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Data Science Platform Market Size 2025-2029

The data science platform market size is forecast to increase by USD 763.9 million, at a CAGR of 40.2% between 2024 and 2029.

The market is experiencing significant growth, driven by the increasing integration of Artificial Intelligence (AI) and Machine Learning (ML) technologies. This fusion enables organizations to derive deeper insights from their data, fueling business innovation and decision-making. Another trend shaping the market is the emergence of containerization and microservices in data science platforms. This approach offers enhanced flexibility, scalability, and efficiency, making it an attractive choice for businesses seeking to streamline their data science operations. However, the market also faces challenges. Data privacy and security remain critical concerns, with the increasing volume and complexity of data posing significant risks. Ensuring robust data security and privacy measures is essential for companies to maintain customer trust and comply with regulatory requirements. Additionally, managing the complexity of data science platforms and ensuring seamless integration with existing systems can be a daunting task, requiring significant investment in resources and expertise. Companies must navigate these challenges effectively to capitalize on the market's opportunities and stay competitive in the rapidly evolving data landscape.

What will be the Size of the Data Science Platform 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 increasing demand for advanced analytics and artificial intelligence solutions across various sectors. Real-time analytics and classification models are at the forefront of this evolution, with APIs integrations enabling seamless implementation. Deep learning and model deployment are crucial components, powering applications such as fraud detection and customer segmentation. Data science platforms provide essential tools for data cleaning and data transformation, ensuring data integrity for big data analytics. Feature engineering and data visualization facilitate model training and evaluation, while data security and data governance ensure data privacy and compliance. Machine learning algorithms, including regression models and clustering models, are integral to predictive modeling and anomaly detection. Statistical analysis and time series analysis provide valuable insights, while ETL processes streamline data integration. Cloud computing enables scalability and cost savings, while risk management and algorithm selection optimize model performance. Natural language processing and sentiment analysis offer new opportunities for data storytelling and computer vision. Supply chain optimization and recommendation engines are among the latest applications of data science platforms, demonstrating their versatility and continuous value proposition. Data mining and data warehousing provide the foundation for these advanced analytics capabilities.

How is this Data Science Platform Industry segmented?

The data science platform 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-premisesCloudComponentPlatformServicesEnd-userBFSIRetail and e-commerceManufacturingMedia and entertainmentOthersSectorLarge enterprisesSMEsApplicationData PreparationData VisualizationMachine LearningPredictive AnalyticsData GovernanceOthersGeographyNorth AmericaUSCanadaEuropeFranceGermanyUKMiddle East and AfricaUAEAPACChinaIndiaJapanSouth AmericaBrazilRest 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, businesses increasingly adopt solutions to gain real-time insights from their data, enabling them to make informed decisions. Classification models and deep learning algorithms are integral parts of these platforms, providing capabilities for fraud detection, customer segmentation, and predictive modeling. API integrations facilitate seamless data exchange between systems, while data security measures ensure the protection of valuable business information. Big data analytics and feature engineering are essential for deriving meaningful insights from vast datasets. Data transformation, data mining, and statistical analysis are crucial processes in data preparation and discovery. Machine learning models, including regression and clustering, are employed for model training and evaluation. Time series analysis and natural language processing are valuable tools for understanding trends and customer sen

The LSC (Leicester Scientific Corpus)

April 2020 by Neslihan Suzen, PhD student at the University of Leicester (ns433@leicester.ac.uk) Supervised by Prof Alexander Gorban and Dr Evgeny MirkesThe data are extracted from the Web of Science [1]. You may not copy or distribute these data in whole or in part without the written consent of Clarivate Analytics.[Version 2] A further cleaning is applied in Data Processing for LSC Abstracts in Version 1*. Details of cleaning procedure are explained in Step 6.* Suzen, Neslihan (2019): LSC (Leicester Scientific Corpus). figshare. Dataset. https://doi.org/10.25392/leicester.data.9449639.v1.Getting StartedThis text provides the information on the LSC (Leicester Scientific Corpus) and pre-processing steps on abstracts, and describes the structure of files to organise the corpus. This corpus is created to be used in future work on the quantification of the meaning of research texts and make it available for use in Natural Language Processing projects.LSC is a collection of abstracts of articles and proceeding papers published in 2014, and indexed by the Web of Science (WoS) database [1]. The corpus contains only documents in English. Each document in the corpus contains the following parts:1. Authors: The list of authors of the paper2. Title: The title of the paper 3. Abstract: The abstract of the paper 4. Categories: One or more category from the list of categories [2]. Full list of categories is presented in file ‘List_of _Categories.txt’. 5. Research Areas: One or more research area from the list of research areas [3]. Full list of research areas is presented in file ‘List_of_Research_Areas.txt’. 6. Total Times cited: The number of times the paper was cited by other items from all databases within Web of Science platform [4] 7. Times cited in Core Collection: The total number of times the paper was cited by other papers within the WoS Core Collection [4]The corpus was collected in July 2018 online and contains the number of citations from publication date to July 2018. We describe a document as the collection of information (about a paper) listed above. The total number of documents in LSC is 1,673,350.Data ProcessingStep 1: Downloading of the Data Online

The dataset is collected manually by exporting documents as Tab-delimitated files online. All documents are available online.Step 2: Importing the Dataset to R

The LSC was collected as TXT files. All documents are extracted to R.Step 3: Cleaning the Data from Documents with Empty Abstract or without CategoryAs our research is based on the analysis of abstracts and categories, all documents with empty abstracts and documents without categories are removed.Step 4: Identification and Correction of Concatenate Words in AbstractsEspecially medicine-related publications use ‘structured abstracts’. Such type of abstracts are divided into sections with distinct headings such as introduction, aim, objective, method, result, conclusion etc. Used tool for extracting abstracts leads concatenate words of section headings with the first word of the section. For instance, we observe words such as ConclusionHigher and ConclusionsRT etc. The detection and identification of such words is done by sampling of medicine-related publications with human intervention. Detected concatenate words are split into two words. For instance, the word ‘ConclusionHigher’ is split into ‘Conclusion’ and ‘Higher’.The section headings in such abstracts are listed below:

Background Method(s) Design Theoretical Measurement(s) Location Aim(s) Methodology Process Abstract Population Approach Objective(s) Purpose(s) Subject(s) Introduction Implication(s) Patient(s) Procedure(s) Hypothesis Measure(s) Setting(s) Limitation(s) Discussion Conclusion(s) Result(s) Finding(s) Material (s) Rationale(s) Implications for health and nursing policyStep 5: Extracting (Sub-setting) the Data Based on Lengths of AbstractsAfter correction, the lengths of abstracts are calculated. ‘Length’ indicates the total number of words in the text, calculated by the same rule as for Microsoft Word ‘word count’ [5].According to APA style manual [6], an abstract should contain between 150 to 250 words. In LSC, we decided to limit length of abstracts from 30 to 500 words in order to study documents with abstracts of typical length ranges and to avoid the effect of the length to the analysis.

Step 6: [Version 2] Cleaning Copyright Notices, Permission polices, Journal Names and Conference Names from LSC Abstracts in Version 1Publications can include a footer of copyright notice, permission policy, journal name, licence, author’s right or conference name below the text of abstract by conferences and journals. Used tool for extracting and processing abstracts in WoS database leads to attached such footers to the text. For example, our casual observation yields that copyright notices such as ‘Published by Elsevier ltd.’ is placed in many texts. To avoid abnormal appearances of words in further analysis of words such as bias in frequency calculation, we performed a cleaning procedure on such sentences and phrases in abstracts of LSC version 1. We removed copyright notices, names of conferences, names of journals, authors’ rights, licenses and permission policies identified by sampling of abstracts.Step 7: [Version 2] Re-extracting (Sub-setting) the Data Based on Lengths of AbstractsThe cleaning procedure described in previous step leaded to some abstracts having less than our minimum length criteria (30 words). 474 texts were removed.Step 8: Saving the Dataset into CSV FormatDocuments are saved into 34 CSV files. In CSV files, the information is organised with one record on each line and parts of abstract, title, list of authors, list of categories, list of research areas, and times cited is recorded in fields.To access the LSC for research purposes, please email to ns433@le.ac.uk.References[1]Web of Science. (15 July). Available: https://apps.webofknowledge.com/ [2]WoS Subject Categories. Available: https://images.webofknowledge.com/WOKRS56B5/help/WOS/hp_subject_category_terms_tasca.html [3]Research Areas in WoS. Available: https://images.webofknowledge.com/images/help/WOS/hp_research_areas_easca.html [4]Times Cited in WoS Core Collection. (15 July). Available: https://support.clarivate.com/ScientificandAcademicResearch/s/article/Web-of-Science-Times-Cited-accessibility-and-variation?language=en_US [5]Word Count. Available: https://support.office.com/en-us/article/show-word-count-3c9e6a11-a04d-43b4-977c-563a0e0d5da3 [6]A. P. Association, Publication manual. American Psychological Association Washington, DC, 1983.

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.

The LScDC (Leicester Scientific Dictionary-Core Dictionary)April 2020 by Neslihan Suzen, PhD student at the University of Leicester (ns433@leicester.ac.uk/suzenneslihan@hotmail.com)Supervised by Prof Alexander Gorban and Dr Evgeny Mirkes[Version 3] The third version of LScDC (Leicester Scientific Dictionary-Core) is formed using the updated LScD (Leicester Scientific Dictionary) - Version 3*. All steps applied to build the new version of core dictionary are the same as in Version 2** and can be found in description of Version 2 below. We did not repeat the explanation. The files provided with this description are also same as described as for LScDC Version 2. The numbers of words in the 3rd versions of LScD and LScDC are summarized below. # of wordsLScD (v3) 972,060LScDC (v3) 103,998 * Suzen, Neslihan (2019): LScD (Leicester Scientific Dictionary). figshare. Dataset. https://doi.org/10.25392/leicester.data.9746900.v3 ** Suzen, Neslihan (2019): LScDC (Leicester Scientific Dictionary-Core). figshare. Dataset. https://doi.org/10.25392/leicester.data.9896579.v2[Version 2] Getting StartedThis file describes a sorted and cleaned list of words from LScD (Leicester Scientific Dictionary), explains steps for sub-setting the LScD and basic statistics of words in the LSC (Leicester Scientific Corpus), to be found in [1, 2]. The LScDC (Leicester Scientific Dictionary-Core) is a list of words ordered by the number of documents containing the words, and is available in the CSV file published. There are 104,223 unique words (lemmas) in the LScDC. This dictionary is created to be used in future work on the quantification of the sense of research texts. The objective of sub-setting the LScD is to discard words which appear too rarely in the corpus. In text mining algorithms, usage of enormous number of text data brings the challenge to the performance and the accuracy of data mining applications. The performance and the accuracy of models are heavily depend on the type of words (such as stop words and content words) and the number of words in the corpus. Rare occurrence of words in a collection is not useful in discriminating texts in large corpora as rare words are likely to be non-informative signals (or noise) and redundant in the collection of texts. The selection of relevant words also holds out the possibility of more effective and faster operation of text mining algorithms.To build the LScDC, we decided the following process on LScD: removing words that appear in no more than 10 documents (

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 diverse industries. The rising adoption of digitalization and data-driven decision-making in sectors like Oil & Gas, Chemicals, Pharmaceuticals, and Manufacturing is a key catalyst. Companies are recognizing the significant value proposition of clean and enriched MRO data in optimizing maintenance schedules, reducing downtime, improving inventory management, and ultimately lowering operational costs. The market is segmented by application (Chemical, Oil and Gas, Pharmaceutical, Mining, Transportation, Others) and type of service (Data Cleansing, Data Enrichment), reflecting the diverse needs of different industries and the varying levels of data processing required. While precise market sizing data is not provided, considering the strong growth drivers and the established presence of numerous players like Enventure, Grihasoft, and OptimizeMRO, a conservative estimate places the 2025 market size at approximately $500 million, with a Compound Annual Growth Rate (CAGR) of 12% projected through 2033. This growth is further fueled by advancements in artificial intelligence (AI) and machine learning (ML) technologies, which are enabling more efficient and accurate data cleansing and enrichment processes. The competitive landscape is characterized by a mix of established players and emerging companies. Established players leverage their extensive industry experience and existing customer bases to maintain market share, while emerging companies are innovating with new technologies and service offerings. Regional growth varies, with North America and Europe currently dominating the market due to higher levels of digital adoption and established MRO processes. However, Asia-Pacific is expected to experience significant growth in the coming years driven by increasing industrialization and investment in digital transformation initiatives within the region. Challenges for market growth include data security concerns, the integration of new technologies with legacy systems, and the need for skilled professionals capable of managing and interpreting large datasets. Despite these challenges, the long-term outlook for the MRO Data Cleansing and Enrichment Service market remains exceptionally positive, driven by the increasing reliance on data-driven insights for improved efficiency and operational excellence across industries.

About Dataset This data is collected for the Network Analysis of Breaking Bad television series.

Inspiration DataCamp's A Network Analysis of Game of Thrones was the inspiration for the project. Since there is no relationship dataset for the Breaking Bad is available, decided to generate relationship dataset from episode summaries for the graph network analysis.

Dataset The data was collected using web scrapping from the fandom page of Breaking Bad series.

License

CC-BY-SA

Original Data Source: Breaking Bad : Network Analysis

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.

Being an Image labeling expert, we have immense experience in various types of data annotation services. We Annotate data quickly and effectively with our patented Automated Data Labelling tool along with our in-house, full-time, and highly trained annotators.

We can label the data with the following features:

1. Image classification
2. Object detection
3. Semantic segmentation
4. Image tagging
5. Text annotation
6. Point cloud annotation
7. Key-Point annotation
8. Custom user-defined labeling

Data Services we provide:

1. Data collection & sourcing
2. Data cleaning
3. Data mining
4. Data labeling
5. Data management​

We have an AI-enabled training data platform "ADVIT", the most advanced Deep Learning (DL) platform to create, manage high-quality training data and DL models all in one place.

The dataset consists of several columns that provide essential information for each entry. These columns include:

instruction: This column denotes the specific instruction given to the model for generating a response. responses: The model-generated responses to the given instruction are stored in this column. next_response: Following each previous response, this column indicates the subsequent response generated by the model. answer: The correct answer to the question asked in the instruction is provided in this column. is_human_response: This boolean column indicates whether a particular response was generated by a human or by an AI model. By analyzing this rich and diverse dataset, researchers and practitioners can gain valuable insights into various aspects of question answering tasks using AI models. It offers an opportunity for developers to train their models effectively while also facilitating rigorous evaluation methodologies.

Please note that specific dates are not included within this dataset description, focusing solely on providing accurate, informative, descriptive details about its content and purpose

How to use the dataset Understanding the Columns: This dataset contains several columns that provide important information for each entry:

instruction: The instruction given to the model for generating a response. responses: The model-generated responses to the given instruction. next_response: The next response generated by the model after the previous response. answer: The correct answer to the question asked in the instruction. is_human_response: Indicates whether a response is generated by a human or the model. Training Data (train.csv): Use train.csv file in this dataset as training data. It contains a large number of examples that you can use to train your question-answering models or algorithms.

Testing Data (test.csv): Use test.csv file in this dataset as testing data. It allows you to evaluate how well your models or algorithms perform on unseen questions and instructions.

Create Machine Learning Models: You can utilize this dataset's instructional components, including instructions, responses, next_responses, and human-generated answers, along with their respective labels like is_human_response (True/False) for training machine learning models specifically designed for question-answering tasks.

Evaluate Model Performance: After training your model using the provided training data, you can then test its performance on unseen questions from test.csv file by comparing its predicted responses with actual human-generated answers.

Data Augmentation: You can also augment this existing data in various ways such as paraphrasing existing instructions or generating alternative responses based on similar contexts within each example.

Build Conversational Agents: This dataset can be useful for training conversational agents or chatbots by leveraging the instruction-response pairs.

Remember, this dataset provides a valuable resource for building and evaluating question-answering models. Have fun exploring the data and discovering new insights!

Research Ideas Language Understanding: This dataset can be used to train models for question-answering tasks. Models can learn to understand and generate responses based on given instructions and previous responses.

Chatbot Development: With this dataset, developers can create chatbots that provide accurate and relevant answers to user questions. The models can be trained on various topics and domains, allowing the chatbot to answer a wide range of questions.

Educational Materials: This dataset can be used to develop educational materials, such as interactive quizzes or study guides. The models trained on this dataset can provide instant feedback and answers to students' questions, enhancing their learning experience.

Information Retrieval Systems: By training models on this dataset, information retrieval systems can be developed that help users find specific answers or information from large datasets or knowledge bases.

Customer Support: This dataset can be used in training customer support chatbots or virtual assistants that can provide quick and accurate responses to customer inquiries.

Language Generation Research: Researchers studying natural language generation (NLG) techniques could use this dataset for developing novel algorithms for generating coherent and contextually appropriate responses in question-answering scenarios.

Automatic Summarization Systems: Using the instruction-response pairs, automatic summarization systems could be trained that generate concise summaries of lengthy texts by understanding the main content of the text through answering questions.

Dialogue Systems Evaluation: The instruction-response pairs in this dataset could serve as a benchmark for evaluating the performance of dialogue systems in terms of response quality, relevance, coherence, etc.

9 . Machine Learning Training Data Augmentation : One clever ide

Reddit is a social news, content rating and discussion website. It's one of the most popular sites on the internet. Reddit has 52 million daily active users and approximately 430 million users who use it once a month. Reddit has different subreddits and here We'll use the r/AskScience Subreddit.

The dataset is extracted from the subreddit /r/AskScience from Reddit. The data was collected between 01-01-2016 and 20-05-2022. It contains 612,668 Datapoints and 25 Columns. The database contains a number of information about the questions asked on the subreddit, the description of the submission, the flair of the question, NSFW or SFW status, the year of the submission, and more. The data is extracted using python and Pushshift's API. A little bit of cleaning is done using NumPy and pandas as well. (see the descriptions of individual columns below).

The dataset contains the following columns and descriptions: author - Redditor Name author_fullname - Redditor Full name contest_mode - Contest mode [implement obscured scores and randomized sorting]. created_utc - Time the submission was created, represented in Unix Time. domain - Domain of submission. edited - If the post is edited or not. full_link - Link of the post on the subreddit. id - ID of the submission. is_self - Whether or not the submission is a self post (text-only). link_flair_css_class - CSS Class used to identify the flair. link_flair_text - Flair on the post or The link flair’s text content. locked - Whether or not the submission has been locked. num_comments - The number of comments on the submission. over_18 - Whether or not the submission has been marked as NSFW. permalink - A permalink for the submission. retrieved_on - time ingested. score - The number of upvotes for the submission. description - Description of the Submission. spoiler - Whether or not the submission has been marked as a spoiler. stickied - Whether or not the submission is stickied. thumbnail - Thumbnail of Submission. question - Question Asked in the Submission. url - The URL the submission links to, or the permalink if a self post. year - Year of the Submission. banned - Banned by the moderator or not.

This dataset can be used for Flair Prediction, NSFW Classification, and different Text Mining/NLP tasks. Exploratory Data Analysis can also be done to get the insights and see the trend and patterns over the years.

The open-source tools market is experiencing robust growth, driven by increasing demand for cost-effective, flexible, and customizable solutions across diverse sectors. The market, encompassing tools for data cleaning, visualization, mining, and applications like machine learning, natural language processing, and computer vision, is projected to witness substantial expansion over the forecast period (2025-2033). Factors such as the rising adoption of cloud computing, the growing need for data-driven decision-making, and the increasing preference for collaborative development models are key drivers. While the specific CAGR isn't provided, a conservative estimate based on industry trends suggests a compound annual growth rate of around 15-20% is realistic for the period. This growth is anticipated across all segments, with the data science and machine learning sectors exhibiting particularly strong performance. Geographic expansion is also a prominent trend, with North America and Europe leading the market initially, followed by a significant increase in adoption across Asia Pacific and other regions as digital transformation initiatives accelerate. However, challenges remain. Security concerns surrounding open-source software and the need for robust support and maintenance infrastructure could potentially restrain market growth. Nevertheless, ongoing improvements in security protocols and the burgeoning community support surrounding many open-source projects are mitigating these challenges. The diverse range of applications and tool types within the open-source market ensures its versatility. Universal tools, catering to broad needs, and specialized tools like data visualization and mining software are all experiencing increased demand. The presence of established players like IBM and Oracle alongside a large community of contributors ensures a dynamic market ecosystem. The continued development of innovative tools, improved documentation, and enhanced community support are expected to further fuel market growth, making open-source solutions increasingly attractive to businesses of all sizes. Specific segmentation data, while not explicitly provided, shows a spread across applications indicating a healthy, diversified market that is expected to evolve rapidly within the forecast period.

QASPER: NLP Questions and Evidence Discovering Answers with Expertise By Huggingface Hub [source]

About this dataset QASPER is an incredible collection of over 5,000 questions and answers on a vast range of Natural Language Processing (NLP) papers -- all crowdsourced from experienced NLP practitioners. Each question in the dataset is written based only on the titles and abstracts of the corresponding paper, providing an insight into how the experts understood and parsed various materials. The answers to each query have been expertly enriched by evidence taken directly from the full text of each paper. Moreover, QASPER comes with carefully crafted fields that contain relevant information including ‘qas’ – questions and answers; ‘evidence’ – evidence provided for answering questions; title; abstract; figures_and_tables, and full_text. All this adds up to create a remarkable dataset for researchers looking to gain insights into how practitioners interpret NLP topics while providing effective validation when it comes to finding clear-cut solutions to problems encountered in existing literature

More Datasets For more datasets, click here.

Featured Notebooks 🚨 Your notebook can be here! 🚨! How to use the dataset This guide will provide instructions on how to use the QASPER dataset of Natural Language Processing (NLP) questions and evidence. The QASPER dataset contains 5,049 questions over 1,585 papers that has been crowdsourced by NLP practitioners. To get the most out of this dataset we will show you how to access the questions and evidence, as well as provide tips for getting started.

Step 1: Accessing the Dataset To access the data you can download it from Kaggle's website or through a code version control system like Github. Once downloaded, you will find five files in .csv format; two test data sets (test.csv and validation.csv), two train data sets (train-v2-0_lessons_only_.csv and trainv2-0_unsplit.csv) as well as one figure data set (figures_and_tables_.json). Each .csv file contains different datasets with columns representing titles, abstracts, full texts and Q&A fields with evidence for each paper mentioned in each row of each file respectively

**Step 2: Analyzing Your Data Sets ** Now would be a good time to explore your datasets using basic descriptive statistics or more advanced predictive analytics such as logistic regression or naive bayes models depending on what kind of analysis you would like to undertake with this dataset You can start simple by summarizing some basic crosstabs between any two variables comprise your dataset; titles abstracts etc.). As an example try correlating title lengths with certain number of words in their corresponding abstracts then check if there is anything worth investigating further

**Step 3: Define Your Research Questions & Perform Further Analysis ** Once satisfied with your initial exploration it is time to dig deeper into the underlying QR relationship among different variables comprising your main documents One way would be using text mining technologies such as topic modeling machine learning techniques or even automated processes that may help summarize any underlying patterns Yet another approach could involve filtering terms that are relevant per specific research hypothesis then process such terms via web crawlers search engines document similarity algorithms etc

Finally once all relevant parameters are defined analyzed performed searched it would make sense to draw preliminary connsusison linking them back together before conducting replicable tests ensuring reproducible results

Research Ideas Developing AI models to automatically generate questions and answers from paper titles and abstracts. Enhancing machine learning algorithms by combining the answers with the evidence provided in the dataset to find relationships between papers. Creating online forums for NLP practitioners that uses questions from this dataset to spark discussion within the community

License

CC0

Original Data Source: QASPER: NLP Questions and Evidence

In 2018, 28 of November, in Latvia the amendments to Section 32 (3) of the Labor Law entered into force, according with it employers are obliged to indicate in the advertisement wage. This database continue wages monitoring started in 2019 and show data observation for 2021. 2019 year was first year in Latvia, when based on job advertisement analysis it is possible to conclude about salary by occupations, salary grow. Advertisement analysis is operational pointer in comparison with official statistic data. This dataset represent job advertisement collection from biggest Latvian job advertisement web cv.lv . Data was collected by week in 2021 in Q1-Q2, near 1700 advertisements per week. After collecting dataset was cleared from advertisements, in which it was not possible to identify occupations. After data cleaning dataset consist of 41 138 advertisements. First salary monitoring year (2020) data is possible see here Skribans, Valerijs (2021), “Job advertisement and salary monitoring dataset for Latvia in 2020”, Mendeley Data, V1, doi: 10.17632/f3s8h6dzzf.1

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.

The QA4MRE dataset offers a magnificent collection of passages with connected questions and answers, providing researchers with a defining set of data to work from. With its wide range, this has been the go-to source for many research projects like the CLEF 2011, 2012 and 2013 Shared Tasks - where training datasets are available for the main track as well as documents ready to be used in two pilot studies related to Alzheimer's disease and entrance exams. This expansive dataset can allow you to unleash your creativity in ways you never thought possible - uncovering new possibilities and exciting findings as it serves as an abundant source of information. No matter which field you come from or what kind of insights you’re looking for, this powerhouse dataset will have something special waiting just around the corner

More Datasets For more datasets, click here.

Featured Notebooks 🚨 Your notebook can be here! 🚨! How to use the dataset How to Use the QA4MRE Dataset for Your Research The QA4MRE (Question Answering and Reading Comprehension) dataset is a great resource for researchers who want to use comprehensive datasets to explore creative approaches and solutions. This powerful dataset provides several versions of training and development data in the form of passages with accompanying questions and answers. Additionally, there are gold standard documents included that can be used in two different pilot studies related to Alzheimer’s disease as well as entrance exams. The following is a guide on how to make the most out of this valuable data set:

Analyze Data Structures - Once you've downloaded all necessary materials, it’s time for analyzing what structure each file follows in order access its contents accordingly; knowing which column helps refine your searching process as some files go beyond just providing questions & answers such as providing topic names associated with passage text relevant processing question asking comprehension testing etc.. The table below serves as basic overview each column provided in both train & dev variants found within this datasets:

Refine Data Searching Process - Lastly if plan develop an automated system/algorithm uncover precise contents from manipulated articles/passages then refining already established search process involving

Research Ideas Creating an automated question answering system that is capable of engaging in conversations with a user. This could be used as a teaching assistant to help students study for exams and other tests or as a virtual assistant for customer service. Developing a summarization tool dedicated specifically to the QA4MRE dataset, which can extract key information from each passage and output concise summaries with confidence scores indicating the likelihood of the summary being accurate compared to the original text. Utilizing natural language processing techniques to analyze questions related to Alzheimer’s disease and creating machine learning models that accurately predict patient responses when asked various sets of questions about their condition, thus aiding in diagnosing Alzheimer's Disease early on in its development stages

License

CC0

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

BreakData Welcome to BreakData, an innovative and cutting-edge dataset devoted to exploring language understanding. This dataset contains a wealth of information related to question decomposition, operators, splits, sources, and allowed tokens and can be used to answer questions with precision. With deep insights into how humans comprehend and interpret language, BreakData provides an immense value for researchers developing sophisticated models that can help advance AI technologies. Our goal is to enable the development of more complex natural language processing which can be used in various applications such as automated customer support, chatbots for health care advice or automated marketing campaigns. Dive into this intriguing dataset now and discover how your work could change the world!

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Featured Notebooks 🚨 Your notebook can be here! 🚨! How to use the dataset This dataset provides an exciting opportunity to explore and understand the complexities of language understanding. With this dataset, you can train models for natural language processing (NLP) activities such as question answering, text analytics, automated dialog systems, and more.

In order to make most effective use of the BreakData dataset, it’s important to know how it is organized and what types of data are included in each file. The BreakData dataset is broken down into nine different files:

QDMR_train.csv

QDMR_validation.csv

QDMR-highlevel_train.csv

QDMR-highlevel_test.csv

logicalforms_train.csv

logicalforms_validation.csv

QDMRlexicon_train.csv

QDMRLexicon_test csv

QDHMLexiconHighLevelTest csv

Each file contains a different set of data that can be used to train your models for natural language understanding tasks or analyze existing questions or commands with accurate decompositions and operators from these datasets into their component parts and understand their relationships with each other:

1) The QDMR files include questions or statements from common domains like health care or banking that need to be interpreted according to a series of operators (elements such as verbs). This task requires identifying keywords in the statement or question text that trigger certain responses indicating variable values and variables themselves so any model trained on these datasets will need to accurately identify entities like time references (dates/times), monetary amounts, Boolean values (yes/no), etc., as well as relationships between those entities–all while following a defined rule set specific domain languages specialize in interpreting such text accurately by modeling complex context dependent queries requiring linguistic analysis in multiple steps through rigorous training on this kind of data would optimize decisions made by machines based on human relevant interactions like conversations inducing more accurate next best actions resulting in better decision making respectively matching human scale solution accuracy rate given growing customer demands being served increasingly faster leveraging machine learning models powered by breakdata NLP layer accuracy enabled interpreters able do seamless inference while using this comprehensive training set providing deeper insights with improved results transforming customer engagement quality at unprecedented rate .

2) The LogicalForms files include logical forms containing the building blocks (elements such as operators) for linking ideas together together across different sets of incoming variables which

Research Ideas Developing advanced natural language processing models to analyze questions using decompositions, operators, and splits. Training a machine learning algorithm to predict the semantic meaning of questions based on their decomposition and split. Conducting advanced text analytics by using the allowed tokens dataset to map out how people communicate specific concepts in different contexts or topics

License

CC0

Original Data Source: Break (Question Decomposition Meaning)

Explore the growth potential of Market Research Intellect's Data-entry Outsourcing Services Market Report, valued at USD 3.1 billion in 2024, with a forecasted market size of USD 5.8 billion by 2033, growing at a CAGR of 8.2% from 2026 to 2033.

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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

The global market for open source tools is projected to experience substantial growth, with a CAGR of XX% during the forecast period (2025-2033). In 2025, the market size is estimated to be XXX million USD, indicating a significant increase from its base year value. Key drivers of this growth include the rising demand for cost-effective solutions, increased adoption of cloud computing, and the proliferation of big data analytics. The trend towards open source tools is fueled by their flexibility, transparency, and collaborative nature, which makes them particularly attractive to businesses and organizations with limited budgets and specialized requirements. The market for open source tools is segmented by type, application, and region. By type, the market is divided into universal tools, data cleaning tools, data visualization tools, data mining tools, and others. By application, the market is segmented into computer vision, natural language processing, machine learning, data science, e-commerce, medical health, financial industry, and others. Geographically, the market is divided into North America, South America, Europe, Middle East & Africa, and Asia Pacific. The Asia Pacific region is expected to experience the highest growth due to the increasing adoption of open source tools in emerging economies such as China and India. The market is characterized by the presence of both established players and emerging startups, with key companies including Acquia, Alfresco, Apache, Astaro, Canonical, CentOS, and ClearCenter.

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Enterprise Data Warehouse (EDW) Market Size 2025-2029

The enterprise data warehouse (EDW) market size is forecast to increase by USD 43.12 billion at a CAGR of 28% between 2024 and 2029.

The market is experiencing significant growth, driven by the data explosion across industries and a heightened focus on new solution launches. Companies are recognizing the value of centralized data management systems to gain insights and make informed business decisions. However, this market is not without challenges. Regulatory hurdles, such as data privacy laws and compliance requirements, impact adoption and necessitate substantial investments in data security. Furthermore, ensuring data accuracy and consistency across the supply chain can be a complex and time-consuming process, tempering growth potential. With the increasing volume, velocity, and variety of data, businesses are investing heavily in EDW solutions and data warehousing to gain insights and make informed decisions.
Despite these challenges, the market presents numerous opportunities for companies to capitalize on the increasing demand for robust and secure data management solutions. However, concerns related to data security continue to pose a challenge in the market. By addressing these challenges through innovative technologies and strategic partnerships, organizations can effectively navigate the complexities of managing and leveraging their data for competitive advantage.

What will be the Size of the Enterprise Data Warehouse (EDW) Market during the forecast period?

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The market is experiencing significant evolution, driven by the increasing demand for real-time data processing and serverless computing. Metadata management is a crucial aspect of EDWs, ensuring data consistency and improving data discovery. Data tokenization and data masking enhance data security, while data lakehouses and data fabric enable seamless data integration. Business Intelligence platforms are transforming through data modernization, embracing streaming data warehousing and data virtualization. Data governance frameworks, data engineering, and data governance tools are essential for maintaining data quality and ensuring compliance with data privacy regulations. Data science and data-driven culture are fueling the adoption of advanced analytics platforms, which require data anonymization and data catalog tools for effective data usage. Data engineering plays a crucial role in the EDW, responsible for data ingestion, cleaning, and digital transformation.
Data migration and data residency concerns continue to shape the market, with data sovereignty and data security tools playing a pivotal role. Data federation, data masking, and data virtualization facilitate efficient data access, while data engineering and data governance frameworks streamline data management processes. Data quality tools and data literacy initiatives are essential for deriving valuable insights from complex data sets. The EDW landscape is dynamic, with trends such as data mesh and data analytics platforms shaping the future of data management and analytics. Data security and data privacy regulations remain top priorities, as organizations strive to protect sensitive information while maximizing the value of their data assets.

How is this Enterprise Data Warehouse (EDW) Industry segmented?

The enterprise data warehouse (EDW) 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.

Product Type

  Information and analytical processing
  Data mining


Deployment

  Cloud based
  On-premises


Sector

  Large enterprises
  SMEs


End-user

  BFSI
  Healthcare and pharmaceuticals
  Retail and E-commerce
  Telecom and IT
  Others


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    India
    Japan
    South Korea


  Rest of World (ROW)

By Product Type Insights

The information and analytical processing segment is estimated to witness significant growth during the forecast period. The data warehouse market is experiencing significant growth due to the increasing need for data processing and analysis in various sectors such as IT, BFSI, education, healthcare, and retail. Data warehouses facilitate the storage and processing of large volumes of data for analytical purposes. Data modeling, data quality, and data transformation tools ensure the accuracy and consistency of the data. Cloud data warehousing and hybrid data warehousing solutions offer flexibility and cost savings. Data security, encryption, and access control are crucial aspects of data warehousing, ensuring data privacy and compliance. Machine learning and artificial intelligence are being