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78 results for “SOURCE IMAGING”
MosqVision-3K: A Balanced Multi-Source Dataset of 3,000 Annotated Images for Culex, Anopheles, and Aedes Mosquito Species Classification
<p><strong>Comprehensive Mosquito Species Image Dataset for Machine Learning</strong><br><strong>Description:</strong><br>This dataset is a meticulously curated collection of high-quality images featuring three major mosquito species: <strong>Culex</strong>, <strong>Anopheles</strong>, and <strong>Aedes</strong>. These species are significant vectors for transmitting vector-borne diseases such as malaria, dengue, and Zika. The dataset has been compiled to support research and development in entomology, vector-borne disease control, and image recognition.<br>With <strong>3,000 images in total</strong>, the dataset is structured to ensure a balanced representation of the three species, each having <strong>1,000 images</strong>. Images were sourced from four reputable platforms, including <strong>MosquitoAlert.com</strong>, <strong>Mendeley Data</strong>, <strong>IEEE DataPort</strong>, and the <strong>Dryad Digital Repository</strong>. These sources ensure a comprehensive and diverse representation of mosquito appearances, including variations in morphology, lighting conditions, and orientations.</p> <h2>The dataset is organized into directories for each species, making it easy to integrate into machine learning workflows for tasks like species identification and classification. The collection also includes metadata and annotations to enhance usability.</h2> <p><strong>Key Features:</strong></p> <ul> <li><strong>Species Represented:</strong> <ul> <li><em>Culex</em></li> <li><em>Anopheles</em></li> <li><em>Aedes</em></li> </ul> </li> <li><strong>Total Images:</strong> 3,000 (1,000 images per species)</li> <li><strong>Image Sources:</strong> <ul> <li><strong>MosquitoAlert.com</strong> (1,234 images)</li> <li><strong>Mendeley Data</strong> (876 images)</li> <li><strong>IEEE DataPort</strong> (748 images)</li> <li><strong>Dryad Digital Repository</strong> (600 images)</li> </ul> </li> </ul> <h2>- <strong>Image Annotations:</strong> Metadata and species labels are included for enhanced usability.</h2> <p><strong>Applications:</strong><br>This dataset is ideal for a variety of applications, including:</p> <ul> <li>Training machine learning models for mosquito species identification.</li> <li>Developing computer vision algorithms for pest control and public health.</li> </ul> <h2>- Enhancing vector control strategies to mitigate disease spread.</h2> <p><strong>Data Structure:</strong><br>The dataset is organized as follows:</p> <pre><code>Mosquito_Dataset/ ├── Anopheles/ │ ├── img_001<span>.jpg</span> │ ├── img_002<span>.jpg</span> │ └── ... ├── Aedes/ │ ├── img_001<span>.jpg</span> │ ├── img_002<span>.jpg</span> │ └── ... └── Culex/ ├── img_001<span>.jpg</span> ├── img_002<span>.jpg</span> └── ... </code></pre> <h2> </h2> <h2><strong>Acknowledgments:</strong><br>We acknowledge the following data sources for their contributions:</h2> <blockquote> <ul> <li>MosquitoAlert.com</li> <li>Mendeley Data</li> <li>IEEE DataPort</li> <li> Dryad Digital Repository</li> </ul> </blockquote>
Deep Learning for Efficient Microseismic Location using Source Migration-based Imaging
<p>The uploaded .rar file contains data and network of mircoseismic location program.</p> <p>'network.py' : network based on U-Net. The input is diffraction stacking images with the size of 1*64*64*64*32. The output has the same size as the input.</p> <p>'Model_SeisLoca_Unet.hdf5' : The model of the network trained by 800 samples.</p> <p>'ValidationSample_SNR1.mat' is corresponding to the validation sample showed in paper, and it includes 'input', 'label', and 'pred'. The 'pred' can be obtained by running 'prediction.py' with correct setups.</p> <p>'TestSample_SNR0.5.mat' is corresponding to the test sample with the SNR equals 1/2 showed in paper, and it includes 'input', 'label', and 'pred'. The 'pred' can be obtained by running 'prediction.py' with correct setups.</p> <p>'Draw_Synthetic_input_label_prediction.m' is a script to draw the input, label, and prediction.</p>
Dataset for Mistic: an open-source multiplexed image t-SNE viewer
<p>This link consists of 10 anonymized non-small cell lung cancer (NSCLC) field of Views (FoVs) to test Mistic.</p> <p><strong>Mistic</strong></p> <p>Understanding the complex ecology of a tumor tissue and the spatio-temporal relationships between its cellular and microenvironment components is becoming a key component of translational research, especially in immune-oncology. The generation and analysis of multiplexed images from patient samples is of paramount importance to facilitate this understanding. In this work, we present Mistic, an open-source multiplexed image t-SNE viewer that enables the simultaneous viewing of multiple 2D images rendered using multiple layout options to provide an overall visual preview of the entire dataset. In particular, the positions of the images can be taken from t-SNE or UMAP coordinates. This grouped view of all the images further aids an exploratory understanding of the specific expression pattern of a given biomarker or collection of biomarkers across all images, helps to identify images expressing a particular phenotype or to select images for subsequent downstream analysis. Currently there is no freely available tool to generate such image t-SNEs.</p> <p><strong>Links</strong></p> <p><br> <a href="https://github.com/MathOnco/Mistic">Mistic code</a></p> <p><a href="https://mistic-rtd.readthedocs.io/">Mistic documentation</a></p> <p><a href="https://www.biorxiv.org/content/10.1101/2021.10.08.463728v1">Paper</a></p> <p> </p>
Source data and code for "A diamond voltage imaging microscope"
<p>This data set contains both the source data and code used to generate figures and establish the conclusions of "A diamond voltage imaging microscope" (DOI: https://doi.org/10.1038/s41566-022-01064-1). It contains:</p> <p>- Raw source data (e.g., video data, fluorescence spectra).</p> <p>- Processed source data (e.g., calibration maps, calculated vales of contrast, sensitivity, etc).</p> <p>- Analysis code used to generate processed source data (this includes both MATLAB and Python scripts. MATLAB scripts require at least version R2021A).</p> <p>- Simulation code (Python) used to fit the equivalent RC circuit model described in the work to the experimental data.</p>
Reprocessed / rebinned to 0.5 degree images: Beta-Lactamase X-ray diffraction data recorded at Diamond Light Source I04 as part of commissioning & development
<p>Derived from https://zenodo.org/record/841060; made more compact (though no longer "raw" data) for use in tutorials</p>
The identified boulders from LCAM DOM image and information of all source craters
<p>This website contains Data Set S1 and Data Set S2, derived from the manuscript '<strong>Oblique impact adjacent to Chang’E-5 sampling site: Fine-scale analysis and implication on the provenance of returned samples</strong>,' authored by Wenhui Wu, Zhaopeng Chen, Xin Ren, Dawei Liu, Xingguo Zeng, Yuan Chen, Wangli Chen, Wei Yan, Bin Liu, Xiaoxia Zhang, and Jianjun Liu, for the Journal of Geophysical Research: Planets.</p>
Seismic dataset in "Source-Independent Passive Seismic Reverse-time Structure Imaging with Grouping Imaging Condition: Method and Application to Microseismic Events Induced by Hydraulic Fracturing"
<p>This dataset contains the seismic data and the velocity model used in the manuscript entitled "Source-Independent Passive Seismic Reverse-time Structure Imaging with Grouping Imaging Condition: Method and Application to Microseismic Events Induced by Hydraulic Fracturing" submitted to Journal of Geophysical Research-Solid Earth.</p>
The identified boulders from the LCAM DOM image and information on all source craters
<p>The derived data from the manuscript titled '<em>Oblique impact adjacent to Chang’E-5 sampling site: Fine-scale analysis and implication on the provenance of returned samples</em>'.</p>
GDCLD:A globally distributed dataset of coseismic landslide mapping via multi-source high-resolution remote sensing images
<p>GDCLD : A globally distributed dataset of coseismic landslide mapping via multi-source high-resolution remote sensing images</p> <p>Fang, C., Fan, X., Wang, X., Nava, L., Zhong, H., Dong, X., Qi, J., and Catani, F.: A globally distributed dataset of coseismic landslide mapping via multi-source high-resolution remote sensing images, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2024-239, in review, 2024.</p> <p> </p> <p>Data description:</p> <p> </p> <p>The training dataset and the validation dataset are composed of UAV, PlanetScope, Gaofen-6 and Map World images of the 5 earthquake regions of Luding, Nippes, Hokkaido, Jiuzhaigou and Mainling. There is no overlapping area in each TIFF. The training dataset and the validation dataset are randomly divided at a ratio of approximately 0.75:0.25.</p> <p> </p> <p>train_dataset:</p> <p>train_data: The train dataset part of the GDCLD data set contains 11162 data matrices (TIFF), and the shape of the matrix is (1024, 1024, 3) (TIFF).</p> <p>train_label: The train dataset part of the GDCLD data set contains 11162 data matrices (TIFF), and the shape of the matrix is (1024, 1024, 1) (TIFF).</p> <p> </p> <p>Validation_dataset</p> <p>val_data: The validation dataset part of the GDCLD data set contains 4459 data matrices (TIFF), and the shape of the matrix is (1024, 1024, 3) (TIFF).</p> <p>val_label: The validation dataset part of the GDCLD data set contains 4459 data matrices (TIFF), and the shape of the matrix is (1024, 1024, 1) (TIFF).</p> <p> </p> <p>Test_dataset (Lushan, Sumatra, Mesetas and Palu dataset)</p> <p>This package contains the original files of remote sensing images from three sources: UAV, Map World, and PlaneScope belonging to the Lushan, Sumatra, Mesetas and Palu earthquake regiones, which are used to display the test area.<br><br>Future work:<br>The future work includes additional landslide data that the authors will continue to upload. In this 2.0 version update, we have added UAV imagery and interpreted data for loess landslides triggered by the December 2023 M6.2 earthquake in Gansu, China, with a resolution of 0.1 m. Due to authorization constraints, we can only provide PNG files without geographic coordinates. Additionally, this update includes PlanetScope imagery of landslides induced by heavy rainfall in Guangdong, China, in 2024, as well as PlanetScope imagery and landslide labels for events triggered by the Hualien earthquake in Taiwan.<br><br>Please note that this landslide dataset is publicly available exclusively for scientific research purposes and must not be used for commercial purposes.</p>
Orbit Image Analysis: An open-source whole slide image analysis tool
<p>This is a whole slide image (WSI) dataset for glomeruli segmentation on kidney tissue, in total 88 images.</p> <p>The train-set (58 images) and test-set (32 images) has been used in the publication "Orbit Image Analysis: An open-source whole slide image analysis tool" to train and test the<br> glomeruli segmentation model.</p>
Source Data for "Imaging biological tissue with high-throughput single-pixel compressive holography"
<p>This file contains five subfolders, which are archived with relevant data that are necessary for reconstructing the holographic images of biological samples and resolution targets, respectively. <br> Here we introduce in order:<br> 1. 'dataset 1' is prepared for holographic reconstruction of stained tissue from mouse tails;<br> 2. 'dataset 2' is provided for holographic reconstruction of 80-um unstained tissue from mouse brains.<br> 3. 'dataset 3' is provided for verification of amplitude resolution in large-FOV mode;<br> 4. 'dataset 4' is provided for verification of amplitude resolution in high-resolution mode;<br> 5. 'dataset 5' is provided for verification of phase resolution in high-resolution mode;<br> 6. 'additional dataset 1' is prepared for additional holographic reconstruction of another stained tissue from mouse tails;<br> 7. 'additional dataset 2' is prepared for additional holographic reconstruction of 100-um unstained tissue from mouse brains;<br> 8. 'additional dataset 3' is prepared for additional holographic reconstruction of 120-um unstained tissue from mouse brains;<br> 9. 'additional dataset 4' is prepared for additional holographic reconstruction of 10-um unstained tissue from mouse brains;</p> <p>Both subfolders have the same structures, including the MATLAB data and raw data collected from the data acquisition card, which are necessary for holographic imaging reconstruction.<br> Here we introduce in order:<br> *) biological_sample.mat: The raw data of imaging biological sample. The format of the data has been converted from .tdms to .mat file.</p> <p>*) target_sample.mat: The raw data of imaging resolution target. The format of the data has been converted from .tdms to .mat file.</p> <p>*) background_curvature.mat: The raw data used to correct for phase contaminations from system aberrations. The format of the data has been converted from .tdms to .mat file.</p> <p>*) biological_sample_rawdata.tdms: The raw data of imaging biological sample. The data was collected through DAC and was in the format of TDMS.</p> <p>*) target_sample_rawdata.tdms: The raw data of imaging resolution target. The data was collected through DAC and was in the format of TDMS.</p> <p>*) background_curvature_rawdata.tdms: The raw data used to correct for phase contaminations from system aberrations. The data was collected through DAC and was in the format of TDMS.<br> </p>
Microseismic source imaging using physics-informed neural networks with hard constraints
<p>Locating subsurface seismic sources is crucial to both seismic monitoring and seismology. We propose a novel direct source imaging framework based on physics-informed neural networks with hard constraints. In this letter, we present the relevant dataset to the paper. </p>
Images of colony growth for 224 Arabidopsis thaliana bacterial strains on 46 carbon sources
<p>This repository contains all data necessary to score colony growth on agar plates using the software tool 'platescan' (github.com/MicrobiologyETHZ/platescan) for the strains presented in Schäfer, Pacheco, et al.</p> <p>The growth of the 224 strains was split into 3 screens. Rounds 1, 2, and half of 3 comprised the core of the screen for all strains, and the second half of 3 comprised the validation set for computing the false discovery rate. The directories containing the images, as well as their corresponding metadata files, are named accordingly:</p> <p> pictures_7dps_screen_*: folders contain pictures of screening plates to assess growth on tested garbon sources. Pictures were taken 7 days after spotting strains on the plates.</p> <p> *_d7_filenames.txt: files link the picture filename to the carbon source that was tested. In addition the screening round and the plate number within the screening round is indicated.</p> <p> strain_metadata_screen*.txt: files indicate the position of the strains on the test plates [row (alphabetical), row (numbered), column (numbered)] and additional comments relevant for analysis (swarmer). Screen_nr and plate_number allow to link data to the corresponding pictures.</p> <p>This repository additionally includes a script to perform downstream processing of the platescan software output,</p> <p> plate_scan_analysis.R.</p> <p>and a file reporting the latescan output values that were manually overwritten due to contamination or poor image quality</p> <p> manual_correction.txt.</p>
Source Images
<p>Source images used for analysis in "Biophysical ordering transitions underlie genome 3D re-organization during cricket spermiogenesis." by G.Orsi et al.</p>
MMV_Im2Im: An Open Source Microscopy Machine Vision Toolbox for Image-to-Image Transformation
<p>This dataset contains trained deep learning models and sample data for the manuscript "MMV_Im2Im: An Open Source Microscopy Machine Vision Toolbox for Image-to-Image Transformation". Please find the software and more information including tutorials here: https://github.com/MMV-Lab/mmv_im2im.</p><p> </p><p>sample_data.zip includes the following datasets:</p><ul><li>Labelfree prediction of nuclear structure from 2D/3D brighteld images<ul><li>2D<ul><li><a href="https://zenodo.org/record/6139958#.Y78QJKrMLtU">https://zenodo.org/record/6139958#.Y78QJKrMLtU</a></li><li><a href="https://zenodo.org/record/6140064#.Y78YeqrMLtU">https://zenodo.org/record/6140064#.Y78YeqrMLtU</a></li><li>Both repositories have a Creative Commons Attribution 4.0 International License</li></ul></li><li>3D<ul><li><a href="https://open.quiltdata.com/b/allencell/packages/aics/hipsc_single_cell_image_dataset">https://open.quiltdata.com/b/allencell/packages/aics/hipsc_single_cell_image_dataset</a></li><li>Terms of use: <a href="https://www.allencell.org/terms-of-use.html">https://www.allencell.org/terms-of-use.html</a></li><li>"Your use of the Content, including creation of derivative works of the services, data and tools, must be for research or other noncommercial purposes unless it is otherwise set forth in these Terms or agreed to in writing by the Allen Institute."</li></ul></li></ul></li><li>2D semantic segmentation of tissues from H&E images<ul><li><a href="https://www.kaggle.com/datasets/sani84/glasmiccai2015-gland-segmentation">https://www.kaggle.com/datasets/sani84/glasmiccai2015-gland-segmentation</a></li><li>"<strong>The dataset used in this competition is provided for research purposes only. Commercial uses are not allowed.</strong><br>If you intend to publish research work that uses this dataset, you must cite our review paper to be published after the competition"</li></ul></li><li>Instance segmentation<ul><li>2D<ul><li><a href="https://bbbc.broadinstitute.org/BBBC010">https://bbbc.broadinstitute.org/BBBC010</a></li><li>Terms of use: <a href="https://bbbc.broadinstitute.org/">https://bbbc.broadinstitute.org/</a></li><li>"Researchers are encouraged to use these image sets as reference points when developing, testing, and publishing new image analysis algorithms for the life sciences."</li></ul></li><li>3D<ul><li><a href="https://open.quiltdata.com/b/allencell/packages/aics/hipsc_single_cell_image_dataset">https://open.quiltdata.com/b/allencell/packages/aics/hipsc_single_cell_image_dataset</a></li><li>Terms of use: <a href="https://www.allencell.org/terms-of-use.html">https://www.allencell.org/terms-of-use.html</a></li><li>"Your use of the Content, including creation of derivative works of the services, data and tools, must be for research or other noncommercial purposes unless it is otherwise set forth in these Terms or agreed to in writing by the Allen Institute."</li></ul></li></ul></li><li>Compare semantic segmentation and instance segmentation<ul><li><a href="https://open.quiltdata.com/b/allencell/packages/aics/hipsc_single_cell_image_dataset">https://open.quiltdata.com/b/allencell/packages/aics/hipsc_single_cell_image_dataset</a></li><li>Terms of use: <a href="https://www.allencell.org/terms-of-use.html">https://www.allencell.org/terms-of-use.html</a></li><li>"Your use of the Content, including creation of derivative works of the services, data and tools, must be for research or other noncommercial purposes unless it is otherwise set forth in these Terms or agreed to in writing by the Allen Institute."</li></ul></li><li>Unsupervised semantic segmentation<ul><li><a href="https://open.quiltdata.com/b/allencell/packages/aics/hipsc_single_cell_image_dataset">https://open.quiltdata.com/b/allencell/packages/aics/hipsc_single_cell_image_dataset</a></li><li>Terms of use: <a href="https://www.allencell.org/terms-of-use.html">https://www.allencell.org/terms-of-use.html</a></li><li>"Your use of the Content, including creation of derivative works of the services, data and tools, must be for research or other noncommercial purposes unless it is otherwise set forth in these Terms or agreed to in writing by the Allen Institute."</li></ul></li><li>Generating synthetic images<ul><li><a href="https://open.quiltdata.com/b/allencell/packages/aics/hipsc_single_cell_image_dataset">https://open.quiltdata.com/b/allencell/packages/aics/hipsc_single_cell_image_dataset</a></li><li>Terms of use: <a href="https://www.allencell.org/terms-of-use.html">https://www.allencell.org/terms-of-use.html</a></li><li>"Your use of the Content, including creation of derivative works of the services, data and tools, must be for research or other noncommercial purposes unless it is otherwise set forth in these Terms or agreed to in writing by the Allen Institute."</li></ul></li><li>Image denoising<ul><li><a href="https://csbdeep.bioimagecomputing.com/scenarios/">https://csbdeep.bioimagecomputing.com/scenarios/</a></li><li>Two datasets: "Denoising in 3D (Planaria nuclei)" and "Denoising in 3D (Tribolium nuclei)"</li><li>Terms of use: <a href="http://csbdeep.bioimagecomputing.com/">http://csbdeep.bioimagecomputing.com/</a></li><li>"The entire CSBDeep toolbox is fully open source and intended to be used from either Python or <a href="https://fiji.sc">Fiji</a>."</li></ul></li><li>Imaging modality transformation<ul><li><a href="https://zenodo.org/record/4624364#.Y9bWOoHMIqJ">https://zenodo.org/record/4624364#.Y9bWOoHMIqJ</a></li><li>Two datasets: "Confocal_2_STED.zip" (Microtubule and Nuclear_Pore_complex)</li><li>Repository has a Creative Commons Attribution 4.0 International License</li></ul></li><li>Staining transformation:<ul><li><a href="https://zenodo.org/record/4751737#.Y9gbv4HMLVZ">https://zenodo.org/record/4751737#.Y9gbv4HMLVZ</a></li><li>Dataset "BC-DeepLIIF_Training_Set.zip" and "BC-DeepLIIF_Validation_Set.zip"</li><li>Repository has a Creative Commons Attribution 4.0 International License</li></ul></li></ul><p> </p>
Effects of Heart Rates and Variability of Heart Rates on Image Quality of Dual-Source CT Coronary Angiography
ClinicalTrials.gov study NCT00632918. IPD Sharing: Not stated. Countries: 1. Publications: 4.
ECG Triggered Dual Source CT for Non-invasive Pre-operative Cardiac Imaging in Morbid Obese Patients
ClinicalTrials.gov study NCT02802579. IPD Sharing: NO. Countries: 1. Publications: 4.
Clinical Utility of Automated Electric Source Imaging in Presurgical Evaluation
ClinicalTrials.gov study NCT04218812. IPD Sharing: Not stated. Countries: 8. Publications: 8.
Automated location invariant animal detection in camera trap images using publicly available data sources
Open the record for dataset details and reuse information.
Orbit Image Analysis: an open-source whole slide image analysis tool
Open the record for dataset details and reuse information.
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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.