This Website Statistics dataset has four resources showing usage of the Lincolnshire Open Data website. Web analytics terms used in each resource are defined in their accompanying Metadata file.

• Website Usage Statistics: This document shows a statistical summary of usage of the Lincolnshire Open Data site for the latest calendar year.

• Website Statistics Summary: This dataset shows a website statistics summary for the Lincolnshire Open Data site for the latest calendar year.

• Webpage Statistics: This dataset shows statistics for individual Webpages on the Lincolnshire Open Data site by calendar year.

• Dataset Statistics: This dataset shows cumulative totals for Datasets on the Lincolnshire Open Data site that have also been published on the national Open Data site Data.Gov.UK - see the Source link.

  Note: Website and Webpage statistics (the first three resources above) show only UK users, and exclude API calls (automated requests for datasets). The Dataset Statistics are confined to users with javascript enabled, which excludes web crawlers and API calls.

These Website Statistics resources are updated annually in January by the Lincolnshire County Council Business Intelligence team. For any enquiries about the information contact opendata@lincolnshire.gov.uk.

Note: This dataset has been archived and is no longer being updated due to a change in analytics platform. You can find the new dataset relating to Website Statistics in the following link; https://lincolnshire.ckan.io/dataset/website-statistics This Website Statistics dataset has three resources showing usage of the Lincolnshire Open Data website. Web analytics terms used in each resource are defined in their accompanying Metadata file. Website Usage Statistics: This document shows a statistical summary of usage of the Lincolnshire Open Data site for the latest calendar year. Website Statistics Summary: This dataset shows a website statistics summary for the Lincolnshire Open Data site for the latest calendar year. Webpage Statistics: This dataset shows statistics for individual Webpages on the Lincolnshire Open Data site by calendar year. Note: The resources above show only UK users, and exclude API calls (automated requests for datasets). For further information, please contact the Lincolnshire County Council Open Data team.

Dataset Overview

This dataset provides detailed information on website traffic, including page views, session duration, bounce rate, traffic source, time spent on page, previous visits, and conversion rate.

Dataset Description

• Page Views: The number of pages viewed during a session.
• Session Duration: The total duration of the session in minutes.
• Bounce Rate: The percentage of visitors who navigate away from the site after viewing only one page.
• Traffic Source: The origin of the traffic (e.g., Organic, Social, Paid).
• Time on Page: The amount of time spent on the specific page.
• Previous Visits: The number of previous visits by the same visitor.
• Conversion Rate: The percentage of visitors who completed a desired action (e.g., making a purchase).

Data Summary

• Total Records: 2000
• Total Features: 7

Key Features

1. Page Views: This feature indicates the engagement level of the visitors by showing how many pages they visit during their session.
2. Session Duration: This feature measures the length of time a visitor stays on the website, which can indicate the quality of the content.
3. Bounce Rate: A critical metric for understanding user behavior. A high bounce rate may indicate that visitors are not finding what they are looking for.
4. Traffic Source: Understanding where your traffic comes from can help in optimizing marketing strategies.
5. Time on Page: This helps in analyzing which pages are retaining visitors' attention the most.
6. Previous Visits: This can be used to analyze the loyalty of visitors and the effectiveness of retention strategies.
7. Conversion Rate: The ultimate metric for measuring the effectiveness of the website in achieving its goals.

Usage

This dataset can be used for various analyses such as:

• Identifying key drivers of engagement and conversion.
• Analyzing the effectiveness of different traffic sources.
• Understanding user behavior patterns and optimizing the website accordingly.
• Improving marketing strategies based on traffic source performance.
• Enhancing user experience by analyzing time spent on different pages.

Acknowledgments

This dataset was generated for educational purposes and is not from a real website. It serves as a tool for learning data analysis and machine learning techniques.

This Website Statistics dataset has three resources showing usage of the Lincolnshire Open Data website. Web analytics terms used in each resource are defined in their accompanying Metadata file. Please Note: due to a change in Analytics platform and accompanying metrics, the current files do not contain a full years data. The files will be updated again in January 2025 with 2024-2025 data. The previous dataset containing Web Analytics has been archived and can be found in the following link; https://lincolnshire.ckan.io/dataset/website-statistics-archived Website Usage Statistics: This document shows a statistical summary of usage of the Lincolnshire Open Data site for the latest calendar year. Website Statistics Summary: This dataset shows a website statistics summary for the Lincolnshire Open Data site for the latest calendar year. Webpage Statistics: This dataset shows statistics for individual Webpages on the Lincolnshire Open Data site by calendar year. Note: The resources above exclude API calls (automated requests for datasets). These Website Statistics resources are updated annually in February by the Lincolnshire County Council Open Data team.

This dataset is the complete directory of all Trygve's web pages. The web page HTML code is found from its URL.

For example, the HTML for http://folk.uio.no/trygver/themes/Personal/pp-index.html is in the file at themes/Personal/pp-index.html

The University of Oslo is terminating its Web service after 25 years of operation. My gigabyte of web pages have been collected over the years and will no longer be accessible over the Net. The pages are stored in this dataset and it may be possible to transfer them to another service if required. It should in any case be possible to read the dataset with an HTML reader.

Context

The problem is to predict user ratings for web pages (within a subject category). The HTML source of a web page is given. Users looked at each web page and indicated on a 3 point scale (hot medium cold) 50-100 pages per domain.

Content

This database contains HTML source of web pages plus the ratings of a single user on these web pages. Web pages are on four separate subjects (Bands- recording artists; Goats; Sheep; and BioMedical).

Acknowledgement

Data originally from the UCI ML Repository. Donated by:

Michael Pazzani Department of Information and Computer Science, University of California, Irvine Irvine, CA 92697-3425 pazzani@ics.uci.edu

Concept based Information Access with Google for Personalized Information Retrieval

Web Questions (Q&A On Web Pages)

Questions are supposed to be answerable by Freebase, a large knowledge graph

Source

Huggingface Hub: link

About this dataset

This dataset consists of 6,642 question/answer pairs. The questions are supposed to be answerable by Freebase, a large knowledge graph. The questions are mostly centered around a single named entity. The questions are popular ones asked on the web (at least in 2013).

How to use the dataset

Research Ideas

-This dataset could be used to train a question-answering system. -This dataset could be used to train a system that predicts what question will be popular on the web. -This dataset could be used to build a knowledge graph from the questions and answers

Acknowledgements

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: train.csv | Column name | Description | |:--------------|:-----------------------------------------------| | url | The URL of the question. (String) | | answers | The answers to the question. (List of Strings) |

File: test.csv | Column name | Description | |:--------------|:-----------------------------------------------| | url | The URL of the question. (String) | | answers | The answers to the question. (List of Strings) |

Mobile Web Pages

## Overview

Mobile Web Pages is a dataset for object detection tasks - it contains Hamburger Menu annotations for 337 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [Public Domain license](https://creativecommons.org/licenses/Public Domain).

This dataset was used in the Kaggle Wikipedia Web Traffic forecasting competition. It contains 145063 daily time series representing the number of hits or web traffic for a set of Wikipedia pages from 2015-07-01 to 2017-09-10.

The original dataset contains missing values. They have been simply replaced by zeros.

Our dataset focuses on detecting clickbait web pages, especially those commonly found in news media websites. To address this specific task, we recognize the need for a curated dataset tailored to clickbait detection. In addition to content analysis, we also explore link information, studying the relationships between web pages. Clickbait articles tend to cluster together, often belonging to the same domain or interconnected through buttons like "NEXT" or "Read More." We call such instances "clustered clickbait." Clustered clickbait articles offer very little valuable information and unnecessarily extend content to excessive lengths, sometimes spanning over 20-30 web pages. This can lead to a loss of interest and trust from the general public, even if some of these domains contain genuinely valuable content To distinguish between clustered clickbait and well-formed articles, we curated a diverse dataset containing samples from numerous domains. This dataset includes both clustered clickbait and well-formed articles, providing a comprehensive resource for training and evaluating clickbait detection models

SWPS40 (Similar Web PageS) dataset is aimed for researchers to supply a ground truth dataset to verify their ranking results based on web page visual similarity. For this purpose, we have collected screenshots and HTML+CSS+Js files of 40 different web pages from different contexts and sectors. The main goal of this dataset is to provide ground truth for visual similarity based rankings collected from many participants. The web page pairs in the dataset were scored by 312 different participants. During the study, each participant scored 100 different page pairs yielding totally 31200 individual scores. In this way, 40 votings have been collected for each page pair (e.g. P1 and P4) In this way, it was aimed to generate a statistically significant ground truth rankings.

The dataset contains relevant and irrelevant image tags of Web pages of 125 different domains. The image dataset contains the web domain, file number, the text of image HTML element, attributes of image elements, the size attributes, the parent HTML element of the image, and relevancy of the image. Each Web domain contains 100 Web pages with varying number of image elements.

The purpose of this project is to improve the accuracy of statistical software by providing reference datasets with certified computational results that enable the objective evaluation of statistical software. Currently datasets and certified values are provided for assessing the accuracy of software for univariate statistics, linear regression, nonlinear regression, and analysis of variance. The collection includes both generated and 'real-world' data of varying levels of difficulty. Generated datasets are designed to challenge specific computations. These include the classic Wampler datasets for testing linear regression algorithms and the Simon & Lesage datasets for testing analysis of variance algorithms. Real-world data include challenging datasets such as the Longley data for linear regression, and more benign datasets such as the Daniel & Wood data for nonlinear regression. Certified values are 'best-available' solutions. The certification procedure is described in the web pages for each statistical method. Datasets are ordered by level of difficulty (lower, average, and higher). Strictly speaking the level of difficulty of a dataset depends on the algorithm. These levels are merely provided as rough guidance for the user. Producing correct results on all datasets of higher difficulty does not imply that your software will pass all datasets of average or even lower difficulty. Similarly, producing correct results for all datasets in this collection does not imply that your software will do the same for your particular dataset. It will, however, provide some degree of assurance, in the sense that your package provides correct results for datasets known to yield incorrect results for some software. The Statistical Reference Datasets is also supported by the Standard Reference Data Program.

Description

The Klarna Product Page Dataset is a dataset of publicly available pages corresponding to products sold online on various e-commerce websites. The dataset contains offline snapshots of 51,701 product pages collected from 8,175 distinct merchants across 8 different markets (US, GB, SE, NL, FI, NO, DE, AT) between 2018 and 2019. On each page, analysts labelled 5 elements of interest: the price of the product, its image, its name and the add-to-cart and go-to-cart buttons (if found). These labels are present in the HTML code as an attribute called klarna-ai-label taking one of the values: Price, Name, Main picture, Add to cart and Cart.

The snapshots are available in 3 formats: as MHTML files (~24GB), as WebTraversalLibrary (WTL) snapshots (~7.4GB), and as screeshots (~8.9GB). The MHTML format is less lossy, a browser can render these pages though any Javascript on the page is lost. The WTL snapshots are produced by loading the MHTML pages into a chromium-based browser. To keep the WTL dataset compact, the screenshots of the rendered MTHML are provided separately; here we provide the HTML of the rendered DOM tree and additional page and element metadata with rendering information (bounding boxes of elements, font sizes etc.). The folder structure of the screenshot dataset is identical to the one the WTL dataset and can be used to complete the WTL snapshots with image information. For convenience, the datasets are provided with a train/test split in which no merchants in the test set are present in the training set.

Corresponding Publication

For more information about the contents of the datasets (statistics etc.) please refer to the following TMLR paper.

GitHub Repository

The code needed to re-run the experiments in the publication accompanying the dataset can be accessed here.

Citing

If you found this dataset useful in your research, please cite the paper as follows:

@article{hotti2024the, title={The Klarna Product Page Dataset: Web Element Nomination with Graph Neural Networks and Large Language Models}, author={Alexandra Hotti and Riccardo Sven Risuleo and Stefan Magureanu and Aref Moradi and Jens Lagergren}, journal={Transactions on Machine Learning Research}, issn={2835-8856}, year={2024}, url={https://openreview.net/forum?id=zz6FesdDbB}, note={} }

This dataset contains 2648 records of web sites of different categories. The first column contains an URL for the web site, while the second column contains the web site category index and name. There are 7 categories in total:

0 – Business (508); 1 – Education (394); 2 – Adult (115); 3 – Games (385); 4 – Health (456); 5 – Sport (299); 6 – Travel (491).

Please note that some URLs can become unavailable over time.

The dataset consists of a collection of legitimate as well as phishing website instances. Each instance contains the URL and the relevant HTML page. The index.sql file is the root file, and it can be used to map the URLs with the relevant HTML pages. The dataset can serve as an input for the machine learning process.

Highlights: - Total number of instances: 80,000 (83,275 instances in the dataset due to the existence of some removed SQL records in preprocessing stage) - Number of legitimate website instances (labelled as 0 in the SQL file): 50,000 - Number of phishing website instances (labelled as 1 in the SQL file): 30,000

Structure: The index.sql file is the root file. It consisted of five fields. 1). rec_id - record number 2). url - URL of the webpage 3). website - Filename of the webpage (i.e. 1635698138155948.html) 4). result - Indicates whether a given URL is phishing or not (0 for legitimate and 1 for phishing). 5). created_date - Webpage downloaded date

Sources: - Legitimate Data [50,000] - These data were collected from two sources. 1). Google search - Simple keyword search on the google search engine was used, and the top 5 URLs of each search were collected. Domain restrictions were used and limited a maximum of 10 collections from a domain to have a diverse collection at the end. 2). Ebbu2017 Phishing Dataset [1] - Nearly 25,874 active URLs were collected from this repository

• Phishing Data [30,000] - Three sources were used. 1). PhishTank - From 01 December 2020 to 31 October 2021 2). OpenPhish - From 29 September 2021 to 31 October 2021 3). PhishRepo [2] - From 29 September 2021 to 31 October 2021

Data Collection Process: - Legitimate Data: - The URLs were collected from the above sources and fetched the relevant webpages separately. - The URLs are in different lengths to minimize the URL lengths issue mentioned by Verma et al. [3].

 - Phishing Data: 
    - The URLs were collected from the above sources, and at the same time, the relevant web pages were fetched. 
    - An automated script continuously monitored PhishTank and OpenPhish to collect the latest phishing URLs. 
    - The collected URLs were fetched simultaneously to minimize the resource unavailable issue since the phishing pages do not exist for a longer period on the web. 
    - PhishRepo provides all the resources relevant to a phishing webpage; therefore, simply use their download function to download PhishRepo data.

References: [1]. Ebbu2017 Phishing Dataset. Accessed 31 October 2021. Available: https://github.com/ebubekirbbr/pdd/tree/master/input. [2]. PhishRepo. Accessed 31 October 2021. Available: https://moraphishdet.projects.uom.lk/phishrepo/. [3]. Verma, Rakesh M., Victor Zeng, and Houtan Faridi. "Data quality for security challenges: Case studies of phishing, malware and intrusion detection datasets.", 2019.

This dataset contains 8,576 URIs with content determined to be in the English language. The URIs were collected from DMOZ. All 8,576 URIs were available on the live Web as of December 2015.This data is used and further described in the journal article:Lulwah M. Alkwai, Michael L. Nelson, and Michele C. Weigle. 2017. Comparing the Archival Rate of Arabic, English, Danish, and Korean Language Web Pages. ACM Transactions on Information Systems (TOIS).This work was an extension of the paper:

Lulwah M. Alkwai, Michael L. Nelson, and Michele C. Weigle. 2015. How Well Are Arabic Websites Archived?. In Proceedings of the 15th IEEE/ACM Joint Conference on Digital Libraries (JCDL). ACM

Here are a few use cases for this project:

1. Web Accessibility Analysis: This model can be used to analyze the accessibility of web pages by identifying different elements and ensuring they follow good practices in design and user accessibility standards, such as having appropriate contrast between text and image, or usage of icons and buttons for UI/UX.

2. Web Page Redesign: By identifying the classes of elements on a webpage, "Reorganized2" could be used by designers and developers to analyze a current website layout and assist in redesigning a more intuitive and user-friendly interface.

3. UX Research and Testing: The model can be utilized in user experience (UX) research. It can help in identifying which elements (buttons, icons, dropdowns) on a webpage are getting more attention thus allowing UX designers to create more effective webpages.

4. Web Scraping: In the field of data mining, the model can serve as a smart web scraper, identifying different elements on a page, thus making web scraping more efficient and targeted rather than pulling irrelevant information.

5. E-commerce Optimization: "Reorganized2" can be used to analyze various e-commerce websites, spotting common design features amongst the most successful ones, especially regarding the usage and placement of 'cart', 'field', and 'dropdown' elements. These insights can be used to optimize other online retail sites.

This dataset contains 145063 time series representing the number of hits or web traffic for a set of Wikipedia pages from 2015-07-01 to 2022-06-30. This is an extended version of the dataset that was used in the Kaggle Wikipedia Web Traffic forecasting competition. For consistency, the same Wikipedia pages that were used in the competition have been used in this dataset as well.The colons (:) in article names have been replaced by dashes (-) to make the .tsf file readable using ourdata loaders.

The original dataset contains missing values. They have been simply replaced by zeros.

The data were downloaded from theWikimedia REST API. According to the conditions of the API, this dataset is licensed underCC-BY-SA 3.0andGFDLlicenses.

Dataset SummaryThe Triple-to-Text Alignment dataset aligns Knowledge Graph (KG) triples from Wikidata with diverse, real-world textual sources extracted from the web. Unlike previous datasets that rely primarily on Wikipedia text, this dataset provides a broader range of writing styles, tones, and structures by leveraging Wikidata references from various sources such as news articles, government reports, and scientific literature. Large language models (LLMs) were used to extract and validate text spans corresponding to KG triples, ensuring high-quality alignments. The dataset can be used for training and evaluating relation extraction (RE) and knowledge graph construction systems.Data FieldsEach row in the dataset consists of the following fields:subject (str): The subject entity of the knowledge graph triple.rel (str): The relation that connects the subject and object.object (str): The object entity of the knowledge graph triple.text (str): A natural language sentence that entails the given triple.validation (str): LLM-based validation results, including:Fluent Sentence(s): TRUE/FALSESubject mentioned in Text: TRUE/FALSERelation mentioned in Text: TRUE/FALSEObject mentioned in Text: TRUE/FALSEFact Entailed By Text: TRUE/FALSEFinal Answer: TRUE/FALSEreference_url (str): URL of the web source from which the text was extracted.subj_qid (str): Wikidata QID for the subject entity.rel_id (str): Wikidata Property ID for the relation.obj_qid (str): Wikidata QID for the object entity.Dataset CreationThe dataset was created through the following process:1. Triple-Reference Sampling and ExtractionAll relations from Wikidata were extracted using SPARQL queries.A sample of KG triples with associated reference URLs was collected for each relation.2. Domain Analysis and Web ScrapingURLs were grouped by domain, and sampled pages were analyzed to determine their primary language.English-language web pages were scraped and processed to extract plaintext content.3. LLM-Based Text Span Selection and ValidationLLMs were used to identify text spans from web content that correspond to KG triples.A Chain-of-Thought (CoT) prompting method was applied to validate whether the extracted text entailed the triple.The validation process included checking for fluency, subject mention, relation mention, object mention, and final entailment.4. Final Dataset Statistics12.5K Wikidata relations were analyzed, leading to 3.3M triple-reference pairs.After filtering for English content, 458K triple-web content pairs were processed with LLMs.80.5K validated triple-text alignments were included in the final dataset.