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229 results for “user study”
Research Artefact: What network simulator questions do users ask? a large-scale study of stack overflow posts
<p><strong>Research Artefact: What network simulator questions do users ask? a large-scale study of stack overflow posts</strong></p> <p>This is a research artefact for the paper: <strong>What network simulator questions do users ask? a large-scale study of stack overflow posts</strong>. This artefact is a repository consisting of the collected dataset including 2,322 network-simulator-related Stack Overflow questions. This artefact aims to enable researchers to replicate our dataset of the paper and reuse the dataset for further research.</p>
DATA FOR STUDY OF PRIVACY ATTITUDE OF USERS OF SOCIAL NETWORKING SITES AND THEIR EXPECTATIONS FROM LAW IN INDIA
<p>In the present study, on Indian population a disproportionate, stratified, purposive, convenience mixed sampling technique has been adopted in order to ensure proper representation of all the stakeholders concerned with regard to the issue of data privacy in India among the population of the study. The stratified sampling technique is popularly used for a large size population and where it is desirable to purposively have an adequate representation of the all the sub-groups. It is estimated that there are about 0.2 million Law Enforcement Officers (Directors General of Police to Assistant Sub- Inspector), 2.2 million Legal Professionals, including 21,586 Judges, 1.5 million Academicians, about 5000 Information Assurance and Privacy Experts, and 450 million Internet Users in India, out of which 196 million use SNSs. It may be clarified here that all these stakeholders are not only the users of social networking sites but also Indian citizens who are stakeholders in enactment and implementation of the privacy law as and when it is enacted.</p> <p>Based on the size of the total population, a statistically adequate sample size of 385, having a 95 per cent Confidence Level, 5 per cent Margin of Error (Confidence Interval), 0.5 Standard Deviation, a 1.96 Z-score was calculated.</p> <p> </p> <p> </p> <p> </p>
Lost at C: Data from the Security-focused User Study
<p><strong>2022 Study on the security implications of Large Language Model Code Assistants</strong></p> <p>This repository contains the results of the 2022 study described in the Paper: `Lost at C: A User Study on the security implications of Large Language Model Code Assistants` Link: https://arxiv.org/pdf/2208.09727.pdf</p> <p>Here, the overall goal is to determine if users with access to code suggestions via a Large Language Model (OpenAI code-cushman-001) in a GitHub Copilot-like arrangement produce code with a higher incidence rate of security-related bugs than those without any such access. In particular we concern ourselves with low-level memory-related bugs such as those often present in buggy C code.</p> <p>To answer this question, a User Study was conducted (N=58) which had users implement a shopping list in C as a singly-linked list. Half the users had access to a custom Copilot-like extension which generated suggestions according to code-cushman-001, and half had no access or coding hints other than provided by Visual Studio Code's default Intellisense.<br> The study was performed in a controlled environment (a virtualized cloud-based desktop).</p> <p>This task was made deliberately difficult than usual via the specifications: participants had to implement the shopping list according to an unusual API containing a number of pitfalls. They had to implement only the implementation of the specification (i.e. the `list.c` file). Users were provided `list.h` as well as a suite of automated (if basic) tests.</p> <p>For more details, you can see the associated paper.</p> <p>The repository contains the user study data as well as the scripts used for analysis and results reproduction.</p>
Resources for User Study of Mapeathor
<p>This dataset contains the resources used to carry out the exercise proposed for the Mapeathor study of usability. It is comprised of:</p> <ul> <li>An ontology diagram representing a subset of the <a href="http://vocab.ciudadesabiertas.es/def/comercio/tejido-comercial/index-en.html"><em>Vocabulary for data representation of the local business census and activities licenses</em></a> </li> <li>Three clean CSV files with data associated with the subset ontology: <ul> <li>bar.csv and restaurant.csv: Information about local businesses (bars and restaurants respectively), with the following fields: <em>id, URL, email, longitude, latitude, phone, maximum_capacity, name</em>.</li> <li>address.csv: Information about the postal address of local businesses, with the following fields: <em>id, restaurant_name, postal_code, country, city, address</em>.</li> </ul> </li> </ul>
GENEA Challenge 2023 user-study video stimuli
<p>This Zenodo repository contains video stimuli in mp4 format from the user studies in the GENEA Challenge 2023.</p> <p> </p> <p><strong>Contents:</strong></p> <p>The file "monadic_videos.zip" contains the video stimuli used in the monadic evaluations of the challenge (human-likeness and speech appropriateness) and the file "dyadic_videos.zip" the video stimuli from the dyadic evaluation (appropriateness for the interlocutor).</p> <p>Video stimuli with mismatched motion are in the corresponding subfolders with the prefix "mismat_".</p> <p>The file "attention_check_examples.zip" contains examples of the attention-check videos used in the challenge.</p> <p> </p> <p>The release contains all videos used for the challenge evaluation, except for the attention-checks, where only a few examples are provided. Except for the attention-check examples for the human-likeness studies, all videos contain speech audio. This audio needs to be removed to replicate the human-likeness evaluation.</p> <p> </p> <p><strong>Attribution:</strong></p> <p>If you use this material, please cite our latest paper on the GENEA Challenge 2023. At the time of writing (2023-08-01) this is our ACM ICMI 2023 paper:</p> <p>Taras Kucherenko, Rajmund Nagy, Youngwoo Yoon, Jieyeon Woo, Teodor Nikolov, Mihail Tsakov, and Gustav Eje Henter. 2023. The GENEA Challenge 2023: A large-scale evaluation of gesture generation models in monadic and dyadic settings. In Proceedings of the ACM International Conference on Multimodal Interaction (ICMI ’23). ACM.</p> <p>Also, please cite the paper about the original dataset from Meta Research:</p> <p>Gilwoo Lee, Zhiwei Deng, Shugao Ma, Takaaki Shiratori, Siddhartha S. Srinivasa, and Yaser Sheikh. 2019. Talking With Hands 16.2M: A large-scale dataset of synchronized body-finger motion and audio for conversational motion analysis and synthesis. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV ’19). IEEE, 763–772.</p> <p> </p> <p>Condition NA in the data contains motion from the Talking With Hands 16.2M dataset at <a href="https://github.com/facebookresearch/TalkingWithHands32M/">https://github.com/facebookresearch/TalkingWithHands32M/</a>. These stimuli, and the audio, are licensed under a CC BY NC 4.0 international license. The motion for all other conditions is released under a CC BY 4.0 international license, whose license text is reproduced in the file LICENSE.txt.</p> <p> </p> <p><strong>More information:</strong></p> <p>To find more GENEA Challenge 2023 material on the web, please see:</p> <ul> <li> <p><a href="https://genea-workshop.github.io/2023/challenge/">https://genea-workshop.github.io/2023/challenge/</a></p> </li> </ul> <p>If you have any questions or comments, please contact:</p> <ul> <li> <p>The GENEA Challenge organisers <genea-challenge@googlegroups.com></p> </li> </ul>
Study Dataset: Shedding Light on CVSS Scoring Inconsistencies: A User- Centric Study on Evaluating Widespread Security Vulnerabilities
<p>This record contains the <strong>study datasets, descriptive results and questionnaires</strong> from the paper "Shedding Light on CVSS Scoring Inconsistencies: A User-Centric Study on Evaluating Widespread Security Vulnerabilities" by Julia Wunder, Andreas Kurtz, Christian Eichenmüller, Freya Gassmann and Zinaida Benenson to appear in Proceedings of the 45th IEEE Symposium on Security and Privacy (2024).</p> <p>The pseudonymous <strong>datasets</strong> contain data from the online surveys (main study with 196 participants and follow-up study with 59 participants). The first row gives the question codes and questions, the following rows gives the answers from the participants (see also README.md).</p> <p>We also provide <strong>descriptive results</strong> from the online surveys as PDF and the questionnaires.</p> <p>Please refer to the README.md file and our paper for further details about the data set and study.</p>
The Olympic gold medalists and Instagram - A longitudinal study on user characteristics
<p>This dataset includes Instagram user characteristics of those Olympic athletes who won gold medals in the individual events of Rio2016. The name of all these gold medalists of individual events are in the dataset (226 athletes), however only 144 athletes (83 men and 61 women) had a publicly available Instagram account in all of the observations during the 4 months period of data gathering. The first round of data gathering (first observation, i.e. OlympicAthletesData_1) took place 9-Aug-2019 to 12-Aug-2019, the second round of data gathering (second observation, i.e. OlympicAthletesData_2) took place 9-Sep-2019 to 12-Sep-2019, the third round of data gathering (third observation, i.e. OlympicAthletesData_3) took place 9-Oct-2019 to 12-Oct-2019, the fourth round of data gathering (fourth observation, i.e. OlympicAthletesData_4) took place 9-Nov-2019 to 12-Nov-2019. The data gathered for each user (in each observation) consists of:</p> <p>1- Name of the individual event </p> <p>2- Country</p> <p>3- Name</p> <p>4- Gender</p> <p>5- Instagram ID</p> <p>6- Number of Posts</p> <p>7- Number of followers</p> <p>8- Number of followings</p> <p>9- Maximum Number of likes (in the last 10 photo posts)</p> <p>10- Number of comments for the post with Maximum Number of likes (in the last 10 photo posts)</p> <p>11- Number of self-presenting posts in the last 10 photo posts (those posts in which the athlete is present)</p> <p>12- Number of pure self-presenting posts in the last 10 photo posts (those posts in which the athlete is the only person who is present)</p> <p>13- Age</p> <p>14- Date of data crawling</p>
Study Data: Obtaining Semi-Formal Models from Qualitative Data: From Interviews into BPMN Models in User-Centered Design Processes
<p>This dataset (Data.zip) contains the raw data of a user study on the investigation of transforming think aloud interviews into BPMN models. All information on how to use the data are provide in the SPSS files and as a readme file. This transformation is executed following a manual additionally provided in Documents.zip. For the training phase, a website was used provided in Website.zip including Screenshots for simpler re-use. Further information are also included as readme file in the zip container.</p> <p>Main research question answered is in how far the manual reduces interpretation and variance in the created models. </p>
User Study Data for "HapticLock: Eyes-Free Authentication for Mobile Devices"
<p>User study data from the HapticLock paper published in the Proceedings of the ACM ICMI 2021 conference.</p>
Dataset: A Study on the Mental Models of Users Concerning Existing Software
<p>In 2022, we conducted a study on the mental models of users concerning existing software.</p> <p>Information on the execution of the study are presented in the paper linked below:</p> <p>https://doi.org/10.1007/978-3-030-98464-9_18</p>
ReachHover User Study Dataset
<p>Dataset for the user study described in the ReachHover paper.</p>
Transcripts (+attributions) from a mixed-presence user study with two wall-sized displays
<p>Transcripts from a mixed-presence experiment with two wall-sized displays.<br>Automatic transcription (and translation when necessary) using Whisper large v3. Resulting sentences were then attributed to individuals.<br>Comes from a study ran in Q4 2023. Accompanies a paper.</p> <p>As for the abbreviations/acronyms used in the file:</p> <ul> <li>Conditions C0 and C1 correspond respectively to "no cues" and "cues enabled" (see paper)</li> <li>Sides A and V respectively correspond to Arena (=circular display) and Viswall (flat display)</li> <li>Speakers CEO, FIN, ICU and LOG respectively correspond to the roles given to these speakers (i.e. CEO, Head of Finance, Head of Intensive Care Unit, and Head of Logistics)</li> <li>Speakers TECH_VIZWALL and FACILITATOR_VIZWALL correspond to members of the research team (see protocol)</li> </ul>
User Study Data for "Enhancing Ultrasound Haptics with Parametric Audio Effects"
<p>Data from an experiment investigation the perception of ultrasound haptic and parametric audio stimuli, as described in a paper at ACM ICMI 2021.</p>
Experimental data for the study: "Hiding Assistive Robots During Training in Immersive VR Does not Affect Users' Motivation, Presence, Embodiment, and Performance"
<p>The datasets contains the motor performance metrics, the gaze fixation time ratios, and the questionnaire responses for a study involving a motor task with a rehabilitation assistive robot and an immersive virtual reality head-mounted display. The study was performed in the Motor Learning and Neurorehabilitation Laboratory at University of Bern. All data are stored in “csv” files. The variables inside the files are explained in “DataFrameDescription.rtf”. For questions, please contact nicolas.wenk@unibe.ch or L.MarchalCrespo@tudelft.nl.</p>
Package Quality for Users and Contributors: An Empirical Study of the npm Ecosystem
<p>Dataset for the submission "Package Quality for Users and Contributors: An Empirical Study of the npm Ecosystem"</p>
GENEA Challenge 2022 User Study Raw Responses
<p>This archive contains user-study response data in JSON format from the GENEA Challenge 2022 (https://youngwoo-yoon.github.io/GENEAchallenge2022/). Included in this data are the raw text responses on the questionnaire that was presented to participants after finishing the evaluation.</p> <p>If you use this material, please cite the following paper:</p> <p>Youngwoo Yoon, Pieter Wolfert, Taras Kucherenko, Carla Viegas, Teodor Nikolov, Mihail Tsakov, and Gustav Eje Henter. 2022. The GENEA Challenge 2022: A large evaluation of data-driven co-speech gesture generation. In Proceedings of the ACM International Conference on Multimodal Interaction (ICMI '22). ACM</p> <p> </p>
Dataset: What can be concluded from User Feedback? - An Empirical Study
<p>In 2022, we conducted a study on the awareness of users concerning existing software in order to compare users' description and their feedback.</p> <p>This data set contain 100 participants' description and feedback about the Komoot hiking app. The aspects were manually coded in the data set.</p>
[Dataset] An End User Based Study on Subtitling for the d/Deaf and Hard of Hearing in Turkey [Unpublished Master's Thesis]
<p>The dataset provided is from an unpublished master's thesis authored by Selma Akseki and supervised by Asst. Prof. Elif Ersözlü at Hacettepe University, Ankara (Türkiye). For more details see https://www.openaccess.hacettepe.edu.tr/xmlui/handle/11655/25767</p> <p>Abstract from the master's thesis reporting on the analysis of this dataset:</p> <p>Reception research in audiovisual translation (AVT), particularly on the<br> intersection between AVT and media accessibility (MA) has been a research<br> avenue to interest for translation scholars in the last couple of decades. However,<br> research in reception studies in countries like Turkey, where MA practices are<br> relatively new in terms of legislative mandates on the subject, are still scarce.<br> This thesis aims to contribute to the field by investigating the reception of subtitles<br> for the d/Deaf and hard of hearing (SDH) by the intended audience, Turkish<br> d/Deaf and hard of hearing (HOH) viewers. The present study places itself in the<br> intersection of Descriptive Translation Studies (DTS) and Reception Studies (RS)<br> within AVT. First, guidelines and current practices of SDH were investigated to<br> reveal the norms with a focus on specific parameters. Second, a questionnaire<br> was designed to elicit the opinions of viewers on these practices. The English<br> template of the Digital TV for All (DTV4ALL) questionnaire was adapted to the<br> Turkish context (Romero-Fresco, 2015). The project in which the original<br> questionnaire was used aimed to facilitate provision of access services and<br> provide feedback from viewers that could be relevant to stakeholders in improving<br> the quality of SDH. The Turkish questionnaire, designed with a similar objective<br> in mind, consisted of questions regarding demographic and personal data,<br> viewing habits and preferences, and opinions on particular SDH parameters.<br> Data was collected from 237 participants through online and paper<br> questionnaires. Findings were compared with previous similar studies and<br> discussed. In conclusion, as regards the specific SDH parameters investigated,<br> current practices seem to accomplish their skopos. The provision of more<br> subtitled programmes on free-to-air linear broadcast with a wider variety of types<br> of programmes, and offering of accessible versions with premieres of<br> programmes are areas that, according to the end users, could be improved on.</p>
Data set supplementing "Characteristics of Users and Nonusers of Symptom Checkers in Germany: Cross-Sectional Survey Study"
<p>This is the data set used to conduct the analyses in the article published by the Journal of Medical Internet Research under the title "Characteristics of Users and Nonusers of Symptom Checkers in Germany: Cross-Sectional Survey Study" (<a href="https://doi.org/10.2196/46231">https://doi.org/10.2196/46231</a>)</p> <p>This data set contains information collected of 1,084 respondents about their awareness, use, and usefulness of symptom checkers and several individual characteristics. </p>
User study data - Summaries with personalized persuasive suggestions to mitigate confirmation bias during interaction with online debates
<p><strong>Description</strong></p> <p>This data was collected to test the effect of debate summaries and personalized persuasive suggestions to engage with them on participants argument recall after engaging with the debate. It contains interaction data and questionnaire results of 212 participants who interacted with one out of four versions of an online debate page.</p> <p><strong>Variables</strong></p> <p>(names/column headers, description, coding)</p> <ul> <li><strong>display_con</strong>: debate display condition, coding: 1: without summary, 2: with summary and neutral suggestion, 3: with summary and personalized persuasive suggestion, 4: with summary and random persuasive suggestion</li> <li><strong>correct_comp</strong>: proportion of correctly recalled arguments (10 arguments)</li> <li><strong>AO_correct_comp</strong>: proportion of correctly recalled attitude-opposing arguments (5 arguments)</li> <li><strong>AC_correct_comp</strong>: proportion of correctly recalled attitude-confirming arguments (5 arguments), coding</li> <li><strong>assigned_topic</strong>: debate topic participant was assigned to</li> <li><strong>clicked_contribute</strong>: indicates whether participant made a contribution to the debate, binary</li> <li><strong>att_strength</strong>: strength of prior attitude, coding: 3: strong, 2: moderate</li> <li><strong>time_debate</strong>: time spent on the debate page in seconds</li> <li><strong>clicked_showmore</strong>: indicates whether participant clicked on the show more button to reveal two additional items of the summary, binary</li> <li><strong>att_change</strong>: change of prior to post attitude, coding: negative values indicate a weakaning, positive a strengthening of the initial attitude (attitude was measured on a seven-point Likert scale)</li> <li><strong>stps_highest</strong>: highest scoring persuasion category (persuasion profile)</li> </ul>
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.