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7 results for “HTTP”
Problem discovery and resolution activities in the Apache HTTP Server Project (March 2001- March 2013).
<p>This is a dynamic visualization of problem discovery and resolution activities observed in the in the development of the Apache HTTP Server Project during the period March 2001- March 2013. The nodes in the network represent problems (software bugs). Anthropomorphic icons represent participants (software developers). The network edges connect participants to problems. Numerical labels record the internal identification numbers or participants and problems. The visible clusters represent the software modules. The central node is the project core module. Participants move closer to problems that attract their attention. When a participant allocates attention to a problem, an edge emerges connecting the two. The edge is green when a participant opens a bug report (i.e., when he discovers a new problem), red when the participant closes the bug report (i.e., when she solves an existing problem), and yellow when any other action is recorded. Problems (white nodes) are green when they first appear. They turn red immediately before being closed, and are yellow when the corresponding bug report is being modified. The animation advances by 0.05 seconds every day of historical time.</p> <p>The animation is produced using the Gource server control visualization tool developed by Andrew Caldwell (<a href="https://gource.io/">https://gource.io/</a>)</p>
Artifacts related to "Using Informed Access Network Selection to Improve HTTP Adaptive Streaming Performance"
<p>This archive contains data related to in the following paper:</p> <p>"Using Informed Access Network Selection to Improve HTTP Adaptive Streaming Performance"</p> <p>(published at the ACM MMSys 2020 conference)</p> <p>Copyright (c) 2020, Theresa Enghardt <theresa@tenghardt.net>, Fachgebiet INET - TU Berlin.</p> <p><br> See https://github.com/fg-inet/MMSys2020_Informed-Access-Network-Selection for more information.</p> <p>This data is released under the Creative Commons Attribution 4.0 International license.</p>
HTTP(S) Traffic Data
<p>This dataset contains HTTP/HTTPS raw traffic data collected by crawling top 2,165 websites using VPN vantage points from 52 countries. The objective of crawling each website from different countries is to discover various geographic-specific hostnames used by the websites to serve content across geography. </p> <p>The capitalized alphabet in the filename, e.g., A in dataA.tar.gz, represent the country names starting with that alphabet from were the data was collected.</p> <p>This dataset was collected in January, 2013.</p>
HTTP Traffic Datasets for Research in Service-Oriented Computing
<p>We present three HTTP datasets for experimenting on various aspects of service-oriented computing.</p> <p>The datasets were generated by creating random traffic targeting the services offered by <a href="https://developers.google.com/tasks">Google Tasks</a>, <a href="https://api.slack.com/methods">Slack</a>, and <a href="https://developer.twitter.com/en/docs/tweets/post-and-engage/overview">Twitter</a>. In order to form transactions, various operations to create, read, update and delete (CRUD) service-specific resources were created and the respective responses were recorded, simulating service interactions by users through applications. The resources the operations interacted with are lists (Google Tasks), messages (Slack) and tweets (Twitter). </p> <p>The input generation used fuzzing techniques. In particular, <a href="https://jmeter.apache.org/">Apache JMeter</a> was used as it has the functionality to fuzz RESTful services (randomly generate various types of API calls by providing different inputs) and recording interactions in a suitable textual format for further processing. The fuzzing was guided by a light-weight semantic service model provided as <a href="https://github.com/OAI/OpenAPI-Specification">Swagger</a> (recently renamed as OpenAPI) spec. </p> <p>The datasets reflect the richness of modern Web APIs for experiments and are in the XML format.</p>
HTTP Datasets for Research in Service-Oriented Computing
<p>We present three HTTP datasets for experimenting on various aspects of service-oriented computing.</p> <p>The datasets were generated by creating random traffic targeting the services offered by <a href="https://developers.google.com/tasks">Google Tasks</a>, <a href="https://api.slack.com/methods">Slack</a> and <a href="https://developer.twitter.com/en/docs/tweets/post-and-engage/overview">Twitter</a>. In order to form transactions, various operations to create, read, update and delete (CRUD) service-specific resources were created and the respective responses were recorded, simulating service interactions by users through applications. The resources the operations interacted with are lists (Google Tasks), messages (Slack) and tweets (Twitter). </p> <p>The input generation used fuzzing techniques. In particular, <a href="https://jmeter.apache.org/">Apache JMeter</a> was used as it has the functionality to fuzz RESTful services (randomly generate various types of API calls by providing different inputs) and recording interactions in a suitable textual format for further processing. The fuzzing was guided by a light-weight semantic service model provided as <a href="https://github.com/OAI/OpenAPI-Specification">Swagger</a> (recently renamed as OpenAPI) spec. </p> <p>The datasets reflect the richness of modern Web APIs for experiments and are in the XML format. </p>
HTTP Dataset 02 for Experimenting on Service-Oriented Computing
<p>This is the newest version.</p>
HTTP Dataset 01 for Experimenting on Service-Oriented Computing
<p>The dataset was generated based on recording the traffic targeted to Slack REST service.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
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.