7 datasets found
  1. Google Analytics Sample

    • console.cloud.google.com
    Updated Jul 15, 2017
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    https://console.cloud.google.com/marketplace/browse?filter=partner:Obfuscated%20Google%20Analytics%20360%20data&inv=1&invt=AbzttQ (2017). Google Analytics Sample [Dataset]. https://console.cloud.google.com/marketplace/product/obfuscated-ga360-data/obfuscated-ga360-data
    Explore at:
    Dataset updated
    Jul 15, 2017
    Dataset provided by
    Googlehttp://google.com/
    License

    MIT Licensehttps://opensource.org/licenses/MIT
    License information was derived automatically

    Description

    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

  2. E

    Google Analytics Statistics By Revenue, Market, Customer, Usage And Facts...

    • electroiq.com
    Updated Jun 23, 2025
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    Electro IQ (2025). Google Analytics Statistics By Revenue, Market, Customer, Usage And Facts (2025) [Dataset]. https://electroiq.com/stats/google-analytics-statistics/
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    Dataset updated
    Jun 23, 2025
    Dataset authored and provided by
    Electro IQ
    License

    https://electroiq.com/privacy-policyhttps://electroiq.com/privacy-policy

    Time period covered
    2022 - 2032
    Area covered
    Global
    Description

    Introduction

    Google Analytics Statistics: Google Analytics is one of the most popular tools to monitor your website’s performance, as it gathers data regarding customer behaviour, engagement, and preferences. They are segmented into two different versions: Google Analytics 4 (GA4) and Google Analytics 360 (GA360). Google Analytics was developed by Google and was released on November 14, 2005.

    This article includes several detailed analyses from different insights, including overall market analysis, user bases, visitors' interaction with a website, such as page views, session duration, traffic sources, and conversion rates.

  3. d

    Data Licensing - ABM Data- 152+ Million Contacts | 13+ Million Companies -...

    • datarade.ai
    .xml, .csv, .xls
    Updated Oct 25, 2024
    + more versions
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    Thomson Data (2024). Data Licensing - ABM Data- 152+ Million Contacts | 13+ Million Companies - Updated Monthly Basis [Dataset]. https://datarade.ai/data-products/thomson-data-data-licensing-abm-data-154-million-contacts-thomson-data
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    .xml, .csv, .xlsAvailable download formats
    Dataset updated
    Oct 25, 2024
    Dataset authored and provided by
    Thomson Data
    Area covered
    Morocco, Nauru, Paraguay, Greenland, Slovakia, Saint Helena, Papua New Guinea, Bangladesh, Brazil, Niger
    Description

    Empower Your Business With Professional Data Licensing Services

    Discover a 360-Degree View of Worldwide Solution Buyers and Their Needs Leverage over 70 insights that will help you make better decisions to manage your sales pipeline, target key accounts with customized messaging, and focus your sales and marketing efforts:

    Here are some of the types of Insights, our data licensing services can provide are:

    Technology Insights: Discover companies’ technology preferences, including their tech stack for essential investments such as CRM systems, marketing and sales automation, email security and hosting, data analytics, and cloud security and providers.

    Departmental Roles and Openings: Access real-time data on the number of roles and job openings across various departments, including IT, Development, Security, Marketing, Sales, and Customer Success. This information helps you gauge the company’s growth trajectory and possible needs.

    Funding Insights: Keep updated of the latest funding, dates, types, and lead investors, providing you with a clear understanding of a company’s potential for growth investments.

    Mobile Application Insights: Find out if the company has a mobile app or web app, enabling you to tailor your pitch effectively.

    Website traffic and advertising spend metrics: Customers can leverage website traffic and advertising data to gain insights into competitor performance, allowing them to refine their marketing strategies and optimize ad spending.

    Access unlimited data and improve conversation by 3X

    • Leverage the data for your Account-Based Marketing (ABM) strategy

    • Leverage ICP (industry, company size, location etc) to identify high- potential Accounts.

    • Utilize GTM strategies to deliver personalized marketing experiences through
      Multi-channel outreach (email, Cell, social media) that resonate with the target audience.

    Who can leverage our Data:

    B2B marketing Teams- Increase marketing leads and enhance conversions.

    B2B sales teams- Build a stronger pipeline and increase your deal wins.

    Talent sourcing/Staffing companies- Leverage our data to identify and engage top talent, streamlining your recruitment process and finding the best candidates faster.

    Research companies/Investors- Insights into the financial investments received by a company, including funding rounds, amounts, and investor details.

    Technology companies: Leverage our Technographic data to reveal the technology stack and tools used by companies, helping tailor marketing and sales efforts.

    Data Source:

    The Database, sourced through multiple sources and validated using proprietary methods on an ongoing basis, is highly customizable. It contains parameters such as employee size, job title, domain, industry, Technography, Ad spends, Funding data, and more, which can be tailored to create segments that perfectly align with your targeting needs. That is exactly why our Database is perfect for licensing!

    FAQs

    1. Can licensed data be resold or redistributed? Answer: No, The customer shall not, directly or indirectly, sell, distribute, license, or otherwise make available the licensed data to any third party that intends to resell, sublicense, or redistribute the data. The Customer must take reasonable steps to ensure that any recipient of the licensed data is using it for internal purposes only and not for resale or redistribution. Any breach of this provision shall be considered a material breach of this Order Form and may result in the immediate termination of the Customer's rights under this agreement, as well as any applicable remedies available under law.

    2. What is the duration of the data license and usage terms? Answer: The data license is valid for 12 months (1 year) for unlimited usage. Customers also have the option to license the data for multiple years. At the end of the first year, Customers can renew the license to maintain continued access.

    3. What happens if the customer misuses the data? Answer: The data can be used without limits for a period of one year or multiple years (depending on the contract tenure); however, Thomson Data actively monitors its usage. If any unusual activity is detected, Thomson Data reserves the right to terminate the account.

    4. How frequently is the data updated? Answer: The data is updated on a quarterly basis and fresh records added on a monthly basis

    5. What is the accuracy rate of the data? Answer: Customers can expect 90% accuracy for all data points, with email accuracy ranging between 85% and 90%. Cell phone data accuracy is around 80%.

    6. What types of information are included in the data? Answer: Thomson Data provides over 70+ data points, including contact details (name, job title, LinkedIn profile, cell number, email address, education, certifications, work experience, etc.), company information, department/team sizes, SIC and NAICS codes, industry classification, technographic detai...

  4. IoT-deNAT: Outbound flow-based network traffic data of IoT and non-IoT...

    • zenodo.org
    • explore.openaire.eu
    Updated Jul 23, 2020
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    Yair Meidan; Yair Meidan; Vinay Sachidananda; Vinay Sachidananda; Hongyi Peng; Racheli Sagron; Yuval Elovici; Yuval Elovici; Asaf Shabtai; Asaf Shabtai; Hongyi Peng; Racheli Sagron (2020). IoT-deNAT: Outbound flow-based network traffic data of IoT and non-IoT devices behind a home NAT [Dataset]. http://doi.org/10.5281/zenodo.3924770
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    Dataset updated
    Jul 23, 2020
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Yair Meidan; Yair Meidan; Vinay Sachidananda; Vinay Sachidananda; Hongyi Peng; Racheli Sagron; Yuval Elovici; Yuval Elovici; Asaf Shabtai; Asaf Shabtai; Hongyi Peng; Racheli Sagron
    Description

    This dataset is comprised of NetFlow records, which capture the outbound network traffic of 8 commercial IoT devices and 5 non-IoT devices, collected during a period of 37 days in a lab at Ben-Gurion University of The Negev. The dataset was collected in order to develop a method for telecommunication providers to detect vulnerable IoT models behind home NATs. Each NetFlow record is labeled with the device model which produced it; for research reproducibilty, each NetFlow is also allocated to either the "training" or "test" set, in accordance with the partitioning described in:

    Y. Meidan, V. Sachidananda, H. Peng, R. Sagron, Y. Elovici, and A. Shabtai, A novel approach for detecting vulnerable IoT devices connected behind a home NAT, Computers & Security, Volume 97, 2020, 101968, ISSN 0167-4048, https://doi.org/10.1016/j.cose.2020.101968. (http://www.sciencedirect.com/science/article/pii/S0167404820302418)

    Please note:

    • The dataset itself is free to use, however users are requested to cite the above-mentioned paper, which describes in detail the research objectives as well as the data collection, preparation and analysis.
    • Following is a brief description of the features used in this dataset.

    # NetFlow features, used in the related paper for analysis

    'FIRST_SWITCHED': System uptime at which the first packet of this flow was switched
    'IN_BYTES': Incoming counter for the number of bytes associated with an IP Flow
    'IN_PKTS': Incoming counter for the number of packets associated with an IP Flow
    'IPV4_DST_ADDR': IPv4 destination address
    'L4_DST_PORT': TCP/UDP destination port number
    'L4_SRC_PORT': TCP/UDP source port number
    'LAST_SWITCHED': System uptime at which the last packet of this flow was switched
    'PROTOCOL': IP protocol byte (6: TCP, 17: UDP)
    'SRC_TOS': Type of Service byte setting when there is an incoming interface
    'TCP_FLAGS': Cumulative of all the TCP flags seen for this flow

    # Features added by the authors

    'IP': Prefix of the destination IP address, representing the network (without the host)
    'DURATION': Time (seconds) between first/last packet switching

    # Label
    'device_model':

    # Partition
    'partition': Training or test

    # Additional NetFlow features (mostly zero-variance)
    'SRC_AS': Source BGP autonomous system number
    'DST_AS': Destination BGP autonomous system number
    'INPUT_SNMP': Input interface index
    'OUTPUT_SNMP': Output interface index
    'IPV4_SRC_ADDR': IPv4 source address
    'MAC': MAC address of the source

    # Additional data
    'category': IoT or non-IoT
    'type': IoT, access_point, smartphone, laptop
    'date': Datepart of FIRST_SWITCHED
    'inter_arrival_time': Time (seconds) between successive flows of the same device (identified by its MAC address)

  5. f

    Selection of English Wikipedia pages (CNs) regarding topics with a direct...

    • plos.figshare.com
    xls
    Updated Jun 1, 2023
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    Mirko Kämpf; Eric Tessenow; Dror Y. Kenett; Jan W. Kantelhardt (2023). Selection of English Wikipedia pages (CNs) regarding topics with a direct relation to the emerging Hadoop (Big Data) market. [Dataset]. http://doi.org/10.1371/journal.pone.0141892.t001
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    xlsAvailable download formats
    Dataset updated
    Jun 1, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Mirko Kämpf; Eric Tessenow; Dror Y. Kenett; Jan W. Kantelhardt
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Apache Hadoop is the central software project, beside Apache SOLR, and Apache Lucene (SW, software). Companies which offer Hadoop distributions and Hadoop based solutions are the central companies in the scope of the study (HV, hardware vendors). Other companies started very early with Hadoop related projects as early adopters (EA). Global players (GP) are affected by this emerging market, its opportunities and the new competitors (NC). Some new but highly relevant companies like Talend or LucidWorks have been selected because of their obvious commitment to the open source ideas. Widely adopted technologies with a relation to the selected research topic are represented by the group TEC.

  6. Daily domestic transport use by mode

    • gov.uk
    Updated Jun 11, 2025
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    Department for Transport (2025). Daily domestic transport use by mode [Dataset]. https://www.gov.uk/government/statistics/transport-use-during-the-coronavirus-covid-19-pandemic
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    Dataset updated
    Jun 11, 2025
    Dataset provided by
    GOV.UKhttp://gov.uk/
    Authors
    Department for Transport
    Description

    Our statistical practice is regulated by the Office for Statistics Regulation (OSR). OSR sets the standards of trustworthiness, quality and value in the Code of Practice for Statistics that all producers of official statistics should adhere to. You are welcome to contact us directly by emailing transport.statistics@dft.gov.uk with any comments about how we meet these standards.

    These statistics on transport use are published monthly.

    For each day, the Department for Transport (DfT) produces statistics on domestic transport:

    • road traffic in Great Britain
    • rail passenger journeys in Great Britain
    • Transport for London (TfL) tube and bus routes
    • bus travel in Great Britain (excluding London)

    The associated methodology notes set out information on the data sources and methodology used to generate these headline measures.

    From September 2023, these statistics include a second rail usage time series which excludes Elizabeth Line service (and other relevant services that have been replaced by the Elizabeth line) from both the travel week and its equivalent baseline week in 2019. This allows for a more meaningful like-for-like comparison of rail demand across the period because the effects of the Elizabeth Line on rail demand are removed. More information can be found in the methodology document.

    The table below provides the reference of regular statistics collections published by DfT on these topics, with their last and upcoming publication dates.

    ModePublication and linkLatest period covered and next publication
    Road trafficRoad traffic statisticsFull annual data up to December 2023 was published in May 2024.

    Quarterly data up to September 2024 was published December 2024.
    Rail usageThe Office of Rail and Road (ORR) publishes a range of statistics including passenger and freight rail performance and usage. Statistics are available at the https://www.orr.gov.uk/published-statistics" class="govuk-link">ORR website.

    Statistics for rail passenger numbers and crowding on weekdays in major cities in England and Wales are published by DfT.
    ORR’s latest quarterly rail usage statistics, covering July to September 2024, was published in December 2024.


    DfT’s most recent annual passenger numbers and crowding statistics for 2023 were published in September 2024.
    Bus usageBus statisticsThe most recent annual publication covered the year ending March 2024.

    The most recent quarterly publication covered October to December 2024.
    TfL tube and bus usageData on buses is covered by the section above. https://tfl.gov.uk/status-updates/busiest-times-to-travel" class="govuk-link">Station level business data is available.
    Cycling usageWalking and cycling statistics, England2023 calendar year published in August 2024.
    Cross Modal and journey by purposeNational Travel Survey2023 calendar year data published in August 2024.

  7. Common languages used for web content 2025, by share of websites

    • statista.com
    • ai-chatbox.pro
    Updated Feb 11, 2025
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    Statista (2025). Common languages used for web content 2025, by share of websites [Dataset]. https://www.statista.com/statistics/262946/most-common-languages-on-the-internet/
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    Dataset updated
    Feb 11, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Feb 2025
    Area covered
    Worldwide
    Description

    As of February 2025, English was the most popular language for web content, with over 49.4 percent of websites using it. Spanish ranked second, with six percent of web content, while the content in the German language followed, with 5.6 percent. English as the leading online language United States and India, the countries with the most internet users after China, are also the world's biggest English-speaking markets. The internet user base in both countries combined, as of January 2023, was over a billion individuals. This has led to most of the online information being created in English. Consequently, even those who are not native speakers may use it for convenience. Global internet usage by regions As of October 2024, the number of internet users worldwide was 5.52 billion. In the same period, Northern Europe and North America were leading in terms of internet penetration rates worldwide, with around 97 percent of its populations accessing the internet.

  8. Not seeing a result you expected?
    Learn how you can add new datasets to our index.

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https://console.cloud.google.com/marketplace/browse?filter=partner:Obfuscated%20Google%20Analytics%20360%20data&inv=1&invt=AbzttQ (2017). Google Analytics Sample [Dataset]. https://console.cloud.google.com/marketplace/product/obfuscated-ga360-data/obfuscated-ga360-data
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Google Analytics Sample

Explore at:
19 scholarly articles cite this dataset (View in Google Scholar)
Dataset updated
Jul 15, 2017
Dataset provided by
Googlehttp://google.com/
License

MIT Licensehttps://opensource.org/licenses/MIT
License information was derived automatically

Description

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

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