Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
1,506
datasets available to search
ShareScore release 0.9.0
Dataset results
1,506 results for “objects”
Dynamic phase object
<p>In Video 1, it is possible to see the temporal evolution of the sample, in this case the optical path difference (OPD) has been calculated to observe the variations of the phase map.</p>
Objective evaluation of Nintendo Wii Fit plus balance program training on postural stability in Multiple Sclerosis patients
<p>The use of the Nintendo Wii system has become a common tool for balance rehabilitation in patients with multiple sclerosis, but few studies verified the effectiveness of such an approach using quantitative measures of postural control. We aimed to evaluate the impact of rehabilitation treatment using the Nintendo Wii Fit Plus balance program on objective stabilometric parameters in multiple sclerosis patients. We enrolled 36 multiple sclerosis patients, with mild-moderate disability, referring to the multiple sclerosis Centre of the University of Catania from September 2013 to June 2014. Twenty participants underwent 20 individual sessions of balance exercise using the Nintendo Wii Fit Plus. They were assessed at baseline (T0) and at the end of rehabilitation program (T1) by Neurocom Balance Manager. Functional independence measure, Barthel index, and Berg balance scale were also administered. At T1, we found a significant improvement in total path length-open eyes, sway area-open eyes, and mean sway velocity-open eyes. Patients showed significant improvement in functional independence measure motor score, Barthel index, and in Berg balance scale. No significant differences between T0 and T1 in closed eyes trials were found. A significant correlation between delta values between T0 and T1 of sway area-open eyes and the Berg balance scale (r = -0.76; P < 0.0001) was found. This study confirmed that balance rehabilitation training performed using the Nintendo Wii with balance board significantly reduced some postural sway parameters in multiple sclerosis patients. It could be a good support to standard rehabilitation program in multiple sclerosis patients.</p>
Data from: Integrating a UAV-derived DEM in object-based image analysis increases habitat classification accuracy on coral reefs
<p>Very shallow coral reefs (< 5 m deep) are naturally exposed to strong sea surface temperature variations, UV radiation and other stressors exacerbated by climate change, raising great concern over their future. As such, accurate and ecologically informative coral reef maps are fundamental for their management and conservation. Since traditional mapping and monitoring methods fall short in very shallow habitats, shallow reefs are increasingly mapped with Unmanned Aerial Vehicles (UAVs). UAV-imagery is commonly processed with Structure-from-Motion (SfM) to create orthomosaics and Digital Elevation Models (DEMs) spanning several hundred metres. Techniques to convert these SfM products to ecologically relevant habitat maps are still relatively underdeveloped. Here we demonstrate that incorporating geomorphometric variables (the DEM and its derivatives) in addition to spectral information (the orthomosaic) can greatly enhance the accuracy of automatic habitat classification. Therefore, we mapped three very shallow reef areas off KAUST on the Saudi Arabian Red Sea coast with an RTK-ready UAV. Imagery was processed with SfM, and classified through Object-Based Image Analysis (OBIA). Within our OBIA workflow, we observed overall accuracy increases of up to 11% when training a Random Forest classifier on both spectral and geomorphometric variables as opposed to traditional methods that only use spectral information. Our work highlights the potential of incorporating a UAV's DEM in OBIA for benthic habitat mapping, a promising but still scarcely exploited asset.</p>
GDS file for Pool-Seq object -- DEST + VA data
<p>See https://github.com/Jcbnunez/Cville-Seasonality-2016-2019</p>
Dataset for "Invariance of Object Detection in Untrained Deep Neural Networks"
<p><strong>Dataset for<br> "Invariance of Object Detection in Untrained Deep Neural Networks"</strong><br> Jeonghwan Cheon, Seungdae Baek, and Se-Bum Paik*<br> *Contact: sbpaik@kaist.ac.kr<br> <br> To run demo codes for "<a href="https://github.com/vsnnlab/Invariance">Invariance of Object Detection in Untrained Deep Neural Networks</a>", please download files below.<br> <br> <strong>1. Image.zip</strong><br> <strong>- Object dataset (Foldername: selectivity_var)</strong>: This set was used to find units that selectively respond to a specific object class. It contains nine object classes (bed, chair, desk, dresser, nightstand, monitor, sofa, table, toilet) and 200 images are prepared to an object class. Each image has different object identities, which means it rendered from different object 3D models (Princeton ModelNet, a 3D CAD model dataset for computer vision and cognitive science [https://modelnet.cs.princeton.edu/]). To render image of object dataset, horizontal viewpoint variation angle was randomly set between -30° and +30°. In object dataset, brightness and contrast of images are statistically comparable across the object class.<br> <strong>- Viewpoint dataset for invariance test (Folder name: invariance_test)</strong>: This set was used to test the viewpoint invariant characteristic of object selective units. This dataset consists of 13 subsets which has different viewpoints from -180° to +180° in linear scale step. It contains 200 different object identities in an object class, which are the same as those used in the object dataset.<br> <strong>- Viewpoint dataset for finding invariant unit (Folder name: invariance_unit)</strong>: This set was used to find object selective units that specifically or invariantly responded to object images of different viewpoints. This dataset consists of five angle-based viewpoint classes (-60°, -30°, 0°, 30°, 60°) with 50 object identities which were not used to find object selective unit<br> <strong>- SVM dataset (Folder name: SVM_var)</strong>: This set was used to train and test SVM which performs object detection task. It contains 60 different object identities in an object class, which were not used to find object selective unit. Specifically, it consists of 18 subsets which has different viewpoint variation range from 0° to 180°. For example, subset with 180° viewpoint variation range contains images which shows different viewpoints of objects within range of -90° and +90°.</p>
[Re] Object Detection Meets Knowledge Graphs - Supporting Datasets
<p><strong>Supporting Datasets for [Re] Object Detection Meets Knowledge Graphs</strong></p> <p>The supporting data needed to reproduce the results of <a href="https://www.ijcai.org/Proceedings/2017/230">Object Detection Meets Knowledge Graphs</a> as a part of the submission to ReScience C journal.</p>
Ethnographic Object Provenance Data
<p><strong>This REPO contains the following:</strong></p> <ol> <li>Data TRiG</li> <li>ShEx Shape for data Model</li> <li>ShapeMap queries for validation</li> <li>SPARQL Queries against competency questions</li> </ol> <p>Validation tool used: <a href="https://rdfshape.weso.es/shexValidate">RDFshape</a><br> </p>
Multi-Objective Evolutioary Algorithms for Synset Dimensionality Reduction
<p>Multi-Objective Evolutioary Algorithms for Synset Dimensionality Reduction</p> <p>This work has been developed to discover the usage of Multi-Objective Evolutionary computation to reduce the dimensionality of synset-based datatsets. </p> <p>The objective of this code is to introduce different dimensionality reduction methods (lossless, low-loss and lossy) as an optimization problem that can be solved using Multi-Objective Evolutionary Algorithms (MOEA).</p>
Familiar object benefit more from transsaccadic feature predictions
<p>The transsaccadic feature prediction mechanism associates peripheral and foveal information<br> belonging to the same object to make predictions about how an object seen in the periphery<br> would appear in the fovea or vice versa. It is unclear if such transsaccadic predictions require<br> experience with the object such that only familiar objects benefit from this mechanism by virtue<br> of having peripheral-foveal associations. In two experiments, we tested whether familiar<br> objects have an advantage over novel objects in peripheral-foveal matching and transsaccadic<br> change detection tasks.<br> In both experiments, observers were unknowingly familiarized with a small set of stimuli by<br> completing a sham orientation change detection task. In the first experiment, observers<br> subsequently performed a peripheral-foveal matching task, where they needed to pick the<br> foveal test object that matched a briefly presented peripheral target. In the second experiment,<br> observers subsequently performed a transsaccadic object change detection task where a<br> peripheral target was exchanged or not exchanged with another target after the saccade, either<br> immediately or after a 300 ms blank period.<br> We found an advantage of familiar objects over novel objects in both experiments. While<br> foveal-peripheral associations explained the familiarity effect in the matching task of the first<br> experiment, the second experiment provided evidence for the advantage of peripheral-foveal<br> associations in transsaccadic object change detection. Introducing a postsaccadic blank<br> improved the change detection performance in general but more for familiar than for novel<br> objects. We conclude that familiar objects benefit from additional object-specific predictions.<br> Keywords: Saccades, Transsaccadic prediction, perceptual learning, object recognition,<br> visual stability.</p>
StreetScouting dataset: A Street-Level Image dataset for finetuning and applying custom object detectors for urban feature selection
<p>The dataset consists of two .zip files.</p> <p>The first .zip file named "annotated dataset" contains a folder named “annotated dataset" with annotated street images. It consists of the folder “images” that has 763 image files. The image format is PNG. 432 images have dimensions of 1080 x 2160 and 331 have dimensions of 866 x 2400. The filenames are random uuids. The “annotated dataset” folder also contains the annotations in the file “coco_annotations.json”. Annotations are provided in COCO format. Table 1 shows the total number of annotated objects per class.</p> <table align="center" summary="Total number of annotated objects per class"> <caption><em>Table 1. Total number of annotated objects per class</em></caption> <thead> <tr> <th scope="col"><strong><em>Class</em></strong></th> <th scope="col"><strong><em>Annotated Objects</em></strong></th> </tr> </thead> <tbody> <tr> <td><em>Tree</em></td> <td>1922</td> </tr> <tr> <td><em>Waste Bin</em></td> <td><em>223</em></td> </tr> <tr> <td><em>Recycling Bin</em></td> <td>181</td> </tr> <tr> <td><em>Lighting Pole</em></td> <td><em>716</em></td> </tr> <tr> <td><em>Shop Storefront</em></td> <td><em>628</em></td> </tr> </tbody> </table> <p>The second .zip file is named "routes" and contains a folder named “routes” with consecutive frames of four different driving routes in the city of Thessaloniki and their corresponding GPS signal. So the folder “routes” contains 4 folders in the following format “VID_<YYYYMMDD>_<HHmmSS>” where Y denotes digits for year, M denotes digits for month, D denotes digits for day, H denotes digits for hour, m denotes digits for minutes and S denotes digits for seconds. Not the filename represents the start of the collection sequence. All street data was collected in 2022. Each route folder has the “images” folder which contains the consecutive street image data. Image data in this folder is in JPEG format. Each filename in ‘images’ has the frame_<id>.jpg format where id denotes the order of the frame. Table 2 shows more details regarding the number of frames and frame dimension of the driving routes.</p> <table align="center" summary="Total number of annotated objects per class"> <caption><em>Table 2. Total number frames and frame dimensions for each of the routes</em></caption> <thead> <tr> <th scope="col"><strong><em>Route Name</em></strong></th> <th scope="col"><strong><em>Frames Number</em></strong></th> <th scope="col"><strong><em>Frame Dimension</em></strong></th> <th scope="col"><strong><em>Route duration</em></strong></th> </tr> </thead> <tbody> <tr> <td><em>VID_20220617_111456</em></td> <td><em>41,650</em></td> <td><em>1080 x 2160</em></td> <td>1h, 9m, 26s</td> </tr> <tr> <td><em>VID_20220210_112926</em></td> <td><em>23.035</em></td> <td><em>866 x 2400</em></td> <td>38m, 26s</td> </tr> <tr> <td><em>VID_20220209_114831</em></td> <td><em>18.000</em></td> <td><em>1080 x 2160</em></td> <td>30m, 3s</td> </tr> <tr> <td><em>VID_20220209_123323</em></td> <td><em>18.273</em></td> <td><em>1080 x 2160</em></td> <td>30m, 30s</td> </tr> </tbody> </table> <p>Each route folder contains a “gps.json” file which contains latitude and longitude information for each frame. This file is essentially a JSON list of objects that each object contains the “frame_name” attribute and the corresponding “coordinates” object which contains the “latitude” and "longitude" attributes.</p>
Data for: Attributes of CloudSat identified echo objects
<p>This data set contains a collection of attributes associated with CloudSat identified echo objects (or contiguous regions of radar/dBZ echo) from15 June 2006 till 17 January 2013. CloudSat is a NASA satellite that carries a 94 GHz (3 mm) nadir pointing cloud profiling radar (CPR). CloudSat makes approximately 14 orbits per day with an equator passing time of 0130 and 1330 local time. Echo objects were identified using CloudSat's 2B-GEOPROF product that includes 2D arrays (alongtrack x vertical) of the radar reflectivity factor and gaseous attenuation correction. Also included in the product is a "cloud mask" with values ranging between 0 and 40 with higher values indicating a greater likelihood of cloud detection.</p> <p>An EO was defined as a contiguous region of cloud mask greater than or eaqual to 20, consisting of at least three pixels with their edges and not merely their corners touching. Each echo object (EO) is assigned multiple attributes. The geographic attributes include minimum, mean, and maximum latitude and longitude, minimum and maximium location along the CloudSat orbit track, and the underlying surface altitude and land mask data, which allows the EOs to be catagorized as occuring over land, sea, or the coast. The geometric attributes include top, mean, and bottom height, width, and the total number of pixels within the EO. Attributes describing the internal structure of the EO are also available including the number of pixels and cells (i.e., group of pixels) greater than 0 dBZ and -17 dBZ. Finally, the time of day of occurance was also recorded to compare the statistics of EOs ocurring during the daytime versus nighttime. In total, we identified 15,181,193 EOs from 15 June 2006 to 17 January 2013. After 17 April 2011, data were only collected during the day due to a battery failure onboard CloudSat. Each attribute is organized as a 1D array where the size of the array corresponds to the number of EOs. This organization allows subsets of EOs to be easily identified using simple "where" statements when writing code.</p> <p>The attributes were used to identify cloud types and analyze global cloud climatology according to season, surface type, and region (i.e., Riley 2009; Riley and Mapes 2009). The varability of EOs across the MJO was also analyzed (Riley et al. 2011).</p>
Datasets for: PARNAS: Objectively selecting the most representative taxa on a phylogeny
<p>The use of next-generation sequencing technology has enabled phylogenetic studies with hundreds of thousands of taxa. Such large-scale phylogenies have become a critical component in genomic epidemiology in pathogens such as SARS-CoV-2 and influenza A virus. However, detailed phenotypic characterization of pathogens or generating a computationally tractable dataset for detailed phylogenetic analyses requires objective subsampling of taxa. To address this need, we propose PARNAS, an objective and flexible algorithm to sample and select taxa that best represent observed diversity by solving a generalized k-medoids problem on a phylogenetic tree. PARNAS solves this problem efficiently and exactly by novel optimizations and adapting algorithms from operations research. For more nuanced selections, taxa can be weighted with metadata or genetic sequence parameters, and the pool of potential representatives can be user-constrained. Motivated by influenza A virus genomic surveillance and vaccine design, PARNAS can be applied to identify representative taxa that optimally cover the diversity in a phylogeny within a specified distance radius. We demonstrated that PARNAS is more efficient and flexible than existing approaches. To demonstrate its utility, we applied PARNAS to (i) quantify SARS-CoV-2 genetic diversity over time, (ii) select representative influenza A virus in swine genes derived from over 5 years of genomic surveillance data, and (iii) identify gaps in H3N2 human influenza A virus vaccine coverage. We suggest that our method, through the objective selection of representatives in a phylogeny, provides criteria for quantifying genetic diversity that has application in the the rational design of multivalent vaccines and genomic epidemiology. PARNAS is available at <a href="https://github.com/flu-crew/parnas">https://github.com/flu-crew/parnas</a>.</p>
Results of the SMS Survey on soil stakeholder interests related to the EU Soil Mission objectives
<p>Soil Mission Support results of the online survey on soil stakeholder interests related to the EU Soil Mission objectives</p> <p>The processed survey results can be found in SMS D3.4 "Report on prioritization of actor needs and criteria for living lab and lighthouse identification" (https://zenodo.org/record/7695582#.ZAdE-NXMI2w)</p>
Figures and Data Set of "The Effelsberg survey of FU Orionis and EX Lupi objects I. - Host environments of FUors/EXors traced by NH3"
<p>This is the full version of Figure 1 and the fits files for the NH<sub>3</sub> detections for the paper "The Effelsberg survey of FU Orionis and EX Lupi objects I. - Host environments of FUors/EXors traced by NH<sub>3</sub>".</p> <p>The paper is published in Astronomy & Astrophysics, and can be found at: <a href="https://doi.org/10.1051/0004-6361/202244911">https://doi.org/10.1051/0004-6361/202244911</a></p> <p> </p>
Toward Early and Objective Hand Osteoarthritis Detection by using EMG during grasps
<p>Dataset analyzed in the study "Toward Early and Objective Hand Osteoarthritis Detection by using EMG during grasps". Use of the data requires proper reference to [1].</p> <p>Dataset contains Electromyographic data from forearm, recorded with an 8-channel sEMG Biometrics Ltd. device. The fields contained in the structure are those detailed in the following scheme::</p> <ul> <li>Group: 0 for healthy subjects; 1 for HOA patients</li> <li>Subject: subject ID;</li> <li>Grasp: grasp ID, according to Figure 1 [1];</li> <li>Raw EMG data (7 columns): Raw sEMG data without any filter and not resampled, for the seven representative spot areas according to [2].</li> </ul> <p>[1] Jarque-Bou, N.J.; Gracia-Ibáñez, V.; Roda-Sales, A.; Bayarri-Porcar, V.; Sancho-Bru, J.L.; Vergara, M. Toward Early and Objective Hand Osteoarthritis Detection by Using EMG during Grasps. <em>Sensors</em> <strong>2023</strong>, <em>23</em>, 2413. https://doi.org/10.3390/s23052413</p> <p>[2] Jarque-Bou, N. J., Vergara, M., Sancho-Bru, J. L., Alba, R.-S. & Gracia-Ibáñez, V. Identification of forearm skin zones with similar muscle activation patterns during activities of daily living. <em>J. NeuroEngineering Rehabil. </em>(2018).</p> <p> </p>
Behavioral responses to a series of similar but distinct novel objects in house sparrows
<p>Generalization refers to the process by which animals assign different stimuli into cognitive categories based on their similarity/dissimilarity to previously experienced stimuli. Stimuli which strongly deviate from previous experience may be avoided due to their novelty (i.e., neophobia), while stimuli which are sufficiently similar to a known/recognized cue may elicit the same response as that cue (i.e., generalization). While generalization has been widely researched, few studies have examined among-individual variance in its expression. Our study quantified among-individual variation in neophobia and generalization in house sparrows (<em>Passer domesticus</em>) by placing a series of different objects next to each subject's food source on successive days and measuring the subject's latency to approach the food source in the presence vs. absence of each object. Similarly to previous work, we found that house sparrows are neophobic on average and exhibit significant among-individual variance in neophobia. More interestingly, we found that the neophobic response declined across presentations of different novel objects. This implies that the sparrows generalized some aspect(s) of the objects. We also found significant among-individual variance in the rate at which approach latency changed across this series, possibly reflecting individual differences in propensity to generalize. These results raise new questions about how neophobia, habituation, and generalization are linked and about the potential for selection to act on these traits under different ecological conditions.</p>
Data of FAIR Assessment in Research Objects with FAIROs
<p>This data has been used to test the FAIRness of Research Objects of the platform ROHub (https://reliance.rohub.org).</p> <p>It is composed by two sets:</p> <p>* Folder research objects: It contains the collection of Research Objects used.</p> <p>* Folder assessment: It contains the assessment of each Research Object using FAIROs.</p>
The optimal time to approach an unfamiliar object: A Bayesian model
<p>Many organisms take time before approaching unfamiliar objects. This caution forms the basis of some well-known assays in the fields of behavioral ecology, comparative psychology and animal welfare, including quantifying the personality traits of individuals and evaluating the extent of their neophobia. In this paper we present a mathematical model which identifies the optimal time an observer should wait before approaching an unfamiliar object. The model is Bayesian, and simply assumes that the longer the observer goes without being attacked by an unfamiliar object, the lower will be the observer's estimated probability that the object is dangerous. Given the information gained, a time is reached at which the expected benefits from approaching the object begin to exceed the costs. The model not only explains why latency to approach may be repeatable among individuals and vary with the object's appearance, but also why individuals habituate to the stimulus, approaching it more rapidly over repeated trials. We demonstrate the applicability of our model by fitting it to published data on the time taken by chicks to attack artificial caterpillars which share no, one, or two signaling traits with snakes (eyespots and posture). We use this example to show that while the optimal time to attack an unfamiliar object reflects the observer's expectation that the object is dangerous, the rate at which habituation arises is also a function of the observer's certainty in their belief. In so doing, we explain why observers become more rapidly habituated to "weaker" stimuli than "stronger" ones. </p>
Legibility of Multi-Robot Systems: examples of coordination objectives
<p>Video examples used to introduce the coordination objectives to the users, when performing user studies to assess legibility of multi-robot systems.</p>
How do patients sleep after orthopedic surgery? Changes in objective sleep parameters and pain in hospitalized patients undergoing hip and knee arthroplasty
<p><strong>Aim.</strong> Sleep impairment after hip and knee arthroplasty is multifactorial and still poorly understood and post-operative pain is a potential cause of sleep deprivation or disturbances. The aim of this study was to assess actigraphy-based sleep characteristics and pain scores in patients undergoing knee or hip joint replacement and hospitalized for ten days after surgery. <strong>Methods.</strong> In this observational cohort study, n=20 subjects (11 males and 9 females; age: 64.0±10.39 years old) completed a daily sleep diary and wore the Actiwatch 2 actigraph (Philips Respironics, Portland, OR) to record sleep parameters for 11 consecutive days, starting the day before surgery and ending the 10<sup>th</sup> day after surgery. Subjective scores of pain, by a visual analog scale (VAS), were constantly monitored for the entire experimental protocol. The following evaluation time-points have been considered for the analysis: pre-surgery (PRE), the first (POST1), the fourth (POST4) and the tenth day (POST10) after surgery. A repeated-measures one-way analysis of variance followed by the Tukey-Kramer post hoc tests, or the equivalent nonparametric Friedman test followed by the Dunn’s multiple comparisons, was applied to test differences in sleep and pain among time-points. Test for correlation between mean VAS score and the sleep parameters at each post-operative time point were performed using Pearson’s method. <strong>Results.</strong> Sleep quantity and timing parameters did not differ from PRE to POST10, during the hospitalization. On the contrary, Sleep Efficiency (p=0.006; η<sup>2</sup><sub>p</sub>=0.45, large), Sleep Latency (p=0.039; η<sup>2</sup><sub>p</sub>=0.37, large), Wake After Sleep Onset (p=0.042; η<sup>2</sup><sub>p</sub>=0.34, large), Immobility Time (p=0.005; η<sup>2</sup><sub>p</sub>=0.47, large) and Fragmentation Index (p=0.016; η<sup>2</sup><sub>p</sub>=0.36, large) displayed significant differences. In detail, Sleep Efficiency and Immobility Time significantly decreased at POST1 compared to PRE by 10.8% (p=0.003; ES: 0.9, moderate) and 9.4% (p=0.005; ES: 0.86, moderate) respectively. Overall, all sleep quality parameters showed a trend of constant improvement from POST1 to POST10. VAS scores were higher in the 1<sup>st</sup> day post-surgery (4.58 ± 2.46; p=0.0011 and ES: 1.40, large) compared to POST10 (1.68 ± 1.58) and in POST4 (3.85 ± 2.31) compared to POST10 (-2.18; p=0.0087 and ES: 1.09, moderate). During time, mean VAS showed significant negative correlations with mean Sleep Efficiency (r = -0.71; p=0.021) and mean Immobility Time (r = - 0.83; p=0.003) and a significant positive correlation with Fragmentation Index (r = 0.70; p=0.023), highlighting that high scores of pain were associated with lower overall sleep quality. <strong>Conclusion.</strong> Sleep quantity and timing parameters were stable during the entire hospitalization whereas sleep quality parameters significantly worsened the 1<sup>st</sup> night after surgery compared to the pre-surgery night and, overall, these parameters showed a trend of constant improvement until discharge. Pain scores were at the highest level the 1<sup>st</sup> day after surgery and at the lowest level before discharge with a negative correlation between pain and sleep quality parameters during time. The present study confirms the important impact of surgery on sleep parameters, especially on the first few post-operative days.</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.