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166 results for “image classification”
BRAIN Journal-An Enhancement over Texture Feature Based Multiclass Image Classification under Unknown Noise-Figure 3. Applying Wiener Filter on noisy image
<p>The Wiener filter is the mean square error-optimal stationary linear filter for images<br> degraded by additive noise and blurring. It removes the additive noise and inverts the blurring<br> simultaneously. The Wiener filtering is a linear estimation of the original image. The approach is<br> based on a stochastic framework.</p>
BRAIN Journal-An Enhancement over Texture Feature Based Multiclass Image Classification under Unknown Noise-Figure 2. Original image affected by Gaussian noise
<p>In this research work we have detected Gaussian noise (fig 2) pattern in our images based on<br> methodology discussed in [18]. Where statistical moments features are extracted from the noise<br> patterns for noise class detection. This experiment detects the Gaussian noise patterns from images.<br> This leads to applying of wiener filter on noisy images, which gives the best noise removal.</p>
Figure 19. Dynamics of the filtered images with the 9 algorithms and the 2 methods of classification-Efficient Filtering of Noisy Fingerprint Images
<p>The classification (Malik, Gautam, Sahai, Jha & Singh, 2013) and ranking stage can be visualized in the Figure 19, the summary of the filtered images is shown in Table 3 and the pseudocode of the current step can be visualized in Figure 18. The overall results show that the two selection criterion: fuzzy and aggregation indicate that the most efficient algorithm is A6 and according to each criterion there can be made certain decisions to choose the best filters for each situation. Also, the results are influenced by the parameters set to calibrate the filtering, the fuzzy profiles, the weighted sum or the vicinity approach.</p>
BRAIN Journal-ANNSVM: A Novel Method for Graph-Type Classification by Utilization of Fourier Transformation, Wavelet Transformation, and Hough Transformation-Figure 8. Simulation of Coiflet 1 (PyWavelets discussion group, 2008), analyzing as one-dimensional images
<p>Using only the wavelet coefficients was inadequate for classification. For example, for the pie chart, we obtained large wavelet coefficients located in the low-frequency domain; however, if we changed a circle in the pie chart to other shapes, such as a radar chart, the wavelet transformation gave results that were similar to those of the original pie chart. The Hough transformation can solve this problem since it detects the shapes of objects</p>
BRAIN Journal-ANNSVM: A Novel Method for Graph-Type Classification by Utilization of Fourier Transformation, Wavelet Transformation, and Hough Transformation-Figure 2. Illustrating the core process of one-dimensional image construction by applying a DFT
<p>First, we collect graph images as raw data, which contain different scales and sizes, and therefore need to be normalized. We clean the images by omitting irrelevant areas. For example, we omit unnecessary text that has nothing to do with our classification procedure. Moreover, to standardize the sizes and shapes of the images, we resize and reshape them to be 64 x 64 squares. Second, we examine each image pixel, each of which contains one color value. After each pixel is projected along the x- and y-axes, we count the number of projected pixels with a color value greater than zero to reduce image dimensionality. We, therefore, obtain two one-dimensional images from the x- and y-axes. </p>
MSD-I: Million Song Dataset with Images for Multimodal Genre Classification
<p>The Million Song Dataset (https://labrosa.ee.columbia.edu/millionsong/) is a collection of metadata and precomputed audio features for 1 million songs. Along with this dataset, a dataset with annotations of 15 top-level genres with a single label per song was released. In our work, we combine the CD2c version of this genre datase (http://www.tagtraum.com/msd_genre_datasets.html) with a collection of album cover images. </p> <p><br> The final dataset contains 30,713 tracks from the MSD and their related album cover images, each annotated with a unique genre label among 15 classes. Based on an initial analysis on the images, we identified that this set of tracks is associated to 16,753 albums, yielding an average of 1.8 songs per album.</p> <p>We randomly divide the dataset into three parts: 70% for training, 15% for validation, and 15% for test, with no artist and album overlap across these sets. This is crucial to avoid possible overfitting, as the classifier may learn to predict the artist instead of the genre. </p> <p> </p> <p>Content:</p> <p>MSD-I dataset (mapping, metadata, annotations and links to images)<br> Data splits and feature vectors for TISMIR single-label classification experiments </p> <p>These data can be used together with the Tartarus deep learning python module https://github.com/sergiooramas/tartarus.</p> <p> </p> <p>Scientific References:</p> <p>Please cite the following paper if using MSD-I dataset or Tartarus software.</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>
Data for "On the impact of Citizen Science-derived data quality on deep learning based classification in marine images"
<p>This dataset contains all the annotations done by either citizen scientists or experts of the publication: "On the impact of Citizen Science-derived data quality on deep learning based classification in marine images"</p> <p><strong>CSP.csv</strong> -> CS annotations of the Citizen Science Primer-experiment</p> <p><strong>CSPExpert.csv</strong> -> Expert annotations of the Citizen Science Primer-experiment</p> <p><strong>CSS.csv</strong> -> CS annotations of the Citizen Science Study</p> <p><strong>CSSExpert.csv</strong> -> Expert annotations of the Citizen Science Study</p> <p>Visual exploration of the image data is possible in the BIIGLE 2.0 image annotation system at <a href="https://biigle.de/projects/139">https://biigle.de/projects/159</a> using the login <em><a href="mailto:cs@example.com">cs@example.com</a></em> and the password <em>plosonecs</em>.</p>
Lumbar Spine Vertebral Compression Fractures (VCFs) Dataset: MRI T1-Weighted Images for Benign and Malignant Classification
<p>This dataset was prepared for the study of classification of benign vertebral compression fractures (VCFs) secondary to osteoporosis and malignant VCFs secondary to neoplastic infiltration. The original study in which it was used aimed to assist in differentiating these conditions using three-dimensional radiomic techniques and artificial neural networks.</p> <p>This dataset was assembled from sagittal T1-weighted magnetic resonance imaging (MRI) scans of the lumbar spine obtained from consecutive patients diagnosed with benign or malignant VCFs at the University Hospital of the Ribeirão Preto Medical School (HCFMRP) between the years 2010 and 2019. The images were acquired using the Philips Achieva 1.5 T and 3 T MRI systems and were stored in the DICOM (Digital Imaging and Communications in Medicine) format.</p> <p>The compilation of the dataset followed a rigorous selection and filtering process. From the initial set of cases of vertebral fractures in the lumbar region, patients who had received prior treatment (such as chemotherapy, radiotherapy, or surgery), those with fractures of traumatic etiology, old fractures, patients under 18 years old, and cases of malignant fractures without biopsy confirmation were excluded. With these exclusions, the final set consists of 91 patients (36 men and 55 women, with a mean age of 64.24 ± 11.75 years), of which 47 have benign VCFs and 44 have malignant VCFs.</p> <p>For the segmentation of fractured vertebrae, the images were pre-processed by normalizing the intensity to 256 gray levels (0 to 255), and histogram equalization was applied to improve contrast. The vertebrae were semi-automatically segmented using the 3D Slicer software. Each segmentation was saved in the "nrrd" format, native to 3D Slicer. The entire segmentation process, as well as the definition and application of exclusion criteria, were supervised by a senior radiologist with 20 years of experience in musculoskeletal radiology.</p> <p>The structure of this dataset includes, in addition to the DICOM exams, a directory containing the requantized images to 256 gray levels in the "nrrd" format and another with the segmentation files in the "seg.nrrd" format. A spreadsheet with detailed information about each patient's class, sex, age, and which vertebral bodies were segmented is also available. All DICOM files in this dataset have been anonymized to ensure patient privacy.</p> <p>This dataset was structured to provide a robust basis for the training and validation of machine learning models focused on the classification of vertebral compression fractures. It was designed to aid in the massive three-dimensional extraction of radiomic features, enabling the search for radiomic signatures capable of assisting radiologists in the accurate characterization of these fractures.</p> <p>For more information on how the dataset was created and used, please refer to the original article: <a href="https://link.springer.com/article/10.1007/s10278-023-00847-4" target="_blank" rel="noopener"><em>Chiari-Correia NS, Nogueira-Barbosa MH, Chiari-Correia RD, Azevedo-Marques PM. A 3D Radiomics-Based Artificial Neural Network Model for Benign Versus Malignant Vertebral Compression Fracture Classification in MRI. J Digit Imaging. 2023;36(4):1565-1577. doi:10.1007/s10278-023-00847-4</em></a></p>
Companion data artifacts: Technical framework demonstration for deep learning-based wood species classification with advanced sub-μ-CT imaging
<p>This is the companion data artifact collection for the IWAWA paper manuscript by Jannik Stebani, Tim Lewandrowski, Kilian Dremel, Simon Zabler and Volker Haag.It is generally to be used with the visualization and prediction showcases implemented in the Binder notebooks launched from this <a href="https://github.com/stebix/woodnet-showcase" target="_blank" rel="noopener">woodnet-showcase</a> GitHub repository.</p> <p>The artifacts amount to the following:</p> <ol> <li><code>acer-artifact.hdf5</code> : Exemplary <code>(256, 256, 256)</code> subvolume from a <em>Acer pseudoplatanus</em> sub-μ-CT scan</li> <li><code>pinus-artifact.hdf5</code> : Exemplary <code>(256, 256, 256)</code> subvolume from a <em>Pinus sylvestris </em>sub-μ-CT scan</li> <li><code>weights-artifact.pth</code> : Exemplary PyTorch trained weights for a woodnet/deep neural network to demonstrate classification of the above samples</li> </ol>
FIG. 8. Laser confocal microscopic images. A. Lipokophila eberhardi, female abdomen showing copulatory tubes. B in New genera and species of Plokiophilidae from Australia, Fiji, and Southeast Asia, with a revised classification of the family (Insecta: Heteroptera: Cimicoidea)
FIG. 8. Laser confocal microscopic images. A. Lipokophila eberhardi, female abdomen showing copulatory tubes. B. Heissophila macrotheleae, female abdomen, showing large asymmetrical "vagina" and absence of copulatory tubes. Abbreviations: ct, copulatory tube; vg, vagina (bursa copulatrix).
Arc fault detection and appliances classification in AC home electrical networks using Recurrence Quantification Plots and Image Analysis
<p>The data provided can be used for the development of methods for the detection of arcing faults in a domestic low-voltage electrical networks (230V - 50 Hz). The data files are current and voltage signatures experimentally measured.</p> <p>The ReadMe file describes :</p> <p>- the test set up and the the procedure followed to make the measurements</p> <p>- the list of household appliances and their main characteristics.</p> <p>- the name of the data files</p> <p>- the type of arcing faults</p> <p> </p>
WHU-OHS: A benchmark dataset for large-scale Hyperspectral Image classification
<p>The WHU-OHS dataset is made up of 42 OHS satellite images acquired from more than 40 different locations in China. The imagery has a spatial resolution of 10 m (nadir) and a swath width of 60 km (nadir). There are 32 spectral channels ranging from the visible to near-infrared range, with an average spectral resolution of 15 nm. We cropped each image into 512 × 512 pixels with a stride of 32. There are 4822, 513, and 2460 sub-images in the training, validation, and test sets, respectively.</p> <p>For transferability test, we choose eight pairs of OHS images, and each pair contains one source image (S) and one target image (T):</p> <p>S1: Changchun</p> <p>T1: Jilin</p> <p>S2: Wuxi</p> <p>T2: Shanghai</p> <p>S3: Guangzhou</p> <p>T3: Zhongshan</p> <p>S4: Xining</p> <p>T4: Lanzhou</p> <p>S5: Hetian</p> <p>T5: Kelamayi</p> <p>S6: Anyi</p> <p>T6: Nanchang</p> <p>S7: Changde</p> <p>T7: Changsha</p> <p>S8: Tianjin</p> <p>T8: Tangshan</p> <p>The 26 OHS images except for the eight pairs:</p> <p>O1: Baoding</p> <p>O2: Chongqing</p> <p>O3: Fujin</p> <p>O4: Huainan</p> <p>O5: Huhehaote</p> <p>O6: Jinzhong</p> <p>O7: Luliang</p> <p>O8: Manasi_1</p> <p>O9: Manasi_2</p> <p>O10: Nanmulin</p> <p>O11: Neimenggu</p> <p>O12: Qingdao</p> <p>O13: Qinghuangdao</p> <p>O14: Shawan</p> <p>O15: Shenyang</p> <p>O16: Shuozhou</p> <p>O17: Songpan</p> <p>O18: Taian</p> <p>O19: Tongjiang_1</p> <p>O20: Tongjiang_2</p> <p>O21: Wuzhong</p> <p>O22: Xundian</p> <p>O23: Xuzhou</p> <p>O24: Yidu</p> <p>O25: Zangzu</p> <p>O26: Zhongshan</p> <p>The image patches have been normalized and scaled by 10000 to reduce storage cost. Divide the pixel values by 10000 and then the image patches can be used directly.</p>
DeepHisto: Dataset for glioma subtype classification from Whole Slide Images
<p>DeepHisto dataset contains tiles (patches) of hematoxylin and eosin stained Whole Slide Images (WSI) of 28 adult-type diffuse glioma cases collected at the National Center of Pathology (NCP), Luxembourg National Health Laboratory (Laboratoire national de santé - LNS) from 2017 to 2021. WSIs were acquired with an IntelliSite Ultra Fast digital slide scanner from Philips containing a 20x/0.75 NA Plan Apo objective with an average slide resolution of 0.25um/pixel.</p> <p>Three primary diffuse glioma subtypes are classified into IDH-mutant, 1p/19q codeleted oligodendroglioma, IDH-mutant astrocytoma, and IDH-wildtype glioblastoma according to the 5th edition of the WHO classification of central nervous system tumors. The brain WSIs of a non-cancer patients were used as normal brain (white and gray matter) controls.</p> <p>Region annotation of WSIs was done by a board-certified pathologist, and the regions of interest are further divided into square 512×512 tiles, each of them associated with a particular class denoting the respective tumor entity, normal brain tissue or necrosis.<br> Tiles are further divided into training and test subsets patient-wise.</p>
Materials for the "Unsupervised classification (clustering) of satellite images" workshop
<p>Dataset for the "<strong>Unsupervised classification (clustering) of satellite images</strong>" workshop on <a href="https://opengeohub.org/summer-school/opengeohub-summer-school-poznan-2023/">OpenGeoHub Summer School 2023</a>. The repository with the code can be found on GitHub: <a href="https://github.com/kadyb/OGH2023">https://github.com/kadyb/OGH2023</a>.</p> <p>In the .zip archive there are two catalogs: "<em>data</em>" and "<em>task</em>", which include:</p> <ul> <li>Landsat 8 scene (7 spectral bands) + metadata;</li> <li>polygons with coverage of Poznań and Szamotuły counties.</li> </ul> <p>The satellite data was downloaded from <a href="https://earthexplorer.usgs.gov/">EarthExplorer</a>.</p>
Cervical intraepithelial neoplasia acetic acid white images - pre-cancerous lesion three-class classification
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Classifications of auroral phenomena in THEMIS All-Sky images obtained via self-supervised learning
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MedMNIST Classification Decathlon: A Lightweight AutoML Benchmark for Medical Image Analysis
<p>This data repository for MedMNIST v1 is out of date! Please check the <a href="http://medmnist.github.io">latest version</a> of MedMNIST v2. </p> <p> </p> <p><strong>Abstract</strong></p> <p>We present MedMNIST, a collection of 10 pre-processed medical open datasets. MedMNIST is standardized to perform classification tasks on lightweight 28x28 images, which requires no background knowledge. Covering the primary data modalities in medical image analysis, it is diverse on data scale (from 100 to 100,000) and tasks (binary/multi-class, ordinal regression and multi-label). MedMNIST could be used for educational purpose, rapid prototyping, multi-modal machine learning or AutoML in medical image analysis. Moreover, MedMNIST Classification Decathlon is designed to benchmark AutoML algorithms on all 10 datasets; We have compared several baseline methods, including open-source or commercial AutoML tools. The datasets, evaluation code and baseline methods for MedMNIST are publicly available at <a href="https://medmnist.github.io/">https://medmnist.github.io/</a>.</p> <p> </p> <p>Please note that this dataset is <strong>NOT</strong> intended for clinical use.</p> <p> </p> <p>We recommend our official <a href="https://github.com/MedMNIST/MedMNIST">code</a> to download, parse and use the MedMNIST dataset:</p> <blockquote> <pre>pip install medmnist</pre> </blockquote> <p> </p> <p><strong>Citation and Licenses</strong></p> <p>If you find this project useful, please cite our ISBI'21 paper as:<br> <em> Jiancheng Yang, Rui Shi, Bingbing Ni. "MedMNIST Classification Decathlon: A Lightweight AutoML Benchmark for Medical Image Analysis," arXiv preprint arXiv:2010.14925, 2020.</em><br> <br> or using bibtex:<br> <em> @article{medmnist,<br> title={MedMNIST Classification Decathlon: A Lightweight AutoML Benchmark for Medical Image Analysis},<br> author={Yang, Jiancheng and Shi, Rui and Ni, Bingbing},<br> journal={arXiv preprint arXiv:2010.14925},<br> year={2020}<br> }</em></p> <p>Besides, please cite the corresponding paper if you use any subset of MedMNIST. Each subset uses the <strong>same license</strong> as that of the source dataset.</p> <p> </p> <p><strong>PathMNIST</strong></p> <p>Jakob Nikolas Kather, Johannes Krisam, et al., "Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study," PLOS Medicine, vol. 16, no. 1, pp. 1–22, 01 2019.</p> <p><em><strong>License</strong>: <a href="https://creativecommons.org/licenses/by/4.0/">CC BY 4.0</a></em></p> <p> </p> <p><strong>ChestMNIST</strong></p> <p>Xiaosong Wang, Yifan Peng, et al., "Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases," in CVPR, 2017, pp. 3462–3471.</p> <p><em><strong>License</strong>: <a href="https://creativecommons.org/publicdomain/zero/1.0/">CC0 1.0</a></em></p> <p> </p> <p><strong>DermaMNIST</strong></p> <p>Philipp Tschandl, Cliff Rosendahl, and Harald Kittler, "The ham10000 dataset, a large collection of multisource dermatoscopic images of common pigmented skin lesions," Scientific data, vol. 5, pp. 180161, 2018.</p> <p>Noel Codella, Veronica Rotemberg, Philipp Tschandl, M. Emre Celebi, Stephen Dusza, David Gutman, Brian Helba, Aadi Kalloo, Konstantinos Liopyris, Michael Marchetti, Harald Kittler, and Allan Halpern: “Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)”, 2018; arXiv:1902.03368.</p> <p><em><strong>License</strong>: <a href="https://creativecommons.org/licenses/by-nc/4.0/">CC BY-NC 4.0</a></em></p> <p> </p> <p><strong>OCTMNIST/PneumoniaMNIST</strong></p> <p>Daniel S. Kermany, Michael Goldbaum, et al., "Identifying medical diagnoses and treatable diseases by image-based deep learning," Cell, vol. 172, no. 5, pp. 1122 – 1131.e9, 2018.</p> <p><em><strong>License</strong>: <a href="https://creativecommons.org/licenses/by/4.0/">CC BY 4.0</a></em></p> <p> </p> <p><strong>RetinaMNIST</strong></p> <p>DeepDR Diabetic Retinopathy Image Dataset (DeepDRiD), "The 2nd diabetic retinopathy – grading and image quality estimation challenge," https://isbi.deepdr.org/data.html, 2020.</p> <p><em><strong>License</strong>: <a href="https://creativecommons.org/licenses/by/4.0/">CC BY 4.0</a></em></p> <p> </p> <p><strong>BreastMNIST</strong></p> <p>Walid Al-Dhabyani, Mohammed Gomaa, Hussien Khaled, and Aly Fahmy, "Dataset of breast ultrasound images," Data in Brief, vol. 28, pp. 104863, 2020.</p> <p><em><strong>License</strong>: <a href="https://creativecommons.org/licenses/by/4.0/">CC BY 4.0</a></em></p> <p> </p> <p><strong>OrganMNIST_{Axial,Coronal,Sagittal}</strong></p> <p>Patrick Bilic, Patrick Ferdinand Christ, et al., "The liver tumor segmentation benchmark (lits)," arXiv preprint arXiv:1901.04056, 2019.</p> <p>Xuanang Xu, Fugen Zhou, et al., "Efficient multiple organ localization in ct image using 3d region proposal network," IEEE Transactions on Medical Imaging, vol. 38, no. 8, pp. 1885–1898, 2019.</p> <p><em><strong>License</strong>: <a href="https://creativecommons.org/licenses/by/4.0/">CC BY 4.0</a></em></p> <div> <div class="gtx-trans-icon"> </div> </div>
A dataset of 1600 images extracted from 5 cm RGB orthophotos for the classification of 12 classes of roofing materials
<p>This dataset contains a collection of 1601 image tiles of 64x64 pixels (3.2x3.2m²) annotated for 12 roofing materials. These tiles were extracted from 5 cm RGB orthophotos acquired by the city of Namur (Belgium) in 2017. The additional data used to create this dataset are (a) a Namur roof section mask, and (b) a set of 1601 material samples acquired using stratified random sampling. The tiles were obtained as follows: the centroid of each roof section containing a sample is used to extract tiles. A size of 64x64 pixels has been chosen so that a tile contains information for only one roof section, in order to learn only the colour and texture of the roof materials. This also avoids adding information outside the given roof section. The tiles are thus extracted for each orthophoto spectral band and labelled with the identifier of the class of roofing materials to which they belong. Here are the 12 material classes considered, preceded by their labels:</p> <p>0- Solar panels<br>1- Brown tiles<br>2- Orange tiles<br>3- Black tiles<br>4- Dark membranes<br>5- White membranes<br>6- Slates containing asbestos<br>7- Slates without asbestos<br>8- Corrugated asbestos-cement sheets<br>9- Gravel<br>10- Vegetation<br>12- Metals</p> <p>There are approximately 140 tiles by material class except for the vegetated roof sections (class 10) which contains only 47 samples due to its rarety.</p> <p>The dataset contains 1 folder for each spectral band. Each folder contains 1601 thumbnails in tif format named as follows:</p> <p><strong>img[tile id]_[class label].tif</strong></p> <p>It is suggested to apply pre-processing to these images as done by<a href="https://doi.org/10.1109/jurse57346.2023.10144142"> Wyard et al. (2023).</a></p>
Data set: Al-Biruni Earth Radius Optimization with Deep Transfer Learning based Scene Image Classification on Remote Sensing Imagery
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Toxic Sentence Classification Dataset with labels of categories such as religion, mental health, race, sex, body image, disability, physical abuse, and politics
<p>The dataset has a collection of various toxic sentences belonging to different categories. It was collected from various sources. It indicates which category each sentence belongs to. The values of the category columns are binary 1 or 0 indicating whether the sentence belongs to that particular category or not. Each sentence belongs to only 1 category. </p> <p> </p> <p>Columns:<br>1.comment_text: Contains toxic sentences that are insensitive and offensive, focusing on various categories.<br>2.mental_health: Binary value 1 indicates that the sentence focuses on mental health.<br>3.Race:Binary value 1 indicates that the sentence is racist.<br>4.sex:Binary value 1 indicates that the sentence focuses on sexuality.<br>5.body_image:Binary value 1 indicates that the sentence focuses on body image.<br>6.disability:Binary value 1 indicates that the sentence focuses on physical disability and related issues.<br>7.religion:Binary value 1 indicates that the sentence can be triggering to people who are extremely religious.<br>8.physical_abuse:Binary value 1 indicates that the sentence focuses on physical abuse issues.<br>9.politics:Binary value 1 indicates that the sentence focuses on political issues.</p>
ScienceDex guides
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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.