A 2021 survey among Italian digital professionals found that the optimization of mobile experience and user experience was considered the most relevant factor by over ** percent of the respondents, while around ** percent of the professionals thought that the relationship between search intent of the users and the website's content was becoming more relevant.

Using four metrics—**Authority Score, Referring Domains, Citation Flow, and Trust Flow**—with an equal weight of 25%, SCImago constructs an overall indicator that reflects media websites’ digital reputation. The results define their relative position in the ranking and permit a comparison of digital development and leadership.

☢️❓The entire dataset is obtained from public and open-access data of SCImago Media Rankings

This is the data repository for the paper Anytime Ranking on Document-Ordered Indexes by Joel Mackenzie, Matthias Petri, and Alistair Moffat. This paper appeared in ACM TOIS in 2021.

Google.com continues to dominate the digital landscape, ranking as the most linked-to website worldwide with 468,745 referring subnets as of December 2024. Facebook.com follows closely in second place with 470,479 referring subnets, while YouTube.com ranked third with 417,827.

This statistic displays a list of websites ranking factors becoming less relevant in Italy in 2019. According to survey results, the length of texts and the richness of content would impact less the ranking of websites for ***** percent of professionals in the digital sector.

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 SCImago Institutions Rankings (SIR) is a classification of academic and research-related institutions ranked by a composite indicator that combines three different sets of indicators based on research performance, innovation outputs and societal impact measured by their web visibility. It provides a friendly interface that allows the visualization of any customized ranking from the combination of these three sets of indicators. Additionally, it is possible to compare the trends for individual indicators of up to six institutions. For each large sector it is also possible to obtain distribution charts of the different indicators. For comparative purposes, the value of the composite indicator has been set on a scale of 0 to 100. However the line graphs and bar graphs always represent ranks (lower is better, so the highest values are the worst).

This repository contains performance measures of dataset ranking models.- Usage: from Results/src run Python results m1 m2 ...such that mi can be omitted, or be any element of the list of model labels ['bayesian-12C', 'bayesian-5L', 'bayesian-5L12C', 'cos-12C', 'cos-5L', 'cos-5L5C', 'j48-12C', 'j48-5L', 'j48-5L5C', 'jrip-12C', 'jrip-5L', 'jrip-5L5C', 'sn-12C', 'sn-5L', 'sn-5L12C']. Results of selected models will be plotted in a 2D line plot. If no model is provided all models will be listed.

Data of investigation published in the article: "Using Machine Learning for Web Page Classification in Search Engine Optimization"

Abstract of the article:

This paper presents a novel approach of using machine learning algorithms based on experts’ knowledge to classify web pages into three predefined classes according to the degree of content adjustment to the search engine optimization (SEO) recommendations. In this study, classifiers were built and trained to classify an unknown sample (web page) into one of the three predefined classes and to identify important factors that affect the degree of page adjustment. The data in the training set are manually labeled by domain experts. The experimental results show that machine learning can be used for predicting the degree of adjustment of web pages to the SEO recommendations—classifier accuracy ranges from 54.59% to 69.67%, which is higher than the baseline accuracy of classification of samples in the majority class (48.83%). Practical significance of the proposed approach is in providing the core for building software agents and expert systems to automatically detect web pages, or parts of web pages, that need improvement to comply with the SEO guidelines and, therefore, potentially gain higher rankings by search engines. Also, the results of this study contribute to the field of detecting optimal values of ranking factors that search engines use to rank web pages. Experiments in this paper suggest that important factors to be taken into consideration when preparing a web page are page title, meta description, H1 tag (heading), and body text—which is aligned with the findings of previous research. Another result of this research is a new data set of manually labeled web pages that can be used in further research.

AS Rank is CAIDA's ranking of Autonomous Systems (AS) (which approximately map to Internet Service Providers) and organizations (Orgs) (which are a collection of one or more ASes). This ranking is derived from topological data collected by CAIDA's Archipelago Measurement Infrastructure and Border Gateway Protocol (BGP) routing data collected by the Route Views Project and RIPE NCC.
ASes and Orgs are ranked by their customer cone size, which is the number of their direct and indirect customers.
Note: We do not have data to rank ASes (ISPs) by traffic, revenue, users, or any other non-topological metric..

The numbers in the parentheses are the ranking orders according to the focus indicators.Top 15 websites with highest PageRank.

TRANSPARENT RANKING: All Repositories by Google Scholar.

Incluye Top 10 repositorios de Colombia 2018 y 2019

Fuente: http://repositories.webometrics.info/en/node/30

Designed and produced by the World Wide Web Foundation, the Web Index is the world’s first measure of the World Wide Web’s contribution to social, economic and political progress in countries across the world. http://thewebindex.org/about/ Scores are given in the areas of universal access; freedom and openness; relevant content; and empowerment. First released in 2012, the 2014-15 Index has been expanded and refined to include a total of 86 countries and features an enhanced data set, particularly in the areas of gender, Open Data, privacy rights and censorship. The Index combines existing secondary data with new primary data derived from an evidence-based expert assessment survey. The Web Index provides an objective and robust evidence base to inform public dialogue on the steps needed for societies to leverage greater value from the Web. It is published annually and resources permitting, it will continue to be expanded to cover more countries in the coming years. It will eventually allow for comparisons of trends over time and the benchmarking of performance across countries, continuously improving our understanding of the Web’s value for humanity.

Ranking top 17 web sites according to flow distances and comparisons with other ranking methods.

This ranking report attempts to identify the best law school home pages based exclusively on objective criteria. The goal is to assess elements that make websites easier to use for sighted as well as visually-impaired users. Most elements require no special design skills, sophisticated technology or significant expenses. Ranking results in this report represent reasonably relevant elements. In this report, 200 ABA-accredited law school home pages are analyzed and ranked for twenty elements in three broad categories: Design Patterns & Metadata; Accessibility & Validation; and Marketing & Communications. As was the case in 2009, there is still no objective way to account for good taste. For interpreting these results, we don't try to decide if any whole is greater or less than the sum of its parts.

Explore the Times Higher Education World University Rankings 2025 below. Trusted worldwide by students, teachers, governments and industry experts, the list ranks 2,092 institutions from 115 countries and territories.

Basic Info:

The Dataset represents the County Health Ranking of all states taking into account the various factors The County Health Rankings can be used to highlight regional variations in health, increase public understanding of the various factors that affect health, and inspire actions to improve community health. The Rankings capitalizes on our innate desire to compete by enabling comparisons across adjacent or comparable counties within states.

Dataset Information:

The CSV file contains the rankings and data details for the measures used in the 2022/23 County Health Rankings.
1) Outcomes and Factors Rankings --Ranks are all calculated and reported WITHIN states
2)**Outcomes and Factors SubRankings** --Ranks are all calculated and reported WITHIN states
3) Ranked Measure Data --The measures themselves are listed in bold.
4) Ranked Measure Sources & Years
5) Additional Measure Data --These are supplemental measures reported on the Rankings web site but not used in calculating the rankings.
6) Additional Measure Sources & Years

The Data Types of all Columns are automatically set to "Object" To change it just use data.apply(pd.to_numeric)

See https://domain-ranking-design-decisions.distrinet-research.be/ for a full project description.

DataForSEO Labs API offers three powerful keyword research algorithms and historical keyword data:

• Related Keywords from the “searches related to” element of Google SERP. • Keyword Suggestions that match the specified seed keyword with additional words before, after, or within the seed key phrase. • Keyword Ideas that fall into the same category as specified seed keywords. • Historical Search Volume with current cost-per-click, and competition values.

Based on in-market categories of Google Ads, you can get keyword ideas from the relevant Categories For Domain and discover relevant Keywords For Categories. You can also obtain Top Google Searches with AdWords and Bing Ads metrics, product categories, and Google SERP data.

You will find well-rounded ways to scout the competitors:

• Domain Whois Overview with ranking and traffic info from organic and paid search. • Ranked Keywords that any domain or URL has positions for in SERP. • SERP Competitors and the rankings they hold for the keywords you specify. • Competitors Domain with a full overview of its rankings and traffic from organic and paid search. • Domain Intersection keywords for which both specified domains rank within the same SERPs. • Subdomains for the target domain you specify along with the ranking distribution across organic and paid search. • Relevant Pages of the specified domain with rankings and traffic data. • Domain Rank Overview with ranking and traffic data from organic and paid search. • Historical Rank Overview with historical data on rankings and traffic of the specified domain from organic and paid search. • Page Intersection keywords for which the specified pages rank within the same SERP.

All DataForSEO Labs API endpoints function in the Live mode. This means you will be provided with the results in response right after sending the necessary parameters with a POST request.

The limit is 2000 API calls per minute, however, you can contact our support team if your project requires higher rates.

We offer well-rounded API documentation, GUI for API usage control, comprehensive client libraries for different programming languages, free sandbox API testing, ad hoc integration, and deployment support.

We have a pay-as-you-go pricing model. You simply add funds to your account and use them to get data. The account balance doesn't expire.

The dataset contains relevance scores for websites recommended to different users, and comprises of 30, 000 user-website pairs. For a user i and website j, the data contains a 136-dimensional feature vector uj i, which consists of user i’s attributes corresponding to website j, such as length of stay or number of clicks on the website. Furthermore, for each user-website pair, the dataset also contains a relevance score, i.e. how relevant the website was to the user.