The global number of internet users in was forecast to continuously increase between 2024 and 2029 by in total 1.3 billion users (+23.66 percent). After the fifteenth consecutive increasing year, the number of users is estimated to reach 7 billion users and therefore a new peak in 2029. Notably, the number of internet users of was continuously increasing over the past years.Depicted is the estimated number of individuals in the country or region at hand, that use the internet. As the datasource clarifies, connection quality and usage frequency are distinct aspects, not taken into account here.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of internet users in countries like the Americas and Asia.

Firms are increasingly transitioning advertising budgets to Internet display campaigns, but this transition poses new challenges. These campaigns use numerous potential metrics for success (e.g., reach or click rate), and because each website represents a separate advertising opportunity, this is also an inherently high-dimensional problem. Further, advertisers often have constraints they wish to place on their campaign, such as targeting specific sub-populations or websites. These challenges require a method flexible enough to accommodate thousands of websites, as well as numerous metrics and campaign constraints. Motivated by this application, we consider the general constrained high-dimensional problem, where the parameters satisfy linear constraints. We develop the Penalized and Constrained optimization method (PaC) to compute the solution path for high-dimensional, linearly constrained criteria. PaC is extremely general; in addition to internet advertising, we show it encompasses many other potential applications, such as portfolio estimation, monotone curve estimation, and the generalized lasso. Computing the PaC coefficient path poses technical challenges, but we develop an efficient algorithm over a grid of tuning parameters. Through extensive simulations, we show PaC performs well. Finally, we apply PaC to a proprietary dataset in an exemplar Internet advertising case study and demonstrate its superiority over existing methods in this practical setting. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.

Statistics of how many adults access the internet and use different types of technology covering: home internet access how people connect to the web how often people use the web/computers whether people use mobile devices whether people buy goods over the web whether people carried out specified activities over the internet For more information see the ONS website and the UKDS website.

Context

There are a lot of unknowns when running an E-commerce store, even when you have analytics to guide your decisions.

Users are an important factor in an e-commerce business. This is especially true in a C2C-oriented store, since they are both the suppliers (by uploading their products) AND the customers (by purchasing other user's articles).

This dataset aims to serve as a benchmark for an e-commerce fashion store. Using this dataset, you may want to try and understand what you can expect of your users and determine in advance how your grows may be.

• For instance, if you see that most of your users are not very active, you may look into this dataset to compare your store's performance.

If you think this kind of dataset may be useful or if you liked it, don't forget to show your support or appreciation with an upvote/comment. You may even include how you think this dataset might be of use to you. This way, I will be more aware of specific needs and be able to adapt my datasets to suits more your needs.

This dataset is part of a preview of a much larger dataset. Please contact me for more.

Content

What is inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too.

The data was scraped from a successful online C2C fashion store with over 9M registered users. The store was first launched in Europe around 2009 then expanded worldwide.

Visitors vs Users: Visitors do not appear in this dataset. Only registered users are included. "Visitors" cannot purchase an article but can view the catalog.

Acknowledgements

We wouldn't be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.

Inspiration

Questions you might want to answer using this dataset:

• Are e-commerce users interested in social network feature ?
• Are my users active enough (compared to those of this dataset) ?
• How likely are people from other countries to sign up in a C2C website ?
• How many users are likely to drop off after years of using my service ?

License

CC-BY-NC-SA 4.0

For other licensing options, contact me.

How many people use social media?

              Social media usage is one of the most popular online activities. In 2024, over five billion people were using social media worldwide, a number projected to increase to over six billion in 2028.

              Who uses social media?
              Social networking is one of the most popular digital activities worldwide and it is no surprise that social networking penetration across all regions is constantly increasing. As of January 2023, the global social media usage rate stood at 59 percent. This figure is anticipated to grow as lesser developed digital markets catch up with other regions
              when it comes to infrastructure development and the availability of cheap mobile devices. In fact, most of social media’s global growth is driven by the increasing usage of mobile devices. Mobile-first market Eastern Asia topped the global ranking of mobile social networking penetration, followed by established digital powerhouses such as the Americas and Northern Europe.

              How much time do people spend on social media?
              Social media is an integral part of daily internet usage. On average, internet users spend 151 minutes per day on social media and messaging apps, an increase of 40 minutes since 2015. On average, internet users in Latin America had the highest average time spent per day on social media.

              What are the most popular social media platforms?
              Market leader Facebook was the first social network to surpass one billion registered accounts and currently boasts approximately 2.9 billion monthly active users, making it the most popular social network worldwide. In June 2023, the top social media apps in the Apple App Store included mobile messaging apps WhatsApp and Telegram Messenger, as well as the ever-popular app version of Facebook.

Context

Phishing is a form of identity theft that occurs when a malicious website impersonates a legitimate one in order to acquire sensitive information such as passwords, account details, or credit card numbers. People generally tend to fall pray to this very easily. Kudos to the commendable craftsmanship of the attackers which makes people believe that it is a legitimate website. There is a need to identify the potential phishing websites and differentiate them from the legitimate ones. This dataset identifies the prominent features of the phishing websites, 10 such features have been identified.

Content

Generally, the open source datasets available on the internet do not comes with the code and the logic which arises certain problems i.e.:

1. Limited Data: The ML algorithms can only be tested with the existing phishing URLs and no new phishing URLS can be checked for its validity.
2. Outdated URLs: The datasets available on the internet has been uploaded long time ago, there are new kind of phishing URLs arising in every second.
3. Outdated Features: The datasets available on the internet has been uploaded long time ago, there are new methodologies arising in phishing techniques.
4. No Access to Backend: There is no stepwise guide describing how the feature has been derived.

On the contrary we are trying to overcome all the above-mentioned problems.

1. Real Time Data: Before applying a Machine Learning algorithm, we can run the script and fetch real time URLs from Phishtank (for phishing URLs) and from moz (for legitimate URLs) 2. Scalable Data: We can also specify the number of URLs we want to feed the model and hence the web scrapper will fetch that much amount of data from the websites. Presently we are using 1401 URLs in this project i.e. 901 Phishing URLs and 500 Legitimate URLS. 3. New Features: We have tried to implement the prominent new features that is there in the current phishing URLs and since we own the code, new features can also be added. 4. Source code on Github: The source code is published on GitHub for public use and can be used for further scope of improvements. This way there will be transparency to the logic and more creators can add there meaningful additions to the code.

Link to the source code

https://github.com/akshaya1508/detection_of_phishing_websites.git

Inspiration

The idea to develop the dataset and the code for this dataset has been inspired by various other creators who have worked on the similar lines.

The dynamic face-to-face interaction networks represent the interactions that happen during discussions between a group of participants playing the Resistance game. This dataset contains networks extracted from 62 games. Each game is played by 5-8 participants and lasts between 45--60 minutes. We extract dynamically evolving networks from the free-form discussions using the ICAF algorithm. The extracted networks are used to characterize and detect group deceptive behavior using the DeceptionRank algorithm.

The networks are weighted, directed and temporal. Each node represents a participant. At each 1/3 second, a directed edge from node u to v is weighted by the probability of participant u looking at participant v or the laptop. Additionally, we also provide a binary version where an edge from u to v indicates participant u looks at participant v (or the laptop).

Stanford Network Analysis Platform (SNAP) is a general purpose, high performance system for analysis and manipulation of large networks. Graphs consists of nodes and directed/undirected/multiple edges between the graph nodes. Networks are graphs with data on nodes and/or edges of the network.

The core SNAP library is written in C++ and optimized for maximum performance and compact graph representation. It easily scales to massive networks with hundreds of millions of nodes, and billions of edges. It efficiently manipulates large graphs, calculates structural properties, generates regular and random graphs, and supports attributes on nodes and edges. Besides scalability to large graphs, an additional strength of SNAP is that nodes, edges and attributes in a graph or a network can be changed dynamically during the computation.

SNAP was originally developed by Jure Leskovec in the course of his PhD studies. The first release was made available in Nov, 2009. SNAP uses a general purpose STL (Standard Template Library)-like library GLib developed at Jozef Stefan Institute. SNAP and GLib are being actively developed and used in numerous academic and industrial projects.

http://snap.stanford.edu/data/index.html#face2face

Context

The data presented here was obtained in a Kali Machine from University of Cincinnati,Cincinnati,OHIO by carrying out packet captures for 1 hour during the evening on Oct 9th,2023 using Wireshark.This dataset consists of 394137 instances were obtained and stored in a CSV (Comma Separated Values) file.This large dataset could be used utilised for different machine learning applications for instance classification of Network traffic,Network performance monitoring,Network Security Management , Network Traffic Management ,network intrusion detection and anomaly detection.

The dataset can be used for a variety of machine learning tasks, such as network intrusion detection, traffic classification, and anomaly detection.

Content :

This network traffic dataset consists of 7 features.Each instance contains the information of source and destination IP addresses, The majority of the properties are numeric in nature, however there are also nominal and date kinds due to the Timestamp.

The network traffic flow statistics (No. Time Source Destination Protocol Length Info) were obtained using Wireshark (https://www.wireshark.org/).

Dataset Columns:

No : Number of Instance. Timestamp : Timestamp of instance of network traffic Source IP: IP address of Source Destination IP: IP address of Destination Portocol: Protocol used by the instance Length: Length of Instance Info: Information of Traffic Instance

Acknowledgements :

I would like thank University of Cincinnati for giving the infrastructure for generation of network traffic data set.

Ravikumar Gattu , Susmitha Choppadandi

Inspiration : This dataset goes beyond the majority of network traffic classification datasets, which only identify the type of application (WWW, DNS, ICMP,ARP,RARP) that an IP flow contains. Instead, it generates machine learning models that can identify specific applications (like Tiktok,Wikipedia,Instagram,Youtube,Websites,Blogs etc.) from IP flow statistics (there are currently 25 applications in total).

**Dataset License: ** CC0: Public Domain

Dataset Usages : This dataset can be used for different machine learning applications in the field of cybersecurity such as classification of Network traffic,Network performance monitoring,Network Security Management , Network Traffic Management ,network intrusion detection and anomaly detection.

ML techniques benefits from this Dataset :

This dataset is highly useful because it consists of 394137 instances of network traffic data obtained by using the 25 applications on a public,private and Enterprise networks.Also,the dataset consists of very important features that can be used for most of the applications of Machine learning in cybersecurity.Here are few of the potential machine learning applications that could be benefited from this dataset are :

1. Network Performance Monitoring : This large network traffic data set can be utilised for analysing the network traffic to identifying the network patterns in the network .This help in designing the network security algorithms for minimise the network probelms.

2. Anamoly Detection : Large network traffic dataset can be utilised training the machine learning models for finding the irregularitues in the traffic which could help identify the cyber attacks.

3.Network Intrusion Detection : This large dataset could be utilised for machine algorithms training and designing the models for detection of the traffic issues,Malicious traffic network attacks and DOS attacks as well.

This folder contains the data from the Phishing Website dataset provided in [1]. All the features are categorical and were preprocessed in integer values. The data can be downloaded from https://archive.ics.uci.edu/dataset/327/phishing+websites. There are 11055 samples with 30 features. Websites belong to 2 domains: websites that use the IP address used instead of the domain name in the URL and websites that use the domain name in the URL. For reference, please refer to: [1] R. Mohammad, F. Thabtah, L. Mccluskey. An assessment of features related to phishing websites using an automated technique In International Conference for Internet Technology and Secured Transactions, 2012

The dataset includes the hyperlink network structure of the government websites in different countries. This version of the dataset includes data from Canada, Japan, and Spain. See 'Related Resources' section below for similar collections. This project aims to develop methodologies to study online political behaviour including use of the Internet to generate new data and experiments; to collect and analyse data on internet-mediated interactions at both individual and organisational levels; and to use this data to re-examine and where necessary develop political science knowledge and theory in light of widespread use of the Internet First, the project will re-examine the logic of collective action, assessing the impact of reduced communication and coordination costs; the changing nature of leadership; and the effects of different information environments on propensity to participate in political mobilisation. This part of the research will involve conducting laboratory and field experiments into online behaviour. Second, the research will develop the Digital-era Governance model for newer 'Web 2.0' applications and other technological developments such as cloud computing. The research will re-examine the nature of citizen-government interactions in this changing environment, examining the impact of Internet-based mediation on information exchange, organisational forms in government and citizen participation in policy-making. This part of the research will involve a comparison of government's online presence in eight countries, using webmetric techniques, and in-depth qualitative analysis of governance models, using elite interviewing and documentary analysis. We used the Heritrix web crawler (https://en.wikipedia.org/wiki/Heritrix) to capture the hyperlink structure of webpages witihin the .gc.ca, .go.jp, and .gob.es domains.

The City's Internet site allows residents to access City services online, learn more about the City of Chicago, and find other pertinent information. The percentage of the City’s Internet website uptime, the amount of time the site was available, and the target uptime for each week are available by mousing over columns. The target availability for this site is 99.5%.

Context

The cleaned text data can be used to adapt LLM to the domain of Norwegian Agriculture within the Norwegian language. In addition, it can be valuable for various NLP tasks such as region classification, or analytical tasks, such as exploring common agricultural practices in Norway.

Content

This dataset focuses on agronomic management practices and production in Norway. It consists of 2292 articles in Norwegian. All data is derived from three Norwegian agricultural-related websites and includes data from the largest advisory service for the agricultural sector, Norsk landbruksrådgivning (Norwegian Agricultural Extension Service, NLR), the most prominent agricultural research institute in Norway, Norsk Institutt for Bioøkonomi (Norwegian Institute for Bioeconomy, NIBIO), and the most comprehensive web page dedicated to plant protection in agriculture, Plantevernleksikonet.

Inspiration

The emergence of LLMs marked a significant step forward, providing a single solution for generating human-like text. However, training an LLM requires substantial amounts of text data, which is not readily available for most natural languages, including Norwegian. And agriculture as an industry has not seen much penetration of AI, - what if we could provide location-specific insights to a farmer?

Acknowledgements

The data from NLR can be expanded in the future, gathering more text data.

The data represent web-scraping of hyperlinks from a selection of environmental stewardship organizations that were identified in the 2017 NYC Stewardship Mapping and Assessment Project (STEW-MAP) (USDA 2017). There are two data sets: 1) the original scrape containing all hyperlinks within the websites and associated attribute values (see "README" file); 2) a cleaned and reduced dataset formatted for network analysis. For dataset 1: Organizations were selected from from the 2017 NYC Stewardship Mapping and Assessment Project (STEW-MAP) (USDA 2017), a publicly available, spatial data set about environmental stewardship organizations working in New York City, USA (N = 719). To create a smaller and more manageable sample to analyze, all organizations that intersected (i.e., worked entirely within or overlapped) the NYC borough of Staten Island were selected for a geographically bounded sample. Only organizations with working websites and that the web scraper could access were retained for the study (n = 78). The websites were scraped between 09 and 17 June 2020 to a maximum search depth of ten using the snaWeb package (version 1.0.1, Stockton 2020) in the R computational language environment (R Core Team 2020). For dataset 2: The complete scrape results were cleaned, reduced, and formatted as a standard edge-array (node1, node2, edge attribute) for network analysis. See "READ ME" file for further details. References: R Core Team. (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/. Version 4.0.3. Stockton, T. (2020). snaWeb Package: An R package for finding and building social networks for a website, version 1.0.1. USDA Forest Service. (2017). Stewardship Mapping and Assessment Project (STEW-MAP). New York City Data Set. Available online at https://www.nrs.fs.fed.us/STEW-MAP/data/. This dataset is associated with the following publication: Sayles, J., R. Furey, and M. Ten Brink. How deep to dig: effects of web-scraping search depth on hyperlink network analysis of environmental stewardship organizations. Applied Network Science. Springer Nature, New York, NY, 7: 36, (2022).

This table shows whether people aged 16 or over have ever used or never used the internet by a range of variables such as age, ethnicity, pay, occupation, qualifications, and disability. The question asked in the Labour Force Survey is "When did you last use the internet?" This question is only asked to people aged 16 and over. The first time this data was available was 2011 Q1. At borough level the data showed ever used or never used. For London and Rest of UK the data is broken down by a range of indicators, including age, ethnic group, weekly pay, occupation levels, qualification levels, and economic activity. The APS sampled around 333,000 people in the UK (around 27,000 in London). As such all figures must be treated with some caution. Data was supplied directly by ONS under request from the Greater London Authority. Numbers rounded to the nearest thousand. Other Internet Access data can be found on the ONS website. This is national data based on the Opinions and Lifestyle Survey.

Ebitda Time Series for United Internet AG NA. United Internet AG, through its subsidiaries, operates as an Internet service provider worldwide. The company operates through Consumer Access, Business Access, Consumer Applications, and Business Applications segments. It offers landline-based broadband and mobile internet products, including home networks, online storage, smart home, and IPTV for private users; and telecommunication products ranging from fiber-optic direct connections to tailored ICT solutions, which include voice, data, and network solutions, as well as infrastructure services to national and international carriers and ISPs. The company also provides applications and services for home users, such as personal information management applications comprising email, to-do lists, appointments, and addresses; and online cloud storage and office software. In addition, it provides business applications for freelancers and small to medium enterprises, such as domains, websites, web hosting, servers, e-shops, group work, online cloud storage, and office software, as well as cloud solutions and infrastructure. It offers its access products through the yourfone, smartmobile.de, 1&1, and 1&1 Versatel brand names; and applications through GMX, mail.com, WEB.DE, home.pl, Arsys, STRATO, IONOS, Fasthosts, we22, InterNetX, united-domains, and World4You brand names. In addition, the company offers professional services in the fields of active domain management; performance-based advertising and sales services under the Sedo brand name; online advertising services under the United Internet Media brand name; and white-label website builder services under the we22 brand, as well as sells IT hardware. The company was founded in 1988 and is headquartered in Montabaur, Germany.

VLC Data: A Multi-Class Network Traffic Dataset Covering Diverse Applications and Platforms

Valencia Data (VLC Data) is a network traffic dataset collected from various applications and platforms. It includes both encrypted and, when applicable, unencrypted protocols, capturing realistic usage scenarios and application-specific behavior.

The dataset covers 18.5 hours, 58 pcapng files, and 24.26 GB, with traffic from:

• Video streaming: Netflix and Prime Video (10–50 min) via Firefox.
• Gaming: Roblox sessions on Windows (20–35 min), recorded outside of virtual machines, despite VM support.
• Video conferencing: Microsoft Teams (20 min) via Firefox.
• Web browsing: Wikipedia, BBC, Google, LinkedIn, Amazon, and OWIN6G (2–5 min) via Firefox or Chrome.
• Audio streaming: Spotify (30–33 min) on multiple OS.
• Web streaming: YouTube in 4K and Full HD (20–30 min).

This dataset is publicly available for traffic analysis across different apps, protocols, and systems.

Table Description:

Type	Applications	Platform	Time [min]	Comments	Filename	Size (MB)
Video Streaming	Netflix	Linux	10	Running Netflix on Firefox Browser	netflix_linux_10m_01	95.1
Video Streaming	Netflix	Linux	20	Running Netflix on Firefox Browser	netflix_linux_20m_01	167.7
Video Streaming	Netflix	Linux	20	Running Netflix on Firefox Browser	netflix_linux_20m_02	237.9
Video Streaming	Netflix	Linux	20	Running Netflix on Firefox Browser	netflix_linux_20m_03	212.6
Video Streaming	Netflix	Linux	25	Running Netflix on Firefox, but 2 min in Menu	netflix_linux_25m_01	610.7
Video Streaming	Netflix	Linux	35	Running Netflix on Firefox, but 1 min in Menu	netflix_linux_35m_01	534.8
Video Streaming	Netflix	Linux	50	Running Netflix on Firefox Browser	netflix_linux_50m_01	660.9
Video Streaming	Netflix	Windows	10	Running Netflix on Firefox Browser	netflix_windows_10m_01	132.1
Video Streaming	Netflix	Windows	20	Running Netflix on Firefox Browser	netflix_windows_20m_01	506.4
Video Streaming	Prime Video	Linux	20	Running Prime Video on Firefox Browser	prime_linux_20m_01	767.3
Video Streaming	Prime Video	Linux	20	Running Prime Video on Firefox Browser	prime_linux_20m_02	569.3
Video Streaming	Prime Video	Windows	20	Running Prime Video on Firefox Browser	prime_windows_20m_01	512.3
Video Streaming	Prime Video	Windows	20	Running Prime Video on Firefox Browser	prime_windows_20m_02	364.2
Gaming	Roblox	Windows	20	Doesn't run in VM	roblox_windows_20m_01	127.5
Gaming	Roblox	Windows	20	Doesn't run in VM	roblox_windows_20m_02	378.5
Gaming	Roblox	Windows	20	Doesn't run in VM	roblox_windows_20m_03	458.9
Gaming	Roblox	Windows	30	Doesn't run in VM	roblox_windows_30m_01	519.8
Gaming	Roblox	Windows	30	Doesn't run in VM	roblox_windows_30m_02	357.3
Gaming	Roblox	Windows	35	Doesn't run in VM	roblox_windows_35m_01	880.4
Audio Streaming	Spotify	Linux	30	Running Spotify app on Ubuntu-Linux	spotify_linux_30m_01	98.2
Audio Streaming	Spotify	Linux	30	Running Spotify app on Ubuntu-Linux	spotify_linux_30m_02	112.2
Audio Streaming	Spotify	Linux	30	Running Spotify app on Ubuntu-Linux	spotify_linux_30m_03	175.5
Audio Streaming	Spotify	Windows	30	Running Spotify app on Windows	spotify_windows_30m_01	50.7
Audio Streaming	Spotify	Windows	30	Doesn't run in VM	spotify_windows_30m_02	63.2
Audio Streaming	Spotify	Windows	33	Running Spotify app on Windows	spotify_windows_33m_01	70.9
Video Conferencing	Teams	Linux	20	Running Teams on Firefox Browser	teams_linux_20m_01	134.6
Video Conferencing	Teams	Linux	20	Running Teams on Firefox Browser	teams_linux_20m_02	343.3
Video Conferencing	Teams	Linux	20	Running Teams on Firefox Browser	teams_linux_20m_03	376.6
Video Conferencing	Teams	Windows	20	Running Teams on Firefox Browser	teams_windows_20m_01	634.1
Video Conferencing	Teams	Windows	20	Running Teams on Firefox Browser	teams_windows_20m_02	517.8
Video Conferencing	Teams	Windows	20	Running Teams on Firefox Browser	teams_windows_20m_03	629.9
Web Browsing	Web	Linux	2	OWIN6G website on Firefox Browser	web_linux_2m_owin6g	1.2
Web Browsing	Web	Linux	2	Wikipedia website on Firefox Browser	web_linux_2m_wikipedia	19.7
Web Browsing	Web	Linux	3	OWIN6G website on Firefox Browser	web_linux_3m_owin6g	4.5
Web Browsing	Web	Linux	3	Wikipedia website on Firefox Browser	web_linux_3m_wikipedia	23.5
Web Browsing	Web	Linux	5	Amazon website on Chrome Browser	web_linux_5m_amazon	262.9
Web Browsing	Web	Linux	5	BBC website on Firefox Browser	web_linux_5m_bbc	55.7
Web Browsing	Web	Linux	5	Google website on Firefox Browser	web_linux_5m_google	22.6
Web Browsing	Web	Linux	5	Linkedin website on Firefox Browser	web_linux_5m_linkedin	39.8
Web Browsing	Web	Windows	3	OWIN6G website on Firefox Browser	web_windows_3m_owin6g	32.6
Web Browsing	Web	Windows	3	Wikipedia website on Firefox Browser	web_windows_3m_wikipedia	94.9
Web Browsing	Web	Windows	5	Amazon website on Chrome Browser	web_windows_5m_amazon	104.0
Web Browsing	Web	Windows	5	BBC website on Firefox Browser	web_windows_5m_bbc	23.1
Web Browsing	Web	Windows	5	Google website on Firefox Browser	web_windows_5m_google	31.5
Web Browsing	Web	Windows	5	Linkedin website on Firefox Browser	web_windows_5m_linkedin	104.1
Web Streaming	Youtube	Linux	20	One Video Streaming, 4K	youtube_linux_20m_01	1,145.6
Web Streaming	Youtube	Linux	20	One Video Streaming, FullHD	youtube_linux_20m_02	389.4
Web Streaming	Youtube	Linux	20	One Video Streaming, FullHD	youtube_linux_20m_03	2,007.1
Web Streaming	Youtube	Linux	20	One Video Streaming, 4K	youtube_linux_20m_04	390.4
Web Streaming	Youtube	Linux	20	One Video Streaming, FullHD	youtube_linux_20m_05	410.1
Web

These data consist of measures of Internet use estimated using small area estimation. The small area estimation is based on census Output Areas (OAs) using the 2013 Oxford Internet Survey (OxIS) and the 2011 British census. There is an estimate for each OA in Great Britain. By combining the 2013 OxIS survey data with the comprehensive small area coverage of the 2011 British census we can use the strengths of one to offset the gaps in the other. Specifically, we follow a two-step process. First, we use the information that is reliably available in OxIS to create model that estimates the proportion of Internet users in OAs. Second, we use the parameters from this model combined with census data to estimate the proportion of Internet users each OA in Britain. Once these estimates are available, we aggregate the estimates up to higher levels of geography. In this way we can estimate Internet use in Glasgow, Manchester and Cardiff as well as other small areas in Britain. This procedure is referred to as indirect, model-based or synthetic estimation. In recent years such SAE techniques have been widely used throughout Europe and North America. See the project website for more details.The objective of the Geography of Digital Inequality project was to explore the geographical contours of Internet use and penetration in Britain. Specifically, the project assembled from existing datasets a new dataset which contains Internet information at fine-grained geographic levels, census output areas (OAs). From OAs we were able to aggregate to higher geographic levels such as counties, Welsh and Scottish Councils, metropolitan areas, or others. Through this unique dataset we explored digital divides and the geography of the Internet, a capability possessed by no other dataset. Specifically, we explored the extent of use versus non-use of the Internet. There were 2 datasets used to assemble this dataset. First, the 2013 Oxford Internet Survey (OxIS) is a random sample of the 2657 people age 14+ from the British population (England, Scotland & Wales). Interviews were conducted face-to-face by an independent survey research company. The response rate for 2013 was 51%. The data collection was a two-stage sample. A random sample of census output areas (OAs) was selected and respondents were randomly sampled within each selected OA. For details, see "Data collection technical report.pdf" which has been uploaded. We use six variables from OxIS: Internet use, region, age, lifestage, gender and education. The questionnaire for OxIS contains about 300 variables and it is available from the OxIS website, see the URL in the "related resources" section. Second, the 2011 British Census. For information on how the census was conducted,see the census website. The URL for the 2011 census is given below in "related resources".

For further information about cycling - see the City of York Council website *Please note that the data published within this dataset is a live API link to CYC's GIS server. Any changes made to the master copy of the data will be immediately reflected in the resources of this dataset.The date shown in the "Last Updated" field of each GIS resource reflects when the data was first published.

Total-Revenue Time Series for United Internet AG NA. United Internet AG, through its subsidiaries, operates as an Internet service provider worldwide. The company operates through Consumer Access, Business Access, Consumer Applications, and Business Applications segments. It offers landline-based broadband and mobile internet products, including home networks, online storage, smart home, and IPTV for private users; and telecommunication products ranging from fiber-optic direct connections to tailored ICT solutions, which include voice, data, and network solutions, as well as infrastructure services to national and international carriers and ISPs. The company also provides applications and services for home users, such as personal information management applications comprising email, to-do lists, appointments, and addresses; and online cloud storage and office software. In addition, it provides business applications for freelancers and small to medium enterprises, such as domains, websites, web hosting, servers, e-shops, group work, online cloud storage, and office software, as well as cloud solutions and infrastructure. It offers its access products through the yourfone, smartmobile.de, 1&1, and 1&1 Versatel brand names; and applications through GMX, mail.com, WEB.DE, home.pl, Arsys, STRATO, IONOS, Fasthosts, we22, InterNetX, united-domains, and World4You brand names. In addition, the company offers professional services in the fields of active domain management; performance-based advertising and sales services under the Sedo brand name; online advertising services under the United Internet Media brand name; and white-label website builder services under the we22 brand, as well as sells IT hardware. The company was founded in 1988 and is headquartered in Montabaur, Germany.

The USGS National Hydrography Dataset (NHD) downloadable data collection from The National Map (TNM) is a comprehensive set of digital spatial data that encodes information about naturally occurring and constructed bodies of surface water (lakes, ponds, and reservoirs), paths through which water flows (canals, ditches, streams, and rivers), and related entities such as point features (springs, wells, stream gages, and dams). The information encoded about these features includes classification and other characteristics, delineation, geographic name, position and related measures, a "reach code" through which other information can be related to the NHD, and the direction of water flow. The network of reach codes delineating water and transported material flow allows users to trace movement in upstream and downstream directions. In addition to this geographic information, the dataset contains metadata that supports the exchange of future updates and improvements to the data. The NHD supports many applications, such as making maps, geocoding observations, flow modeling, data maintenance, and stewardship. For additional information on NHD, go to https://www.usgs.gov/core-science-systems/ngp/national-hydrography.

DWR was the steward for NHD and Watershed Boundary Dataset (WBD) in California. We worked with other organizations to edit and improve NHD and WBD, using the business rules for California. California's NHD improvements were sent to USGS for incorporation into the national database. The most up-to-date products are accessible from the USGS website. Please note that the California portion of the National Hydrography Dataset is appropriate for use at the 1:24,000 scale.

For additional derivative products and resources, including the major features in geopackage format, please go to this page: https://data.cnra.ca.gov/dataset/nhd-major-features Archives of previous statewide extracts of the NHD going back to 2018 may be found at https://data.cnra.ca.gov/dataset/nhd-archive.

In September 2022, USGS officially notified DWR that the NHD would become static as USGS resources will be devoted to the transition to the new 3D Hydrography Program (3DHP). 3DHP will consist of LiDAR-derived hydrography at a higher resolution than NHD. Upon completion, 3DHP data will be easier to maintain, based on a modern data model and architecture, and better meet the requirements of users that were documented in the Hydrography Requirements and Benefits Study (2016). The initial releases of 3DHP include NHD data cross-walked into the 3DHP data model. It will take several years for the 3DHP to be built out for California. Please refer to the resources on this page for more information.

The FINAL,STATIC version of the National Hydrography Dataset for California was published for download by USGS on December 27, 2023. This dataset can no longer be edited by the state stewards. The next generation of national hydrography data is the USGS 3D Hydrography Program (3DHP).

Questions about the California stewardship of these datasets may be directed to nhd_stewardship@water.ca.gov.