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1,389 results for “multimodality”
scGeneAI pbmc multimodal dataset
<p>The input pbmc_multimodal_2023 dataset used in the full-size examples in scGenAI is uploaded here</p>
CREATTIVE3D multimodal dataset of user behavior in virtual reality
<p>In the context of the <a href="https://project.inria.fr/creattive3d/">ANR CREATTIVE3D</a> project, we join the expertise of computer science, neuroscience, and clinical practitioners, with the aim to analyze the impact that a simulated low-vision condition has on user navigation behavior in complex road crossing scenes: a common daily situation where the difficulty to access and process visual information (e.g., traffic lights, approaching cars) in a timely fashion can lead to serious consequences on a person's safety and well-being. As a secondary objective, we also aim to investigate the potential role virtual reality could play in rehabilitation and training protocols for low-vision patients.</p> <p>This dataset contains the data as part of the study described in <a href="https://hal.science/hal-04102737">An Integrated Framework for Understanding Multimodal Embodied Experiences in Interactive Virtual Reality</a>.</p> <p>The dataset is metadata for the pre-print <a href="https://inria.hal.science/hal-04429351">Exploring, walking, and interacting in virtual reality with simulated low vision: a living contextual dataset</a></p> <p>To use this dataset, please cite:</p> <blockquote> <pre>@unpublished{wu:hal-04429351, TITLE = {{Exploring, walking, and interacting in virtual reality with simulated low vision: a living contextual dataset}}, AUTHOR = {Wu, Hui-Yin and Robert, Florent Alain Sauveur and Gallo, Franz Franco and <br> Pirkovets, Kateryna and Quere, Cl{\'e}ment and Delachambre, Johanna and <br> Ramano{\"e}l, Stephen and Gros, Auriane and Winckler, Marco and Sassatelli, Lucile and <br> Hayotte, Meggy and Menin, Aline and Kornprobst, Pierre}, URL = {https://inria.hal.science/hal-04429351}, NOTE = {working paper or preprint}, YEAR = {2023}, MONTH = Dec, KEYWORDS = {Virtual reality ; Dataset ; Context ; Low vision ; 3D environments ; User study}, PDF = {https://inria.hal.science/hal-04429351/file/2023_CREATTIVE3D_dataset_arxiv_.pdf}, HAL_ID = {hal-04429351}, HAL_VERSION = {v1}, }<br><br>@inproceedings{robert2023integrated, title={An integrated framework for understanding multimodal embodied experiences in interactive virtual reality}, author={Robert, Florent and Wu, Hui-Yin and Sassatelli, Lucile and Ramanoel, Stephen and <br> Gros, Auriane and Winckler, Marco}, booktitle={Proceedings of the 2023 ACM International Conference on Interactive Media Experiences}, pages={14--26}, year={2023} }</pre> </blockquote> <h3> </h3> <h3>Versions</h3> <p>2024-12-18: Updated readme with description of labels, columns, and suggestions on how to start exploring the dataset. We also provide the questionnaire responses and observation notes in English (questionnaire_translation_EN.csv).</p>
Synthetic Multimodal Drone Delivery Dataset
<h1><strong>README: Synthetic Logistics Dataset Structure and Components</strong></h1> <p>This dataset provides a structured representation of logistics data designed to evaluate and optimize hybrid truck-and-drone delivery networks. It captures a comprehensive set of parameters essential for modeling real-world logistics scenarios, including spatial coordinates, environmental conditions, and operational constraints. The data is meticulously organized into distinct keys, each representing a critical aspect of the delivery network, enabling researchers and practitioners to conduct flexible and in-depth analyses. </p> <p>The dataset is a curated subset derived from the research presented in the paper "Synthetic Dataset Generation for Optimizing Multimodal Drone Delivery Systems" by Altinsel et al. (2024), published in Drones. It serves as a practical resource for studying the interplay between ground-based and aerial delivery systems, with a focus on efficiency, environmental impact, and operational feasibility. </p> <p><strong>Altinses, D., Torres, D. O. S., Gobachew, A. M., Lier, S., & Schwung, A. (2024). Synthetic Dataset Generation for Optimizing Multimodal Drone Delivery Systems. <em>Drones (2504-446X)</em>, <em>8</em>(12).</strong></p> <p>Each data file contains information on ten customer locations, specified by their x and y coordinates, which facilitate the modeling of delivery routes and service areas. Additionally, the dataset includes communication data represented as a two-dimensional grid, which can be used to assess signal strength, connectivity, or other network-related factors that influence drone operations. </p> <p>A key feature of this dataset is the inclusion of wind data, structured as a two-dimensional grid with four distinct features per grid point. These features likely represent wind velocity components (such as horizontal and vertical directions) along with auxiliary parameters like turbulence intensity or wind shear, which are crucial for drone path planning and energy consumption estimation. The wind data enables researchers to simulate realistic environmental conditions and evaluate their impact on drone performance, stability, and battery life. </p> <p>By integrating geospatial, environmental, and operational data, this dataset supports a wide range of applications, from route optimization and energy efficiency studies to risk assessment and resilience planning in multimodal delivery systems. Its synthetic nature ensures reproducibility while maintaining relevance to real-world logistics challenges, making it a valuable tool for advancing research in drone-assisted delivery networks. </p> <p> </p> <h3><strong>The 4 wind channels represent:</strong></h3> <ol> <li> <p><strong><code>X</code> and <code>Y</code> (Grid Positions)</strong></p> <ul> <li> <p>These define <strong>where the arrows start</strong> (usually a meshgrid).</p> </li> </ul> </li> <li> <p><strong><code>U</code> and <code>V</code> (Arrow Directions)</strong></p> <ul> <li> <p><code>U</code> = Horizontal component (e.g., gradient in <code>x</code>).</p> </li> <li> <p><code>V</code> = Vertical component (e.g., gradient in <code>y</code>).</p> </li> </ul> </li> </ol> <p> </p> <h3><strong>How to load the files using Python:</strong></h3> <p>data = np.loadtxt('data.txt')</p> <p>#### Just for Wind data:</p> <p>data = data.reshape((4,16,16)) </p>
SILKNOW Multimodal Cultural Heritage Dataset
<p>SILKNOW Multimodal Cultural Heritage Dataset. Includes text descriptions, images, labels, and predictions made by individual modality classifiers.</p> <p>The data resulted from an export of the SILKNOW Knowledge Graph. See: <a href="https://zenodo.org/record/5743090">https://zenodo.org/record/5743090</a></p> <p>Repository with code using this dataset available at: <a href="https://github.com/silknow/multimodal_cultural_heritage">https://github.com/silknow/multimodal_cultural_heritage</a></p>
Multimodal Dataset of 3D point clouds and CT-volumes
<p>The multimodal dataset for evaluating algorithms for aligning CT volumes and point clouds which is presented in 'Multimodal registration across 3D point clouds and CT-volumes'. (Saiti, E., and T. Theoharis. "Multimodal registration across 3D point clouds and CT-volumes." <em>Computers & Graphics</em> 106 (2022): 259-266.) The multimodal dataset consistsof real micro-CT scans and their synthetically generated 3D models (point clouds) .</p>
Data: multimodal cell tracking from systemic administration to tumour growth by combining gold nanorods and reporter genes
<p>This data set includes multispectral optoacoustic tomography images supporting an article on cell tracking (preprint: bioRxiv 199836; https://doi.org/10.1101/199836). The corresponding bioluminescence results are included too, as well as the spectra used for the multispectral processing. </p>
M3-OCTA:Leveraging Multimodal Fusion for Enhanced Diagnosis of Multiple Retinal Diseases in Ultra-wide OCTA
<p>Ultra-wide optical coherence tomography angiography (UW-OCTA) is an emerging imaging technique that offers significant advantages over traditional OCTA by providing an exceptionally wide scanning range of up to 24 x 20 mm^{2}, covering both the anterior and posterior regions of the retina. However, the currently accessible UW-OCTA datasets suffer from limited comprehensive hierarchical information and corresponding disease annotations. To address this limitation, we have curated the pioneering M3OCTA dataset, which is the first multimodal (i.e., multilayer), multi-disease, and widest field-of-view UW-OCTA dataset. Furthermore, the effective utilization of multi-layer ultra-wide ocular vasculature information from UW-OCTA remains underdeveloped. To tackle this challenge, we propose the first cross-modal fusion framework that leverages multi-modal information for diagnosing multiple diseases. Through extensive experiments conducted on our openly available M3OCTA dataset, we demonstrate the effectiveness and superior performance of our method, both in fixed and varying modalities settings. The construction of the M3OCTA dataset, the first multimodal OCTA dataset encompassing multiple diseases, aims to advance research in the ophthalmic image analysis community.</p> <p>Our proposed M3OCTA is the first multi-modal based ultra-wide retinal OCTA dataset, involving 1637 scans from 1046 eyes of 620 individuals imaged in Zigong First People’s Hospital through 24×20 scan mode. Specifically, 1067 scans contains choroid large vessel image; images of 1310 scans from 496 people are labeled as six classes in multi-label setting, including healthy, diabetic retinopathy (DR), diabetic macular edema (DME), Retinal Vein Occlusion (RVO), Hypertension (HBP) and Vitreous Hemorrhage (VH), and then split into train, validation and test set as 6:2:2. The remaining unlabeled data are only used in the pretraining step. Details of our M3OCTA and other public ones are listed in Table.1. Compared with others, M3OCTA dataset demonstrates superiorities in several aspects including the number of modalities, number of patients, image resolution, and FOV.</p> <p> </p> <p><strong>You can request this dataset through signing the attached agreement. The download link will send to you. </strong></p>
Data for "Multimodal Soft Valve Enables Physical Responsiveness for Pre-emptive Resilience of Soft Robots"
<p>This dataset contains all the data and CAD models needed to replicate the study presented in "Multimodal Soft Valve Enables Physical Responsiveness for Pre-emptive Resilience of Soft Robots".</p>
Computational Design of Multimodal Combinatorial Mechanical Metamaterials
<p>This dataset contains the data used to design multimodal mechanical metamaterials as described in the paper 'Prospecting for Pluripotency in Metamaterial Design', as published in Phys. Rev. Research 7(2), 023299.</p> <p>In this paper, the data is used to design 5×5 unit cells with desired deformation (zero) modes. The dataset contains the data used to train neural networks (CNN_data.zip), the designs generated by genetic algorithm (step_i.zip) and their mode structures (step_ii.zip), and the designs obtained through our design approach as described in the paper (step_ii.zip). Additionally, there is data comparing the efficiency of using a genetic algorithm or a hill climbing method to generate designs with a large number of intensive modes (step_i.zip).</p>
MSMD - Multimodal Sheet Music Dataset
<p>MSMD is a synthetic dataset of 497 pieces of (classical) music that contains both audio and score representations of the pieces aligned at a fine-grained level (344,742 pairs of noteheads aligned to their audio/MIDI counterpart). It can be used for training and evaluating multimodal models that enable crossing from one modality to the other, such as retrieving sheet music using recordings or following a performance in the score image.</p> <p>Please find further information and a corresponding Python package on this Github page: <a href="https://github.com/CPJKU/msmd">https://github.com/CPJKU/msmd</a></p> <p>If you use this dataset, please cite:<br> [1] Matthias Dorfer, Jan Hajič jr., Andreas Arzt, Harald Frostel, Gerhard Widmer.<br> <a href="https://transactions.ismir.net/articles/10.5334/tismir.12/">Learning Audio-Sheet Music Correspondences for Cross-Modal Retrieval and Piece Identification</a> (<a href="https://transactions.ismir.net/articles/10.5334/tismir.12/galley/8/download/">PDF</a>).<br> Transactions of the International Society for Music Information Retrieval, issue 1, 2018.</p>
Dataset of the study: Explaining recovery from coma with multimodal neuroimaging
Open the record for dataset details and reuse information.
Kuangetal-PPIG24-Designing-a-multimodal-IDE-with-developers
<p>This replication package includes:</p> <ol> <li>The conceptual design and personas.</li> <li>The co-design workshop output and the slides used in the workshop.</li> <li>The IDE prototype exported in PDF for a quick overview and in PNG for reuse on Figma.</li> <li>A demo of the IDE prototype on Figma, which is used for the user test.</li> </ol>
OpenMapCD: A Multimodal Benchmark Dataset for Change Detection Between Optical Remote Sensing and Map Data
<p><strong>Overview: </strong></p> <ol> <li>OpenMapCD, the <strong>first large-scale multimodal dataset</strong> for change detection on optical remote sensing imagery and map (OpenStreetMap) data, <strong>supporing basic binary change detection and further semantic change detection</strong></li> <li>OpenMapCD is highly geographically diverse, with <strong>1288</strong> benchmark samples with 1024x1024 pixels from <strong>40 </strong>regions across six continents and out-of-distribution data in two areas in Japan</li> <li>Advancing land-cover mapping, binary change detection and semantic change detection tasks, and GIS system updating<br><br></li> </ol> <p><strong>Research Paper: <br></strong></p> <ul> <li>Arxiv paper: <a href="https://arxiv.org/abs/2310.02674v3">https://arxiv.org/html/2310.02674v3</a></li> <li>TGRS paper: <a href="https://ieeexplore.ieee.org/document/10551264">https://ieeexplore.ieee.org/document/10551264</a></li> </ul> <p><strong><br>Project Page:</strong><br>The benchmark code is available at: <a href="https://github.com/ChenHongruixuan/ObjFormer">https://github.com/ChenHongruixuan/ObjFormer</a><br><br><strong>Reference:</strong></p> <pre><code>@ARTICLE{Chen2024ObjFormer, author={Chen, Hongruixuan and Lan, Cuiling and Song, Jian and Broni-Bediako, Clifford and Xia, Junshi and Yokoya, Naoto}, journal={IEEE Transactions on Geoscience and Remote Sensing}, title={ObjFormer: Learning Land-Cover Changes From Paired OSM Data and Optical High-Resolution Imagery via Object-Guided Transformer}, year={2024}, volume={62}, number={}, pages={1-22}, doi={10.1109/TGRS.2024.3410389} }</code></pre>
AVbook, a high-frame-rate corpus of narrative audiovisual speech for investigating multimodal speech perception
<p><strong>Please cite</strong><br> Varano E, Guilleminot P, Reichenbach T. <em>AVbook, a high-frame-rate corpus of narrative audiovisual speech for investigating multimodal speech perception</em>. J Acoust Soc Am. 2023 May 1;153(5):3130. doi: 10.1121/10.0019460. PMID: 37249407.<br> <br> Seeing a speaker's face can help substantially in understanding them, in particular in challenging listening conditions. Research into the neurobiological mechanisms behind the audiovisual integration has recently begun to employ continuous natural speech. However, these efforts are impeded by a lack of high-quality audiovisual recordings of a speaker narrating a longer text. Here we seek to close this gap by developing AVbook, an audiovisual speech corpus designed for cognitive neuroscience studies and audiovisual speech recognition. The corpus consists of 3.6 hours of audiovisual recordings of two speakers, one male and one female, reading 59 passages from a narrative English text. The recordings were acquired at a high frame rate of 119.88 frames per second. The corpus includes a sets of multiple-choice questions to test attention to the different passages. We verified the efficacy of these questions in a pilot study. A short written summary is also provided for each recording. To enable audiovisual synchronization when presenting the stimuli, four videos of an electronic clapperboard were recorded with the corpus. The corpus is available for download to support research into the neurobiology of audiovisual speech processing as well as the development of computer algorithms for audiovisual speech recognition.</p>
MultiSubs: A Large-scale Multimodal and Multilingual Dataset
<p>MultiSubs is a dataset of multilingual subtitles gathered from <a href="https://opus.nlpl.eu/OpenSubtitles.php">the OPUS OpenSubtitles dataset</a>, which in turn was sourced from <a href="http://www.opensubtitles.org/">opensubtitles.org</a>. We have supplemented some text fragments (visually salient nouns in this release) within the subtitles with web images, where the word sense of the fragment has been disambiguated using a cross-lingual approach. </p> <p>Please refer to our paper for a more detailed description of the dataset:</p> <p>Josiah Wang, Pranava Madhyastha, Josiel Figueiredo, Chiraag Lala, Lucia Specia (2021). <a href="https://arxiv.org/abs/2103.01910">MultiSubs: A Large-scale Multimodal and Multilingual Dataset</a>. CoRR, abs/2103.01910. Available at: <a href="https://arxiv.org/abs/2103.01910">https://arxiv.org/abs/2103.01910</a></p>
Multimodal poster presentation feedback dataset
<p>This is a dataset used for a multimodal poster presentation experiment and data analysis performed at the Division of Speech, Music and Hearing (TMH) at KTH Royal Institute of Technology in Stockholm Sweden in 2019 and 2020. The compressed elan_files.zip file contains raw ELAN annotated data, and the various .csv files contain formatted data for training for statistical learning models. Used in an upcoming publication.</p>
Multimodal 3D Digitisation of a Simulated Crime Scene
<p><em>The peer-reviewed publication affiliated to this dataset has been published in Forensic Sci.</em> <strong>2021</strong><em> an MDPI journal, and can be accessed here: </em><a href="https://doi.org/10.3390/forensicsci1020008">https://doi.org/10.3390/forensicsci1020008</a><em>. Please cite this when using the dataset.</em><br> <br> Usually, crimes and more specifically assassinations take part indoors and at particular locations. Under this scope, we demonstrate a range of 3D scanning modalities in a simulated indoor crime scene. We propose 3D scanning as a complement to traditional photographic documentation of the scene. For better comprehension of the reconstruction quality obtained from each modality and/or further research on the topic, we release the 3D files of the scene to the public. We also provide ground-truth measurements as a comparison to those that could be obtained using the 3D reconstructions.</p> <p>The modalities that were utilized are:</p> <ul> <li> <p>FARO Focus M70</p> </li> <li> <p>FARO Freestyle 3DX</p> </li> <li> <p>DSLR and Pix4D photogrammetry software</p> </li> <li> <p>RGB-D scanner based on Asus XTION</p> </li> <li> <p>iPhone 12 Pro Max and Trnio 3D Scanner application</p> </li> </ul>
Multimodal metaphor corpus
<p>This corpus consists of videos with subtitles that have been annotated for metaphors. All videos are CC BY.</p> <p>Citation: <strong>Khalid Alnajjar, Mika Hämäläinen and Shuo Zhang (2022) Ring That Bell: A Corpus and Method for Multimodal Metaphor Detection in Videos. In <em>the Proceedings of the Third Workshop on Figurative Language Processing</em>.</strong></p> <p> The splits used in the paper are in splits.json and clips.zip contains the clipped data used in the research paper. Full_videos.zip has the full video files and the annotated subtitles. Attributions.txt lists the URLs and CC BY licenses for each file.</p>
MuMu: Multimodal Music Dataset
<p>MuMu is a Multimodal Music dataset with multi-label genre annotations that combines information from the Amazon Reviews dataset and the Million Song Dataset (MSD). The former contains millions of album customer reviews and album metadata gathered from Amazon.com. The latter is a collection of metadata and precomputed audio features for a million songs. </p> <p>To map the information from both datasets we use MusicBrainz. This process yields the final set of 147,295 songs, which belong to 31,471 albums. For the mapped set of albums, there are 447,583 customer reviews from the Amazon Dataset. The dataset have been used for multi-label music genre classification experiments in the related publication. In addition to genre annotations, this dataset provides further information about each album, such as genre annotations, average rating, selling rank, similar products, and cover image url. For every text review it also provides helpfulness score of the reviews, average rating, and summary of the review. </p> <p>The mapping between the three datasets (Amazon, MusicBrainz and MSD), genre annotations, metadata, data splits, text reviews and links to images are available here. Images and audio files can not be released due to copyright issues.</p> <ul> <li>MuMu dataset (mapping, metadata, annotations and text reviews)</li> <li>Data splits and multimodal feature embeddings for ISMIR multi-label classification experiments </li> </ul> <p>These data can be used together with the Tartarus deep learning library https://github.com/sergiooramas/tartarus.</p> <p>NOTE: This version provides simplified files with metadata and splits.</p> <p><strong>Scientific References</strong></p> <p>Please cite the following papers if using MuMu dataset or Tartarus library.</p> <p>Oramas, S., Barbieri, F., Nieto, O., and Serra, X (2018). Multimodal Deep Learning for Music Genre Classification, Transactions of the International Society for Music Information Retrieval, V(1).</p> <p>Oramas S., Nieto O., Barbieri F., & Serra X. (2017). Multi-label Music Genre Classification from audio, text and images using Deep Features. In Proceedings of the 18th International Society for Music Information Retrieval Conference (ISMIR 2017). https://arxiv.org/abs/1707.04916</p> <p> </p>
TRANSIT long-distance multimodal trips model results - a Spanish case study
<p>The files downloaded present the results per scenario considered obtained with the agent-based model developed for the assessment of the Intermodal Timetable Synchronisation solution proposed in the scope of the TRANSIT project (<a href="https://www.transit-h2020.eu/">https://www.transit-h2020.eu/</a>).</p> <p>An agent-based modelling framework called <a href="https://github.com/StefanoPenazzi/jtap/tree/main">J-TAP</a> has been developed and put at work to implement a Spanish long-distance multimodal trips model. The enhanced version of J-TAP used in this work can be found on a <a href="https://github.com/NommonSolutionsAndTechnologies/jtap">github repository</a>.</p> <p>The case study is focused on modelling the long-distance travel patterns of the residents in the Valencia (Spain) area. The destinations considered include the whole of Spain. The period under study is a full year from March 2019 to February 2020 (both inclusive).The files are structured in three different scenarios:</p> <ol> <li><strong>CS01 - Baseline</strong>. The current state of the network is considered and the actual long-distance travel patterns are obtained.</li> <li><strong>CS02 - HSR connection with Madrid-Barajas airport</strong>. The long-distance travel patterns are modelled with hard measures, the high-speed rail is connected to Madrid-Barajas airport.</li> <li><strong>CS03 - HSR connection with Madrid-Barajas airport and timetable synchronisation</strong>. The effects of the timetable synchronisation are modelled.</li> </ol> <p>Each scenario includes the following files:</p> <ul> <li>ctapModelParameters. A folder containing all the information extracted from the <a href="https://neo4j.com/product/graph-data-science/?utm_program=emea-prospecting&utm_source=google&utm_medium=cpc&utm_campaign=emea-search-offers&utm_adgroup=dynamic&utm_content=dynamic&utm_placement=&utm_network=g&gclid=Cj0KCQiAwJWdBhCYARIsAJc4idAo4CEi9lU8TXwmBym8MNHpEIZHPBs3x_4phxbu76y1XKbYlFoZCjIaAiGhEALw_wcB">neo4j</a> graph database created to model the multimodal network and the agents. J-TAP contains packages that simplify network creation in the graph database. This information is stored in .json files (e.g., "Os2DsTravelCostParameter.json" contains the generalised cost for each OD pair and transport mode, "AttractivenessParameter.json" contains the attractiveness by destination, activity, time of the year and agent, etc.). The solver included in the J-TAP framework uses this information to calculate the agents plans.</li> <li>population.json. The result of the J-TAP optimisation. It includes the fitness value for each agent plan evaluated during the J-TAP execution. The best plan for each agent is selected as the plan performed by the agent. An agent plan includes: <ul> <li>activities - Sequence of activities.</li> <li>locations - Sequence of locations</li> <li>ts - Initial time of the activity</li> <li>te - Final time of the activity</li> </ul> </li> <li>LinkTimeFlow.csv. It is obtained after processing the previous file. It contains the number of agents using each link in the network (i.e., road, rail, air and cross links) in each time interval. The first column represents the link id and the rest of columns indicates the number of agents in each interval.</li> </ul> <p>The J-TAP simulation framework is explained in detail in TRANSIT's deliverable <a href="http://www.nommon-files.es/transit/TRANSIT-D5.1_Modelling_Framework_v02.00.00.pdf">D5.1. TRANSIT Modelling and Simulation Framework</a> and the complete description of the case studies and scenarios tested is included in TRANSIT's deliverable <a href="http://www.nommon-files.es/transit/TRANSIT-D6.1_Assessment_of_Intermodal_Concepts_00.02.00.pdf">D6.1. Impact Assessment of New Intermodal Concepts and Passenger Information Services: Conclusions and Recommendations</a>.</p> <p>Thank you for downloading the dataset! It would be very helpful if you share your view on the data show with us. </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.