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.
48
datasets available to search
ShareScore release 0.9.0
Dataset results
48 results for “salience”
Data supplementing the article Schomaker, J., Walper, D., Wittmann, B.C., & Einhäuser, W. (2017). Attention in natural scenes: Affective-motivational factors guide gaze independently of visual salience. Vision Research, 133, 161-175.
<p>These data supplement the article Schomaker, J., Walper, D., Wittmann, B.C., & Einhäuser, W. (2017). Attention in natural scenes: Affective-motivational factors guide gaze independently of visual salience. Vision Research, 133, 161-175.</p> <p>Use is free for academic purposes, provided the aforementioned article is appropriately cited.</p> <p>The directory contains the following files</p> <p>stimuli.tar.gz - stimuli used in this study; note that this is based on the MONS database, but some deviations from the final version of the database do exist.</p> <p>ratings.mat contains the variables<br> arousal - mean arousal rating<br> valence - mean valence rating<br> valence2 - squared mean valence rating (after subtracting midpoint)<br> motivationalValue - mean motivation rating<br> motivaionalValue2 - squared mean motivation rating (after subtracting midpoint)</p> <p>All variables are 104x3, where the first dimension is the stimulus number, and the second dimension the motivation ground truth (aversive, neutral, appetitive)</p> <p><br> Experiment 1</p> <p>fixationsExperiment1.mat contains the variables fixationX, fixationY, fixationDuration, fixaitonOnset, fixationInitial, which contain for each fixation horizontal and vertical coordinate, the duration, the time of the onset relative to the trial onset and whether it is the initial fixation. All variables have dimensions 16x104x3x50, where the first dimension is the observer, the second the scene, the third the condition and the forth a counter of fixations. Whenever there are less than 50 fixations the remainder are filled with NaN.</p> <p><br> boundingBoxesExperiment1.mat contains for each critical object the bounding box coordinates x,y of upper left corner and width and height as variables boundingBoxX, boundingBoxY, boundingBoxW, boundingBoxH respectively. Note that this is relative to the eyetracker coordinates of experiment 1 (full display 1024x768, presentation in the center) and will therefore not match the coordinates of the images in the archive or the bounding box coordinates of experiment 2. Dimensions are 104x3, the dimensions representing scene number and condition, respectively.</p> <p><br> figure2.m uses these data to computes figure 2 of the article from these data</p> <p><br> dataForExperiment1.Rdata contains the data frame data, which contains for each fixation the values of the predictors used in the model of table 1. This is computed from the matlab data listed above in addition to the peak values of the AWS salience in the object.</p> <p><br> table1.R computes and prints the models for table 1</p> <p> </p> <p>Experiment 2</p> <p>fixationsExperiment2.mat contains fixation data for experiment 2. Variable names as in experiment 1. Dimensions are 18x99x3x3x50, where the first dimension is the observer, the second the image number, the third the visual condition, the third the motivational condition and the fifth the fixation count. Since only one visual condition was shown to each observer per motivational condition, there is an additional variable 'hasData', which is 1 if the image was presented to the observer in this condition and 0 otherwise. Since fixations can be outside the image and will therefore be excluded, there is also an additional variable fixationNumber to keep a correct count of the fixation number in the trial.</p> <p>boundingBoxesExperiment2.mat contains bounding box data for experiment 2 in image (and fixation) coordinates. Notation as for experiment 1, but coordinates refer to image and eyetracking coordinates used for experiment 2 and therefore can differ occasionally.</p> <p><br> figure3and4.m generates figures 3 and 4 of the article from these data files.</p> <p>dataForExperiment2.Rdata contains the data frame data, which contains for each fixation the values of the predictors used in the model of tables 2 amd 3. This is computed from the matlab data listed above in addition to the peak values of the AWS salience in the object. The fields imgMot and imgVis contain the motivational ground truth and the salience manipulation, respectively.</p> <p>table2.R uses the Rdata file to compute the models for table 2 of the article and print summary results</p> <p>table3.R uses the Rdata file to compute the models for table 3 of the article and print summary results. Note that the computation can take substantial time; results might deviate slightly depending on the exact version of R and its libraries used.</p> <p> </p>
Data supplementing Einhäuser, W., Neubert, C. R., Grimm, S., & Bendixen, A. (2024). High visual salience of alert signals can lead to a counterintuitive increase of reaction times. Scientific Reports, 14, 8858.
<p>These files supplement the publication <br>Einhäuser, W., Neubert, C. R., Grimm, S., & Bendixen, A. (2024). High visual salience of alert signals can lead to a counterintuitive increase of reaction times. <em>Scientific Reports, </em>14, 8858. https://doi.org/10.1038/s41598-024-58953-4</p> <p>The files data_expX.mat, where X is the experiment number (1-4), contain the data as described below. </p> <p>The files dataTraining_expX.mat contain the data of the first (training) block of each experiment. They are needed only for the supplemental material. </p> <p>To exemplify the usage, the functions figure2and3.m, figure4.m, figure5.m, figure6.m and Table1.m output the paper's figures and the data of Table 1, respectively; figureS2.m, figureS3.m, figureS4.m and figureS5.m output the figures of the supplemental material (figure S1 needs substantial amounts of external source code to compute the salience maps and is therefore not included).</p> <p><br>data_exp1.mat contains the following variables<br>For alert trials, variable of dimensions subjects x blocks x alert trials (20x10x64); note that only used participants and blocks with alert trials (2 through 11) are included in the data set:<br>alert_aud - the salience level of the alert tone (1-8, corresponding to 54dB(A) through 89 dB(A))<br>alert_vis - the salience level of the alert frame (1-8, corresponding to 0.10 to 8.50 Weber contrasts in logarithmic steps)<br>alert_side - the side on which the alert frame and the tone were presented (1-left, 2-right)<br>alert_fixOk - derived from eye movement data, was the first fixation closer to the alert square than to the center?<br>alert_primaryRT - primary-task reaction time (for alert trials)<br>alert_alertRT - alert-task reaction time <br>alert_correctAlert - was the response (up/down) to the alert correct?<br>alert_intrusionAlert - was there an intrusion (left/right pressed before up or down)?<br>alert_correctPrimary - was the primary task conducted correctly?<br>alert_intrusionPrimary - was there an intrusion for the primary task?<br>alert_timeToFixation - time to first fixation on alert square <br>alert_fixationToResp - time from beginning of fixation to response to the alert <br>alert_fixDur - duration of first fixation after trial onset</p> <p>For no-alert trials, variable of dimensions subjects x blocks x no-alert trials (20x10x448):<br>noalert_correctPrimary - was the primary task conducted correctly?<br>noalert_intrusionPrimary - was there an intrusion for the primary task? (i.e., up/down pressed before left/right)?</p> <p>For all trials, variable of dimensions subjects x blocks x no-alert trials (20x10x512):<br>all_correctPrimary - was the primary task conducted correctly?<br>all_intrusionPrimary - was there an intrusion for the primary task? (i.e., up/down pressed before left/right)?<br>all_RT - reaction time in the primary task<br>all_isAlertTrial - was the trial an alert trial? (useful to map no-alert trials and alert trials on all trials)</p> <p>In addition, there are some raw eye movement data for the alert blocks:<br>alert_eyeX, alert_eyeY - dimension 20 x 10 x 64 x 6000; x and y position in pixel coordinates relative to trial (and alert) onset, 1ms/sample, ends at conclusion of trials, filled up with NaN if duration was less than 6000ms <br>alert_eyeFixX, alert_eyeFixY, alert_eyeFixTon, alert_eyeFixDur - 20 x 10 x 64 x 15; x and y position, onset (in ms relative to trial onset) and duration of fixations during the trial (from onset to primary-task response), filled with NaN when less than 15 fixations were made. Note that the first entry of alert_eyeFixDur along the forth dimension will usually equal the alert_fixDur</p> <p><br>data_exp2.mat contains the same variables as data_exp1.mat with the following exceptions:<br>alert_vis - contains only two levels (1 and 2) corresponding to Weber contrasts of 0.10 and 2.39, respectively<br>alert_dur - the level of duration of the alert frame (1 through 8, corresponding to 25ms, 50ms, 100ms, 200ms, 300ms, 400ms, 600ms, 800ms)<br>alert_aud is not included (all tones were at 54 dB(A))<br>there are only 19 participants; hence the variables are of size 19 x ...<br>note: block 8 for subject 6 contains only 450 trials (57 alert trials), the remainder is filled with NaN.</p> <p><br>data_exp3.mat contains the same variables as data_exp1.mat with the following exceptions:<br>alert_aud - contains only two levels (1 and 2) corresponding to sound levels of 54 dB(A) and 79 dB(A) respectively<br>alert_dur - the level of duration of the alert tone (1 through 8, corresponding to 25ms, 50ms, 100ms, 200ms, 300ms, 400ms, 600ms, 800ms)<br>alert_vis is not included (all alert frames were at 0.10 contrast)</p> <p> </p> <p>data_exp4.mat contains the same variables as data_exp1.mat with the following exceptions:<br>alert_aud - contains only two levels (1 and 2) corresponding to sound levels of 54 dB(A) and 79 dB(A) respectively<br>alert_vis is not included and replaced by<br>alert_condBefore - alert frame contrast level before the saccade (1 - 0.10 contrast, 2 - 2.39 contrast)<br>alert_condAfter - alert frame contrast level after the saccade (1 - 0.10 contrast, 2 - 2.39 contrast)</p> <p><br>dataTraining_expX.mat contains for the first (training) block of experiment X (X being 1, 2, 3 or 4) the following variables of size 20x512 (participant x trial) [19x512 in case of Experiment 2]:<br>all_correctPrimary - was the primary task conducted correctly?<br>all_RT - reaction time in the primary task<br>[Note that there are no alert trials in this block and these data are only used in the supplementary material (part 4)]</p>
360-degree Saliency Dataset
<p>This file is related to the paper "A. Nguyen, and Z. Yan. 2019. A Saliency Dataset for 360-Degree Videos. In Proceedings of the 10th ACM on Multimedia Systems Conference (Accepted to MMSys 2019)"</p> <p>This is the zipped pickle file that store the 360-degree saliency map. A python program is required to open the pickle file.</p> <p>Each pickle file is a collection of records. Each record has three main fields: timestamp, head_orientation vector, and saliency map</p>
Haptic Saliency Model for Rigid Textured Surfaces
<p>When touching an object, we focus more on some of its parts rather than touching the whole object’s surface, i.e. some parts are more salient than others. Here we investigated how different physical properties of rigid, plastic, relieved textures determine haptic exploratory behavior. We produced haptic stimuli whose textures were locally defined by random distributions of four independent features: amplitude, spatial frequency, orientation and isotropy. Participants explored two stimuli one after the other and in order to promote exploration we asked them to judge their similarity. We used a linear regression model to relate the features and their gradients to the exploratory behavior (spatial distribution of touch duration). The model predicts human behavior significantly better than chance, suggesting that exploratory movements are to some extent driven by the low level features we investigated. Remarkably, the contribution of each predictor changed as a function of the spatial scale in which it was defined, showing that haptic exploration preferences are spatially tuned, i.e. specific features are most salient at different spatial scales.</p> <p>Metzger, A., Toscani, M., Valsecchi, M. & Drewing, K. (2018) Haptic saliency model for rigid textured surfaces. In Prattichizzo, D., Shinoda, H., Tan, H. Z., Ruffaldi, E. & Frisoli, A. (Eds.), Haptics: Science, Technology, and Applications, 11th International Conference, EuroHaptics 2018, Pisa, Italy, June 13-16, 2018, Proceedings, Part I (pp. 389–400). Springer International Publishing, Cham.</p> <p> </p> <p>Data of the experiment is stored in a zip file, containing all data relative to the publication. The 'movement' folder containes participnts' movement data. The 'stimuli' folder containes the 2D and 3D models of the stimuli. </p> <p>Explanaition and coding of the data is provided in the file VARIABLE_CODES.txt.</p>
UEyes: Understanding Visual Saliency across User Interface Types
<p>UEyes is a large eye-tracking-based dataset including 62 participants and 1,980 UI screenshots, covering four major UI types: webpage, desktop UI, mobile UI, and poster. </p> <p>Please cite the following paper:</p> <p>UEyes: Understanding Visual Saliency across User Interface Types</p> <p>https://dl.acm.org/doi/10.1145/3544548.3581096</p> <p>Yue Jiang, Luis A. Leiva, Hamed Rezazadegan Tavakoli, Paul R. B. Houssel, Julia Kylmälä, and Antti Oulasvirta. 2023. UEyes: Understanding Visual Saliency across User Interface Types. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI '23). Association for Computing Machinery, New York, NY, USA, Article 285, 1–21. https://doi.org/10.1145/3544548.3581096</p>
Antagonistic effects of predator colour morph abundance and saliency on prey anti-predator responses
Open the record for dataset details and reuse information.
Valence and salience encoding in the central amygdala
Open the record for dataset details and reuse information.
Meaning maps and saliency models based on deep convolutional neural networks are insensitive to image meaning when predicting human fixations - data
<p>Data from the paper:<em> Meaning maps and saliency models based on deep convolutional neural networks are insensitive to image meaning when predicting human fixations.</em></p> <p>Preprint: https://www.biorxiv.org/content/10.1101/840256v1</p> <p>Marek A. Pedziwiatr<br> marek.pedziwi@gmail.com<br> September 2020</p> <p> </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>
EyeCatchingMaps, a Dataset to Assess Saliency Models on Maps
<div>EyeCatchingMaps, a Dataset to Assess Saliency Models on Maps</div> <div> </div> <div> <p>This dataset is made up of a total of 322 maps. It is made up of maps from google maps, ign and osm.</p> <p>It also includes maps from school textbooks.</p> </div> <div> </div> <div>In the folder map we have all the map of the datatset</div> <div> </div> <div>in the folder coord_on_map, for each map, a file containing all the fixation points of each participant, named ’coord_fixation_name_map.png.csv’ in CSV format with the following elements and during a fixation, a person looks at an area and can move slightly. The fixation will then be represented by a few points:</div> <div>- worl_index : the id of fixation</div> <div>- id_fixation : the id of fixation point</div> <div>- time : the time of the fixation point in relation to the time of the survey</div> <div>- x : x coordinate of the point of fixation on the image</div> <div>- y : y coordinate of the point of fixation on the image</div> <div>- dispersion : distance between all gaze locations during a fixation, in degree</div> <div>- accuracy : in degree</div> <div>- precision :in degree</div> <div>- participant : Participant ID</div> <div>- distance : distance between eye-tracker and screen incm</div> <div>- time_to_map : the time of the fixation point in relation to the map display</div> <div>- height : height of the map</div> <div>- width : width of the map</div> <div> </div> <div>In the folder heatmap we have heatmaps for each map named ’heatmap_name_map.png’.</div> <div> </div> <div> </div> <div> <div> <div>in the folder 'script' there are the different scripts used to produce the data</div> </div> </div>
Synth-Salience Choral Set
<p>The <strong>Synth-salience Choral Set</strong> (SSCS) is a publicly available dataset for voice assignment based on pitch salience. </p> <p>The dataset was created to support research on voice assignment based on pitch salience. By definition, an “ideal” pitch salience representation of a music recording is zero everywhere where there is no perceptible pitch, and has a positive value that reflects the pitches’ perceived energy at the frequency bins of the corresponding F0 values. In practice, for a normalized synthetic pitch salience function we assume a value equal to the maximum energy (salience), i. e., 1, in the time-frequency bins that correspond to the notes present in a song, and 0 elsewhere. We obtain such a synthetic pitch salience representation directly by processing the digital (MusicXML, MIDI) score of a music piece, using the desired time and frequency quantization, i. e., a time-frequency grid. </p> <p>To build the SSCS, we collect scores of four-part (SATB) a cappella choral music from the <a href="https://www.cpdl.org/wiki/index.php/Main_Page">Choral Public Domain Library (CPDL)</a> using their API. We assemble a collection of <strong>5381 scores</strong> in MusicXML format, which we subsequently convert into MIDI files for an easier parsing.</p> <p><br> Each song in the dataset comprises five CSV files: one with the polyphonic pitch salience representation of the four voices (*_mix.csv) and four additional files with the monophonic pitch salience representation of each voice separately (*_S/A/T/B.csv). In both cases, the asterisk refers to the name of the song, which is shared between all representations from the same song.<br> Besides the pitch salience files, we provide a metadata CSV file (sscs_metadata.csv) which indicates the associated CPDL URL for each song in the dataset. Note that this dataset contains the input/output features used in the cited study, i.e., salience functions, and not audio files nor scores. However, the accompanying metadata file allows researchers to access the associated open access scores for each example in the dataset.</p> <p>When using this dataset for your research, please cite:</p> <p>Helena Cuesta and Emilia Gómez (2022). <strong>Voice Assignment in Vocal Quartets using Deep Learning Models based on Pitch Salience</strong>. Transactions of the International Society for Music Information Retrieval (TISMIR). <em>To appear.</em></p> <p>Helena Cuesta (2022). <strong>Data-driven Pitch Content Description of Choral Singing Recordings</strong>. PhD thesis. Universitat Pompeu Fabra, Barcelona.</p> <p> </p>
Post-revision data and R Script for Murtagh et al. 'The scent of enrichment: Exploring the effect of odour and biological salience on behaviour during enrichment of kennelled dogs.'
<p>Data and analysis file (written in R studio) for the paper Murtagh et al. 'The scent of enrichment: Exploring the effect of odour and biological salience on behaviour during enrichment of kennelled dogs.' Updated based on reviewer comments and additional analyses requested (master data and analysis filed labelled with 'rev' suffix).</p>
Activation and connectivity maps - A chronometric relationship between circuits underlying learning and error monitoring in the basal ganglia and salience network
<p>Activation and connectivity maps of the study "A chronometric relationship between circuits underlying learning and error monitoring in the basal ganglia and salience network".</p> <ul> <li>Error-correct.nii corresponds to the statistical map of group-level differences in BOLD signal between correct and erroneous responses shown in figure 4;</li> <li>Late-initial.nii corresponds to the statistical map of group-level differences in BOLD signal between the initial and late learning periods shown in figure 5;</li> <li>Conn_error-correct_dACC.nii corresponds to the results from the seed-to-voxel gPPI analysis, using the dACC as seed region, showing areas of higher functional connectivity in erroneous compared to correct responses, shown in figure 8.</li> </ul>
Event-driven Proto-object based saliency in 3D space to attract a robot's attention - Supplementary Video
<p>To interact with its environment, a robot working in 3D space needs to organise its visual input in terms of objects or their<br> perceptual precursors, proto-objects. Among other visual cues, depth is a submodality used to direct attention to visual features<br> and objects. Current depth-based proto-object attention models have been implemented for standard RGB-D cameras that<br> produce synchronous frames. In contrast, event cameras are neuromorphic sensors that loosely mimic the function of the<br> human retina by asynchronously encoding per-pixel brightness changes at very high temporal resolution, thereby providing<br> advantages like high dynamic range, efficiency (thanks to their high degree of signal compression), and low latency. We<br> propose a bio-inspired bottom-up attention model that exploits event-driven sensing to generate depth-based saliency maps<br> that allow a robot to interact with complex visual input. We use event-cameras mounted in the eyes of the iCub humanoid robot<br> to directly extract edge, disparity and motion information. Real-world experiments demonstrate that our system robustly selects<br> salient objects near the robot in the presence of clutter and dynamic scene changes, for the benefit of downstream applications<br> like object segmentation, tracking and robot interaction with external objects.</p>
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: Influence of periaqueductal gray on other salience network nodes predicts social sensitivity
Open the record for dataset details and reuse information.
Salience-In-News-And-Tweets: Salience in News and Tweets
<p>New release of the "Salience in News and Tweets" dataset.</p>
Reward salience but not spatial attention dominates the value representation in the orbitofrontal cortex
<p>We have uploaded the behavioral and electrophysiological data collected in the study of 'Reward salience but not spatial attention dominates the value representation in the orbitofrontal cortex'. The description of the data the code used to analyze it could be found at https://github.com/tmyang-lab/reward_salience_in_OFC.</p>
Competition between salience and informational value for saccade adaptation
<p>Dataset from the following publication:</p> <p>Wolf, C., Wagner, I., & Schütz, A.C. (under review). Competition between salience and informational value for saccade adaptation. <em>Journal of Vision</em>.</p> <p>For every experiment, there is a zip folder with the underlying data. Column descriptions can be found in pdf files. More informations can be found in the README.txt files in every folder.</p> <p>For further questions, please contact:<br> chr.wolf[at]wwu.de or a.schuetz[at]uni.marburg.de</p>
Morphological heart age from CTA: Predictions, Performance and Saliency Maps
<p>This data distribution contains regression results for age prediction from computed tomography angiography images from the SCAPIS dataset, as well as proof-of-concept experiments concerning the prediction of known volumetric features estimated through segmentation of the images.</p> <p>Each sub-folder represents one experiment.</p> <p>For each experiment (sex-stratified), there is a csv file '0.csv'<br>with the following structure</p> <p>Row 1: subject id1, subject id2, subject id3, ...<br>Row 2: reference value1, reference value2, reference value3, ...<br>Row 3: predicted value1, predicted value2, predicted value3, ...</p> <p>There is also a file 'results_summary.txt' which provides a text output of the quality measures corresponding to each experiment:<br>Mean Absolute Error (MAE)<br>R^2 (R2)<br>Pearson correlation (r_p)<br>Spearman correlation (r_s)<br>Intraclass Correlation Coefficient (ICC)</p> <p>The following sub-folders/experiments contain saliency maps:<br>- main (the main experiment with PCA from the whole heart)<br>- main_linear (the main experiment without PCA from the whole heart)<br>- poc_lvv (proof-of-concept: left ventricle volume)<br>- poc_rvv (proof-of-concept: right ventricle volume)<br>- poc_lav (proof-of-concept: left atrium volume)<br>- poc_rav (proof-of-concept: right atrium volume)<br>- poc_myov (proof-of-concept: myocardium volume)<br>- poc_av (proof-of-concept: aorta volume)</p> <p>The feature subsets use the following encoding (for the feature subset experiments, the path contains the numbers representing the included features):<br>1: Median Density<br>2: Median Volume<br>3: Stddev Density<br>4: Stddev Volume</p> <p>Ethics:<br>The subjects/images are all anonymized, with a chronological age rounded to whole months.</p> <p>Ethics approval was obtained from the Swedish Ethical Review Authority (Dnr 2022-07308-01) to conduct this research study related to human subjects, with associated sex and age information. All subjects provided informed written consent for their collected data to be used for research and for that research to be published. The study adheres to the Declaration of Helsinki. SCAPIS has been approved as a multicentre trial by the ethics committee at Umea University and adheres to the Declaration of Helsinki.</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.