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3,435 results for “Visualization”
Remote Rapid Visual Screening (RRVS) Buildings Survey Data - DESTRESS - France
<p>The dataset contains a set of structural and non-structural attributes collected using the GFZ RRVS methodology in Alsace, France, within the framework of the DESTRESS project. The survey has been carried out between May and June 2017 using a Remote Rapid Visual Screening system developed by GFZ and employing omnidirectional images from Google StreetView (vintage: February 2011) and footprints from OpenStreetMap.<br> Surveyor: Konstantinos G. Megalooikonomou (GFZ-Potsdam)<br> The attributes are encoded according to the GEM taxonomy v2.0 (see https://taxonomy.openquake.org). <br> The following attributes are defined (not all are observable in the RRVS survey):<br />code,description<br> lon, longitude in fraction of degrees<br> lat, latitude in fraction of degrees<br> object_id, unique id of the building surveyed <br> MAT_TYPE,Material Type<br> MAT_TECH,Material Technology<br> MAT_PROP,Material Property<br> LLRS,Type of Lateral Load-Resisting System<br> LLRS_DUCT,System Ductility<br> HEIGHT,Height<br> YR_BUILT,Date of Construction or Retrofit<br> OCCUPY,Building Occupancy Class - General<br> OCCUPY_DT,Building Occupancy Class - Detail<br> POSITION,Building Position within a Block<br> PLAN_SHAPE,Shape of the Building Plan<br> STR_IRREG,Regular or Irregular<br> STR_IRREG_DT,Plan Irregularity or Vertical Irregularity<br> STR_IRREG_TYPE,Type of Irregularity<br> NONSTRCEXW,Exterior walls<br> ROOF_SHAPE,Roof Shape<br> ROOFCOVMAT,Roof Covering<br> ROOFSYSMAT,Roof System Material<br> ROOFSYSTYP,Roof System Type<br> ROOF_CONN,Roof Connections<br> FLOOR_MAT,Floor Material<br> FLOOR_TYPE,Floor System Type<br> FLOOR_CONN,Floor Connections</p>
Path Following in Non-Visual Conditions - Screen shots and audio sample
<p>Screen shots and audio file in addition to the publication "Path Following in Non-Visual Conditions" by Alan Del Piccolo, Davide Rocchesso, and Stefano Papetti. Under revision for IEEE Transaction on Haptics (1 June 2018).</p> <p>Developed in Max (https://cycling74.com).</p> <p>Short description:</p> <ul> <li><em>interface.png</em>: the interface for managing the experiment. Output levels, trial repetitions and feedback conditions can be adjusted from here. The underlying Max patch is depicted in "main patch.png"</li> <li><em>main patch.png</em>: the main patch controlling the experiment. It receives the data from the Soundplane's controller (top left), invokes finger position detection and feedback generation (bottom left), manages the trial repetition and feedback modes (bottom center), and enables the adjustment of the feedback levels (right). </li> <li><em>mapToImage.png</em>: the patch that maps the participant's finger position on the Soundplane to the relative position over the loaded path shape. The position is shown by the white circle on the bottom left of the image.</li> <li><em>rolling_feedback.png</em>: the SDT "rolling model" configured for the use in the experiment. Note that the input levels are generated in the track_detection_SP2 patch.</li> <li><em>sdt_rolling.png</em>: a configuration of the SDT "rolling model" adjusted to output a signal similar to the one used in the experiment (see record.wav) as a stand-alone, namely without using the experiment's patches.</li> <li><em>track_detection_SP2.png</em>: the patch that manages the feedback generation (top left), the recording of execution time (bottom left), and the recording of position and force (center).</li> <li><em>record.wav</em>: a recording of the signal used in the experiment for both audio and vibrotactile feedback.</li> </ul>
Dataset Comparison of MRI-based automated segmentation methods and functional neurosurgery targeting with direct visualization of the Ventro-intermediate thalamic nucleus at 7T
<p>Scientific Reports - Nature - DOI : 10.1038/s41598-018-37825-8</p> <p>##################################<br> "Comparison of MRI-based automated segmentation methods and functional neurosurgery targeting with direct visualization of the Ventro-intermediate thalamic nucleus at 7T"<br> ##################################</p> <p>E. Najdenovska*, C. Tuleasca*, J. Jorge, P. Maeder, J.P. Marques, T. Roine, D. Gallichan, J.-P. Thiran, M. Levivier, and M. Bach Cuadra</p> <p>*Equally contributed authors</p> <p><br> Copyright (c) - All rights reserved. University of Lausanne. 2018.</p> <p><br> To reproduce the analyses presented in the referred study, in this repository you could find the MR images acquired from nine young healthy subjects (YS1-YS5), four elderly healthy subject (ES1-ES4) and two drug-resistant tremor patients treated treated with Vim radiosurgery by Gamma Knife (P1 and P2).</p> <p>The provided dataset includes the following NifTI files:</p> <p>- MPRRAGE @3T<br> - DWI @3T (together with the corresponding bvals and bvecs)<br> - MP2RAGE @7T<br> - SWI @7T<br> - binary masks of the manual delineation of both left and right Vim respectively that were done on the SWI (as NifTI files as well).</p> <p>Additionally, for the young cohort (YS1-YS5) we include as well the images used for building the quadrilateral of Guiot:<br> - T2-w @3T<br> - T2 CISS @3T</p> <p>For the patients (P1 and P2), a follow-up MPRAGE (acquired at 3T) with Gadolinium enhancement is also provided.</p> <p>——————————————<br> Notes:<br> 1. For YS3 MP2RAGE at 7T is missing, instead MPRAGE at 3T was used</p> <p>2. The code performing the thalamic nuclei clustering could be found in Zenodo (DOI: 10.5281/zenodo.123768)</p>
MA14KD [AGGREGATED] Dataset: Visual Attraction of Movie Trailers
<p><strong>MA14KD</strong> (Movie Attract 14K Dataset) provides a set of <strong>181 aggregated VISUAL features </strong>extracted from <strong>14074 movie</strong> <strong>and tv series trailers</strong>. The movie IDs are in agreement with the movie IDs provided by another rating dataset that also contains movie genres and tags (see the description within the file). More details can be found in the following publication:</p> <p><em>Farshad B. Moghaddam, Mehdi Elahi, Reza Hosseini, Christoph Trattner, Marko Tkalcic, <strong>Predicting Movie Popularity and Ratings with Visual Features</strong>, IEEE SMAP’19, 9-10 June 2019, Larnaca, Cyprus</em></p>
MA14KD [ORIGINAL] Dataset: Visual Attraction of Movie Trailers
<p><strong>MA14KD</strong> (Movie Attract 14K Dataset) provides a set of <strong>10 VISUAL features </strong>extracted from <strong>14074 movie</strong> <strong>and tv series trailers</strong>. The movie IDs are in agreement with the movie IDs provided by another rating dataset that also contains movie genres and tags (see the description within the file). More details can be found in the following publication:</p> <p><em>Farshad B. Moghaddam, Mehdi Elahi, Reza Hosseini, Christoph Trattner, Marko Tkalcic, <strong>Predicting Movie Popularity and Ratings with Visual Features</strong>, IEEE SMAP’19, 9-10 June 2019, Larnaca, Cyprus</em></p>
Metagenome quality metrics and taxonomical annotation visualization through the integration of MAGFlow and BIgMAG (Sup. Material)
<p>Dataset encompassing:</p> <ul> <li>The recovered MAGs by 6 different metagenomics pipelines (ATLAS, DATMA, MetaWRAP, MUFFIN, nf-core/mag and SnakeMAGs) using a mock community as input (SRR8359173 and SRR9328980), complemented with the output from MAGFlow (v1.0.0) using these MAGs as input for their quality assessment and taxonomical annotation. </li> <li>The MAGs produced by nf-core/mag using rice/rhizosphere sequenced libraries (PRJNA663614, PRJNA448773 and PRJNA645385) in either single assembly/single binning or co-assembly/co-binning mode, complemented with the output from MAGFlow (v1.0.0) using these MAGs as input for their quality assessment and taxonomical annotation.</li> <li>Scripts, commands and configuration files to run the different pipelines (ATLAS, DATMA, MetaWRAP, MUFFIN, nf-core/mag and SnakeMAGs) and reproduce the experimental conditions.</li> <li>Outputs, commands and scripts to run Metabinner and Semibin in their default configuration using the rice soil samples co-assembly, along with the MAGFlow (v1.1.0) output to compare these binners against MetaBAT2.</li> </ul>
Graphic Illustration of Litsa Wooten's Talk: Visualizing Mongolian Mammal Specimens and their Parasites Through Time
<p><a href="https://lib.ku.edu/people/courtney-foat" target="_blank" rel="noopener">Courtney Foat</a>, Advisor for Strategic Initiatives & Organizational Engagement at the University of Kansas, graphically recorded this talk by Litsa Wooten at the Digital Data 2024 Conference in Lawrence, Kansas in May of 2024. We include this resource, with permission, because of its relevance to our NSF-supported Workshop: Digital Collections Data and Tracking Disease.</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>
Dataset: Rainbow color map distorts and misleads research in hydrology – guidance for better visualizations and science communication
<p>The rainbow color map is scientifically incorrect and hinders people with color vision deficiency to view visualizations in a correct way. Due to perceptual non-uniform color gradients within the rainbow color map the data representation is distorted what can lead to misinterpretation of results and flaws in science communication. Here we present the data of a paper survey of 797 scientific publication in the journal Hydrology and Earth System Sciences. With in the survey all papers were classified according to color issues. Find details about the data below.</p> <ul> <li><code>year</code> = year of publication (YYYY)</li> <li><code>date</code> = date (YYYY-MM-DD) of publication</li> <li><code>title</code> = full paper title from journal website</li> <li><code>authors</code> = list of authors comma-separated</li> <li><code>n_authors</code> = number of authors (integer between 1 and 27)</li> <li><code>col_code</code> = color-issue classification (see below)</li> <li><code>volume</code> = Journal volume</li> <li><code>start_page</code> = first page of paper (consecutive)</li> <li><code>end_page</code> = last page of paper (consecutive)</li> <li><code>base_url</code> = base url to access the PDF of the paper with <code>/volume/start_page/year/</code></li> <li><code>filename</code> = specific file name of the paper PDF (e.g. <code>hess-9-111-2005.pdf</code>)</li> </ul> <p>Color classification is stored in the <code>col_code</code> variable with:</p> <ul> <li><code>0</code> = chromatic and issue-free,</li> <li><code>1</code> = red-green issues,</li> <li><code>2</code>= rainbow issues and</li> <li><code>bw</code>= black and white paper.</li> </ul> <p> </p> <p>See more details (e.g., sample code to analyse the survey data) on https://github.com/modche/rainbow_hydrology</p> <p>Paper: Stoelzle, M. and Stein, L.: Rainbow color map distorts and misleads research in hydrology – guidance for better visualizations and science communication, Hydrol. Earth Syst. Sci., 25, 4549–4565, https://doi.org/10.5194/hess-25-4549-2021, 2021.</p> <p> </p> <p> </p>
Animation to visualize the electron beam damage induced in calcium silicate hydrate phases
<p>This dataset visualizes the electron beam damage induced by a scanning electron microscope (SEM) in calcium silicate hydrates (C-S-H). The specimen used is 28 days hydrated alite (water/solid = 0.5). It was scanned using a thermofischer scientific Helios G4 UX microscope at 350 V/25 pA with a stage bias of 200 V.</p> <p>This animation was an afterthought. Therefore, the dataset provides multiple magnifications and resolutions and some of the images are not in focus. Nevertheless, It can be seen, that the C-S-H needle in the right half of the image significantly deformes within a timespan of 124 seconds of constant scanning of that region.</p> <p><strong>File content:</strong></p> <ul> <li>All images ending with "raw" are the raw images provided by the SEM software including all metadata.</li> <li>The file "C3S_CSH_e-beam-damage_aligned stack.tif" contains the aligned image set using the SIFT algorithm. It contains the correct scaling if opened with ImageJ.</li> <li>The file "C3S_CSH_e-beam-damage_animation.gif" provides the final animation including a overlayed scalebar.</li> </ul>
Visual Dictionary of Tibetan Verb Valency: Data
<p>This repository contains a JSON version of the data powering the <a href="https://bit.ly/VisualDictionary-TibetanValency">Visual Dictionary of Tibetan Verb Valency</a> together with its documentation. The structure of the data is explained in the documentation section on 'Dictionary data'.</p> <p>The Visual Dictionary of Tibetan Verb Valency was produced as part of the UKRI-funded project <a href="https://gtr.ukri.org/projects?ref=AH%2FP004644%2F1">Lexicography in Motion a History of the Tibetan Verb</a> (LIM) at SOAS.</p>
TBPos: Dataset for Large-Scale Precision Visual Localization (database files)
<p>Large-scale dataset for visual localization, provided in the format of the well-known InLoc dataset (Taira et al, 2018). Contains co-registered RGB point clouds and a script for generating the rest of the 'database' files for visual localization by the InLoc algorithm. Note: query images are provided in a separate repository.</p>
Toloka Visual Question Answering Dataset
<p>Our dataset consists of the images associated with textual questions. One entry (instance) in our dataset is a question-image pair labeled with the ground truth coordinates of a bounding box containing the visual answer to the given question. The images were obtained from a CC BY-licensed subset of the Microsoft Common Objects in Context dataset, <a href="https://cocodataset.org/">MS COCO</a>. All data labeling was performed on the Toloka crowdsourcing platform, <a href="https://toloka.ai/">https://toloka.ai/</a>.</p> <p>Our dataset has 45,199 instances split among three subsets: <strong>train</strong> (38,990 instances), <strong>public test</strong> (1,705 instances), and <strong>private test</strong> (4,504 instances). The entire train dataset was available for everyone since the start of the challenge. The public test dataset was available since the evaluation phase of the competition, but without any ground truth labels. After the end of the competition, public and private sets were released.</p> <p>The datasets will be provided as files in the comma-separated values (CSV) format containing the following columns.</p> <table> <tbody> </tbody> </table> <table> <tbody> <tr> <td><strong>Column</strong></td> <td><strong>Type</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>image</td> <td>string</td> <td>URL of an image on a public content delivery network</td> </tr> <tr> <td>width</td> <td>integer</td> <td>image width</td> </tr> <tr> <td>height</td> <td>integer</td> <td>image height</td> </tr> <tr> <td>left</td> <td>integer</td> <td>bounding box coordinate: left</td> </tr> <tr> <td>top</td> <td>integer</td> <td>bounding box coordinate: top</td> </tr> <tr> <td>right</td> <td>integer</td> <td>bounding box coordinate: right</td> </tr> <tr> <td>bottom</td> <td>integer</td> <td>bounding box coordinate: bottom</td> </tr> <tr> <td>question</td> <td>string</td> <td>question in English</td> </tr> </tbody> </table> <p>This upload also contains a ZIP file with the images from MS COCO.</p>
Visualizing the Impact of COVID-19 and the Vaccination Data in 2021
<p>COVID-19 has been a hot topic in recent years. While numerous visualizations have showcased the distribution of COVID-19 cases and deaths, few demonstrate the temporal relationships between cases, deaths, and COVID-19 vaccinations. Our visualization aims to fill this gap by showcasing the temporal evolution of COVID-19 cases, deaths, and vaccinations in the U.S. while also comparing them geographically by U.S. states.</p> <p>Our dataset was obtained from two different organizations: the New York Times and Our World in Data. The New York Times dataset focuses on COVID-19 cases and deaths within each county/state of the US in 2021, while the dataset from Our World in Data contains information on the various vaccination data of each state throughout the year. We chose to focus on 2021 since that is when the first data was collected for the us_state_vaccinations.csv, in addition to the reason that the majority of vaccination data from 2022 are not as consistent and missing a lot.</p> <p>Our visualizations are targeted towards individuals who want to learn more about the timeline of COVID-19 cases, deaths, and vaccinations data and the complex relationships among them. This includes public health officials who need to make informed decisions regarding interventions to mitigate the spread of COVID-19, journalists and media organizations who want to report accurate information about the pandemic to the public, and the general public who are interested in understanding the impact of COVID-19 on their local communities.</p> <p>We implemented our visualizations using Python's Altair and Streamlit libraries, using drop-down selection bars, time/date sliders, multi-select widgets, and various types of linked views. These interactive features are implemented using built-in functions from the Streamlit and Altair libraries, including st.selectbox, st.multiselect, st.slider, alt.selection_interval, and alt.selection_single. The details of the code that we wrote to implement these visualizations can be found on our project's GitHub page (https://github.com/Tony-Xiayi-Ding/COVID-19-Visualizations).</p> <p>Our visualizations showed that the temporal evolution of COVID-19 cases and deaths exhibited a striking similarity, with a rather consistent trend over time. Additionally, the cases and deaths count generally remained at much slower increasing rates during seasons with higher temperatures and at much higher increasing rates during colder months, highlighting the complex interplay of demographic and seasonal factors in shaping the pandemic in the U.S. Moreover, the overall trend for case fatality rate was decreasing for most states, and states that were close to each other shared similar trends of case fatality rate. Furthermore, states with higher average temperatures shared similar trends in case fatality rates that were quite different from those states that were relatively colder. Lastly, as total vaccinations per hundred increased over time, the relative case fatality rate dropped, and coastal states were found to have slightly higher total vaccinations per hundred values, potentially due to their higher population densities and that the residents in those states are more aware of the importance of getting vaccinated due to their elevated chances of contracting COVID-19.</p> <p>The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.</p> <p>References:</p> <p>1. New York Times. (2022). Covid-19-data/US-counties-2021.csv. GitHub. Retrieved February 12, 2023, from https://github.com/nytimes/covid-19-data/blob/master/us-counties-2021.csv</p> <p>2. Our World in Data. (2023). Covid-19-data/US_state_vaccinations.CSV. GitHub. Retrieved February 12, 2023, from https://github.com/owid/covid-19-data/blob/master/public/data/vaccinations/us_state_vaccinations.csv</p> <p>3. U.S. Department of Health and Human Services. (2023). What is a FIPS code and why do I need one? National Institutes of Health. Retrieved February 12, 2023, from https://nitaac.nih.gov/resources/frequently-asked-questions/what-fips-code-and-why-do-i-need-one</p>
3D-rhi-synth-2000- Synthetic Rhinophyma Visual Dataset
<p>In the real world, only a handful of data is available for the Rhinophyma skin condition, typically numbering in the hundreds. This repository contains a Synthetic Dataset of Rhinophyma, generated through 3D head models of one male and one female. The purpose of this data generation is to address the data scarcity of the Rhinophyma skin condition within the medical visual data and computer vision community. By generating such data, we aim to bridge the gap in data scarcity for this disease condition, as well as introduce a proof-of-concept methodology for generating synthetic data for specialized disease conditions.</p> <p>The <code>highlight</code> folder 'highlight_female_male_rendered' provides a glimpse of the entire dataset. The file <code>'2000_deformations.npy</code>' contains the 2000 values of deformations applied during rendering.</p> <p>The dataset is divided into two main folders: 'female_rendered' and 'male_rendered'. Within each of these folders, there are three subfolders: 'configu', 'images', and 'points.</p> <p>1. 'configu': This subfolder contains `.json` files with configuration details for each model. The files include various parameters, such as:<br> - "total_num_cameras": the total number of cameras.<br> - "active_camera_name": the name of the active camera.<br> - "camera_focal_len": the camera's focal length.<br> - "camera_loc": the camera's location.<br> - "camera_rot": the camera's rotation.<br> - "nose_deformation_severity": a measure of the severity of nose deformation.<br> - "label": the label for the model (e.g., "Severe").<br> - "nose_variants": additional details about nose variants.</p> <p>2. 'images': This subfolder contains the rendered images in resolution 960x540. They are named according to the following convention e.g.'Nose_Deformation_Severity_0_2.716669764843742_Camera_00001', with specific details related to the deformation severity and camera number. There are images for 10 different cameras.</p> <p>3. 'points': This subfolder contains polygon files corresponding to each model. These files represent the deformations applied to the models during rendering.</p> <p>|-- Dataset Root<br> |-- female_rendered<br> | |-- configu<br> | | |-- Nose_Deformation_Severity_0_2.212930927821943_Camera_00001.json<br> | | |-- Nose_Deformation_Severity_0_2.212930927821943_Camera_00002.json<br> | | |-- ...<br> | |<br> | |-- images<br> | | |-- Nose_Deformation_Severity_0_2.212930927821943_Camera_00001.png<br> | | |-- Nose_Deformation_Severity_0_2.212930927821943_Camera_00002.png<br> | | |-- ...<br> | |<br> | |-- points<br> | | |-- Nose_Deformation_Severity_0_2.212930927821943.ply<br> | | |-- ...<br> | <br> |-- male_rendered<br> |-- configu<br> | |-- Nose_Deformation_Severity_0_2.716669764843742_Camera_00001.json<br> | |-- Nose_Deformation_Severity_0_2.716669764843742_Camera_00002.json<br> | |-- ...<br> |<br> |-- images<br> | |-- Nose_Deformation_Severity_0_2.716669764843742_Camera_00001.jpg<br> | |-- Nose_Deformation_Severity_0_2.716669764843742_Camera_00002.jpg<br> | |-- ...<br> |<br> |-- points<br> |-- Nose_Deformation_Severity_0_2.716669764843742.ply<br> |-- ...</p> <p>Together, these folders and files comprise a dataset designed to represent and analyze the Rhinophyma condition in both male and female 3D head models that we have created. These 3D models will be made available upon a genuine request to the authors of this dataset.</p>
Comparing effects of auditory and visual disturbances on smallmouth bass parental care behaviors during the summer of 2025 at Douglas Lake, Michigan, USA
A prevalent source of sensory pollution within aquatic systems is recreational motorboats that can impact aquatic organisms through several exposure mechanisms. Auditory and visual sensory disturbances are particularly important as fish may utilize these cues during critical reproductive behaviors such as parental care. Here, we conducted a field study in Douglas Lake, Michigan, and located wild smallouth bass nests actively guarded by males. We exposed smallmouth bass to two sequential treatments of playback auditory noise and visual disturbances. Using an underwater drone, parental care behaviors of smallmouth bass were monitored before, during, and after both auditory and visual disturbances. The results show that auditory and visual disturbances may alter smallmouth bass parental care behaviors differently.
Count data of air-breathing fauna from visual transect surveys including water temperature, time, sea and weather conditions in Shark Bay Marine Park, Western Australia from February 2008 to July 2014
This dataset provides information on the relative abundances of air breathing fauna (dugongs, dolphins, sea snakes, marine birds, and sea turtles) in the study area of the Eastern Gulf of Shark Bay, Western Australia. The dataset comprises transects that quantify animal abundances in three microhabitats (shallow seagrass banks, seagrass bank edges, and deep sandy channels). These microhabitats vary in their food supply as well as their potential to facilitate or inhibit detection and escape from predators, mainly the tiger shark (Galeocerdo cuvier). As a result these data have been used to examine risk-specific habitat use behaviors of these fauna, in addition to general abundance estimates.
leaf litter decomposition experiment In QPA and QPB - visual shrimp observations 2019
Visual shrimp observations from pools associated with 2019 in-situ leaf litter decomposition experiment. Shrimp abundance was recorded over two-minute intervals within pools in Quebrada Prieta A and Quebrada Prieta B. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
Associative Prediction of Visual Shape in the Hippocampus
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Layer VASO in visual system
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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.