Find research datasets worth reusing

openother-pdJun 2020View details →

GazeMining: A Dataset of Video and Interaction Recordings on Dynamic Web Pages. Labels of Visual Change, Segmentation of Videos into Stimulus Shots, and Discovery of Visual Stimuli.

Recording setup Recordings have been taken place on 12th March 2019. Gaze data has been recorded with a Tobii 4C eye tracker with Pro license at 90 Hz. Resolution of the viewport was set to 1024x768. The display had a size of 24 inches and a resolution of 1680x1050 pixels. We polled the DOM tree every 50 milliseconds for fixed elements. We recorded the Web browsing of four participants, who followed the protocol as stored under "Dataset_visual_change/Instructions.doc". Description of the dataset The dataset consists of following three subsets. 1. Dataset_visual_change The recordings of each participant p1-p4 on twelve Web sites are in the corresponding directories. For each Web site, there are nine to eleven files: <ul> <li><site>.json: datacast</li> <li><site>.webm: video recording</li> <li><site>.features.csv: computer-vision features per observation</li> <li><site>.features_meta.csv: meta information about features</li> <li><site>.labels-l<X>.csv: labels of observations</li> <li><site>_meta.csv: meta information about recording</li> <li><site>_scroll_cache.csv: cache of estimated scrolling</li> <li><site>_scroll_cache_map.csv: mapping of observations to scroll cache entries</li> <li><site>_times.csv: timestamps of frames in the video recording</li> <li><site>_layer_pixels.csv: first row is the pixel count of root layer, second row is pixel count of all fixed elements</li> </ul> 2. Dataset_stimuli Stimulus shots and visual stimuli computed with the framework. Value-based, edge-based, signal-based, and SIFT-based features have been used. The labels of the first participant's session had been used to train a random forest classifier with 100 trees for visual change classification, using the named features. The discovery has been performed on each Web site from the dataset and the results are placed in the respective directories. Inside each directory, there is one directory for the detected shots and one for the discovered stimuli. In the shots directory, there is one overview as <participant>_<site>.csv file. For each shot, there are four further files: <ul> <li><participant>_<site>_<shot>.png: stitched frame of the stimulus shot</li> <li><participant>_<site>_<shot>-blind.csv: frames from animations that are not contributing to the stitched frame</li> <li><participant>_<site>_<shot>-gaze.csv: gaze data (in stitched frame space)</li> <li><participant>_<site>_<shot>-mouse.csv: mouse data (in stitched frame space)</li> </ul> The shots have been merged to stimuli, which are placed in the stimuli directory. The stimuli are grouped per layer (scrollable, fixed elements, etc.) and meta information is available in <layer_index>-<xpath>-meta.csv files. Furthermore, there are directories per layer, storing the discovered stimuli. Each discovered visual stimulus is represented by four files: <ul> <li><stimulus_id>.png: stitched frame of the visual stimulus</li> <li><stimulus_id>-gaze.csv: gaze data (in stitched frame space)</li> <li><stimulus_id>-mouse.csv: mouse data (in stitched frame space)</li> <li><stimulus_id>-shots.csv: contained stimulus shots</li> </ul> 3. Dataset_evaluation We have performed two evaluations of the visual stimuli discovery. One computational estimating the quality of stimuli. One case-study of an expert's task. There are two respective directories with the annotation data. Changelog [1.0.2] Add counts of layer pixels per participant. [1.0.1] Change to CC0 license. [1.0.1] Add labels of third annotator "l3". [1.0.0] Initial release.

opencc-by-nc-nd-4.0Aug 2021View details →

Predicting readers' prototypical eye-movement behavior using MASC, a model of Attention in the Superior Colliculus: Stimulus materials, model code, data, and statistical analyses.

The goal of the present research was to determine the role of rudimentary visuo-motor pathways, from the retina and the primary visual cortex to the superior colliculus (SC), in the guidance of human eye movement during reading. To this end, we used MASC, our model of Attention in the Superior Colliculus (Adeli et al., Journal of Neuroscience 2017), a model that relies on well-established saccade-programming principles in the SC. MASC predicts sequences of fixations over an input image by spatially integrating incoming signals in the space of the SC. Here, MASC computed the distribution of luminance contrast over sentences' images (visual-saliency map), after blurring it proportional to retinal eccentricity (retina transformation). It then projected the visual-saliency map into SC space, using a logarithmic afferent-mapping function (magnification factor). Input signals were averaged over retinotopically organized populations of neurons (point images) of constant size, first in the visual map and then in a spatially-registered motor map. The most active population was identified through a winner-take-all process. After jitter applied to the winning population, the next fixation location was determined using inverse efferent mapping. This sequence of events was then repeated to predict following fixation locations, but inserting after each saccade an inhibitory spatial tag (Inhibition of Saccade Return; ISR -referred to as IOR in the uploaded files). All MASC's parameters, but one, were biologically determined, using electrophysiological data in macaque; the ISR window was the one fit parameter. MASC was tested by comparing its predicted sequences of fixations over sentences from the French-Sentence Corpus (FSC) to the eye-movement behavior of 40 French-native speakers reading the same sentences for comprehension (Albrengues et al., Plos One 2019). Then, MASC was dissected to determine the crucial processing steps enabling prediction of human behavior (10 comparison models -see the general README file). Finally, to address crucial issues in the reading literature, i.e., the role of inter-word spacing and character print size in eye-movement guidance, MASC was additionally tested in four additional display conditions: the same sentences from the FSC, but with blank spaces between words being either filled or removed, or with the screen width angle being multiplied by 2 or 4, such that characters were larger in angular size (0.5° and 1°) than in the original experiment (0.25°). MASC's predicted effects of inter-word spacing and print size were compared to previously published data. All material relevant to the project is reported here, including the FSC materials (bitmap and information text files), the Matlab code for our MASC model, raw simulation data for MASC and all our comparison models, as well as MASC's simulations in the different display conditions, the scripts we developed in R to transform raw simulation data into data matrices for statistical analyses of (word-based) eye-movement behavior, the resulting data matrices for all models as well as the data matrix for FSC readers, the R-scripts for statistical comparison of oculomotor behavior between data sets and conditions, literature-review tables of previously published data (for comparison with MASC's predictions), and the R-scripts generating the figures summarizing our results. Further information can be found in the general README file as well as in the README files attached to each folder. The authors' respective contributions to the project, the licence attached to the included materials and their condition of use are listed in the general README file. A manuscript reporting and discussing these modeling data is in preparation (Vitu, F., Adeli, H. & Zelinsky, G. J.); A reference will be provided here when the manuscript appears in a journal. Other references to be cited: - For the model code: Adeli, H., Vitu, F., & Zelinsky, G. J. (2017). A model of the superior colliculus predicts fixation locations during scene viewing and visual search. Journal of Neuroscience, 37(6), 1453-1467. http://www.jneurosci.org/content/37/6/1453 - For FSC materials and data: Albrengues, C., Lavigne, F., Aguilar, C., Castet, E., & Vitu, F. (2019). Linguistic processes do not beat visuo-motor constraints, but they modulate where the eyes move regardless of word boundaries: Evidence against top-down word-based eye-movement control during reading. PLoS ONE 14(7): e0219666. https://doi.org/10.1371/journal.pone.0219666

opencc-by-4.0Nov 2022View details →

Dataset: Stimulus salience conflicts and colludes with endogenous goals during urgent choices

This dataset (packaged as the zip file 3CS_datashare.zip) accompanies the article titled "Stimulus salience conflicts and colludes with endogenous goals during urgent choices" by EE Oor, TR Stanford, and E Salinas, iScience  26:106253 (2023). The experimental results in the paper are based on behavioral data collected from three monkey subjects during performance of visuomotor tasks, as described in the article. This dataset contains the trial-by-trial results collected for each subject and upon which all subsequent analyses were based. In addition to the trial-wise data arrays (stored in three *.csv files), the dataset includes Matlab functions and scripts (*.m files) used to analyze the data and generate figures in the article. Instructions and specifics are detailed in the README file.

opencc-by-4.0Sep 2023View details →

Aversive stimulus-tuned responses in the CA1 of the dorsal hippocampus (dataset 8)

Multichannel electrophysiology data for the manuscript with the same title. (2306_24)

controlledIPD-YESFeb 2026View details →

Pain Protocol: Nociception Coma Scale-Revised With Personalized Stimulus

ClinicalTrials.gov study NCT06012357. IPD Sharing: YES. Countries: 1. Publications: 1.

Tactile/kinesthetic Stimulus Program

ClinicalTrials.gov study NCT05486663. IPD Sharing: NO. Countries: 1. Publications: 6.

controlledIPD-YESFeb 2026View details →

STIMULUS MDS-US : Sabatolimab Added to HMA in Higher Risk MDS

ClinicalTrials.gov study NCT04878432. IPD Sharing: YES. Countries: 1. Publications: 0.

Stimulus Intensity in Left Ventricular Leads

ClinicalTrials.gov study NCT01060449. IPD Sharing: Not stated. Countries: 1. Publications: 1.

Use of Music and Voice Stimulus on Coma Patients

ClinicalTrials.gov study NCT00959829. IPD Sharing: Not stated. Countries: 1. Publications: 1.

Variable Visual Stimulus as a Novel Approach for Gait Rehabilitation

ClinicalTrials.gov study NCT03737331. IPD Sharing: NO. Countries: 1. Publications: 4.

The Incretin Secretion in the Gut System Related to the Physiological Stimulus

ClinicalTrials.gov study NCT00994435. IPD Sharing: Not stated. Countries: 1. Publications: 1.

The Effect of Multisensory Stimulus Method on Pain and Physiological Parameters in Infants

ClinicalTrials.gov study NCT06291519. IPD Sharing: Not stated. Countries: 1. Publications: 8.

Study of Stimulus Parameters in Flicker Electroretinogram (ERG)

ClinicalTrials.gov study NCT02466607. IPD Sharing: Not stated. Countries: 1. Publications: 1.

ANI and NoL Index Variations After Standard Nociceptive Stimulus at 0, 50, 25 % of Inhaled N2O in the Anesthetic Mixture

ClinicalTrials.gov study NCT02701478. IPD Sharing: NO. Countries: 1. Publications: 1.

Baby Smell Visual Stimulus Program Cortical and Breast Oxygenation Milk Amount Mother-Infant Attachment

ClinicalTrials.gov study NCT06058208. IPD Sharing: NO. Countries: 1. Publications: 5.

Influence of Depth of Anesthesia on Pupillary Reactivity to a Standardized Stimulus

ClinicalTrials.gov study NCT02595476. IPD Sharing: Not stated. Countries: 1. Publications: 1.

REsting and Stimulus-based Paradigms to Detect Organized NetworkS and Predict Emergence of Consciousness

ClinicalTrials.gov study NCT03504709. IPD Sharing: Not stated. Countries: 1. Publications: 5.

Efficacy of Two Interventions Increasing Sensory Stimulus in Elderly Patients With Oropharyngeal Dysphagia

ClinicalTrials.gov study NCT01762228. IPD Sharing: Not stated. Countries: 1. Publications: 11.