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13 results for “Voltage imaging”
High-Voltage Disconnector State Identification: synthetic and real images of substation disconnectors
<p>This dataset contains the training and test images used in the work detailed in the article: Barpp Gomes, V., Marchesi, B., Gruber, Y.A. <em>et al.</em> Exploring Synthetic Data for Training Deep Learning Models for High-Voltage Disconnector State Identification. <em>J Control Autom Electr Syst</em> (2025). <a href="https://doi.org/10.1007/s40313-025-01204-2">https://doi.org/10.1007/s40313-025-01204-2</a></p> <p>Contais about 940,000 synthetic (CGI-rendered) and 60,000 real (camera-captured) samples of four types of substation disconnectors, on both open and closed states:</p> <ul> <li>230 kV center break (type 1, as indicated in the article);</li> <li>230 kV center break (type 2);</li> <li>230 kV double side break</li> <li>525 kV horizontal semi-pantograph</li> </ul> <p>Each zip file contains images of one type of substation disconnector. Images are sized 320x128 and are organized in folders, as follows:</p> <ul> <li>00_train_synth: Synthetic training images.</li> <li>01_train_real: A small set of real training images, as indicated in the article.</li> <li>02_test_real_normal1: One set of real test images.</li> <li>03_test_real_normal2: Another set of real test images, from a different time period.</li> <li>04_test_real_maneuvers: A special set of real test images in which the switches have been operated (are in different states).</li> </ul>
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>
Source code and data for manuscript "Large-scale deep tissue voltage imaging with targeted illumination confocal microscopy"
<p>Source code and data for manuscript "Large-scale deep tissue voltage imaging with targeted illumination confocal microscopy", <em>Nat Methods</em> (2024), https://doi.org/10.1038/s41592-024-02275-w.</p>
Simultaneous two-photon voltage or calcium imaging and multi-channel LFP recordings in barrel cortex of awake and anesthetized mice
<p>Neuronal population activity, both spontaneous and sensory-evoked, generates propagating waves in cortex. However, high spatiotemporal-resolution mapping of these waves is difficult as calcium imaging, the work horse of current imaging, does not reveal subthreshold activity.</p> <p>Here, we present a platform combining voltage or calcium two-photon imaging with multi-channel local field potential (LFP) recordings in different layers of the barrel cortex from anesthetized and awake head-restrained mice. A chronic cranial window with access port allows injecting a viral vector expressing GCaMP6f or the voltage-sensitive dye (VSD) ANNINE-6plus, as well as entering the brain with a multi-channel neural probe. We present both average spontaneous activity and average evoked signals in response to multi-whisker air-puff stimulations.</p> <p>Time domain analysis shows the dependence of the evoked responses on the cortical layer and on the state of the animal, here separated into anesthetized, awake but resting, and running. The simultaneous data acquisition allows to compare the average membrane depolarization measured with ANNINE-6plus with the amplitude and shape of the LFP recordings. The calcium imaging data connects these data sets to the large existing database of this important second messenger. Interestingly, in the calcium imaging data, we found a few cells which showed a decrease in calcium concentration in response to vibrissa stimulation in awake mice.</p> <p>This system offers a multimodal technique to study the spatiotemporal dynamics of neuronal signals through a 3D architecture in vivo. It will provide novel insights on sensory coding, closing the gap between electrical and optical recordings.</p>
Scanless two-photon voltage imaging example code and dataset
<p>This data and code are associated with the manuscript "Scanless two-photon voltage imaging" published in Nature Communications in June 2024 (DOI: <a href="10.1038/s41467-024-49192-2">10.1038/s41467-024-49192-2</a><span>). </span></p>
Preliminary data and analysis from ultrafast calcium or voltage imaging recordings at 8-bits resolution using a Kinetix camera
<p><span>This dataset was obtained from brain slices of the mouse. Data are from transversal hippocampal slices from </span><span>30-40 postnatal days old C57Bl6 mice (of both genders), stained with the Ca<sup>2+</sup> indicator Fluo-4 AM; or from layer-5 pyramidal neurons loaded intracellularly either with the Ca<sup>2+</sup> indicator Oregon Green BAPTA-5N or with the voltage sensitive dye JPW1114. <span> </span>Details are in the Read_me file. This dataset cannot be used for publications without permission of the contact person.</span></p>
Datesets and images of the publication "Probing crystallinity and grain structure of 2D materials and 2D-like van der Waals heterostructures by low-voltage electron diffraction" - DOI: 10.1002/pssa.202300148
<p>Datasets and images of the publication "Probing crystallinity and grain structure of 2D materials and 2D-like van der Waals heterostructures by low-voltage electron diffraction" - DOI: <a href="https://www.doi.org/10.1002/pssa.202300148">10.1002/pssa.202300148</a></p> <p>The Jupyter Notebooks for analyzing the datasets and generating all the figures are available at <a href="https://gitlab.com/JohMu/tds_hios_manuscript">https://gitlab.com/JohMu/tds_hios_manuscript</a>.</p> <p><strong>MoS<sub>2</sub> 4D-STEM dataset:</strong></p> <ul> <li>192x192 scan pixels</li> <li>200x200 camera pixels</li> <li>Acceleration voltage: 20kV</li> <li>Camera length: 10.56 mm</li> <li>Camera pixel size: 4x5.86 µm = 23.44 µm (original dataset with 4x4 binning)</li> <li>File location: Figure 2_3_S1.zip -> 230101205338_20kV_hexz0_camz-10_posi_003_good\scan_data_bin2_centered_crop-imgNx200.h5</li> <li>The original raw dataset (23 GB, 192x192 scan pixels, 800x800 camera pixels, camera pixel size: 5.86 µm), the scan reference dataset and the Jupyter Notebook for the shift-compensation is available from the author. The dataset uploaded here is binned by a factor of 4 and shift-compensated.</li> </ul> <p><strong>C60/MoS<sub>2</sub> 4D-STEM dataset:</strong></p> <ul> <li>113x113 scan pixels</li> <li>512x512 camera pixels</li> <li>Acceleration voltage: 20kV</li> <li>Camera length: 20.56 mm</li> <li>Camera pixel size: 5.86 µm</li> <li>File location: Figure 4.zip -> scan_data_scan113x113_gzip.h5</li> </ul> <p> </p>
Simultaneous two-photon voltage or calcium imaging and multi-channel LFP recordings in barrel cortex of awake and anesthetized mice
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Positive-going hybrid indicators for voltage imaging in excitable cells and tissues
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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>
Synthetic High-Voltage Power Line Insulator Images
<p>This database contains Synthetic High-Voltage Power Line Insulator Images.</p> <p>There are two sets of images: one for image segmentation and another for image classification.</p> <p>The first set contains images with different types of materials and landscapes, including the following landscape types: Mountains, Forest, Desert, City, Stream, Plantation. Each of the above-mentioned landscape types consists of 2,627 images per insulator type, which can be Ceramic, Polymeric or made of Glass, with a total of 47,286 distinct images.</p> <p>The second file contains synthetic that simulate the most common impurities found on high-voltage transmission line insulator strings: salt, volcanic soot, bird excrement and a clean insulator. Each type of dirt has 3,608 images, with 1,202 images for each type of insulator material, with a total of 14,432 images.</p>
Real-time Neuron Segmentation for Voltage Imaging
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Statistically unbiased prediction enables accurate denoising of voltage imaging data
<p>Here we report SUPPORT (Statistically Unbiased Prediction utilizing sPatiOtempoRal information in imaging daTa), a self-supervised learning method for removing Poisson-Gaussian noise in voltage imaging data. SUPPORT is based on the insight that a pixel value in voltage imaging data is highly dependent on its spatially neighboring pixels in the same time frame, even when its temporally adjacent frames do not provide useful information for statistical prediction. Such spatiotemporal dependency is captured and utilized to accurately denoise voltage imaging data in which the existence of the action potential in a time frame cannot be inferred by the information in other frames. Through simulation and experiments, we show that SUPPORT enables precise denoising of voltage imaging data while preserving the underlying dynamics in the scene.</p> <p>We also show that SUPPORT can be used for denoising time-lapse fluorescence microscopy images of <em>Caenorhabditis elegans</em> (<em>C. elegans</em>), in which the imaging speed is not faster than the locomotion of the worm, as well as static volumetric images of <em>Penicillium</em> and mouse embryos. SUPPORT is exceptionally compelling for denoising voltage imaging and time-lapse imaging data, and is even effective for denoising calcium imaging data.</p> <p>For more details, please see the accompanying research publication "<a href="https://www.nature.com/articles/s41592-023-02005-8">Statistically unbiased prediction enables accurate denoising of voltage imaging data</a>".</p> <p> </p> <p>Datasets for publication titled "Statistically unbiased prediction enables accurate denoising of voltage imaging data"</p> <p><strong>Voltage imaging of paQuasAr6a: paQuasAr6a.zip</strong><br> paQuasAr6a/Q6a_Cell1<br> paQuasAr6a/Q6a_Cell2<br> paQuasAr6a/Q6a_Cell3<br> paQuasAr6a/Q6a_Cell4<br> paQuasAr6a/Q6a_Cell5<br> paQuasAr6a/Q6a_Cell6<br> paQuasAr6a/175118PP046_P8_pulse (10 ms)_q6<br> paQuasAr6a/181625PP046_P5_q6_pulse(50ms)<br> paQuasAr6a/183616PP046_P8_pulse (10 ms)_q6<br> paQuasAr6a/183717PP046_P5_q6_pulse(50ms)</p> <p><strong>Voltage imaging of Voltron2: Voltron2.zip</strong><br> Voltron2/Voltron_Cell1<br> Voltron2/Voltron_Cell2<br> Voltron2/Voltron_Cell3<br> Voltron2/Voltron_Cell3_2<br> Voltron2/Voltron_Cell4<br> Voltron2/Voltron_Cell5<br> Voltron2/Voltron_Cell6<br> Voltron2/Voltron_Cell7 (100f_s)<br> Voltron2/Voltron_Cell7_2 (100 f_s)</p> <p><strong>Voltage imaging of zArchon (zebrafish spinal cord): zebrafish_spinal_cord_N.zip</strong></p> <p><strong>Voltage imaging of SomArchon (mouse hippocampus neuron): SomArchon.zip</strong></p> <p><strong>Volumetric structural imaging of mouse embryo (Expansion microscopy): Expansion_microscopy.zip</strong><br> Expansion_microscopy/JEME208_2x_expanded_bone.tif<br> Expansion_microscopy/JEME208_2x_expanded_intenstine.tif<br> Expansion_microscopy/JEME209_2x_expanded_bone_1.tif<br> Expansion_microscopy/JEME209_2x_expanded_bone_2.tif<br> Expansion_microscopy/JEME209_2x_expanded_tail.tif</p> <p><strong>Volumetric structural imaging of <em>penicillium</em>: Penicillium.zip</strong><br> Penicillium/penicillium_low_snr.tif<br> --> Low SNR image<br> Penicillium/penicillium_high_snr.tif<br> --> High SNR image</p> <p><strong>Calcium imaging of zebrafish: Zebrafish.zip</strong><br> Zebrafish/zebrafish_multiple_brain_regions.tif<br> --> Multiple brain regions<br> Zebrafish/zebrafish_Cerebellar_plate.tif<br> --> Cerebellar plate<br> Zebrafish/zebrafish_Dorsal_telencephalon.tif<br> --> Dorsal telencephalon<br> Zebrafish/zebrafish_Medulla_oblongata.tif<br> --> Medulla oblongata<br> Zebrafish/zebrafish_Olfactory_bulb.tif<br> --> Olfactory bulb<br> Zebrafish/zebrafish_Optic_tectum.tif<br> --> Optic tectum<br> Zebrafish/zebrafish_Habenula.tif<br> --> Habenula</p>
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