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3,435 results for “visualization”
Transposition confusability during visual word recognition
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Visual and auditory brain areas share a representational structure that supports emotion perception: fMRI data
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Data to "Humans Can Visually Judge Grasp Quality and Refine Their Judgments Through Visual and Haptic Feedback"
<p>This record contains experimental and analysis scripts (written in Matlab) as well as raw and processed data to reproduce the results shown in:</p> <p>Maiello, G.^, Schepko, M.^, Klein, L. K., Paulun, V. C., and Fleming, R. W. (2021) Humans Can Visually Judge Grasp Quality and Refine Their Judgments Through Visual and Haptic Feedback. Front. Neurosci. 14:591898.<br> doi: 10.3389/fnins.2020.591898</p> <p>A preprint version of the manuscript is available at: https://doi.org/10.1101/2020.08.11.246173</p>
Dataset supporting the paper: Symbolic Versus Numerical Computation and Visualization of Parameter Regions for Multistationarity of Biological Networks
<p>Dataset supporting the paper:</p> <p>Matthew England, Hassan Errami, Dima Grigoriev, Ovidiu Radulescu, Thomas Sturm, and Andreas Weber. Symbolic Versus Numerical Computation and Visualization of Parameter Regions for Multistationarity of Biological Networks. In Proceedings of CASC ’17, Beijing, China, September 18-22 2017, 15 pages. Springer, 2017.</p> <p>The files whose name starts with "SamplePoints" are text files containing the data that produced the plots in the paper.</p> <p>The files whose name starts with "Sys" show the Maple computations used to produce the data. The mw files are to be run with the Maple Computer Algebra System (https://www.maplesoft.com/products/maple/). Pdf printouts of these have also been included for those who do not have access to Maple.</p> <p> </p>
Data for Cell-type-specific inhibitory circuitry from a connectomic census of mouse visual cortex
<p>Data for the paper: Cell-type-specific inhibitory circuitry from a connectomic census of mouse visual cortex, Nature 640, 2025</p> <p>In brief, this data archive includes information about the skeleton morphology and synaptic features of neurons whose cell bodies fell within a 100 micron by 100 micron column spanning all layers of mouse visual cortex. See <a href="https://www.microns-explorer.org/cortical-mm3">MICrONs-Explorer</a> for a full description of the broader volume and how it was collected.</p> <p>The data here include both data tables of cell locations, neuronal features, synapse lists, and more, as well as files containing morphological descriptions of all neurons used for the analysis in the initial version of the preprint. See the README.md file for more complete information about the individual files.</p> <p>Note: Data has been updated with post-publication files.</p>
Interactive maps for the visualization of ESRIUM automated driving tests with various EGNSS localization solutions
<p>In order to make the test results available to a broader audience in an easy manner, we have generated interactive maps. These maps are attached to this report and can be viewed in a web-browser. </p><p>Due to the large number of datasets, we have color-coded them on the map and in the menu. An arbitrary number of datasets can be selected at a time.</p><p>Due to the high accuracy of the EGNSS receivers, one can clearly identify the lane on which the vehicle was driving, and where the vehicle was performing a lane-change. However, the satellite/areal-images are not perfectly geo-referenced, thus one can notice a slight offset between satellite/areal-images and real-world lanes.</p><p> </p><p><strong>How to use the map?</strong></p><ul><li>The map can be used in a similar manner than other map-applications, such as google maps. By using the mouse, you can set the focus on the area of your interest. By using the +/- buttons (top left), you can zoom in/out.</li><li>By hovering over the layer-symbol (top right), a popup emerges. Here, you can select different background-tiles (such as satellite/areal-images). In addition, you can select different datasets which should be visualized on the map.</li></ul><p><strong>Background-tiles:</strong></p><ul><li>Basemap – Sat - Satellite/Areal images (from Basemap) -Symbolic map with high resolution (from Basemap)</li><li>Basemap – HighDPI Symbolic map with high resolution (from Basemap)</li><li>OpenStreetMap - Symbolic map (from OpenStreetMap)</li><li>OpenTopoMap - Symbolic map including topology information (from OpenTopoMap)</li></ul><p><strong>Datasets:</strong></p><ul><li>GNSS (Vehicle) - Position of vehicle, according to on-board GPS receiver</li><li>EGNSS (AsteRx SB3 Pro+) - Position of vehicle, according to AsteRx SB3 Pro+ receiver</li><li>EGNSS (mosaic-X5) - Position of vehicle, according to mosaic-X5 receiver</li><li>EGNSS (mosaic-H) - Position of vehicle, according to mosaic-H receiver</li><li>PVT Mode: EGNSS (AsteRx SB3 Pro+) - PVT Mode of AsteRx SB3 Pro+ receiver</li><li>PVT Mode: EGNSS (mosaic-X5) - PVT Mode of mosaic-X5 receiver</li><li>PVT Mode: EGNSS (mosaic-H) - PVT Mode of mosaic-H receiver</li><li>in-lane Offset Change-Request - Position, at which an in-lane offset change (relative to middle of the current lane) was requested via C-ITS</li><li>Lane Change to left - Position, at which a lane-change towards left was performed </li><li>Lane Change to right - Position, at which a lane-change towards right was performed</li></ul><p>Interactive maps are attached are two precision levels one with 4 and the other in 7 digits. The list files and the corresponding test conditions are listed below. </p><p>Test velocities [km/h]: 90, 110, 130 </p><p>interactive map files: </p><p>speed: 90 km/h</p><ul><li>Testrun_01.html</li><li>Testrun_03.html</li><li>Testrun_04.html</li></ul><p>speed: 110 km/h</p><ul><li>Testrun_05.html</li><li>Testrun_06.html</li><li>Testrun_07.html</li></ul><p>speed: 130 km/h </p><ul><li>Testrun_08.html</li><li>Testrun_09.html</li><li>Testrun_10.html</li></ul>
Data from: Visual pigment chromophore usage in Nicaraguan Midas cichlids: Phenotypic plasticity and genetic assimilation of cyp27c1 expression
<p>Code and Data associated with "Visual pigment chromophore usage in Nicaraguan Midas cichlids: Phenotypic plasticity and genetic assimilation of <em>cyp27c1</em> expression"</p> <h2><span>Abstract</span></h2> <p><span>The wide-ranging photic conditions found across aquatic habitats may act as selective pressures potentially driving rapid evolution and diversity in the visual system of teleost fishes. Fine-tuning of visual sensitivities in many fish species relies on regulating the two components of visual pigments, the opsin protein and the chromophore. Many studies have focused on opsin gene expression or opsin sequence divergence in fishes inhabiting contrasting habitats. However, variation in chromophore usage across photic habitats has received less attention. Species from the Nicaraguan Midas cichlid complex, <em>Amphilophus </em>cf <em>citrinellus </em>[Günther 1864], have independently colonized seven isolated crater lakes of varying photic conditions resulting in repeated examples of small adaptive radiations. Here, we investigate variation in <em>cyp27c1</em>, the main enzyme involved in chromophore exchange, in response to photic environments in the wild, we measure its genetic component using laboratory-reared fish and test the effect of different rearing light conditions on <em>cyp27c1</em> expression. We found that photic environments significantly predict variation in <em>cyp27c1</em> expression in wild populations and that this variation seems to be genetically assimilated in two populations. We found that light-induced <em>cyp27c1</em> expression is variable across populations (i.e., genotype-by-environment interactions) and correlated with local photic conditions thus highlighting <em>cyp27c1</em> as a key factor of visual ecology in cichlid fishes.</span></p> <p><span>Keywords: <em>cyp27c1 </em>gene expression, sensory ecology, visual plasticity, Neotropical cichlids </span></p>
CAbiNet: Joint clustering and visualization of cells and genes for single-cell transcriptomics
<p>We here provide the data sets to reproduce the results in our manuscript "CAbiNet: Joint clustering and visualization of cells and genes for single-cell transcriptomics". Our package "CAbiNet" can be downloaded from https://github.com/VingronLab/CAbiNet. The scripts to reproduce the results in our manuscript can be found from https://github.com/VingronLab/CAbiNet_paper.</p><p>You can find the description of folders in 'Data.zip' in the README.md file.</p>
Attention-based frontal-posterior coupling for visual consciousness in the human brain
<ol> <li>DataCode_Fig1_Attentional_Capture_Image_Detectability.m</li> <li>DataCode_Fig1_Attentional_Capture_Image_Detectability.mat</li> <li>DataCode_FigS2_Attentional_Capture_Image_Detecability.mat <ul> <li>.m Code (1) using .mat Data (2 and 3) illustrate main behavioral findings in our manuscript. Panel figures shown in Figure.1 and Figure.S2 could be well replicated using these materials.<br><br></li> </ul> </li> <li>Au_Step06_0601_unit_2C.m</li> <li>Au_Step06_0601_unit_mC.m</li> <li>Train_DSVM_xilei.m</li> <li>Classify_DSVM.m</li> <li>svmclassify.m</li> <li>svmtrain_xilei.m <ul> <li>.m Code (4) and .m code (5) using child .m functions (6, 7, 8 and 9) illustrate core codes used to discriminate neural pattern differences on a 2-class issue (image presence versus image absence) or a 3-class issue (animal, object or face), respectively. </li> </ul> </li> <li>Note_Location_activeChannels_distanceTest.m</li> <li>Note_Location_activeChannels_distanceTest.mat</li> <li>Note_Location_activeChannels.mat <ul> <li>.m Code (10) using .mat Data (11 and 12) illustrate our method used to calculate distance between responsive contacts. Based on that, we also made a statistical inference against a chance-level distribution. Panel figure shown in Figure.2F could be well replicated using these materials.<br><br></li> </ul> </li> <li>easy_ImgC.m <ul> <li>.m Code (13) illustrate our method used to calculate imaginary coherence between responsive contacts. A Rayleigh Z correction was also performed and outputed.<br><br></li> </ul> </li> <li>easy_visibility.m <ul> <li>.m Code (14) illustrate our method used to calculate an index of visibility from which measures of interest tied to an invisible image was subtracted from that of a visible image. </li> </ul> </li> </ol> <p> </p>
New Challenges in Point Cloud Visual Quality Assessment: A Systematic Review (Dataset)
<p>This dataset is a collection of annotated information on the scientific papers screened and analyzed for the systematic review of the literature in Point Cloud Visual Quality Assessment. </p> <p>The data is structured as follows:</p> <ul> <li>General information <ul> <li>Document title</li> <li>Authors</li> <li>Year of publication</li> <li>Venue (Conference or Journal title)</li> <li>Citations (number)</li> <li>URL/DOI</li> </ul> </li> </ul> <ul> <li>About the content <br> <ul> <li>Content Type: Point clouds (PC), Colored Point clouds (CPC), Meshes, Dynamic Point Clouds (DPC)</li> <li>Content source: Source of the content used in a subjective QA test or the evaluation of one or more QA metrics</li> </ul> </li> </ul> <ul> <li>About metric benchmarks <ul> <li>Subjective Ground-truth Data: Dataset(s) Source of the subjective scores used as ground-truth in a QA metric benchmark</li> <li>Assessed Metrics: Types of metrics assessed in a benchmark (JPEG standards, IQM, NR, State-of-the-art, others)</li> <li>Performance Measures: PLCC, SROCC, KRCC, RMSE, OR, others</li> </ul> </li> </ul> <ul> <li>About Objective QA metrics <ul> <li>Metric: Name given to the metric introduced in this paper</li> <li>Base: 3D-based or Projection-based</li> <li>Categories: Categories that characterize the approach of the proposed metric (Feature-based, Learning-Based, Perceptual-based, IQM, others) </li> <li>Reference: Full-Reference (FR), Reduced-Reference (RR) or No-Reference (NR)</li> </ul> </li> </ul> <ul> <li>About Subjective QA experiments <ul> <li>Display: Type of display (2D, 3D, AR, MR, VR) and interaction approach (passive, interactive, 3DoF, 6DoF) used in the described experiment.</li> <li>Rendering: Type of rendering used to display the stimuli (Points, Squares, Cubes, Surface)</li> <li>Lab/Remote: The experiment was run in one or more lab environments, or remotely (Lab, Cross-Lab, Remote)</li> <li>Rating: Subjective rating methodology used in the experiment (ACR, DSIS, PWC, others)</li> <li>Dataset: Name of the new subjective dataset if the experiment's results were published.</li> <li>Observers: Number of observers </li> <li>Distortion type: Types of distortions applied to the stimuli and assessed in the experiment</li> </ul> </li> </ul>
Visualization and quantification of coral reef soundscapes using CoralSoundExplorer software
<p>Support material for the research paper "Visualization and quantification of coral reef soundscapes using CoralSoundExplorer software"</p>
PsPM-FER01: PSR, SCR, ECG and respiration measurements from a discriminant delay fear conditioning task with visual CS and electrical US.
<p>This dataset includes pupil size response (PSR), skin conductance response (SCR), electrocardiogram (ECG) and respiration measurements. Also included are CS and US information and shock expectancy ratings at the end of the experiment for 30 healthy unmedicated participants (12 males and 18 females aged 23.9+/-4.4 years) participating in a classical (Pavlovian) discriminant delay fear conditioning task. Fear acquisition consisted of 10 CS- and 32 CS+ trials (16 CSa+/16 CSb+). Half of the CS+ trials were paired with an electric shock. CS were colored triangles (yellow/red/blue). US consisted of a 500 ms train of 250 square pulses with individual pulse width of 0.2 ms. SOA between the CS onset and US was 3.5 seconds. CS and US co-terminated. The ITI was randomly determined as discrete values between 7-11 seconds (mean 9 seconds).</p> <p> </p>
Confocal Microscopy Visualizes Particle-Crack Interactions in Epoxy Composites with Optical Force Probe-Crosslinked Rubber Particles
<p>Data (*.csv and *.lif) corresponding to Figures 2-7 of the manuscript and Figures S1-S2 of the Supporting Information.</p>
Dataset for the study Late development of audio-visual integration in the vertical plane
<p>It is not clear how multisensory skills develop and how visual experience impacts on multisensory spatial development. Conflicting results show that visual calibration precedes multisensory integration for the audio-visual spatial bisection task (Gori et al., 2012a, 2012b) while in other tasks such as spatial localization, visual calibration occurs after multisensory development (Rohlf et al., 2020). Results in blind individuals can say something about the role of vision on perceptual development. Scientific evidences show that blind individuals have impairments in bisecting the auditory space (Gori et al., 2014) but not in localizing auditory sources (Lessard et al., 1998). Such results suggest that sensory calibration and impairment are linked. We studied the development of audio-visual multisensory localization in the vertical plane in sighted individuals from 5 years to adulthood to address this hypothesis. We hypothesize that typical children would show late audio-visual integration for the vertical plane, preceded by visual dominance. Unimodal and bimodal audio-visual thresholds and PSEs were measured and compared with the Bayesian optimal-integration model (maximum likelihood estimation). Results show that the development of multisensory integration in the vertical plane is not evident at 5 years, suggesting visual dominance for vertical audio-visual localization. These results support the idea that multisensory perception in the vertical domain depends on sensory calibration. We discuss these scientific results proposing that the process of cross-sensory calibration is task-specific and highlighting the importance of linking the impairment and development to better determine how our brain works.</p> <p>Data are in textual tab delimited format. Columns report for each subject: age, age_bin, condition, jnd.</p> <p> </p>
Data for: What's in a game: Video game visual-spatial demand location exhibits a double dissociation with reading speed
<p>The aggregate data in these datasets were used in analyses for "What’s in a game: Video game visual-spatial demand location exhibits a double dissociation with reading speed".</p>
Typewriter indentations in a letter by W. H. Auden - visualized with photometric stereo
<p>This figure shows a section of a letter from W. H. Auden to Stella Musulin.</p> <p>Top: conventional photograph.<br> Middle: raking light photograph. Indentations in the paper become visible, but hardly legible.<br> Bottom: a false-color visualization generated with photometric stereo. The simultaneous display of depth, albedo and curvature gradient allow the discrimination of four text layers.</p>
Experimental data for the study: "Naturalistic visualization of reaching movements using head-mounted displays improves movement quality and proves high usability compared to conventional computer screens"
<p>The datasets contains the motor performance metrics and the questionnaire responses for two experiments involving a motor task with a VR controller (experiment 1, healthy old participants) or a rehabilitation assistive device (experiment 2, brain-injured patients) and three visualization technologies: an immersive virtual reality (IVR) head-mounted display (HMD), an augmented reality (AR) HMD, and a computer screen (2D screen). The study was performed in the Motor Learning and Neurorehabilitation Laboratory at the University of Bern. All data are stored in “csv” files. The variables inside the files are explained in “DataFrameDescription.rtf”. For questions, please contact L.MarchalCrespo@tudelft.nl.</p>
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>
SubPipe: A Submarine Pipeline Inspection Dataset for Segmentation and Visual-inertial Localization
<h1><strong>Abstract</strong></h1> <p>This paper presents SubPipe, an underwater dataset for SLAM, object detection, and image segmentation. <br><br>SubPipe has been recorded using a lightweight autonomous underwater vehicle (LAUV), operated by OceanScan MST, and carrying a sensor suite including two cameras, a side-scan sonar, and an inertial navigation system, among other sensors. The AUV has been deployed in a pipeline inspection environment with a submarine pipe partially covered by sand. The AUV's pose ground truth is estimated from the navigation sensors. The side-scan sonar and RGB images include object detection and segmentation annotations, respectively. State-of-the-art segmentation, object detection, and SLAM methods are benchmarked on SubPipe to demonstrate the dataset's challenges and opportunities for leveraging computer vision algorithms.<br>To the authors' knowledge, this is the first annotated underwater dataset providing a real pipeline inspection scenario. The dataset and experiments are publicly available <a href="https://github.com/remaro-network/SubPipe-dataset">online.</a></p> <p>On Zenodo we provide <em>three</em> versions for SubPipe. One is the full version (<strong>SubPipe.zip</strong>, ~80GB unzipped) and two subsamples: <strong>SubPipeMini.zip</strong>, ~12GB unzipped and <strong>SubPipeMini2.zip</strong>, ~16GB unzipped. Both subsamples are only parts of the entire dataset (SubPipe.zip). SubPipeMini is a subset, containing semantic segmentation data, and it has interesting camera data of the underwater pipeline. On the other hand, SubPipeMini2 is mainly focused on underwater side-scan sonar images of the seabed including ground truth object detection bounding boxes of the pipeline.</p> <p><strong>For (re-)using/publishing SubPipe, please include the following copyright text:</strong></p> <p><em><strong>SubPipe</strong> is a public dataset of a submarine outfall pipeline, property of Oceanscan-MST. This dataset was acquired with a Light Autonomous Underwater Vehicle by Oceanscan-MST, within the scope of Challenge Camp 1 of the</em> <em>H2020 </em><a href="https://remaro.eu/"><em>REMARO</em></a><em> project.</em></p> <p><em>More information about OceanScan-MST can be found at </em><a href="https://www.oceanscan-mst.com/"><em>this link</em></a><em>.</em></p> <h1><strong>Cam0 — GoPro Hero 10</strong></h1> <h4>Camera parameters:</h4> <ul> <li>Resolution: 1520×2704</li> <li>fx = 1612.36</li> <li>fy = 1622.56</li> <li>cx = 1365.43</li> <li>cy = 741.27</li> <li>k1,k2, p1, p2 = [−0.247, 0.0869, −0.006, 0.001]</li> </ul> <h1><strong>Side-scan Sonars</strong></h1> <p>Each sonar image was created after 20 “ping” (after every 20 new lines) which corresponds to approx. ~1 image / second.</p> <p>Regarding the object detection annotations, we provide both COCO and YOLO formats for each annotation. A single COCO annotation file is provided per each chunk and per each frequency (low frequency vs. high frequency), whereas the YOLO annotations are provided for each SSS image file.</p> <p>Metadata about the side-scan sonar images contained in this dataset:</p> <table> <tbody> <tr> <td><strong>Images for object detection</strong></td> <td> </td> </tr> <tr> <td># Low Frequency (LF):</td> <td> 5000</td> </tr> <tr> <td>LF image size:</td> <td>2500 × 500</td> </tr> <tr> <td># High Frequency (HF):</td> <td> 5030</td> </tr> <tr> <td>HF Image size</td> <td>5000 × 500</td> </tr> <tr> <td><strong>Total number of images:</strong></td> <td>10030</td> </tr> <tr> <td><strong>Annotations</strong><strong><br></strong></td> <td> </td> </tr> <tr> <td># Low Frequency:</td> <td> 3163</td> </tr> <tr> <td># High Frequency:</td> <td> 3172</td> </tr> <tr> <td><strong>Total number of annotations:</strong></td> <td><strong> </strong>6335</td> </tr> </tbody> </table>
Dataset supplementing Schütz, I. & Einhäuser, W. (2018) Visual awareness in binocular rivalry modulates induced pupil fluctuations.
<p>This dataset supplements the publication:</p> <p>Schütz, I. & Einhäuser, W. (2018). Visual awareness in binocular rivalry modulates induced pupil fluctuations.</p> <p><br> Raw data are available in two formats: the actual raw EDF data files as returned by the eyetracking device (*.edf) for reference, and as MATLAB files into which all relevant information has been extracted (*.mat) for analysis.</p> <p>Variables in the pft_*.mat files include<br> - raw eye position in the variable scan (t,x,y,p), where t is the timestamp of the eyetracker, x/y the position on the screen and p the pupil diameter in arbitrary units<br> - fixation, saccade and blink events in variables fix, sac and blink<br> - raw button presses in cell arrays buttonDn and buttonUp (6 - left button, 7 - right button) including timestamps<br> - stimulus presentation cycle timestamps in the variable stimperiod<br> - timestamps for auditory attentional instruction in the variable attends</p> <p>For analysis, data from all participants and sessions is imported into pftdata.mat, where it is stored in cell arrays of the format "samples{subject_no, condition}".</p> <p><br> Data Files<br> ==========</p> <p>- rawdata.zip<br> - rawdata/*.edf: raw EyeLink 2000 EDF data files<br> - rawdata/*.mat: EyeLink data converted to MATLAB data file</p> <p>- analysis.zip<br> - pftdata.mat: preprocessed eye tracking and response data for analysis<br> - face.png, house.png: stimulus images used for the experiment<br> - resp_anova.csv: response data for ANOVA (generated by preprocessing.m)<br> - fig4_anova.csv: complex plane data for R T-Test (generated by figure4_complex_plane.m)<br> - analysis code files, see below</p> <p><br> Analysis Functions<br> ==================</p> <p>Run the following functions in the listed order to reproduce figures and data in results/.</p> <p>- preprocessing.m:<br> - convert EDF data files to ASCII using SR-Research edf2asc, import into MATLAB<br> - remove EyeLink detected blinks and interpolate (cubic spline)<br> - z-score pupil data within each experimental block<br> - add button press / reported percept to sample data<br> - save response data for RM-ANOVA in R</p> <p>- figure1_methods.m:<br> - recreates Figure 1 (stimulus figure from images)</p> <p>- figure2_example_plot.m:<br> - recreate Figure 2 (example data from one participant)</p> <p>- figure3_averaged_response.m<br> - recreates Figure 2 (averaged F1 FFT component by condition)</p> <p>- figure4_complex_plane.m:<br> - recreates Figure 4 (complex plane analysis of pupil response)</p> <p>- stats_auc_decoding.m:<br> - moment-by-moment decoding analysis using AUC<br> - recreates stats_AUC.txt</p> <p>- pft_statistics.R:<br> - R statistics, recreates stats_responses.txt and stats_Zvalues.txt</p> <p><br> Output Files<br> ============</p> <p>results/<br> - Paper Figures (not layouted): figure1.tif, figure2.png, figure3.png, figure4.png<br> - stats_responses.txt: behavioral analyses results,<br> - stats_Zvalues.txt: complex plane analysis results<br> - stats_AUC.txt: moment-by-moment AUC decoding results</p> <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.