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1,617 results for “user”
User-Profiling Dataset
<p>The User-Profiling Dataset provides hand labeled user action videos and frames. The dataset includes over 40k images from 236 video clips containing five different user actions, and five different sequences of actions.</p>
A Dataset of User Generated Videos from Edinburgh Festival 2016
<p>Files include footage from performances of the trEd Dance group (edfest8, edfest9 and edfest10) and Rebecca Wilson’s ‘The Strawberry Show’ (edfest6 and edfest7) taken during Edinburgh Festival 2016 by BBC R&D as part of the COGNITUS project funded from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 687605. BBC©2016. Further details about the content are available in the accompanying paper <em>"An Open Access Dataset of User Generated Videos from Edinburgh Festival 2016"</em>. If you have any queries please contact BBC R&D at cognitus-h2020@rd.bbc.co.uk. This notice must remain attached to any copy of the content.</p> <p> </p>
Dynamics of Instagram Users
<p>These two data sets are gathered from Instagram users who were chosen randomly.</p> <p>The Main data set encompasses data for 1K users including 500 men and 500 women. The Test data set encompasses data for 100 users including 50 men and 50 women.</p> <p>Data gathered for each user includes :</p> <p>1- number of posts</p> <p>2- number of followers</p> <p>3- number of followings</p> <p>4- number of likes for the tenth previous post</p> <p>5- number of likes for the eleventh previous post</p> <p>6- number of likes for the twelfth previous post</p> <p>7- number of self-presenting posts from nine previous posts</p> <p>8- gender</p>
AnalyzAIRR: A user-friendly guided workflow for AIRR data analysis: example data and analysis source-code
<p>This repository contains:</p> <ul> <li>Annotated TCR-seq data files named <em>tripod-XX-XXXX</em></li> <li>The metadata corresponding to the annotated files</li> <li>The RepSeqExperiment object, which integrates the annotated files and the metadata and was used in the analysis pipeline</li> <li>The analysis script to generate the plots of the different figures</li> </ul>
Unveiling Competition Dynamics in Mobile App Markets through User Reviews
<p>This replication package contains the datasets and evaluation results for the research titled <i>"<strong>Unveiling Competition Dynamics in Mobile App Markets through User Reviews"</strong>, </i>by Quim Motger, Xavier Franch, Vincenzo Gervasi and Jordi Marco.</p><p>Latest version of the full code is available at: <a href="https://github.com/quim-motger/app-market-analysis">https://github.com/quim-motger/app-market-analysis</a></p>
Utilizing Creator Profiles for Predicting Valuable User Enhancement Reports
<p>This is the dataset for paper "Utilizing Creator Profiles for Predicting Valuable User Enhancement Reports"</p>
US4USec: A User Story Model for Usable Security
<p>Excel sheet containing information used for the construction of the US4USec: A User Story Model for Usable Security </p>
Additional Resources for End-user Comprehension of Transfer Risks in Smart Contracts
<p>Google Docs versions of most of the PDFs can be found on https://linktr.ee/tethersurvey</p> <p> </p> <p>Additionally, a survey data visualizer is found on https://tether-survey.onrender.com/</p> <p> </p>
Additional Resources for Understanding End-User Perception of Transfer Risks in Smart Contracts
<p>This contains further resources for the work titled "Understanding End-User Perception of Transfer Risks in Smart Contracts", which is set to appear in CHI 2025.</p> <p>This work details an investigation into user understanding of transfer risks in ERC-20 blockchain smart contracts. An example transfer risk is a user being unable to transfer due to their account being blacklisted by the owner of the contract.</p> <p>A large portion of this work focuses on the most popular Ethereum smart contract (USD Tether). This details responses to a 110-participant survey on smart contract users, establishing their knowledge of transfer risks and various other perceptions. Included also are the results of statistical tests on these responses, the follow-up message to the respondents and more.</p> <p>Another portion of this work investigates the presence of transfer risks in other top ERC-20 contracts. The results of this investigation is also found here.</p> <p>This also includes results of blockchain analytics to establish the top ERC-20 addresses, as well as code used for processing.</p>
Interviewing users of services of selected urban areas (Granada)
<p>As part of the EU project SAFE (https://eusafe.fa.uni-lj.si/), University of Granada investigated living conditions and infrastructure offers in Granada (Spain) in order to work out possible suggestions for improvement. Students of University of Granada conducted a survey in Granada in March 2024. The dataset is a compilation of the raw data.</p>
Interviewing users of services of selected urban areas (Bydgoszcz)
<p>As part of the EU project SAFE (https://eusafe.fa.uni-lj.si/), WSG University investigated living conditions and infrastructure offers in Bydgoszcz (Poland) in order to work out possible suggestions for improvement. Students of WSG University conducted an oral survey of passers-by in Bydgoszcz in January 2024. The dataset is a compilation of the raw data.</p>
Artifacts for "Reviewing War: Unconventional User Reviews as a Side Channel to Circumvent Information Controls"
<p>List of keywords used to build the text classifier for the paper <em>Reviewing War: Unconventional User Reviews as a Side Channel to Circumvent Information Controls</em>. For more information on how these keywords were obtained, see the "Data Labeling" section of this paper.</p> <p>The provided CSV file contains 3 columns:</p> <ul> <li>"keyword": Lowercased keyword in either English, Russian, Ukranian or Polish.</li> <li>"weight": Score indicating the keyword's war-related affinity, with 3 being the most related. Negative weights are used to correct for exceptions in our classifier.</li> <li>"topic": Subject of the keyword used for topic analysis.</li> </ul>
Estimating human joint moments unifies exoskeleton control and reduces user effort
<p>Robotic lower-limb exoskeletons can augment human mobility, but current systems require extensive, context-specific considerations, limiting their real-world viability. Here, we present a unified exoskeleton control framework that autonomously adapts assistance based on instantaneous user joint moment estimates from a temporal convolutional network (TCN). When deployed on our hip exoskeleton, the TCN achieved an average RMSE of 0.142 ± 0.021 Nm/kg and R<sup>2</sup> of 0.840 ± 0.045 across 35 ambulatory conditions without any subject-specific calibration. Further, the unified controller significantly reduced user metabolic cost and lower-limb positive work during level ground and incline walking compared to walking without wearing the exoskeleton (P < 0.05). This advancement bridges the gap between in-lab exoskeleton technology and real-world human ambulation, making exoskeleton control technology viable for a broad community.</p>
Multi-modal User Interactions for Recommendations
<p>This repository contains the data for <a href="https://doi.org/10.1145/3626772.3657881">Dataset and Models for Item Recommendation Using Multi-Modal User Interactions</a>.</p> <p>We publish a real-world dataset from the insurance domain with multi-modal user interactions that can be used in recommendation models. The dataset is anonymized.</p> <p>There are 6 different datasets:</p> <div> <h3><strong>data_users.csv</strong></h3> </div> <p>This data contains the users. Each user has had one or more purchase events with conversations and/or web sessions prior to that purchase. The data contains 5 columns:</p> <ul> <li>user_id. The ID of a user.</li> <li>purchase_event_id. The ID of a purchase event.</li> <li>conversation_id. The ID of a conversation.</li> <li>session_id. The ID of a web session.</li> <li>event_number. A number specifying the order of conversations/web sessions.</li> </ul> <div> <h3><strong>data_conversations_keyword.csv</strong></h3> </div> <p>This data contains the conversations that the user had prior to the user's purchase event. Each conversation consists of multiple sentences represented with keywords. The data contains 4 columns:</p> <ul> <li>conversation_id. The ID of a conversation.</li> <li>sentence_number. A number specifying the order of sentences.</li> <li>sentence_speaker. The speaker of the sentence (user or agent).</li> <li>keywords. List with the IDs of the keywords in the sentence.</li> </ul> <div> <h3><strong>data_conversations_embedding.csv</strong></h3> </div> <p>The data contains the conversations that the user had prior to the user's purchase event. Each conversation consists of multiple sentences represented with text embeddings. The data contains 771 columns:</p> <ul> <li>conversation_id. The ID of a conversation.</li> <li>sentence_number. A number specifying the order of sentences.</li> <li>sentence_speaker. The speaker of the sentence (user or agent).</li> <li>embedding_1 - embedding_768. Text embeddings computed with a pre-trained language-specific BERT model.</li> </ul> <div> <h3><strong>data_sessions.csv</strong></h3> </div> <p>This data contains the web sessions that the user made prior to the user's purchase event. Each web session consists of multiple actions. The data contains 3 columns:</p> <ul> <li>session_id. The ID of a web session.</li> <li>action_number. A number specifying the order of actions.</li> <li>action_tags. List with the IDs of the section, object and type of an action.</li> </ul> <div> <h3><strong>data_purchase_events.csv</strong></h3> </div> <p>This data contains the purchase events. Each event consists of one or more item purchases made by the same user. The data contains 2 columns:</p> <ul> <li>purchase_event_id. The ID of a purchase event.</li> <li>item_id. The ID of an item.</li> </ul> <div> <h3><strong>data_post_filter.csv</strong></h3> </div> <p>This data contains the items that were possible for the user to buy at the time of the user's purchase event. The data contains 2 columns:</p> <ul> <li>purchase_event_id. The ID of a purchase event.</li> <li>item_id. The ID of an item.</li> </ul>
The codes and datasets for the paper titled "Don't Confuse! Redrawing GUI Navigation Flow in Mobile Apps for Visually Impaired Users"
<h3>Project Title:</h3> <p>Redrawing GUI Navigation Flow in Mobile Apps for Visually Impaired Users</p> <h3>Description:</h3> <p>This project enhances GUI navigation accessibility for visually impaired users by analyzing GUI structures, identifying issues, and optimizing navigation flow.</p> <h3>Contents:</h3> <ol> <li><strong>Risk Warnings</strong></li> <li><strong>Variable Explanations</strong></li> <li><strong>Function Descriptions</strong></li> <li><strong>Usage Instructions</strong></li> <li><strong>Contact Information</strong></li> </ol> <h3>1. Risk Warnings:</h3> <ul> <li>Navigation analysis focuses on visible nodes only.</li> <li>Code maintenance issue in loop C.</li> <li>Prior reading of the "Info" button warning is essential.</li> <li>Potential information loss in the reordering algorithm.</li> </ul> <h3>2. Variable Explanations:</h3> <ul> <li>Constants: parameter1, parameter2, outputSign.</li> <li>Global Variables: nodeString, nodeList, intToRect, intToDir, intToInfo, infoToInt, intToSubRect, intToSubKind.</li> <li>Local Variables: sortedSon, sign, acceptable.</li> </ul> <h3>3. Function Descriptions:</h3> <ul> <li><strong>isNodeVisibleOnScreen</strong>: Checks if a node is visible on the screen.</li> <li><strong>Gestalt</strong>: Conducts a depth-first search traversal of all nodes and records the Gestalt_inspired order.</li> <li><strong>checkNodeNecessity</strong>: Further checks if a node is necessary for navigation.</li> <li><strong>DFS</strong>: Used for the reordering algorithm.</li> </ul> <h3>4. Usage Instructions:</h3> <ul> <li>Ensure thorough understanding of risk warnings.</li> <li>Modify and maintain the code as necessary.</li> <li>Read the warning prompt before using the "Info" button.</li> <li>Exercise caution with potential information loss in reordering.</li> </ul> <h3>5. Contact Information:</h3> <ul> <li>Developer: Mengxi Zhang</li> <li>Email: <a target="_new">zmxalakay@126.com</a></li> </ul> <p><strong>Note</strong>: This README provides a brief overview. Refer to the User_Guidelines documentation for detailed information.</p>
Automatic User Story Generation: A Comprehensive Systematic Literature Review - Data Extraction
<p>This document presents the data extraction performed for the Systematic Literature Review in Automatic User Story Generation.</p>
Interviewing users of services of selected urban areas (Vantaa)
<p>As part of the EU project SAFE (https://eusafe.fa.uni-lj.si/), Laurea Unversity of Applied Sciences investigated living conditions and infrastructure offers in Vantaa (Finland) in order to work out possible suggestions for improvement. Students of Laurea Unversity of Applied Sciences conducted a survey in Vantaa in November 2023. The dataset is a compilation of the raw data.</p>
Data for: Evaluation of Methods for Eliciting and Specifying Usability Requirements using User Stories: A Controlled Experiment
<p>Este projeto contém os materiais utilizados na pesquisa intitulada Evaluation of Methods for Eliciting and Specifying Usability Requirements using User Stories: A Controlled Experiment: TCLE, Formulário de Caracterização, Cenário, Oráculo, User Stories, Protótipo, Storyboards, Avaliação de ferramentas em escala de Likert e Dados coletados do formulário.</p>
Technical and user evaluation of mobile application for pedestrian safety
<p>This file contains the evaluation results of the tests carried out a Versailles during the Show project with external participants:</p> <ul> <li>Technical indicators have been collected in one sheet</li> <li>Feedbacks from the user questionnaires have been collected in one other sheet</li> </ul>
Dataset for Interactive Profiling Narrative (IPN) with Toxicity Tolerance Score of each individual user to different categories of toxicity
<p>he dataset is the collection of the results of the Interactive Profiling Narrative that was developed as a part of the project Listener Aware Content Detoxification.<br>It contains the toxicity tolerance scores of each individual user to different categories.<br>High scores indicate that the user is extremely sensitive to the particular category where as low scores indicate that the user isn't that triggered by the category.Medium scores show moderate tolerance.</p> <p>Columns:<br>1.UserId: The unique id given to each user (helps preserve anonymity).<br>2.Race: The user's toxicity tolerance to the category race. High score means racist comment trigger them. <br>3.Sex: The user's toxicity tolerance to the category sexuality. High scores indicate sensitivity to comments on sexual identity.<br>4.Body_image: The user's sensitivity to comments regarding body image. High scores indicate that remarks on body appearance strongly affect the user.<br>5.Disability:The user's sensitivity to comments about disabilities. A high score means the user is highly sensitive to potentially ableist remarks.<br>6.Religion_culture: The user's sensitivity to content involving religion or culture. High scores show the user is easily triggered by insensitive comments about religious or cultural aspects.<br>7.Physical_abuse: User's sensitivity to comments regarding physical abuse. High scores indicate the user is strongly affected by remarks on physical abuse.<br>8.Mental_health: This measures the user's sensitivity to comments on mental health. High scores suggest that the user is particularly affected by comments stigmatizing mental health issues.<br>9.Politics:The user's sensitivity to political content. High scores indicate that political discussions or comments are likely to evoke a strong reaction in the user</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.