MIT Licensehttps://opensource.org/licenses/MIT
License information was derived automatically
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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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.
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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:
# 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)
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
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.
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:
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.
Mode | Publication and link | Latest period covered and next publication |
---|---|---|
Road traffic | Road traffic statistics | Full annual data up to December 2023 was published in May 2024. Quarterly data up to September 2024 was published December 2024. |
Rail usage | The 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 usage | Bus statistics | The most recent annual publication covered the year ending March 2024. The most recent quarterly publication covered October to December 2024. |
TfL tube and bus usage | Data 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 usage | Walking and cycling statistics, England | 2023 calendar year published in August 2024. |
Cross Modal and journey by purpose | National Travel Survey | 2023 calendar year data published in August 2024. |
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.
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MIT Licensehttps://opensource.org/licenses/MIT
License information was derived automatically
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