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1,617 results for “users”
Luxembourgish word embedding (User comments from RTL.lu)
<p>This dataset is a word embedding model trained on Luxembourgish user comments from the media platform RTL.lu. It contains data from roughly 544k Luxembourgish texts published between December 2008 and December 2018. See the documentation file for detailed info.</p>
UI Dark Patterns and Where to Find Them: A Study on Mobile Applications and User Perception
<p>UI Dark Patterns and Where to Find Them: A Study on Mobile Applications and User Perception</p> <p> </p> <p>This dataset contains:</p> <ul> <li>survey_data.xlsx: Read-only spreadsheet containing the answers of 541 participants of our online survey (48 participants opted to not make their answers publicly available);</li> <li>classification_data.xlsx: Read-only spreadsheet containing the overall and the individual categorization of 240 mobile apps with respect to the presence of dark patterns; and,</li> <li>Videos.zip: videos of 15 apps (10 minutes each) used to classify the apps. The complete set of videos is considerably large and can be provided upon request.</li> </ul>
dataset for paper Vanhaebost J, Faouzi M, Mangin P, Michaud K: New reference tables and user-friendly Internet application for predicted heart weights. Int J Legal Med 2014, 128(4):615-620.
<p>The heart weight is the most important parameter in the determination of cardiac hypertrophy. The obtained heart weight value should be compared against tables of normal weights by age, gender and body weight and height</p> <p>In the study by Vanhaebost<em> et al</em>. has been shown in the Swiss population that the heart weight increases along with the increase of the body weight, body height, BMI and body surface area (BSA). The mean heart weight is greater in men than in women at a similar body weight. The reference tables for predicted heart weights obtained from this study are presented as an user-friendly internet application (<a href="http://calc.chuv.ch/Heartweight">http://calc.chuv.ch/Heartweight</a>) enabling the comparison of heart weights observed at autopsy with the reference values.</p>
User 1_360 stereo video
<p>Complete 360 stereoscopic version for User 1.</p> <p><br> Video Composition of the full experience:<br> Interrogation room story video and video<br> compositing for conversation between the<br> users.<br> Equirectangular.<br> Stereoscopic.</p>
User Interaction Evaluation of 3D Handicraft Products Application
<p>The dataset for analysis during the study for evaluation of 3D handicraft products application for smartphones usage</p>
Traveller pre and post-questionnaires of mainstream and in depth users of the 2nd iteration phase
<p>The dataset contains the travellers’ pre- and post-questionnaires of the second evaluation phase in the MyCorridor project, which was a semi-real-world test. Each spreadsheet indicates the source of the data (pre, post, mainstream or in depth users). The questions are related to easiness to use the app, usefulness of the App, social desirability, attitude towards public transport, sharing modes and general mind sets, perceived accessibility to local transport, perceived accessibility to innovative mobility services and perceived overall trustworthiness, safety and security when using transport services.</p>
Surveying the communities of users of MATLAB and similar languages (Responses)
<p>This upload includes responses gathered from a survey applied to the users of MATLAB and its clone languages. It covers the participants' programming experience, how they interact with these languages, the importance they give to the reusability of their programs, object-oriented programming and how satisfied they are with these languages.</p>
Following/Followers and Tags on 0.1 million Twitter Users
<p><strong>Abstract</strong> (our paper)</p> <p>Why does Smith follow Johnson on Twitter? In most cases, the reason why users follow other users is unavailable. In this work, we answer this question by proposing TagF, which analyzes the who-follows-whom network (matrix) and the who-tags-whom network (tensor) simultaneously. Concretely, our method decomposes a coupled tensor constructed from these matrix and tensor. The experimental results on million-scale Twitter networks show that TagF uncovers different, but explainable reasons why users follow other users.</p> <p><strong>Data</strong></p> <p>coupled_tensor:<br> The first column is the source user id (from user id), the second column is the destination user id (to user id), and the third column is the tag id.</p> <p>users.id:<br> The first column is the user id for coupled_tensor, and the second column is the user id on Twitter.</p> <p>tags.id:<br> The first column is the tag id for coupled_tensor, and the second column is the tag (<em>i.e.</em> slug or list name) on Twitter. On the tags, ###follow### and ###friend### are special tags expressing follower and following.</p> <p><strong>Publication</strong></p> <p>This dataset was created for our study. If you make use of this dataset, please cite:<br> Yuto Yamaguchi, Mitsuo Yoshida, Christos Faloutsos, Hiroyuki Kitagawa. Why Do You Follow Him? Multilinear Analysis on Twitter. <em>Proceedings of the 24th International Conference on World Wide Web (WWW '15 Companion)</em>. pp.137-138, 2015.<br> http://doi.org/10.1145/2740908.2742715</p> <p><strong>Code</strong></p> <p>Our code outputting experiment results made available at:<br> https://github.com/yamaguchiyuto/tagf</p> <p><strong>Note</strong></p> <p>If you would like to use larger dataset, the dataset on 1 million seed users made available at:<br> http://dx.doi.org/10.5281/zenodo.16267<br> (The dataset on 0.1 million seed users is not subset of the dataset on 1 million seed users.)</p>
SURF: Replication Package for: "What Would Users Change in My App? Summarizing App Reviews for Recommending Software Changes"
<p>Description of the content of folder "SURF_replication_package": 1) "Experiment I" contains: a) the folder "summaries" which contains all the html summaries generated through SURF and browsed by study participants involved in the Experiment I. b) the folder "XMLreviews" which contains, for each of the apps involved in the Experiment I, the corresponding XML file containing all the collected reviews for that app. These xml files have been used as input files for the SURF tool for generating the summaries contained in the "summaries" folder c) "Experiment_I_results.xlsx" which contains all the answers to our survey collected from the Experiment I participants.</p> <p>2) "Experiment II" contains: a) the folder "summaries" which contains the two html summaries generated through SURF and browsed by study participants in the Experiment II. b) the folder "XMLreviews" which contains, for each of the two apps involved in the Experiment II, the corresponding XML file containing all the collected reviews for that app. These xml files have been used as input of the SURF tool for generating the summaries contained in the "summaries" folder. c) "Experiment_II_results.xlsx" which contains all the user feedbacks extracted/validated by survey participants in the two sub-experiments. d) "Experiment_II_survey_answers.xlsx" which contains all the answers to our survey collected in the Experiment II participants.</p> <p>3) "Survey.pdf" which contains the pdf version of the survey performed by the participants</p> <p>4) "SURF_tool.zip" contains: a) "SURF.jar", which contains the class files of a prototypical implementation of SURF b) "README.txt" which contains the instructions to run the SURF tool c) the "lib" folder, which contains all the java libraries needed for running SURF.</p>
CERN Analysis Preservation User Stories
<p>This comic-like drawing is a graphical representation of the User Stories identified for CERN Analysis Preservation.</p>
Two datasets with user generated audio recordings
<p>We provide two open access datasets of <strong>user generated audio recordings </strong>captured with mobile devices such as smartphones and portable cameras . The provided audio files originate from two different public events, a <strong>musical concert</strong> and a <strong>football match</strong>. Also, for each event, we provide two different types of collections; the original <strong>unorganized</strong> collection of uncompressed audio files and an additional <strong>organized</strong> collection, where the different recordings corresponding to similar parts of the event are grouped into specific folders and time-aligned so they can be played back in unison. The interested researcher is invited to read the accompanying paper "<em>Two open access datasets of user generated audio recordings</em>" for finding out more details about these datasets and the way that they can be useful in the context of research related to the organization and reproduction of user generated content.</p>
Exit poll survey data from users of Helsinki University Library
<p>Survey data from exit poll user surveys at five Helsinki University Library locations collected from 2015 to 2016. Patrons coming out the library premises were asked about their user category (student at the University of Helsinki, researcher or lecturer, student at another institution, other library user) and the reason for their library visit. Also the duration of the visit was noted and wether they needed help during their stay at the library. The file contains 3084 rows of tabular data in CSV format. The language is Finnish.</p>
CROSSCULT user ontology
<p>CC-UserOntology is the user ontology to be used in the CROSSCULT project, aiming to capture rich information in user profiles to enable innovative applications in relation to cultural heritage reflection and re-interpretation.</p>
Explaining non-adoption of electronic government services by citizens. A study among non-users of public e-services in Latvia
<p>This data was collected as part of the H2020 Citadel project, http://www.citadel-h2020.eu, project no. 726755. The objective was to analyse citizen motives for not using electronic government services. Using interviews among users of Citizens´ Service Centres in Latvia, the data is used to analyses the motives of citizens not to use electronic government services but to rely on non-electronic equivalents or on in-person assistance. Findings and fieldwork details are available in D2.1 of this project.</p>
An Open Access User Generated Video Dataset from 2016 Edinburgh Festival
<p>A user generated video dataset captured during the 2016 Edinburgh festival. The provided dataset was collected using a smart phone and is available with no post-processing. The videos mainly cover the Edinburgh streets and the festival atmosphere, and do not cover any performances. The dataset can be used for evaluation of various research tools, such as video quality assessment and enhancement.</p>
Different adaptation error types in affective computing have different effects on user experience: a Wizard-of-Oz study
<p>The record consists of one Excel file that contains individual participant data for the study "Different adaptation error types in affective computing have different effects on user experience: a Wizard-of-Oz study". The study included 97 participants who were randomly divided into five groups corresponding to five adaptation behaviors (SingleSmall, SingleModerate, ImmediateLow, ImmediateHigh, IrreversibleHigh). Each participant took part in three 11-minute intervals. Difficulty changed every 60 seconds in each 11-minute interval, and there are thus 11 difficulty values per interval. At the end of each interval, participants self-reported their experience using the NASA Task Load Index (6 items) and Intrinsic Motivation Inventory (8 items). After the third interval, participants were asked to rate how much they liked the 3 intervals on a visual analog scale that was converted to 1-100 numerical scores.</p>
Survey Results - User Accuracy Effects on Algorithmic Accuracy
<p>The survey was hosted on Qualtrics and participants recruited via Cloud Research. Participants are US-only. The data includes those who did not finish. No PII data was collected. </p><p>The survey included a deception scenario for a mortgage application followed by a battery of questions to assess ratings of the algorithm, assess participant honesty, and assess algorithmic awareness.</p>
arxiv_supplementary_material_User_study_dataset
<div> <div>**Supplemental Material Description for "Expanding Horizons in HCI Research Through LLM-Driven Qualitative Analysis"**</div> <br> <div>The Excel workbook titled `arxiv_supplymentary_material_User_study_dataset.xlsx` provided as supplementary material holds the dataset necessary for understanding the evaluation methods used in the study. The contents are organized into several sheets, described as follows:</div> <br> <div>1. **Paper**: In this sheet, the research paper selection is listed, including an identifier (ID), the title of the paper, its publication year, the venue where it was presented, the DOI, and information regarding the availability of raw data. This dataset only includes papers that offer open access to raw participant data for qualitative analysis, foundational to the research process.</div> <br> <div>2. **GPT_paper_summary**: This sheet contains summaries of the selected papers, created using GPT-4. Included for each paper are the ID, the title, and the GPT-4-generated summary, which were required by the system for initializing the analysis.</div> <br> <div>3. **G1, G2, … , G5**: Recorded in these tabs are the results from multiple rounds of applying the evaluation system. Each 'Gx' tab corresponds to one trial, and contains both the paper ID as well as the qualitative comments crafted by the LLM, mirroring the deductive reasoning typically manifest in human analysis.</div> <br> <div>It is important to note that the data shared excludes any material that is not cleared for public release, including full-text articles and the raw responses of the study participants. The dataset, therefore, aligns with copyright and privacy policies while providing an adequate basis for verification and exploration by other researchers.</div> <br> <div>The LLM-generated discussions are compared with the original papers' discussions to assess the capabilities of LLMs in reconstructing meaningful narrative in the absence of the source material. This dataset supports the credibility of the study's outcomes and presents an opportunity for others in the field to undertake similar research endeavors.</div> </div>
Automated Insights Dataset (AID) and User Interface Depth Dataset (UID)
<p>The Automated Insights Dataset (AID) brings metadata from the 200 most downloaded free apps from each of the 32 categories on the Google Play Store, totaling 6400 apps, with information that goes beyond that presented by app stores, also bringing metadata from AppBrain. The User Interface Depth Dataset (UID) brings a high-quality sampling of the AID, and delves into the identification of 7540 components of 50 component types and the capture of 1948 screenshots of the interface of 400 apps. The component set was based on components of Google Material Design and Android Studio.</p> <ul> <li>The datasets can be viewed in the spreadsheets named "Automated Insights Dataset (AID).xlsx" and "User Interface Depth Dataset (UID).xlsx".</li> <li>The "UID - Screenshots.zip" file contains screenshots of the apps present in the UID, organized in folders by app IDs.</li> <li>The "Source code of the developed tools.zip" file contains Python codes and complementary files used to collect the datasets.</li> <li>The "Discarded apps.zip" file contains the apps discarded in the analysis, it presents screenshots of some apps, collected elements and the reasons that led to these apps being discarded.</li> <li>The "Data explanation.zip" file contains graphical representations of the UID components and textual representations of each data present in the UID and AID, allowing a better understanding of the criteria used.</li> </ul>
NoVAGraphS FSA User-Agent Corpus
<ul> <li><strong>Paper:</strong> Di Nuovo E., Sanguinetti M., Balestrucci P.F,Anselma L., Bernareggi C., Mazzei A. (2024),Educational Dialogue Systems for Visually Impaired Students: Introducing a Task-Oriented User-Agent Corpus. Accepted paper at the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)</li> <li><strong>Contact person: </strong>Elisa Di Nuovo,<strong> </strong>elisa.dinuovo@gmail.com</li> </ul> <h2>Dataset Summary</h2> <p>Collection of user-agent interactions revolving around the description of Finite State Automata.</p> <h2>Daset Description</h2> <p>The corpus consists of a CSV file encoded in UTF-8 comprising the following columns:</p> <ul> <li><strong>CODE_ID</strong>: the id of the interaction</li> <li><strong>Turn</strong>: the turn number within the interaction</li> <li><strong>Participant</strong>: it identifies the sender (<code>U</code> for the user, <code>S</code> for the agent)</li> <li><strong>Text</strong>: the utterance content</li> <li><strong>VIP</strong>: it determines whether the user is a Visually-Impaired Person</li> <li><strong>Token count</strong>: the number of tokens in the utterance (counted using <code>Spacy</code> tokenizer)</li> <li><strong>DAs_GOLD</strong> and <strong>Errors_GOLD</strong>: the columns including the assigned labels for Dialog Acts and Errors, respectively</li> <li><strong>FSA_ID</strong>: the id of the Finite State Automaton that is being referred to within the conversation (it corresponds to the PNG and HTML file names containing the relevant information on the FSA)</li> </ul> <h2>Additional Data</h2> <ul> <li>Two PNG files with the graphical representation of the automata</li> <li>Two HTML files containing the state tables of the automata</li> <li>RASA configuration files used to train the DIET classifier on the DAs</li> </ul> <h2>Access Request</h2> <p>To access the data users need to fill out the following Google <a href="https://docs.google.com/forms/u/0/d/e/1FAIpQLSeZJnUMj8tb-KtNK5nx1SkZMCjlCZIjuCFP7CYToaCila3QbA/formResponse" target="_blank" rel="noopener">form</a></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.