25 datasets found
  1. Average load time for mobile sites in APAC 2018, by country

    • statista.com
    Updated Jul 9, 2025
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    Statista (2025). Average load time for mobile sites in APAC 2018, by country [Dataset]. https://www.statista.com/statistics/1034822/apac-average-mobile-site-load-time-by-country/
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    Dataset updated
    Jul 9, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2018
    Area covered
    Asia-Pacific
    Description

    Within the Asia Pacific region, China had the fastest load time for mobile sites with *** seconds, followed by Singapore with an average load time of seven seconds. Nevertheless, the load times are still slower than the recommended load time of ***** seconds. The same research found that for every second of delay in mobile site load time, there was a ** percent drop in conversions.

  2. Load times: Android and iPhone

    • statista.com
    Updated Mar 17, 2011
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    Statista (2011). Load times: Android and iPhone [Dataset]. https://www.statista.com/statistics/272176/website-load-times-with-android-and-iphone/
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    Dataset updated
    Mar 17, 2011
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Mar 2011
    Area covered
    Worldwide
    Description

    The statistic shows the average load time of websites regarding the iPhone 4.3 and Android 2.3.

  3. u

    Website Speed Impact Statistics - What’s Included in a Website Maintenance...

    • uysys.com
    Updated Aug 13, 2025
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    UYSYS (2025). Website Speed Impact Statistics - What’s Included in a Website Maintenance Plan? [Dataset]. https://www.uysys.com/whats-included-in-a-website-maintenance-plan/
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    Dataset updated
    Aug 13, 2025
    Dataset authored and provided by
    UYSYS
    License

    https://www.uysys.com/terms-conditions/https://www.uysys.com/terms-conditions/

    Description

    Data showing how website loading speed affects visitor bounce rates

  4. Average time taken to load a web page on 3G & 4G in the UK in 2014, by...

    • statista.com
    Updated Nov 13, 2014
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    Statista (2014). Average time taken to load a web page on 3G & 4G in the UK in 2014, by provider [Dataset]. https://www.statista.com/statistics/398680/average-time-taken-to-load-a-web-page-on-3g-and-4g-by-provider-in-the-uk/
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    Dataset updated
    Nov 13, 2014
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Mar 2014 - Jun 2014
    Area covered
    United Kingdom
    Description

    This statistic compares the time taken to load a web page on 3G and 4G networks in the United Kingdom (UK) as of *********, by provider. In *********, the page-loading time on Three's 4G network was **** seconds and that on its 3G network was **** seconds, faster than any of the other providers.

  5. S

    Landing Page Statistics By Types And Facts (2025)

    • sci-tech-today.com
    Updated Jun 23, 2025
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    Sci-Tech Today (2025). Landing Page Statistics By Types And Facts (2025) [Dataset]. https://www.sci-tech-today.com/stats/landing-page-statistics-updated/
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    Dataset updated
    Jun 23, 2025
    Dataset authored and provided by
    Sci-Tech Today
    License

    https://www.sci-tech-today.com/privacy-policyhttps://www.sci-tech-today.com/privacy-policy

    Time period covered
    2022 - 2032
    Area covered
    Global
    Description

    Introduction

    Landing Page Statistics: Landing pages are dedicated web pages designed to convert visitors into leads or customers by focusing on a single, clear call to action. In 2024, the median landing page conversion rate across industries is 6.6%, with top-performing pages exceeding 20%. Email-driven traffic achieves the highest average conversion rate at 19.3%, outperforming paid search (10.9%) and paid social (12%).

    Mobile devices account for 82.9% of landing page traffic, yet desktop users exhibit a higher average conversion rate of 12.1% compared to 11.2% for mobile users. Speed is crucial; a one-second delay in page load time can reduce conversions by 7%. Incorporating videos can boost conversions by 86%, and personalized landing pages can convert 202% better than generic ones.

    Design elements significantly impact performance. Landing pages with five or fewer form fields convert 120% better than those with more fields. Pages with a single, clear call to action achieve a 13.5% conversion rate, compared to 11.9% for pages with multiple CTAs. Additionally, 38.6% of marketers report that videos enhance landing page conversion rates more than any other element.

    Let us check out some of the Landing page statistics concerning landing page performance and the secrets of landing page success.

  6. d

    Web Traffic Data | 500M+ US Web Traffic Data Resolution | B2B and B2C...

    • datarade.ai
    .csv, .xls
    Updated Feb 22, 2024
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    Allforce (2024). Web Traffic Data | 500M+ US Web Traffic Data Resolution | B2B and B2C Website Visitor Identity Resolution [Dataset]. https://datarade.ai/data-products/traffic-continuum-from-solution-publishing-500m-us-web-traf-solution-publishing
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    .csv, .xlsAvailable download formats
    Dataset updated
    Feb 22, 2024
    Dataset authored and provided by
    Allforce
    Area covered
    United States of America
    Description

    Unlock the Potential of Your Web Traffic with Advanced Data Resolution

    In the digital age, understanding and leveraging web traffic data is crucial for businesses aiming to thrive online. Our pioneering solution transforms anonymous website visits into valuable B2B and B2C contact data, offering unprecedented insights into your digital audience. By integrating our unique tag into your website, you unlock the capability to convert 25-50% of your anonymous traffic into actionable contact rows, directly deposited into an S3 bucket for your convenience. This process, known as "Web Traffic Data Resolution," is at the forefront of digital marketing and sales strategies, providing a competitive edge in understanding and engaging with your online visitors.

    Comprehensive Web Traffic Data Resolution Our product stands out by offering a robust solution for "Web Traffic Data Resolution," a process that demystifies the identities behind your website traffic. By deploying a simple tag on your site, our technology goes to work, analyzing visitor behavior and leveraging proprietary data matching techniques to reveal the individuals and businesses behind the clicks. This innovative approach not only enhances your data collection but does so with respect for privacy and compliance standards, ensuring that your business gains insights ethically and responsibly.

    Deep Dive into Web Traffic Data At the core of our solution is the sophisticated analysis of "Web Traffic Data." Our system meticulously collects and processes every interaction on your site, from page views to time spent on each section. This data, once anonymous and perhaps seen as abstract numbers, is transformed into a detailed ledger of potential leads and customer insights. By understanding who visits your site, their interests, and their contact information, your business is equipped to tailor marketing efforts, personalize customer experiences, and streamline sales processes like never before.

    Benefits of Our Web Traffic Data Resolution Service Enhanced Lead Generation: By converting anonymous visitors into identifiable contact data, our service significantly expands your pool of potential leads. This direct enhancement of your lead generation efforts can dramatically increase conversion rates and ROI on marketing campaigns.

    Targeted Marketing Campaigns: Armed with detailed B2B and B2C contact data, your marketing team can create highly targeted and personalized campaigns. This precision in marketing not only improves engagement rates but also ensures that your messaging resonates with the intended audience.

    Improved Customer Insights: Gaining a deeper understanding of your web traffic enables your business to refine customer personas and tailor offerings to meet market demands. These insights are invaluable for product development, customer service improvement, and strategic planning.

    Competitive Advantage: In a digital landscape where understanding your audience can make or break your business, our Web Traffic Data Resolution service provides a significant competitive edge. By accessing detailed contact data that others in your industry may overlook, you position your business as a leader in customer engagement and data-driven strategies.

    Seamless Integration and Accessibility: Our solution is designed for ease of use, requiring only the placement of a tag on your website to start gathering data. The contact rows generated are easily accessible in an S3 bucket, ensuring that you can integrate this data with your existing CRM systems and marketing tools without hassle.

    How It Works: A Closer Look at the Process Our Web Traffic Data Resolution process is streamlined and user-friendly, designed to integrate seamlessly with your existing website infrastructure:

    Tag Deployment: Implement our unique tag on your website with simple instructions. This tag is lightweight and does not impact your site's loading speed or user experience.

    Data Collection and Analysis: As visitors navigate your site, our system collects web traffic data in real-time, analyzing behavior patterns, engagement metrics, and more.

    Resolution and Transformation: Using advanced data matching algorithms, we resolve the collected web traffic data into identifiable B2B and B2C contact information.

    Data Delivery: The resolved contact data is then securely transferred to an S3 bucket, where it is organized and ready for your access. This process occurs daily, ensuring you have the most up-to-date information at your fingertips.

    Integration and Action: With the resolved data now in your possession, your business can take immediate action. From refining marketing strategies to enhancing customer experiences, the possibilities are endless.

    Security and Privacy: Our Commitment Understanding the sensitivity of web traffic data and contact information, our solution is built with security and privacy at its core. We adhere to strict data protection regulat...

  7. I

    Global Website Speed Testing Tool Market Strategic Recommendations 2025-2032...

    • statsndata.org
    excel, pdf
    Updated Aug 2025
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    Stats N Data (2025). Global Website Speed Testing Tool Market Strategic Recommendations 2025-2032 [Dataset]. https://www.statsndata.org/report/website-speed-testing-tool-market-95706
    Explore at:
    excel, pdfAvailable download formats
    Dataset updated
    Aug 2025
    Dataset authored and provided by
    Stats N Data
    License

    https://www.statsndata.org/how-to-orderhttps://www.statsndata.org/how-to-order

    Area covered
    Global
    Description

    The Website Speed Testing Tool market has increasingly become a critical component of online performance optimization, as businesses recognize that page load speed significantly impacts user experience, search engine rankings, and conversion rates. With internet users growing more impatient and demanding faster load

  8. S

    Global Web Page Performance Testing Tools Market Growth Opportunities...

    • statsndata.org
    excel, pdf
    Updated Aug 2025
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    Stats N Data (2025). Global Web Page Performance Testing Tools Market Growth Opportunities 2025-2032 [Dataset]. https://www.statsndata.org/report/web-page-performance-testing-tools-market-39338
    Explore at:
    excel, pdfAvailable download formats
    Dataset updated
    Aug 2025
    Dataset authored and provided by
    Stats N Data
    License

    https://www.statsndata.org/how-to-orderhttps://www.statsndata.org/how-to-order

    Area covered
    Global
    Description

    As businesses increasingly shift towards digital platforms, the demand for Web Page Performance Testing Tools has surged, emerging as a critical component for optimizing online presence and enhancing user experience. These tools allow organizations to analyze various performance metrics, such as load times, responsi

  9. Web Analytics Market Analysis, Size, and Forecast 2025-2029: North America...

    • technavio.com
    pdf
    Updated Apr 29, 2025
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    Technavio (2025). Web Analytics Market Analysis, Size, and Forecast 2025-2029: North America (US and Canada), Europe (France, Germany, Italy, and UK), APAC (China, India, Japan, and South Korea), and Rest of World (ROW) [Dataset]. https://www.technavio.com/report/web-analytics-market-industry-analysis
    Explore at:
    pdfAvailable download formats
    Dataset updated
    Apr 29, 2025
    Dataset provided by
    TechNavio
    Authors
    Technavio
    License

    https://www.technavio.com/content/privacy-noticehttps://www.technavio.com/content/privacy-notice

    Time period covered
    2025 - 2029
    Area covered
    United States, Canada
    Description

    Snapshot img

    Web Analytics Market Size 2025-2029

    The web analytics market size is forecast to increase by USD 3.63 billion, at a CAGR of 15.4% between 2024 and 2029.

    The market is experiencing significant growth, driven by the rising preference for online shopping and the increasing adoption of cloud-based solutions. The shift towards e-commerce is fueling the demand for advanced web analytics tools that enable businesses to gain insights into customer behavior and optimize their digital strategies. Furthermore, cloud deployment models offer flexibility, scalability, and cost savings, making them an attractive option for businesses of all sizes. However, the market also faces challenges associated with compliance to data privacy and regulations. With the increasing amount of data being generated and collected, ensuring data security and privacy is becoming a major concern for businesses.
    Regulatory compliance, such as GDPR and CCPA, adds complexity to the implementation and management of web analytics solutions. Companies must navigate these challenges effectively to maintain customer trust and avoid potential legal issues. To capitalize on market opportunities and address these challenges, businesses should invest in robust web analytics solutions that prioritize data security and privacy while providing actionable insights to inform strategic decision-making and enhance customer experiences.
    

    What will be the Size of the Web Analytics Market during the forecast period?

    Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
    Request Free Sample

    The market continues to evolve, with dynamic market activities unfolding across various sectors. Entities such as reporting dashboards, schema markup, conversion optimization, session duration, organic traffic, attribution modeling, conversion rate optimization, call to action, content calendar, SEO audits, website performance optimization, link building, page load speed, user behavior tracking, and more, play integral roles in this ever-changing landscape. Data visualization tools like Google Analytics and Adobe Analytics provide valuable insights into user engagement metrics, helping businesses optimize their content strategy, website design, and technical SEO. Goal tracking and keyword research enable marketers to measure the return on investment of their efforts and refine their content marketing and social media marketing strategies.

    Mobile optimization, form optimization, and landing page optimization are crucial aspects of website performance optimization, ensuring a seamless user experience across devices and improving customer acquisition cost. Search console and page speed insights offer valuable insights into website traffic analysis and help businesses address technical issues that may impact user behavior. Continuous optimization efforts, such as multivariate testing, data segmentation, and data filtering, allow businesses to fine-tune their customer journey mapping and cohort analysis. Search engine optimization, both on-page and off-page, remains a critical component of digital marketing, with backlink analysis and page authority playing key roles in improving domain authority and organic traffic.

    The ongoing integration of user behavior tracking, click-through rate, and bounce rate into marketing strategies enables businesses to gain a deeper understanding of their audience and optimize their customer experience accordingly. As market dynamics continue to evolve, the integration of these tools and techniques into comprehensive digital marketing strategies will remain essential for businesses looking to stay competitive in the digital landscape.

    How is this Web Analytics Industry segmented?

    The web analytics industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

    Deployment
    
      Cloud-based
      On-premises
    
    
    Application
    
      Social media management
      Targeting and behavioral analysis
      Display advertising optimization
      Multichannel campaign analysis
      Online marketing
    
    
    Component
    
      Solutions
      Services
    
    
    Geography
    
      North America
    
        US
        Canada
    
    
      Europe
    
        France
        Germany
        Italy
        UK
    
    
      APAC
    
        China
        India
        Japan
        South Korea
    
    
      Rest of World (ROW)
    

    .

    By Deployment Insights

    The cloud-based segment is estimated to witness significant growth during the forecast period.

    In today's digital landscape, web analytics plays a pivotal role in driving business growth and optimizing online performance. Cloud-based deployment of web analytics is a game-changer, enabling on-demand access to computing resources for data analysis. This model streamlines business intelligence processes by collecting, integra

  10. MAP for website - Satellite Maps Western Hemisphere

    • noaa.hub.arcgis.com
    Updated Aug 4, 2023
    + more versions
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    NOAA GeoPlatform (2023). MAP for website - Satellite Maps Western Hemisphere [Dataset]. https://noaa.hub.arcgis.com/maps/4406a7daa7b94b5f8c8364f7f2dc9bf2
    Explore at:
    Dataset updated
    Aug 4, 2023
    Dataset provided by
    National Oceanic and Atmospheric Administrationhttp://www.noaa.gov/
    Authors
    NOAA GeoPlatform
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Area covered
    Description

    This application is intended for informational purposes only and is not an operational product. The tool provides the capability to access, view and interact with satellite imagery, and shows the latest view of Earth as it appears from space.For additional imagery from NOAA's GOES East and GOES West satellites, please visit our Imagery and Data page or our cooperative institute partners at CIRA and CIMSS.This website should not be used to support operational observation, forecasting, emergency, or disaster mitigation operations, either public or private. In addition, we do not provide weather forecasts on this site — that is the mission of the National Weather Service. Please contact them for any forecast questions or issues. Using the Maps​What does the Layering Options icon mean?The Layering Options widget provides a list of operational layers and their symbols, and allows you to turn individual layers on and off. The order in which layers appear in this widget corresponds to the layer order in the map. The top layer ‘checked’ will indicate what you are viewing in the map, and you may be unable to view the layers below.Layers with expansion arrows indicate that they contain sublayers or subtypes.What does the Time Slider icon do?The Time Slider widget enables you to view temporal layers in a map, and play the animation to see how the data changes over time. Using this widget, you can control the animation of the data with buttons to play and pause, go to the previous time period, and go to the next time period.Do these maps work on mobile devices and different browsers?Yes!Why are there black stripes / missing data on the map?NOAA Satellite Maps is for informational purposes only and is not an operational product; there are times when data is not available.Why does the imagery load slowly?This map viewer does not load pre-generated web-ready graphics and animations like many satellite imagery apps you may be used to seeing. Instead, it downloads geospatial data from our data servers through a Map Service, and the app in your browser renders the imagery in real-time. Each pixel needs to be rendered and geolocated on the web map for it to load.How can I get the raw data and download the GIS World File for the images I choose?The geospatial data Map Service for the NOAA Satellite Maps GOES satellite imagery is located on our Satellite Maps ArcGIS REST Web Service ( available here ).We support open information sharing and integration through this RESTful Service, which can be used by a multitude of GIS software packages and web map applications (both open and licensed).Data is for display purposes only, and should not be used operationally.Are there any restrictions on using this imagery?NOAA supports an open data policy and we encourage publication of imagery from NOAA Satellite Maps; when doing so, please cite it as "NOAA" and also consider including a permalink (such as this one) to allow others to explore the imagery.For acknowledgment in scientific journals, please use:We acknowledge the use of imagery from the NOAA Satellite Maps application: LINKThis imagery is not copyrighted. You may use this material for educational or informational purposes, including photo collections, textbooks, public exhibits, computer graphical simulations and internet web pages. This general permission extends to personal web pages. About this satellite imageryWhat am I looking at in these maps?In this map you are seeing the past 24 hours (updated approximately every 10 minutes) of the Western Hemisphere and Pacific Ocean, as seen by the NOAA GOES East (GOES-16) and GOES West (GOES-18) satellites. In this map you can also view four different ‘layers’. The views show ‘GeoColor’, ‘infrared’, and ‘water vapor’.This maps shows the coverage area of the GOES East and GOES West satellites. GOES East, which orbits the Earth from 75.2 degrees west longitude, provides a continuous view of the Western Hemisphere, from the West Coast of Africa to North and South America. GOES West, which orbits the Earth at 137.2 degrees west longitude, sees western North and South America and the central and eastern Pacific Ocean all the way to New Zealand.What does the GOES GeoColor imagery show?The 'Merged GeoColor’ map shows the coverage area of the GOES East and GOES West satellites and includes the entire Western Hemisphere and most of the Pacific Ocean. This imagery uses a combination of visible and infrared channels and is updated approximately every 15 minutes in real time. GeoColor imagery approximates how the human eye would see Earth from space during daylight hours, and is created by combining several of the spectral channels from the Advanced Baseline Imager (ABI) – the primary instrument on the GOES satellites. The wavelengths of reflected sunlight from the red and blue portions of the spectrum are merged with a simulated green wavelength component, creating RGB (red-green-blue) imagery. At night, infrared imagery shows high clouds as white and low clouds and fog as light blue. The static city lights background basemap is derived from a single composite image from the Visible Infrared Imaging Radiometer Suite (VIIRS) Day Night Band. For example, temporary power outages will not be visible. Learn more.What does the GOES infrared map show?The 'GOES infrared' map displays heat radiating off of clouds and the surface of the Earth and is updated every 15 minutes in near real time. Higher clouds colorized in orange often correspond to more active weather systems. This infrared band is one of 12 channels on the Advanced Baseline Imager, the primary instrument on both the GOES East and West satellites. on the GOES the multiple GOES East ABI sensor’s infrared bands, and is updated every 15 minutes in real time. Infrared satellite imagery can be "colorized" or "color-enhanced" to bring out details in cloud patterns. These color enhancements are useful to meteorologists because they signify “brightness temperatures,” which are approximately the temperature of the radiating body, whether it be a cloud or the Earth’s surface. In this imagery, yellow and orange areas signify taller/colder clouds, which often correlate with more active weather systems. Blue areas are usually “clear sky,” while pale white areas typically indicate low-level clouds. During a hurricane, cloud top temperatures will be higher (and colder), and therefore appear dark red. This imagery is derived from band #13 on the GOES East and GOES West Advanced Baseline Imager.How does infrared satellite imagery work?The infrared (IR) band detects radiation that is emitted by the Earth’s surface, atmosphere and clouds, in the “infrared window” portion of the spectrum. The radiation has a wavelength near 10.3 micrometers, and the term “window” means that it passes through the atmosphere with relatively little absorption by gases such as water vapor. It is useful for estimating the emitting temperature of the Earth’s surface and cloud tops. A major advantage of the IR band is that it can sense energy at night, so this imagery is available 24 hours a day.What do the colors on the infrared map represent?In this imagery, yellow and orange areas signify taller/colder clouds, which often correlate with more active weather systems. Blue areas are clear sky, while pale white areas indicate low-level clouds, or potentially frozen surfaces. Learn more about this weather imagery.What does the GOES water vapor map layer show?The GOES ‘water vapor’ map displays the concentration and location of clouds and water vapor in the atmosphere and shows data from both the GOES East and GOES West satellites. Imagery is updated approximately every 15 minutes in real time. Water vapor imagery, which is useful for determining locations of moisture and atmospheric circulations, is created using a wavelength of energy sensitive to the content of water vapor in the atmosphere. In this imagery, green-blue and white areas indicate the presence of high water vapor or moisture content, whereas dark orange and brown areas indicate little or no moisture present. This imagery is derived from band #10 on the GOES East and GOES West Advanced Baseline Imager.What do the colors on the water vapor map represent?In this imagery, green-blue and white areas indicate the presence of high water vapor or moisture content, whereas dark orange and brown areas indicate less moisture present. Learn more about this water vapor imagery.About the satellitesWhat are the GOES satellites?NOAA’s most sophisticated Geostationary Operational Environmental Satellites (GOES), known as the GOES-R Series, provide advanced imagery and atmospheric measurements of Earth’s Western Hemisphere, real-time mapping of lightning activity, and improved monitoring of solar activity and space weather.The first satellite in the series, GOES-R, now known as GOES-16, was launched in 2016 and is currently operational as NOAA’s GOES East satellite. In 2018, NOAA launched another satellite in the series, GOES-T, which joined GOES-16 in orbit as GOES-18. GOES-17 became operational as GOES West in January 2023.Together, GOES East and GOES West provide coverage of the Western Hemisphere and most of the Pacific Ocean, from the west coast of Africa all the way to New Zealand. Each satellite orbits the Earth from about 22,200 miles away.

  11. Information Organizations and Websites Performance

    • kaggle.com
    zip
    Updated Sep 17, 2020
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    Information Management Research Lab (2020). Information Organizations and Websites Performance [Dataset]. https://www.kaggle.com/imrlab/information-organizations-websites-global-report
    Explore at:
    zip(3013660 bytes)Available download formats
    Dataset updated
    Sep 17, 2020
    Authors
    Information Management Research Lab
    License

    Attribution-NonCommercial-ShareAlike 4.0 (CC BY-NC-SA 4.0)https://creativecommons.org/licenses/by-nc-sa/4.0/
    License information was derived automatically

    Description

    Notice: You can check the new version 0.9.6 at the official page of Information Management Lab and at the Google Data Studio as well.

    Description of the Report and Topic Justification

    Now that the ICTs have matured, Information Organizations such as Libraries, Archives and Museums, also known as LAMs, proceed into the utilization of web technologies that are capable to expand the visibility and findability of their content. Within the current flourishing era of the semantic web, LAMs have voluminous amounts of web-based collections that are presented and digitally preserved through their websites. However, prior efforts indicate that LAMs suffer from fragmentation regarding the determination of well-informed strategies for improving the visibility and findability of their content on the Web (Vállez and Ventura, 2020; Krstić and Masliković, 2019; Voorbij, 2010). Several reasons related to this drawback. As such, administrators’ lack of data analytics competency in extracting and utilizing technical and behavioral datasets for improving visibility and awareness from analytics platforms; the difficulties in understanding web metrics that integrated into performance measurement systems; and hence the reduced capabilities in defining key performance indicators for greater usability, visibility, and awareness.

    In this enriched and updated technical report, the authors proceed into an examination of 504 unique websites of Libraries, Archives and Museums from all over the world. It is noted that the current report has been expanded by up to 14,81% of the prior one Version 0.9.5 of 439 domains examinations. The report aims to visualize the performance of the websites in terms of technical aspects such as their adequacy to metadata description of their content and collections, their loading speed, and security. This constitutes an important stepping-stone for optimization, as the higher the alignment with the technical compliencies, the greater the users’ behavior and usability within the examined websites, and thus their findability and visibility level in search engines (Drivas et al. 2020; Mavridis and Symeonidis 2015; Agarwal et al. 2012).

    One step further, within this version, we include behavioral analytics about users engagement with the content of the LAMs websites. More specifically, web analytics metrics are included such as Visit Duration, Pages per Visit, and Bounce Rates for 121 domains. We also include web analytics regarding the channels that these websites acquire their users, such as Direct traffic, Search Engines, Referral, Social Media, Email, and Display Advertising. SimilarWeb API was used to gather web data about the involved metrics.

    In the first pages of this report, general information is presented regarding the names of the examined organizations. This also includes their type, their geographical location, information about the adopted Content Management Systems (CMSs), and web server software types of integration per website. Furthermore, several other data are visualized related to the size of the examined Information Organizations in terms of the number of unique webpages within a website, the number of images, internal and external links and so on.

    Moreover, as a team, we proceed into the development of several factors that are capable to quantify the performance of websites. Reliability analysis takes place for measuring the internal consistency and discriminant validity of the proposed factors and their included variables. For testing the reliability, cohesion, and consistency of the included metrics, Cronbach’s Alpha (a), McDonald’s ω and Guttman λ-2 and λ-6 are used.
    - For Cronbach’s, a range of .550 up to .750 indicates an acceptable level of reliability and .800 or higher a very good level (Ursachi, Horodnic, and Zait, 2015). - McDonald’s ω indicator has the advantage to measure the strength of the association between the proposed variables. More specifically, the closer to .999 the higher the strength association between the variables and vice versa (Şimşek and Noyan, 2013). - Gutman’s λ-2 and λ-6 work verifiably to Cronbach’s a as they estimate the trustworthiness of variance of the gathered web analytics metrics. Low values less than .450 indicate high bias among the harvested web metrics, while values higher than .600 and above increase the trustworthiness of the sample (Callender and Osburn, 1979). -Kaiser–Meyer–Olkin (KMO) and Bartlett’s Test of Sphericity indicators are used for measuring the cohesion of the involved metrics. KMO and Bartlett’s test indicates that the closer the value is to .999 amongst the involved items, the higher the cohesion and consistency of them for potential categorization (Dziuban and Shirkey, 1974). Both descriptive statistics and reliability analyses were performed via JASP 0.14.1.0 software.

    To this end, this report contributes to the knowledge expansion of all the interest parties and stakeholders related to the research topic of improving the visibility and findability of LAMs and their content on the Web. It constitutes a well-informed compass, that could be adopted by such organizations, in order to implement potential strategies that combine both domain knowledge and data-driven culture in terms of awareness optimization on the internet realm.

    The Research Team Behind the Project

    The whole project is managed and optimized on a weekly basis by a big young and smiley team of scientists (alphabetically referred in the next section). All of them are undergraduate students at the Department of Archival, Library and Information Studies of the University of West Attica.

    They are responsible for the overall process of publishing the Technical Report which includes the initial organizations’ identification, and subsequently, websites testing, data gathering, curation and pre-processing, analysis, validation and visualization. Of course, the Team will continue to expand the capabilities of this report while involving new features, metrics, and further information regarding Libraries, Archives and Museums websites from all over the world.

    Notice: includes a plurality of technical and behavioral factors and variables concerning the examined information organizations' websites. Potentially, more features will be included on the next versions.

    Report Version 0.9.6 Correspondence: Ioannis C. Drivas (PhDc) idrivas@uniwa.gr | http://users.uniwa.gr/idrivas/ Research Lab of Information Management Department of Archival, Library Science and Information Studies University of West Attica.

  12. Z

    Data from: Replication Data for "Exploring Genetic Improvement of the Carbon...

    • data.niaid.nih.gov
    Updated Oct 5, 2023
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    Gregory Gay (2023). Replication Data for "Exploring Genetic Improvement of the Carbon Footprint of Web Pages" [Dataset]. https://data.niaid.nih.gov/resources?id=zenodo_8347914
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    Dataset updated
    Oct 5, 2023
    Dataset provided by
    Haozhou Lyu
    Gregory Gay
    Maiko Sakamoto
    License

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

    Description

    Overview

    In this study, we explore automated reduction of the carbon footprint of web pages through genetic improvement, a process that produces alternative versions of a program by applying program transformations intended to optimize qualities of interest. We introduce a prototype tool that imposes transformations to HTML, CSS, and JavaScript code, as well as image resources, that minimize the quantity of data transferred and memory usage while also minimizing impact to the user experience (measured through loading time and number of changes imposed).

    In an evaluation, our tool outperforms two baselines---the original page and randomized changes---in the average case on all projects for data transfer quantity, and 80% of projects for memory usage and load time, often with large effect size. Our results illustrate the applicability of genetic improvement to reduce the carbon footprint of web components, and offer lessons that can benefit the design of future tools.

    Data Contained in This Package

    • experiment_data/Subject Project-XX-X.xlsx

    Each spreadsheet contains data collected as part of our experiments, including the fitness scores of the final solutions.

  13. Multilingual Scraper of Privacy Policies and Terms of Service

    • zenodo.org
    bin, zip
    Updated Apr 24, 2025
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    David Bernhard; David Bernhard; Luka Nenadic; Luka Nenadic; Stefan Bechtold; Karel Kubicek; Karel Kubicek; Stefan Bechtold (2025). Multilingual Scraper of Privacy Policies and Terms of Service [Dataset]. http://doi.org/10.5281/zenodo.14562039
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    zip, binAvailable download formats
    Dataset updated
    Apr 24, 2025
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    David Bernhard; David Bernhard; Luka Nenadic; Luka Nenadic; Stefan Bechtold; Karel Kubicek; Karel Kubicek; Stefan Bechtold
    License

    Attribution-NonCommercial-NoDerivs 4.0 (CC BY-NC-ND 4.0)https://creativecommons.org/licenses/by-nc-nd/4.0/
    License information was derived automatically

    Description

    Multilingual Scraper of Privacy Policies and Terms of Service: Scraped Documents of 2024

    This dataset supplements publication "Multilingual Scraper of Privacy Policies and Terms of Service" at ACM CSLAW’25, March 25–27, 2025, München, Germany. It includes the first 12 months of scraped policies and terms from about 800k websites, see concrete numbers below.

    The following table lists the amount of websites visited per month:

    MonthNumber of websites
    2024-01551'148
    2024-02792'921
    2024-03844'537
    2024-04802'169
    2024-05805'878
    2024-06809'518
    2024-07811'418
    2024-08813'534
    2024-09814'321
    2024-10817'586
    2024-11828'662
    2024-12827'101

    The amount of websites visited should always be higher than the number of jobs (Table 1 of the paper) as a website may redirect, resulting in two websites scraped or it has to be retried.

    To simplify the access, we release the data in large CSVs. Namely, there is one file for policies and another for terms per month. All of these files contain all metadata that are usable for the analysis. If your favourite CSV parser reports the same numbers as above then our dataset is correctly parsed. We use ‘,’ as a separator, the first row is the heading and strings are in quotes.

    Since our scraper sometimes collects other documents than policies and terms (for how often this happens, see the evaluation in Sec. 4 of the publication) that might contain personal data such as addresses of authors of websites that they maintain only for a selected audience. We therefore decided to reduce the risks for websites by anonymizing the data using Presidio. Presidio substitutes personal data with tokens. If your personal data has not been effectively anonymized from the database and you wish for it to be deleted, please contact us.

    Preliminaries

    The uncompressed dataset is about 125 GB in size, so you will need sufficient storage. This also means that you likely cannot process all the data at once in your memory, so we split the data in months and in files for policies and terms.

    Files and structure

    The files have the following names:

    • 2024_policy.csv for policies
    • 2024_terms.csv for terms

    Shared metadata

    Both files contain the following metadata columns:

    • website_month_id - identification of crawled website
    • job_id - one website can have multiple jobs in case of redirects (but most commonly has only one)
    • website_index_status - network state of loading the index page. This is resolved by the Chromed DevTools Protocol.
      • DNS_ERROR - domain cannot be resolved
      • OK - all fine
      • REDIRECT - domain redirect to somewhere else
      • TIMEOUT - the request timed out
      • BAD_CONTENT_TYPE - 415 Unsupported Media Type
      • HTTP_ERROR - 404 error
      • TCP_ERROR - error in the network connection
      • UNKNOWN_ERROR - unknown error
    • website_lang - language of index page detected based on langdetect library
    • website_url - the URL of the website sampled from the CrUX list (may contain subdomains, etc). Use this as a unique identifier for connecting data between months.
    • job_domain_status - indicates the status of loading the index page. Can be:
      • OK - all works well (at the moment, should be all entries)
      • BLACKLISTED - URL is on our list of blocked URLs
      • UNSAFE - website is not safe according to save browsing API by Google
      • LOCATION_BLOCKED - country is in the list of blocked countries
    • job_started_at - when the visit of the website was started
    • job_ended_at - when the visit of the website was ended
    • job_crux_popularity - JSON with all popularity ranks of the website this month
    • job_index_redirect - when we detect that the domain redirects us, we stop the crawl and create a new job with the target URL. This saves time if many websites redirect to one target, as it will be crawled only once. The index_redirect is then the job.id corresponding to the redirect target.
    • job_num_starts - amount of crawlers that started this job (counts restarts in case of unsuccessful crawl, max is 3)
    • job_from_static - whether this job was included in the static selection (see Sec. 3.3 of the paper)
    • job_from_dynamic - whether this job was included in the dynamic selection (see Sec. 3.3 of the paper) - this is not exclusive with from_static - both can be true when the lists overlap.
    • job_crawl_name - our name of the crawl, contains year and month (e.g., 'regular-2024-12' for regular crawls, in Dec 2024)

    Policy data

    • policy_url_id - ID of the URL this policy has
    • policy_keyword_score - score (higher is better) according to the crawler's keywords list that given document is a policy
    • policy_ml_probability - probability assigned by the BERT model that given document is a policy
    • policy_consideration_basis - on which basis we decided that this url is policy. The following three options are executed by the crawler in this order:
      1. 'keyword matching' - this policy was found using the crawler navigation (which is based on keywords)
      2. 'search' - this policy was found using search engine
      3. 'path guessing' - this policy was found by using well-known URLs like example.com/policy
    • policy_url - full URL to the policy
    • policy_content_hash - used as identifier - if the document remained the same between crawls, it won't create a new entry
    • policy_content - contains the text of policies and terms extracted to Markdown using Mozilla's readability library
    • policy_lang - Language detected by fasttext of the content

    Terms data

    Analogous to policy data, just substitute policy to terms.

    Updates

    Check this Google Docs for an updated version of this README.md.

  14. Share of global mobile website traffic 2015-2025

    • statista.com
    Updated Sep 11, 2025
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    Statista (2025). Share of global mobile website traffic 2015-2025 [Dataset]. https://www.statista.com/statistics/277125/share-of-website-traffic-coming-from-mobile-devices/
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    Dataset updated
    Sep 11, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    Worldwide
    Description

    In the second quarter of 2025, mobile devices (excluding tablets) accounted for 62.54 percent of global website traffic. Since consistently maintaining a share of around 50 percent beginning in 2017, mobile usage surpassed this threshold in 2020 and has demonstrated steady growth in its dominance of global web access. 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.

  15. Romanian Institute of Statistics

    • hosted-metadata.bgs.ac.uk
    Updated Dec 13, 2012
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    Romanian Institute of Statistics, Bd Libertatii nr.16 sector 5, +4021 3181824; +4021 3181842 , romstat@insse.ro (2012). Romanian Institute of Statistics [Dataset]. https://hosted-metadata.bgs.ac.uk/geonetwork/srv/api/records/70c8d9f8-a012-4a19-8663-b66d2e4c8e61
    Explore at:
    Dataset updated
    Dec 13, 2012
    Dataset provided by
    British Geological Surveyhttps://www.bgs.ac.uk/
    NSI Romaniahttp://www.insse.ro/cms/ro
    Area covered
    Description

    TEMPO-Online provides the following functions and services: Free access to statistical information.Export of tables in .csv and .xls formats and its printing. What is the content of TEMPO-Online? The National Institute of Statistics offers a statistical database, TEMPO-Online, that gives the possibility to access a large range of information.The content of the above-mentioned database consists of:Approximately 1100 statistical indicators, divided in socio-economical fields and sub-fields; Metadata associated to the statistical indicators (definition, starting and ending year of the time series, the last period of data loading, statistical methodology, the last updating); Detailed indicators at statistical characteristics group and/or sub-group level ( ex. The total number of employees at the end of the year by employee category, activities of the national economy - sections, sexes, areas and counties); Time series starting with 1990 - till today: With a monthly, quarterly, semi-annual and annual frequency; At national level, development region level, county and commune level. Search according to key words The search key words allows the finding of various objects (tables with statistical variables divided on time series). The search will give back results based on the matrix code and on the key words in the title or in the definition of a matrix. The result of the search will show on a list with specific objects. For a key word, one can use the searching section from the menu bar on the left.Tables As a whole, the tables that result following an interrogation have a flexible structure. For instance, the user may select the variables and attributes with the help of the interrogation interface, according to his needs.The user can save the table that results following an interrogation in .csv and .xls formats and its printingNote: in order to access tables at place level (very large), the user has to select each county with the respective places, so that the access be faster and avoid technical blocks.

    Website: http://statistici.insse.ro/shop/?lang=en

  16. S

    Global Website Speed and Performance Test Tool Market Business Opportunities...

    • statsndata.org
    excel, pdf
    Updated Jul 2025
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    Stats N Data (2025). Global Website Speed and Performance Test Tool Market Business Opportunities 2025-2032 [Dataset]. https://www.statsndata.org/report/website-speed-and-performance-test-tool-market-317638
    Explore at:
    pdf, excelAvailable download formats
    Dataset updated
    Jul 2025
    Dataset authored and provided by
    Stats N Data
    License

    https://www.statsndata.org/how-to-orderhttps://www.statsndata.org/how-to-order

    Area covered
    Global
    Description

    In today's digital landscape, where user experience is paramount, the demand for Website Speed and Performance Test Tools has surged significantly. These tools play a crucial role in helping businesses assess and optimize their website performance, ensuring that pages load quickly and run efficiently. As user expect

  17. I

    Global Web Server Accelerator Tool Market Economic and Social Impact...

    • statsndata.org
    excel, pdf
    Updated Aug 2025
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    Stats N Data (2025). Global Web Server Accelerator Tool Market Economic and Social Impact 2025-2032 [Dataset]. https://www.statsndata.org/report/web-server-accelerator-tool-market-172998
    Explore at:
    excel, pdfAvailable download formats
    Dataset updated
    Aug 2025
    Dataset authored and provided by
    Stats N Data
    License

    https://www.statsndata.org/how-to-orderhttps://www.statsndata.org/how-to-order

    Area covered
    Global
    Description

    The Web Server Accelerator Tool market is experiencing significant growth as businesses increasingly prioritize website performance and user experience. These tools optimize the delivery and processing of content, reducing load times and enhancing server efficiency. With the ever-evolving digital landscape, where sp

  18. Evaluation of STENCIL load times.

    • plos.figshare.com
    xls
    Updated Jun 4, 2023
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    Qi Sun; Ali Nematbakhsh; Prashant K. Kuntala; Gretta Kellogg; B. Franklin Pugh; William K. M. Lai (2023). Evaluation of STENCIL load times. [Dataset]. http://doi.org/10.1371/journal.pcbi.1009859.t001
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jun 4, 2023
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Qi Sun; Ali Nematbakhsh; Prashant K. Kuntala; Gretta Kellogg; B. Franklin Pugh; William K. M. Lai
    License

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

    Description

    Evaluation of STENCIL load times.

  19. H

    Health and Retirement Study (HRS)

    • dataverse.harvard.edu
    • search.dataone.org
    Updated May 30, 2013
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    Anthony Damico (2013). Health and Retirement Study (HRS) [Dataset]. http://doi.org/10.7910/DVN/ELEKOY
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    May 30, 2013
    Dataset provided by
    Harvard Dataverse
    Authors
    Anthony Damico
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    analyze the health and retirement study (hrs) with r the hrs is the one and only longitudinal survey of american seniors. with a panel starting its third decade, the current pool of respondents includes older folks who have been interviewed every two years as far back as 1992. unlike cross-sectional or shorter panel surveys, respondents keep responding until, well, death d o us part. paid for by the national institute on aging and administered by the university of michigan's institute for social research, if you apply for an interviewer job with them, i hope you like werther's original. figuring out how to analyze this data set might trigger your fight-or-flight synapses if you just start clicking arou nd on michigan's website. instead, read pages numbered 10-17 (pdf pages 12-19) of this introduction pdf and don't touch the data until you understand figure a-3 on that last page. if you start enjoying yourself, here's the whole book. after that, it's time to register for access to the (free) data. keep your username and password handy, you'll need it for the top of the download automation r script. next, look at this data flowchart to get an idea of why the data download page is such a righteous jungle. but wait, good news: umich recently farmed out its data management to the rand corporation, who promptly constructed a giant consolidated file with one record per respondent across the whole panel. oh so beautiful. the rand hrs files make much of the older data and syntax examples obsolete, so when you come across stuff like instructions on how to merge years, you can happily ignore them - rand has done it for you. the health and retirement study only includes noninstitutionalized adults when new respondents get added to the panel (as they were in 1992, 1993, 1998, 2004, and 2010) but once they're in, they're in - respondents have a weight of zero for interview waves when they were nursing home residents; but they're still responding and will continue to contribute to your statistics so long as you're generalizing about a population from a previous wave (for example: it's possible to compute "among all americans who were 50+ years old in 1998, x% lived in nursing homes by 2010"). my source for that 411? page 13 of the design doc. wicked. this new github repository contains five scripts: 1992 - 2010 download HRS microdata.R loop through every year and every file, download, then unzip everything in one big party impor t longitudinal RAND contributed files.R create a SQLite database (.db) on the local disk load the rand, rand-cams, and both rand-family files into the database (.db) in chunks (to prevent overloading ram) longitudinal RAND - analysis examples.R connect to the sql database created by the 'import longitudinal RAND contributed files' program create tw o database-backed complex sample survey object, using a taylor-series linearization design perform a mountain of analysis examples with wave weights from two different points in the panel import example HRS file.R load a fixed-width file using only the sas importation script directly into ram with < a href="http://blog.revolutionanalytics.com/2012/07/importing-public-data-with-sas-instructions-into-r.html">SAScii parse through the IF block at the bottom of the sas importation script, blank out a number of variables save the file as an R data file (.rda) for fast loading later replicate 2002 regression.R connect to the sql database created by the 'import longitudinal RAND contributed files' program create a database-backed complex sample survey object, using a taylor-series linearization design exactly match the final regression shown in this document provided by analysts at RAND as an update of the regression on pdf page B76 of this document . click here to view these five scripts for more detail about the health and retirement study (hrs), visit: michigan's hrs homepage rand's hrs homepage the hrs wikipedia page a running list of publications using hrs notes: exemplary work making it this far. as a reward, here's the detailed codebook for the main rand hrs file. note that rand also creates 'flat files' for every survey wave, but really, most every analysis you c an think of is possible using just the four files imported with the rand importation script above. if you must work with the non-rand files, there's an example of how to import a single hrs (umich-created) file, but if you wish to import more than one, you'll have to write some for loops yourself. confidential to sas, spss, stata, and sudaan users: a tidal wave is coming. you can get water up your nose and be dragged out to sea, or you can grab a surf board. time to transition to r. :D

  20. UK e-retailers: mobile page load speed rating 2017-2018

    • statista.com
    Updated Dec 20, 2023
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    Statista Research Department (2023). UK e-retailers: mobile page load speed rating 2017-2018 [Dataset]. https://www.statista.com/topics/5425/retail-technology-in-the-uk/
    Explore at:
    Dataset updated
    Dec 20, 2023
    Dataset provided by
    Statistahttp://statista.com/
    Authors
    Statista Research Department
    Area covered
    United Kingdom
    Description

    This statistic presents UK retailers' mobile page load speeds as rated by Google from 2017 to 2018. As displayed in the chart, in 2018 62 percent of UK retailers who sell online had a poor rating on their page load speed across mobile devices. The percentage of e-retailers whose mobile page load speed was rated as fair decreased from 32 percent to 26 percent in 2018. Only one percent of UK e-retailers had mobile page load speeds that received an excellent Google rating in 2018, representing a decrease from two percent in 2017.

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Statista (2025). Average load time for mobile sites in APAC 2018, by country [Dataset]. https://www.statista.com/statistics/1034822/apac-average-mobile-site-load-time-by-country/
Organization logo

Average load time for mobile sites in APAC 2018, by country

Explore at:
Dataset updated
Jul 9, 2025
Dataset authored and provided by
Statistahttp://statista.com/
Time period covered
2018
Area covered
Asia-Pacific
Description

Within the Asia Pacific region, China had the fastest load time for mobile sites with *** seconds, followed by Singapore with an average load time of seven seconds. Nevertheless, the load times are still slower than the recommended load time of ***** seconds. The same research found that for every second of delay in mobile site load time, there was a ** percent drop in conversions.

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