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63 results for “visual detection”
Sex-dependent shifts in visual detection thresholds under turbid conditions in an African cichlid
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Data from: Nest structure affects auditory and visual detectability, but not predation risk, in a tropical songbird community
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Raw data from: Visual and motor signatures of locomotion dynamically shape a population code for feature detection in Drosophila, part 2
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Data from: Visual and motor signatures of locomotion dynamically shape a population code for feature detection in Drosophila
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Raw data from: Visual and motor signatures of locomotion dynamically shape a population code for feature detection in Drosophila, part 3
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Raw data from: Visual and motor signatures of locomotion dynamically shape a population code for feature detection in Drosophila, part 1
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ClinePlotR: Visualizing genomic clines and detecting outliers in R
<p class="Normal1">Patterns of multi-locus differentiation (i.e., genomic clines) often extend broadly across hybrid zones and their quantification can help diagnose how species boundaries are shaped by adaptive processes, both intrinsic and extrinsic. In this sense, the transitioning of loci across admixed individuals can be contrasted as a function of the genome-wide trend, in turn allowing an expansion of clinal theory across a much wider array of biodiversity. However, computational tools that serve to interpret and consequently visualize 'genomic clines' are limited.</p> <p>Here, we introduce the <span class="MsoSubtleReference">ClinePlotR R</span>-package for visualizing genomic clines and detecting outlier loci using output generated by two popular software packages, <span class="MsoSubtleReference">bgc </span>and <span class="MsoSubtleReference">Introgress.</span></p> <p><span class="MsoSubtleReference">ClinePlotR </span>bundles both input generation (i.e, filtering datasets and creating specialized file formats) and output processing (e.g., MCMC thinning and burn-in) with functions that directly facilitate interpretation and hypothesis testing. Tools are also provided for post-hoc analyses that interface with external packages such as <span class="MsoSubtleReference">ENMeval </span>and <span class="MsoSubtleReference">RIdeogram</span></p> <p>Our package increases the reproducibility and accessibility of genomic cline methods, thus allowing an expanded user base and promoting these methods as mechanisms to address diverse evolutionary questions in both model and non-model organisms.</p>
Multi-Domain Dataset for Robots (MDDRobots) - Multi-Domain Indoor Dataset for Visual Place Recognition and Anomaly Detection by Mobile Robots
<h2><strong>License</strong></h2> <p>The MDDRobots dataset is made available under the CC BY 4.0 license <a href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</a>.</p> <h2><strong>Summary</strong></h2> <p>The Multi-Domain Dataset for Robots (MDDRobots) contains data for computer vision problems, indoor visual place recognition, and anomaly detection. The recorded images are from different cameras and indoor environmental conditions. </p> <p>It is obligatory to cite the following paper in every work that uses the dataset: <br><strong>Wozniak, P., Krzeszowski, T. & Kwolek, B. Multi-Domain Indoor Dataset for Visual Place Recognition and Anomaly Detection by Mobile Robots. <em>Sci Data</em> 12, 817 (2025). https://doi.org/10.1038/s41597-025-05124-3</strong></p> <h2><strong>Data description</strong></h2> <p>The data are divided into five sets (containing data for different cameras), which have further subsets. Each of the subsets: Training, Test 1, Test 2, and Test 3 consists of nine image sequences. A total of 89,550 three-channel RGB color images in PNG format are organized into 20 zip folders with a whole size of 34.3 GB. Each image in the sequence has a label that represents a room. The number of images for each subset differs due to the split into training and testing data. The difference also results from different methods of recording the image sequences. In order to have balanced data in the subsets, each room in the sequence has the same number of images. Different environmental changes were introduced in each subset. The data from Test 1 are closest to those from the training set. The differences between the sequences are mainly due to changes in the route, robot, and recording equipment. The rooms are well lighted, but not overexposed. The sequences from Test 3 present changed conditions, such as a different time of day, a changed lighting system, and intensive layout changes. The key change is the different paths of the human and the robot. This means a different perspective from previously recorded scenes. The Test 2 sequences pose the most difficult challenge because they contain various recorded activities performed by people moving around rooms. People can occlude important parts of the scene and pass in front of the camera. The images were anonymized by manually blurring the faces of observed people.</p> <h2><strong>Dataset structure<br></strong></h2> <ul> <li>RobotPiCamera_DataSet <ul> <li>DataSet_RobotPiCamera_RGB_train</li> <li>DataSet_RobotPiCamera_RGB_test1</li> <li>DataSet_RobotPiCamera_RGB_test2</li> <li>DataSet_RobotPiCamera_RGB_test3</li> </ul> </li> <li> Xtion_DataSet <ul> <li>DataSet_XTION_RGB_train</li> <li>DataSet_XTION_RGB_test1</li> <li>DataSet_XTION_RGB_test2</li> <li>DataSet_XTION_RGB_test3</li> </ul> </li> <li> GOPRO_DataSet <ul> <li>DataSet_GOPRO_RGB_train</li> <li>DataSet_GOPRO_RGB_test1</li> <li>DataSet_GOPRO_RGB_test2</li> <li>DataSet_GOPRO_RGB_test3</li> </ul> </li> <li>iPhone_DataSet <ul> <li>DataSet_IPHONE_RGB_train</li> <li>DataSet_IPHONE_RGB_test1</li> <li>DataSet_IPHONE_RGB_test2</li> <li>DataSet_IPHONE_RGB_test3</li> </ul> </li> <li>P40PRO_DataSet <ul> <li>DataSet_P40PRO_RGB_train</li> <li>DataSet_P40PRO_RGB_test1</li> <li>DataSet_P40PRO_RGB_test2</li> <li>DataSet_P40PRO_RGB_test3</li> </ul> </li> </ul> <p><em>Example folder content: DataSet_P40PRO_RGB_train\Corridor1_RGB - 00000000.png, 00000001.png, 00000002.png, 00000003.png, ... 00000599.png.</em></p> <p>Total Images (Images per Place)</p> <table> <tbody> <tr> <td>Subset</td> <td>Mounted</td> <td>Training</td> <td>Test 1</td> <td>Test 2</td> <td>Test 3</td> </tr> <tr> <td>Pi Camera</td> <td>Robot</td> <td>7200 (800)</td> <td>5400 (600)</td> <td>5400 (600)</td> <td>5400 (600)</td> </tr> <tr> <td>Xtion</td> <td>Robot</td> <td>7200 (800) </td> <td>1800 (200) </td> <td>1800 (200)</td> <td>1800 (200) </td> </tr> <tr> <td>GoPro</td> <td>Hand</td> <td>5400 (600)</td> <td>4500 (500)</td> <td>4500 (500)</td> <td>4500 (500)</td> </tr> <tr> <td>iPhone</td> <td>Hand</td> <td>5400 (600) </td> <td>4500 (500)</td> <td>4500 (500)</td> <td>4500 (500) </td> </tr> <tr> <td>P40Pro</td> <td>Hand</td> <td>5400 (600)</td> <td>4050 (450)</td> <td>3150 (350) </td> <td>3150 (350) </td> </tr> </tbody> </table> <h2><br>Further information</h2> <p>For any questions, comments or other issues please contact Piotr Woźniak <p.wozniak@prz.edu.pl>.</p>
Pupil dilation during orienting of attention and conscious detection of visual targets in patients with left spatial neglect
<p>Right Brain-Damaged patients (RBD) with left spatial neglect (N+), are characterised by deficits in orienting and re-orienting attention to stimuli in the contralesional left side of space. In a recent ERPs study with visual stimuli (Lasaponara et al., 2018) we have pointed out that the pathological attentional bias of N+ is matched with exaggerated novelty reaction and contextual updating of targets in the right ipsilesional space and reduced novelty reaction and contextual updating of targets in the left contralesional space. To characterise further the attentional performance of N+, here we measured Pupil Dilation (PDil), which is a reliable marker of noradrenergic-locus coeruleus activity and response to unexpected events/rewards. Compared to Neutral and Valid targets, N+ patients displayed a pathological reduction of PDil in response to infrequent Invalid targets in the left side of space, while in Healthy Controls (HC) and RBD without neglect (N-) the same targets enhanced PDil with respect to Neutral and frequent Valid targets. Invalid targets in the right side of space enhanced PDil in all experimental groups. Interestingly, both N- and N+ showed a consistent number of target omissions both in the left and right side of space. With respect to seen targets, N- showed reduced PDil in response to unseen targets both in the left and right side of space. In contrast, N+ had reduced PDil in response to unseen targets in the left side of space though not in the right side, where seen and unseen targets evoked comparable levels of PDil. These results disclose, for the first time, the PDil correlates of spatial attention in left spatial neglect and suggest that the pathological attentional bias suffered by N+ might enhance the autonomic responses reflected in PDil to unseen ipsilesional stimuli. This enhancement can contribute to biasing contextual updating and predictive coding of stimuli in the ipsilesional space, thus worsening the pathological attentional bias of N+.</p>
VWFP: Virtual World Fallen People Dataset for Visual Fallen People Detection
<p>A synthetic dataset for <em>visual fallen people detection </em>comprising images extracted from the highly photo-realistic video game Grand Theft Auto V developed by Rockstar North. Each image is labeled by the game engine providing bounding boxes and statuses (fallen or non-fallen) of people present in the scene.<br> The dataset comprises 6,071 synthetic images depicting 7,456 fallen and 26,125 non-fallen pedestrian instances in various looks, camera positions, background scenes, lightning, and occlusion conditions.</p>
Data used in the paper Automatically Detecting Visual Bugs in HTML5 <canvas> Games
<p>This repository contains the snapshots (i.e., screenshot, <canvas> object representation pairs) and game assets collected from our test <canvas> game for use in the paper <code>Automatically Detecting Visual Bugs in HTML5 <canvas> Games</code>, accepted at ASE 2022.</p> <p>The data in this repository can be used to benchmark new visual bug detection approaches for HTML5 <canvas> games. The source code for our test <canvas> game and our 24 synthetic visual bugs can be found at the following <a href="https://github.com/asgaardlab/canvas-visual-bugs-testbed">link</a>.</p> <p><a href="https://asgaardlab.github.io/canvas-visual-bugs-testbed/">Project page</a><br> <br> <strong>Directory structure</strong></p> <pre> data/ | - assets/ | - exp_0/ | | - a/ | | | - 0.png | | | - 0.json | | | ... | | | - 9.png | | | - 9.json | | ... | | - j/ | - exp_appearance_1 | ... | - exp_state_6 </pre> <p><br> <strong>Data description</strong></p> <table> <tbody> <tr> <td>assets/</td> <td>Source images from the test <canvas> game</td> </tr> <tr> <td>exp_0/</td> <td>Snapshots with no visual bugs injected (okay/non-buggy experiment)</td> </tr> <tr> <td>exp_*_{1,2,3,4,5,6}/</td> <td>Snapshots for each of the injected visual bugs (buggy experiments)</td> </tr> <tr> <td>*/{a,b,c,d,e,f,g,h,i,j}/</td> <td>10 runs of data for each experiment</td> </tr> <tr> <td>*/{0,1,2,3,4,5,6,7,8,9}.png</td> <td>10 snapshots per experiment, each snapshot has a screenshot (png)</td> </tr> <tr> <td>*/{0,1,2,3,4,5,6,7,8,9}.json</td> <td>10 snapshots per experiment, each snapshot has a COR<sup>1</sup> (json)</td> </tr> </tbody> </table> <p><sup>1</sup>COR = <canvas> objects representation</p>
Collective detection based on visual information in animal groups
<p></p><p> We investigate key principles underlying individual, and collective, visual detection of stimuli, and how this relates to the internal structure of groups. While the individual and collective detection principles are generally applicable, we employ a model experimental system of schooling golden shiner fish ( Notemigonus crysoleucas ) to relate theory directly to empirical data, using computational reconstruction of the visual fields of all individuals. This reveals how the external visual information available to each group member depends on the number of individuals in the group, the position within the group, and the location of the external visually detectable stimulus. We find that in small groups, individuals have detection capability in nearly all directions, while in large groups, occlusion by neighbours causes detection capability to vary with position within the group. To understand the principles that drive detection in groups, we formulate a simple, and generally applicable, model that captures how visual detection properties emerge due to geometric scaling of the space occupied by the group and occlusion caused by neighbours. We employ these insights to discuss principles that extend beyond our specific system, such as how collective detection depends on individual body shape, and the size and structure of the group. </p><p></p>
Visual Oral Examination for Early Detection of Oral Cancer in High-Risk Individuals
ClinicalTrials.gov study NCT06231537. IPD Sharing: NO. Countries: 1. Publications: 36.
Early Detection and Prevention of Amblyopia and Visual Impairment Through Systematic Pediatric Vision Screening
ClinicalTrials.gov study NCT07112560. IPD Sharing: UNDECIDED. Countries: 1. Publications: 5.
Synergistic Effect of G-Eye Balloon for Behind the Folds Visualization With CADe (Discovery System) on Adenoma Detection Rate.
ClinicalTrials.gov study NCT05220345. IPD Sharing: YES. Countries: 1. Publications: 0.
Reliability of Fluorescent Camera and Caries Detection Dye Versus the Visual Tactile Method
ClinicalTrials.gov study NCT03051594. IPD Sharing: YES. Countries: 1. Publications: 1.
MRI-targeted Biopsy of the Prostate: Software Versus Visual Registration in the Accuracy of Prostate Cancer Detection
ClinicalTrials.gov study NCT03819751. IPD Sharing: NO. Countries: 1. Publications: 9.
Heidelberg Edge Perimetry (HEP) Detecting Glaucomatous Visual Field Defects
ClinicalTrials.gov study NCT02526654. IPD Sharing: NO. Countries: 0. Publications: 1.
In-Vivo Validation of Novel Diagnostic Devices Versus Visual ICDAS-II Criteria in Detection of Early Occlusal Caries
ClinicalTrials.gov study NCT06790485. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Validity of Fluorescence-Based Devices Versus Visual-Tactile Method in Detection of Secondary Caries Around Resin Composite Restorations: Invivo Diagnostic Accuracy Study
ClinicalTrials.gov study NCT04426604. IPD Sharing: Not stated. Countries: 1. Publications: 1.
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.