Daily utilization metrics for data.lacity.org and geohub.lacity.org. Updated monthly

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

Preliminary research efforts regarding Social Media Platforms and their contribution to website traffic in LAMs. Through the Similar Web API, the leading social networks (Facebook, Twitter, Youtube, Instagram, Reddit, Pinterest, LinkedIn) that drove traffic to each one of the 220 cases in our dataset were identified and analyzed in the first sheet. Aggregated results proved that Facebook platform was responsible for 46.1% of social traffic (second sheet).

Use the CMC_CG_Combo dataset, unless you want to recollect and DIY!

Context

On a quest to compare different cryptoexchanges, I came up with the idea to compare metrics across multiple platforms (at the moment just two). CoinGecko and CoinMarketCap are two of the biggest websites for monitoring both exchanges and cryptoprojects. In response to over-inflated volumes faked by crypto exchanges, both websites came up with independent metrics for assessing the worth of a given exchange.

Content

Collected on May 10, 2020

CoinGecko's data is a bit more holistic, containing metrics across a multitude of areas (you can read more in the original blog post here. The data from CoinGecko consists of the following:

-Exchange Name -Trust Score (on a scale of N/A-10) -Type (centralized/decentralized) -AML (risk: How well prepared are they to handle financial crime?) -API Coverage (Blanket Measure that includes: (1) Tickers Data (2) Historical Trades Data (3) Order Book Data (4) Candlestick/OHLC (5) WebSocket API (6) API Trading (7) Public Documentation -API Last Updated (When was the API last updated?) -Bid Ask Spread (Average buy/sell spread across all pairs) -Candlestick (Available/Not) -Combined Orderbook Percentile (See above link) -Estimated_Reserves (estimated holdings of major crypto) -Grade_Score (Overall API score) -Historical Data (available/not) -Jurisdiction Risk (risk: risk of Terrorist activity/bribery/corruption?) -KYC Procedures (risk: Know Your Customer?) -License and Authorization (risk: has exchange sought regulatory approval?) -Liquidity (don't confuse with "CMC Liquidity". THIS column is a combo of (1) Web traffic & Reported Volume (2) Order book spread (3) Trading Activity (4) Trust Score on Trading Pairs -Negative News (risk: any bad news?) -Normalized Trading Volume (Trading Volume normalized to web traffic) -Normalized Volume Percentile (see above blog link) -Orderbook (available/not) -Public Documentation (got well documented API available to everyone?) -Regulatory Compliance (risk rating from compliance perspective) -Regulatory last updated (last time regulatory metrics were updated) -Reported Trading Volume (volume as listed by the exchange) -Reported Normalized Trading Volume (Ratio of normalized to reported volume [0-1]) -Sanctions (risk: risk of sanctions?) -Scale (based on: (1) Normalized Trading Volume Percentile (2) Normalized Order Book Depth Percentile -Senior Public Figure (risk: does exchange have transparent public relations? etc) -Tickers (tick tick tick...) -Trading via API (can data be traded through the API?) -Websocket (got websockets?)

-Green Pairs (Percentage of trading pairs deemed to have good liquidity) -Yellow Pairs (Percentage of trading pairs deemed to have fair liquidity -Red Pairs (Percentage of trading pairs deemed to have poor liquidity) -Unknown Pairs (percentage of trading pairs that do not have sufficient order book data)

~

Again, CoinMarketCap only has one metric (that was recently updated and scales from 1-1000, 1000 being very liquid and 1 not. You can go check the article out for yourself. In the dataset, this is the "CMC Liquidity" column, not to be confused with the "Liquidity" column, which refers to the CoinGecko Metric!

Acknowledgements

Thanks to coingecko and cmc for making their data scrapable :)

[CMC, you should try to give us a little more access to the figures that define your metric. Thanks!]

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

AI Agent Platform Agent Meta and Traffic Dataset in AI Agent Marketplace | AI Agent Directory | AI Agent Index from DeepNLP

This dataset is collected from AI Agent Marketplace Index and Directory at http://www.deepnlp.org, which contains AI Agents's meta information such as agent's name, website, description, as well as the monthly updated Web performance metrics, including Google,Bing average search ranking positions, Github Stars, Arxiv References, etc. The dataset is helpful for… See the full description on the dataset page: https://huggingface.co/datasets/DeepNLP/ai-agent-platform.

Per the Federal Digital Government Strategy, the Department of Homeland Security Metrics Plan, and the Open FEMA Initiative, FEMA is providing the following web performance metrics with regards to FEMA.gov.rnrnInformation in this dataset includes total visits, avg visit duration, pageviews, unique visitors, avg pages/visit, avg time/page, bounce ratevisits by source, visits by Social Media Platform, and metrics on new vs returning visitors.rnrnExternal Affairs strives to make all communications accessible. If you have any challenges accessing this information, please contact FEMAWebTeam@fema.dhs.gov.

During a 2024 survey among marketers worldwide, around 86 percent reported using Facebook for marketing purposes. Instagram and LinkedIn followed, respectively mentioned by 79 and 65 percent of the respondents.

              The global social media marketing segment

              According to the same study, 59 percent of responding marketers intended to increase their organic use of YouTube for marketing purposes throughout that year. LinkedIn and Instagram followed with similar shares, rounding up the top three social media platforms attracting a planned growth in organic use among global marketers in 2024. Their main driver is increasing brand exposure and traffic, which led the ranking of benefits of social media marketing worldwide.

              Social media for B2B marketing

              Social media platform adoption rates among business-to-consumer (B2C) and business-to-business (B2B) marketers vary according to each subsegment's focus. While B2C professionals prioritize Facebook and Instagram – both run by Meta, Inc. – due to their popularity among online audiences, B2B marketers concentrate their endeavors on Microsoft-owned LinkedIn due to its goal to connect people and companies in a corporate context.

This data set features a hyperlink to the New York State Department of Transportation’s (NYSDOT) Traffic Data (TD) Viewer web page, which includes a link to the Traffic Data interactive map. The Traffic Data Viewer is a geospatially based Geographic Information System (GIS) application for displaying data contained in the roadway inventory database. The interactive map has five viewable data categories or ‘layers’. The five layers include: Average Daily Traffic (ADT); Continuous Counts; Short Counts; Bridges; and Grade Crossings throughout New York State.

Web traffic statistics for the several City-Parish websites, brla.gov, city.brla.gov, Red Stick Ready, GIS, Open Data etc. Information provided by Google Analytics.

This is a E-commerce website logs data created for helping the data analysts to practice exploratory data analysis and data visualization. The dataset has data on when the website was accessed, IP address of the source, Country, language in which website was accessed, amount of sales made by that IP address.

Included columns:

Time and duration of of accessing the website
Country, Language & Platform in which it was accessed
No. of bytes used & IP address of the person accessing website
Sales or return amount of that person

Technological advancements and the widespread availability of internet access have fueled the rapid global expansion of online gambling. These platforms offer users the flexibility to play anytime and anywhere, coupled with the allure of substantial profits and immersive gameplay, which contributes to their rising popularity. However, beneath this appeal lie significant risks, including addiction, financial loss, and potential involvement in criminal activities. In Europe alone, online gambling revenue has grown by approximately 9% annually and is expected to represent 41% of the total gambling industry revenue by 2026. Moreover, online gambling is increasingly associated with cybercrimes such as theft, fraud, and defacement attacks targeting government and educational websites, often combined with black-hat SEO techniques to boost traffic to illicit gambling sites and tarnish institutional reputations. To better understand the infrastructure behind these activities, this study involved accessing several online gambling websites and applications through three one-hour gameplay sessions. The resulting dataset identifies various gambling-related IP addresses, the services they utilize, and their countries of origin, providing valuable insights into the digital and geographical landscape of online gambling operations.

A log of dataset alerts open, monitored or resolved on the open data portal. Alerts can include issues as well as deprecation or discontinuation notices.

Open source data platform and multidisciplinary online repository where research groups and different organizations store and make public their datasets, managed by Scayle. Collection of public datasets are available through open.scayle.es and can be reused. NetFlow is network protocol developed by Cisco for collection and monitoring of network traffic flow data generated. Netflow datasets have been used to train machine learning models.

This dataset encompasses mobile app usage, web clickstream and location visitation behavior, collected from over 150,000 triple-opt-in first-party US Daily Active Users (DAU). The only omnichannel meter at scale representing iOS and Android platforms.

The Intelligent Road Network dataset provided by the Transport Department includes traffic directions, turning restrictions at road junctions, stopping restrictions, on-street parking spaces and other road traffic data for supporting the development of intelligent transport system, fleet management system and car navigation etc. by the public.

Esri China (HK) has prepared this File Geodatabase containing a Network Dataset for the Intelligent Road Network to support Esri GIS users to use the dataset in ArcGIS Pro without going through long configuration steps. Please refer to this guideline to use the Road Network Dataset in ArcGIS Pro for routing analysis. This network dataset has been configured and deployed the following restrictions:

Speed LimitTurnIntersectionTraffic FeaturesPedestrian ZoneTraffic Sign of ProhibitionVehicle RestrictionThe coordinate system of this dataset is Hong Kong 1980 Grid.The objectives of uploading the network dataset to ArcGIS Online platform are to facilitate our Hong Kong ArcGIS users to utilize the data in a spatial ready format and save their data conversion effort.For details about the schema and information about the content and relationship of the data, please refer to the data dictionary provided by Transport Department at https://data.gov.hk/en-data/dataset/hk-td-tis_15-road-network-v2.For details about the data, source format and terms of conditions of usage, please refer to the website of DATA.GOV.HK at https://data.gov.hk.Dataset last updated on: 2021 July

The global number of Facebook users was forecast to continuously increase between 2023 and 2027 by in total 391 million users (+14.36 percent). After the fourth consecutive increasing year, the Facebook user base is estimated to reach 3.1 billion users and therefore a new peak in 2027. Notably, the number of Facebook users was continuously increasing over the past years. User figures, shown here regarding the platform Facebook, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period and count multiple accounts by persons only once.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).

Total-Other-Income-Expense-Net Time Series for Semrush Holdings Inc. Semrush Holdings, Inc. develops an online visibility management software-as-a-service platform in the United States, the United Kingdom, and internationally. The company enables companies to identify and reach the right audience for their content through the right channels. Its platform enables the company's customers to understand trends and act upon insights to improve online visibility, and drive traffic to their websites and social media pages, as well as online listings, distribute targeted content to their customers, and measure the effectiveness of their digital marketing campaigns. The company serves small and midsize businesses, enterprises, and marketing agencies, including consumer internet, digital media, education, financial services, healthcare, retail, software, telecommunications, and others. Semrush Holdings, Inc. was founded in 2008 and is headquartered in Boston, Massachusetts.

Global Database’s B2B Sales & Marketing dataset helps revenue teams identify, engage, and convert ideal customers—faster. Covering over 300 million companies across 195+ countries, our data is sourced directly from official business registries, then enriched with:

✅ Verified contact details (emails, phone numbers, LinkedIn profiles)

✅ Job titles, seniority, and department filters

✅ Company firmographics: industry, size, location, ownership

✅ Website traffic insights and tech stack details

✅ Global company hierarchies and UBO/shareholder data

💡 Ideal For:

Sales prospecting & outbound campaigns

CRM data enrichment

ABM strategy execution

Channel and market expansion

🔄 Flexible Delivery: Access data via web platform, bulk files, or REST API. Integrate seamlessly with Salesforce, HubSpot, Microsoft Dynamics, and more.

📢 Stay Current: Receive alerts when companies change status, ownership, or financials—so your team always acts on accurate, up-to-date insights.

The MUTCD Official Rulings is a resource that allows web site visitors to obtain information about requests for changes, experiments, and interpretations related to the MUTCD that have been received by the FHWA. Copies of various documents (such as incoming request letters, response letters from the FHWA, progress reports, and final reports) that are available in both pdf and html formats may be viewed on this web site. The current status of experiments, as well as any contact information for the requestor that has been made a part of the public record, is also available.

This dataset consists of 24-hour traffic volumes which are collected by the City of Tempe high (arterial) and low (collector) volume streets. Data located in the tabular section shares with its users total volume of vehicles passing through the intersection selected along with the direction of flow.

Historical data from this feature layer extends from 2016 to present day.

Contact: Sue Taaffe

Contact E-Mail: sue_taaffe@tempe.gov

Contact Phone: 480-350-8663

Link to embedded web map:http://www.tempe.gov/city-hall/public-works/transportation/traffic-counts

Link to site containing historical traffic counts by node: https://gis.tempe.gov/trafficcounts/Folders/

Data Source: SQL Server/ArcGIS Server

Data Source Type: Geospatial

Preparation Method: N/A

Publish Frequency: As information changes

Publish Method: Automatic

Data Dictionary