Mobile accounts for approximately half of web traffic worldwide. In the last quarter of 2024, mobile devices (excluding tablets) generated 62.54 percent of global website traffic. Mobiles and smartphones consistently hoovered around the 50 percent mark since the beginning of 2017, before surpassing it in 2020. Mobile traffic Due to low infrastructure and financial restraints, many emerging digital markets skipped the desktop internet phase entirely and moved straight onto mobile internet via smartphone and tablet devices. India is a prime example of a market with a significant mobile-first online population. Other countries with a significant share of mobile internet traffic include Nigeria, Ghana and Kenya. In most African markets, mobile accounts for more than half of the web traffic. By contrast, mobile only makes up around 45.49 percent of online traffic in the United States. Mobile usage The most popular mobile internet activities worldwide include watching movies or videos online, e-mail usage and accessing social media. Apps are a very popular way to watch video on the go and the most-downloaded entertainment apps in the Apple App Store are Netflix, Tencent Video and Amazon Prime Video.

Traffic statistics for the For government franchise on the Queensland Government website. Source: Google Analytics.

Sessions, page views, users, devices and referrals for the DataWorks platform using information from Google Analytics. The interactive dashboard provides data by month from September 2018 for sessions, page views, users, devices and referrals to DataWorks. Previous years are covered by static dashboards. Excel and csv files give data on the use of datasets published on our Data Works platform. Please note, each file uploaded on the site is listed and this may include files now deleted or changed. Our Open Data is also published on Data.Gov.UK - Calderdale

Valuation Office Website Traffic and Stats . Published by Tailte Éireann – Surveying. Available under the license Creative Commons Attribution 4.0 (CC-BY-4.0).This dataset provides the number of users to the Valuation Office website....

The ckanext-ds-stats extension for CKAN provides analytics and statistics capabilities by integrating with Google Analytics. It leverages dga-stats and ga-report to pull data from Google Analytics and display relevant statistics within the CKAN interface, primarily focusing on package resource downloads. Facilitating data-driven decisions about dataset usage and popularity, this extension helps CKAN administrators understand how users interact with their data catalog. It also supports cross-domain tracking using Google's site linking feature. Key Features: Google Analytics Integration: Utilizes the Google Analytics API to retrieve website usage data, providing insights into dataset and resource access patterns. Download Tracking: Tracks and displays the number of downloads for individual resources on package pages, providing immediate feedback on resource popularity. Bounce Rate Tracking: Records bounce rate information for a specified page (typically the home page), enabling assessment of landing page effectiveness. Cross-Domain Tracking: Supports cross-domain tracking to consolidate analytics data from multiple related domains into a single Google Analytics property. Event Tracking: (Potentially for CKAN 1.x) Enables tracking of events beyond resource downloads, providing a more holistic view of user interactions with the CKAN instance. Configurable Analytics Settings: Offers several configuration options, including resource_prefix to easily filter resource downloads in Google Analytics, and domain settings to specify the tracking domain.

Vehicle travel time and delay data on sections of road in Hamilton City, based on Bluetooth sensor records. To get data for this dataset, please call the API directly talking to the HCC Data Warehouse: https://api.hcc.govt.nz/OpenData/get_traffic_link_stats?Page=1&Start_Date=2021-06-02&End_Date=2021-06-03. For this API, there are three mandatory parameters: Page, Start_Date, End_Date. Sample values for these parameters are in the link above. When calling the API for the first time, please always start with Page 1. Then from the returned JSON, you can see more information such as the total page count and page size. For help on using the API in your preferred data analysis software, please contact dale.townsend@hcc.govt.nz. NOTE: Anomalies and missing data may be present in the dataset.

Column_Info

Relationship









Disclaimer

Hamilton City Council does not make any representation or give any warranty as to the accuracy or exhaustiveness of the data released for public download. Levels, locations and dimensions of works depicted in the data may not be accurate due to circumstances not notified to Council. A physical check should be made on all levels, locations and dimensions before starting design or works.

Hamilton City Council shall not be liable for any loss, damage, cost or expense (whether direct or indirect) arising from reliance upon or use of any data provided, or Council's failure to provide this data.

While you are free to crop, export and re-purpose the data, we ask that you attribute the Hamilton City Council and clearly state that your work is a derivative and not the authoritative data source. Please include the following statement when distributing any work derived from this data:

‘This work is derived entirely or in part from Hamilton City Council data; the provided information may be updated at any time, and may at times be out of date, inaccurate, and/or incomplete.'

Vehicle travel time and delay data on sections of road in Hamilton City, based on Bluetooth sensor records. To get data for this dataset, please call the API directly talking to the HCC Data Warehouse: https://api.hcc.govt.nz/OpenData/get_traffic_link_stats?Page=1&Start_Date=2021-06-02&End_Date=2021-06-03. For this API, there are three mandatory parameters: Page, Start_Date, End_Date. Sample values for these parameters are in the link above. When calling the API for the first time, please always start with Page 1. Then from the returned JSON, you can see more information such as the total page count and page size. For help on using the API in your preferred data analysis software, please contact dale.townsend@hcc.govt.nz. NOTE: Anomalies and missing data may be present in the dataset.

Column_InfoLink_Id, int : Unique link identifierTravel_Time, int : Average travel time in seconds to travel along the linkAverage_Delay, int : Average travel delay in seconds, calculated as the difference between the free flow travel time and observed travel timeDate, varchar : Starting date and time for the recorded delay and travel time, in 15 minute periods

Relationship

This table reference to table Traffic_Link

Analytics

For convenience Hamilton City Council has also built a Quick Analytics Dashboard over this dataset that you can access here.

Disclaimer

Hamilton City Council does not make any representation or give any warranty as to the accuracy or exhaustiveness of the data released for public download. Levels, locations and dimensions of works depicted in the data may not be accurate due to circumstances not notified to Council. A physical check should be made on all levels, locations and dimensions before starting design or works.

Hamilton City Council shall not be liable for any loss, damage, cost or expense (whether direct or indirect) arising from reliance upon or use of any data provided, or Council's failure to provide this data.

While you are free to crop, export and re-purpose the data, we ask that you attribute the Hamilton City Council and clearly state that your work is a derivative and not the authoritative data source. Please include the following statement when distributing any work derived from this data:

‘This work is derived entirely or in part from Hamilton City Council data; the provided information may be updated at any time, and may at times be out of date, inaccurate, and/or incomplete.'

In November 2024, Google.com was the most popular website worldwide with 136 billion average monthly visits. The online platform has held the top spot as the most popular website since June 2010, when it pulled ahead of Yahoo into first place. Second-ranked YouTube generated more than 72.8 billion monthly visits in the measured period. The internet leaders: search, social, and e-commerce Social networks, search engines, and e-commerce websites shape the online experience as we know it. While Google leads the global online search market by far, YouTube and Facebook have become the world’s most popular websites for user generated content, solidifying Alphabet’s and Meta’s leadership over the online landscape. Meanwhile, websites such as Amazon and eBay generate millions in profits from the sale and distribution of goods, making the e-market sector an integral part of the global retail scene. What is next for online content? Powering social media and websites like Reddit and Wikipedia, user-generated content keeps moving the internet’s engines. However, the rise of generative artificial intelligence will bring significant changes to how online content is produced and handled. ChatGPT is already transforming how online search is performed, and news of Google's 2024 deal for licensing Reddit content to train large language models (LLMs) signal that the internet is likely to go through a new revolution. While AI's impact on the online market might bring both opportunities and challenges, effective content management will remain crucial for profitability on the web.

The ckanext-datavic-stats extension, a fork of CKAN's built-in statistics plugin, provides enhanced statistical analysis capabilities specifically tailored for data portals like data.gov.au. It modifies the core statistics functionality to provide more relevant and accurate insights. The extension focuses on presenting a refined view of data usage and engagement, optimizing the presentation of key metrics for informed decision-making. Key Features: Exclusion of Private Datasets: Removes private datasets from almost all statistical calculations (except for the top users section), ensuring that public statistics accurately reflect openly accessible data. This prevents skewing of results due to internal or non-public data. Summary Page Enhancement: Introduces or enhances a summary stats page, allowing administrators and users to quickly grasp key metrics related to dataset usage and engagement. This includes important data regarding the number of available public datasets. Activity Summary Page: Adds a dedicated activity summary page that aggregates public data activity metrics, providing a consolidated view of user interactions, dataset updates, and other relevant portal events. This enhances transparency and allows for better tracking of data portal usage. Organization-Level Public/Private Dataset Counts: Provides a dedicated page for organizations outlining the count of both public and private datasets they manage. This enhances organizational transparency and allows for easy auditing of data visibility settings. Use Cases: Data Portals: Enhances the statistical reporting capabilities of open data portals by focusing statistics on public datasets, providing portal administrators and users with more actionable insights into the use of openly accessible data and ensuring key statistics reflect the portals publicly available data offerings. Data Governance: Helps organisations monitor and maintain their data portfolios, with clear indicators of how much data is public. Technical Integration: This extension modifies the existing CKAN statistics plugin, seamlessly integrating with CKAN's user interface and backend. It likely overrides or extends the default statistical calculations and templates to implement its specific features without requiring substantial alterations to the core CKAN system. Benefits & Impact: By focusing statistics on public datasets, ckanext-datavic-stats provides portal administrators a clearer picture of which data is used and how. Added summary pages and organization data counts provide better ways for end users to understand their catalog's data, helping increase its value and utility. The exclusion of private datasets makes metrics more relevant for external users interested in open data usage trends and more targeted insights that can inform promotion and improvement strategies.

This Kaggle dataset comes from an output dataset that powers my March Madness Data Analysis dashboard in Domo. - Click here to view this dashboard: Dashboard Link - Click here to view this dashboard features in a Domo blog post: Hoops, Data, and Madness: Unveiling the Ultimate NCAA Dashboard

This dataset offers one the most robust resource you will find to discover key insights through data science and data analytics using historical NCAA Division 1 men's basketball data. This data, sourced from KenPom, goes as far back as 2002 and is updated with the latest 2025 data. This dataset is meticulously structured to provide every piece of information that I could pull from this site as an open-source tool for analysis for March Madness.

Key features of the dataset include: - Historical Data: Provides all historical KenPom data from 2002 to 2025 from the Efficiency, Four Factors (Offense & Defense), Point Distribution, Height/Experience, and Misc. Team Stats endpoints from KenPom's website. Please note that the Height/Experience data only goes as far back as 2007, but every other source contains data from 2002 onward. - Data Granularity: This dataset features an individual line item for every NCAA Division 1 men's basketball team in every season that contains every KenPom metric that you can possibly think of. This dataset has the ability to serve as a single source of truth for your March Madness analysis and provide you with the granularity necessary to perform any type of analysis you can think of. - 2025 Tournament Insights: Contains all seed and region information for the 2025 NCAA March Madness tournament. Please note that I will continually update this dataset with the seed and region information for previous tournaments as I continue to work on this dataset.

These datasets were created by downloading the raw CSV files for each season for the various sections on KenPom's website (Efficiency, Offense, Defense, Point Distribution, Summary, Miscellaneous Team Stats, and Height). All of these raw files were uploaded to Domo and imported into a dataflow using Domo's Magic ETL. In these dataflows, all of the column headers for each of the previous seasons are standardized to the current 2025 naming structure so all of the historical data can be viewed under the exact same field names. All of these cleaned datasets are then appended together, and some additional clean up takes place before ultimately creating the intermediate (INT) datasets that are uploaded to this Kaggle dataset. Once all of the INT datasets were created, I joined all of the tables together on the team name and season so all of these different metrics can be viewed under one single view. From there, I joined an NCAAM Conference & ESPN Team Name Mapping table to add a conference field in its full length and respective acronyms they are known by as well as the team name that ESPN currently uses. Please note that this reference table is an aggregated view of all of the different conferences a team has been a part of since 2002 and the different team names that KenPom has used historically, so this mapping table is necessary to map all of the teams properly and differentiate the historical conferences from their current conferences. From there, I join a reference table that includes all of the current NCAAM coaches and their active coaching lengths because the active current coaching length typically correlates to a team's success in the March Madness tournament. I also join another reference table to include the historical post-season tournament teams in the March Madness, NIT, CBI, and CIT tournaments, and I join another reference table to differentiate the teams who were ranked in the top 12 in the AP Top 25 during week 6 of the respective NCAA season. After some additional data clean-up, all of this cleaned data exports into the "DEV _ March Madness" file that contains the consolidated view of all of this data.

This dataset provides users with the flexibility to export data for further analysis in platforms such as Domo, Power BI, Tableau, Excel, and more. This dataset is designed for users who wish to conduct their own analysis, develop predictive models, or simply gain a deeper understanding of the intricacies that result in the excitement that Division 1 men's college basketball provides every year in March. Whether you are using this dataset for academic research, personal interest, or professional interest, I hope this dataset serves as a foundational tool for exploring the vast landscape of college basketball's most riveting and anticipated event of its season.

Table of statistics of exhumed burials from Oakwood Chapel Cemetery in Austin, Texas. Information comes from Hicks & Co report created for the city of Austin https://www.austintexas.gov/sites/default/files/files/Parks/Oakwood/Oakwood%20Monitoring%20and%20Exhumations%20Vol%20I%20and%20II%20-%20Public%20Distribution%20Copies%20-%20April%202020.pdfAll Together Here interprets the archaeology project at the Oakwood Cemetery Chapel. During the rehabilitation of the Chapel in 2016, there was a painful discovery: the Chapel had been constructed over preexisting burials.Oakwood Cemetery is in the heart of Austin. When it was established in 1839, it was on the outskirts of a 1.5 square mile frontier town. The cemetery holds the namesakes of many Austin buildings, streets, and beloved parks. The final resting place for more than 23,000 people, Oakwood Cemetery remains an enduring link to the community, cultures, and life stories that built the great city just beyond its gates.The Oakwood Cemetery Chapel was built in 1914, 75 years after the cemetery was established, to host funeral and memorial services on site. Reopened in 2019, the Chapel is a visitor center where we can, as an act of remembrance, learn about our cultural heritage through the people who were buried in the surrounding cemetery. The Chapel is part of the Museums and Cultural Programs Division of the Parks and Recreation Department of the City of Austin. Oakwood Cemetery Chapel's digital history exhibits are published here: www.austintexas.gov/page/oakwood-cemetery-chapel-resources

The trak extension for CKAN enhances the platform's tracking capabilities by providing tools to import Google Analytics data and modify the presentation of page view statistics. It introduces a paster command for importing page view data from exported Google Analytics CSV files, enabling users to supplement CKAN's built-in tracking. The extension also includes template customizations to alter how page view counts are displayed on dataset and resource listing pages. Key Features: Google Analytics Data Import: Imports page view data directly from a stripped-down CSV of Google Analytics data using a dedicated paster command (csv2table). The CSV should contain a list of page views, where each row starts with '/'. The PageViews column is expected to be the 3rd column. Customizable Page View Display: Changes the default presentation of page view statistics within CKAN, removing the minimum view count restriction (default is 10) so all views can be seen and modifies UI elements. Altered Page Tracking Stats: Alters the placement of page tracking statistics, moving them below Package Data (on dataset list pages) and Resource Data (on resource list pages) for better integration of tracking data. UI/UX Enhancements: Replaces the flame icon typically used for page tracking and substitutes it with more subtle background styling to modernize the presentation of tracking data. Backend Data Manipulation Uses a 'floor date' of 2011-01-01 for page view calculation. Entries are made in the trackingraw table for each view, with a unique UUID. Integration with CKAN: The extension integrates into CKAN's core functionalities by introducing a new paster command and modifying existing templates for displaying page view statistics. It relies on CKAN's built-in tracking to be enabled, but supplements its capabilities with imported data and presentation adjustments. After importing data using the csv2table paster command, the standard tracking update and search-index rebuild paster tasks need to be run to process the imported data and update the search index.. Benefits & Impact: By importing data from Google Analytics, the trak extension allows administrators to see a holistic view of page views. It changes the user experience to facilitate tracking statistics in a more integrated fashion. This allows for a better understanding of the impact and utilization of resources within the CKAN instance, based on Google Analytics data.

According to research by Sucuri, 60.04% of websites analyzed contained at least one backdoor, 52.6% of websites contained some form of SEO spam; 95.62% of those websites run on WordPress.

The Industrial On-Site Control Display market has emerged as a critical component in enhancing operational efficiency and visibility across various sectors. These systems play a vital role in providing real-time data and visual insights directly on the production floor, allowing operators to monitor processes, troub

NFL Football Stats

My family has always been serious about fantasy football. I've managed my own team since elementary school. It's a fun reason to talk with each other on a weekly basis for almost half the year.

Ever since I was in 8th grade I've dreamed of building an AI that could draft players and choose lineups for me. I started off in Excel and have since worked my way up to more sophisticated machine learning. The one thing that I've been lacking is really good data, which is why I decided to scrape pro-football-reference.com for all recorded NFL player data.

From what I've been able to determine researching, this is the most complete public source of NFL player stats available online. I scraped every NFL player in their database going back to the 1940s. That's over 25,000 players who have played over 1,000,000 football games.

The scraper code can be found here. Feel free to user, alter, or contribute to the repository.

The data was scraped 12/1/17-12/4/17

Shameless plug

When I uploaded this dataset back in 2017, I had two people reach out to me who shared my passion for fantasy football and data science. We quickly decided to band together to create machine-learning-generated fantasy football predictions. Our website is https://gridironai.com. Over the last several years, we've worked to add dozens of data sources to our data stream that's collected weekly. Feel free to use this scraper for basic stats, but if you'd like a more complete dataset that's updated every week, check out our site.

The data is broken into two parts. There is a players table where each player has been assigned an ID and a game stats table that has one entry per game played. These tables can be linked together using the player ID.

Player Profile Fields

• Player ID: The assigned ID for the player.
• Name: The player's full name.
• Position: The position the player played abbreviated to two characters. If the player played more than one position, the position field will be a comma-separated list of positions (i.e. "hb,qb").
• Height: The height of the player in feet and inches. The data format is

The dga-stats extension for CKAN enhances the platform's built-in statistics functionality. Adapted from CKAN's original statistics plugin, it provides data.gov.au-specific modifications focused on dataset visibility and summary reporting. By excluding private datasets from the majority of statistics and introducing dedicated summary pages, the extension aims to provide a clearer and more relevant overview of the CKAN instance's data landscape, helping users and administrators to better understand data usage and trends. Key Features: Exclusion of Private Datasets: Removes private datasets from most statistics calculations, providing a public-facing view of dataset popularity and usage. This ensures that only publicly accessible data influences key performance indicators reported through the dashboard. The exception is top users which include interactions with private datasets. Summary Page: Adds a dedicated summary page providing a high-level overview of key metrics on the CKAN instance. This offers a one-stop-shop for quick access to essential information about the portal's data holdings. Activity Summary Page: Introduces a page specifically designed to aggregate and display activity-related statistics. It offers insights into user engagement and data interaction patterns. Organization Public/Private Dataset Count Page: Provides a breakdown of public and private datasets within each organization. This reporting feature provides information about how organizations are managing their data within the platform. Technical Integration: The dga-stats extension modifies the existing CKAN statistics plugin. It achieves this by integrating custom code and configurations within the CKAN framework. Benefits & Impact: The dga-stats extension provides enhanced visibility into data usage on CKAN instances, optimized for platforms like data.gov.au. These enhancements offer improved insights for site administrators and increased transparency for users by emphasizing public dataset statistics and providing comprehensive summary reporting.

Carnivores countries w stats

Esports Performance Rankings and Results

Performance Rankings and Results from Multiple Esports Platforms

By [source]

About this dataset

This dataset provides a detailed look into the world of competitive video gaming in universities. It covers a wide range of topics, from performance rankings and results across multiple esports platforms to the individual team and university rankings within each tournament. With an incredible wealth of data, fans can discover statistics on their favorite teams or explore the challenges placed upon university gamers as they battle it out to be the best. Dive into the information provided and get an inside view into the world of collegiate esports tournaments as you assess all things from Match ID, Team 1, University affiliations, Points earned or lost in each match and special Seeds or UniSeeds for exceptional teams. Of course don't forget about exploring all the great Team Names along with their corresponding websites for further details on stats across tournaments!

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

Download Files First, make sure you have downloaded the CS_week1, CS_week2, CS_week3 and seeds datasets on Kaggle. You will also need to download the currentRankings file for each week of competition. All files should be saved using their originally assigned name in order for your analysis tools to read them properly (ie: CS_week1.csv).

Understand File Structure Once all data has been collected and organized into separate files on your desktop/laptop computer/mobile device/etc., it's time to become familiar with what type of information is included in each file. The main folder contains three main data files: week1-3 and seedings. The week1-3 contain teams matched against one another according to university, point score from match results as well as team name and website URL associated with university entry; whereas the seedings include a ranking system amongst university entries which are accompanied by information regarding team names, website URLs etc.. Furthermore, there is additional file featured which contains currentRankings scores for each individual player/teams for an first given period of competition (ie: first week).

Analyzing Data Now that everything is set up on your end it’s time explore! You can dive deep into trends amongst universities or individual players in regards to specific match performances or standings overall throughout weeks of competition etc… Furthermore you may also jumpstart insights via further creation of graphs based off compiled date from sources taken from BUECTracker dataset! For example let us say we wanted compare two universities- let's say Harvard University v Cornell University - against one another since beginning of event i we shall extract respective points(column),dates(column)(found under result tab) ,regions(csilluminating North America vs Europe etc)general stats such as maps played etc.. As well any other custom ideas which would come along in regards when dealing with similar datasets!

Research Ideas

• Analyze the performance of teams and identify areas for improvement for better performance in future competitions.
• Assess which esports platforms are the most popular among gamers.
• Gain a better understanding of player rankings across different regions, based on rankings system, to create targeted strategies that could boost individual players' scoring potential or team overall success in competitive gaming events

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: CS_week1.csv | Column name | Description | |:---------------|:----------------------------------------------| | Match ID | Unique identifier for each match. (Integer) | | Team 1 | Name of the first team in the match. (String) | | University | University associated with the team. (String) |

File: CS_week1_currentRankings.csv | Column name | Description | |:--------------|:-----------------------------------------------------------|...

Reddit is a web traffic powerhouse: in March 2024 approximately 2.2 billion visits were measured to the online forum, making it one of the most-visited websites online. The front page of the internet Formerly known as “the front page of the internet”, Reddit is an online forum platform with over 130,000 sub-forums and communities. The platform allows registered users, called Redditors, to post content. Each post is open to the entire Reddit community to vote upon, either by down- or upvotes. The most popular posts are featured directly on the front page. Subreddits are available by category and Redditors can follow selected subreddits relevant to their interest and also control what content they see on their custom front page. Some of the most popular subreddits are r/AskReddit or r/AMA – the “Ask Me Anything” format. According to the company, Reddit hosted 1,800 AMAs in 2018, with a wide range of topics and hosts. One of the most popular Reddit AMA of 2022 by number of upvotes was by actor Nicolas Cagem with more than 238.5 thousand upvotes. Reddit usage The United States account for the biggest share of Reddit's desktop traffic, followed by the UK, and Canada. As of March 2023, Reddit ranked among the most popular social media websites in the United States.

Context

The data has been extracted from UN website (link) and contains information about population & age stats, mortality rates etc. of various countries.

Content

The datasets with prefix - WPP2019* are datasets as downloaded from the above link. Whereas the rest of the datasets with *_manual suffix have been cleaned manually for the purpose of using in the Covid19-global competition to differentiate between countries.

Countries that are not available here include but are relevant to the competition include: Andorra, Cruise Ship, Greenland, Guernsey, Holy See, Jersey, Kosovo, Liechtenstein, Monaco, San Marino

Acknowledgements

All thanks to UN for maintaining these stats in an easily accessible website.