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173 results for “convolution neural network”
A tempοral Deep Convolutional Neural Network model on Sentinel-1 Image Time Series for pixel-wise Flood Classification (dataset)
<p>This is a dataset which has been designed to be used for flood time series classification. Each time series is annotated as flood or no-flood and represents a pixel-wise time series derived from stack of Sentinel-1 IW GRD images that have been pre-processed according to <a href="http://doi.org/10.5281/zenodo.6510223">https://doi.org/10.5281/zenodo.6510223</a>.</p>
Global Positioning System Based on Optical Flow and Convolutional Neural Network
<p>Two datasets used in the paper Global Positioning System Based on Optical Flow and Convolutional Neural Network, to evaluate the proposed CNN model to the task of position estimation. The datasets cover 2 different modes of motion of a drone, and were captured using Google API.</p>
Multiwavelength classification of X-ray selected galaxy cluster candidates using convolutional neural networks
<p>Classification dataset used in Kosiba et al. 2020 (10.1093/mnras/staa1723) consisting of candidate clusters in the XCLASS survey</p> <p>Training and testing images and corresponding labels low-z (clusters, 0<z<0.3), hi-z (clusters, z>0.3), nearby galaxy, point source (point, double source, star/AGN), and other (artefact, edge)</p>
Goat-CNN: A Lightweight Convolutional Neural Network for Pose-Independent Body Condition Score Estimation in Goats
<p>Here we introduce the dataset utilized in our published paper entitled "<a href="https://www.sciencedirect.com/science/article/pii/S2666154324002114">Goat-CNN: A Lightweight Convolutional Neural Network for Pose-Independent Body Condition Score Estimation in Goats</a>".</p> <p>Contained within the "bcs" folder are all the videos collected for this study. Each video file is named with a format denoting its respective details. The first number signifies the sequence of collection, the second denotes the ear tag, and the final figure represents the body condition score (BCS) value.</p> <p>For example: "1_158734_2.50" indicates the first sampling of an animal with the ear tag "158734" and a BCS value of "2.50".</p> <p>Additionally, we provide two Python scripts in this repository. The first script, "Video2Frame.py", facilitates the splitting of videos into individual frames. The second script, "Frames2npy.py", converts these frames into two numpy-friendly files with the extension ".npy". These files contain both the images ("X_train_bcs300.npy") and their corresponding labels ("Y_train_bcs300.npy").</p> <p>Furthermore, for the convenience of swift experimentation, we have included the desired .npy files within the repository.</p> <p>To load these files into your Python environment, you can use the following code snippet:</p> <div> <div>th4figs = '/content/drive/MyDrive/compag_2023/'</div> <br> <div>path4images = "/content/drive/MyDrive/CodeRefarm/datasets/BCS/X_train_bcs300.npy"</div> <div>Xtrain = np.load(path4images)</div> <br> <div>path4labels = "/content/drive/MyDrive/CodeRefarm/datasets/BCS/Y_train_bcs300.npy"</div> <div>Ytrain = np.load(path4labels).astype(float)</div> <br> <div>print("X train : ", Xtrain.shape)</div> <div>print("Y train : ", Ytrain.shape)</div> <div> <div> <div> <div> <div> <div> <div> </div> </div> <div> </div> </div> </div> </div> </div> <div> <div> <div> <div> <div> <div> <div> <div> <pre>X train : (5332, 300, 300, 3) Y train : (5332,)<br> </pre> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div>
Dataset for training SENMAP, a automatic tool to curate LTR-retrotransposons using convolutional neural networks
<p>Transposable elements (TEs) are specific structures of the genome of species, which can move from one location to another. For that reason, they can cause mutations or changes that can be negative, such as the appearance of diseases, or beneficial, such as participating in fundamental roles in the evolution of genomes and genetic diversity. Long Terminal Repeat retrotransposons (LTR-RT) are the most abundant in plant species, hence the importance of studying these structures in particular. Over the time, these elements can suffer changes called nested insertions, which can inactivate or modify the functioning of the element, for that they are no longer consider as intact element and cannot be used for identification and classification studies. We create a dataset containing 56,442 LTR-RTs targed as "non-intact" elements and 49,215 considered as "intact". </p> <p>We formated the sequences IDs in order to keep relevant information as the superfamily and the lineage, as well as the category (Negative for "non-intact" and Positive for "intact" elements). </p> <p> This dataset (the npy files obtained from the fasta file) was used for training SENMAP, a convolutional neural network architecture to obtain intact LTR-RT sequences in plant genomes, which is composed by four convolutional layers, LeakyReLU as activation function and BinaryFocalLoss as loss function. Achieving an F1-score percentage of 91.37% with test data, identifying low quality sequences rapidly and efficiently, contributing to curate libraries of LTR retrotransposons of plants genomes published in large-scale sequencing projects due to the post-genomic era.</p>
Figure 1. CNN architecture (adopted from Krizhevsky et al. '12)-Measuring Customer Behavior with Deep Convolutional Neural Networks
<p>The architecture of a CNN can be described as following. A small pixel region goes to input neurons and then connects to a first convolution hidden layer (Figure1). There we can see a set of learnable filters, which are activated during the presentation some particular type of feature in pixel region in the input. On this phase, CNN does shift invariance, which is carried by feature map. Subsampling layer goes next. There we have two processes: local averaging and sampling. As a result, we get declining resolution of feature map. To correspond this task CNN needs supervised learning. Before starting the experiment, we gave a set of labeled videos with different emotional experience. The system analyses images and finds similar features. Then the system creates a map, where it arranges videos in accordance with similar features. Thereby, images with similar emotions form certain class. To test the system, we add other videos and correct the system when it refers them improperly. The proposed model consists of four convolutional layers, followed by max-pooling layers, and three fully-connected layers with a final classificatory presented with MLP (with six basic outputs, corresponding to basic emotions for emotion classification and two outputs for motion classification for typical and non-typical behavior). The input data was presented as infrared camera output.</p>
Classification of Phonocardiograms with Convolutional Neural Networks-Figure 5. An example of a convolutional layer (Convolutional Neural Networks (LeNet), 2018)
<p>Figure 5 shows an example of a convolutional layer. Layer m-1 includes four feature maps. The m is a hidden layer and it includes two feature maps such as h0 and h1. The neuron outputs at h0 and h1 are calculated from the neuron outputs entering the 2x2 receiving area at the layer below layer m-1. Thus, the weights are W0, and W1 and h,0 and h1 are weight tensors that have three dimensions. The leading dimension indexes the map entries summaries, pointing to the other two neuron outputs. To combine all of them Wijkl , with each pixel of the layer's minus k map, it refers to the weight of the link between neuron outputs (i, j) in the coordinates of the feature map m-1 (Convolutional Neural Networks (LeNet), 2018).</p>
Classification of Phonocardiograms with Convolutional Neural Networks-Figure 4. a) Sparse Connectivity, b) Shared Weights (Convolutional Neural Networks (LeNet), 2018)
<p>A CNNs are biologically inspired variants of a multilayer perceptron. CNNs establish a built-in local correlation by applying a local link model between the neurons of adjacent layers. As shown in Figure 4a, the inputs of the hidden units in the m-layer are obtained from a subset of the units having the built-in areas in the m-1 layer. This ensures that a number of layers arrive consecutively, resulting in a filtration. It can encode 5 features such as a neuron in the m+1 hidden layer. On CNNs, each filter hi is repeated on the entire image surface. These repeated units form a feature map that shares the weight and bias parameters. 3 hidden units of the same feature map are shown in Figure 4b. The weights shown by parallel lines in the figure were also limited as same. To learn such shared parameters, the gradient method can be used with only a small modification to the original parameters. The sum of the inverse gradients of the shared parameters equals the gradient of the shared weights.</p>
Classification of Phonocardiograms with Convolutional Neural Networks-Figure 2. PCGs for sample sound file of 103-1305031931979-B
<p>Heart sounds that provide valuable diagnostic information in clinical examinations are among the most important physiological signals in the human body. However, heart sounds include noise, such as external sounds and lung sounds, caused by signal recording conditions. Noisy heart sound signal negatively affects the diagnosis of the doctor (Denga & Hanb, 2018). Digital filters are often used to filter biomedical signal. Digital filtering is defined as the acquisition of desired frequency values according to the characterization of the desired filter in order to improve the signal according to the intended use (Shenoi, 2005; Thede, 1995). Based on the experience gained from previous studies, an elliptic filter was used in this study (Deperlioglu, 2018; Guraksin et. al., 2009) . The heart sound signal covering the first two steps is given in Figure 2 for sample sound file of 103_1305031931979_B in the PASCAL Btraining data set.</p>
Classification of Phonocardiograms with Convolutional Neural Networks-Figure 1. Block diagram of classification process
<p>Heart and vascular diseases are the first cause of death in the world. The heartbeat, which is the result of the blood pumping of the heart, gives information about the functioning of the heart valves. Thus, features can be obtained that can differentiate between normal and abnormal heart sound signals. With the ECG signal, it is possible to visualize the parts of the heartbeat signal. However, ECG measurement is usually expensive and requires extra time. For this reason, studies for the detection of heart diseases generally focus on the analysis of heart sounds. In this context, it is aimed to make a successful classification using deep learning method in this study. The block diagram of the application is shown in Figure 1. The details of each block in the diagram are explained as follows. For all classification study, MATLAB r2017a software was used.</p>
Classification of Phonocardiograms with Convolutional Neural Networks-Figure 6. Confusion matrix of multilayer feedforward network
<p>Multilayer feedforward network was used for classification with ANN. In this application, the ANN structure and parameters were obtained after a review of previous studies and very much number of trial runs. There is a total of 10 neurons in the hidden layer in ANN. The Bayesian regularization backpropagation was used for learning algorithm and the mean square error function was also used the performance algorithm. 134 samples in the data set were used for training data, 29 samples were used for validation data, and 29 samples were used for testing data. The confusion matrix obtained at the end of the classification was given in Figure 6. As seen from the confusion matrix, the accuracy of classification 82.8% was achieved in the ANN classification. The ANN performed with a sensitivity of 92.40% and a specificity of 88.82%.</p>
Classification of Phonocardiograms with Convolutional Neural Networks-Figure 3. Multilayer feed forward network (Gurney, 2004)
<p>Feedforward network is a non-repeating network with the processing units or nodes in the layer, and all nodes in a layer are linked to the nodes of the previous layers. There are different weights on the connection. There is no feedback loop, the signal input can only flow in one direction. Multilayer feedforward network is feedforward ANN concept with multiple weighted layers as seen figure 3. This network is called hidden layers because it has one or more hidden layers between the input and output layers (Gurney, 2004; Tutorials Point, 2017).</p>
Classification of Phonocardiograms with Convolutional Neural Networks-Figure 7. Confusion matrix of convolutional neural network
<p>The final layer of CNN is the classification layer. This layer uses the possibilities returned by the softmax activation function for each input to mutually assign one of the special classes. The confusion matrix obtained at the end of the classification was given in Figure 7. As seen from the confusion matrix, the accuracy of classification 97.9% was achieved in the CNN classification. The CNN performed with sensitivity of 99.47% and specificity of 98.42%.</p>
Domain-Independent Reviews' Sentiment Polarity Classification using Shallow Word2Seq Convolutional Neural Network
<p>Reviews and comments are perceptions about specific services or products. They are embedded with hidden sentiments the reviewer has towards certain subjects. Business owners use customer reviews to understand customers’ perceptions about specific services or products. The ability to understand reviews’ sentiment from different domains or areas give decision makers and business owners the opportunity to make critical business decisions which can help them to increase profits of their businesses. Previous studies had focussed on classifying sentiment polarity by using traditional machine learning and deep learning methods. However, these suffered from low model generalization ability, causing the models to perform better only on single domain datasets rather than multiple domain datasets. The problem is the inability of the classification model to learn domain-restricted knowledge from multi-domain datasets. Aiming to improve the accuracy of the cross-domain classification, this paper proposes a method which uses Word2Seq Convolutional Neural Network (CNN) to classify reviews’ sentiment across multiple domain datasets (i.e. digital worker, movie, product, hotel and restaurant reviews). The evaluation showed that the proposed method had achieved the state-of-the-art performance. The high classification performance also promoted the reliability and effectiveness of implementing the Word2Seq CNN to classify reviews’ sentiment across different domains and learn domain restricted knowledge while improving the model generalization ability.</p> <p>The uploaded dataset is a sampled dataset with 5000 observations for both training and testing sets.</p>
A Convolutional Neural Network classifier identifies tree species in mixed-conifer forest from hyperspectral imagery
<p>Published online: <a href="https://www.mdpi.com/2072-4292/11/19/2326">https://www.mdpi.com/2072-4292/11/19/2326</a></p> <p>DOI: 10.3390/rs11192326</p> <p><strong>Abstract:</strong></p> <p>In this study, we automate tree species classification and mapping using field-based training data, high spatial resolution airborne hyperspectral imagery, and a convolutional neural network classifier (CNN). We tested our methods by identifying seven dominant trees species as well as dead standing trees in a mixed-conifer forest in the Southern Sierra Nevada Mountains, CA (USA) using training, validation, and testing datasets composed of spatially-explicit transects and plots sampled across a single strip of imaging spectroscopy. We also used a three-band ‘Red-Green-Blue’ pseudo true-color subset of the hyperspectral imagery strip to test the classification accuracy of a CNN model without the additional non-visible spectral data provided in the hyperspectral imagery. Our classifier is pixel-based rather than object based, although we use three-dimensional structural information from airborne Light Detection and Ranging (LiDAR) to identify trees (points > 5 m above the ground) and the classifier was applied to image pixels that were thus identified as tree crowns. By training a CNN classifier using field data and hyperspectral imagery, we were able to accurately identify tree species and predict their distribution, as well as the distribution of tree mortality, across the landscape. Using a window size of 15 pixels and eight hidden convolutional layers, a CNN model classified the correct species of 713 individual trees from hyperspectral imagery with an average F-score of 0.87 and F-scores ranging from 0.67–0.95 depending on species. The CNN classification model performance increased from a combined F-score of 0.64 for the Red-Green-Blue model to a combined F-score of 0.87 for the hyperspectral model. The hyperspectral CNN model captures the species composition changes across ~700 meters (1935 to 2630 m) of elevation from a lower-elevation mixed oak conifer forest to a higher-elevation fir-dominated coniferous forest. High resolution tree species maps can support forest ecosystem monitoring and management, and identifying dead trees aids landscape assessment of forest mortality resulting from drought, insects and pathogens. We publicly provide our code to apply deep learning classifiers to tree species identification from geospatial imagery and field training data</p> <p>Digital Publication of the training data polygons and hyperspectral imagery used in the manuscript "A Convolutional Neural Network classifier identifies tree species in mixed-conifer forest from hyperspectral imagery".</p> <p>Code is available in a Jupyter Notebook and can be found here: <a href="https://github.com/jonathanventura/canopy">https://github.com/jonathanventura/canopy</a></p> <p>National Ecological Observatory Network. 2018. Provisional data downloaded from <a href="http://data.neonscience.org/">http://data.neonscience.org</a> on 22 June 2018. Battelle, Boulder, CO, USA</p>
Data archive for 'Convolutional neural networks facilitate river barrier detection and evidence severe habitat fragmentation in the Mekong River biodiversity hotspot'
<p>This repository contains the code and databases used in the paper 'Convolutional neural networks facilitate river barrier detection and evidence severe habitat fragmentation in the Mekong River biodiversity hotspot'. </p> <p>The 'Mekong River Barrier Database (MRBD)' folder contains the basin-scale barrier database developed in this study. This database contains more than 13,000 unique barriers, which were identified by using the convolutional neural networks-based object detection method from Google Earth’s satellite imagery.</p> <p>The 'FCOS' folder contains the barrier detection model (FCOS ResNext-101-FPN), trained for detecting river barriers from remotely sensed images within the MMDetection framework. The 'FCOS_x101_v2' folder contains the enhanced FCOS model.</p> <p>The 'R_script' folder contains R files used in the paper. Coordinate.R was used to extract coordinates from bounding boxes in each TIF image. CAFI.R was used to calculate the CAFI index in each sub-catchment.</p> <p>The 'Barrier image training set' folder contains over 10,000 river barrier satellite images and their associated JSON files, forming the 'training, validation, and test datasets' used during the model training process. This dataset is made available to the user community in raw, in the hope that others will contribute to its future development, thereby enhancing its use and utility.</p> <p>For more information on the MMDetection framework, refer to the following GitHub repository: <a href="https://github.com/open-mmlab/mmdetection">https://github.com/open-mmlab/mmdetection</a></p>
Series AC Arc Fault Detection Method Based on High-Frequency Coupling Sensor and Convolution Neural Network
<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>Test for to produce an arcing fault : Open contact electrodes and Carbonized path wires</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>
Visual Genome - Visual Relationship Detection - Scene Graph Generation using Message Passing Neural Networks and Graph Convolutional Networks
<p>This repository contains a processed version of <strong>Visual Genome</strong> for <em>Visual Relationship Detection</em>, from the Diploma (MSc) thesis <strong>Scene Graph Generation using Message Passing Neural Networks and Graph Convolutional Networks</strong> by Miltiadis Kofinas, supervised by Christos Diou and Anastasios Delopoulos.</p> <p>The original thesis is written in Greek</p> <blockquote> <p><strong>Νευρωνικά Δίκτυα Ανταλλαγής Μηνυμάτων και Συνελικτικά Δίκτυα Γράφων για Εξαγωγή Γράφου Σκηνής Εικόνων</strong><br> Μιλτιάδης Κοφινάς<br> <a href="https://ikee.lib.auth.gr/record/300900">https://ikee.lib.auth.gr/record/300900</a></p> </blockquote> <p>A summarized English version of the thesis can be accessed <a href="https://www.dropbox.com/s/m87ixw8c8ecrswm/mkofinas_thesis_english_scene_graph_generation.pdf?dl=0">here</a>.</p> <p>It contains region proposals for VGG-16 for all images, and metadata about the bounding box distribution and the predicate classes.</p>
Dataset: Modelling surface color discrimination under different lighting environments using image chromatic statistics and convolutional neural networks
<p><strong>Associated publication</strong></p> <p>[1] Samuel Ponting*, <strong>Takuma Morimoto</strong>*, Hannah E. Smithson, “Modelling surface color discrimination under different lighting environments using image chromatic statistics and convolutional neural networks”, *equal contribution, bioRxiv, <a href="https://www.google.com/url?q=https%3A%2F%2Fdoi.org%2F10.1101%2F2022.11.02.514864&sa=D&sntz=1&usg=AOvVaw3KwSo7KmqPzBR1UMc1MHmk">https://doi.org/10.1101/2022.11.02.514864</a></p> <p>[2] Takuma Morimoto, and Hannah E. Smithson, “Discrimination of spectral reflectance under complex environmental illumination,” Journal of the Optical Society of America A, 35, 4, B244-B255 (2018) https://doi.org/10.1364/JOSAA.35.00B244</p> <p> </p> <p>Datasets contain 2 folders and 1 mat file.</p> <p> </p> <p><strong>(Folder 1) Stimuli</strong></p> <p><strong>(Folder 2) Psychophysics_data</strong></p> <p><strong>(Mat file) stimulusMagnitudeToMacLeodBoynton.mat</strong></p> <p> </p> <p>Details are described below.</p> <p> </p> <p>----------------------------------------------------------------------------------------------------------------------------------------</p> <p><strong>(Folder 1) Stimuli</strong></p> <p> </p> <p><strong>Overview of datasets</strong></p> <p>This Image dataset includes 57,600 images (2 gloss levels * 3 environments * 100 stimulus magnitudes * 8 hue directions * 12 camera angles from 0 to 330 degree in 30 degree step) in .mat format.</p> <p> </p> <p>The half of images were used in psychophysical experiment (camera angles: 0, 60, 120, 180, 240, 300 degrees).</p> <p>Other half images were used for testing chromatic statistics models and CNN-based models [1] (camera angles: 30, 90, 150, 210, 270, 330 degrees).</p> <p> </p> <p><strong>Each image file</strong></p> <p>Filename denotes a condition name and the camera angle as formatted in a following way.</p> <p> </p> <p>stim_”environment” _”glossiness”_”hueAngle”_”magnitude”_”cameraAngle”.mat</p> <p>e.g. “stim_en1_glossy_hue45_n45_cameraAngle90.mat”</p> <p> </p> <p>Stimulus magnitude 100 is a maximum saturation, and 1 corresponds to equal energy white (which was used as a distractor object).</p> <p> </p> <p>Each image file contains two valuables : MacLeodBoynton, XYZ</p> <p> </p> <p>Each variable contains an image of 128*128*3 pixels (height*width*channel).</p> <p> </p> <p>MacLeod-Boynton: MacLeod-Boynton chromaticity image (1st channel: L/(L+M), 2nd channel: S/(L+M), and 3rd channel L+M)</p> <p>XYZ: XYZ coordinates calculated based on 2-degree CIE 1931 xyz color matching function (1st channel: X, 2nd channel: Y, and 3rd channel Z)</p> <p> </p> <p>Luminance and L+M are both relative (normalised by the maximum luminance across all 57,600 images).</p> <p> </p> <p>----------------------------------------------------------------------------------------------------------------------------------------</p> <p><strong>(Folder 2) Psychophysics_data</strong></p> <p>Filename denotes the condition and observers formatted in a following way.</p> <p> </p> <p>data_”environment” _”specularities”_”sessionNumber”_”obsever”.mat</p> <p>e.g. data_en2_matte_session4_JH.mat or .csv</p> <p> </p> <p>Each file includes following variables:</p> <p> </p> <p>(Variable 1) threshold</p> <p>Thresholds are stored in MacLeod-Boynton (MB) chromaticity coordinates for all 8 hue directions (from 0 to 315 degree in 45 degree step).</p> <p> </p> <p>MacLeod-Boynton chromaticity coordinates were calculated in a following way. </p> <p>These scalings are in accordance with description in CVRL main site (Chromaticity coordinates tab ).</p> <p> </p> <p>First of all, L, M, and S cone signals were calculated based on Stockman & Sharpe cone fundamentals (energy in linear scale available at at http://www.cvrl.org).</p> <p>Each sensitivity curve was normalised to have 1.0 at the peak.</p> <p> </p> <p>Then, MB coordinates were calculated using equation (1-3).</p> <p> </p> <p>L/(L+M) = Lw*L/(Lw*L+Mw*M) - (1)</p> <p>S/(L+M) = Sw*S/(Lw*L+Mw*M) - (2)</p> <p>L+M = Lw*L+Mw*M - (3)</p> <p> </p> <p>where Lw = 0.689903; Mw = 0.348322;Sw = 1.93540.</p> <p> </p> <p>L, M and S denote L-cone, M-cone, S-cone excitations, respectively.</p> <p> </p> <p>Under this calculation, equal energy white becomes L/(L+M) = 0.7078 and S/(L+M) = 1.</p> <p> </p> <p>(Variable 2) staircase</p> <p> </p> <p>Since we ran 8 interleaved staircase (for 8 hue angles), information about 8 staircases are stored in this single variable.</p> <p>(staircase(1) corresponds to 0 degree, and staircase(8) corresponds to 315 degree)</p> <p> </p> <p>There are 5 fields:</p> <p>(i) groundtruth, (ii) response, (iii) correct, (iv) magnitude, (v) cameraAngle</p> <p> </p> <p>For each trial, the location of objects was defined in a following way.</p> <p>| 1 3 |</p> <p>| 2 4 |</p> <p> </p> <p>And each field stores following information for all trials in the staircase.</p> <p> </p> <p>(i) groundtruth</p> <p>Location of the target object</p> <p> </p> <p>(ii) response</p> <p>Location that the participant chose</p> <p> </p> <p>(iii) correct</p> <p>If the response was correct (1) or incorrect (0)</p> <p> </p> <p>(iv) magnitude</p> <p>Stimulus magnitude of target object in each trial from 1 to 100 (1 for equal energy white and 100 for maximum saturation).</p> <p> </p> <p>(v) Camera angle</p> <p>Camera angles assigned for four objects in each trial.</p> <p>This data and (i) groundtruth allow reconstruct of the exact image for each trial.</p> <p> </p> <p>----------------------------------------------------------------------------------------------------------------------------------------</p> <p><strong>(Mat file) stimulusMagnitudeToMacLeodBoynton.mat</strong></p> <p>This file stores a variable ‘stimulusMagnitudeToMacLeodBoynton’ (8*100*2) which describes correspondence map between stimulus magnitude and MacLeod-Boynton chromaticity.</p> <p> </p> <p>1st channel: hue direction from 0 degree to 315 degree, 45 degree step</p> <p>2nd channel: magnitude from 1 to 100</p> <p>3rd channel: MacLeod-Boynton coordinate, 1 being L/(L+M) and 2 being S/(L+M)</p> <p> </p>
Spatiotemporal Estimation of TROPOMI NO2 Column with Depthwise Partial Convolutional Neural Network
<p>Public Repository of the model outputs of TROPOMI NO2 datasets for 2019 and 2020.</p> <p>Comprises:</p> <p>Saved Partial Convolution Neural Network models (PCNN, PCNN-ST, and DW-PCNN) and code to load the models.</p> <p>Datasets (in Netcdf4 format) from PCNN model outputs, Inverse Distance Weighting, Inverse Distance Weighting with Kriging, spatial coordinates, time, target NO2 for imputation, and masks.</p> <p> </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.