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218 results for “electron imaging”
Refinements for Bragg coherent X-ray diffraction imaging: Electron backscatter diffraction alignment and strain field computation
<p>Here we present the final crystal reconstructions and analysis scripts for the paper titled "Refinement for Bragg coherent X-ray diffraction imaging: Electron backscatter diffraction alignment and strain field computation" published in Journal of Applied Crystallography, 55, 2022. Please see the README file for more information.</p>
Scanning electron microscope images of spruce needle homogenate and scanning electron microscope images of isolated small cellular particles from spruce needle homogenate
<p>Scanning electron microscope images of spruce needle homogenate and of isolated small cellular particles from spruce needle homogenate are presented. Each image is supplemented by description of the preparation of the sample and the data on the imaging technique and equipment. The data are curated by Veronika Kralj-Iglic and University of Ljubljana, Faculty of Health Sciences, Laboratory of Clinical Biophysics, and Anna Romolo, presently at University of Ljubljana, Faculty of Electrical Engineering, Laboratory of Physics, Ljubljana, Slovenia. Present address of Marko Jeran is: Department of Inorganic Chemistry and Technology, “Jožef Stefan” Institute, Ljubljana, Slovenia.</p>
IODP Expedition 383 Scanning electron microscope images
Microscopic images of discrete samples were acquired using a scanning electron microscope (SEM) and captured as image files. These files were uploaded along with a brief description and a record of the microscopic conditions when the image was taken.
IODP Expedition 378 Scanning electron microscope images
<p>Microscopic images of discrete samples were acquired using a scanning electron microscope (SEM) and captured as image files. These files were uploaded along with a brief description and a record of the microscopic conditions when the image was taken.</p>
IODP Expedition 367 Scanning electron microscope images
Microscopic images of discrete samples were acquired using a scanning electron microscope (SEM) and captured as image files. These files were uploaded along with a brief description and a record of the microscopic conditions when the image was taken.
Dataset for Automated Image Analysis for Single-Atom Detection in Catalytic Materials by Transmission Electron Microscopy
<p>Raw and processed image data resulting from the paper "Automated Image Analysis for Single-Atom Detection in Catalytic Materials by Transmission Electron Microscopy", by S. Mitchell, F. Parés, D. Faust Akl, S. M. Collins, D. M. Kepaptsoglou, Q. M. Ramasse, D. Garcia-Gasulla, J. Pérez-Ramírez, and N. López (JACS, 2021). </p> <p>The corresponding code can be found under: <a href="https://github.com/HPAI-BSC/AtomDetection_ACSTEM">GitHub - HPAI-BSC/AtomDetection_ACSTEM</a></p>
Synthetic cryo electron microscopy single particle images containing biomolecular complexes with continuous conformational variability used for validating DeepHEMNMA method and validation results
<p>This archive contains a synthetic dataset used for validating DeepHEMNMA method and the validation results. DeepHEMNMA is a deep learning extension of HEMNMA approach for analyzing continuous conformational variability of biomolecular complexes in cryo electron (cryo-EM) microscopy single particle images. We provide a training set of 20,000 images and an inference set of 50,000 images. The training images were used (1) to estimate the conformational and rigid-body parameters with HEMNMA and (2) to train the neural network using the parameters previously estimated with HEMNMA (the file with the HEMNMA-estimated parameters is provided). The inference images were used to infer the parameters with the trained neural network. Also, we provide (1) the input PDB structure, its normal modes, and the conformational and rigid-body parameters used to synthesize the 20,000 training images (ground-truth parameters) and (2) the conformational and rigid-body parameters inferred from the set of 50,000 inference images.</p> <p>The DeepHEMNMA method and the method for synthesizing images have been fully described in the following article: "Hamitouche I and Jonic S (2022), DeepHEMNMA: ResNet-based hybrid analysis of continuous conformational heterogeneity in cryo-EM single particle images. Front Mol Biosci 9, 965645. <a href="https://doi.org/10.3389/fmolb.2022.965645">https://doi.org/10.3389/fmolb.2022.965645</a> (in press)". Additionally, this article describes a test of DeepHEMNMA using one experimental cryo-EM dataset (available in EMPIAR database under the accession code EMPIAR-10016). </p>
IODP Expedition 379 Scanning electron microscope images
Microscopic images of discrete samples were acquired using a scanning electron microscope (SEM) and captured as image files. These files were uploaded along with a brief description and a record of the microscopic conditions when the image was taken.
IODP Expedition 360 Scanning electron microscope images
Microscopic images of discrete samples were acquired using a scanning electron microscope (SEM) and captured as image files. These files were uploaded along with a brief description and a record of the microscopic conditions when the image was taken.
IODP Expedition 397 Scanning electron microscope images
Microscopic images of discrete samples were acquired using a scanning electron microscope (SEM) and captured as image files. These files were uploaded along with a brief description and a record of the microscopic conditions when the image was taken.
IODP Expedition 398 Scanning electron microscope images
Microscopic images of discrete samples were acquired using a scanning electron microscope (SEM) and captured as image files. These files were uploaded along with a brief description and a record of the microscopic conditions when the image was taken.
IODP Expedition 356 Scanning electron microscope images
Microscopic images of discrete samples were acquired using a scanning electron microscope (SEM) and captured as image files. These files were uploaded along with a brief description and a record of the microscopic conditions when the image was taken.
IODP Expedition 359 Scanning electron microscope images
Microscopic images of discrete samples were acquired using a scanning electron microscope (SEM) and captured as image files. These files were uploaded along with a brief description and a record of the microscopic conditions when the image was taken.
CLIC Calorimeter 3D images: Electron showers at Fixed Angle
<p>Energy deposits from single-particle showers in the ECAL+HCAL calorimeters of the CLIC detector</p> <p>Simulation performed with GEANT4 (https://geant4.web.cern.ch) and DD4HEP software (https://dd4hep.web.cern.ch/dd4hep/)</p> <p>Electrons entering the detector at variable energy and fixed direction (perpendicular to the ECAL inner surface)</p> <p>See https://arxiv.org/abs/1912.06794 for details</p>
Diagnostic electron microscopy of viruses with low-voltage electron microscopes. Raw image files with brief description.
<p>The zipped data container contains the raw (unprocessed) images that we have used for the preparation of our manuscript entiteled:</p> <p>"Diagnostic electron microscopy of viruses with low-voltage electron microscopes" <a href="https://doi.org/10.1369%2F0022155420929438">https://doi.org/10.1369/0022155420929438</a></p> <p>Lars Möller, Gudrun Holland, Michael Laue</p> <p>Advanced Light and Electron Microscopy (ZBS 4), Centre for Biological Threats and Special Pathogens, Robert Koch Institute, D-13353 Berlin, Germany</p> <p>The brief description of the data set comprises the abstract of the manuscript, the figures (including captions) and a description of the materials and methods used for their generation.</p>
Transmission Electron Microscopy Dataset for Image Deblurring
<p>The dataset consists of images corrupted by motion blur together with corresponding high-quality images from two different samples, one of thin sectioned kidney tissue and one of a calibration grid. The data was collected using a MiniTEM microscope (Vironova AB). The motion corrupted images are created by moving the sample under the microscope. Each low-quality (motion blurry) imaging sequence has corresponding high-quality images (captured by stopping the microscope at each position in the sequence). The high-quality frames have a size of 2048 x 2048 pixels with an overlap of 50% between adjacent frames. The low-quality (motion blurry) frames are captured with a size of 1024x1024 with the same motion direction (approximately vertically upwards). All images were captured at a field of view of 32μm, and with a per image exposure time of 15ms and stored as 16 bit tiff files. Both samples are imaged with the same settings and have four imaging sequences each.</p> <p>The dataset contains the raw image files as well as a partitioning into training, validation and testing. For these images, five low-quality images have been registered to each high-quality image. For the five registered images the intersection of all is cropped and stored. 1 of the 4 imaging sequences are chosen as the test set and the last part of another of the imaging sequences as a validation set. The rest is put in the training set.</p> <p><em><strong>Folder Structures:</strong></em></p> <ul> <li><strong>Raw data:</strong> <ul> <li>Raw data is the unprocessed data and each sample folder contains 4 image sequences. In each of these folders low-quality (motion blurry) images are stored in folder “Low” and corresponding high-quality images are stored in “GT”</li> </ul> </li> <li><strong>TrainValTest:</strong> <ul> <li>TrainValTest consist of data where the low-quality frames have been registered to the high-quality frames and divided into a training, validation and test set.</li> <li>Each of the Train, Val, Test folders contains 3 subfolders. “Low” contains folders names the same as the files in “GT” where each folder contains five low-quality (motion blurry) images, registered the that corresponding high-quality image. “GT” contains the corresponding high-quality images down sampled to the same spatial size as the low-quality images. “GT_hr” contains the same images as “GT” but not down sampled.</li> </ul> </li> </ul>
IODP Expedition 366 Scanning electron microscope images
<p>Microscopic images of discrete samples were acquired using a scanning electron microscope (SEM) and captured as image files. These files were uploaded along with a brief description and a record of the microscopic conditions when the image was taken.</p>
Scanning electron microscope images of Dunaliela tertiolecta and Phaeodactylum tricornutum cultures and scanning electron microscope images and cryogenic electron microscope images of isolated small cellular particles from respective conditioned media
<p>Scanning electron microscope images of cultures of microalgae<em> Dunaliela</em><em> </em><em>tertiolecta</em><em> </em>and <em>Phaeodactylum</em><em> </em><em>tricornutum</em><em> </em>and scanning electron microscope images and cryogenic electron microscope images of isolated small cellular particles from respective conditioned media are presented. Each image is supplemented by description of the preparation of the sample and the data on the imaging technique and equipment. The data are curated by Veronika Kralj-Iglic and Anna Romolo, University of Ljubljana, Faculty of Health Sciences, Laboratory of Clinical Biophysics.</p> <p> </p> <p> </p>
IODP Expedition 352 Scanning electron microscope images
<p>Microscopic images of discrete samples were acquired using a scanning electron microscope (SEM) and captured as image files. These files were uploaded along with a brief description and a record of the microscopic conditions when the image was taken.</p>
Datasets for Lorentz electron ptychography towards sub-nanometer resolution imaging of magnetic textures
<p>These data sets are the raw experimental data used in a Letter titled, Lorentz electron ptychography for imaging magnetic textures beyond the diffraction limit published on Nature Nanotechnology. The related paper should be cited whenever the datasets are used.</p> <p>Reference:</p> <p>Zhen Chen, Emrah Turgut, Yi Jiang, Kayla X. Nguyen, Matthew J. Stolt, Song Jin, Daniel C. Ralph, Gregory D. Fuchs, David A. Muller, Lorentz electron ptychography for imaging magnetic textures beyond the diffraction limit. Nature Nanotechnology, in press, https://doi.org/10.1038/s41565-022-01224-y (2022).</p> <p>The file format is Matlab's *.mat file with version 7.3.</p> <p>The diffraction patterns are stored as the variable 'cbed'.</p> <p>Experimental conditions can be found in data_info.txt and the related paper.</p> <p> </p>
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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.