maintaining deep learning systems. Despite these hurdles, the market's potential for innovation and disruption is immense, making it an exciting space for businesses to explore further. Semi-supervised learning, data labeling, and data cleaning facilitate efficient training of deep learning models. Cloud analytics is another significant trend, as companies seek to leverage cloud computing for cost savings and scalability. 

What will be the Size of the market During the Forecast Period?

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Deep learning, a subset of machine learning, continues to shape industries by enabling advanced applications such as image and speech recognition, text generation, and pattern recognition. Reinforcement learning, a type of deep learning, gains traction, with deep reinforcement learning leading the charge. Anomaly detection, a crucial application of unsupervised learning, safeguards systems against security vulnerabilities. Ethical implications and fairness considerations are increasingly important in deep learning, with emphasis on explainable AI and model interpretability. Graph neural networks and attention mechanisms enhance data preprocessing for sequential data modeling and object detection. Time series forecasting and dataset creation further expand deep learning's reach, while privacy preservation and bias mitigation ensure responsible use.

In summary, deep learning's market dynamics reflect a constant pursuit of innovation, efficiency, and ethical considerations. The Deep Learning Market in the US is flourishing as organizations embrace intelligent systems powered by supervised learning and emerging self-supervised learning techniques. These methods refine predictive capabilities and reduce reliance on labeled data, boosting scalability. BFSI firms utilize AI image recognition for various applications, including personalizing customer communication, maintaining a competitive edge, and automating repetitive tasks to boost productivity. Sophisticated feature extraction algorithms now enable models to isolate patterns with high precision, particularly in applications such as image classification for healthcare, security, and retail.

How is this market segmented and which is the largest segment?

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Application

  Image recognition
  Voice recognition
  Video surveillance and diagnostics
  Data mining


Type

  Software
  Services
  Hardware


End-user

  Security
  Automotive
  Healthcare
  Retail and commerce
  Others


Geography

  North America

    US

By Application Insights

The Image recognition segment is estimated to witness significant growth during the forecast period. In the realm of artificial intelligence (AI) and machine learning, image recognition, a subset of computer vision, is gaining significant traction. This technology utilizes neural networks, deep learning models, and various machine learning algorithms to decipher visual data from images and videos. Image recognition is instrumental in numerous applications, including visual search, product recommendations, and inventory management. Consumers can take photographs of products to discover similar items, enhancing the online shopping experience. In the automotive sector, image recognition is indispensable for advanced driver assistance systems (ADAS) and autonomous vehicles, enabling the identification of pedestrians, other vehicles, road signs, and lane markings.

Furthermore, image recognition plays a pivotal role in augmented reality (AR) and virtual reality (VR) applications, where it tracks physical objects and overlays digital content onto real-world scenarios. The model training process involves the backpropagation algorithm, which calculates

This dataset provides Hindi-English language identification data, specifically designed for testing machine learning models [1]. It is an integral part of the broader LinCE (Linguistic Code-switching Evaluation) collection, which is an expansive compilation of language technologies and data [2]. This resource facilitates a multitude of purposes, including language identification (LID), part-of-speech (POS) tagging, Named-Entity Recognition (NER), and sentiment analysis (SA) [2]. It is highly valuable for training robust models efficiently with machine learning techniques, enabling the automatic detection and classification of various linguistic tasks [2]. The LinCE collection itself explores six distinct languages: Spanish, Hindi, Nepali, Spanish-English, Hindi-English, and Spanish Multi-Source-English (MSAEA), making this dataset a valuable tool for those looking to unlock the power of language through analysis within a diverse linguistic context [2].

Columns

The dataset contains the following key columns: * words: The textual words within the dataset, represented as a string [1]. * idx: An index identifier for each record [1]. * lid: The language identification label assigned to the text [1].

Distribution

The data file is typically provided in a CSV format [3]. This specific test dataset contains 1,853 individual records or rows [1]. While a sample file will be updated separately to the platform, the structured nature of this data allows for straightforward integration into analytical workflows [3].

Usage

This dataset is ideal for a variety of applications and use cases, including: * Testing machine learning models developed for language identification [1]. * Training ML models to automatically detect and classify tasks such as POS tagging or NER from different language variations [2]. * Building cross-linguistic models across multiple languages [2]. * Exploratory research within natural language processing (NLP) [2]. * Developing multilingual sentiment analysis systems [2]. * Training models to identify and classify named entities across multiple languages, regardless of the specific language or coding scheme [2]. * Developing AI-powered cross-lingual translators that accurately translate text between languages [2].

Coverage

The dataset specifically focuses on Hindi-English language identification [1]. As part of the wider LinCE project, it aligns with a collection that encompasses Spanish, Hindi, Nepali, Spanish-English, Hindi-English, and Spanish Multi-Source-English (MSAEA) [2]. While the listed region for the dataset's availability is GLOBAL, specific geographic or time range coverage for the data content itself is not detailed [4].

License

CC0

Who Can Use It

This dataset is particularly useful for: * Data scientists and machine learning engineers focused on natural language processing. * NLP researchers and linguists interested in language analysis, code-switching, and multilingual models [2]. * Developers and academics looking to build and test models for language identification, part-of-speech tagging, named-entity recognition, and sentiment analysis [2]. * Anyone aiming to uncover the insights from language data and develop advanced multilingual AI applications [2].

Dataset Name Suggestions

• Hindi-English Language ID Test Data
• LinCE Hindi-English LID Dataset
• Multilingual Code-switching Evaluation: Hindi-English
• NLP Hindi-English Language Identifier
• Cross-lingual Language Detection (Hindi-English)

Attributes

Original Data Source: LinCE (Linguistic Code-switching Evaluation)

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Text Analytics Market Size 2024-2028

The text analytics market size is forecast to increase by USD 18.08 billion, at a CAGR of 22.58% between 2023 and 2028.

The market is experiencing significant growth, driven by the increasing popularity of Service-Oriented Architecture (SOA) among end-users. SOA's flexibility and scalability make it an ideal choice for text analytics applications, enabling organizations to process vast amounts of unstructured data and gain valuable insights. Additionally, the ability to analyze large volumes of unstructured data provides valuable insights through data analytics, enabling informed decision-making and competitive advantage. Furthermore, the emergence of advanced text analytical tools is expanding the market's potential by offering enhanced capabilities, such as sentiment analysis, entity extraction, and topic modeling. However, the market faces challenges that require careful consideration. System integration and interoperability issues persist, as text analytics solutions must seamlessly integrate with existing IT infrastructure and data sources.
Ensuring compatibility and data exchange between various systems can be a complex and time-consuming process. Addressing these challenges through strategic partnerships, standardization efforts, and open APIs will be essential for market participants to capitalize on the opportunities presented by the market's growth.

What will be the Size of the Text Analytics Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2018-2022 and forecasts 2024-2028 - in the full report.
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The market continues to evolve, driven by advancements in technology and the increasing demand for insightful data interpretation across various sectors. Text preprocessing techniques, such as stop word removal and lexical analysis, form the foundation of text analytics, enabling the extraction of meaningful insights from unstructured data. Topic modeling and transformer networks are current trends, offering improved accuracy and efficiency in identifying patterns and relationships within large volumes of text data. Applications of text analytics extend to fake news detection, risk management, and brand monitoring, among others. Data mining, customer feedback analysis, and data governance are essential components of text analytics, ensuring data security and maintaining data quality.

Text summarization, named entity recognition, deep learning, and predictive modeling are advanced techniques that enhance the capabilities of text analytics, providing actionable insights through data interpretation and data visualization. Machine learning algorithms, including machine learning and deep learning, play a crucial role in text analytics, with applications in spam detection, sentiment analysis, and predictive modeling. Syntactic analysis and semantic analysis offer deeper understanding of text data, while algorithm efficiency and performance optimization ensure the scalability of text analytics solutions. Text analytics continues to unfold, with ongoing research and development in areas such as prescriptive modeling, API integration, and data cleaning, further expanding its applications and capabilities.

The future of text analytics lies in its ability to provide valuable insights from unstructured data, driving informed decision-making and business growth.

How is this Text Analytics Industry segmented?

The text analytics industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Deployment

  Cloud
  On-premises


Component

  Software
  Services


Geography

  North America

    US


  Europe

    France
    Germany


  APAC

    China
    Japan


  Rest of World (ROW)

By Deployment Insights

The cloud segment is estimated to witness significant growth during the forecast period.

Text analytics is a dynamic and evolving market, driven by the increasing importance of data-driven insights for businesses. Cloud computing plays a significant role in its growth, as companies such as Microsoft, SAP SE, SAS Institute, IBM, Lexalytics, and Open Text offer text analytics software and services via the Software-as-a-Service (SaaS) model. This approach reduces upfront costs for end-users, as they do not need to install hardware and software on their premises. Instead, these solutions are maintained at the company's data center, allowing end-users to access them on a subscription basis. Text preprocessing, topic modeling, transformer networks, and other advanced techniques are integral to text analytics.

Fake news detection, spam filtering, sentiment analysis, and social media monitoring are essential applications. Deep learning, m

About the MNAD Dataset The MNAD corpus is a collection of over 1 million Moroccan news articles written in modern Arabic language. These news articles have been gathered from 11 prominent electronic news sources. The dataset is made available to the academic community for research purposes, such as data mining (clustering, classification, etc.), information retrieval (ranking, search, etc.), and other non-commercial activities.

Dataset Fields

Title: The title of the article Body: The body of the article Category: The category of the article Source: The Electronic News paper source of the article

About Version 1 of the Dataset (MNAD.v1) Version 1 of the dataset comprises 418,563 articles classified into 19 categories. The data was collected from well-known electronic news sources, namely Akhbarona.ma, Hespress.ma, Hibapress.com, and Le360.com. The articles were stored in four separate CSV files, each corresponding to the news website source. Each CSV file contains three fields: Title, Body, and Category of the news article.

The dataset is rich in Arabic vocabulary, with approximately 906,125 unique words. It has been utilized as a benchmark in the research paper: "A Moroccan News Articles Dataset (MNAD) For Arabic Text Categorization". In 2021 International Conference on Decision Aid Sciences and Application (DASA).

This dataset is available for download from the following sources: - Kaggle Datasets : MNADv1 - Huggingface Datasets: MNADv1

About Version 2 of the Dataset (MNAD.v2) Version 2 of the MNAD dataset includes an additional 653,901 articles, bringing the total number of articles to over 1 million (1,069,489), classified into the same 19 categories as in version 1. The new documents were collected from seven additional prominent Moroccan news websites, namely al3omk.com, medi1news.com, alayam24.com, anfaspress.com, alyaoum24.com, barlamane.com, and SnrtNews.com.

The newly collected articles have been merged with the articles from the previous version into a single CSV file named MNADv2.csv. This file includes an additional column called "Source" to indicate the source of each news article.

Furthermore, MNAD.v2 incorporates improved pre-processing techniques and data cleaning methods. These enhancements involve removing duplicates, eliminating multiple spaces, discarding rows with NaN values, replacing new lines with " ", excluding very long and very short articles, and removing non-Arabic articles. These additions and improvements aim to enhance the usability and value of the MNAD dataset for researchers and practitioners in the field of Arabic text analysis.

This dataset is available for download from the following sources: - Kaggle Datasets : MNADv2 - Huggingface Datasets: MNADv2

Citation If you use our data, please cite the following paper:

bibtex @inproceedings{MNAD2021, author = {Mourad Jbene and Smail Tigani and Rachid Saadane and Abdellah Chehri}, title = {A Moroccan News Articles Dataset ({MNAD}) For Arabic Text Categorization}, year = {2021}, publisher = {{IEEE}}, booktitle = {2021 International Conference on Decision Aid Sciences and Application ({DASA})} doi = {10.1109/dasa53625.2021.9682402}, url = {https://doi.org/10.1109/dasa53625.2021.9682402}, }

The MNAD corpus is a collection of over 1 million Moroccan news articles written in modern Arabic language. These news articles have been gathered from 11 prominent electronic news sources. The dataset is made available to the academic community for research purposes, such as data mining (clustering, classification, etc.), information retrieval (ranking, search, etc.), and other non-commercial activities.

Dataset Fields

• Title: The title of the article
• Body: The body of the article
• Category: The category of the article
• Source: The Electronic News paper source of the article

About Version 1 of the Dataset (MNAD.v1)

Version 1 of the dataset comprises 418,563 articles classified into 19 categories. The data was collected from well-known electronic news sources, namely Akhbarona.ma, Hespress.ma, Hibapress.com, and Le360.com. The articles were stored in four separate CSV files, each corresponding to the news website source. Each CSV file contains three fields: Title, Body, and Category of the news article.

The dataset is rich in Arabic vocabulary, with approximately 906,125 unique words. It has been utilized as a benchmark in the research paper: "A Moroccan News Articles Dataset (MNAD) For Arabic Text Categorization". In 2021 International Conference on Decision Aid Sciences and Application (DASA).

This dataset is available for download from the following sources: - Kaggle Datasets : MNADv1 - Huggingface Datasets: MNADv1

About Version 2 of the Dataset (MNAD.v2)

Version 2 of the MNAD dataset includes an additional 653,901 articles, bringing the total number of articles to over 1 million (1,069,489), classified into the same 19 categories as in version 1. The new documents were collected from seven additional prominent Moroccan news websites, namely al3omk.com, medi1news.com, alayam24.com, anfaspress.com, alyaoum24.com, barlamane.com, and SnrtNews.com.

The newly collected articles have been merged with the articles from the previous version into a single CSV file named MNADv2.csv. This file includes an additional column called "Source" to indicate the source of each news article.

Furthermore, MNAD.v2 incorporates improved pre-processing techniques and data cleaning methods. These enhancements involve removing duplicates, eliminating multiple spaces, discarding rows with NaN values, replacing new lines with " ", excluding very long and very short articles, and removing non-Arabic articles. These additions and improvements aim to enhance the usability and value of the MNAD dataset for researchers and practitioners in the field of Arabic text analysis.

This dataset is available for download from the following sources: - Kaggle Datasets : MNADv2 - Huggingface Datasets: MNADv2

Citation

If you use our data, please cite the following paper:

@inproceedings{MNAD2021,
  author  = {Mourad Jbene and 
         Smail Tigani and 
         Rachid Saadane and 
         Abdellah Chehri},
  title   = {A Moroccan News Articles Dataset ({MNAD}) For Arabic Text Categorization},
  year   = {2021},
  publisher = {{IEEE}},
  booktitle = {2021 International Conference on Decision Aid Sciences and Application ({DASA})}
  doi    = {10.1109/dasa53625.2021.9682402},
  url    = {https://doi.org/10.1109/dasa53625.2021.9682402},
}

The global data scraping tools market, valued at $15.57 billion in 2025, is experiencing robust growth. While the provided CAGR is missing, a reasonable estimate, considering the expanding need for data-driven decision-making across various sectors and the increasing sophistication of web scraping techniques, would be between 15-20% annually. This strong growth is driven by the proliferation of e-commerce platforms generating vast amounts of data, the rising adoption of data analytics and business intelligence tools, and the increasing demand for market research and competitive analysis. Businesses leverage these tools to extract valuable insights from websites, enabling efficient price monitoring, lead generation, market trend analysis, and customer sentiment monitoring. The market segmentation shows a significant preference for "Pay to Use" tools reflecting the need for reliable, scalable, and often legally compliant solutions. The application segments highlight the high demand across diverse industries, notably e-commerce, investment analysis, and marketing analysis, driving the overall market expansion. Challenges include ongoing legal complexities related to web scraping, the constant evolution of website structures requiring adaptation of scraping tools, and the need for robust data cleaning and processing capabilities post-scraping. Looking forward, the market is expected to witness continued growth fueled by advancements in artificial intelligence and machine learning, enabling more intelligent and efficient scraping. The integration of data scraping tools with existing business intelligence platforms and the development of user-friendly, no-code/low-code scraping solutions will further boost adoption. The increasing adoption of cloud-based scraping services will also contribute to market growth, offering scalability and accessibility. However, the market will also need to address ongoing concerns about ethical scraping practices, data privacy regulations, and the potential for misuse of scraped data. The anticipated growth trajectory, based on the estimated CAGR, points to a significant expansion in market size over the forecast period (2025-2033), making it an attractive sector for both established players and new entrants.

Snapshot img

Alternative Data Market Size 2025-2029

The alternative data market size is forecast to increase by USD 60.32 billion, at a CAGR of 52.5% between 2024 and 2029.

The market is experiencing significant growth, driven by the increased availability and diversity of data sources. This expanding data landscape is fueling the rise of alternative data-driven investment strategies across various industries. However, the market faces challenges related to data quality and standardization. As companies increasingly rely on alternative data to inform business decisions, ensuring data accuracy and consistency becomes paramount. Addressing these challenges requires robust data management systems and collaboration between data providers and consumers to establish industry-wide standards. Companies that effectively navigate these dynamics can capitalize on the wealth of opportunities presented by alternative data, driving innovation and competitive advantage.

What will be the Size of the Alternative Data Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
Request Free SampleThe market continues to evolve, with new applications and technologies shaping its dynamics. Predictive analytics and deep learning are increasingly being integrated into business intelligence systems, enabling more accurate risk management and sales forecasting. Data aggregation from various sources, including social media and web scraping, enriches datasets for more comprehensive quantitative analysis. Data governance and metadata management are crucial for maintaining data accuracy and ensuring data security. Real-time analytics and cloud computing facilitate decision support systems, while data lineage and data timeliness are essential for effective portfolio management. Unstructured data, such as sentiment analysis and natural language processing, provide valuable insights for various sectors. Machine learning algorithms and execution algorithms are revolutionizing trading strategies, from proprietary trading to high-frequency trading. Data cleansing and data validation are essential for maintaining data quality and relevance. Standard deviation and regression analysis are essential tools for financial modeling and risk management. Data enrichment and data warehousing are crucial for data consistency and completeness, allowing for more effective customer segmentation and sales forecasting. Data security and fraud detection are ongoing concerns, with advancements in technology continually addressing new threats. The market's continuous dynamism is reflected in its integration of various technologies and applications. From data mining and data visualization to supply chain optimization and pricing optimization, the market's evolution is driven by the ongoing unfolding of market activities and evolving patterns.

How is this Alternative Data Industry segmented?

The alternative data industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. TypeCredit and debit card transactionsSocial mediaMobile application usageWeb scrapped dataOthersEnd-userBFSIIT and telecommunicationRetailOthersGeographyNorth AmericaUSCanadaMexicoEuropeFranceGermanyItalyUKAPACChinaIndiaJapanRest of World (ROW)

By Type Insights

The credit and debit card transactions segment is estimated to witness significant growth during the forecast period.Alternative data derived from card and debit card transactions plays a pivotal role in business intelligence, offering valuable insights into consumer spending behaviors. This data is essential for market analysts, financial institutions, and businesses aiming to optimize strategies and enhance customer experiences. Two primary categories exist within this data segment: credit card transactions and debit card transactions. Credit card transactions reveal consumers' discretionary spending patterns, luxury purchases, and credit management abilities. By analyzing this data through quantitative methods, such as regression analysis and time series analysis, businesses can gain a deeper understanding of consumer preferences and trends. Debit card transactions, on the other hand, provide insights into essential spending habits, budgeting strategies, and daily expenses. This data is crucial for understanding consumers' practical needs and lifestyle choices. Machine learning algorithms, such as deep learning and predictive analytics, can be employed to uncover patterns and trends in debit card transactions, enabling businesses to tailor their offerings and services accordingly. Data governance, data security, and data accuracy are critical considerations when dealing with sensitive financial d

ObjectiveThe aim of this study is to use network pharmacology and data mining to explore the role of traditional Chinese medicine (TCM) in ischemic stroke (IS) intervention by ferroptosis regulation. The results will provide reference for related research on ferroptosis in IS.MethodsThe ferroptosis-related targets were obtained from the GeneCards, GeneCLiP3, and FerrDdb databases, while the IS targets were sourced from the GeneCards and DisGeNET databases. Venny was used to identify IS targets associated with ferroptosis. A protein-protein interaction (PPI) analysis was then conducted, and machine learning screening was used to validate these potential targets. The potential targets that met specific criteria and their related compounds allowed us to select TCMs. A mechanistic analysis of the potential targets was conducted using the DAVID database. PPI network diagrams, target-compound network diagrams, and target-compound-TCM network diagrams were then constructed. Finally, molecular docking technology was used to verify the binding activities of the TCM compounds and core components with the identified targets. In addition, the properties, flavors, meridian tropism, and therapeutic effects of the candidate TCMs were analyzed and statistically evaluated.ResultsA total of 706 targets associated with ferroptosis in IS were obtained, and 14 potential ferroptosis targets in IS were obtained using machine learning. Furthermore, 413 compounds and 301 TCMs were screened, and the binding activities of the targets to the TCM compounds and the core prescriptions were stable. The candidate TCMs primarily exhibited cold, warm, bitter taste, pungent taste, liver meridian, heat-cleaning medicinal, and tonify deficiency properties.ConclusionsThis study investigated ferroptosis regulation for IS intervention using TCM. We began by investigating the targets of IS and ferroptosis, and we also analyzed the relevant mechanism of ferroptosis in IS. The results of this study provide reference for related research on ferroptosis in IS.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Cipher Mining: (CIFR) Is This the Next Big Crypto Mining Play?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Buried Venture: Is Buenaventura (BVN) Mining Company, Inc. a Good Investment?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

BlackRock World Mining (BRWM): Mining for Gains?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data