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2,139 results for “recognition”
Multi-sensor dataset for testing merge of Hyperspectral, HD and 3D cloud information for image recognition
<p>This data contain multisensor image dataset constructed for benchmarking purposes. It contains multiple images of the constructed scenes -- on which objects made of different materials are placed to test image recognition scenarios. The scene is recorded from various angles by imagining sensors, i.e. HSI camera, HD camera on mobile chassis and MS Kinect to provide complete information.<br> </p> <p><strong>Equipment</strong><br> The imaging was performed with use of three devices for three different approaches to data. Those three devices' imaging characteristics are widely different when it comes to angle and resolution which required them to be separately positioned to acquire the matching images. Therefore while HD camera was being transferred on the moving platform (chassis), both Kinect and SOC710 were placed on a stationary position which was moved between the frames by hand.</p> <p><em>Hyperspectral data</em></p> <p><br> Hyperspectral data acquisition was performed with Surface Optics SOC710 camera. This camera records spectra at VNIR range $377-1046$ nm; the output image has dimensions $696 \times 520$ with 128 bands and $12$ bit dynamic range.</p> <p>The camera is equipped with sensor line translation unit and can be used from static stand as a conventional camera (i.e. it does not require mechanical translation of the observed sample or rotary stand, as in traditional ‘push broom’ hyperspectral cameras). The lighting was provided with four ambient lamps and adjusted for each scenario separately, so that most of the dynamic range of the camera was used and image saturation is avoided. Captured hyperspectral images were subject to a standard calibration procedure, including: the removal of a dark frame, spectral and radiometric calibration as well as reflectance normalization using the calibration panel. </p> <p><em>3D point clouds</em></p> <p><br> The Kinect sensor incorporates several advanced sensing hardware. The depth sensor consists of the IR projector combined with the IR camera, which is a monochrome complementary metaloxide semiconductor (CMOS) sensor. The IR projector is an IR laser that passes through a diffraction grating and turns into a set of IR dots. The relative geometry between the IR projector and the IR camera as well as the projected IR dot pattern are known. If we can match a dot observed in an image with a dot in the projector pattern, we can reconstruct it in 3D using triangulation. Because the dot pattern is relatively random, the matching between the IR image and the projector pattern can be done in a straightforward way by comparing small neighborhoods using, for example, normalized cross correlation. The depth value is encoded with gray values; the darker a pixel, the closer the point is to the camera in space. The black pixels indicate that no depth values are available for those pixels. This might happen if the points are too far (and the depth values cannot be computed accurately), are too close (there is a blind region due to limited fields of view for the projector and the camera), are in the cast shadow of the projector (there are no IR dots), or reflect poor IR lights </p> <p><em>HD Images</em></p> <p><br> The HD images were acquired using 5 Megapixel HD camera mounted on a mobile chassis made by Dawn Robotics, that allowed the camera to be moved freely on the scene. Both camera and mobile chassis was controlled by a Raspberry PI unit which was also responsible to position the camera in accord to the data being collected by other sources. <br> </p> <p><strong>Data</strong></p> <p>The dataset consists of three scenes consisting of various objects -- minerals, fruit, wood plastic and metal -- placed on a stand. The objects, depending on the view are partially covered and seen from different perspective. Each scene is captured from 8 different angles.</p> <p>Scene 1 (denoted <em>SceneEagle</em>) uses mostly inorganic materials, such as wood, metal, plastic and glass all placed on the vertical stand.<br> Scene 2 (<em>SceneFruit</em>) uses fruits normal and artificial, that are similar on HD photography and 3D cloud of point, but differs in hyperspectral image.<br> Scene 3 (<em>SceneFruit2</em>) uses the fruits but also includes printed full colour images of same fruits that are 2-dimensional.</p> <p> </p> <p>The data are formatted as follows:<br> - The HIS images are available in both \text{*.hdr} and \text{*.cube} formats. The separate files with calibrating panel is provided for each frame.<br> - Kinect clouds are provided in \text{*.obj} format, typical for Kinect output files.<br> - Matched Hyperspectral clouds are also provided as \text{*.obj} files<br> - HD photo files are provided in \text{*.jpg} files.<br> </p> <p><br> <strong>Acknowledgements</strong></p> <p>This work has been supported by the National Science Centre, based on decision no. DEC2012/07/N/ST6/03656.</p> <p> </p>
Data from: Evidence for normal novel object recognition abilities in developmental prosopagnosia
<p>The issue of the face specificity of recognition deficits in developmental prosopagnosia (DP) is fundamental to the organisation of high-level visual memory and has been increasingly debated in recent years. Previous DP investigations have found some evidence of object recognition impairments, but have almost exclusively used familiar objects (e.g., cars), where performance may depend on acquired object-specific experience and related visual expertise. An object recognition test not influenced by experience could provide a better, less contaminated measure of DPs' object recognition abilities. To investigate this, in the current study we tested 30 DPs and 30 matched controls on a novel object memory test (NOMT Ziggerins) and the Cambridge Face Memory Test (CFMT). DPs were impaired on the CFMT but showed no differences in accuracy or reaction times to controls on the NOMT. We found similar results when comparing DPs to a larger sample of 274 web-based controls. Additional individual analyses demonstrated that the rates of object recognition impairment in DPs did not differ from the rate of impairment in either control group. Together, these results demonstrate unimpaired object recognition in DPs for a class of novel objects that serves as a powerful index for broader novel object recognition capacity.</p>
Raw data for: No reproductive benefits of dear enemy recognition in a territorial songbird
<p>Territorial animals often learn to distinguish their neighbors from unfamiliar conspecifics. This cognitive ability facilitates the dear enemy effect, where individuals respond less aggressively to neighbors than to other individuals, and is hypothesized to be adaptive by reducing unnecessary aggressive interactions with individuals that are not a threat to territory ownership. A key prediction of this hypothesis, that individuals with better ability to learn to recognize neighbors should have higher fitness, has never been tested. We used a series of song playbacks to measure the change in response of male great tits on their breeding territories to a simulated establishment of a neighbor on an adjacent territory. Males reduced their approach to the speaker and sang fewer songs on later repetitions of the playback trials, consistent with a dear enemy effect through habituation learning. However, not all males discriminated between the neighbor and stranger playbacks at the end of the series of trials, and there was evidence that individuals consistently differed from one another in performing this discrimination. We monitored nests and analyzed offspring paternity to determine male reproductive success. Unexpectedly, individuals that learned to recognize their neighbors did not have higher reproductive success, and in fact one measure, total offspring biomass, was lower for learners. Although the general capability to recognize neighbors is most likely adaptive, we speculate that individuals who decrease their responsiveness to familiar neighbors too quickly may be at a disadvantage, perhaps leading to selection for slower dear enemy recognition learning.</p>
voiceHome-2 corpus - automatic speech recognition baseline - acoustic model
<p>This entry contains the acoustic model used for evaluation of distant-microphone speech recognition performance in:</p> <p>Nancy Bertin, Ewen Camberlein, Romain Lebarbenchon, Emmanuel Vincent, Sunit Sivasankaran, Irina Illina, Frédéric Bimbot<br> <a href="https://hal.inria.fr/hal-01923108">VoiceHome-2, an extended corpus for multichannel speech processing in real homes</a><br> <em>Speech Communication</em>, 2019, 106, pp.68-78. <a href="https://dx.doi.org/10.1016/j.specom.2018.11.002">⟨10.1016/j.specom.2018.11.002⟩</a></p>
Generating Physically Sound Training Data for Image Recognition of Additively Manufactured Parts Data and Scripts
<p>The repository contains the data corresponding to the Paper "Generating Physically Sound Training Data for Image Recognition of Additively Manufactured Parts".</p> <p>Random30, Random50, Random100, Similiar10, Similar30 and Similar50.zip contain the data sets (obj Files).</p> <p>R30_physical_images.zip and sim50_physical_images.zip contain the photos made from the physical components which are used for the evaluation.</p>
Historical German Children's Playbooks - 6 Digitized Books with Images, OCR-Fulltext, and Named Entity Recognition
<p>The dataset consists of 6 digitized books with 1750 images and OCR-fulltext.</p> <p>Additionally, named entity recognition has been carried out on basis of flair's de-ner model, see https://github.com/flairNLP for details.</p>
Data from: Recognition of endophytic Trichoderma species by leaf-cutting ants and their potential in a Trojan-horse management strategy
Interactions between leaf-cutting ants, their fungal symbiont (Leucoagaricus) and the endophytic fungi within the vegetation they carry into their colonies are still poorly understood. If endophytes antagonistic to Leucoagaricus were found in plant material being carried by these ants, then this might indicate a potential mechanism for plants to defend themselves from leaf-cutter attack. In addition, it could offer possibilities for the management of these important Neotropical pests. Here, we show that, for Atta sexdens rubropilosa, there was a significantly greater incidence of Trichoderma species in the vegetation removed from the nests—and deposited around the entrances—than in that being transported into the nests. In a no-choice test, Trichoderma-infested rice was taken into the nest, with deleterious effects on both the fungal gardens and ant survival. The endophytic ability of selected strains of Trichoderma was also confirmed, following their inoculation and subsequent reisolation from seedlings of eucalyptus. These results indicate that endophytic fungi which pose a threat to ant fungal gardens through their antagonistic traits, such as Trichoderma, have the potential to act as bodyguards of their plant hosts and thus might be employed in a Trojan-horse strategy to mitigate the negative impact of leaf-cutting ants in both agriculture and silviculture in the Neotropics. We posit that the ants would detect and evict such 'malign' endophytes—artificially inoculated into vulnerable crops—during the quality-control process within the nest, and, moreover, that the foraging ants may then be deterred from further harvesting of 'Trichoderma-enriched' plants.
EEG data for "Conversation electrified: ERP correlates of speech act recognition in underspecified utterances"
<p>Please refer to the publication in Plos One for a description of the experiment and data analysis: Gisladottir RS, Chwilla DJ, Levinson SC (2015) Conversation Electrified: ERP Correlates of Speech Act Recognition in Underspecified Utterances. PLoS ONE 10(3): e0120068. doi: 10.1371/journal.pone.0120068</p>
Supporting Data: A large-scale dataset of solar event reports from automated feature recognition modules.
<p>This is the supporting dataset for the paper:</p> <p>A large-scale dataset of solar event reports from automated feature recognition modules. Michael A. Schuh, Rafal A. Angryk, Petrus C. Martens. Journal of Space Weather and Space Climate, 2016.</p>
Experiments of the Paper "MORTY: A Toolbox for Mode Recognition and Tonic Identification"
<p>This package contains the complete experimental data explained in:</p> <blockquote> <p>Karakurt, A., Şentürk S., & Serra X. (In Press). MORTY: A Toolbox for Mode Recognition and Tonic Identification. 3rd International Digital Libraries for Musicology Workshop. </p> </blockquote> <p>Please cite the paper above, if you are using the data in your work.</p> <p>The zip file includes the folds, features, training and testing data, results and evaluation file. It is part of the experiments hosted in github (https://github.com/sertansenturk/makam_recognition_experiments/tree/dlfm2016) in the folder call ".<strong>/data</strong>". We host the experimental data in Zenodo (http://dx.doi.org/10.5281/zenodo.57999) separately due to the file size limitations in github.</p> <p>The files generated from audio recordings are labeled with 16 character long MusicBrainz IDs (in short "MBID"s) Please check http://musicbrainz.org/ for more information about the unique identifiers. The structure of the data in the zip file is explained below. In the paths given below <em>task</em> is the computational task ("tonic," "mode" or "joint"), <em>training_type</em> is either "single" (-distribution per mode) or "multi" (-distribution per mode), <em>distribution</em> is either "pcd" (pitch class distribution) or "pd" (pitch distribution), <em>bin_size</em> is the bin size of the distribution in cents, <em>kernel_width</em> is the standard deviation of the Gaussian kernel used in smoothing the distribution, <em>distance</em> is either the distance or the dissimilarity metric, <em>num_neighbors</em> is the number or neighbors checked in <em>k</em>-nearest neighbor classification and <em>min_peak</em> is the minimum peak ratio. 0 <em>kernel_width</em> implies no smoothing. <em>min_peak </em>always takes the value 0.15. For a thorough explanation please refer to the companion page (http://compmusic.upf.edu/node/319) and the paper itself.</p> <ul> <li><strong>folds.json: </strong>Divides the test dataset (https://github.com/MTG/otmm_makam_recognition_dataset/releases) into training and testing sets according to stratified 10-fold scheme. The annotations are also distributed to sets accordingly. The file is generated by the Jupyter notebook <em>setup_feature_training.ipynb (4th code block)</em> in the github experiments repository (https://github.com/sertansenturk/makam_recognition_experiments/blob/master/setup_feature_training.ipynb).</li> <li><strong>Features: </strong>The path is <strong>data/features/[distribution--bin_size--kernel_width]/[MBID--(hist </strong><em>or </em><strong>pdf)].json</strong>. "pdf" stands for probability density function, which is used to obtain the multi-distribution models in the training step and "hist" stands for the histogram, which is used to obtain the single-distribution models in the training step. The features are extracted using the Jupyter notebook <em>setup_feature_training.ipynb (5th code block)</em> in the github experiments repository (https://github.com/sertansenturk/makam_recognition_experiments/blob/master/setup_feature_training.ipynb)</li> <li><strong>Training: </strong>The path is <strong>data/training/[training_type--distribution--bin_size--kernel_width]/fold(0:9).json]</strong>. There are 10 folds in each folder, each of which stores the training model (file paths of the <em>distribution</em>s in "multi" <em>training_type</em> or the <em>distribution</em>s itself in "single" <em>training_type</em>) trained for the fold using the parameter set. The training files are generated by the Jupyter notebook <em>setup_feature_training.ipynb (6th code block)</em> in the github experiments repository (https://github.com/sertansenturk/makam_recognition_experiments/blob/master/setup_feature_training.ipynb)</li> <li><strong>Testing: </strong>The path is <strong>data/testing/[task]/[training_type--distribution--bin_size--kernel_width--distance--num_neighbors--min_peak]</strong>. Each path has the folders <strong>fold(0:9)</strong>, which have the evaluation and the results files obtained from each fold. The path also has the <strong>overall_eval.json</strong> file, which stores the overall evaluation of the experiment. The optimal value of <em>min_peak </em>is selected in the 4th code block, testing is carried in the 6th code clock and the evaluation is done in the 7th code block in the Jupyter notebook <em>testing_evaluation.ipynb</em> in the github experiments repository (https://github.com/sertansenturk/makam_recognition_experiments/blob/master/testing_evaluation.ipynb). <br> <strong>data/testing/ </strong>folder also contains a summary of all the experiments in the files <strong>data/testing/evaluation_overall.json </strong>and <strong>data/testing/evaluation_perfold.json</strong>. These files are created in MATLAB while running the statistical significance scripts. <strong>data/testing/evaluation_perfold.mat </strong>is the same with the json file of the same filename, stored for fast reading.</li> </ul> <p>For additional information please contact the authors.</p> <p>This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.</p>
Vanellus chilensis dataset accompanying PLOS ONE paper "Automated Sound Recognition Provides Insights into the Behavioral Ecology of a Tropical Bird"
<p>Southern Lapwing <em>Vanellus chilensis</em> dataset accompanying the PLOS ONE article</p> <p>O. Jahn, T. Ganchev, M.I. Marinez and K.L. Schuchmann: Automated Sound Recognition Provides Insights into the Behavioral Ecology of a Tropical Bird. DOI:10.1371/journal.pone.0169041</p> <p>---<br> BL01:<br> Background Library 01 consists of</p> <p>BL01 >> BL_CHVACH_free_final:<br> 54 hand-cleaned (<em>Vanellus chilensis</em>-free) background files and</p> <p>BL01 >> PSC008_forest:<br> 36 original soundscapes recorded inside forest, which may contain a few target signals from overflying lapwings.</p> <p>---<br> PONE_VACH_AnnualCycle_Statistics:<br> Contains the Excel files<br> - 2013CHVACH_BreedingCycle_Statistics_PONE: statistics on <em>V. chilensis</em> activity patterns, Apr. 2013 to Sep. 2013.<br> - 2013CHVACH_FalseNegatives_PONE: determination of the false negative rate based on an expert-annotated sample of 26 soundcsape recordings<br> - 2013CHVACH_FalsePositives_PONE: determination of the false positive rate based on an expert-annotated random sample of 1250 automated <em>V. chilensis</em> detections.</p> <p>---<br> TL01_BIAVCHCHVACH_20130813v2_HandCleaned:<br> Training library for the development of the <em>V. chilensis</em> recognizer, consisting of 90 hand-filtered recordings of the target species.</p> <p>---<br> VACH_Detector_results >> VACHdetectorOutput_PONE.zip:<br> TXT detector output files, listing timestamps of potential <em>V. chilensis</em> sound events.</p> <p>---<br> VACH_FNrate_20160706:<br> Validation library used to determine the false negative rate. The library consists of 26 expert-annotated sound files. Annotations were made in Adobe Audion v3.0.</p> <p>---<br> VL01_VACH_MonoB<br> and<br> VL01_VACH_MonoB:<br> Validation library used for the fine-tuning of the recognizer settings.</p> <p>---<br> Important notes on the annotation of the VACH_FNrate_20160706 library:</p> <p>1) In general, we used the procedure described in Ganchev et al. 2015 for the annotation of VACH validation libraries (see next section).</p> <p>2) However, the detector-generated timestamps were not changed! For the following reasons, it is not possible to use the VACH_FN_rate library as a validation library for the development of improved recognizer versions:</p> <p>(a) The VACH detector overlooked many of the weaker signals within a VACH call series. Therfore the detector-generated annotations are incomplete.<br> (b) For the same reason some automatically-generated detections may refer to a single VACH call event (double hits).</p> <p>Details on the method used for the annotation of the VACH validation libraries are described in Ganchev et al. 2015, pp.6100f: 2.1.3.3.Vanellus chilensis validation dataset.</p>
A dataset for high-level activity recognition based on low level audio events
<p>The high level activities are:<br> - kitchencleanup<br> - music<br> - no activity<br> - other activity<br> - talk<br> - tv</p> <p>Each recording of low-level audio events is stored in a separate file.</p> <p>Files are organized in 6 folders, each folder corresponding to a separate file.</p> <p>The format of is file is json-like. In particular, each row has the following format:</p> <p>{"prob": 0.88557562121157585, "energy": 0.024511212402412885, "t": 1485110417, "event": "speech"}</p> <p>This dataset can be evaluated with the python code metaClassifier/evaluate.py of the AUOR repository:<br> https://github.com/tyiannak/AUROS</p>
Test-B1 ICDAR2017 Competition on Handwritten Text Recognition on the READ Dataset (ICDAR2017 HTR)
<ul> <li><strong>Test-B1</strong>: a batch of page images annotated with the geometry of regions where to detect text line and recognize.</li> </ul>
Test-B2 ICDAR2017 Competition on Handwritten Text Recognition on the READ Dataset (ICDAR2017 HTR)
<p><strong>Test-B2</strong>: a batch of page images annotated with the geometry of regions where to detect text line and recognize.</p>
Dataset for ICDAR2017 Competition on Handwritten Text Recognition on the READ Dataset (ICDAR2017 HTR)
<p><strong>Train-A:</strong> Dataset of pages with manually revised baselines and the corresponding transcripts associated to them. This batch is small, 50 pages. Please, keep in mind that only the baselines have been manually corrected, The polygons associated to each line have not been manually reviewed.</p> <p><strong>Train-B:</strong> Dataset of pages without any layout or text line information. The corresponding transcripts are provided at page level with line breaks. It has 10k pages, though for convenience it is divided into two 5k page batches. This information is provided in PAGE format.</p> <p><strong>Test A:</strong> Dataset of pages with manually revised baselines. This batch has 65 pages. The polygons associated to each line have not been manually reviewed.</p> <p><strong>Test-B1:</strong> The same dataset of pages of the Test A, but annotated only with the geometry of regions. Text line information is not provided. </p> <p><strong>Test-B2:</strong> Dataset of page images annotated with the geometry of regions where to detect text line and recognize. It has 57 pages.</p> <p><strong>Baseline.tgz:</strong> Baseline system trained using the first 40 pages of Train-A. The system is based on the deep learning toolkit to transcribe handwritten text images called Laia.</p> <p>More information at:</p> <p>https://scriptnet.iit.demokritos.gr/competitions/~icdar2017htr/</p> <p> </p>
The FORTH-TRACE dataset for human activity recognition of simple activities and postural transitions using a Body Area Network
<p>The dataset is collected from 15 participants wearing 5 Shimmer wearable sensor nodes on the locations listed in Table 1. The participants performed a series of 16 activities (7 basic and 9 postural transitions), listed in Table 2.</p> <p>The captured signals are the following:</p> <ul> <li>3-axis accelerometer</li> <li>3-axis gyroscope</li> <li>3-axis magnetometer</li> </ul> <p>The sampling rate of the devices is set to 51.2 Hz.</p> <p>DATASET FILES</p> <p>The dataset contains the following files:</p> <ul> <li>partX/partXdev1.csv</li> <li>partX/partXdev2.csv</li> <li>partX/partXdev3.csv</li> <li>partX/partXdev4.csv</li> <li>partX/partXdev5.csv</li> </ul> <p>Where X corresponds to the participant ID, and numbers 1-5 to the device IDs indicated in Table 1.</p> <p>Each .csv file has the following format:</p> <ul> <li>Column1: Device ID</li> <li>Column2: accelerometer x</li> <li>Column3: accelerometer y</li> <li>Column4: accelerometer z</li> <li>Column5: gyroscope x</li> <li>Column6: gyroscope y</li> <li>Column7: gyroscope z</li> <li>Column8: magnetometer x</li> <li>Column9: magnetometer y</li> <li>Column10: magnetometer z</li> <li>Column11: Timestamp</li> <li>Column12: Activity Label</li> </ul> <p>Table 1: LOCATIONS</p> <ol> <li>Left Wrist</li> <li>Right Wrist</li> <li>Torso</li> <li>Right Thigh</li> <li>Left Ankle</li> </ol> <p>Table 2: ACTIVITY LABELS</p> <p>(Arrows (->) indicate transitions between activities)</p> <ol> <li>stand</li> <li>sit</li> <li>sit and talk</li> <li>walk</li> <li>walk and talk</li> <li>climb stairs (up/down)</li> <li>climb stairs (up/down) and talk</li> <li>stand -> sit</li> <li>sit -> stand</li> <li>stand -> sit and talk</li> <li>sit and talk -> stand</li> <li>stand -> walk</li> <li>walk -> stand</li> <li>stand -> climb stairs (up/down), stand -> climb stairs (up/down) and talk</li> <li>climb stairs (up/down) -> walk</li> <li>climb stairs (up/down) and talk -> walk and talk</li> </ol>
pLMMoRF: A web server that accurately predicts membrane-interacting molecular recognition features by employing a protein language model
<p>pLMMMoRF predictor scrips and MemMoRF prediction of the human proteome.</p>
Figure 35 in A preliminary report on the World species of Bemisia Quaintance and Baker and its congeners (Hemiptera: Aleyrodidae) with a comparative analysis of morphological variation and its role in the recognition of species Raymond Gill
Figure 35. Paratype, Bemisia rosae Danzig, 25 km s/o Orapa?, 10-VI-78, ex: rose, E. Danzig, coll.
Fig. 2 in Cephalic labial gland secretions of males as species recognition signals in bumblebees: are there really geographical variations in the secretions of the Bombus terrestris subspecies? (Hymenoptera: Apidae: Bombus)
Fig. 2: Legends on p. 106
Use of artificial intelligence techniques for the recognition of human emotions: a bibliometric analysis
<p>Human emotion recognition with AI uses physiological, audiovisual, and linguistic signals. Despite its importance and great progress in emotion recognition, several challenges remain in generalization and evaluation through standards and shared data, as well as other research gaps. Therefore, the objective is to analyze the scientific production on the use of artificial intelligence techniques for the recognition of human emotions. This study uses bibliometric analysis following the guidelines of the PRISMA-2020 statement for literature reviews. Based on the results of the bibliometrics on the use of artificial intelligence techniques in for the recognition of human emotions, significant conclusions are obtained that improve the understanding of the current panorama in this field of research. A growing interest in the subject is observed during the years 2023, 2022, 2021 and 2020, which demonstrates the relevance and potential of artificial intelligence in the recognition of human emotions. A cubic polynomial growth in the number of scientific articles is observed, demonstrating a constant expansion of knowledge and support for future trends. Leading authors and journals are identified, highlighting global collaboration in China and India. The thematic evolution shows maturity and progressive specialization, with emerging concepts that promise future research and innovative applications.</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.