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92 results for “visual learning”
OLVSL_ Object-location visual statistical learning
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Raw data of individuals with Down syndromre, individuals with Williams syndrome, healthy children and adults in a visual learning task, a conditional learning task and a transitive inference task.
<p>Raw data of 17 individuals with Down syndrome (8 girls/women; average age: 17.8 years; range: 7.2-30.8 years at the beginning of the study) in a visual learning task, a 3-item conditional learning task, and a 5-item conditional learning and transitive inference task.</p> <p>Raw data of 27 individuals with Williams syndrome (16 girls/women; average age: 23.7; range: 9.4-43.8 at the beginning of the study) in a visual learning task, a 3-item conditional learning task, and a 5-item conditional learning and transitive inference task.</p> <p>Raw data of 71<strong> </strong>healthy children (31 girls; average age: 6.42 years; range: 2.95-11.64 years at the beginning of the study) in a visual learning task, a 3-item conditional learning task, and a 5-item conditional learning and transitive inference task.</p> <p>Raw data of 22 healthy adults (11 femaleswomen; average age: 26.05 years; range: 20.32-29.76 years at the beginning of the study) in a visual learning task, a 3-item conditional learning task, and a 5-item conditional learning and transitive inference task.</p>
Dataset from: "Reward expectation facilitates context learning and attentional guidance in visual search"
<p>Dataset for Bergmann N, Koch D, Schubö A (2019). Reward expectation facilitates context learning and attentional guidance in visual search, <em>Journal of Vision</em>, 19(3). <a href="https://doi.org/10.1167/19.3.10">https://doi.org/10.1167/19.3.10</a></p>
Conditional Visual Associative Learning Task
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Galaxy Zoo DECaLS: Detailed Visual Morphology Measurements from Volunteers and Deep Learning for 314,000 Galaxies
<p>This repository contains the data released in the paper "Galaxy Zoo DECaLS: Detailed Visual Morphology Measurements from Volunteers and Deep Learning for 314,000 Galaxies" <em>(DOI to follow on publication).</em></p> <p>We release detailed morphology catalogues, both volunteer and automated, for Galaxy Zoo DECaLS.</p> <p>- gz_decals_volunteers_1_and_2 contains volunteer classifications for galaxies classified during the GZD-1 and GZD-2 campaigns.</p> <p>- gz_decals_volunteers_5 similarly contains classifications from the GZD-5 campaign. Note that GZD-5 used a modified schema designed to better detect mergers and weak bars, and includes many galaxies with only approx. five volunteer responses.</p> <p>- gz_decals_auto_posteriors contains the predicted posteriors for volunteer responses to all galaxies used in any campaign. The full posteriors are recorded as Dirichlet distribution concentrations. gz_decals_auto_posteriors also summarises these posteriors as the automated equivalent of previous Galaxy Zoo data releases;<strong> the expected vote fractions (mean posteriors)</strong>. Note that not all posteriors/vote fractions are relevant for every galaxy; we suggest assessing relevance using the estimated fraction of volunteers that would have been asked each question.</p> <p>We include a schema document, schema.md, to define the column names in each catalogue.</p> <p>We also release the galaxy images shown to volunteers on www.galaxyzoo.org during GZD-5. The images on which the automated classifier was trained may be derived from these volunteer-facing images. These images are split into four zip files, each of which contains images named by iauname inside a subfolder named by the first four characters in their iauname. Not all images were labelled during GZD-5 - refer to the catalog for training labels. We are working with the Zenodo team to add these large files to this repository - meanwhile, you can download them from The University of Manchester <a href="https://docs.google.com/document/d/1YgpnxiSJ7ffOW6FY8pX0pw93LTu8rLIdPL2PYhxW1fo/edit?usp=sharing">here</a>.</p> <p>The .csv and .parquet files contain identical data. Parquet is a fast column-oriented binary format which can be read with pd.read_parquet(loc, columns=[some columns]).</p> <p>You may also be interested in the <a href="https://github.com/mwalmsley/zoobot">github repository</a> which contains code to reproduce the model and to fine-tune it for new tasks (including pretrained weights).</p> <p>We will release updates if needed via Zenodo versioning. We recommend using the latest version of this repository. You can check the version you are currently viewing on the right-hand sidebar.</p> <p>Please cite the paper (DOI to follow on publication) when using the data in this repository.</p> <p>---</p> <p>History</p> <p>v0.0.1 (submission) provides the catalog files.</p> <p>v0.0.2 (first revision) renames the catalog files, adds flags for poorly sized galaxies, and includes the galaxy images via the University of Manchester</p>
Raw data of healthy children and adults in a visual learning task, a conditional learning task and a transitive inference task.
<p>Raw data of 71<strong> </strong>healthy children (31 girls; average age: 6.42 years; range: 2.95-11.64 years at the beginning of the study) in a visual learning task, a 3-item conditional learning task, and a 5-item conditional learning and transitive inference task.</p><p>Raw data of 22 healthy adults (11 femaleswomen; average age: 26.05 years; range: 20.32-29.76 years at the beginning of the study) in a visual learning task, a 3-item conditional learning task, and a 5-item conditional learning and transitive inference task.</p>
Data for "Identification of 4876 Bent-Tail Radio Galaxies in the FIRST Survey using Deep Learning Combined with Visual Inspection"
<p>The data are the full versions of tables that will be published in the manuscript titled "Identification of 4876 Bent-Tail Radio Galaxies in the FIRST Survey using Deep Learning Combined with Visual Inspection" by The Astrophysical Journal Supplement Series.</p> <p>The table file named "FIRST_bt_table1.csv" is the full table for "A catalog of 4876 BTRGs identified from VLA FIRST survey". </p> <p>The table file named "FIRST_bt_table2.csv" is the full table for "Cluster details for BTRGs". </p>
Learned value modulates the access to visual awareness during continuous flash suppression
<p>Data from Experiment 1 and Experiment 2 are reported in separate files. </p> <p>Each line contains the mean suppression time of a target grating under continuous flash suppression expressed in seconds for one participant. </p> <p>Each column refers to a different condition:<br> HREV = visual stimuli associated with high monetary reward<br> LREV = visual stimuli associated with low monetary reward<br> base = baseline measurements before associative learning<br> P1 = first measurement after associative learning<br> P2 = second measurement after associative learning<br> P3 = third measurement after associative learning</p> <p>For experiment 1, a short (20 trials) associative learning recall session was performed between P1 and P2 and between P2 and P3.</p> <p> </p> <p> </p> <p> </p>
Advancing Vanadium Redox Flow Battery Analysis: A Deep Learning Framework for High-Throughput 3D Visualization and Bubble Quantification via Synchrotron X-ray Tomography
<p>Dataset and model of UTILE-Redox - Deep Learning based Tool for Autonomous 3D Bubble Analysis of Vanadium Flow Batteries from Synchrotron X-ray Imaging. This project focuses on the deep learning-based automatic analysis of Vanadium Redox Flow Batteries (VRFB) Synchrotron X-ray tomographies. This repository contains the Python implementation of the UTILE-Redox software for automatic volume analysis, feature extraction, and visualization of the results.</p>
lilGym: Natural Language Visual Reasoning with Reinforcement Learning, model files
<p>Baselines models for the paper <a href="https://lil.nlp.cornell.edu/lilgym"><em>lil</em>Gym: Natural Language Visual Reasoning with Reinforcement Learning</a>.</p>
Reconstruction of Natural Visual Scenes from Primary Visual Cortical Neural Activity Using Adversarial Learning
<p>This data set and code goes with "Reconstruction of Natural Visual Scenes from Primary Visual Cortical Neural Activity Using Adversarial Learning"</p>
Data from: Texas field crickets (Gryllus texensis) use visual cues to place learn but perform poorly when intra- and extra-maze cues conflict
<p>Central place foraging field crickets are an ideal system for studying the adaptive value of learning and memory, but more research is needed on ecology-relevant cognition in these invertebrates. Here, we test the visuospatial place learning of Texas field crickets (<em>Gryllus texensis</em>) in a radial arm maze. Our study expands previous work on <em>G. texensis</em> cognition for accuracy measures and extends our previous findings on females to both sexes. Additionally, our study examines whether crickets use intra- or extra-maze cues to locate a food reward using a maze rotation putting the cues in conflict. We found that male and female crickets improved performance over trials when measured by accuracy variables but not latency variables; thigmotaxis negatively impacted performance in both sexes. In a reward-absent trial, both male and female crickets demonstrated place memory. When intra- and extra-maze cues conflicted during a rotation trial, crickets' performance was not better than chance. Our rotation results suggest that crickets may experience reciprocal overshadowing of conflicting cues – a result most often seen in other taxa with conflicting multi-modal cues. We conclude that crickets do not rely solely on: (1) a single-cue association; (2) route-following; or (3) their own scent cues to navigate the maze. Instead, male and female Texas field crickets seem to learn the location of the reward using a combination of proximal and distal cues. The possibility to test large numbers of wild-caught or laboratory-reared individuals opens the door to future investigations on the evolutionary ecology of visuospatial learning in these invertebrates.</p>
Data from two studies of learning in visual span working memory tasks.
<p>Data from two working memory span tasks. The span set size could be up to six, and each row in each .csv is one response (i.e., one "click" on an item). Thus, each trial's data is spread out on multiple rows, with accuracy being a binary variable.</p>
Figure 3. Data visualization-DATA MINING LEARNING MODELS AND ALGORITHMS ON A SCADA SYSTEM DATA REPOSITORY
<p>Data visualization is also a very useful technique because it helps to deter-<br> mine the di±culty of the learning problem. We visualized with Weka single<br> attributes (1-d) and pairs of attributes (2-d). The ¯gure 3 shows the variation<br> of the temperature in time.</p>
Adaptive processing and perceptual learning in visual cortical areas V1 and V4
<p>Neurons in visual cortical areas primary visual cortex (V1) and V4 are adaptive processors, influenced by perceptual task. This is reflected in their ability to segment the visual scene into task-relevant and task-irrelevant stimulus components and by changing their tuning to task-relevant stimulus properties according to the current top-down instruction. Differences between the information represented in each area were seen. While V1 represented detailed stimulus characteristics, V4 filtered the input from V1 to carry the binary information required for the two-alternative judgement task. Neurons in V1 were activated at locations where the behaviorally relevant stimulus was placed well outside the grating-mapped receptive field. By systematically following the development of the task-dependent signals over the course of perceptual learning, we found that neuronal selectivity for task-relevant information was initially seen in V4 and, over a period of weeks, subsequently in V1. Once the learned information was represented in V1, on any given trial, task-relevant information appeared initially in V1 responses, followed by a 12-ms delay in V4. We propose that the shifting representation of learned information constitutes a mechanism for systems consolidation of memory.</p>
Visualizing histopathologic deep learning classification and anomaly detection using nonlinear feature space dimensionality reduction
<p>Representative Testing/Validation WSIs used in the manuscript "Visualizing histopathologic deep learning classification and anomaly detection using nonlinear feature space dimensionality reduction"</p>
Visualizing histopathologic deep learning classification and anomaly detection using nonlinear feature space dimensionality reduction
<p>Training image dataset used in the manuscript "Visualizing histopathologic deep learning classification and anomaly detection using nonlinear feature space dimensionality reduction"</p>
Prototyping 3D Virtual Learning Environments with X3D-based Content and Visualization Tools-Figure 10. Room model generated with Autodesk 123D Catch - the 3D model (screen capture from GLC Player)
<p>Structure from motion was used for rapid modeling of a small room with all its objects. Two files were generated, a Wavefront obj and mtl (corresponding to the texture). The 3D model was post-processed with MeshLab, during which several filters were applied to clean up the model. The mesh model was also connected with the scanned model, by choosing at least 4 connection points. The 2D and 3D results are shown in Figures 9, 10. A post-processing could also be performed using the Autodesk 123D Catch web application.</p>
Prototyping 3D Virtual Learning Environments with X3D-based Content and Visualization Tools-Figure 2. The model of the 3D virtual campus - details from the building interior (3D modeling by Marius Hodea)
<p>The processing workflow for 3D modeling and design for a 3DVLE represents a time- consuming stage in the overall pipeline production. One reason is that a range of technologies and tools are typically used. In (Cudworth 2014) a 3-week period is indicated for experienced users to perform the 3D modeling of a virtual space. In our case, a 3-month work was needed for designing a working model of a 3D virtual campus (see Figure 1 and Figure 2 for final results).</p>
Prototyping 3D Virtual Learning Environments with X3D-based Content and Visualization Tools-Figure 1. The model of the 3D virtual campus - an outdoor view (3D modeling by Marius Hodea)
<p>The processing workflow for 3D modeling and design for a 3DVLE represents a time- consuming stage in the overall pipeline production. One reason is that a range of technologies and tools are typically used. In (Cudworth 2014) a 3-week period is indicated for experienced users to perform the 3D modeling of a virtual space. In our case, a 3-month work was needed for designing a working model of a 3D virtual campus (see Figure 1 and Figure 2 for final results).</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.