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6 results for “webpages”
A Comprehensive Dataset for Webpage Classification (Part 3: Benign 2)
<p>This dataset, split across three parts due to Zenodo's size constraints, serves as a fundamental resource for enhancing webpage classification techniques. It encompasses 1,069,715 URLs, each annotated with labels to signify their categorization into Malicious, Benign, or Adult content, and further into 20 detailed sublabels for granular analysis. The dataset is designed to facilitate the evaluation and benchmarking of machine learning models, notably Stochastic Gradient Descent (SGD) and Support Vector Classifier (SVC), across a variety of tokenization methods and input types, including URLs, raw HTML, and parsed HTML content.</p> <p>The primary objective of assembling this dataset is to support research into effective webpage classification, thereby improving content prioritization and filtering in web crawling applications. It has been meticulously curated to provide a robust framework for studying the impact of different feature representation techniques on classification accuracy.</p> <p>The dataset is structured as JSON lines (jsonl) files, with each entry detailing a URL's label, sublabel, source, status code, and HTML content. This comprehensive dataset is divided into three parts due to size constraints on Zenodo, each targeting specific content categories to ensure ease of use and accessibility for researchers:</p> <ul> <li><strong>Part 1: Adult & Malicious</strong> encompasses URLs classified under Adult and Malicious categories, offering insights into content that requires stringent filtering.</li> <li><strong>Part 2: Benign 1</strong> and <strong>Part 3: Benign 2</strong> cover benign URLs, facilitating the study of safe web content and its classification nuances.</li> </ul> <p>We also created a .csv file without the HTML content so it is easier to work with URLs only, this .csv file contains the next columns `['uid', 'url', 'label', 'sublabel']`</p> <p>By providing this dataset, we aim to contribute significantly to the field of webpage classification, offering a valuable asset for researchers and practitioners looking to advance the state of web crawling technology and its applications.</p> <p> </p> <p>JSON line format for each line:</p> <pre><code>{"url": "<URL>", "label": "<Label>", "sublabel": "<Sublabel>", "source": "<Source of this URL>", "status_code": <Status Code>, "html": "<HTML Textual Content>"}</code><br><br></pre> <p><br><strong>Other parts of this dataset:</strong></p> <ul> <li><a title="A Comprehensive Dataset for Webpage Classification (Part 1: Adult & Malicious)" href="../records/10775260">A Comprehensive Dataset for Webpage Classification (Part 1: Adult & Malicious)</a></li> <li><a title="A Comprehensive Dataset for Webpage Classification (Part 2: Benign 1)" href="../records/10795435">A Comprehensive Dataset for Webpage Classification (Part 2: Benign 1)</a></li> </ul> <p> </p> <p><strong>Citation</strong></p> <p>if you use this dataset, please cite us:</p> <p><code>Al-Maamari, M., Istaiti, M., Zerhoudi, S., Dinzinger, M., Granitzer, M. and Mitrovic, J., A COMPREHENSIVE DATASET FOR WEBPAGE CLASSIFICATION.</code></p> <p>https://ca-roll.github.io/downloads/A_Comprehensive_Dataset_for_Webpage_Classification.pdf</p> <p> </p> <p><code>Granitzer, M., Voigt, S., Fathima, N.A., Golasowski, M., Guetl, C., Hecking, T., Hendriksen, G., Hiemstra, D., Martinovič, J., Mitrović, J. and Mlakar, I., 2023. Impact and development of an Open Web Index for open web search. <em>Journal of the Association for Information Science and Technology.</em></code></p> <p>https://doi.org/10.1002/asi.24818</p>
A Comprehensive Dataset for Webpage Classification (Part 2: Benign 1)
<p>This dataset, split across three parts due to Zenodo's size constraints, serves as a fundamental resource for enhancing webpage classification techniques. It encompasses 1,069,715 URLs, each annotated with labels to signify their categorization into Malicious, Benign, or Adult content, and further into 20 detailed sublabels for granular analysis. The dataset is designed to facilitate the evaluation and benchmarking of machine learning models, notably Stochastic Gradient Descent (SGD) and Support Vector Classifier (SVC), across a variety of tokenization methods and input types, including URLs, raw HTML, and parsed HTML content.</p> <p>The primary objective of assembling this dataset is to support research into effective webpage classification, thereby improving content prioritization and filtering in web crawling applications. It has been meticulously curated to provide a robust framework for studying the impact of different feature representation techniques on classification accuracy.</p> <p>The dataset is structured as JSON lines (jsonl) files, with each entry detailing a URL's label, sublabel, source, status code, and HTML content. This comprehensive dataset is divided into three parts due to size constraints on Zenodo, each targeting specific content categories to ensure ease of use and accessibility for researchers:</p> <ul> <li><strong>Part 1: Adult & Malicious</strong> encompasses URLs classified under Adult and Malicious categories, offering insights into content that requires stringent filtering.</li> <li><strong>Part 2: Benign 1</strong> and <strong>Part 3: Benign 2</strong> cover benign URLs, facilitating the study of safe web content and its classification nuances.</li> </ul> <p>We also created a .csv file without the HTML content so it is easier to work with URLs only, this .csv file contains the next columns `['uid', 'url', 'label', 'sublabel']`</p> <p>By providing this dataset, we aim to contribute significantly to the field of webpage classification, offering a valuable asset for researchers and practitioners looking to advance the state of web crawling technology and its applications.</p> <p> </p> <p>JSON line format for each line:</p> <pre><code>{"url": "<URL>", "label": "<Label>", "sublabel": "<Sublabel>", "source": "<Source of this URL>", "status_code": <Status Code>, "html": "<HTML Textual Content>"}</code><br><br></pre> <p><br><strong>Other parts of this dataset:</strong></p> <ul> <li><a title="A Comprehensive Dataset for Webpage Classification (Part 1: Adult & Malicious)" href="../records/10775260">A Comprehensive Dataset for Webpage Classification (Part 1: Adult & Malicious)</a></li> <li><a title="A Comprehensive Dataset for Webpage Classification (Part 3: Benign 2)" href="../records/10795437">A Comprehensive Dataset for Webpage Classification (Part 3: Benign 2)</a></li> </ul> <p> </p> <p><strong>Citation</strong></p> <p>if you use this dataset, please cite us:</p> <p><code>Al-Maamari, M., Istaiti, M., Zerhoudi, S., Dinzinger, M., Granitzer, M. and Mitrovic, J., A COMPREHENSIVE DATASET FOR WEBPAGE CLASSIFICATION.</code></p> <p>https://ca-roll.github.io/downloads/A_Comprehensive_Dataset_for_Webpage_Classification.pdf</p> <p> </p> <p><code>Granitzer, M., Voigt, S., Fathima, N.A., Golasowski, M., Guetl, C., Hecking, T., Hendriksen, G., Hiemstra, D., Martinovič, J., Mitrović, J. and Mlakar, I., 2023. Impact and development of an Open Web Index for open web search. <em>Journal of the Association for Information Science and Technology.</em></code></p> <p>https://doi.org/10.1002/asi.24818</p>
A Comprehensive Dataset for Webpage Classification (Part 1: Adult & Malicious)
<p>This dataset, split across three parts due to Zenodo's size constraints, serves as a fundamental resource for enhancing webpage classification techniques. It encompasses 1,069,715 URLs, each annotated with labels to signify their categorization into Malicious, Benign, or Adult content, and further into 20 detailed sublabels for granular analysis. The dataset is designed to facilitate the evaluation and benchmarking of machine learning models, notably Stochastic Gradient Descent (SGD) and Support Vector Classifier (SVC), across a variety of tokenization methods and input types, including URLs, raw HTML, and parsed HTML content.</p> <p>The primary objective of assembling this dataset is to support research into effective webpage classification, thereby improving content prioritization and filtering in web crawling applications. It has been meticulously curated to provide a robust framework for studying the impact of different feature representation techniques on classification accuracy.</p> <p>The dataset is structured as JSON lines (jsonl) files, with each entry detailing a URL's label, sublabel, source, status code, and HTML content. This comprehensive dataset is divided into three parts due to size constraints on Zenodo, each targeting specific content categories to ensure ease of use and accessibility for researchers:</p> <ul> <li><strong>Part 1: Adult & Malicious</strong> encompasses URLs classified under Adult and Malicious categories, offering insights into content that requires stringent filtering.</li> <li><strong>Part 2: Benign 1</strong> and <strong>Part 3: Benign 2</strong> cover benign URLs, facilitating the study of safe web content and its classification nuances.</li> </ul> <p>We also created a .csv file without the HTML content so it is easier to work with URLs only, this .csv file contains the next columns `['uid', 'url', 'label', 'sublabel']`</p> <p>By providing this dataset, we aim to contribute significantly to the field of webpage classification, offering a valuable asset for researchers and practitioners looking to advance the state of web crawling technology and its applications.</p> <p>JSON line format for each line:</p> <p><code>{"url": "<URL>", "label": "<Label>", "sublabel": "<Sublabel>", "source": "<Source of this URL>", "status_code": <Status Code>, "html": "<HTML Textual Content>"}</code><br><br><strong>Other parts of this dataset:</strong></p> <ul> <li><a title="A Comprehensive Dataset for Webpage Classification (Part 2: Benign 1)" href="../records/10795435">A Comprehensive Dataset for Webpage Classification (Part 2: Benign 1)</a></li> <li><a title="A Comprehensive Dataset for Webpage Classification (Part 3: Benign 2)" href="../records/10795437">A Comprehensive Dataset for Webpage Classification (Part 3: Benign 2)</a></li> </ul> <p> </p> <p><strong>Citation</strong></p> <p>if you use this dataset, please cite us:</p> <p><code>Al-Maamari, M., Istaiti, M., Zerhoudi, S., Dinzinger, M., Granitzer, M. and Mitrovic, J., A COMPREHENSIVE DATASET FOR WEBPAGE CLASSIFICATION.</code></p> <p>https://ca-roll.github.io/downloads/A_Comprehensive_Dataset_for_Webpage_Classification.pdf</p> <p> </p> <p><code>Granitzer, M., Voigt, S., Fathima, N.A., Golasowski, M., Guetl, C., Hecking, T., Hendriksen, G., Hiemstra, D., Martinovič, J., Mitrović, J. and Mlakar, I., 2023. Impact and development of an Open Web Index for open web search. <em>Journal of the Association for Information Science and Technology.</em></code></p> <p>https://doi.org/10.1002/asi.24818</p>
Museum Webpages Performance Dataset | Ver 1
<p>This dataset thoroughly evaluates the web performance metrics for 234 museums worldwide. It includes 23 variables that assess technical performance, usability, and organizational details. The dataset contains information such as the organization’s name, domain, country, physical address, and the type of content management system (CMS) utilized. Performance metrics are available for both mobile and desktop platforms, addressing accessibility compliance, adherence to best practices, and search engine optimization (SEO). Specific metrics for mobile and desktop speed performance include First Contentful Paint (FCP), Total Blocking Time (TBT), Speed Index, Largest Contentful Paint (LCP), and Cumulative Layout Shift (CLS).</p> <p>Data collection was conducted using PageSpeed Insights. The CMS employed by each organization was identified through the WhatCMS tool. This dataset serves as a useful resource for researchers, web developers, and cultural institutions seeking to enhance digital inclusivity, optimize website performance, and benchmark their digital presence against global standards. It also provides a solid foundation for studying web optimization, accessibility, and the broader digital transformation of cultural heritage institutions.<br><br>It is noted that information regarding dataset's authors has been erased as the dataset is used in a research paper submitted to <em>Metrics </em>for peer review evaluation and potential publication<em>. </em></p>
Datasample - Speed Performance of 121 LAMs webpages worldwide
<p>This particular dataset is composed of 121 cases of homepages speed performance coming from Libraries, Archives and Museums worldwide. Data for 12 metrics are taking place. 6 for mobile and six for desktop. PageSpeed Insights of Google has been used to retrieved data.</p>
Data analysis of tools functionalities for an exploratory data webpage
<p>This publication contains the results of the data analysed for the study published by Calvera-Isabal, Santos & Hernández-Leo (2023). It includes the dataset collected from the workshops developed with teachers and TEL experts.</p> <p>This work has been funded by PID2020-112584RB-C33 funded by MCIN/AEI/10.13039/501100011033, the CS Track project, EU Horizon 2020 programme [grant agreement No 872522], grant for activities to increase the social impact of research in 2021 from Universitat Pompeu Fabra (UPF) and H2O Learn project PID2020-112584RB-C33 funded by MCIN/ AEI / 10.13039/501100011033.</p> <p>Please, contact miriam.calvera@upf.edu for data access.</p> <p>Calvera-Isabal, M., Santos, P., & Hernández-Leo, D. (2023). Towards Citizen Science-Inspired Learning Activities: The Co-design of an Exploration Tool for Teachers Following a Human-Centred Design Approach. International Journal of Human–Computer Interaction, 1-22.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.