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136 results for “eye tracking”
A comparative usability analysis of eye-tracking and mouse click data taken from digital libraries
<p>This dataset is the result of a study, in which we analyzed parallels and differences between clicks as well as eye movements on two different digital library homepages. For this analysis we used diverse tracking tools for mouse clicks and eye tracking data that where further studied with respect to specific areas of interest (AOI). </p> <p>The dataset contains two screenshots indicating the areas of interest (AOIs; entitled “AreasOfInterest_Kartenportal.jpg and AreasOfInterest_Webportal.jpg), which separate the homepages into analyzable parts. It also contains eight screenshots of the homepages containing the total amount of collected clicks (each name starting with “clicks”) and two screenshots with the eye tracking heat maps (starting with “Heatmap”). The screenshots have directly been extracted from the click and eye tracking tools and matched with the before mentioned AOIs in order to gain the total count of clicks and views as well as the view duration the concerned area.</p> <p>All data are synthesized in a document containing three sheets with different tables: a first one with the initial data compilation for all AOIs of the two analyzed homepages (entitled “Data”), a second one with a more visual compiled data analysis for both homepages and all AOIs (entitled “Data2) and last one with the duration of the view as well as the duration of the fixation and the compiled click data (entitled « Eye tracking study data »).</p>
LRP predicts smooth pursuit eye movement onset during the ocular tracking of self-generated movements
<p>Dataset relative to the following publication:</p> <p>Chen, J., Valsecchi, M. & Gegenfurtner, K.R. (2016). LRP predicts smooth pursuit eye movement onset during the ocular tracking of self-generated movements. <em>Journal of Neurophysiology, </em>in press</p> <p>Each folder contains the data relative to one experiment and the script that was used to generate them. Please refer to "Description on data format.txt" for the usage of the data.</p> <p>Additional information can be deducted from the experimental scripts.</p>
Eye tracking data set of academics making an omelette: An egg-breaking work
<p>Just as there are numerous ways to cook an egg, there are numerous ways to recreate a YouTube video of cooking an omelette. We created a dataset of 10 academics replicating a viral video of making an omelette. We evaluated the saccade behavior during the varying subtasks and found differences related to the actions (whisking, sprinkling, etc.) and the objects (eggs, butter, plate, etc.). This dataset can further offer insight into eye movements in complex tasks and is potentially even applicable for task planning and </p>
Analysis of Smooth Pursuit Eye Movements in Clinical Context by Tracking the Target and Eyes
<p>Eyemove dataset obtained at Teikyo University.</p> <p>If you use the dataset, please state clearly that you have used our data.</p> <p>The mp4 files are the video of the examination.<br> Excel files are the position of the optic disc analyzed by SSD and the ocular position data analyzed by VOG.</p>
Dataset of the first and second student study of eye tracking with Feynman diagrams
<p>This dataset is part of my thesis "The Educational Use of Feynman DIagrams: Opportunities, Challenges, Practices".</p> <p>It comprises of questionnaire answers, as well as quantitative and qualitative eye tracking data produced by students working on <a href="../records/11501410">this learning material</a>.</p> <p>The file "Questionnaire-answers.xlsx" contains answers to questionnaires used in the second student study, while the file "hssip-c.xlsxl" contains the answers to the cognitive load questionnaire used in the first student study.</p> <p>Scripts for the analysis of the questionnaires can be found <a href="../records/11519651">here</a>.</p> <p>The thesis with all relevant information is published <a href="http://dx.doi.org/10.53846/goediss-10576">here</a>.</p>
Eye Tracking Data for Research on the Educational Use of Feynman Diagrams
<p>These data were collected within an eye tracking study as part of the PhD Thesis "The Educational Use of Feynman Diagrams: Opportunities, Challenges, Practices". The data were collected on a learning environment published under <a href="https://doi.org/10.5281/zenodo.11486241">this link</a>.</p> <p>Files including "et-data" are raw eye tracking data, mostly used to create and analyse transition-based metrics, files including "metrics" are metrics calculated by the Tobii Pro Lab software mostly used to calculate gaze durations on certain areas of interest. Files containing "questionnaire" are data from different questionnaires. The file"le-questions-analysis.xlsx" contains an item analysis of the answers within the learning environment. </p> <p>Analysis scripts are published <a href="../records/11519651">here</a>.</p> <p>The thesis with all relevant information is published <a href="http://dx.doi.org/10.53846/goediss-10576">here</a>.</p>
Gaze cueing by multiple people - averaged eye tracking data by participant
<p>Data for article Gaze cueing by multiple people.</p> <p>Averaged eye tracking data by participant that was included.</p> <p>Task 1 - time to first fixation and total fixation duration for each Area of Interest </p> <p>Task 2 - time to first fixation for Area of Interest as determined by the task </p>
"Haptic aesthetics and bodily properties of Ori Gersht's digital art: a behavioral and eye-tracking study." - datasets and script
<p>"Haptic aesthetics and bodily properties of Ori Gersht’s digital art: a behavioral and eye-tracking study."</p> <p>Datasets and R script for analyses of behavioural scores and visual parameter.</p>
Data from an eye-tracking based study of solution strategies in similar mathematical and physical tasks
<p>Eye-tracking data:</p> <p>The .nas file "ET_data" contains the eyetracking project in software tobii studio with gaze data of all 131 participants.<br> To open this project, the software tobii studio is required.</p> <p>----------------------------------------<br> Interview data:</p> <p>All .txt files contain transcribed interview data in German language. <br> The files are labeled by subject number (e.g., P01 for the first participant) and the item for which the interview was recorded (e.g., M8).<br> The files contain interview data of participants explaining their solution strategy for the given item.</p> <p>----------------------------------------<br> Descriptive data:</p> <p>The file descriptive_data.csv contains the descriptive data of all participants (N=131).<br> The column labels are as follows:</p> <p>code: code of participant, p01-p131 <br> survey_period: survey period, summer_2021 / autumn_2020 / spring_2020<br> age: age of participant in years <br> grade: grade of participant <br> gen: gender, male (m) / female (f) / diverse (d) <br> ger_nat_speak: German native speaker, yes / no <br> repeater: Repeater of the last grade?, yes / no<br> math_course: basic course (basic) or advanced course (advanced) in mathematics<br> physics_course: basic course (basic) or advanced course (advanced) in physics<br> grade_math: last grade in mathematics, 1-6 <br> grade_phy: last grade mark in physics, 1-6 <br> grade_ger: last report mark in german, 1-6 <br> glasses: wearer of glasses?, yes / no<br> motivation: four-point lickert scale, very motivated (1) to not motivated at all (0) <br> test: physics tasks before math tasks (pm) or vice versa (mp) <br> For each test item 2 columns are created. Example Item k1:<br> ac_k1: answer correctness of item k1, not correct and/or guessed (0), correct (1). <br> cf_k1: confidence of item k1, four-point lickert scale, very confident (1) to guessed (0) </p>
ICSE'23: How Do We Read Formal Claims? Eye-Tracking and the Cognition of Proofs about Algorithms (Replication Materials)
<p>Formal methods are used successfully in high-assurance software, but they require rigorous mathematical and logical training that practitioners often lack. As such, integrating formal methods into software has been associated with numerous challenges. While educators have placed emphasis on formalisms in undergraduate theory courses, such courses often struggle with poor student outcomes and satisfaction. In this paper, we present a controlled eye-tracking human study (n=34) investigating the problem-solving strategies employed by students with different levels of incoming preparation (as assessed by theory coursework taken and pre-screening performance on a proof comprehension task), and how educators can better prepare low-outcome students for the rigorous logical reasoning that is a core part of formal methods in software engineering. We find that incoming preparation is not a good predictor of student outcomes for formalism comprehension tasks, and that student self-reports are not accurate at identifying factors associated with high outcomes for such tasks. Instead, and importantly, we find that differences in outcomes can be attributed to performance for proofs by induction and recursive algorithms, and that better-performing students exhibit significantly more attention switching behaviors, a result that has several implications for pedagogy in terms of the design of teaching materials. Our results suggest the need for a substantial pedagogical intervention in core theory courses to better align student outcomes with the objectives of mastery and retaining the material, and thus bettering preparing students for high-assurance software engineering.</p> <p>This artifact makes publicly available the de-identified eye-tracking and facial behavior analysis data that we collected in our controlled study of cognition of proofs about algorithms. We also include our Python scripts (as several Jupyter notebooks) used for the statistical analyses of the collected data. </p>
Eye-Control Trial: Wearable Eye-Tracking Device as Means of Communication
ClinicalTrials.gov study NCT04582149. IPD Sharing: YES. Countries: 1. Publications: 1.
Probing the Role of Feature Dimension Maps in Visual Cognition: Impact of Salience Level (Eye-tracking Follow-up Study)
ClinicalTrials.gov study NCT06852534. IPD Sharing: YES. Countries: 1. Publications: 14.
Data from: Active vision in freely moving marmosets using head-mounted eye tracking
Open the record for dataset details and reuse information.
Impostor Syndrome in Final Year Computer Science Students: An Eye Tracking and Biometrics Study
<p>These are the artifacts that are associated with the paper: <strong>Impostor Syndrome in Final Year Computer Science Students: An Eye Tracking and Biometrics Study</strong></p> <p>This paper was accepted for publication at the 18th International Conference on Augmented Cognition</p> <p>The preprint of the paper is available at: https://arxiv.org/abs/2404.10194</p>
Publication data of How to improve data quality in dog eye tracking
<p>Publication data of How to improve data quality in dog eye tracking</p>
Eye-tracking data of translation evaluation
<p>Students and lecturers were asked to evaluate translations and select their favourite. On some of the slides, they were also provided with extrinsic information. </p> <p>Dataset includes project files, which need to be opened using GazePoint software. We have also included an Excel file which provides explanations on the participant background and selections. </p> <p> </p>
Benchmark Webcam Eye-Tracking Software
<p>These data correspond to the industrial systematic review and benchmark test set related to the paper <strong><em>A Benchmark Study of Webcam Eye Tracking Software in Robotic Process Automation</em>.</strong></p> <p>The industrial systematic review is an approach adapted from Kitchenham's method that gathers all the eye tracking tools obtained after running a search in search engines, entering defined queries, and filtering by inclusion/exclusion criteria that we define in our planning. More precisely, this review includes all the different results obtained from the search queries (<em>Eye tracking</em> or <em>Gaze tracking</em> <em>or Eye tracker</em>) and (<em>software</em> or <em>app</em> or <em>tool</em> or <em>github</em>) entered in the search engines <em>DuckDuckgo</em>, <em>Bing</em> and <em>Google</em>.</p> <p>After the search, the eye tracking software results will be filtered according to the exclusion/inclusion criteria (C):</p> <p>· C1: The software must be available and documented or supported.</p> <p>· C2: The software must work on desktop computers or laptops, excluding mobile devices.</p> <p>· C3: The software should be open-source and/or free software, and if it is not, a demo version should be available even if it is for a trial period.</p> <p>· C4: The software must provide the option to use the built-in/native camera of a PC or webcam as hardware for eye tracking. It is excluded head-mounted devices, Virtual Reality glasses and commercial eye tracker hardware.</p> <p>· C5: The software must provide Eye tracking as software, not as a feature or extension of a service unrelated about specifically predicting the POG as functionality.</p> <p>· C6: The software must be installable in the established benchmark test setup.</p> <p>The software that passes the inclusion/exclusion criteria are those evaluated in a benchmark.</p> <p> For this evaluation, there are 4 different objects where eye tracking software tests are performed. These objects are called <em>Circle</em>, <em>Buttons</em>, <em>Cartesian</em> <em>system</em> and <em>Email</em> <em>Invoice</em> and differ from each other in the elements and location of the targets for testing each eye tracking software.</p> <p>The results obtained from tests objects are strategically differentiated into two distinct categories to becnhmark eye tracking software according to Accuracy, Precision and POG Coincidence metrics:</p> <p>· <u>Accuracy and precision.</u> Measures directly the accuracy and precision of the eye tracking software in the <em>Cartesian system</em> object.</p> <p> o Accuracy. It is quantified as the angular error between the point representing the centroid of the estimated POG from the eye tracking software and the point representing the centroid of the target, at which the subject is gazing.</p> <p> o Precision. It is calculated using the root mean square (RMS) of the sampled points obtained from the screenshots of one test session.</p> <p>· <u>POG coincidence</u>. Classifies where the estimated POG is in each screenshot. This category applies to <em>Circle</em>, <em>Buttons</em> and <em>Invoice email</em> objects.</p> <p> o Match on target (green circle with a diameter of 1cm). The estimated POG centroid by the eye tracking software in the screenshot is inside the green circle area or touches its circumference or limit.</p> <p> o Match in a close target (blue circle with a radius of 2cm that contains the green circle (target) inscribed within it. The estimated POG centroid by the eye tracking software in the screenshot is inside the blue circle area or touches its circumference or limit.</p> <p> o Off-target. The estimated POG centroid by the eye tracking software in the screenshot is outside the target limit</p> <p> </p> <p>The structure of the data packages is organized as follows:</p> <p><strong>SLR_eye_tracking_software.xlsx: </strong>This .xlsx file corresponds entirely to the entire industrial systematic review on eye tracking software. It consists of 3 sheets:</p> <ul> <li><em>SLR</em>: This encapsulates the eye tracking software acquired through search queries inputted across diverse search engines. It is composed of 4 columns.</li> <ul> <li>SOFTWARE: Web platform or application that provides the eye tracking software.</li> <li>SOFTWARE URL: URL to the repository or host page of the software.</li> <li>SEARCH URL: URL of the page mentioning the software</li> <li>QUERY SEARCH: Keywords used for the search and entered in the search engines.</li> </ul> <li><em>Conducting</em>: This page shows the classification of eye tracking software according to the established inclusion/exclusion criteria, where each column corresponds to one of the criteria. Software that does not pass the successive criteria is discarded. Columns are as follows:</li> <ul> <li>AVAILABILITY; DOCUMENTATION AND SUPPORT (C1): Software that is downloadable and supported for viable use (Available, Not Available). Documentation and/or support about the software setup, run and outputs (Yes, No).</li> <li>PLATFORM (C2): Type of device and operating system where the sofware is run (PC: Windows, Ubuntu, MacOs, ...; Mobile: iOs, Android ...; Multiplatform).</li> <li>LICENSE (C3):</li> <ul> <li>OPENSOURCE: Public access to the source code (Yes, No).</li> <li>FREE SOFTWARE: It can be downloaded, used and distributed free of charge for non-commercial purposes (Yes, No).</li> <li>TRIAL DEMO: Availability of trial version if the software is neither open source nor free (Yes, No, -). *If it is opensource, trial demo value is “-“.</li> </ul> <li>HARDWARE (C4): Hardware required to run the software (Commercial, Webcam, Both, It is not determined).</li> <li>PURPOSE (C5): Eye tracking purposes or functionalities (Predict POG, AOI generation, Head motion capture, ...).</li> <li>BENCHMARK SETUP COMPATIBILITY (C6): The software shows compatibility with the benchmark setup (Yes, No).</li> </ul> <li><em>All Tools</em>: On this page, all the information obtained from each of the eye tracking software is displayed, without discarding based on the inclusion/exclusion criteria. In addition to the columns described on the conducting page, additional columns with extra information are included.</li> <ul> <li>SOFTWARE END-USE CONTEXT: Purpose of use of the software (Generic, Specific).</li> <li>SOURCE CODE: Eye tracker source code language (Java, C++, Python, JavaScript or it is not determined).</li> <li>INSTALLATION: Installation of the software in the system (Executable, SaaS, WebApp, Compiled, ...).</li> </ul> </ul> <p><strong>benchmark.xlsx: </strong>This file represents the results of the benchmark carried out on eye tracking software. It consists of two sheets.</p> <p> · <em>Accuracy + Precision</em>: It corresponds to the results obtained from the POG accuracy and precision tests conducted based on the Cartesian system object. Each column represents the following data:</p> <p> o <em>Software: </em>Eye tracking software tested.<em> </em></p> <p> o <em>Test ID: </em>Test identification.</p> <p> o <em>Screenshot ID: </em>Screenshot identification.</p> <p> o <em>Centroid POG X: </em>X-coordinate of the centroid of the (POG) estimated by the eye tracking software.</p> <p> o <em>Centroid POG Y: </em>Y-coordinate of the centroid of the (POG) estimated by the eye tracking software.</p> <p> o <em>Target X: </em>X-coordinate of the centroid of the target.</p> <p> o <em>Target Y: </em>Y-coordinate of the centroid of the target.</p> <p> o <em>Distance X (px): </em>Distance from the Centroid POG X to Target X (in pixels).</p> <p> o <em>Distance Y (px): </em>Distance from the Centroid POG Y to Target Y (in pixels).</p> <p> o <em>Euc.Distance (px): </em>Euclidean distance (in pixels) from the centroid of the POG to the centroid of the target point.</p> <p> o <em>Euc.Distance (cm): </em>Euclidean distance (in centimeters) from the centroid of the POG to the centroid of the target point.</p> <p> o <em>Average Euc.Distance (px): </em>Average Euclidean distance (in pixels) from the centroid of the Point of Gaze (POG) to the centroid of the target point, referred to the same test.</p> <p> o <em>Average euc.Distance (cm): </em>Average Euclidean distance (in centimeters) from the centroid of the Point of Gaze (POG) to the centroid of the origin point, referred to the same test.</p> <p> o <em>Precision: </em>Value of the Root Mean Square (RMS) of the average distance from the centroid of the Point of Gaze (POG) to the centroid of the target point.</p> <p> o <em><u>Accuracy (º</u></em><em>): Accuracy estimated (in degrees) according to the setup (60 centimeters and 0º eyes to screen target distance).</em></p> <p> o <em><u>Accuracy per points (º):</u></em><em> </em>Accuracy estimated (in degrees) per points referred to the same Test ID</p> <p> o <em><u>Average Accuracy per points (º):</u></em><em> </em>Average Accuracy (in degrees) per points referred to the same Test ID.</p> <p> · <em>Coincidence: </em>It corresponds to the results of the benchmark from the POG coincidence tests conducted based on Circle, Buttons and Invoice Email objects. Each column represents the following data:</p> <p> o Software: Eye tracking software tested.</p> <p> o Scenario: Object tested.</p> <p> o Test ID: Test identification.</p> <p> o Match Target: Number of Match target POG.</p> <p> o Close Target: Number of close target POG</p> <p> o Off Target: Number of Off target POG.</p> <p> o Match Target + Close Target: Sum of the match target POG and Close Target POG.</p> <p> o Total: Number of total POG: Total number of recounted points (50).</p> <p> o %Match Target: Percentage of match target POG.</p> <p> o %Close Target: Percentage of close target POG.</p> <p> o %Off Target: Percentage of off target POG.</p> <p> o %Match Target + Close Target: Sum of the match target POG and close target POG percentage.</p> <p><strong>benchmark_tests/</strong></p> <p> · <strong>tests_instructions.txt</strong></p> <p> It is an instruction manual containing information on how the tests are conducted in each object.</p> <p> · <strong>tests_scenarios/</strong></p> <p><strong> </strong>This folder contains the four objects designed for the benchmark evaluation.</p> <p> o <strong>circle.html</strong></p> <p> o <strong>buttons.html</strong></p> <p> o <strong>cartesian_system.html</strong></p> <p> o <strong>invoice_email.html</strong></p> <p> </p> <p> · <strong>eye-tracking_software_tests/</strong></p> <p> o <strong>GazeRecorder/</strong></p> <p> - <strong>buttons/</strong></p> <p> · <strong>T1/</strong></p> <p> o <strong>X_Y_Z.txt</strong></p> <p> o <strong>Recording_XXXXXXXXX_YYYY.mht</strong></p> <p> · <strong>T2/</strong></p> <p> o <strong>X_Y_Z.txt</strong></p> <p> o <strong>Recording_XXXXXXXXX_YYYY.mht</strong></p> <p> · <strong>T3/</strong></p> <p> o <strong>X_Y_Z.txt</strong></p> <p> o <strong>Recording_XXXXXXXXX_YYYY.mht</strong></p> <p> - <strong>circle/…</strong></p> <p> - <strong>invoice_email/…</strong></p> <p> - <strong>cartesian_system/</strong></p> <p> · <strong>T1/</strong></p> <p> o <strong>Recording_XXXXXXXXX_YYYY.mht</strong></p> <p> o <strong>1.png</strong></p> <p> o <strong>2.png</strong></p> <p> o <strong>…</strong></p> <p> o <strong>20.png</strong></p> <p> · <strong>T2/</strong></p> <p> o <strong>Recording_XXXXXXXXX_YYYY.mht</strong></p> <p> o <strong>1.png</strong></p> <p> o <strong>2.png</strong></p> <p> o <strong>…</strong></p> <p> o <strong>20.png</strong></p> <p> - <strong>(*) POG_GazeRecorder.png</strong></p> <p><strong> </strong></p> <p>The folder <em>eye tracking_software_tests</em> contains the results of the tests in a stratified manner. At the first level, there are folders with data belonging to the respective eye tracking software to be tested (GazeRecorder, Eyedid, and Webgazer.js). At the next level, corresponding to each software, there would be folders for the test objects (buttons, circle, cartesian_system, and invoice_email). Within each object test folder, we find folders corresponding to the Test ID. Within each Test ID, there are two files:</p> <p> · <strong>X_Y_Z.txt. </strong></p> <p> o X represents the number of match target POG estimated by the eye tracking software.</p> <p> o Y represents the number of of close target POG estimated by the eye tracking software.</p> <p> o Z represents the number of off-target POG estimated by the eye tracking software.</p> <p> · <strong>Recording_XXXXXXXXX_YYYY.mht. </strong></p> <p> o It is the User Interface log generated by the stepRecorder application for Windows. It contains 30 screenshots about the POG estimation for the coincidence tests objects (Buttons, Circle and Invoice Email) and 20 screenshots for the accuracy and precision test object (Cartesian System).</p> <p> · <strong>1.png to 20.png </strong></p> <p> o These screenshots are exported from the .mht file in order to obtain the centroid of the Point of Gaze (POG) and calculate accuracy and precision accurately, as it is not possible directly from the .mht file.</p> <p> </p> <p>(*) The Point of Gaze (POG) in GazeRecorder is not displayed as a small dot, as is common and as it happens in EyeDid and Webgazer.js, but rather as an eye encapsulated in a rectangle. POG_GazeRecorder.png shows the centroid for the POG estimated by that eye tracking software.</p>
Pointing movements and eye-tracking data_Facilitated Communication Users
<p>The repository contains all the pre-sorted data used for the analysis described in the paper. Data are divided into three:</p> <ul> <li>in A, we report the movement data of each pointing gesture considered in the analysis.</li> <li>in B we report keystrokes' related data.</li> <li>in C we report the sorted eye-tracking data.</li> </ul> <h3>A. User Correct Movements:</h3> <p>Each participant's data is organized into a 1xN cell array in Matlab, where N represents the number of pointing gestures analyzed. Each cell contains an Nx8 column vector with the following information:</p> <ol> <li> <p><strong>Time Information (column 1)</strong>:</p> <ul> <li>Time associated with the pointing gesture (milliseconds).</li> </ul> </li> <li> <p><strong>Arm Coordinates (columns 2,3 and 4)</strong>:</p> <ul> <li>X-axis coordinates (millimetres).</li> <li>Y-axis coordinates (millimetres).</li> <li>Z-axis coordinates (millimetres).</li> </ul> </li> <li> <p><strong>EMG Deltoid Activation (Facilitator) (columns 5, and 6) </strong>:</p> <ul> <li>Rectified EMG deltoid activation.</li> <li>Envelope EMG deltoid activation.</li> </ul> </li> <li> <p><strong>EMG Deltoid Activation (User) (columns 7 and 8)</strong>:</p> <ul> <li>Rectified EMG deltoid activation.</li> <li>Envelope EMG deltoid activation.</li> </ul> </li> </ol> <h3>B. Users' Keys Pressed with Probability:</h3> <p>Each participant's data is organized into an Nx6 string array, where N represents the number of pointing gestures analyzed. Each array contains the following information:</p> <ol> <li> <p><strong>Key Press Time</strong>:</p> <ul> <li>Absolute time the key is pressed (milliseconds, as recorded by the key-logger).</li> </ul> </li> <li> <p><strong>Time Between Keystrokes</strong>:</p> <ul> <li>Difference in milliseconds between two consecutive keystrokes.</li> </ul> </li> <li> <p><strong>Key Pressed</strong>:</p> <ul> <li>The key that has been pressed.</li> </ul> </li> <li> <p><strong>Pointing Time</strong>:</p> <ul> <li>Time taken by the arm to complete the forward phase of the pointing gesture (seconds).</li> </ul> </li> <li> <p><strong>Character Position</strong>:</p> <ul> <li>Position of the pressed character within the word (spacebar hits are assigned the number 300).</li> </ul> </li> <li> <p><strong>Key Selection Probability</strong>:</p> <ul> <li>Probability (percentage) of the key being selected.</li> </ul> </li> </ol> <h3><strong>C. EyeTracking data sorted</strong></h3> <p>Each participant's data is organized into an N×6 cell array, where N represents the number of pointing gestures analyzed through eye-tracking. The contents of each row are as follows:</p> <ol> <li> <p><strong>Fixation Data (Nx5 vector)</strong>:</p> <ul> <li><strong>N</strong> is the number of fixations related to one pointing gesture.</li> <li>Each vector contains: <ul> <li> <p><strong>The standardized time </strong>is determined by synchronizing the eye fixation with the arm movement. Given the movement duration is scaled from 0 to 10, we identify the moment when the eye-fixation occurs.</p> <p>This standardized time refers to the duration of the fixation relative to the duration of the pointing gesture. In <strong>column 1</strong>, we report the gross time, averaging the beginning and end of the fixation. In<strong> column 4</strong>, we provide the exact standardization at the start, and in <strong>column 5</strong>, the exact standardization at the end of the movement. During analysis, these times are synchronized with the movement duration, and we use the data from column 4.</p> </li> <li> <p><strong>Euclidean distance</strong> between the fixated key and the target key (2nd column).</p> </li> <li><strong>Duration</strong> of each fixation (3rd column).</li> </ul> </li> </ul> </li> <li> <p><strong>Sequence of Fixated Keys</strong>:</p> <ul> <li>Contains the sequence of keys fixated by the user during each pointing gesture.</li> </ul> </li> <li> <p><strong>Arm Movement Information</strong>:</p> <ul> <li>Includes details on the arm movement (same as reported in<strong> file A</strong>) corresponding to the eye-tracking fixation sequence.</li> </ul> </li> <li> <p><strong>Target Key Pressed</strong>:</p> <ul> <li>Indicates the target key pressed by the participant.</li> </ul> </li> <li> <p><strong>Probability of Key Pressed</strong>:</p> <ul> <li>Reports the likelihood of each key being pressed,<strong> as detailed in file B.</strong></li> </ul> </li> <li> <p><strong>Euclidean Distance Between Consecutive Keys</strong>:</p> <ul> <li>Measures the Euclidean distance between two consecutively pressed keys using the keyboard as a reference (refer to the paper text for more details).</li> </ul> </li> </ol> <p> </p> <p> </p>
Dataset for BPM2024, Educators Forum: Comprehension of (business) process models via tokens: an eye-tracking approach
Open the record for dataset details and reuse information.
data and appendix for paper "From Analytical Purposes to Data Visualizations: A Decision Process Guided by a Conceptual Framework and Eye Tracking"
<p>the folder contains the following:</p> <p>1. Data from the pre-experiment questionnaire provided to the participants (pre-experiment_data.xlsx)</p> <p>2. Questions and answer data (answers_accuracy.xlsx)</p> <p>3. Online figures appendix (figures.pdf)</p>
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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.