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1,389 results for “multimodality”
Foraging preferences and interspecific competition generate multimodal complexity-stability relationships in an adaptive food-web framework
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Data from: Multimodal mimicry of hosts in a radiation of parasitic finches
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Data from: Functionally redundant multimodal predator cues elicit changes in prey foraging behavior
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Assessing the severity of laparotomy and partial hepatectomy in male rats – a multimodal approach
<p>A total of 136 adult male WistarHan rats were randomly assigned to three groups: Sham-laparotomy (Sham) group [7 days post-surgical survival time (n = 10)]; 50% partial hepatectomy (PH) group; 70% PH group. Both PH groups had three postoperative survival times of 1, 3, or 7 days (n = 21 for each time-point). Postoperative severity assessment was performed daily using a model-specific score sheet, behavioral testing in the Open Field (OF), telemetric data analysis, and analysis of blood and fecal samples. Parameters of surgical and postoperative distress and severity in the animals were analyzed.</p> <p>Rats were trained to the OF test thrice every alternate training day (D-20, D-18, and D-16). On D-15 (surgery I), a telemetric transmitter (hereafter referred to as transmitter implantation, TI) was surgically implanted subcutaneously in the left flank in 12 animals of each 7-day survival group. All animals in the Sham group were implanted a dummy device. Transmitter implantation was followed by a 12-day recovery phase with no further intervention. To ensure comparability, this test-free recovery period was standardized for all animals, even if they were not telemetrically monitored. Thereafter, retraining was performed on D-2 and -1 in all animals, followed by Sham, 50% PH, or 70% PH (surgery II), on D0. OF tests were performed on POD1, POD3, POD4, and POD7 (depending on survival time). Body weight was measured before individual training, as well as before and after each surgery; in addition, the body weight was measured once daily during postoperative scoring. Postoperative scoring was conducted three times a day from POD1 to POD3 and once a day in the morning from POD4 to POD7. On euthanasia day (POD1, POD3, or POD7) rats were reopened (surgery III) under general anesthesia and analgesia and were euthanized by final blood withdrawal from the inferior vena cava. Organs were removed for subsequent examinations and processed accordingly.</p>
Multimodal Biomedical Dataset for Evaluating Registration Methods (patches from TMA Cores)
<p>The dataset consists of 206 aligned Bright-Field (BF) and Second-Harmonic Generation (SHG) images. All images are of the same size of 834x834 pixels. The training set contains 40 image pairs. A validation set 1 of 25 image pairs for tuning the hyperparameters for the training. A validation set 2 of 7 image pairs for tuning the registration method. The test set for evaluation: 134 image pairs (times two).</p> <p>For each image pair in the test set, a random rotation up to +/-30 degrees and random translations in x and y for up to 100px were applied. For each image pair that was transformed for the test set, we apply the transformation to both modalities and keep their corresponding reference images as well as transformed images, in order to allow for registration using each modality as a reference and the other as a floating image. The metadata.csv file includes the coordinates of the four corners of the reference image as well as the coordinates of the corners of the transformed image after displacement. The origin of this coordinate system is the upper left corner of the reference image, given by (0,0).</p> <p>Example of naming convention in the test set:</p> <ul> <li>R_1B_F8_BF.tif: reference image in Bright-Field</li> <li>R_1B_F8_SHG.tif: reference image in Second-Harmonic Generation</li> <li>T_1B_F8_BF.tif: transformed image in Bright-Field</li> <li>T_1B_F8_SHG.tif: transformed image in Second-Harmonic Generation.</li> </ul> <p>Where the transformations applied to T_1B_F8_BF.tif and T_1B_F8_SHG.tif are identical and hence the coordinates of the corners in metadata.csv are valid for both image pairs.</p> <p>The metadata.csv file contains the following columns:</p> <ul> <li>Filename: identifier for image pair</li> <li>X1_Ref: x-coordinate of upper left corner of reference image</li> <li>Y1_Ref: y-coordinate of upper left corner of reference image</li> <li>X2_Ref: x-coordinate of lower left corner of reference image</li> <li>Y2_Ref: y-coordinate of lower left corner of reference image</li> <li>X3_Ref: x-coordinate of upper right corner of reference image</li> <li>Y3_Ref: y-coordinate of upper right corner of reference image</li> <li>X4_Ref: x-coordinate of lower right corner of reference image</li> <li>Y4_Ref: y-coordinate of lower right corner of reference image</li> <li>X1_Trans: x-coordinate of upper left corner of transformed image</li> <li>Y1_Trans: y-coordinate of upper left corner of transformed image</li> <li>X2_Trans: x-coordinate of lower left corner of transformed image</li> <li>Y2_Trans: y-coordinate of lower left corner of transformed image</li> <li>X3_Trans: x-coordinate of upper right corner of transformed image</li> <li>Y3_Trans: y-coordinate of upper right corner of transformed image</li> <li>X4_Trans: x-coordinate of lower right corner of transformed image</li> <li>Y4_Trans: y-coordinate of lower right corner of transformed image</li> <li>Displacement: Mean Euclidean distance between reference corner points and transformed corner points</li> </ul> <p>The data set was originally produced by the authors of <em>Aligned Collagen Is a Prognostic Signature for Survival in Human Breast Carcinoma</em> (<a href="https://www.sciencedirect.com/science/article/pii/S0002944010002336">https://www.sciencedirect.com/science/article/pii/S0002944010002336</a>). The registered and non-registered sub-image pairs in this data set were created by Johan Öfverstedt and Elisabeth Wetzer.</p>
Perceived Mental Workload Classification using Intermediate Fusion Multimodal Deep Learning
<p><em>This repository contains all code -from data collection to perceived mental workload classification- used in the "Perceived Mental Workload Classification using Intermediate Fusion Multimodal Deep Learning" research and serves as supplementary material. <strong>A clear README file is provided, please refer to that for information about the individual scripts and their usage. </strong></em></p> <p>Mental workload detection has been attempted using various bio-signals. Recently, deep learning has allowed for novel methods and results within the BCI community. However, studies currently often only use a single modality to classify mental workload, whereas a plethora of modalities have proven to be valuable in this task.</p> <p>A dataset on which these scripts was also made publicly available under the following DOI: 10.4121/12932801<br> This dataset contains data collected during research into mental workload (MWL) detection using deep learning. It is being made public as supplementary data for publications, as well as for reuse in research that seeks to classify MWL using multimodal physiological data. The goal of this repository and dataset is to serve as a testing ground for the creation of deep neural networks that can classify MWL using multimodal physiological data.</p> <p>The data in this dataset was collected in the Behavioural, Management, and Social Sciences Lab, University of Twente, Enschede, The Netherlands in June/July 2020.</p>
Multimodal mechanosensing enables treefrog embryos to escape egg-predators
<p><span>Mechanosensory-cued hatching (MCH) is widespread, diverse, and improves survival in many animals. From flatworms and insects to frogs and turtles, embryos use mechanosensory cues and signals to inform hatching timing, yet mechanisms mediating mechanosensing <i>in ovo</i> are largely unknown. The arboreal embryos of red-eyed treefrogs, <i>Agalychnis callidryas, </i><span>hatch prematurely to escape predation, cued by physical disturbance in snake attacks. When otoconial organs in the developing vestibular system become functional, this response strengthens, but its earlier occurrence indicates another sensor must contribute. </span>Post-hatching, tadpoles use lateral line neuromasts to detect water motion. We ablated neuromast function with gentamicin to assess their role in <i>A. callidryas</i>' hatching response to disturbance<span>. </span>Prior to vestibular function, this<span> nearly eliminated the hatching response to a complex simulated attack cue, egg-jiggling, revealing that neuromasts mediate early MCH. Vestibular function onset increased hatching, independent of neuromast function, indicating young embryos use multiple mechanosensory systems. MCH increased developmentally. All older embryos hatched in response to egg-jiggling, but neuromast function reduced response latency. In contrast, neuromast ablation had no effect on timing or level of hatching in motion-only vibration playbacks. It appears only a subset of egg-disturbance cues stimulate neuromasts; thus embryos in attacked clutches may receive uni- or multimodal stimuli. <i>A. callidryas</i> embryos have more neuromasts than described for any other species at hatching, suggesting precocious sensory development may facilitate MCH. </span>Our findings provide insight into the behavioral roles of two mechanosensory systems <i>in ovo </i>and open possibilities for exploring sensory perception across taxa in early life stages.</span></p>
Data from: Wind- and rain-induced vibrations impose different selection pressures on multimodal signaling
The world is a noisy place, and animals have evolved a myriad of strategies to communicate in it. Animal communication signals are, however, often multimodal; their components can be processed by multiple sensory systems, and noise can thus affect signal components across different modalities. We studied the effect of environmental noise on multimodal communication in the túngara frog (Physalaemus pustulosus). Males communicate with rivals using airborne sounds combined with call-induced water ripples. We tested males under control as well as noisy conditions in which we mimicked rain- and wind-induced vibrations on the water surface. Males responded more strongly to a multimodal playback in which sound and ripples were combined, compared to a unimodal sound-only playback, but only in the absence of rain and wind. Under windy conditions, males decreased their response to the multimodal playback, suggesting that wind noise interferes with the detection of rival ripples. Under rainy conditions, males increased their response, irrespective of signal playback, suggesting that different noise sources can have different impacts on communication. Our findings show that noise in an additional sensory channel can affect multimodal signal perception and thereby drive signal evolution, but not always in the expected direction.
Data from: Simultaneous pulsed flowering in a temperate legume: causes and consequences of multimodality in the shape of floral display schedules
1. In plants, the temporal pattern of floral displays, or display schedules, delimits an individual's mating opportunities. Thus, variation in the shape of display schedules can affect the degree of population synchrony and the strength of phenological assortative mating by flowering onset date. A good understanding of the mechanisms regulating the timing of flowering onset has been developed, but we know less about factors influencing subsequent patterns of floral display. 2. We observed unusual multimodal display schedules in temperate populations of the annual legume Chamaecrista fasciculata. Here we ask if 'flowering pulses' are simultaneous among individuals and populations and explore potential underlying mechanisms and consequences of pulsed flowering. 3 .We monitored daily flower production for individual plants from genetically divergent populations during a series of field experiments that manipulated three potential influencers of display schedule shape: average daily temperature, pollinator availability, and watering schedules. We measured floral longevity to isolate the contributions of flower retention and flower deployment to display schedules. We assessed relationships between flowering and environmental variables and compared estimates of population synchrony, individual synchrony, and the strength of assortative mating with those of 29 unimodally-flowering species from the area. 4. We observed simultaneous flowering pulses in all experiments, with peaks aligned among individuals and populations despite variation in flowering onset and/or duration. Pulses were not the result of increases in average temperature, pollinator availability, or variation in watering schedules. Seasonal fluctuations in temperature correlated with floral longevity and flower deployment, suggesting that the shape of display schedules may be plastic in response to temperature. Average population and individual synchrony differed only slightly from those of the species with unimodal schedules, while the average strength of assortative mating for flowering onset date was strongly reduced (0.21 in C. fasciculata vs. 0.35 for the 29 other species). 5. Synthesis. Researchers should take caution in assuming that components of display schedules are genetically or developmentally correlated with flowering onset. Variation in the shape of display schedules can influence patterns of gene-flow within or between populations, with potential effects on the strength of phenological assortative mating and subsequent responses to selection.
Recursiveness in Multimodal Generative Artificial Intelligence
<p>The dataset contains images extracted from the <a href="https://cocodataset.org/#home">COCO</a> dataset that have been tested in the Recursive Modality Changes process: a caption is extracted from an image and used to generate a new image. The process is repeated in a loop. For the extraction of the description was used GPT-4o and for the generation of the images DALL-E3. A second experiment with a subset of the previous images have been done with Flux.1 and Phi-3.5 </p> <p> </p> <div> <div> </div> <h2>Description</h2> </div> <div> <p>The dataset contains experiments of applying the RMC of length 40 generations from images that contains elements of the following categories: apples, elephants, fire-hydrants, persons, toilets, and trains. In total, there are 10 RMC loops per category (40*10*6 = 2,400 images) and the comparison between the images and descriptions using the metrics <a href="https://richzhang.github.io/PerceptualSimilarity/">LPIPS VGG</a>, TF-IDF, <a href="https://www.tensorflow.org/text/api_docs/python/text/BertTokenizer">BERT tokenizer</a>, <a href="https://arxiv.org/abs/2201.12086">BLIP</a>.</p> <ul> <li>1_coco_dataset: information from COCO images of each category</li> <li>2_categories: loops of each category <ul> <li>results_{category} <ul> <li>experiments <ul> <li>results_dall-e-3_hd_{style}_{category} -> hd (high definition), style (vivid or natural) -> all the experiments of that {style} and {category} <ul> <li>results_all.xlsx -> similarity with metrics LPIPS, TF-IDF, BLIP, BERT aggregated</li> <li>experiments <ul> <li>{date}_{style}_{coco_id} -> each experiment from a coco image <ul> <li>imgs -> images</li> <li>imgs_resized -> resized images</li> <li>experiment.json -> json with data of the experiment (description, number of generations, etc.)</li> <li>results.xlsx -> metrics of each individual loop</li> </ul> </li> </ul> </li> </ul> </li> </ul> </li> <li>images_all.xlsx -> summary of all the images generated and their descriptions</li> <li>inter-experiments_results.xlsx -> aggregated results of inter-trajectory experiments of this {category}</li> <li>intra-experiments_results.xlsx -> aggregated results of intra-trajectory experiments of this {category}</li> </ul> </li> </ul> </li> <li>3_combined results: combined results of all categories <ul> <li>rsults_hd_labels_{style} -> all the results from images generated with the same style (vivid or natural) <ul> <li>images_all.xlsx -> summary of all images</li> <li>inter-exeriments_results_all.xlsx</li> <li>intra-experiments_results_all.xlsx</li> <li>results_all_labels.xlsx -> summary of results per {category}</li> </ul> </li> </ul> </li> <li>4_different_styles -> experiments comparing styles (natural and vivid)</li> </ul> <h2>Paper</h2> <ul> <li>Paper: </li> <li>Cite:</li> </ul> <p><code>@misc{</code><br><code>}</code></p> </div>
Processed data for Jowhar et al, "A ubiquitous GC content signature underlies multimodal mRNA regulation by DDX3X"
<p>Processed data for Jowhar et al, "A ubiquitous GC content signature underlies multimodal mRNA regulation by DDX3X".</p> <p>It contains the data objects to reproduce all figures of the paper.</p> <p>More info here: <strong><a href="https://github.com/calviellolab/DDX3X_GC_paper">https://github.com/calviellolab/DDX3X_GC_paper</a></strong></p>
Data for Airlines network analysis on an air-rail multimodal system. Journal of Open Aviation Science, 1(2)
<h2>About</h2> <p>This dataset contains all the input data required to generate the analysis and results of the article Delgado, L., Trapote-Barreira, C., Montlaur, A., Bolić, T., & Gurtner, G. (2023). <em>Airlines’ network analysis on an air-rail multimodal system</em>. Journal of Open Aviation Science, 1(2). <a href="https://doi.org/10.59490/joas.2023.7223" rel="nofollow">https://doi.org/10.59490/joas.2023.7223</a></p> <p>The code used is available on GitHub: <a href="https://github.com/UoW-ATM/joas_air_rail_network_analysis">https://github.com/UoW-ATM/joas_air_rail_network_analysis</a></p> <p>The path to the input data can be modified in the scripts provided in the GitHub repository. With the default setting, the input is in a folder called data.</p> <h2>Dataset structure</h2> <ul> <li>data_computed <ul> <li><em>rail_used_emissions.csv</em></li> <li><em>rail_used_emissions_2.csv</em></li> <li><em>routes_emissinos_v2.csv</em></li> </ul> </li> <li>flights_data4 <ul> <li>year=2023 <ul> <li>month=05 <ul> <li><em>1st_week_0523.csv</em></li> </ul> </li> </ul> </li> </ul> </li> <li>renfe <ul> <li>renfe_mid_long <ul> <li><em>agency.txt</em></li> <li><em>calendar.txt</em></li> <li><em>calendar_dates.txt</em></li> <li><em>routes.txt</em></li> <li><em>stops.txt</em></li> <li><em>stop_times.txt</em></li> <li><em>trips.txt</em></li> </ul> </li> </ul> </li> <li><em>aircraftDatabase.csv</em></li> <li><em>airport_static.csv</em></li> <li><em>code_seats.csv</em></li> <li><em>manual_fixed_airports.csv</em></li> <li><em>mat_corrected.csv</em></li> <li><em>type_code_missing.csv</em></li> </ul> <h2>Data description</h2> <h3>data_computed</h3> <p>This folder contains pre-computed values by the authors on rail and air emissions. These are estimated:</p> <ul> <li>For rail using <a href="http://ecopassenger.hafas.de/" rel="nofollow">EcoPassenger</a>.</li> <li>For flights based on the emission model from Montlaur, A., Delgado, L., & Trapote-Barreira, C. (2021). <a href="https://doi.org/10.3390/su131810401" rel="nofollow"><em>Analytical Models for CO<sub>2</sub> Emissions and Travel Time for Short-to-Medium-Haul Flights Considering Available Seats</em></a>. Sustainability 13.18 (2021) and from some specific flights using EUROCONTROL's <a href="https://www.eurocontrol.int/platform/integrated-aircraft-noise-and-emissions-modelling-platform">IMPACT </a>model.</li> </ul> <h3>flights_data4</h3> <p>Information from flights_data4 table from <a href="https://opensky-network.org/data/impala">OpenSky</a>. Please refer to the <a href="https://opensky-network.org/about/terms-of-use">terms of use of OpenSky</a> for the restrictions on the further use of these data.</p> <p>The file <em>1st_week_0523.csv </em>contains the information of the table flights_data4 from OpenSky for the week of the 1st May 2023 (01/05/2023 to 07/05/2003). This was downloaded with the SQL query </p> <p>SELECT * FROM flights_data4 WHERE lastseen >= 1682899200 AND firstseen <= 1683504000;</p> <p>Note that the lastseen and firstseen are in UnixTime and correspond to 2023-05-01 00:00:00 UTC and 2023-05-08 00:00:00 UTC respectively. This ensures capturing all flights landing on 01/05/2023 even if they departed the day before and departing on 07/05/2023 even if landing the day after.</p> <h3>renfe</h3> <p>renfe folder contains the General Transit Feed Specification (GTFS) data from the <a href="https://data.renfe.com/dataset/horarios-de-alta-velocidad-larga-distancia-y-media-distancia">Renfe</a> rail operator with the high-speed, long and medium distances timetables. Note that Renfe provides the data under a <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank" rel="noopener">Creative Commons Attribution 4.0</a> license. </p> <h3>Other datasets</h3> <ul> <li><em>aircraftDatabase.csv </em>: Database containing information on aircraft information (type, manufacturer, license, etc.) as a function of their transponder icao24 code. Obtained from <a href="https://opensky-network.org/aircraft-database">OpenSky</a>.</li> <li><em>airport_static.csv</em>: Airport ICAO code, latitude and longitude.</li> <li><em>code_seats.csv</em>: allows each aircraft model to be related to the seats in the cabin. It has been extracted from airline websites and other sources.</li> <li><em>manual_fixed_airports.csv</em>: List of manually modified airports for arrival/departure to fix wrong rotations from OpenSky data. For each airport ICAO code, it provides the one that should be used instead and some information on that airport (e.g., name)</li> <li><em>mat_corrected.csv</em>: identifies the rotation of each aircraft in the week of study. This is especially relevant for fleet analysis as it is necessary to know the start and end airport of each rotation for each day. It has been identified with an ad-hoc algorithm, and some data has been corrected with <a href="https://www.flightradar24.com/">FlightRadar24</a> data support.</li> <li><em>type_code_missing.csv</em>: relates the transponders' icao24 identifiers missing from OpenSky to the aircraft type (complement aircraftDatabase), compiled from <a href="https://www.flightradar24.com/" rel="nofollow">FlightRadar24</a>.</li> </ul>
AUTH - Multimodal facial expressions dataset
<p>This is a dataset suitable for active and static single modality and multimodal facial expression recognition methods as well as other active perception tasks.. It contains (i) sequences of facial 3D models, (ii) Webots simulation environments and (ii) videos, along with synchronized audio (speech), captured from a grid of virtual cameras at 2 lighting levels. Each part of the dataset can be utilized in a standalone manner, i.e., it can be downloaded and utilized separately from the rest.</p>
Supplementary Information: Multimodal binding and inhibition of bacterial ribosomes by the 2 antimicrobial peptides Api137 and Api88
<div> <div>This dataset contains important data files for the MD simulation that are part of this publication.</div> <div> </div> <div>The "simulations" directory contains Gromacs parameter files (.mdp) and the run input files (.tpr) as well as the final coordinate files (.gro) of each individual production simulation.</div> <div> </div> <div>The directory "figure3" contains the raw data used to create Figure 3 in the manuscript.</div> <div> </div> <div>The subdirectory "a" contains the data for the PCA projection plot in subfigure 3a. It includes projections of the simulation ensembles of Api88 conformation I-III on to the two dominant conformational modes (.xvg) and the respective extreme conformations (.pdb). The projections of the three initial models and the optimized structure set are also included.</div> <div> </div> <div>Subdirectory "b" contains a numpy array (.npy) with the data for the correlation heatmap in subfigure 3b.</div> <div> </div> <div>Subdirectory "c" contains the results of several correlation-optimization searches. Each directory "N#_maps", where # is to be replaced by the number of structures in the set, contains the search results for N correlation-optimized structures in the Api88 trajectories in the form of a pickled python dictionary (state.pkl). The dictionary has the following keys:</div> <ul> <li>used: Already used sets of MD structures (frozenset)</li> <li>selection: Structure set selected in the last iteration (set)</li> <li>weights: weights of each structure in the selected structure set (numpy array)</li> <li>iteration: Counter of the last iteration (int)</li> </ul> <div> </div> <div>The directory "supplentary_figure_correlation_time" contains the data for a plot of the optimized correlation coefficient as a function of simulation time. The results of the optimization algorithms (as pickled python objects) are included in the subdirectories with the associated trajectory length as a name.</div> </div> <p> </p>
SpectralWaste Dataset: Multimodal Data for Waste Sorting Automation
<p>This dataset contains multimodal images captured in a real waste processing facility specialized in plastics, cartons, and cans. The data was collected with a true-to-life prototype of the conveyor belt installed on the waste separation line, closely mimicking the actual installation. The setup has two synchronized cameras: a line-scan RGB camera (Teledyne DALSA Linea) and line-scan hyperspectral sensor (Specim FX17) that captures 224 contiguous spectral bands in a range from 900 to 1700 nm.</p> <p>The images are annotated for semantic segmentation with categories based on the requirements of the facility. Each one represents elements that commonly cause operational problems in recycling lines and impact the efficiency of the sorting process. Among these problems, machinery jams pose significant issues because they can cause complete stoppages in the process until the obstructing object is removed. The categories include: <em>film</em> and <em>basket</em>, large objects that can clog the conveyor belts as they are not easily breakable; <em>video tape </em>and <em>filament</em>, representing long objects prone to entangling with mechanical parts and requiring manual intervention; <em>trash bag</em>, which encompasses closed bags containing waste that need to be mechanically opened for further processing; and <em>cardboard</em>, paper objects whose recovery adds value by sending them to another recycling process.</p> <p>Our project page can be found at <a href="https://sites.google.com/unizar.es/spectralwaste">https://sites.google.com/unizar.es/spectralwaste</a>.</p>
Image distortion data from "An open-source MRI compatible frame for multimodal presurgical mapping in macaque and capuchin monkeys"
Open the record for dataset details and reuse information.
Multimodal Fish Feeding Intensity Assessment in Aquaculture Part 3
<p>Multimodal Fish Feeding Intensity Assessment in Aquaculture (The paper you can find on Arxiv: <a href="https://arxiv.org/pdf/2309.05058.pdf" rel="nofollow">https://arxiv.org/pdf/2309.05058.pdf</a>) </p>
M-Arg: Multimodal Multimodal Argument Mining Dataset
<p>No description provided.</p>
Infrared-Emitting Multimodal Nanostructures for Controlled In Vivo Magnetic Hyperthermia
<p>Dataset of https://zenodo.org/record/5034409#.YcmFH2jMJPZ</p>
Dataset - Low-hysteresis shape-memory ceramics designed by multimode modelling
<p>Accompanying Dataset for the article "Low-hysteresis shape-memory ceramics designed by multimode modelling". The dataset contains two .csv files, one each for tetragonal and monoclinic phase, containing lattice parameter data used to train the ML models. Lattice parameters are in angstroms, compositions are in mole percent (cation site), temperatures are in degrees Celsius.</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.