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15
datasets available to search
ShareScore release 0.9.0
Dataset results
15 results for “eyetracking”
PsPM-REW2: SCR, ECG, respiration, and eyetracking measurements in a Pavlovian appetitive conditioning task with juice delivery with acquisition and recall test after one week
<p>This dataset contains skin conductance responses (SCR), electrocardyogram (ECG), respiration, pupil size (PSR), and gaze coordinates measurements for 34 healthy unmedicated participants (20 females and 14 males aged 24.9 +/- 4.1) participating in a Pavlovian differential cued rewarding conditioning experiment with 2 sessions. <br>CSs were isoluminant colored triangles presented on the screen center with grey background.<br>US was a sip of the favourite juice selected individually. SOA between the CS onset and US was 5.5 s. Participants underwent the conditioning task with CSs (CS+ 50% reinforced) and US delivery in session 1 with 2 blocks, and were tested in a retention/extinction task one week later in session 2 with 2 blocks. No US was delivered during session 2. The ITI was jittered on each trial uniformly at random between 9 to 16 s. The blocks in each session were recorded on the same day with self-paced breaks.</p>
PsPM-REW1: SCR, ECG, respiration, and eyetracking measurements in a Pavlovian appetitive conditioning task with juice delivery with acquisition and recall test after one week
<p>This dataset contains skin conductance responses (SCR), electrocardyogram (ECG), respiration, pupil size (PSR), and gaze coordinates measurements for 37 healthy unmedicated participants (26 females and 11 males aged 24.2 +/- 4.2) participating in a Pavlovian differential cued rewarding conditioning experiment with 2 sessions. <br>CSs were isoluminant colored triangles presented on the screen center with grey background.<br>US was a sip of the favourite juice selected individually. SOA between the CS onset and US was 5.5 s. Participants underwent the conditioning task with CSs (CS+ 50% reinforced) and US delivery in session 1 with 2 blocks, and were tested in a retention/extinction task one week later in session 2 with 2 blocks. No US was delivered during session 2. The ITI was jittered on each trial uniformly at random between 9 to 16 s. The blocks in each session were recorded on the same day with self-paced breaks.</p>
PsPM-VIS: SCR, ECG, respiration and eyetracker measurements in a delay fear conditioning task with visual CS and electrical US
<p>This dataset consists of a three-block experiment conducted with 29 healthy unmedicated participants (17 females and 12 males aged 25.3 +/- 3.7). The experiment contains a classical (Pavlovian) discriminant delay fear conditioning test. CSs are 2 full-screen fractals of approximate brightness, contrast, and spatial frequency. US is a train of electric square pulses delivered with a constant current stimulator on participants' dominant forearm through a pin-cathod/ring-anode configuration. SOA between the CS and US is 3.5 s. The first 2 blocks are fear acquisition, with 15 CS+US+, 15 CS+US-, and 30 CS- in each block, and the last block is an extinction phase with 20 CS- and 20 CS+ trials without US delivery. The order of trials in each block was randomized. No fixation cross was presented during CS. ITI is randomly determined on each trial to be an integer between 7 - 11 s. During ITI, a black fixation cross was presented in the center of a grey background (RGB 0.7, 0.7, 0.7). The blocks were recorded on the same day with a self-paced break. For all three blocks this dataset contains skin conductance responses (SCR), electrocardyogram (ECG), respiration, pupil size (PSR), and gaze coordinates measurements.</p>
Virtually Real eyetracking data (beeswarm) for the short film "Lux" (2019) by Wendy Pillonel
<p>Two short feature films were recorded with a virtual background by using a green screen in a film studio and previously scanned 3D spaces in a virtual production environment and conventionally (in the corresponding real spaces) to compare the aesthetics and perception of these films. One scene of each film is shown in two variants (real vs. virtual background) to make a comparison.</p> <p>These quicktime movie represent the Eye-Tracking Data for Scenes 1,2 and 3 of "Lux" (2019) by Wendy Pillonel in beeswarm form for both the real and the virtual version of the film.</p> <p>Virtually Real is a research project by the Zurich University of the Arts, Institute for the Performing Arts and Film and the University of Bern, Institute of Psychology.</p>
DELANA - An eyetracking dataset from facilitating a series of laptop-based lessons
<p>This dataset contains eye-tracking data from a two subjects (an expert and a novice teachers), facilitating three collaborative learning lessons (2 for the expert, 1 for the novice) in a classroom with laptops and a projector, with real master-level students. These sessions were recorded during a course on the topic of digital education and learning analytics at [EPFL](http://epfl.ch).</p> <p>This dataset has been used in several scientific works, such as the [CSCL 2015](http://isls.org/cscl2015/) conference paper "The Burden of Facilitating Collaboration: Towards Estimation of Teacher Orchestration Load using Eye-tracking Measures", by Luis P. Prieto, Kshitij Sharma, Yun Wen & Pierre Dillenbourg. The analysis and usage of this dataset is available publicly at https://github.com/chili-epfl/cscl2015-eyetracking-orchestration</p>
JDC2014 - An eyetracking dataset from facilitating a semi-authentic multi-tabletop lesson
<p>This dataset contains eye-tracking data from a single subject (a researcher), facilitating three collaborative learning lessons in a multi-tabletop classroom, with real 10-12 year old students. These sessions were recorded during an "open doors day" at the [CHILI Lab](http://chili.epfl.ch).</p> <p>This dataset has been used in several scientific works, such as the [CSCL 2015](http://isls.org/cscl2015/) conference paper "The Burden of Facilitating Collaboration: Towards Estimation of Teacher Orchestration Load using Eye-tracking Measures", by Luis P. Prieto, Kshitij Sharma, Yun Wen & Pierre Dillenbourg. The analysis and usage of this dataset is available publicly at https://github.com/chili-epfl/cscl2015-eyetracking-orchestration</p>
ISL2015NOVEL (ERRATUM) - An eyetracking dataset from facilitating secondary multi-tabletop classrooms
<p>This dataset is a complement (a correction, actually) to the "ISL2015NOVEL - An eyetracking dataset from facilitating secondary multi-tabletop classrooms" dataset, also published in Zenodo (see https://zenodo.org/record/198681 for further info on the dataset). This erratum contains a CSV file with the manual videocoding of episodes, that substitutes the (erroneous) original one provided there (in the ISL2015NOVEL-CodingData.zip file).</p>
Virtually Real eyetracking data (beeswarm) for the short film "Sonnenwende" (2019) byTimo von Gunten
<p>Two short feature films were recorded with a virtual background by using a green screen in a film studio and previously scanned 3D spaces in a virtual production environment and conventionally (in the corresponding real spaces) to compare the aesthetics and perception of these films. One scene of each film is shown in two variants (real vs. virtual background) to make a comparison.</p> <p>These quicktime movie represent the Eye-Tracking Data for Scenes 1,2 and 3 of "Sonnenwende" (2019) by Timo von Gunten in beeswarm form for both the real and the virtual version of the film.</p> <p>Virtually Real is a research project by the Zurich University of the Arts, Institute for the Performing Arts and Film and the University of Bern, Institute of Psychology.</p>
Jetris - An eyetracking dataset from a Tetris-like task
<p>This dataset contains eye-tracking data from a set of 16 subjects, playing a series of short games of Tetris (for up to 5 minutes each), in different conditions (e.g., collaborative vs competitive).</p> <p>This dataset has been used in several scientific works, such as the [CSCL 2015](http://isls.org/cscl2015/) conference paper "The Burden of Facilitating Collaboration: Towards Estimation of Teacher Orchestration Load using Eye-tracking Measures", by Luis P. Prieto, Kshitij Sharma, Yun Wen & Pierre Dillenbourg. The analysis and usage of this dataset such paper is available publicly at https://github.com/chili-epfl/cscl2015-eyetracking-orchestration</p>
ISL2014BASELINE - An eyetracking dataset from facilitating secondary geometry lessons
<p>This dataset contains eye-tracking data from a single subject (a researcher), facilitating two geometry lessons in a secondary school classroom, with 11-12 year old students using laptops and a projector. These sessions were recorded in the frame of the MIOCTI project (http://chili.epfl.ch/miocti).</p> <p>This dataset has been used in several scientific works, such as the ECTEL 2015 (http://ectel2015.httc.de/) conference paper "Studying Teacher Orchestration Load in Technology-Enhanced Classrooms: A Mixed-method Approach and Case Study", by Luis P. Prieto, Kshitij Sharma, Yun Wen & Pierre Dillenbourg (the analysis and usage of this dataset is available publicly at https://github.com/chili-epfl/ectel2015-orchestration-school)</p>
ISL2015NOVEL - An eyetracking dataset from facilitating secondary multi-tabletop classrooms
<p><strong>IMPORTANT NOTE: One of the files in this dataset is incorrect, see this dataset's erratum at https://zenodo.org/record/203958</strong></p> <p>This dataset contains eye-tracking data from a single subject (an experienced teacher), facilitating two geometry lessons in a secondary school classroom, with 11-12 year old students using tangible paper tabletops and a projector. These sessions were recorded in the frame of the MIOCTI project (http://chili.epfl.ch/miocti).</p> <p>This dataset has been used in several scientific works, such a submitted journal paper "Orchestration Load Indicators and Patterns: In-the-wild Studies Using Mobile Eye-tracking", by Luis P. Prieto, Kshitij Sharma, Lukasz Kidzinski & Pierre Dillenbourg (the analysis and usage of this dataset is available publicly at https://github.com/chili-epfl/paper-IEEETLT-orchestrationload)</p>
EEG and eyetracking response to static and moving stimuli
<p>This dataset contains processed EEG and eyetracking recordings from an experiment in which twelve participants viewed a black disk either flashed in one position or moving in a straight line in one of six directions. Static stimuli were presented at nodes on a hexagonal grid, while moving stimuli moved in straight lines along the axes of the grid.</p>
EEG and eyetracking response to static and moving stimuli
Open the record for dataset details and reuse information.
Eyetracking and keystroke logging metrics for measuring automaticity in translation
<p>This dataset contains the behavioural data from 35 novice translators and 30 experienced translators (65 participants in total) during their translation process. The data are specifically generated eyetracking and keystroke logging metrics for measuring automaticity in the complex activity of translation, which includes four aspects, namely, speed, degree of parallel processing, effortlessness, and the pattern of attention allocated to the source text and target text areas. The dataset is provided for the paper Automaticity in Translation: Effects of Time Pressure and Work Experience.</p>
Eyetracking and Neurovision Rehabilitation of Oculomotor Dysfunction in Mild Traumatic Brain Injury
ClinicalTrials.gov study NCT03319966. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
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