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.
190
datasets available to search
ShareScore release 0.9.0
Dataset results
190 results for “Eye Movements”
Data on eye movements in people with glaucoma and peers with normal vision
<p>Eye movements were recorded from 44 elderly glaucoma patients and 32 age-similar healthy vision controls whilst watching three separate small video clips.</p>
[Saliency4ASD] A dataset of eye movements for the children with autism spectrum disorder
<p>Social difficulties are the hallmark features of Autism Spectrum Disorder (ASD) and can lead to atypical visual attention towards stimuli. Eye movements encode rich information about attention and psychological factors of an individual, which could help to characterize the traits of ASD. Learning atypical eye movements of the individuals with ASD towards various stimuli is important and has many application scenarios. However, due to the lack of open datasets, research in this sense is still limited. In this work, we present an open dataset of eye movements of children with Autism Spectrum Disorder. It consists of 300 natural scene images and the corresponding eye movement data collected from 14 children with ASD and 14 healthy controls. In particular, fixation maps and scanpaths are available in the dataset. Based on this dataset, researchers could analyze the visual traits of children with ASD and design specialized visual attention models to promote research in related fields, as well as design specialized models to identify the individuals with ASD</p>
Data on eye movements of glaucoma patients with asymmetrical visual field loss during free viewing.
<p>Raw eye tracking data and processed eye movement data were recorded from fifteen participants with assymmetrical visual field loss (visual field worse in one eye) while they freely viewed 270 images of nature with each eye monocularly.</p>
Data from Dowiasch et al. (2015) Effects of aging on eye movements in the real world
<p>The effects of aging on eye movements are well studied in the laboratory. Increased saccade latencies or decreased smooth-pursuit gain are well established findings. The question remains whether these findings are influenced by the rather untypical environment of a laboratory; that is, whether or not they transfer to the real world. We measured 34 healthy participants between the age of 25 and 85 during two everyday tasks in the real world: (I) walking down a hallway with free gaze, (II) visual tracking of an earth-fixed object while walking straight-ahead. Eye movements were recorded with a mobile light-weight eye tracker, the EyeSeeCam (ESC). We find that age significantly influences saccade parameters. With increasing age, saccade frequency, amplitude, peak velocity, and mean velocity are reduced and the velocity/amplitude distribution as well as the velocity profile become less skewed. In contrast to laboratory results on smooth pursuit, we did not find a significant effect of age on tracking eye-movements in the real world. Taken together, age-related eye-movement changes as measured in the laboratory only partly resemble those in the real world. It is well-conceivable that in the real world additional sensory cues, such as head-movement or vestibular signals, may partially compensate for age-related effects, which, according to this view, would be specific to early motion processing. In any case, our results highlight the importance of validity for natural situations when studying the impact of aging on real-life performance.</p>
Supplemental material to the article "Improving silent reading performance through feedback on eye movements: A feasibility study"
<p>This repository accompanies the article:</p> <p>Korinth, S., & Fiebach, C.J. (2018). Improving silent reading performance through feedback on eye movements: A feasibility study. Scientific Studies of Reading.</p> <p>For a more convenient download files were zipped. Organized into four subfolders it provides the data and the R-scripts allowing the replication of all analyses and the manuscript's figures. For further information please refer to the README.txt files in the individual subfolders.</p> <p>Please note, a former version of this repository contained the same data and the same analyses and was used for manuscript review purposes. Changes to the current version concern only the manuscript structure (i.e., analyses presented in previous manuscript version as appendices were integrated into the main text). </p>
gazeNet: End-to-end eye-movement event detection with deep neural networks
<p>This repository contains synthetic eye-movement dataset used to train deep learning based eye-movement event detection algorithm described in Zemblys, R., Niehorster, D.C. & Holmqvist, K. (2018). gazeNet: End-to-end eye-movement event detection with deep neural networks. Behavior research methods, pp 1–25. <a href="https://doi.org/10.3758/s13428-018-1133-5">https://doi.org/10.3758/s13428-018-1133-5</a></p> <p>Code used to train a model can be found on github <a href="https://github.com/r-zemblys/gazeNet">here</a>. Code to generate synthetic eye-movement data can be downloaded from <a href="https://github.com/r-zemblys/gazeGenNet">here</a>.</p>
Data from Churan et al. 2018 Eye movements during path integration
<p>Subjects</p> <p>Six human subjects (two male and four female, mean age 23 years) took part in the experiment. The subjects had normal or corrected‐to‐normal vision and normal hearing.</p> <p>Apparatus</p> <p>Experiments were conducted in a darkened (but not completely dark) sound attenuated room. Subjects were seated at a distance of 114 cm from a tangential screen (70° x 55° visual angle) and their head‐position was stabilized by a chin‐rest. Visual stimuli were generated on a windows PC using an in‐house built stimulus package and were back‐projected onto the screen by a CRT‐Projector (Electrohome Marquee 8000) at a resolution of 1152 x 864 pixels and a frame rate of 100 Hz. The auditory stimuli were also generated using MATLAB and presented to the subjects by head‐phones (Philips SHS390). The eye position was recorded by a video‐based eye‐tracker (EyeLink II, SR Research) at a sampling rate of 500 Hz and an average accuracy of ~0.5°. During the distance reproduction, the subjects controlled the speed of simulated self‐motion using an analog joystick (Logitech ATK3) that was placed on a desk in front of them. The speed of the simulated self‐motion was proportional to the inclination angle of the joystick. The data from the joystick were acquired at a rate of 100 Hz and minimal change in speed of simulated self‐motion that could be triggered by the joystick was 1/1000 of the maximum range of speeds used in the experiments.</p> <p>Stimuli</p> <p>The visual stimulus consisted of a horizontal plane of white (luminance: 90 cd/m<sup>2</sup>) randomly placed small squares on a dark (<0.1 cd/m<sup>2</sup>) background that filled the lower half of the screen. The size of the squares was scaled between 0.2° and 1.9° in order to simulate depth. The direction of the simulated self‐motion was always straight‐ahead.The distances are always quantified in arbitrary units (AU) and the speed of simulated self‐motion in AU/s.The auditory stimuli were sinusoidal tones (SPL approximately 80 dB) with a frequency proportional to the simulated speed. The frequencies were in a range between 220 and 440 Hz and changed linearly as a function of the speed of the simulated self‐motion, which was in the range of 0–20 AU/s.</p> <p>Procedure</p> <p>Each trial consisted of two phases. During the “Encoding phase” the subjects were presented with a simulated self‐motion at one of the three speeds (8, 12 or 16 AU/s). The presentation lasted 4 seconds each which resulted in three different traveled distances (32, 48, 64 AU). The presentation was always bimodal, i.e., visual motion was accompanied by a sound representing the respective speed. The sound frequencies corresponding to the three speeds used during the Encoding phase were 308, 354, and 396 Hz, respectively. The task of the subjects in this phase was to monitor the distance covered for later reproduction. After the Encoding phase, a dark screen was presented for 500 msec and then the subjects had to reproduce the previously observed distance using a joystick. In different conditions of this “Reproduction phase,” either only the visual display was presented (visual condition) or only the auditory stimulus was presented while the screen was dark (auditory condition) or both sources of information were available at the same time (bimodal condition). During reproduction, the subjects were able to change the simulated speed by changing the inclination of the joystick. After the subjects had reached the distance they perceived to be identical to that during the Encoding phase, they had to press a joystick button to complete the trial. The subjects were allowed to move their eyes freely during the Encoding and the Reproduction phases. There were thus nine different experimental conditions: three different speeds in three different modalities. In each experimental condition, 80 trials were recorded. All conditions were presented in a pseudo‐randomized order and the subjects were not informed in advance about the sensory modality of the Reproduction phase.</p> <p>Data</p> <p>Eye position as well as the speed of the simulated self‐motion were recorded at a sampling rate of 500 Hz.<br> The file 'all_data.mat' is a MATLAB data file that consists of the cell structure 'all_data' has the elements 'pas' that contains data from the Encoding phase and 'akt' that contains data from the Reproduction phase.<br> The sub-structure 'pas' consists of 6x9x80 elements. The first dimension represents single subjects (6)<br> The second dimension represents the nine different conditions: 1. 8AU/s auditory 2. 8AU/s visual 3. 8AU/s bimodal 4. 12AU/s auditory 5. 12AU/s visual 6. 12AU/s bimodal 7. 16AU/s auditory 8. 16AU/s visual 9. 16AU/s bimodal<br> The third dimension represents the number of (80) trials recorded for each subject and condition.<br> Each element of 'pas' is a matrix consisting of two rows, the first giving the horizontal eye position and the second row giving the vertical eye position. The sampling rate (columns) was 500 Hz. The simulated self-motion started at first sample and ended 4 sec (=2000 samples) later.</p> <p>The sub-structure 'akt' has the same general shape. The only difference is that each element consists of three rows; horizontal eye-position, vertical eye-position, and (the actively chosen) speed of simulated self-motion.</p>
DynamicRead: Eye Movement Data of Reading on Handheld Mobile Devices under Dynamic Conditions
<p>The DynamicRead dataset contains eye movement data from 20 participants engaged in reading tasks on a handheld mobile device, while sitting and walking. Participants read 10 texts using four gaze interaction methods based on scrolling techniques, as well as one touch-based interaction method. The dataset captures the impact of motion on eye movement and features an unstable eye-movement sampling frequency ranging from 8-12fps/hz, which poses a challenge for traditional eye-movement toolkits. This dataset can be valuable for academic research on the impacts of motion on eye movement and the development of robust gaze interaction methods for handheld mobile devices under dynamic conditions.<br> <br> ###<br> Folder Structure:<br> -'eye_data' -- contains eye movement of reading data<br> -'text_screenshot' -- contains different reading material screenshot<br> -'heatmap_img' -- contains heat map of each reading page <br> -'scanpath_img' -- contains scan path of each reading page<br> -'text' -- contains reading material<br> -'code' -- contains R code for reading eye movement visualisation</p> <p><br> Name Method<br> - GAP1_GazeA_log.txt -- Group A, Participant No1, Scrolling technique: GazeA<br> - Gaze A: Auto-scrolling<br> - Gaze B: Heatbox<br> - Gaze C: Eye-Swipe<br> - Gaze D: Moving-bar<br> </p>
Hummingbirds use compensatory eye movements to stabilize both rotational and translational visual motion
Open the record for dataset details and reuse information.
Asymmetric retinal direction tuning predicts optokinetic eye movements across stimulus conditions
Open the record for dataset details and reuse information.
Supporting information: Analysis of attentional bias towards attractive and unattractive body regions among overweight males and females: An eye-movement study.
<p>This is the data set as supporting information, provided as .csv file.</p> <p>Further information is available on request.</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>
Data for manuscript Eye movements whilst walking improve local motion information - Durant S. & Zanker J. M.
<p>Scene camera videos with gaze position parked</p> <p>x,y gaze positions from all the videos</p> <p>summary description of which frames were used</p>
Visual sensitivity for luminance and chromatic stimuli during the execution of smooth pursuit and saccadic eye movements
<p>Dataset relative to the following publication:</p> <p>Braun, D. I., Schütz, A. C., & Gegenfurtner, K. R. (2017). Visual sensitivity for luminance and chromatic stimuli during the execution of smooth pursuit and saccadic eye movements. Vision Research</p>
Discrimination of curvature from motion during smooth pursuit eye movements and fixation
<p>One folder contains all the data for the main experiment. In the folder you can find a file dataREADME, which explains the format and the content of the individual files. A second folder contains the data files for the additional control experiment with shorter presentation durations. The structure of the data is the same. It includes two folders. One which constains the perceptual data for the shorter presentation durations and one with eye traces of the same participants for the oculometric thresholds. The name of the folder indicates also the length of the presentation duration, either 150 or 300 ms.</p>
Dynamic integration of information about salience and value for smooth pursuit eye movements
<p>Dataset from the following publication:</p> <p>Schütz, A. C., Lossin, F., & Gegenfurtner, K. R. (2015). Dynamic integration of information about salience and value for smooth pursuit eye movements. <em>Vision Research, 113</em>, 169-178. <a>doi:10.1016/j.visres.2014.08.009 <span></span></a><a></a> .</p>
Publication data of Dog eye movements are slower than human eye movements
<p>Publication data of Dog eye movements are slower than human eye movements</p> <p> https://doi.org/10.16910/jemr.12.8.4</p> <p>The data in the previous version contained unnecessary information, which has been removed in this updated version.</p>
The Potential of Naturalistic Eye Movement tasks in the Diagnosis of Alzheimer's Disease: A Review- Screening
<p>The Potential of Naturalistic Eye Movement tasks in the Diagnosis of Alzheimer’s Disease: A Review- Screening file</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>
Current foveal inspection and previous peripheral preview influence subsequent eye movement decisions
<p>Data from:</p> <p>Wolf, C., Belopolsky, A.V., & Lappe, M. (2022). Current foveal inspection and previous peripheral preview influence subsequent eye movement decisions. iScience.</p> <p>The zip folder "Data" contains one .dat file with the data of every individual. "Data" contains all trials of all individuals. Each file has three columns.<br> 1st column: condition index, range: 1-4,<br> 1: no preview / noise inspection;<br> 2: no preview / face inspection;<br> 3: preview / noise inspection;<br> 4: preview / face inspection<br> 2nd column: decision outcome, range 0-1<br> 0: noise image selected<br> 1: face image selected<br> 3rd column: fixation duration in seconds</p> <p>The zip folder "FixData" contains 3 .dat files. Every row in SacIndex.dat corresponds to one trial, every column to one millisecond after primary saccade offset (1000 columns in total) and thus depicts the fixation duration on the inspection target.<br> 0: participant was fixating inspection target<br> 1: participant was making a smaller saccade that started and landed on the inspection target<br> NaN: Fixation period for that trial has ended.<br> The two additional files "ParticipantIndex.dat" and "ConditionIndex.dat" are column vectors that contain one entry for every trial (the participant number or the condition index respectively). The condition index is the same as in the other zip folder (see above). Please note that the trial order in FixData does not reflect the actual trial order of the experiment.</p> <p>For questions please contact chr.wolf[at]wwu.de</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.