Set of data (cualitative and cuantitative parameters) and screenshots of Web pages from all countries in the world

This dataset contains statistics related to the Unleashed website (http://uladl.com). Unleashed is an open data competition, an initiative of the Office for Digital Government, Department of the Premier and Cabinet. The data is used to monitor the level of engagement activity with the audience, and make the communication effective in regards to the event.

TagX Web Browsing Clickstream Data: Unveiling Digital Behavior Across North America and EU Unique Insights into Online User Behavior TagX Web Browsing clickstream Data offers an unparalleled window into the digital lives of 1 million users across North America and the European Union. This comprehensive dataset stands out in the market due to its breadth, depth, and stringent compliance with data protection regulations. What Makes Our Data Unique?

Extensive Geographic Coverage: Spanning two major markets, our data provides a holistic view of web browsing patterns in developed economies. Large User Base: With 300K active users, our dataset offers statistically significant insights across various demographics and user segments. GDPR and CCPA Compliance: We prioritize user privacy and data protection, ensuring that our data collection and processing methods adhere to the strictest regulatory standards. Real-time Updates: Our clickstream data is continuously refreshed, providing up-to-the-minute insights into evolving online trends and user behaviors. Granular Data Points: We capture a wide array of metrics, including time spent on websites, click patterns, search queries, and user journey flows.

Data Sourcing: Ethical and Transparent Our web browsing clickstream data is sourced through a network of partnered websites and applications. Users explicitly opt-in to data collection, ensuring transparency and consent. We employ advanced anonymization techniques to protect individual privacy while maintaining the integrity and value of the aggregated data. Key aspects of our data sourcing process include:

Voluntary user participation through clear opt-in mechanisms Regular audits of data collection methods to ensure ongoing compliance Collaboration with privacy experts to implement best practices in data anonymization Continuous monitoring of regulatory landscapes to adapt our processes as needed

Primary Use Cases and Verticals TagX Web Browsing clickstream Data serves a multitude of industries and use cases, including but not limited to:

Digital Marketing and Advertising:

Audience segmentation and targeting Campaign performance optimization Competitor analysis and benchmarking

E-commerce and Retail:

Customer journey mapping Product recommendation enhancements Cart abandonment analysis

Media and Entertainment:

Content consumption trends Audience engagement metrics Cross-platform user behavior analysis

Financial Services:

Risk assessment based on online behavior Fraud detection through anomaly identification Investment trend analysis

Technology and Software:

User experience optimization Feature adoption tracking Competitive intelligence

Market Research and Consulting:

Consumer behavior studies Industry trend analysis Digital transformation strategies

Integration with Broader Data Offering TagX Web Browsing clickstream Data is a cornerstone of our comprehensive digital intelligence suite. It seamlessly integrates with our other data products to provide a 360-degree view of online user behavior:

Social Media Engagement Data: Combine clickstream insights with social media interactions for a holistic understanding of digital footprints. Mobile App Usage Data: Cross-reference web browsing patterns with mobile app usage to map the complete digital journey. Purchase Intent Signals: Enrich clickstream data with purchase intent indicators to power predictive analytics and targeted marketing efforts. Demographic Overlays: Enhance web browsing data with demographic information for more precise audience segmentation and targeting.

By leveraging these complementary datasets, businesses can unlock deeper insights and drive more impactful strategies across their digital initiatives. Data Quality and Scale We pride ourselves on delivering high-quality, reliable data at scale:

Rigorous Data Cleaning: Advanced algorithms filter out bot traffic, VPNs, and other non-human interactions. Regular Quality Checks: Our data science team conducts ongoing audits to ensure data accuracy and consistency. Scalable Infrastructure: Our robust data processing pipeline can handle billions of daily events, ensuring comprehensive coverage. Historical Data Availability: Access up to 24 months of historical data for trend analysis and longitudinal studies. Customizable Data Feeds: Tailor the data delivery to your specific needs, from raw clickstream events to aggregated insights.

Empowering Data-Driven Decision Making In today's digital-first world, understanding online user behavior is crucial for businesses across all sectors. TagX Web Browsing clickstream Data empowers organizations to make informed decisions, optimize their digital strategies, and stay ahead of the competition. Whether you're a marketer looking to refine your targeting, a product manager seeking to enhance user experience, or a researcher exploring digital trends, our cli...

The dataset provides 12 months (August 2016 to August 2017) of obfuscated Google Analytics 360 data from the Google Merchandise Store , a real ecommerce store that sells Google-branded merchandise, in BigQuery. It’s a great way analyze business data and learn the benefits of using BigQuery to analyze Analytics 360 data Learn more about the data The data includes The data is typical of what an ecommerce website would see and includes the following information:Traffic source data: information about where website visitors originate, including data about organic traffic, paid search traffic, and display trafficContent data: information about the behavior of users on the site, such as URLs of pages that visitors look at, how they interact with content, etc. Transactional data: information about the transactions on the Google Merchandise Store website.Limitations: All users have view access to the dataset. This means you can query the dataset and generate reports but you cannot complete administrative tasks. Data for some fields is obfuscated such as fullVisitorId, or removed such as clientId, adWordsClickInfo and geoNetwork. “Not available in demo dataset” will be returned for STRING values and “null” will be returned for INTEGER values when querying the fields containing no data.This public dataset is hosted in Google BigQuery and is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery

Job Offers Web Scraping Search

Targeted Results to Find the Optimal Work Solution

By [source]

About this dataset

This dataset collects job offers from web scraping which are filtered according to specific keywords, locations and times. This data gives users rich and precise search capabilities to uncover the best working solution for them. With the information collected, users can explore options that match with their personal situation, skillset and preferences in terms of location and schedule. The columns provide detailed information around job titles, employer names, locations, time frames as well as other necessary parameters so you can make a smart choice for your next career opportunity

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For more datasets, click here.

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How to use the dataset

This dataset is a great resource for those looking to find an optimal work solution based on keywords, location and time parameters. With this information, users can quickly and easily search through job offers that best fit their needs. Here are some tips on how to use this dataset to its fullest potential:

• Start by identifying what type of job offer you want to find. The keyword column will help you narrow down your search by allowing you to search for job postings that contain the word or phrase you are looking for.

• Next, consider where the job is located – the Location column tells you where in the world each posting is from so make sure it’s somewhere that suits your needs!

• Finally, consider when the position is available – look at the Time frame column which gives an indication of when each posting was made as well as if it’s a full-time/ part-time role or even if it’s a casual/temporary position from day one so make sure it meets your requirements first before applying!

• Additionally, if details such as hours per week or further schedule information are important criteria then there is also info provided under Horari and Temps Oferta columns too! Now that all three criteria have been ticked off - key words, location and time frame - then take a look at Empresa (Company Name) and Nom_Oferta (Post Name) columns too in order to get an idea of who will be employing you should you land the gig!

  All these pieces of data put together should give any motivated individual all they need in order to seek out an optimal work solution - keep hunting good luck!

Research Ideas

• Machine learning can be used to groups job offers in order to facilitate the identification of similarities and differences between them. This could allow users to specifically target their search for a work solution.
• The data can be used to compare job offerings across different areas or types of jobs, enabling users to make better informed decisions in terms of their career options and goals.
• It may also provide an insight into the local job market, enabling companies and employers to identify where there is potential for new opportunities or possible trends that simply may have previously gone unnoticed

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: web_scraping_information_offers.csv | Column name | Description | |:-----------------|:------------------------------------| | Nom_Oferta | Name of the job offer. (String) | | Empresa | Company offering the job. (String) | | Ubicació | Location of the job offer. (String) | | Temps_Oferta | Time of the job offer. (String) | | Horari | Schedule of the job offer. (String) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit .

Author: Víctor Yeste. Universitat Politècnica de Valencia.The object of this study is the design of a cybermetric methodology whose objectives are to measure the success of the content published in online media and the possible prediction of the selected success variables.In this case, due to the need to integrate data from two separate areas, such as web publishing and the analysis of their shares and related topics on Twitter, has opted for programming as you access both the Google Analytics v4 reporting API and Twitter Standard API, always respecting the limits of these.The website analyzed is hellofriki.com. It is an online media whose primary intention is to solve the need for information on some topics that provide daily a vast number of news in the form of news, as well as the possibility of analysis, reports, interviews, and many other information formats. All these contents are under the scope of the sections of cinema, series, video games, literature, and comics.This dataset has contributed to the elaboration of the PhD Thesis:Yeste Moreno, VM. (2021). Diseño de una metodología cibermétrica de cálculo del éxito para la optimización de contenidos web [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/176009Data have been obtained from each last-minute news article published online according to the indicators described in the doctoral thesis. All related data are stored in a database, divided into the following tables:tesis_followers: User ID list of media account followers.tesis_hometimeline: data from tweets posted by the media account sharing breaking news from the web.status_id: Tweet IDcreated_at: date of publicationtext: content of the tweetpath: URL extracted after processing the shortened URL in textpost_shared: Article ID in WordPress that is being sharedretweet_count: number of retweetsfavorite_count: number of favoritestesis_hometimeline_other: data from tweets posted by the media account that do not share breaking news from the web. Other typologies, automatic Facebook shares, custom tweets without link to an article, etc. With the same fields as tesis_hometimeline.tesis_posts: data of articles published by the web and processed for some analysis.stats_id: Analysis IDpost_id: Article ID in WordPresspost_date: article publication date in WordPresspost_title: title of the articlepath: URL of the article in the middle webtags: Tags ID or WordPress tags related to the articleuniquepageviews: unique page viewsentrancerate: input ratioavgtimeonpage: average visit timeexitrate: output ratiopageviewspersession: page views per sessionadsense_adunitsviewed: number of ads viewed by usersadsense_viewableimpressionpercent: ad display ratioadsense_ctr: ad click ratioadsense_ecpm: estimated ad revenue per 1000 page viewstesis_stats: data from a particular analysis, performed at each published breaking news item. Fields with statistical values can be computed from the data in the other tables, but total and average calculations are saved for faster and easier further processing.id: ID of the analysisphase: phase of the thesis in which analysis has been carried out (right now all are 1)time: "0" if at the time of publication, "1" if 14 days laterstart_date: date and time of measurement on the day of publicationend_date: date and time when the measurement is made 14 days latermain_post_id: ID of the published article to be analysedmain_post_theme: Main section of the published article to analyzesuperheroes_theme: "1" if about superheroes, "0" if nottrailer_theme: "1" if trailer, "0" if notname: empty field, possibility to add a custom name manuallynotes: empty field, possibility to add personalized notes manually, as if some tag has been removed manually for being considered too generic, despite the fact that the editor put itnum_articles: number of articles analysednum_articles_with_traffic: number of articles analysed with traffic (which will be taken into account for traffic analysis)num_articles_with_tw_data: number of articles with data from when they were shared on the media’s Twitter accountnum_terms: number of terms analyzeduniquepageviews_total: total page viewsuniquepageviews_mean: average page viewsentrancerate_mean: average input ratioavgtimeonpage_mean: average duration of visitsexitrate_mean: average output ratiopageviewspersession_mean: average page views per sessiontotal: total of ads viewedadsense_adunitsviewed_mean: average of ads viewedadsense_viewableimpressionpercent_mean: average ad display ratioadsense_ctr_mean: average ad click ratioadsense_ecpm_mean: estimated ad revenue per 1000 page viewsTotal: total incomeretweet_count_mean: average incomefavorite_count_total: total of favoritesfavorite_count_mean: average of favoritesterms_ini_num_tweets: total tweets on the terms on the day of publicationterms_ini_retweet_count_total: total retweets on the terms on the day of publicationterms_ini_retweet_count_mean: average retweets on the terms on the day of publicationterms_ini_favorite_count_total: total of favorites on the terms on the day of publicationterms_ini_favorite_count_mean: average of favorites on the terms on the day of publicationterms_ini_followers_talking_rate: ratio of followers of the media Twitter account who have recently published a tweet talking about the terms on the day of publicationterms_ini_user_num_followers_mean: average followers of users who have spoken of the terms on the day of publicationterms_ini_user_num_tweets_mean: average number of tweets published by users who spoke about the terms on the day of publicationterms_ini_user_age_mean: average age in days of users who have spoken of the terms on the day of publicationterms_ini_ur_inclusion_rate: URL inclusion ratio of tweets talking about terms on the day of publicationterms_end_num_tweets: total tweets on terms 14 days after publicationterms_ini_retweet_count_total: total retweets on terms 14 days after publicationterms_ini_retweet_count_mean: average retweets on terms 14 days after publicationterms_ini_favorite_count_total: total bookmarks on terms 14 days after publicationterms_ini_favorite_count_mean: average of favorites on terms 14 days after publicationterms_ini_followers_talking_rate: ratio of media Twitter account followers who have recently posted a tweet talking about the terms 14 days after publicationterms_ini_user_num_followers_mean: average followers of users who have spoken of the terms 14 days after publicationterms_ini_user_num_tweets_mean: average number of tweets published by users who have spoken about the terms 14 days after publicationterms_ini_user_age_mean: the average age in days of users who have spoken of the terms 14 days after publicationterms_ini_ur_inclusion_rate: URL inclusion ratio of tweets talking about terms 14 days after publication.tesis_terms: data of the terms (tags) related to the processed articles.stats_id: Analysis IDtime: "0" if at the time of publication, "1" if 14 days laterterm_id: Term ID (tag) in WordPressname: Name of the termslug: URL of the termnum_tweets: number of tweetsretweet_count_total: total retweetsretweet_count_mean: average retweetsfavorite_count_total: total of favoritesfavorite_count_mean: average of favoritesfollowers_talking_rate: ratio of followers of the media Twitter account who have recently published a tweet talking about the termuser_num_followers_mean: average followers of users who were talking about the termuser_num_tweets_mean: average number of tweets published by users who were talking about the termuser_age_mean: average age in days of users who were talking about the termurl_inclusion_rate: URL inclusion ratio

Point of Interest (POI) is defined as an entity (such as a business) at a ground location (point) which may be (of interest). We provide high-quality POI data that is fresh, consistent, customizable, easy to use and with high-density coverage for all countries of the world.

This is our process flow:

Our machine learning systems continuously crawl for new POI data
Our geoparsing and geocoding calculates their geo locations
Our categorization systems cleanup and standardize the datasets
Our data pipeline API publishes the datasets on our data store

A new POI comes into existence. It could be a bar, a stadium, a museum, a restaurant, a cinema, or store, etc.. In today's interconnected world its information will appear very quickly in social media, pictures, websites, press releases. Soon after that, our systems will pick it up.

POI Data is in constant flux. Every minute worldwide over 200 businesses will move, over 600 new businesses will open their doors and over 400 businesses will cease to exist. And over 94% of all businesses have a public online presence of some kind tracking such changes. When a business changes, their website and social media presence will change too. We'll then extract and merge the new information, thus creating the most accurate and up-to-date business information dataset across the globe.

We offer our customers perpetual data licenses for any dataset representing this ever changing information, downloaded at any given point in time. This makes our company's licensing model unique in the current Data as a Service - DaaS Industry. Our customers don't have to delete our data after the expiration of a certain "Term", regardless of whether the data was purchased as a one time snapshot, or via our data update pipeline.

Customers requiring regularly updated datasets may subscribe to our Annual subscription plans. Our data is continuously being refreshed, therefore subscription plans are recommended for those who need the most up to date data. The main differentiators between us vs the competition are our flexible licensing terms and our data freshness.

Data samples may be downloaded at https://store.poidata.xyz/us

About the NUDA Dataset
Media bias is a multifaceted problem, leading to one-sided views and impacting decision-making. A way to address bias in news articles is to automatically detect and indicate it through machine-learning methods. However, such detection is limited due to the difficulty of obtaining reliable training data. To facilitate the data-gathering process, we introduce NewsUnravel, a news-reading web application leveraging an initially tested feedback mechanism to collect reader feedback on machine-generated bias highlights within news articles. Our approach augments dataset quality by significantly increasing inter-annotator agreement by 26.31% and improving classifier performance by 2.49%. As the first human-in-the-loop application for media bias, NewsUnravel shows that a user-centric approach to media bias data collection can return reliable data while being scalable and evaluated as easy to use. NewsUnravel demonstrates that feedback mechanisms are a promising strategy to reduce data collection expenses, fluidly adapt to changes in language, and enhance evaluators' diversity.

General

This dataset was created through user feedback on automatically generated bias highlights on news articles on the website NewsUnravel made by ANON. Its goal is to improve the detection of linguistic media bias for analysis and to indicate it to the public. Support came from ANON. None of the funders played any role in the dataset creation process or publication-related decisions.

The dataset consists of text, namely biased sentences with binary bias labels (processed, biased or not biased) as well as metadata about the article. It includes all feedback that was given. The single ratings (unprocessed) used to create the labels with correlating User IDs are included.

For training, this dataset was combined with the BABE dataset. All data is completely anonymous. Some sentences might be offensive or triggering as they were taken from biased or more extreme news sources. The dataset does not identify sub-populations or can be considered sensitive to them, nor is it possible to identify individuals.

Description of the Data Files

This repository contains the datasets for the anonymous NewsUnravel submission. The tables contain the following data:

NUDAdataset.csv: the NUDA dataset with 310 new sentences with bias labels
Statistics.png: contains all Umami statistics for NewsUnravel's usage data
Feedback.csv: holds the participantID of a single feedback with the sentence ID (contentId), the bias rating, and provided reasons
Content.csv: holds the participant ID of a rating with the sentence ID (contentId) of a rated sentence and the bias rating, and reason, if given
Article.csv: holds the article ID, title, source, article metadata, article topic, and bias amount in %
Participant.csv: holds the participant IDs and data processing consent

Collection Process

Data was collected through interactions with the Feedback Mechanism on NewsUnravel. A news article was displayed with automatically generated bias highlights. Each highlight could be selected, and readers were able to agree or disagree with the automatic label. Through a majority vote, labels were generated from those feedback interactions. Spammers were excluded through a spam detection approach.

Readers came to our website voluntarily through posts on LinkedIn and social media as well as posts on university boards. The data collection period lasted for one week, from March 4th to March 11th (2023). The landing page informed them about the goal and the data processing. After being informed, they could proceed to the article overview.

So far, the dataset has been used on top of BABE to train a linguistic bias classifier, adopting hyperparameter configurations from BABE with a pre-trained model from Hugging Face.
The dataset will be open source. On acceptance, a link with all details and contact information will be provided. No third parties are involved.

The dataset will not be maintained as it captures the first test of NewsUnravel at a specific point in time. However, new datasets will arise from further iterations. Those will be linked in the repository. Please cite the NewsUnravel paper if you use the dataset and contact us if you're interested in more information or joining the project.

The dataset collection in question is a compilation of related data tables sourced from the website of Tilastokeskus (Statistics Finland) in Finland. The data present in the collection is organized in a tabular format comprising of rows and columns, each holding related data. The collection includes several tables, each of which represents different years, providing a temporal view of the data. The description provided by the data source, Tilastokeskuksen palvelurajapinta (Statistics Finland's service interface), suggests that the data is likely to be statistical in nature and could be related to regional statistics, given the nature of the source. This dataset is licensed under CC BY 4.0 (Creative Commons Attribution 4.0, https://creativecommons.org/licenses/by/4.0/deed.fi).

The FineWeb dataset consists of more than 15T tokens of cleaned and deduplicated English web data from CommonCrawl. The data processing pipeline is optimized for LLM performance and runs on the datatrove library, our large-scale data processing library.

FineWeb was originally meant to be a fully open replication of RefinedWeb, with a release of the full dataset under the ODC-By 1.0 license. However, by carefully adding additional filtering steps, we managed to push the performance of FineWeb well above that of the original RefinedWeb, and models trained on our dataset also outperform models trained on other commonly used high-quality web datasets (like C4, Dolma-v1.6, The Pile, SlimPajama, RedPajam2) on our aggregate group of benchmark tasks.

Associative Tag Recommendation Exploiting Multiple Textual FeaturesFabiano Belem, Eder Martins, Jussara M. Almeida Marcos Goncalves In Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval, July. 2011AbstractThis work addresses the task of recommending relevant tags to a target object by jointly exploiting three dimen- sions of the problem: (i) term co-occurrence with tags preassigned to the target object, (ii) terms extracted from mul- tiple textual features, and (iii) several metrics of tag relevance. In particular, we propose several new heuristic meth- ods, which extend previous, highly effective and efficient, state-of-the-art strategies by including new metrics that try to capture how accurately a candidate term describes the object’s content. We also exploit two learning to rank techniques, namely RankSVM and Genetic Programming, for the task of generating ranking functions that combine multiple metrics to accurately estimate the relevance of a tag to a given object. We evaluate all proposed methods in various scenarios for three popular Web 2.0 applications, namely, LastFM, YouTube and YahooVideo. We found that our new heuristics greatly outperform the methods on which they are based, producing gains in precision of up to 181%, as well as another state-of-the-art technique, with improvements in precision of up to 40% over the best baseline in any scenario. Some further improvements can also be achieved, in some scenarios, with the new learning-to-rank based strategies, which have the additional advantage of being quite flexible and easily extensible to exploit other aspects of the tag recommendation problem.Bibtex Citation@inproceedings{belem@sigir11, author = {Fabiano Bel\'em and Eder Martins and Jussara Almeida and Marcos Gon\c{c}alves}, title = {Associative Tag Recommendation Exploiting Multiple Textual Features}, booktitle = {{Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval (SIGIR'11)}}, month = {{July}}, year = {2011} }

The dataset has been collected in the frame of the Prac1 of the subject Tipology and Data Life Cycle of the Master's Degree in Data Science of the Universitat Oberta de Catalunya (UOC).

The dataset contains 25 variables and 52478 records corresponding to books on the GoodReads Best Books Ever list (the larges list on the site).

Original code used to retrieve the dataset can be found on github repository: github.com/scostap/goodreads_bbe_dataset

The data was retrieved in two sets, the first 30000 books and then the remainig 22478. Dates were not parsed and reformated on the second chunk so publishDate and firstPublishDate are representet in a mm/dd/yyyy format for the first 30000 records and Month Day Year for the rest.

Book cover images can be optionally downloaded from the url in the 'coverImg' field. Python code for doing so and an example can be found on the github repo.

The 25 fields of the dataset are:

| Attributes | Definition | Completeness |
| ------------- | ------------- | ------------- | 
| bookId | Book Identifier as in goodreads.com | 100 |
| title | Book title | 100 |
| series | Series Name | 45 |
| author | Book's Author | 100 |
| rating | Global goodreads rating | 100 |
| description | Book's description | 97 |
| language | Book's language | 93 |
| isbn | Book's ISBN | 92 |
| genres | Book's genres | 91 |
| characters | Main characters | 26 |
| bookFormat | Type of binding | 97 |
| edition | Type of edition (ex. Anniversary Edition) | 9 |
| pages | Number of pages | 96 |
| publisher | Editorial | 93 |
| publishDate | publication date | 98 |
| firstPublishDate | Publication date of first edition | 59 |
| awards | List of awards | 20 |
| numRatings | Number of total ratings | 100 |
| ratingsByStars | Number of ratings by stars | 97 |
| likedPercent | Derived field, percent of ratings over 2 starts (as in GoodReads) | 99 |
| setting | Story setting | 22 |
| coverImg | URL to cover image | 99 |
| bbeScore | Score in Best Books Ever list | 100 |
| bbeVotes | Number of votes in Best Books Ever list | 100 |
| price | Book's price (extracted from Iberlibro) | 73 |

Unlock the full potential of LinkedIn data with our extensive dataset that combines profiles, company information, and job listings into one powerful resource for business decision-making, strategic hiring, competitive analysis, and market trend insights. This all-encompassing dataset is ideal for professionals, recruiters, analysts, and marketers aiming to enhance their strategies and operations across various business functions. Dataset Features

Profiles: Dive into detailed public profiles featuring names, titles, positions, experience, education, skills, and more. Utilize this data for talent sourcing, lead generation, and investment signaling, with a refresh rate ensuring up to 30 million records per month. Companies: Access comprehensive company data including ID, country, industry, size, number of followers, website details, subsidiaries, and posts. Tailored subsets by industry or region provide invaluable insights for CRM enrichment, competitive intelligence, and understanding the startup ecosystem, updated monthly with up to 40 million records. Job Listings: Explore current job opportunities detailed with job titles, company names, locations, and employment specifics such as seniority levels and employment functions. This dataset includes direct application links and real-time application numbers, serving as a crucial tool for job seekers and analysts looking to understand industry trends and the job market dynamics.

Customizable Subsets for Specific Needs Our LinkedIn dataset offers the flexibility to tailor the dataset according to your specific business requirements. Whether you need comprehensive insights across all data points or are focused on specific segments like job listings, company profiles, or individual professional details, we can customize the dataset to match your needs. This modular approach ensures that you get only the data that is most relevant to your objectives, maximizing efficiency and relevance in your strategic applications. Popular Use Cases

Strategic Hiring and Recruiting: Track talent movement, identify growth opportunities, and enhance your recruiting efforts with targeted data. Market Analysis and Competitive Intelligence: Gain a competitive edge by analyzing company growth, industry trends, and strategic opportunities. Lead Generation and CRM Enrichment: Enrich your database with up-to-date company and professional data for targeted marketing and sales strategies. Job Market Insights and Trends: Leverage detailed job listings for a nuanced understanding of employment trends and opportunities, facilitating effective job matching and market analysis. AI-Driven Predictive Analytics: Utilize AI algorithms to analyze large datasets for predicting industry shifts, optimizing business operations, and enhancing decision-making processes based on actionable data insights.

Whether you are mapping out competitive landscapes, sourcing new talent, or analyzing job market trends, our LinkedIn dataset provides the tools you need to succeed. Customize your access to fit specific needs, ensuring that you have the most relevant and timely data at your fingertips.

The total amount of data created, captured, copied, and consumed globally is forecast to increase rapidly, reaching *** zettabytes in 2024. Over the next five years up to 2028, global data creation is projected to grow to more than *** zettabytes. In 2020, the amount of data created and replicated reached a new high. The growth was higher than previously expected, caused by the increased demand due to the COVID-19 pandemic, as more people worked and learned from home and used home entertainment options more often. Storage capacity also growing Only a small percentage of this newly created data is kept though, as just * percent of the data produced and consumed in 2020 was saved and retained into 2021. In line with the strong growth of the data volume, the installed base of storage capacity is forecast to increase, growing at a compound annual growth rate of **** percent over the forecast period from 2020 to 2025. In 2020, the installed base of storage capacity reached *** zettabytes.

The Billion Triple Challenge (BTC) 2019 Dataset is the result of a large-scale RDF crawl (accepting RDF/XML, Turtle and N-Triples) conducted from 2018/12/12 until 2019/01/11 using LDspider. The data are stored as quads where the fourth element encodes the location of the Web document from which the associated triple was parsed. The dataset contains 2,155,856,033 quads, collected from 2,641,253 RDF documents on 394 pay-level domains. Merging the data into one RDF graph results in 256,059,356 unique triples. These data (as quads or triples) contain 38,156 unique predicates and instances of 120,037 unique classes.

If you would like to use this dataset as part of a research work, we would ask you to please consider citing our paper:

José-Miguel Herrera, Aidan Hogan and Tobias Käfer. "BTC-2019: The 2019 Billion Triple Challenge Dataset ". In the Proceedings of the 18th International Semantic Web Conference (ISWC), Auckland, New Zealand, October 26–30, 2019 (Resources track).

The dataset is published in three main parts:

Quads: (*nq.gz): contains the quads retrieved during the crawl (N-Quads, GZipped). These data are divided into individual files for each of the top 100 pay-level-domains by number of quads contributed (btc2019-[domain]_000XX.nq.gz). While most domains have one file, larger domains are further split into parts (000XX) with approximately 150,000,000 quads each. Finally, quads from the 294 domains not in the top 100 are merged into one file: btc2019-other_00001.nq.gz.

Triples: (btc2019-triples.nt.gz): contains the unique triples resulting from taking all quads, dropping the fourth element (indicating the location of the source document) and computing the unique triples.

VoID (void.nt): contains a VoID file offering statistics about the dataset.

For parsing the files, we recommend a streaming parser, such as Raptor, RDF4j/Rio, or NxParser.

The data are sourced from 2,641,253 RDF documents. The top-10 pay-level-domains in terms of documents contributed are:

dbpedia.org 162,117 documents (6.14%)

loc.gov 150,091 documents (5.68%)

bnf.fr 146,186 documents (5.53%)

sudoc.fr 144,877 documents (5.49%)

theses.fr 141,228 documents (5.35%)

wikidata.org 141,207 documents (5.35%)

linkeddata.es 130,459 documents (4.94%)

getty.edu 130,398 documents (4.94%)

fao.org 92,838 documents (3.51%)

ontobee.org 92,812 documents (3.51%)

The data contain 2,155,856,033 quads. The top-10 pay-level-domains in terms of quads contributed are:

wikidata.org 2,006,338,975 quads (93.06%)

dbpedia.org 36,686,161 quads (1.70%)

idref.fr 22,013,225 quads (1.02%)

bnf.fr 12,618,155 quads (0.59%)

getty.edu 7,453,134 quads (0.35%)

sudoc.fr 7,176,301 quads (0.33%)

loc.gov 6,725,390 quads (0.31%)

linkeddata.es 6,485,114 quads (0.30%)

theses.fr 4,820,874 quads (0.22%)

ontologycentral.com 4,633,947 quads (0.21%)

The data contain 256,059,356 unique triples. The top-10 pay-level-domains in terms of unique triples contributed are:

wikidata.org 133,535,555 triples (52.15%)

dbpedia.org 32,981,420 triples (12.88%)

idref.fr 16,820,681 triples (6.57%)

bnf.fr 11,769,268 triples (4.60%)

getty.edu 6,571,525 triples (2.57%)

linkeddata.es 5,898,762 triples (2.30%)

loc.gov 5,362,064 triples (2.09%)

sudoc.fr 4,972,647 triples (1.94%)

ontologycentral.com 4,471,962 triples (1.75%)

theses.fr 4,095,897 triples (1.60%)

If you wish to download all N-Quads files, the following may be useful to copy and paste in Unix:

wget https://zenodo.org/record/2634588/files/btc2019-acropolis.org.uk_00001.nq.gz wget https://zenodo.org/record/2634588/files/btc2019-aksw.org_00001.nq.gz wget https://zenodo.org/record/2634588/files/btc2019-babelnet.org_00001.nq.gz wget https://zenodo.org/record/2634588/files/btc2019-bbc.co.uk_00001.nq.gz wget https://zenodo.org/record/2634588/files/btc2019-berkeleybop.org_00001.nq.gz wget https://zenodo.org/record/2634588/files/btc2019-bibliotheken.nl_00001.nq.gz wget https://zenodo.org/record/2634588/files/btc2019-bl.uk_00001.nq.gz wget https://zenodo.org/record/2634588/files/btc2019-bne.es_00001.nq.gz wget https://zenodo.org/record/2634588/files/btc2019-bnf.fr_00001.nq.gz wget https://zenodo.org/record/2634588/files/btc2019-camera.it_00001.nq.gz wget https://zenodo.org/record/2634588/files/btc2019-cervantesvirtual.com_00001.nq.gz wget https://zenodo.org/record/2634588/files/btc2019-chemspider.com_00001.nq.gz wget https://zenodo.org/record/2634588/files/btc2019-cnr.it_00001.nq.gz wget https://zenodo.org/record/2634588/files/btc2019-comicmeta.org_00001.nq.gz wget https://zenodo.org/record/2634588/files/btc2019-crossref.org_00001.nq.gz wget https://zenodo.org/record/2634588/files/btc2019-cvut.cz_00001.nq.gz wget https://zenodo.org/record/2634588/files/btc2019-d-nb.info_00001.nq.gz wget https://zenodo.org/record/2634588/files/btc2019-datacite.org_00001.nq.gz wget https://zenodo.org/record/2634588/files/btc2019-dbpedia.org_00001.nq.gz wget https://zenodo.org/record/2634588/files/btc2019-dbtune.org_00001.nq.gz wget https://zenodo.org/record/2634588/files/btc2019-drugbank.ca_00001.nq.gz wget https://zenodo.org/record/2634588/files/btc2019-ebi.ac.uk_00001.nq.gz wget https://zenodo.org/record/2634588/files/btc2019-ebu.ch_00001.nq.gz wget https://zenodo.org/record/2634588/files/btc2019-ebusiness-unibw.org_00001.nq.gz wget https://zenodo.org/record/2634588/files/btc2019-edamontology.org_00001.nq.gz wget https://zenodo.org/record/2634588/files/btc2019-europa.eu_00001.nq.gz wget https://zenodo.org/record/2634588/files/btc2019-fao.org_00001.nq.gz wget https://zenodo.org/record/2634588/files/btc2019-gbv.de_00001.nq.gz wget https://zenodo.org/record/2634588/files/btc2019-geonames.org_00001.nq.gz wget https://zenodo.org/record/2634588/files/btc2019-geospecies.org_00001.nq.gz wget https://zenodo.org/record/2634588/files/btc2019-geovocab.org_00001.nq.gz wget https://zenodo.org/record/2634588/files/btc2019-gesis.org_00001.nq.gz wget https://zenodo.org/record/2634588/files/btc2019-getty.edu_00001.nq.gz wget https://zenodo.org/record/2634588/files/btc2019-github.io_00001.nq.gz wget https://zenodo.org/record/2634588/files/btc2019-githubusercontent.com_00001.nq.gz wget https://zenodo.org/record/2634588/files/btc2019-glottolog.org_00001.nq.gz wget https://zenodo.org/record/2634588/files/btc2019-iconclass.org_00001.nq.gz wget https://zenodo.org/record/2634588/files/btc2019-idref.fr_00001.nq.gz wget https://zenodo.org/record/2634588/files/btc2019-iflastandards.info_00001.nq.gz wget https://zenodo.org/record/2634588/files/btc2019-ign.fr_00001.nq.gz wget https://zenodo.org/record/2634588/files/btc2019-iptc.org_00001.nq.gz wget https://zenodo.org/record/2634588/files/btc2019-kanzaki.com_00001.nq.gz wget https://zenodo.org/record/2634588/files/btc2019-kasei.us_00001.nq.gz wget https://zenodo.org/record/2634588/files/btc2019-kit.edu_00001.nq.gz wget https://zenodo.org/record/2634588/files/btc2019-kjernsmo.net_00001.nq.gz wget https://zenodo.org/record/2634588/files/btc2019-korrekt.org_00001.nq.gz wget https://zenodo.org/record/2634588/files/btc2019-kulturarvsdata.se_00001.nq.gz wget https://zenodo.org/record/2634588/files/btc2019-kulturnav.org_00001.nq.gz wget https://zenodo.org/record/2634588/files/btc2019-l3s.de_00001.nq.gz wget https://zenodo.org/record/2634588/files/btc2019-lehigh.edu_00001.nq.gz wget https://zenodo.org/record/2634588/files/btc2019-lexvo.org_00001.nq.gz wget https://zenodo.org/record/2634588/files/btc2019-linkeddata.es_00001.nq.gz wget https://zenodo.org/record/2634588/files/btc2019-linkedopendata.gr_00001.nq.gz wget https://zenodo.org/record/2634588/files/btc2019-linkedresearch.org_00001.nq.gz wget https://zenodo.org/record/2634588/files/btc2019-loc.gov_00001.nq.gz wget https://zenodo.org/record/2634588/files/btc2019-lu.se_00001.nq.gz wget https://zenodo.org/record/2634588/files/btc2019-mcu.es_00001.nq.gz wget https://zenodo.org/record/2634588/files/btc2019-medra.org_00001.nq.gz wget https://zenodo.org/record/2634588/files/btc2019-myexperiment.org_00001.nq.gz wget https://zenodo.org/record/2634588/files/btc2019-ndl.go.jp_00001.nq.gz wget https://zenodo.org/record/2634588/files/btc2019-nih.gov_00001.nq.gz wget https://zenodo.org/record/2634588/files/btc2019-nobelprize.org_00001.nq.gz wget https://zenodo.org/record/2634588/files/btc2019-okfn.gr_00001.nq.gz wget https://zenodo.org/record/2634588/files/btc2019-ontobee.org_00001.nq.gz wget https://zenodo.org/record/2634588/files/btc2019-ontologycentral.com_00001.nq.gz wget https://zenodo.org/record/2634588/files/btc2019-openei.org_00001.nq.gz wget https://zenodo.org/record/2634588/files/btc2019-openlibrary.org_00001.nq.gz wget https://zenodo.org/record/2634588/files/btc2019-orcid.org_00001.nq.gz wget https://zenodo.org/record/2634588/files/btc2019-ordnancesurvey.co.uk_00001.nq.gz wget https://zenodo.org/record/2634588/files/btc2019-oszk.hu_00001.nq.gz wget https://zenodo.org/record/2634588/files/btc2019-other_00001.nq.gz wget https://zenodo.org/record/2634588/files/btc2019-persee.fr_00001.nq.gz wget https://zenodo.org/record/2634588/files/btc2019-pokepedia.fr_00001.nq.gz wget https://zenodo.org/record/2634588/files/btc2019-princeton.edu_00001.nq.gz wget https://zenodo.org/record/2634588/files/btc2019-productontology.org_00001.nq.gz wget https://zenodo.org/record/2634588/files/btc2019-rdaregistry.info_00001.nq.gz wget https://zenodo.org/record/2634588/files/btc2019-rdvocab.info_00001.nq.gz wget https://zenodo.org/record/2634588/files/btc2019-reegle.info_00001.nq.gz wget https://zenodo.org/record/2634588/files/btc2019-rhiaro.co.uk_00001.nq.gz wget https://zenodo.org/record/2634588/files/btc2019-schema.org_00001.nq.gz wget https://zenodo.org/record/2634588/files/btc2019-sf.net_00001.nq.gz wget https://zenodo.org/record/2634588/files/btc2019-simia.net_00001.nq.gz wget https://zenodo.org/record/2634588/files/btc2019-sti2.at_00001.nq.gz wget https://zenodo.org/record/2634588/files/btc2019-stoa.org_00001.nq.gz wget

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TheLook is a fictitious eCommerce clothing site developed by the Looker team. The dataset contains information about customers, products, orders, logistics, web events and digital marketing campaigns. The contents of this dataset are synthetic, and are provided to industry practitioners for the purpose of product discovery, testing, and evaluation. This public dataset is hosted in Google BigQuery and is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets.What is BigQuery .

Context

The World Bank is an international financial institution that provides loans to countries of the world for capital projects. The World Bank's stated goal is the reduction of poverty. Source: https://en.wikipedia.org/wiki/World_Bank

Content

This dataset combines key education statistics from a variety of sources to provide a look at global literacy, spending, and access.

For more information, see the World Bank website.

Fork this kernel to get started with this dataset.

Acknowledgements

https://bigquery.cloud.google.com/dataset/bigquery-public-data:world_bank_health_population

http://data.worldbank.org/data-catalog/ed-stats

https://cloud.google.com/bigquery/public-data/world-bank-education

Citation: The World Bank: Education Statistics

Dataset Source: World Bank. This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source - http://www.data.gov/privacy-policy#data_policy - and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

Banner Photo by @till_indeman from Unplash.

Inspiration

Of total government spending, what percentage is spent on education?

Introducing Job Posting Datasets: Uncover labor market insights!

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A collection of 22 data set of 50+ requirements each, expressed as user stories.

The dataset has been created by gathering data from web sources and we are not aware of license agreements or intellectual property rights on the requirements / user stories. The curator took utmost diligence in minimizing the risks of copyright infringement by using non-recent data that is less likely to be critical, by sampling a subset of the original requirements collection, and by qualitatively analyzing the requirements. In case of copyright infringement, please contact the dataset curator (Fabiano Dalpiaz, f.dalpiaz@uu.nl) to discuss the possibility of removal of that dataset [see Zenodo's policies]

The data sets have been originally used to conduct experiments about ambiguity detection with the REVV-Light tool: https://github.com/RELabUU/revv-light

This collection has been originally published in Mendeley data: https://data.mendeley.com/datasets/7zbk8zsd8y/1

Overview of the datasets [data and links added in December 2024]

The following text provides a description of the datasets, including links to the systems and websites, when available. The datasets are organized by macro-category and then by identifier.

Public administration and transparency

g02-federalspending.txt (2018) originates from early data in the Federal Spending Transparency project, which pertain to the website that is used to share publicly the spending data for the U.S. government. The website was created because of the Digital Accountability and Transparency Act of 2014 (DATA Act). The specific dataset pertains a system called DAIMS or Data Broker, which stands for DATA Act Information Model Schema. The sample that was gathered refers to a sub-project related to allowing the government to act as a data broker, thereby providing data to third parties. The data for the Data Broker project is currently not available online, although the backend seems to be hosted in GitHub under a CC0 1.0 Universal license. Current and recent snapshots of federal spending related websites, including many more projects than the one described in the shared collection, can be found here.

g03-loudoun.txt (2018) is a set of extracted requirements from a document, by the Loudoun County Virginia, that describes the to-be user stories and use cases about a system for land management readiness assessment called Loudoun County LandMARC. The source document can be found here and it is part of the Electronic Land Management System and EPlan Review Project - RFP RFQ issued in March 2018. More information about the overall LandMARC system and services can be found here.

g04-recycling.txt(2017) concerns a web application where recycling and waste disposal facilities can be searched and located. The application operates through the visualization of a map that the user can interact with. The dataset has obtained from a GitHub website and it is at the basis of a students' project on web site design; the code is available (no license).

g05-openspending.txt (2018) is about the OpenSpending project (www), a project of the Open Knowledge foundation which aims at transparency about how local governments spend money. At the time of the collection, the data was retrieved from a Trello board that is currently unavailable. The sample focuses on publishing, importing and editing datasets, and how the data should be presented. Currently, OpenSpending is managed via a GitHub repository which contains multiple sub-projects with unknown license.

g11-nsf.txt (2018) refers to a collection of user stories referring to the NSF Site Redesign & Content Discovery project, which originates from a publicly accessible GitHub repository (GPL 2.0 license). In particular, the user stories refer to an early version of the NSF's website. The user stories can be found as closed Issues.

(Research) data and meta-data management

g08-frictionless.txt (2016) regards the Frictionless Data project, which offers an open source dataset for building data infrastructures, to be used by researchers, data scientists, and data engineers. Links to the many projects within the Frictionless Data project are on GitHub (with a mix of Unlicense and MIT license) and web. The specific set of user stories has been collected in 2016 by GitHub user @danfowler and are stored in a Trello board.

g14-datahub.txt (2013) concerns the open source project DataHub, which is currently developed via a GitHub repository (the code has Apache License 2.0). DataHub is a data discovery platform which has been developed over multiple years. The specific data set is an initial set of user stories, which we can date back to 2013 thanks to a comment therein.

g16-mis.txt (2015) is a collection of user stories that pertains a repository for researchers and archivists. The source of the dataset is a public Trello repository. Although the user stories do not have explicit links to projects, it can be inferred that the stories originate from some project related to the library of Duke University.

g17-cask.txt (2016) refers to the Cask Data Application Platform (CDAP). CDAP is an open source application platform (GitHub, under Apache License 2.0) that can be used to develop applications within the Apache Hadoop ecosystem, an open-source framework which can be used for distributed processing of large datasets. The user stories are extracted from a document that includes requirements regarding dataset management for Cask 4.0, which includes the scenarios, user stories and a design for the implementation of these user stories. The raw data is available in the following environment.

g18-neurohub.txt (2012) is concerned with the NeuroHub platform, a neuroscience data management, analysis and collaboration platform for researchers in neuroscience to collect, store, and share data with colleagues or with the research community. The user stories were collected at a time NeuroHub was still a research project sponsored by the UK Joint Information Systems Committee (JISC). For information about the research project from which the requirements were collected, see the following record.

g22-rdadmp.txt (2018) is a collection of user stories from the Research Data Alliance's working group on DMP Common Standards. Their GitHub repository contains a collection of user stories that were created by asking the community to suggest functionality that should part of a website that manages data management plans. Each user story is stored as an issue on the GitHub's page.

g23-archivesspace.txt (2012-2013) refers to ArchivesSpace: an open source, web application for managing archives information. The application is designed to support core functions in archives administration such as accessioning; description and arrangement of processed materials including analog, hybrid, and
born digital content; management of authorities and rights; and reference service. The application supports collection management through collection management records, tracking of events, and a growing number of administrative reports. ArchivesSpace is open source and its