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979 results for “image dataset”
Active Region Magnetograms for Solar Flare Prediction: Extra Dataset Images for ARs 2470 through 2731
<p>This dataset is the extra images associated with Dryad dataset <a href="https://doi.org/10.5061/dryad.qjq2bvqmj">https://doi.org/10.5061/dryad.qjq2bvqmj</a>. These images are consistently sized images of active region magnetograms from the National Aeronautics and Space Administration's (NASA's) Solar Dynamics Observatory (SDO). These data are the full sized images (600x600 pixels) for active regions (ARs) 2470 through 2731 in .fits format. These are images that were removed from the preconfigured dataset https://doi.org/10.5061/dryad.jq2bvq898.</p>
Active Region Magnetograms for Solar Flare Prediction: Extra Dataset Images for ARs 1981 through 2469
<p>This dataset is the extra images associated with Dryad dataset <a href="https://doi.org/10.5061/dryad.qjq2bvqmj">https://doi.org/10.5061/dryad.qjq2bvqmj</a>. These images are consistently sized images of active region magnetograms from the National Aeronautics and Space Administration's (NASA's) Solar Dynamics Observatory (SDO). These data are the full sized images (600x600 pixels) for active regions (ARs) 1981 through 2469 in .fits format. These are images that were removed from the preconfigured dataset https://doi.org/10.5061/dryad.jq2bvq898.</p>
Active Region Magnetograms for Solar Flare Prediction: Extra Dataset Images for ARs 1528 through 1980
<p>This dataset is the extra images associated with Dryad dataset <a href="https://doi.org/10.5061/dryad.qjq2bvqmj">https://doi.org/10.5061/dryad.qjq2bvqmj</a>. These images are consistently sized images of active region magnetograms from the National Aeronautics and Space Administration's (NASA's) Solar Dynamics Observatory (SDO). These data are the full sized images (600x600 pixels) for active regions (ARs) 1528 through 1980 in .fits format. These are images that were removed from the preconfigured dataset https://doi.org/10.5061/dryad.jq2bvq898.</p>
DeepWeeds: A Multiclass Weed Species Image Dataset for Deep Learning
<p>DeepWeeds: A Multiclass Weed Species Image Dataset for Deep Learning</p> <p>This repository makes available the source code and public dataset for the work, "DeepWeeds: A Multiclass Weed Species Image Dataset for Deep Learning", published with open access by Scientific Reports: <a href="https://www.nature.com/articles/s41598-018-38343-3">https://www.nature.com/articles/s41598-018-38343-3</a>. The DeepWeeds dataset consists of 17,509 images capturing eight different weed species native to Australia in situ with neighbouring flora. In our work, the dataset was classified to an average accuracy of 95.7% with the ResNet50 deep convolutional neural network.</p> <p>The source code, images and annotations are licensed under <a href="https://creativecommons.org/licenses/by/4.0/">CC BY 4.0</a> license. The contents of this repository are released under an <a href="https://github.com/AlexOlsen/DeepWeeds/blob/master/LICENSE">Apache 2</a> license.</p> <p> </p> <p>Download the dataset images and our trained models</p> <ul> <li>images.zip (468 MB)</li> <li>models.zip (477 MB)</li> </ul> <p>Due to the size of the images and models they are hosted outside of the Github repository. The images and models must be downloaded into directories named "images" and "models", respectively, at the root of the repository. If you execute the python script (deepweeds.py), as instructed below, this step will be performed for you automatically.</p> <p> </p> <p><strong>TensorFlow Datasets</strong></p> <p>Alternatively, you can access the DeepWeeds dataset with <a href="https://www.tensorflow.org/datasets">TensorFlow Datasets</a>, TensorFlow's official collection of ready-to-use datasets. <a href="https://www.tensorflow.org/datasets/catalog/deep_weeds">DeepWeeds</a> was officially added to the TensorFlow Datasets catalog in August 2019.</p> <p> </p> <p><strong>Weeds and locations</strong></p> <p>The selected weed species are local to pastoral grasslands across the state of Queensland. They include: "Chinee apple", "Snake weed", "Lantana", "Prickly acacia", "Siam weed", "Parthenium", "Rubber vine" and "Parkinsonia". The images were collected from weed infestations at the following sites across Queensland: "Black River", "Charters Towers", "Cluden", "Douglas", "Hervey Range", "Kelso", "McKinlay" and "Paluma". The table and figure below break down the dataset by weed, location and geographical distribution.</p> <p> </p> <p> </p><p><strong>Data organization</strong></p> <p></p> <p>Images are assigned unique filenames that include the date/time the image was photographed and an ID number for the instrument which produced the image. The format is like so: <code>YYYYMMDD-HHMMSS-ID</code>, where the ID is simply an integer from 0 to 3. The unique filenames are strings of 17 characters, such as 20170320-093423-1.</p> <p> </p> <p><strong>labels</strong></p> <p>The labels.csv file assigns species labels to each image. It is a comma separated text file in the format:</p> <pre><code>Filename,Label,Species ... 20170207-154924-0,jpg,7,Snake weed 20170610-123859-1.jpg,1,Lantana 20180119-105722-1.jpg,8,Negative ... </code></pre> <p><em>Note: The specific label subsets of training (60%), validation (20%) and testing (20%) for the five-fold cross validation used in the paper are also provided here as CSV files in the same format as "labels.csv".</em></p> <p> </p> <p><strong>models</strong></p> <p>We provide the most successful ResNet50 and InceptionV3 models saved in Keras' hdf5 model format. The ResNet50 model, which provided the best results, has also been converted to UFF format in order to construct a TensorRT inference engine.</p> <pre><code>resnet.hdf5 inception.hdf5 resnet.uff </code></pre> <p> </p> <p><strong>deepweeds.py</strong></p> <p>This python script trains and evaluates Keras' base implementation of ResNet50 and InceptionV3 on the DeepWeeds dataset, pre-trained with ImageNet weights. The performance of the networks are cross validated for 5 folds. The final classification accuracy is taken to be the average across the five folds. Similarly, the final confusion matrix from the associated paper aggregates across the five independent folds. The script also provides the ability to measure the inference speeds within the TensorFlow environment.</p> <p>The script can be executed to carry out these computations using the following commands.</p> <ul> <li>To train and evaluate the ResNet50 model with five-fold cross validation, use <code>python3 deepweeds.py cross_validate --model resnet</code>.</li> <li>To train and evaluate the InceptionV3 model with five-fold cross validation, use <code>python3 deepweeds.py cross_validate --model inception</code>.</li> <li>To measure inference times for the ResNet50 model, use <code>python3 deepweeds.py inference --model models/resnet.hdf5</code>.</li> <li>To measure inference times for the InceptionV3 model, use <code>python3 deepweeds.py inference --model models/inception.hdf5</code>.</li> </ul> <p> </p> <p><strong>Dependencies</strong></p> <p>The required Python packages to execute deepweeds.py are listed in requirements.txt.</p> <p> </p> <p><strong>tensorrt</strong></p> <p>This folder includes C++ source code for creating and executing a ResNet50 TensorRT inference engine on an NVIDIA Jetson TX2 platform. To build and run on your Jetson TX2, execute the following commands:</p> <pre><code>cd tensorrt/src make -j4 cd ../bin ./resnet_inference </code></pre> <p> </p> <p><strong>Citations</strong></p> <p>If you use the DeepWeeds dataset in your work, please cite it as:</p> <p>IEEE style citation: “A. Olsen, D. A. Konovalov, B. Philippa, P. Ridd, J. C. Wood, J. Johns, W. Banks, B. Girgenti, O. Kenny, J. Whinney, B. Calvert, M. Rahimi Azghadi, and R. D. White, “DeepWeeds: A Multiclass Weed Species Image Dataset for Deep Learning,” <em>Scientific Reports</em>, vol. 9, no. 2058, <strong>2</strong> 2019. [Online]. Available: <a href="https://doi.org/10.1038/s41598-018-38343-3">https://doi.org/10.1038/s41598-018-38343-3</a> ”</p> <p> </p> <p><strong>BibTeX</strong></p> <pre><code>@article{DeepWeeds2019, author = {Alex Olsen and Dmitry A. Konovalov and Bronson Philippa and Peter Ridd and Jake C. Wood and Jamie Johns and Wesley Banks and Benjamin Girgenti and Owen Kenny and James Whinney and Brendan Calvert and Mostafa {Rahimi Azghadi} and Ronald D. White}, title = {{DeepWeeds: A Multiclass Weed Species Image Dataset for Deep Learning}}, journal = {Scientific Reports}, year = 2019, number = 2058, month = 2, volume = 9, issue = 1, day = 14, url = "https://doi.org/10.1038/s41598-018-38343-3", doi = "10.1038/s41598-018-38343-3" } </code></pre>
Dataset for surface waves height prediction through the video and image processing
<p>Image-based study of surface waves is a long lasting topic in ocean science and remote sensing. We believe that modern computers and new programming techniques can make a break-through in this area.</p> <p> </p> <p>This dataset provides some video files of surface wind waves of two kinds. First is a video snapshot of a quite large area. Second one is a zoom-in video of a spar-buoy (a stick) located in this field. According to the zoom-in video we may see the actual height of the wave in this particular point. This should be treated as a reliable data and so it can be used to calibrate the brightness field. I.e. the users of this dataset are welcome to train their model to obtain the height of the wave out of its brightness on the zoom-out large-area videos.</p> <p> </p> <p>All video files are readable by a conventional software. Records were taken at mild wind conditions in a gulf (fjord or skerry) of the Ladoga Lake. See "readme.pdf" for the details</p>
Active region magnetograms for solar flare prediction: Extra images dataset
<p>In this dataset, we provide a comprehensive collection of magnetograms from the National Aeronautics and Space Administration's (NASA's) Solar Dynamics Observatory (SDO). The dataset incorporates data from three sources and provides SDO Helioseismic and Magnetic Imager (HMI) magnetograms of solar active regions as well as labels of corresponding flaring activity. This dataset will be useful for image analysis or solar physics research related to magnetic structure, its evolution over time, and its relation to solar flares. The dataset will be of interest to those researchers investigating automated solar flare prediction methods, including supervised and unsupervised machine learning (classical and deep), binary and multi-class classification, and regression. This dataset contains those images that were removed from the preconfigured datasets (see usage notes below).</p>
Image dataset for cow identification, including code to train deep learning model, as well as analysis of results (SmARtview, 51088)
<p>This dataset and code was a result of the UKRI project "SmARtview: An AI-powered Augmented Reality Tool for Animal Health and Productivity", linked here: <a href="https://gtr.ukri.org/projects?ref=51088">https://gtr.ukri.org/projects?ref=51088</a></p> <p>These files are intended to be used for an accompanying publication in an academic journal.</p> <p>Anyone is free to use the contents for research and teaching purposes.</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>
OLID I: An Open Leaf Image Dataset of Bangladesh's Major Crops
<p>Artificial intelligence (AI) has taken the globe by storm since its inception, and the enormous agriculture sector is no exception. The progress of any AI-assisted mechanism is heavily reliant on massive training data. Although the application of AI in plant leaf management has garnered prominence in recent years, there is still a dearth of data, especially in the case of tropical and subtropical crops. In light of this, we present a public dataset containing 4,749 leaf images which include healthy, nutritionally deficient, and pest-infested leaves of tomato (<em>Solanum lycopersicum</em>), eggplant (<em>Solanum melongena</em>), cucumber (<em>Cucumis sativus</em>), bitter gourd (<em>Momordica charantia</em>), snake gourd (<em>Trichosanthes cucumerina</em>), ridge gourd (<em>Luffa acutangula</em>), ash gourd (<em>Benincasa hispida)</em>, and bottle gourd (<em>Lagenaria siceraria</em>). The dataset comprises 57 unique classes with high-resolution photos (3024 x 3024). The images have been captured at three different sites in Bangladesh in natural field settings and arduously labeled by an expert panel. This collection features the highest number of plant stress classes and the first multi-label classification problem in the agro-domain. The effective utilization of our dataset will result in an abundance of leaf disease diagnosis algorithms, pest identification and classification tools, and nutritional deficiency estimation strategies, to highlight a few.</p>
Unsupervised microscopy image registration datasets
<p>This repository contains the raw microscopy datasets for the article entitled "<strong>Unsupervised multimodal image registration with deep learning for biomedical microscopy".</strong></p> <p> </p>
COVID-19 medical image datasets
<p>This repository contains three curated datasets for the medical image classification described in the paper entitled "Explainable deep transfer learning fine-tunning with domain adaptation enables trustworthy COVID-19 prediction".</p>
Image dataset for GenerativeGI: Creating Generative Art with Genetic Improvement
<p>Full dataset of our results for our submission, "GenerativeGI: Creating Generative Art with Genetic Improvement," to the Special Issue on Genetic Improvement at the Automated Software Engineering journal. This zip archive contains the images and corresponding population data (including the genome that created each image) for all results reported in our submission.</p>
Volumetric segmentation of biological cells and subcellular structures for optical diffraction tomography images - dataset
<p>This dataset includes 4 files with segmentation results for 4 different ODT reconstructions of SH-SY5Y neuroblastoma cell. The segmentation results contain:</p> <ol> <li>3D binary masks of biological cells obtained through Cellpose [1] and <a href="https://github.com/biopto/ODT-SAS.git">ODT-SAS</a>;</li> <li>3D binary masks of organelles: nucleoli and lipid structures (LS) obtained through slice-by-slice manual segmentation and ODT-SAS.</li> </ol> <p>All files are .*mat files.</p> <p>The files <em>REC_SH-SY5Y_1.mat, REC_SH-SY5Y_2.mat</em> and<em> REC_SH-SY5Y_3.mat</em> consist of 7 variables:</p> <p>RECON – tomographic reconstruction of SH-SY5Y neuroblastoma cell;<br> n_imm – refractive index of object immersion medium;<br> dx – object space sample size in XY [<span class="math-tex">\(\mu m\)</span>];<br> rayXY – xy-coordinates of illumination vectors;</p> <p>maskManual – table with manually determined 3D binary masks of organelles;<br> maskCellpose – 3D binary mask of biological cell obtained through Cellpose;<br> maskODTSAS – table with 3D binary masks of biological cell and their organelles obtained through ODT-SAS.</p> <p>File <em>REC_SH-SY5Y_4.mat</em> includes masks for the ODT-SAS and Cellpose segmentation of three closely packed cells and consists of 5 variables: RECON, n_imm, dx, maskCellpose and maskODTSAS.<br> <br> Access a particular 3D binary mask from 'maskManual' and 'maskODTSAS' tables, using the following names: 'Cell', 'Nucleoli', 'LS'.<br> For example:</p> <pre><code>cellMask = maskODTSAS.Cell{1};</code></pre> <p><br> [1] Stringer, C., Wang, T., Michaelos, M., & Pachitariu, M. (2021). Cellpose: a generalist algorithm for cellular segmentation. Nature methods, 18(1), 100-106.</p> <p> </p>
Schistosoma Haematobium Egg Image Dataset
<p>This dataset comprises the following components:</p> <p>1. SHdataset: It contains 12,051 microscopic images taken from 103 urine samples, along with their corresponding segmentation masks manually annotated for <em>Schistosoma haematobium</em> eggs. The dataset is randomly partitioned into 80-20 train-test splits.</p> <p>2. diagnosis_test_dataset: This dataset includes 65 clinical urine samples. Each sample consists of 117 Field-of-View (FoV) images required to capture the entire filter membrane. Additionally, the dataset includes the diagnosis results provided by an expert microscopist.</p> <p>Samples were obtained from school-age children who had observed the presence of blood in their urine. These clinical urine samples were collected in 20 mL sterile universal containers as part of a field study conducted in the Federal Capital Territory (FCT), Abuja, Nigeria, in collaboration with the University of Lagos, Nigeria. The study received ethical approval from the Federal Capital Territory Health Research Ethics Committee (FCT-HREC) Nigeria (Reference No. FHREC/2019/01/73/18-07-19).</p> <p>The standard urine filtration procedure was used to process the clinical urine samples. Specifically, 10 mL of urine was passed through a 13 mm diameter filter membrane with a pore size of 0.2 μm. After filtration, the membrane was placed on a microscopy glass slide and covered with a coverslip to enhance the flatness of the membrane for image capture. The images were acquired using a digital microscope called the Schistoscope and were saved in PNG format with a resolution of 2028 X 1520 pixels and a size of approximately 2 MB.</p> <p>The annotation and microscopy analysis were performed by a team of two experts from the ANDI Centre of Excellence for Malaria Diagnosis, College of Medicine, University of Lagos, and Centre de Recherches Medicales des Lambaréné, CERMEL, Lambarene. The experts used the coco annotation tool to annotate the 12,051 images, creating polygons around the <em>Schistosoma haematobium</em> eggs. The output of the annotation process was a JSON file containing specific details about the image storage location, size, filename, and coordinates of all annotated regions.</p> <p>The segmentation mask images were generated from the JSON file using a Python program. The SHdataset was used to develop an automated diagnosis framework for urogenital schistosomiasis, while the diagnosis_test_dataset was used to compare the performance of the developed framework with the results from the expert microscopist.</p> <p>For further details about the dataset, more information can be found in the following articles:</p> <p>1. Oyibo, P., Jujjavarapu, S., Meulah, B., Agbana, T., Braakman, I., van Diepen, A., Bengtson, M., van Lieshout, L., Oyibo, W., Vdovine, G., and Diehl, J.C. (2022). "Schistoscope: an automated microscope with artificial intelligence for detection of Schistosoma haematobium eggs in resource-limited settings." <em>Micromachines</em>, 13(5), p.643.</p> <p>2. Oyibo, P., Meulah, B., Bengtson, M., van Lieshout, L., Oyibo, W., Diehl, J.C., Vdovine, G., and Agbana, T. (2023). "Two-stage automated diagnosis framework for urogenital schistosomiasis in microscopy images from low-resource settings." <em>Journal of Medical Imaging</em>. [Accepted Manuscript]</p>
Engineered cardiac microbundle time-lapse microscopy image dataset
<p>The "Microbundle Time-lapse Dataset" contains 24 experimental time-lapse images of cardiac microbundles using three distinct types of experimental testbed of beating lab grown hiPSC-based cardiac microbundles. Of the 24 experimental time-lapse images, 23 examples are brightfield videos, and a single example is a phase contrast video. We categorize the different experimental testbeds into 3 types, where "Type 1" includes movies obtained from standard experimental microbundle platforms termed microbundle strain gauges [1,2,3]. We refer to data collected from non-standard platforms termed FibroTUGs [4] as "Type 2" data, and "Type 3" data represents a highly versatile and diverse nanofabricated experimental platform [5,6].</p> <p><strong>References:</strong></p> <p>[1] Boudou T, Legant WR, Mu A, Borochin MA, Thavandiran N, Radisic M, Zandstra PW, Epstein JA, Margulies KB, Chen CS. A microfabricated platform to measure and manipulate the mechanics of engineered cardiac microtissues. Tissue Engineering Part A. 2012 May 1;18(9-10):910-9.</p> <p>[2] Xu F, Zhao R, Liu AS, Metz T, Shi Y, Bose P, Reich DH. A microfabricated magnetic actuation device for mechanical conditioning of arrays of 3D microtissues. Lab on a Chip. 2015;15(11):2496-503.</p> <p>[3] Bielawski KS, Leonard A, Bhandari S, Murry CE, Sniadecki NJ. Real-time force and frequency analysis of engineered human heart tissue derived from induced pluripotent stem cells using magnetic sensing. Tissue Engineering Part C: Methods. 2016 Oct 1;22(10):932-40.</p> <p>[4] DePalma SJ, Davidson CD, Stis AE, Helms AS, Baker BM. Microenvironmental determinants of organized iPSC-cardiomyocyte tissues on synthetic fibrous matrices. Biomaterials science. 2021;9(1):93-107.</p> <p>[5] Jayne RK, Karakan MÇ, Zhang K, Pierce N, Michas C, Bishop DJ, Chen CS, Ekinci KL, White AE. Direct laser writing for cardiac tissue engineering: a microfluidic heart on a chip with integrated transducers. Lab on a Chip. 2021;21(9):1724-37.</p> <p>[6] Karakan MÇ. A Direct-Laser-Written Heart-on-a-Chip Platform for Generation and Stimulation of Engineered Heart Tissues (Doctoral dissertation, Boston University, 2023).</p>
Test Dataset of AI Altered Images for Photogrammetric Reconstruction
<p>As a part of a test of the capability of using AI altered images in photogrammetry, I generated these image sets using the Prisma AI app. All of these datasets will result in a model in Agisoft Metashape. Prisma AI acts as a filter, changing an input image to the style of another artwork. </p> <p>Original images were collected using a Samsung S22 camera on the 1x zoom rear lens. The subject is a rock decoration in the Utrecht University Botanical Garden.</p> <p>Results can be seen on Sketchfab: https://sketchfab.com/gspeed0689/collections/prisma-photogrammetry-2e334a36c6e942798370d594de5e6f0c </p> <p>Included datasets:</p> <ul> <li>true-color.zip - The original images from the Samsung S22</li> <li>aviator-lowres - Processed images at normal (low resolution) quality with the filter labelled Aviator</li> <li>bubblegum-lowres - Processed images at normal (low resolution) quality with the filter labelled Bubblegum</li> <li>gothic-lowres - Processed images at normal (low resolution) quality with the filter labelled Gothic</li> <li>gothic-hd.zip - Processed images at HD (paid subscription required) quality with the filter labelled Gothic</li> <li>true-color.fbx - 3D model created with Agisoft Metashape using the parameters listed below and the images from true-color.zip</li> <li>aviator-lowres.fbx - 3D model created with Agisoft Metashape using the parameters listed below and the images from aviator-lowres.zip</li> <li>bubblegum-lowres.fbx - 3D model created with Agisoft Metashape using the parameters listed below and the images from bubblegum-lowres.zip</li> <li>gothic-lowres.fbx - 3D model created with Agisoft Metashape using the parameters listed below and the images from gothic-lowres.zip</li> <li>gothic-hd.fbx - 3D model created with Agisoft Metashape using the parameters listed below and the images from gothic-hd.zip</li> </ul> <p>Processing parameters in Agisoft Metashape v 1.7:</p> <ul> <li>Alignment defaults</li> <li>Dense cloud creation at "High" quality</li> <li>Mesh defaults</li> </ul>
Dataset for: Influence of colour vision on attention to, and impression of, complex aesthetic images
<p>Humans exhibit colour vision variations due to genetic polymorphisms, with trichromacy being the most common, while some people are classified as dichromats. Whether genetic differences in colour vision affect the way of viewing complex images remains unknown. Here, we investigated how people with different colour vision focused their gaze on aesthetic paintings by eye-tracking while freely viewing digital rendering of paintings and assessed individual impressions through a decomposition analysis of adjective ratings for the images. Gaze concentrated areas among trichromats were more highly correlated than those among dichromats. However, compared to the brief dichromatic experience with the simulated images, there was little effect of innate colour vision differences on impressions. These results indicate that chromatic information is instructive as a cue for guiding attention, whereas the impression of each person is generated according to their own sensory experience and normalised through one's own colour space.</p>
VNMPF-LIS: Validation Network Multiplatform Precipitation Feature (VNMPF) Dataset with International Space Station Lightning Imaging Sensor (ISS LIS) Data
<p>The Multiplatform Precipitation Feature (MPF) database combines ground- and space-based precipitation observations and retrievals from the Global Precipitation Measurement (GPM) mission Validation Network (VN) with space-based lightning measurements from the Lightning Imaging Sensor on board the International Space Station (ISS LIS). The data are synthesized in a thunderstorm-like, feature-based framework that encapsulates the microphysical, kinematic, and electrical properties of the observed storm.<br> <br> A VNMPF includes:</p> <p>- Radar information, GPM orbit, and ISS orbit <br> - Time/date information<br> - Geographical information<br> - Radar reflectivity characteristics<br> - Lightning energetic and identification information (where there is lightning)<br> - 3-dimensional wind information (where radars in dual-Doppler configuration are available)<br> <br> Version 1: 2017-2020</p> <p>Version 2: 2017-2022, updated VN winds </p>
Lung CT Deformable Image Registration Validation Dataset
<p>This dataset contains 30 different cases of CT image pairs, with a high number of vessel bifurcation landmark pairs identified in each case. These landmarks can be used for deformable image registration (DIR) algorithm validation and quality assurance. Images are obtained from several publicly available image repositories as well as clinical scans from Barnes Jewish Hospital.</p> <p> </p> <p>Guidelines for loading and visualizing data can be found on our Github at </p> <p>https://github.com/deshanyang/Lung-DIR-QA</p> <p> </p> <p>If you use our dataset, please cite the paper at </p> <p>https://doi.org/10.1002/mp.17026</p>
Image for the dataset in "Extraction of stratigraphic exposures on visible images using a supervised machine learning technique"
<p>This is the original and hand-masked image for the dataset used in a research paper "Extraction of stratigraphic exposures on visible images using a supervised machine learning technique".</p> <p>The content is</p> <ul> <li>Original images with hand-masked images (original_images_NOGUCHIandShoji.zip) <ul> <li>training/* : original images used for the training dataset generation (60 files)</li> <li>training_masks/* : hand-masked images for the training dataset generation (60 files)</li> <li>validation/* : original images used for the training dataset generation (10 files)</li> <li>validation_masks/* : hand-masked images for the validation dataset generation (10 files)</li> <li>test/* : original images used as the test data (5 files)</li> <li>test_masks/* : hand-masked images used as the test data (5 files).</li> </ul> </li> </ul> <p>Note that original images include images obtained using <em>google-image-download</em>, a Python script published on GitHub (<a href="https://github.com/Joeclinton1/google-images-download/tree/patch-1">https://github.com/Joeclinton1/google-images-download/tree/patch-1</a>, Copyright © 2015-2019 Hardik Vasa). The whole images we obtained by <em>google-image-download</em> were labeled as noncommercial reuse with modification.</p> <p>For more details, please refer to a research paper "Extraction of stratigraphic exposures on visible images using a supervised machine learning technique".</p> <p>Correspondence: Rina Noguchi (r-noguchi@env.sc.niigata-u.ac.jp)</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.