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7 results for “Microscope Simulation”
Videos of the processed microscope images and time series of the petrophysical parameters from image processing and geochemical simulation and of the measured induced polarisation [Video][Dataset]
<p>Supporting Information for the manuscript <em>Microfluidics and spectral induced polarization for direct observation and petrophysical modeling of calcite dissolution</em> published in Geophysical Research Letters</p> <ul> <li><strong>Data Set S1.</strong> Porosity, water saturation, and calcite sample perimeter from image<br>processing.</li> <li><strong>Data Set S2.</strong> Porosity, water conductivity, and pH from geochemical simulation.</li> <li><strong>Data Set S3.</strong> Real and imaginary components of the complex electrical conductivity at<br>2.5 Hz and CEC from petrophysical modeling.</li> <li><strong>Movie S1.</strong> Dissolution of the calcite sample with the detected contour superimposed in<br>white on the grayscale images. Time, length scale, and flow direction are indicated. In<br>case of problems launching the file, we recommend using VLC Media Player software.</li> <li><strong>Movie S2.</strong> Segmented images of the CO2 bubbles produced by the calcite dissolution.<br>Time, length scale, and flow direction are indicated. In case of problems launching the<br>file, we recommend using VLC Media Player software.</li> </ul>
Microscope-Cockpit find nuclei code and microscope simulation configuration
<p>This file contains instructions for setting up a simulated microscope<br> environment using Microscope-Cockpit and Python-Microscope. This<br> environment includes a large tiled image of which segments are<br> returned to simulate stage movement and different colour channels<br> returned to simulate changing an emission filter. This simulated<br> microscope is then used to test the findNuclei script showing the ease<br> of extending Cockpit functionality with Python libraries,<br> Python-openCV is used in this case.<br> </p>
Leveraging IoT Data Stream for Near-Real-Time Calibration of City-Scale Microscopic Traffic Simulation
<p>This repository includes input and output data of the methodology presented in the <a href="https://arxiv.org/abs/2210.17315">paper</a> for generating a calibrated dynamic microscopic traffic simulation.</p> <ul> <li>The input data includes the network, initial normalized origin-destination matrix, and hourly traffic counts from stationary city sensors.</li> <li>The output is a 24-hour calibrated microscopic traffic simulation for the city of Tartu, Estonia.</li> </ul> <p>All source codes are available at <a href="https://github.com/Khoshkhah/NRTCalib">https://github.com/Khoshkhah/NRTCalib</a>.<br> </p>
Simulated pollen microscope slides for segmentation/bounding box regression and classification
<p><strong>Pollen dataset</strong></p> <p>Sebastian Seurig, Institute for Medical Informatics and Biometry, Carl Gustav Carus Faculty of Medicine, TU Dresden<br> (<a href="https://orcid.org/0000-0001-6511-0102">https://orcid.org/0000-0001-6511-0102</a>)<br> Peter Steinbach, Helmholtz AI, Artificial Intelligence Cooperation Unit, Helmholtz-Zentrum Dresden-Rossendorf<br> (<a href="https://orcid.org/0000-0002-4974-230X">https://orcid.org/0000-0002-4974-230X</a>)<br> Nico Scherf, Institute for Medical Informatics and Biometry, Carl Gustav Carus Faculty of Medicine, TU Dresden and Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig<br> (<a href="https://orcid.org/0000-0003-4003-9121">https://orcid.org/0000-0003-4003-9121</a>)<br> Ingo Röder, Institute for Medical Informatics and Biometry, Carl Gustav Carus Faculty of Medicine, TU Dresden</p> <p>This artificial dataset was created for testing machine learning models on segmentation or bounding box regression and classification tasks.<br> It contains 80.000 (1280x1280 RGB) images of airborne pollen of different sizes within their natural range:</p> <ul> <li>2x10.000 images each Chenopodium bonus-henricus and Corylus colurna making up the medium-sized pollen</li> <li>10.000 images Urtica dioica making up the smaller pollen</li> <li>10.000 images Secale cereale making up the larger pollen</li> <li>4x10.000 images each containing artifacts such as dust or bursted pollen and a mix of pollen in the following ratios:<br> - 'equal_split' 25% each class<br> - 'smaller_pollen_split' 70% Urtica dioica, *<br> - 'middle_pollen_split' 40% Corylus colurna, 10% Chenopodium bonus-henricus, *<br> - 'bigger_pollen_split' 70% Secale cereale, *<br> - * the rest of the pollen split equally between the remaining classes<br> Labels consist of :</li> <li>monochrome masks of each pollen slide (ignoring artifacts) for segmentation</li> <li>x and y-coordinates of the bounding boxes containing all pixels of each of the pollen for regression</li> <li>class names for each labeled pollen.</li> </ul> <p>For further questions and suggestions, please do not hesitate to contact the authors.</p> <p>Extensive code and configuration settings for the generator used can be found at <a href="https://github.com/seurig/slide-generator">https://github.com/seurig/slide-generator</a>.<br> All raw images used to generate this dataset were taken from <a href="https://pollen.tstebler.ch/">https://pollen.tstebler.ch/</a>.</p>
Microscopic simulations of high harmonic generation from semiconductors
<p>Dataset of the publication “Microscopic simulations of high harmonic generation from semiconductors” by A. Trautmann, R. Zuo, G. Wang, W.-R. Hannes, S. Yang, L. H. Thong, C. Ngo, J. T. Steiner, M. Ciappina, M. Reichelt, H. T. Duc, X. Song, W. Yang, and T. Meier, Proc. SPIE 11999, Ultrafast Phenomena and Nanophotonics XXVI, 1199909 (2022) ( <a href="https://doi.org/10.1117/12.2607447">https://doi.org/10.1117/12.2607447</a> ). The zip file includes the data on which the plots are based.</p>
Microscopic simulations of the dynamics of excitonic many-body correlations coupled to quantum light
<p>Dataset of the publication "Microscopic simulations of the dynamics of excitonic many-body correlations coupled to quantum light" H. Rose, P. R. Sharapova, and T. Meier, Proc. SPIE 12884, Ultrafast Phenomena and Nanophotonics XXVIII, 1288403 (2024). ( https://doi.org/10.1117/12.2690245 ). The zip file includes the data on which the plots shown in figures 1 and 2 are based.</p>
Simulation of Anisotropic Microscopic Wave-induced Fluid Flow Effects and the Influential Factors Analysis
<p><strong>The dataset includes:</strong><br>Code for Numerical Simulation (DSSS) <br>Code for generating Digital Rock<br>Code for the Holton Sequence<br>Code for the WIFF-Squirt Flow Model</p> <p>SEM images from natural shale sample<br>Examples of Digital Rock that have been used in our current research</p> <p>(All codes were developed in Matlab 2017 or newer versions.)</p>
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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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.