Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
33
datasets available to search
ShareScore release 0.9.0
Dataset results
33 results for “user interaction”
Anthropomorphic Mechanisms for User Acceptance in Human-Robot Interaction - PRISMA pass data
<p>This is the data produced in the course of selecting relevant literature for the <em>"User Acceptance in Human-Robot Interaction"</em> literature review article.</p> <p><strong>Contents:</strong></p> <ul> <li>Initial pass records: <em>prisma0_wos.xlsx + prisma0_scopus.xlsx</em></li> <li>Initial pass eligibility assessment:<em><strong> </strong>prisma0_eval.xlsx</em></li> <li>Second pass records, filtering and coarse assessment:<em><strong> </strong>prisma1.xlsx</em></li> <li>Third pass records, filtering and coarse assessment:<em><strong> </strong>prisma2.xlsx</em></li> <li>Fine eligibility assessment of 2nd and 3rd pass: <em>prisma_avalanche_1_and_2_report_update_04_26.pdf</em></li> </ul> <p> </p>
Synthetic dataset of user interactions - postpartum depression.csv
<p>A synthetic data set composed of 200 users' utterances as possible answers to questions related to these topics:</p> <p>(i) Feeling sad or Tearful<br>(ii) Irritable towards baby & partner<br>(iii) Trouble sleeping at night<br>(iv) Problems concentrating or making decision<br>(v) Overeating or loss of appetite<br>(vi) Feeling of guilt<br>(vii) Problems of bonding with baby <br>(viii) Suicide attempt</p>
DUX: A dataset of User Interactions and User Emotions
<p>User experience evaluation is becoming increasingly important, and so is emotion recognition. Recognizing users' emotions based on their interactions alone would not be intrusive to users and could be easily implemented in most applications. This is still an area of active research and requires data containing both the user interactions and the corresponding emotions. Currently, there is no public dataset for emotion recognition from keystroke, mouse and touchscreen dynamics. We have created such a dataset for keyboard and mouse interactions through a dedicated user study and made it publicly available for other researchers. This paper examines our study design and the process of creating the dataset. We conducted the study using a test application for travel expense reports with 50 participants. We want to be able to detect predominantly negative emotions, so we added emotional triggers to our test application. However, further research is needed to determine the relationship between user interactions and emotions.</p>
User Study Data for Paper "A case study in designing trustworthy interactions: implications for socially assistive robotics"
<p>Experimental data collected for the user study described in Frontiers paper "A case study in designing trustworthy interactions: implications for socially assistive robotics" by Mengyu Zhong et al. Citation: <i>Zhong, Mengyu, et al. "A case study in designing trustworthy interactions: implications for socially assistive robotics." Frontiers in Computer Science 5.1152532 (2023). </i></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>
A Service Robot in the Wild: Analysis of Users Intentions, Robot Behaviors, and Their Impact on the Interaction
<p>This file contains human-robot interaction data acquired during an experiment conducted at the University of Applied Sciences and Arts of Southern Switzerland (SUPSI). The campaign focuses on collecting non-identifying data, such as torso trajectories and the internal state of the system, from people in the proximity of a robot. The study spans three days in two different environments at the University Campus Est in Lugano, Switzerland.</p> <div> <div> <div> <div> <p>The campaign adheres to ethical guidelines and is approved by SUPSI's local ethics committee.</p> <p>Duration: Total of 5 hours and 7 minutes.</p> <p>Participants: 1777 individuals tracked.</p> <p><strong>Environments:</strong></p> <ul> <li>Entrance to the campus canteen (demographically diverse, including students and staff).</li> <li>Corridor between classrooms (mainly attended by students).</li> </ul> <p><strong>Data Types</strong>:</p> <ul> <li>Robot Sensor: Timestamps, user ID, 3D torso pose in Robot Sensor frame, interaction intention detector output.</li> <li>Environment Sensor: Timestamps, user ID, 3D poses of torso and hands in Environment Sensor frame, 2D torso positions in the sensor’s field of view.</li> <li>Robot State: Currently selected behavior, state (idle or performing an offering motion).</li> </ul> <p><strong>Key Events</strong>:</p> <ul> <li>Pick Motion: User's hand movement within 0.3 meters of the box.</li> <li>Robot Offer: Robot begins an offering motion.</li> <li>Successful Offer: Pick Motion within 6 seconds of a Robot Offer.</li> </ul> </div> </div> </div> </div> <div> <div> <div> </div> </div> </div>
Real-world human-robot interaction data with robotic pets in user homes in the United States and South Korea
<p>Socially-assistive robots (SARs) hold significant potential to transform the management of chronic healthcare conditions (e.g. diabetes, Alzheimer's, dementia) outside the clinic walls. However doing so entails embedding such autonomous robots into people's daily lives and home living environments, which are deeply shaped by the cultural and geographic locations within which they are situated. That begs the question of whether we can design autonomous interactive behaviors between SARs and humans based on universal machine learning (ML) and deep learning (DL) models of robotic sensor data that would work across such diverse environments. To investigate this, we conducted a long-term user study with 26 participants across two diverse locations (the United States and South Korea) with SARs deployed in each user's home for several weeks. We collected robotic sensor data every second of every day, combined with sophisticated ecological momentary assessment (EMA) sampling techniques, to generate a large-scale dataset of over 270 million data points representing 173 hours of randomly-sampled naturalistic interaction data between the human and robot pet. Interaction behaviors included activities like playing, petting, talking, cooking, etc.</p>
Real-world human-robot interaction data with robotic pets in user homes in the United States and South Korea
Open the record for dataset details and reuse information.
Interactive Tagging Networks (Following/Followers and Tags on 1 million Twitter Users)
<p><strong>Abstract</strong> (our paper)</p> <p>How do users behave if they can tag each other in social networks? In this paper, we answer this question by studying the interactive tagging network constructed by Twitter lists. Twitter lists can be regarded as the tagging process; a user (i.e., tagger) creates a list with a name (i.e., tag) and adds other users (i.e., tagged users) into the list. This tagging network is by nature different from the resource tagging networks (e.g., Flickr and Delicious) because users on this network can tag each other. We address the following research questions: (RQ1) What is the common patterns and the difference between the interactive tagging network and the resource tagging networks? (RQ2) Do users tag each other on the interactive tagging network? And if so, to what extent? (RQ3) What is the difference between the two types of relationships on Twitter: who-tags-whom and who-follows-whom? By quantitatively studying million-scale networks, we found the pervasive patterns across the different tagging networks, and the interactive patterns within the interactive tagging network. This study sheds light on the underlying characteristics of the interactive tagging network, which is relevant to the social scientists and the system designers of the tagging systems.</p> <p><strong>Data</strong></p> <p>twitter.seed.users:<br> The first column is the user id, and the second column is the json of the user objects on Twitter. This is the set of 1 million seed users to collect the following data.</p> <p>twitter.tagging.network:<br> The first column is the source user id (from user id), the second column is the destination user id (to user id), the third column is the tag (<em>i.e.</em> slug or list name), and the fourth column is the list id.</p> <p>twitter.tagging-out-going-from-seed-users.network:<br> The first column is the source user id (from user id), the second column is the destination user id (to user id), the third column is the tag (<em>i.e.</em> slug or list name), and the fourth column is the list id. This is only the out-going edges from the seed users, <em>i.e.</em>, this is a subset of twitter.tagging.network.</p> <p>twitter.following.network:<br> The first column is the source user id (from user id), and the second column is the destination user id (to user id).</p> <p>twitter.following-closed-seed-users.network:<br> The first column is the source user id (from user id), and the second column is the destination user id (to user id). This is not used in the following publication paper, but will be useful in other studies.</p> <p><strong>Publication</strong></p> <p>This data set was created for our study. If you make use of this data set, please cite:<br> Yuto Yamaguchi, Mitsuo Yoshida, Christos Faloutsos, Hiroyuki Kitagawa. Patterns in Interactive Tagging Networks. <em>Proceedings of the Ninth International AAAI Conference on Web and Social Media (ICWSM-15)</em>. pp.513-522, 2015.<br> http://www.aaai.org/ocs/index.php/ICWSM/ICWSM15/paper/view/10556</p> <p><strong>Code</strong></p> <p>Our code outputting experiment results made available at:<br> https://github.com/yamaguchiyuto/icwsm15</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>
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>
Dataset of adaptive Children-Robot Interaction for Education based on Autonomous Multimodal Users' Readings
<p># Dataset of adaptive Children-Robot Interaction for Education based on Autonomous Multimodal Users’ Readings </p> <p>## Background</p> <p>This dataset is generated from multiple interactions between a Social Robot (NAO) and 5th grade students from a private school in São Paulo, Brazil. </p> <p>In the interaction, the robot approached the content that teachers were approaching at the time with the participants students about the wasting system in Brazil.</p> <p>The measures here are the readings that the R-CASTLE system did for each answer the students gave to the questions the robot asked. </p> <p>For more information about how these measures were collected, please refer to this thesis at: https://doi.org/10.11606/T.55.2020.tde-31082020-093935</p> <p>Since the goal of the R-CASTLE is to provide autonomous adaptation, we built a ground-truth dataset based on human feedback of an expert in education operating the robot in loco. The person was teleoperating the robot to change its behaviour (or not) according to observed values of the participants as Face Gaze, Facial emotion displayed, Number of spoken words, the correctness of the answer (based on pre-defined answers), and the time students took to answer. These measures are the 5th columns of this csv file. The evaluator could decide to increase (1), maintain (0), or decrease (-1) the level of difficulties of the following questions depending on the mentioned observed measures. This is the human true label, stored in the 6th column. </p> <p>## Description:<br>Each row of this file is a tuple of the autonomous reading the robot made in the 5 first columns, plus the true label in the 6th row (True Value) and the Final Crisp Value using fuzzy classification in the 7th row (Final Crisp Value).</p> <p><br>Deviations (integer): number of face deviations of the participant during the question answering identified by the system.</p> <p>EmotionCount (integer): a balance between "good" and "bad" emotions (good - bad) identified by the system.</p> <p>NumberWord (integer): number of words comprised in the sentence the participant gave.</p> <p>SucRate/Ans/RWa: (between 0 and 1, where 0 is completely wrong and 1 is completely right): The success rate of the participant’s answer to that question, based on the expected answer programmed by their teachers.</p> <p>Time2ans (float): The time spent to answer the question since the robot has finished the question until the end of the participant’s speech in seconds.</p> <p>True Value (-1, 0, 1): Ground-truth value. Value of adaptation chosen by the human observing the interaction if the system needed to decrease, maintain, or increase the level of difficulty of asked questions. </p> <p>Final Crisp Value (float): value of calculated fuzzy output based on the implementations in the paper: https://doi.org/10.1145/3395035.3425201</p> <p><br>## Creators <br>Daniel Tozadore: dtozadore@gmail.com<br>Roseli Romero: rafrance@icmc.usp.br</p> <p><br>## License: <br>[Creative Commons Licenses](https://creativecommons.org/share-your-work/cclicenses/)</p>
Human-Robot Interaction Conversational User Enjoyment Scale (HRI CUES) Dataset - Anonymized
<p>Human-Robot Interaction Conversational User Enjoyment Scale (HRI CUES) and this corresponding dataset aim to provide tools for measuring user enjoyment from an external perspective to supplement self-reported user enjoyment responses in human-robot interaction research, with future potential application for autonomous detection of user enjoyment in real-time in robots and agents for adapting conversations contingently to provide enjoyable and long-lasting interactions.</p> <p>The dataset consists of 25 older adults' (12 men, 13 women) open-domain dialogue with an autonomous companion robot with an integrated large language model (GPT-3.5, text-davinci-003) from participatory design workshops conducted in March 2023. The conversations are annotated for user enjoyment based on HRI CUES by 3 expert annotators, as described in the paper (arXiv:2405.01354). Robot architecture and participatory design workshops are described in DOI: 10.21203/rs.3.rs-2884789/v1.</p> <p><strong>Exchanges</strong> file contains the participant ID, the number of the turn (conversation exchange by Robot-Participant response), the start and end of the turn, the anonymized transcript for the turn, and three annotator scores for the user enjoyment in the exchange. </p> <p><strong>Overall </strong>file contains the participant ID, self-reported user perception scores from the questionnaire ("I was satisfied with my conversation with the robot", "It was fun talking to the robot", "The conversation with the robot was interesting", "It felt strange talking to the robot") and three annotator scores for the user enjoyment in the overall interaction.</p> <p>The conversations are in Swedish. Participants' mean age is 74.6 (SD=5.8). 20 participants had no prior interaction with a robot, and only one had previously talked with a robot. The average interaction duration is 7.4 min (SD=1.5) with 12 to 29 turns. Each turn lasts 5 to 61 seconds (M=17.7, SD=7.2). The total duration of the interactions is 174 min, corresponding to 590 turns. </p> <p><em>Videos of the interactions are available upon request, contingent upon a signed agreement to maintain data confidentiality in accordance with GDPR regulations.</em></p> <p>Anonymization macros:</p> <p>[P_NAME]: Participant's name (may include surname). The robot always uses the first name even when the surname is given.</p> <p>[NAME_REMOVED]: A name of another person mentioned by the participant.</p> <p>[LOCATION_REMOVED]: Small town/village/area where the participant lives or lived.</p> <p>[MEDICAL_INFO_REMOVED]: Medical information shared by the participant.</p> <p>[AGE_REMOVED]: Participant's or other person's age.</p> <p>[INFORMATION_REMOVED]: Sensitive information shared by the participant.</p> <p>[MISTAKEN_NAME]: Speech recognition error resulted in the name being misunderstood.</p>
Supplementary material for the ITP'23 article "Lessons for Interactive Theorem Proving Researchers from a Survey of Coq Users"
<p>This artifact contains the supplementary files for the ITP'23 article "Lessons for Interactive Theorem Proving Researchers from a Survey of Coq Users". More specifically, it contains:</p> <ul> <li>the Limesurvey structure exported file (<code>Limesurvey/survey-structure.lss</code>);</li> <li>the HTML print of the survey in English and Chinese (<code>Limesurvey/questionnaire_english.html</code> and <code>Limesurvey/questionnaire_chinese.html</code>);</li> <li>the Jupyter notebook (<code>Coq-survey-analysis.ipynb</code>) and the Stata code (<code>regressions/Regressions_and_Romano-Wolf.do</code>) that were used to produce the results;</li> <li>the plots for the answers to all the closed questions, as well as plots for some interactions between answers to multiple closed questions in <code>png</code> and <code>svg</code> formats (<code>assets/</code>);</li> <li>manual analysis of some open text questions (<code>coded_answers/</code>);</li> <li>answers to open text questions (<code>open_answers/</code>).</li> </ul> <p>This artifact does <em>not</em> contain the full raw data from the survey. These data have been deleted, following the GDPR compliance statement that was displayed at the beginning of the survey. The open text answers that are made available through this artifact have been sanitized to remove any personally identifiable element.</p> <p>File listing</p> <ul> <li>README.md: this README</li> <li>Limesurvey <ul> <li>questionnaire_english.html: survey HTML print in English</li> <li>questionnaire_chinese.html: survey HTML print in Chinese</li> <li>survey-structure.lss: Limesurvey structure export</li> </ul> </li> <li>Coq-survey-analysis.ipynb: Jupyter notebook used to produce plots</li> <li>regressions <ul> <li>Regressions_and_Romano-Wolf.do: Stata code used to do the regressions appearing in the article</li> </ul> </li> <li>assets <ul> <li>many <code>png</code> and <code>svg</code> files for plots showing quantitative results</li> </ul> </li> <li>coded_answers <ul> <li>renaming.md: manual analysis of the answers to the open text question "If you wish to elaborate on why Coq should / should not be renamed, feel free to do it here."</li> <li>renaming_choices.md: manual analysis of the answers to the open text question "If you wish to share any specific arguments in favor or against some specific name choices, please do so here."</li> <li>contributing_experience.csv: answers to the open text question "Feel free to elaborate on the contributing experience, what we can do better, or why you do not contribute." with manual analysis</li> <li>doc_improvements-grouped.docx manual analysis of the answers to the open text question "Feel free to elaborate on any of the items listed above, their importance, etc. Are there other improvements that you think would be important?" (in the context of a question on "How important are improvements to the following aspects of the Coq documentation?")</li> </ul> </li> <li>open_answers: Each table has been reordered and has a different indexing, so relating answers from different tables is not possible. Furthermore, answers have been checked and sanitized to remove any personally identifiable elements. <ul> <li>ci_feedback.csv: answers to the question "If you have general feedback on CI in the Coq ecosystem, feel free to share it here." Also shared at: <a href="https://github.com/coq-community/manifesto/issues/141">https://github.com/coq-community/manifesto/issues/141</a></li> <li>contributing_experience.csv: answers to the question "Feel free to elaborate on the contributing experience, what we can do better, or why you do not contribute."</li> <li>coqide_improvements.csv: answers to the question "What improvements, bug fixes and new features would you most like to see in CoqIDE?" Also shared at: <a href="https://github.com/coq/coq/issues/16580">https://github.com/coq/coq/issues/16580</a></li> <li>coq_improvements.csv: answers to the question "Feel free to elaborate on any of the items listed above, their importance, etc. Are there other improvements that you think would be important? Also, feel free to tell us how Coq does compared to other proof assistants you have experience with." (in the context of a question on "In order to make you more productive in Coq and to encourage others to learn and use Coq, how important are improvements in the following areas?")</li> <li>coqtail_improvements.csv: answers to the question "What improvements, bug fixes and new features would you most like to see in Coqtail?" Also shared at: <a href="https://github.com/whonore/Coqtail/issues/277">https://github.com/whonore/Coqtail/issues/277</a></li> <li>distracting_company_coq_features.csv</li> <li>doc_improvements.csv: answers to the question "Feel free to elaborate on any of the items listed above, their importance, etc. Are there other improvements that you think would be important?" (in the context of a question on "How important are improvements to the following aspects of the Coq documentation?")</li> <li>extraction_targets.csv: answers to the question "If you're interested in new extraction targets, which languages do you want?" Also analyzed quantitatively in: assets/extraction-targets-barplot.png</li> <li>jscoq_improvements.csv: answers to the question "What improvements, bug fixes and new features would you most like to see in jsCoq?" Also shared at: <a href="https://github.com/jscoq/jscoq/issues/261">https://github.com/jscoq/jscoq/issues/261</a></li> <li>jupyter_improvements.csv: answers to the question "What improvements, bug fixes and new features would you most like to see in coq_jupyter?" Also shared at: <a href="https://github.com/EugeneLoy/coq_jupyter/issues/46">https://github.com/EugeneLoy/coq_jupyter/issues/46</a></li> <li>jupyter_support.csv: answers to the question "Have you had any issues or lack of support for coq_kernel for any service? If so, feel free to share here." Also shared at: <a href="https://github.com/EugeneLoy/coq_jupyter/issues/46">https://github.com/EugeneLoy/coq_jupyter/issues/46</a></li> <li>languages.csv: answers to the question "What languages would be the most useful to support?" Also analyzed quantitatively in: assets/languages-barplot.png</li> <li>learning_experience.csv: answers to the question "How was your experience while learning Coq? For example, what were the easiest and/or most difficult parts of the process? Do you have suggestions to improve the experience?"</li> <li>proof_general_customizations.csv: answers to the question "Do you use specific customizations or fixups of Proof General or Company-Coq (in your ~/.emacs)? If yes, briefly speaking, what are these customizations and would you like to have some of them applied by default?" Also shared at: <a href="https://github.com/ProofGeneral/PG/issues/671">https://github.com/ProofGeneral/PG/issues/671</a></li> <li>proof_general_improvements.csv: answers to the question "What improvements, bug fixes and new features would you most like to see in Proof General?" Also shared at: <a href="https://github.com/ProofGeneral/PG/issues/671">https://github.com/ProofGeneral/PG/issues/671</a></li> <li>renaming.csv: answers to the question "If you wish to elaborate on why Coq should / should not be renamed, feel free to do it here."</li> <li>renaming_choices.csv: answers to the question "If you wish to share any specific arguments in favor or against some specific name choices, please do so here."</li> <li>survey_issues.csv: answers to the question "Did you encounter any issues with the survey that you'd like to report or do you have other feedback that we should hear about?"</li> <li>vim_compatibility.csv: answers to the question "What Vim / NeoVim features or plugins would you like to have better integrated with Coqtail? " Also shared at: <a href="https://github.com/whonore/Coqtail/issues/277">https://github.com/whonore/Coqtail/issues/277</a></li> <li>vscoq_improvements.csv: answers to the question "What improvements, bug fixes and new features would you most like to see in VsCoq?" Also shared at: <a href="https://github.com/coq-community/vscoq/issues/308">https://github.com/coq-community/vscoq/issues/308</a></li> </ul> </li> </ul>
User-Item interactions dataset from a Public Service Media
<p>Dataset from a Public Service Media (PSM) of user and item interactions.</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>
Interaction Framework within Collaborative Virtual Environments for Multiple Users each interacting with Multiple Degrees-Of-Freedom Controllers
<p>Collaboration is a process in which two or more agents work together to achieve shared goals. However, many existing platforms cannot generate a collaborative environment to engage multiple users with multiple controllers in a seamless manner. To address this need, this video and work in progress will describe LISU (Library for Interactive Settings and User-modes) an input management computing framework that enables collaboration across multiple input controllers as its default. Within the system team members cohabit any real-time simulation environments simultaneously and are then able to jointly control visualisation software across multiple controllers while being continually monitored and evaluated at a low level, allowing research questions to be answered.</p>
Data from: Trap nests for bees and wasps to analyse trophic interactions in changing environments - a systematic overview and user guide
1. Trap nests are artificially made nesting resources for solitary cavity-nesting bees and wasps and allow easy quantification of multiple trophic interactions between bees, wasps, their food objects and natural enemies. 2. We synthesized all trap nest studies available in the ISI Web of Science™ to provide a comprehensive overview of trap nest research and identify common practical challenges and promising future research directions. 3. Trap nests have been used on all continents and across climate zones and publication numbers have increased exponentially since the first studies in the 1950s. Originally used for detailed exploratory natural history observations, trap nests are now also an established method in hypothesis-driven ecology and to assess environmental changes. We identify the potential of trap nests for environmental monitoring by assessing trophic interaction networks of the groups involved. While pollen collection by bees or prey hunting by wasps has often been addressed, and interactions with natural enemies were included in almost half of all publications, surprisingly few studies have quantified trophic interaction networks in response to natural and anthropogenic environmental changes. 4. By simultaneously revealing a multitude of trophic interactions, trap nests have the potential to broaden our understanding how species interaction networks are influenced by manifold environmental changes, which are pressing topics in ecological research. To foster the use of trap nests in future studies, we identify common challenges and offer guidance on practical solutions.
Usability, Acceptability, User Experience, Human-Device Interaction, and Ergonomics in Two Mobile FES-Cycling Systems for Individuals with Spinal Cord Injury
<p>This database originates from a study comparing two FES-cycling systems: the <strong>commercial BerkelBike Pro</strong> and a <strong>recumbent FES-bike prototype</strong> developed by the team at Politecnico di Milano. The aim of the study was to evaluate and compare the <strong>usability</strong>, <strong>acceptability</strong>, <strong>user experience</strong>, <strong>human-device interaction</strong>, and <strong>ergonomics</strong> of these two devices for individuals with spinal cord injury (SCI).</p> <p>The study involved 15 participants with SCI, covering a wide range of ages (18 to 65 years) and injury types (both complete and incomplete, at acute and chronic phases). Each participant underwent three sessions with both FES-cycling devices:</p> <ul> <li><strong>First session</strong>: Dedicated to setting up the system for each participant. For both the BerkelBike Pro and the FES-bike prototype, the system settings were tailored to meet individual needs, ensuring optimal comfort and function.</li> <li><strong>Second and third sessions</strong>: Focused on actual training, where participants used the devices to engage in cycling activities.</li> </ul> <p>At the end of the <strong>third session</strong>, participants completed a series of questionnaires to assess the usability, acceptability, user experience, and ergonomics of both devices. These questionnaires form the core of the study’s data collection and are central to understanding participants' interactions with the FES-cycling systems.</p> <p>The database is organized as follows:</p> <ol> <li><strong>Demographic data</strong>: The first page contains demographic information, including participants' age, gender, height, weight, time after injury, injury type (ASIA grade), and injury level.</li> <li><strong>PGWBI scores</strong>: The second page includes baseline data collected using the <strong>Psychological General Well-Being Index (PGWBI)</strong>, which assesses the participants' emotional and psychological state before engaging with the devices. The PGWBI consists of 22 questions grouped into 6 items: "anxiety", "depression", "positivity", "self-control", "health" and "vitality”. The responses are assessed on a 6-point scale ranging from 0 to 5. The score contributions for each question are then added together and transformed to achieve the final score that can range from 0 to 110.</li> <li><strong>SUS scores</strong>: The third page contains the results from the <strong>System Usability Scale (SUS)</strong>, a standard 10-item questionnaire that evaluates the usability of each FES-cycling system based on user ratings. The SUS is evaluated using a 5-point Likert scale, where 1 corresponds to strongly disagree, while 5 to strongly agree. The score contributions for each question are then added together and multiplied by 2.5 to achieve the final score that can range from 0 to 100, where higher scores indicate better usability.</li> <li><strong>TAM-3 scores</strong>: The fourth page presents data from the <strong>Technology Acceptance Model 3 (TAM-3)</strong>, which measures how participants perceive the ease of use and the usefulness of the devices. . The TAM-3 consists of 50 questions grouped into 14 items. These items include “perceived usefulness”, “perceived ease of use”, “self-efficacy”, “perception of external control”, “playfulness”, “anxiety”, “enjoyment”, “subjective norm”, “voluntariness”, “image”, “relevance”, “output quality”, “result demonstrability” and “behavioral intention”. The items are investigated using a 7-point Likert scale, where 1 corresponds to strongly disagree, while 7 to strongly agree.</li> <li><strong>UEQ scores</strong>: The fifth page includes responses from the <strong>User Experience Questionnaire (UEQ)</strong>, evaluating participants' experience with the devices. The UEQ consists of 26 questions grouped in six items: “attractiveness”, “perspicuity”, “efficiency”, “dependability”, “stimulation” and “novelty”. Questions are scored using a 7-point Likert scale, where 1 corresponds to strongly disagree, while 7 to strongly agree. Then scores per item are transformed using a scale ranging from -3 to +3, with +3 representing the most positive value (extremely good) and -3 the most negative one (horribly bad). Values between -0.8 and 0.8 represent a neutral evaluation of the corresponding scale, values > 0.8 represent a positive evaluation and values < -0.8 represent a negative one.</li> <li><strong>Custom questionnaire scores</strong>: The sixth page contains data from a <strong>custom-designed questionnaire</strong>, created specifically for this study to assess the ergonomics of the two FES-cycling systems, with particular attention to human-device interaction at both the physical and psychological levels. It consists of 12 questions covering four items: the transfer from/to the bikes and the initial tuning, bike comfort, its accessibility, and the ease of interaction. Questions are evaluated using a 5-point Likert scale, where 1 corresponds to strongly disagree/very uncomfortable, while 5 to strongly agree/very comfortable.</li> </ol> <p>This comprehensive data collection allows for a thorough comparison of the two FES-cycling systems in terms of user experience and overall acceptability in the SCI population.</p> <p>Please cite the following manuscript when using this database:<br>Nossa R, Biffi E, Sanna N, Diella E, Guanziroli E, Ferrari F, Ferrante S, Molteni F, Pedrocchi A, Tarabini M, Ambrosini E. Assessment of User Experience, Acceptability, Usability, Human-Device Interaction, and Ergonomics in Two Mobile FES-Cycling Systems for Individuals With Spinal Cord Injury. Artif Organs. 2025 Apr 16. doi: 10.1111/aor.15007. Epub ahead of print. PMID: 40237144.</p>
Likert-Skala for User friendly interaction in intelligent digital assistance systems.
<p>A dataset for a usability study.</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
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.