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1,025 results for “Vision”
Dataset: National Vision Holdings, Inc. (EYE) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Vision Sensing Acquisition Corp. (VSACW) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Vision Sensing Acquisition Corp. (VSACU) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Vision Sensing Acquisition Corp. (VSAC) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Vision Marine Technologies Inc. (VMAR) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Rail Vision Ltd. (RVSN) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Rail Vision Ltd. (RVSNW) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Nova Vision Acquisition Corporation (NOVVU) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Nova Vision Acquisition Corporation (NOVVR) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Nova Vision Acquisition Corporation (NOVVW) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Nova Vision Acquisition Corporation (NOVV) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Keen Vision Acquisition Corporation (KVACU) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Keen Vision Acquisition Corporation (KVACW) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Supplementary material for "Cretaceous lacewing larvae with binocular vision demonstrate the convergent evolution of sophisticated simple eyes" by Haug C. et al.
<p>Supplementary matereial including R-Code and data for elliptic Fourier analysis for the publication: "<span>Cretaceous lacewing larvae with binocular vision demonstrate the convergent evolution of sophisticated simple eyes" </span></p> <p><span>Carolin Haug, Roland R. Melzer, Florian Braig, Simon J. Linhart, Derek E. G. Briggs, Alejandro Caballero, Yanzhe Fu, Gideon T. Haug, Marie K. Hörnig, Joachim T. Haug</span></p> <p> </p> <p><span>Abstract: </span></p> <p><span>Many insects and their relatives are renowned for sophisticated compound eyes, which are also preserved in the fossil record. Yet there are also other types of eyes, notably the so-called stemmata of holometabolans, such as beetles, bees, and butterflies. Stemmata are not as effective as compound eyes, except in some predatory larvae. Here we report three lacewing larvae with large forward-directed stemmata from Cretaceous Kachin amber, Myanmar. The stemmata are large relative to those of other fossil lacewing larvae, comparable to the simple eyes of modern larvae capable of image formation. The head is very wide in one larva, representing a new type of morphology as demonstrated by a quantitative comparison of the head and stylets of over 400 fossil and extant lacewing larvae. The arrangement of the exceptionally large stemmata of the larvae reported here provides stereoscopic vision. These new specimens demonstrate the convergent evolution of highly developed simple eyes in at least two additional lineages of lacewings, showcasing the enormous diversity of lacewing larvae in the Cretaceous.</span></p>
Comparative genomics sheds new light on the convergent evolution of infrared vision in snakes
<p>Infrared vision is a highly specialized sensory system that evolved independently in three clades of snakes. Apparently, convergent evolution occurred in the transient receptor potential ankyrin 1 (<em>TRPA1</em>) proteins of infrared-sensing snakes. However, this gene can only explain how infrared signals are received, and not the transduction and processing of those signals. We sequenced the genome of <em>Xenopeltis unicolor</em>, a key outgroup species for pythons, and performed a genome-wide analysis of convergence between two clades of infrared-sensing snakes. Our results revealed pervasive molecular adaptation in pathways associated with neural development and other functions, with parallel selection on loci associated with trigeminal nerve structural organization. Additionally, we found evidence of convergent amino acid substitutions in a set of genes, including <em>TRPA1 </em>and<em> TRPM2</em>. Analysis also identified convergent accelerated evolution in non-coding elements near 12 genes involved in facial nerve structural organization and optic nerve development. Thus, convergent evolution occurred across multiple dimensions of infrared vision in vipers and pythons, as well as amino acid substitutions, non-coding elements, genes, and functions. These changes enabled independent groups of snakes to develop and utilize infrared vision.</p>
Vision dataset for ColRobot WP6 use-case
<p>A specific 2D sensing system, comprising of a 2D camera, a backlit feeder and its associated processing has been developed for the ColRobot kitting use-case. This sensing system is integrated with the gripper and the robot.</p> <p>The objective of the sensing system is to detect and locate the kitting parts (notably screws, washers and nuts) in order to grasp them.</p> <p>The sensing system must be able:</p> <ul> <li>To be rapidly reconfigured for parts of new dimensions (in case of evolution of the kitting specifications), without reprogramming.</li> <li>To be able to isolate parts among similar (but not identical) parts.</li> <li>To be able to avoid detecting parts that are overlapping.</li> </ul> <p>To be able to avoid detecting parts that are too close to each other (clearance should be above 6mm).</p> <p>Constraints are imposed on the processing algorithm to avoid grasping issues:</p> <ul> <li>Do not detect overlapping parts.</li> <li>Do not detect parts too close to another part (identical or not), i.e. keep 6mm clearance around any detected part.</li> </ul> <p>All input pictures are taken with the same sensor, at the same distance to the backlit feeder.</p> <p>Processing parameters are the same for all images.</p> <p>The list of parts to be detected with their parameters is given below in the Data input section.</p> <p>For all input images, all parts given in the input parameters are searched for and an output image is created if found highlighting a point of interest of the part, and an associated oriented frame.</p> <p>On some input images, additional (non-referenced) parts are present in order to test the discrimination properties of the algorithm (false positives).</p> <p>The dataset takes the following format:</p> <ul> <li>Input data: <ul> <li>List of input images.</li> <li>Table of parameters to be given to the processing algorithm for each input image. <ul> <li>For each part, a part type, and an ordered list of dimensions (semantics depending on the part type).</li> </ul> </li> </ul> </li> <li>Output data: <ul> <li>A structured folder of output images <ul> <li>For each input images, a clone of the input image is created for each part described in the input parameters, only if found, and highlighting: <ul> <li>A point of interest (static in the part frame for a given part type).</li> <li>An associated frame describing the part orientation.</li> </ul> </li> </ul> </li> </ul> </li> </ul>
Humans trust central vision more than peripheral vision even in the dark
<p>Dataset relative to the following publication:</p> <p>Gloriani, A. H., & Schütz, A. C. (2019). Humans trust central vision more than peripheral vision even in the dark. Current Biology, 29, 1206–1210.</p>
Fig. 2 in All the better to see you with: a review of odonate color vision with transcriptomic insight into the odonate eye
Fig. 2 Image representing the body and wing coloration of damselflies (a-e). (a) Platycyphya caligata courtesy of J. Abbott. (b) Calopteryx maculata courtesy of J. Abbott. (c) An andromrophic mating wheel of Ischnura ramburii with male on top and andromorph female on the bottom. Courtesy of S. Coleman. (d) Megaloprepus coerulatus courtesy of T. Davenport. (e) An gynomrophic mating wheel of Ischnura ramburii with male on top and gynomorph female on bottom. Courtesy of S. Coleman
Fig. 1 in All the better to see you with: a review of odonate color vision with transcriptomic insight into the odonate eye
Fig. 1 Diagram of the ventral ommatidium of Sympetrum (redrawn from Armett-Kibel and Menertzhagen 1983)
Synthetic Lunar Terrain: A Multimodal Open Dataset for Training and Evaluating Neuromorphic Vision Algorithms
<p><strong>Synthetic Lunar Terrain (SLT) </strong>is a dataset based on a reconstruction of a typical <strong>cratered lunar surface landscape </strong>at the <a href="https://set.adelaide.edu.au/atcsr/space-research/exterres-laboratory" target="_blank" rel="noopener">EXTERRES Laboratory</a> at University of Adelaide, Roseworthy Campus. On a surface area of <strong>3.6m x 4.8m</strong>, multiple synthetic craters with different sizes and geometries were sculpted into<strong> lunar regolith simulant</strong>. A <strong>9kW metal-halide lamp</strong> illuminated the scene, providing high contrast drop-shadows from the rims of craters and similar surface features that are characteristic for the Earth's moon.</p> <p>The purpose of this dataset is to provide multimodal recordings of visual information to develop and test algorithms on a hardware analogue of the moon rather than relying on computer simulations. In particular, comparisons between <strong>neuromorphic vision sensors</strong> like <strong>event-based cameras</strong> and imaging with <strong>conventional monocular cameras</strong> are at the core of this work. For this purpose, an event-based camera (Gen4 Prophesee with Prophesee-Sony IMX636 sensor) was mounted downward-pointing next to a optical camera (Basler a2A1920-160ucPRO with Sony IMX392 sensor) on an extendable rod which was moved above the surface in a slow and continuous sweep. In total, SLT consists of camera recordings from 21 different positions/settings, with clockwise and anti-clockwise motions under varying, extreme lighting conditions.</p> <p>The event-stream and grayscale image data can be further referenced via a detailed <strong>3D point cloud</strong> obtained by a FARO Focus S70 3D Scanner. This 3D Scan was post-processed, realigned and resampled into a 3D point cloud of <strong>~6.25M points, </strong>with a surface density of <strong>1.862 p/mm²</strong>, providing a ground-truth for the crater geometries.</p> <p>In detail, SLT contains the following:</p> <ul> <li>eventbased.zip: <ul> <li><strong>42 camera orbits</strong> in the binary <strong>EVT 3.0</strong> format (<a href="https://docs.prophesee.ai/stable/data/encoding_formats/evt3.html" target="_blank" rel="noopener">Prophesee docs</a>) </li> <li>corresponding <strong>.mp4 </strong>event-frame video rendering for visualization purposes (33.333ms accumulation time at 30FPS)</li> <li>corresponding<strong> .bias</strong> file containing settings used during recording</li> </ul> </li> <li>code.zip: <ul> <li>Standalone C++ code of the <strong>metavision EVT3-to-RAW file decoder</strong>, allowing to convert the binary EVT 3.0 format into a plaintext <strong>.csv </strong>that includes <ul> <li>the coordinates of the event-pixel,</li> <li>the polarity change,</li> <li>and the time-stamp of the event.</li> </ul> </li> <li>This code is an unmodified redistribution from the <a href="https://www.prophesee.ai/metavision-intelligence/" target="_blank" rel="noopener">Metavision SDK</a>, version 4.6.0, released by Prophesee under Apache License 2.0.</li> </ul> </li> <li> optical.zip: <ul> <li><strong>42 image sequences</strong> in <strong>.tif</strong> format (LZW, 1920x1200px, 8bit, grayscale) <ul> <li>Length of image sequences varies between about 300 to 700 images per sequence</li> </ul> </li> </ul> </li> <li>3d_scan.zip: <ul> <li><strong>SLT3d_scan.ply:</strong> 3D point cloud of the scene Stanford Polygon File Format</li> <li><strong>SLT3d_scan.xyz:</strong> 3D point cloud with plaintext x y z coordinates, white-space separated</li> </ul> </li> <li>cratermap.png: <ul> <li>Annotations of <strong>130 different surface features</strong> that have been manually identified as crater-like with approximate x,y-coordinates.</li> </ul> </li> <li>positionmap.png: <ul> <li>Illustration of the different positions from which the rod was moved over the scene (not to scale).</li> </ul> </li> <li>sample.zip: <ul> <li>A sample containing 1 event-camera orbit with the corresponding image sequence (for convenience only, to test the dataset without the need to download it's entirety)</li> </ul> </li> </ul> <p>The global coordinate frame of this dataset puts the origin at the centre of the scene. The shorter side of the terrain is roughly aligned with the x-axis, the longer side with the y-axis. The z-axis represents height/depth (compare with <strong>cratermap.png</strong>). The different conditions (compare with <strong>positionmap.png</strong>) from which the data was taken are encoded as follows:</p> <ul> <li><strong>A1, ..., A9</strong> refer to the left side of the scene (negative x)</li> <li><strong>B1, ..., B9 </strong>refer to the right side of the scene (positive x)</li> <li><strong>S1, S2, S3</strong> and <strong>S4 </strong>describe special lighting conditions and/or parameter settings</li> <li><strong>CW </strong>refers to a "clockwise" sweeping of the camera-rod, relative to the position</li> <li><strong>ACW</strong> refers to an "anti-clockwise" sweeping of the camera-rod, relative to the position</li> </ul> <p>The light from the metal-halide lamp was directed through a small opening, shining along the positive y-axis. In some of the setups, an obstacle was placed between the surface and the opening, blocking out part of the light to create a light-dark separator on the surface, emulating the <strong>terminator</strong> on the Moon between it's day and night side, resulting in highly contrastive images.</p> <p>We encourage you to consult and cite our related publication, should you find SLT useful.</p> <ul> <li>Märtens, M., Farries, K., Culton, J. and Chin, TJ. "<strong>Synthetic Lunar Terrain: A Multimodal Open Dataset for Training and Evaluating Neuromorphic Vision Algorithms</strong>", Proceedings of "<em>International Symposium on Artificial Intelligence, Robotics and Automation in Space (I-SAIRAS), 2024</em>", pp. 609-614</li> </ul> <p> </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.