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229 results for “user studies”
MEDIATOR Driving Simulator Study Germany: Questionnaire Data User Evaluation HMI
<p>The dataset provided resulted from a driving simulator study conducted by Chemnitz University of Technology (TUC) within work package 3 of the MEDIATOR project. The study focused on the user evaluation of the Mediator system and its functionalities, including the innovative Human Machine interface (HMI). The user evaluation centred on acceptance, trust, usability, comfort and the experience of Transitions of Control (TOCs). The core idea of the Mediator system is to mediate between the human driver and the automated system. Thereby, the Mediator system aims at establishing both as a team that is aware of each other’s strengths, limitations as well as current states in order to achieve safe TOCs, which are actively proposed by the HMI. The main focus of the driving simulator study was on comfort TOCs from manual to automated driving, simulated automation degradation and related TOCs by the human driver, comfort critical situations (i.e., close approach to the rear-end of a traffic jam) as well as the influence of driver characteristics. The provided dataset contains the questionnaire data of 74 German-speaking participants.</p> <p> </p> <p>This document contains information about the study methodology and coding of the variables. For a detailed description, please consult Mediator deliverable D3.3 ‘Results of the MEDIATOR driving simulator evaluation studies’ (Part II – Driving simulator study Germany). Please note that selected passages of this deliverable were adopted (partly in a slightly modified manner) in this document.</p> <p> </p> <p>This dataset is licensed under a <a href="https://spdx.org/licenses/CC-BY-4.0.html">Creative Commons Attribution 4.0 International</a> License.</p> <p> </p> <p>The research leading to this dataset received funding from the European Commission Horizon 2020 programme under the project MEDIATOR (<a href="https://mediatorproject.eu/">https://mediatorproject.eu/</a>), grant agreement number 814735.</p> <p> </p> <p>If you use the dataset, please cite it as: MEDIATOR (2023). MEDIATOR Driving Simulator Study Germany: Questionnaire Data User Evaluation HMI. <a href="https://doi.org/10.5281/zenodo.7638299">https://doi.org/10.5281/zenodo.7638299</a></p> <p> </p> <p>For further information, please contact: <a href="mailto:cornelia.hollander@psychologie.tu-chemnitz.de">cornelia.hollander@psychologie.tu-chemnitz.de</a>.</p>
162 Human Error Descriptions and Categorizations from a User Study
<p><i><strong>Software Engineers' Human Errors</strong></i></p><p>This dataset contains descriptions of 162 human errors experienced by software engineering students during a user study described in the following publication:</p><ul><li>Benjamin S. Meyers and Andrew Meneely. Taxonomy-Based Human Error Assessment for Senior Software Engineering Students. Special Interest Group on Computer Science Education (SIGCSE) Technical Symposium. Forthcoming in 2024.</li></ul><p><i><strong>Included Files</strong></i></p><p>The "experienced_human_errors.csv" file contains a dataset of 162 human errors experienced during our user study. Participants documented their human errors in a Google Form with 8 questions.</p><p><i><strong>CSV Fields</strong></i></p><ul><li><strong>PARTICIPANT</strong>: Anonymous participant ID.</li><li><strong>INTERVIEW_DATE</strong>: Date of interview discussing human error.</li><li><strong>ID</strong>: Unique ID for experienced human error. Prefixed with "P1" for Phase 1 or "P2" for Phase 2.</li><li><strong>FINAL_CATEGORIZATION</strong>: Agreed upon T.H.E.S.E. categorization following discussion with interview facilitator.</li><li><strong>QUESTION_1</strong>: Anonymized participant answer to Question 1.</li><li><strong>QUESTION_2</strong>: Anonymized participant answer to Question 2.</li><li><strong>QUESTION_3</strong>: Anonymized participant answer to Question 3.</li><li><strong>QUESTION_4</strong>: Anonymized participant answer to Question 4.</li><li><strong>QUESTION_5</strong>: Anonymized participant answer to Question 5.</li><li><strong>QUESTION_6</strong>: Anonymized participant answer to Question 6.</li><li><strong>QUESTION_7</strong>: Anonymized participant answer to Question 7.</li><li><strong>QUESTION_8</strong>: Anonymized participant answer to Question 8.</li></ul><p><i><strong>Interview Questions</strong></i></p><ol><li>Please briefly describe the human error that you experienced.</li><li>If the human error you experienced resulted in a defect that was committed, please provide a link (or Git commit hash) to the commit below.</li><li>Is your human error a slip, lapse, or mistake?</li><li>Now, please examine the Taxonomy of Human Errors in Software Engineering (T.H.E.S.E.) and choose the specific human error that most accurately describes the human error you experienced. If you experienced multiple human errors, please submit this form once for each human error.</li><li>If there are other categories of human error that also describe the human error that you experienced, please note them here.</li><li>If you chose a 'General' or 'Other' category in Question (4), this question is required. Do you believe there is a missing human error category that better describes the human error that you experienced? If yes, please describe it below.</li><li>On a scale of 1 (not at all confident) to 5 (completely confident), how confident are you in your classification in the previous question?</li><li>Do you have any additional comments about this human error?</li></ol><p><i><strong>Anonymity</strong></i></p><p>Institutional Review Board approval for this research involving human subjects was granted by the Human Subjects Research Office at RIT on March 18, 2022. Participants signed an informed consent form acknowledging that (1) their participation was entirely voluntary and had no impact on their grades, and (2) their survey responses would be published in an anonymized format. All data released with this publication has been anonymized by replacing any personally identifiable information with participant identifiers.</p><p><i><strong>Contact</strong></i></p><p>Please contact Benjamin S. Meyers (<a href="mailto:bsm9339@rit.edu">email</a>) with questions about this data and its collection.</p><p><i><strong>Acknowledgments</strong></i></p><p>Collection of this data has been sponsored in part by the National Science Foundation (grant 1922169), by the NSA Science of Security Lablet program (grant H98230-17-D-0080/2018-0438-02), and by a Department of Defense DARPA SBIR program (grant 140D63-19-C-0018).</p>
User study data: Nudges to Mitigate Confirmation Bias during Web Search for Opinion Formation, automatic vs. reflective study
<p>Data of two user studies (282 and 307 participants), investigating the risks and benefits of warning labels with and without obfuscations to mitigate confirmation bias during web search on debated topics.</p> <p> </p> <p>Study Variables (study 1 and study 2)</p> <p> </p> <p> display_con: Search result display<br> - Study 1<br> - 1: targeted warning label with obfuscation<br> - 2: random warning label with obfuscation<br> - 3: regular (no intervention)<br> - Study 2<br> - 1: targeted warning label with obfuscation<br> - 2: targeted warning label without obfuscation<br> - 3: random warning label with obfuscation<br> - 4: random warning label without obfuscation<br> - 5: regular (no intervention)<br>- CRT_cat: Cognitive reflection<br> - 1: intuitive<br> - 2: analytic<br>- topic: Assigned debated topic<br> - 1: Is drinking milk healthy for humans? <br> - 2: Is homework beneficial?<br> - 3: Should people become vegetarian?<br> - 4: Should students have to wear school uniforms?<br>- clicksup_prop: Clicks on attitude-confirming (AC) search results (proportion of all clicks)<br>- clickwarn_prop: Clicks on warning label (WL) search results (proportion of all clicks)<br>- show_clicked: Clicks on show-button (number of clicks, only in conditions with obfuscation)<br>- accuracy_bias: Accuracy bias estimation (Difference between a) observed bias (as the proportion of attitude-confirming clicks) and b) perceived bias (reported in the post-interaction questionnaire and re-coded into values from 0 to 1), positive values indicate an overestimation of bias)<br>- att_change: Attitude change (Difference between attitude reported in the pre-interaction questionnaire and the post-interaction questionnaire. Negative values indicate an attitude change in the attitude-opposing direction, while positive values indicate an attitude strengthening in the attitude-supporting direction.)<br>- knowledge_1: Self-reported prior knowledge (Reported on a seven-point Likert scale ranging from non-existent to excellent as a response to how they would describe their knowledge on the topic they were assigned to)<br>- N_clicks: Cumulative clicks (Number of all clicks on search results)<br>- NFC: Need for Cognition (Mean response to 4-item subset of the NFC questionnaire)<br>- UX_usability: Usability (Mean of responses on a seven-point Likert scale to the module "usability"from the meCUE 2.0 questionnaire)<br>- UX_usefulness: Usefulness (Mean of responses on a seven-point Likert scale to the module "usefulness"from the meCUE 2.0 questionnaire)</p>
User study Data: Boosting Intellectual Humility During Search on Debated Topics
<pre><strong>User study data </strong> The following column headers correspond to the following study variables: Intervention = Intervention (CONTROL= control, DUMMYCONTROL = ATI control, PRIME = prime, QUESTIONNAIRE = remind, FULL = reinforce) DV1_AC_Clicks = Attitude confirming clicks DV2_Lowest_Rank = Lowest rank clicked DV3_Dwell_Time = Dwell time DV4_Task_Completion = Task completion time DV5_Cumulative_Clicks = Cumulative clicks IH = Intellectual Humility Ranking = Ranking Topic = Topic rationale = Rationale for behavior (free text) rationale_category = Rationale for behavior (category, one of IH_driven = driven by IH, ranking_driven = ranking, bias_driven = confirmation bias, content/form_driven = content/form, task_driven/unclear = task/unclear)) Att_change = Attitude change Knowledge = Knowledge gain (1 = no knowledge gain, 5 = substantial knowledge gain) NASA.Mental = Reflection on search task, mental demand NASA.Temporal = Reflection on search task, temporal demand NASA.Performance = Reflection on search task, performance NASA.Effort = Reflection on search task, effort NASA.Frustration = Reflection on search task, frustration</pre>
User Study Data from "Point-and-Shake: Selecting from Levitating Object Displays"
<p>This dataset contains anonymous user study data from the two experiments described in the corresponding CHI 2018 publication.</p>
Supporting information of a study for the definition and evaluation of a graphical user interface for housing co-design
<p>This dataset is from a study that intends to define, prototype and test a graphical user interface for a housing co-design system. To define the requirements of the interface, we conducted interviews with professionals of architecture, urbanism and social sciences areas, as well as with housing cooperatives and inhabitants of these institutions. An interface solution was prototyped, tested and refined. Then we conducted a heuristic evaluation and a summative evaluation. Such evaluations involved the testing of a high-fidelity prototype, to receive feedback from UX/UI experts, potential users (inhabitants) and architects.</p> <p>S1_File refers to the interview protocol used with the three groups of interviewees. We share the English and Portuguese versions of the interviews with professionals and the original (Portuguese) and translated versions of the remaining ones since these were conducted in Portuguese.</p> <p>S2_File is a dataset reporting the results of the interviews. Each question includes the answers given and the identification (anonymized) of the interviewees who responded to that question.</p> <p>S3_File describes the usability issues identified by the experts during the heuristic evaluation of the high-fidelity prototype. The first page organizes the issues by severity (left) and priority (right). The remaining pages have a table for each issue, including rows for problem designation, heuristic violated, problem description, solution proposal, severity degree, and an image of the interface pointing to the referred issue.</p> <p>S4_File refers to the results of the heuristic evaluation. It includes the identification of each issue, which expert (anonymized) identified such issue, and the heuristic it violates, with the sum of the times each heuristic was violated at the end of each column. At the right, a table presents the consolidation of issues, organized by priority, with columns identifying the issue, severity level, frequency, and priority.</p> <p>S5_File is the script given to potential users to experiment with the interface during the summative evaluation. This script guides the user through the tasks to perform since the prototype does not have all the features functioning.</p> <p>S6_File refers to the questionnaires applied during the summative evaluation with inhabitants. It includes a preliminary questionnaire, a Single Ease Question (SEQ) questionnaire, a System Usability Scale (SUS) questionnaire, and a Graphical User Interface (GUI) questionnaire.</p> <p>S7_File refers to the results of the summative evaluation with inhabitants (potential users).</p> <ul> <li>Page A refers to the preliminary questionnaire with demographic information such as age, gender, education, relationship with digital technologies, etc. Each field corresponds with each inhabitant (anonymised) and the sum and percentage. In the middle, a table presents a summary of the consolidation. In the right possible relations are presented. </li> <li>Page B presents the results of the SEQ questionnaire, identifying the ratings each inhabitant (anonymized) gave each task. A summary of such values is at the right. </li> <li>On page C, the result of each rating for the SUS questionnaire given by each inhabitant (anonymized) is shown. At the bottom is the calculation of the SUS score.</li> <li>Page D presents the GUI questionnaire results for each inhabitant (anonymized), with the average and SD identified for each question. A summary of such results is on the right.</li> <li>Page E holds the notes taken by the researchers based on their observations regarding task performance. The information is organized in tables for each step of each task and includes the completeness, attempts, and time taken for each inhabitant (anonymized) to complete such task. Also, the sum, percentage, average, and SD are registered. Next to each task is a table identifying how many participants accomplished the task at the first attempt.</li> <li>Page F refers to the strong and weak aspects identified by the inhabitants. Strong and weak aspects are identified, as well as which inhabitant (anonymized) has identified them. The sum and percentage are also given. At the right, there is a table with the consolidation of results by combining similar answers. </li> </ul> <p>S8_File refers to the results of the discussion with architects after experiencing the interface. Such results relate to the positive and negative aspects that the architects identified in the interface and its usefulness for architecture. The left table identifies the strong and weak aspects that architects (anonymized) identified and the sum and percentage associated with them. The table on the right consolidates such results, with similar responses combined.</p>
Sparse camera volumetric video applications. A comparison of visual fidelity, user experience, and adaptability: Subjective fidelity study data
<p>Supplementary Material to the Paper: <em>Sparse camera volumetric video applications. A comparison of visual fidelity, user experience, and adaptability</em></p> <p>This folder contains all collected data and scripts that were used to analyze the subjective fidelity study.</p>
User Study Data for "Perception of Ultrasound Haptic Focal Point Motion"
<p>Data from two experiments about the perception of ultrasound haptic feedback.</p>
Natural Language-Guided Programming User Study
<p>In this dataset you find the user study data that was used in the <strong><em>Natural Language-Guided Programming</em></strong> paper, which is accepted for Onward! 2021. A preprint can be found here <a href="https://arxiv.org/pdf/2108.05198.pdf">https://arxiv.org/pdf/2108.05198.pdf</a>. The dataset consists of the following files:</p> <ul> <li> <p>benchmark.json contains 201 test cases. Each test case consists of context, a natural language intent and target code. The test cases are intended to evaluate a model that can predict code giving a piece of context code and a natural language intent. The test cases were derived from Jupyter notebooks that were crawled from Github projects with permissive licenses. In the project_metadata field you find information about the original project such as its git url and license.</p> </li> <li> <p>predictions-annotated.json contains predictions of the three models used in the paper for 100 test cases in benchmark.json. Each prediction is accompanied with qualitive assesments from three annotators.</p> </li> <li> <p>train-index.jsonl is the list of github projects that were used for training the models.</p> </li> <li> <p>eval-index.jsonl is a list of github projects that we kept separate for evaluation. The benchmark.json was created from a random subset of the projects in this list.</p> </li> </ul> <p>For more details we refer to the paper.</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>
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>
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>
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>
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>
Online Repository of the Study "I want to RIDE my e-bicycle!": Supporting Developers Categorizing User Issues of a Mobility-as-a-Service Platform
<p><strong>Online Repository of the Study </strong><em>“I want to RIDE my e-bicycle!": Supporting Developers Categorizing User Issues of a Mobility-as-a-Service Platform</em></p> <p><strong>Introduction</strong></p> <p>In the Mobility-as-a-Service (MaaS) context, e-bikes are important and environmental-friendly transportation resources providing flexibility, time and cost savings, and reducing traffic congestion. Additional to user satisfaction and marketing advantages, the resolution of user-reported issues is regulated in many cities. In order to efficiently solve the issues, it is essential to quickly identify their types (e.g., software- or hardware-related?) to assign them to the responsible team. But for popular e-mobility services, the manual analysis of the reports is inefficient because of its tediousness, high time requirements, and error-proneness. </p> <p>Our empirical study, carried out in the context of a <em>Mobility as a Service </em>start-up company, proposes an approach for the automated identification of relevant concerns reported by users of e-bike services. The company has more than 20,000 private customers across seven different countries and dedicates considerable effort in analyzing user behavior. However, the current manual process of analyzing and triaging user-reported issues hinders MaaS-company’s ability to grow and expand its services. </p> <p>To help MaaS providers identify relevant user-reported issues, In the study, we (i) manually inspect about 3,000 user-reported issues received by the MaaS company; (ii) design a taxonomy modeling the types of relevant issues reported by users; and (iii) propose MaaS-RIDE, an approach to automatically classify the user-reported issues according to the categories of the devised taxonomy. </p> <p>Our results demonstrate that MaaS-RIDE is able to accurately (F-measure ≥ 93%) identify software and hardware user-reported issues. This result is critical for e-bike sharing companies to address such issues in an agile way and achieve the required user satisfaction.</p> <p><strong>Dataset Overview</strong></p> <p>The dataset is composed of the following different sorts of data: </p> <ul> <li> “<em>Data_and_preprocessing</em>” folder <ul> <li>o the user-reported issues data</li> <li>o the user-reported issues data processed as Bag of Words for Machine Learning training. <ul> <li>For this look at the sub-folder “<em>input_data_for_ML</em>” and the following matrices: <ul> <li><em>tf-idf-matrix-of-comment_finals_with_oracle_info_low_level.csv</em></li> <li><em>tf-idf-matrix-of-comment_finals_with_oracle_info.csv</em></li> </ul> </li> <li>Moreover, a sample of selected issues was reported in the replication package: <ul> <li>see file “<em>randomSamples.csv</em>” (due to a non-disclosure agreement with our industrial partner, we are unauthorized to share the whole raw user reports used in our experiments)</li> <li> “RQ1” folder: Types of E-bikes User-reported Issues</li> </ul> </li> </ul> </li> <li> the resulting taxonomy after the analysis of the issues</li> <li> “RQ2” folder: Classifying E-bikes Issue types</li> <li> the trained models </li> <li> the results of the models</li> </ul> </li> </ul> <p>The following sections describe more in detail what each of those folders and files contain.</p> <p><strong>“Data_and_preprocessing” folder</strong></p> <ul> <li><strong>User-reported issues subset.</strong></li> </ul> <p>In an industrial setting, due to privacy reasons, we disclose only an example subset of the user-reported issues, this information is in the file <em>randomSamples.csv</em>.</p> <p>The <em>randomSamples.csv </em>a subset that was generated randomly adding 20 examples using a stratified sampling from the High-level categories and 20 from the Low-level categories. This subset is not exhaustive but serves the purpose of showing the reviewers the kind of issues that this particular industrial set is confronted with. The file contains:</p> <ul> <li> <ul> <li> the Id of the user report; </li> <li> the column "comment_final"<strong> </strong>contains the issue text after the replacement of information that needed anonymization (e.g., vehicle-plates, personal names, addresses and timestamps); </li> <li> the column "High_level_category" contains the selected category from the 5 first level categories of the presented <em>Three-level taxonomy of e-bike user reported issues</em>; </li> <li>• the columns ‘Low_level_category" and "Fine_grained_topic" contain the assigned, if existing, respective category. </li> </ul> </li> <li><strong>Bag of Words Term by Document matrix.</strong></li> </ul> <p>An important input for training the ML models is the Bag of Words representation generated after processing the 2,989 manually-labeled user issues. The result of this process is a Term-by-Document matrix. We share this matrix in the files in the sub-folder <em>input_data_for_ML </em>where they are labeled for High- and Low-level categories. </p> <p>In the <em>tf-idf-matrix-of-comment_finals_with_oracle_info.csv</em> and <em>tf-idf-matrix-of-comment_finals_with_oracle_info_low_level.csv</em> files, the first column refers to the issue “Id”, the last column “oracle” is the labeled category, the rest of the columns represent the terms contained in the 2,989 user-reported issues and in each row the weight of the i−𝑡ℎ term contained in the j−𝑡ℎ user issue by using the tf-idf score.</p> <p><strong>“RQ1” folder</strong></p> <ul> <li><strong>“Three-level taxonomy of e-bike user-reported issues.pdf<em>” file</em></strong></li> </ul> <p>The taxonomy derives from the manual analysis of the 2,989 user issues. We found that a three-level taxonomy provides significant granularity to the MaaS-company. The taxonomy encompasses 5 High-level categories, 16 Low-level categories, and 15 Low-level subcategories of e-bike user-reported issues. The file <em>Three-level taxonomy of e-bike user-reported issues.pdf</em> presents the taxonomy categories and in the columns “Nr.” and “%” it shows the number of occurrences within the analyzed dataset, and the corresponding percentages.</p> <p><strong>“RQ2” folder</strong></p> <ul> <li><strong>“Trained Models” folder</strong></li> </ul> <p>We provide the trained machine and deep learning models in the sub-folder <em>ML_DL_models</em>. Our approach experimented with classic machine learning models based on the Bag-of-Words approach using SVM, on Word Embeddings using FastText, and Language models leveraging BERT. The SVM and BERT models were trained using the open source low-code data analytics platform KNIME and were used to classify issues corresponding to the first and second levels of the taxonomy from the “RQ1” folder. A 10-fold cross validation strategy was used to assess the classification performance. </p> <p>The fastText model was trained by using default values of parameters (https://fasttext.cc/docs/en/options.html) and a 10-fold cross-validation strategy. With fastText, we classified issues corresponding only to the first level of the taxonomy from “RQ1” folder, since fastText is more effective when more data points are available in the training set (i.e., lower levels in the taxonomy have fewer well-represented issue types).</p> <ul> <li><strong>“Model results” folder</strong></li> </ul> <p>In the sub-folder model_results we provide the tables summarizing the results of using the proposed MaaS-RIDE approach, with which we automatically identify and categorize user-reported issues according to the High-level and Low-level categories of the taxonomy devised in RQ1, which are relevant for the MaaS-company. </p>
Disentangling Web Search on Debated Topics - User Study Data
<p>Data of an exploratory, open-ended user study (N = 255) to advance knowledge and uncover relations between the different facets of web search on debated topics. We explored the relations between factors inherent to the searcher and search system (user characteristics, exposure bias), search intercations (confirmation bias, position bias, search effort), and post-search epistemic states (attitude change, knowledge gain). This data set contains the following variables for each of the 255 participants: SERP ranking bias, prior knowledge, attitude strength, receptiveness to opposing views, attitude-confirming clicks, click rank deviation, number of clicks, time on SERP, hover depth, attitude change, knowledge gain.</p>
Unveiling Perspectives - user study data
<p>Data collected with a user study (N=198), investigating how search behavior when searching on debated topics is affected by stance labels that indicate the stance of each search result. The data set contains independent (SERP display condition, SERP ranking condition), dependent (clicking diversity), and exploratory variables (number of clicks on neutral/opposing/supporting search results, mean click viewpoint, number of clicks, mean click rank, task completion time, attitude change, topic) .</p> <p> </p>
Code and Data for the Study "A User-Centric Model of Connectivity in Street Networks"
<p>This resource contains the code and results used in the paper:</p> <p>Corcoran, P. and R. Lewis (Pending) “A User-Centric Model of Connectivity in Street Networks”</p> <p>Please consult <strong>UserGuide.pdf</strong> for further information. </p>
Questionnaires answers and data processing for a mixed-presence user study with two wall-sized displays
<p>Questionnaire answers and data processing tabs that was part of a mixed-presence experiment with two wall-sized displays.<br>Was used for a study in Q4 2023. Accompanies a paper.<br>Complements the protocol for that study that can be found at https://zenodo.org/doi/10.5281/zenodo.12663837 and contains answers for the questionnaires that can be found at https://zenodo.org/doi/10.5281/zenodo.12664007</p>
Using ELN Functionality of Kadi4Mat (KadiWeb) in a Materials Science Case Study of a User Facility
<p>This record contains the dataset belonging to the paper "Using ELN Functionality of Kadi4Mat (KadiWeb) in a Materials Science Case Study of a User Facility"</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.