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766 results for “Microscope”
Example Microscopy Metadata JSON files produced using Micro-Meta App to document the acquisition of example images using a custom-built TIRF Epifluorescence Structured Illumination Microscope
<p><strong>Example Microscopy Metadata JSON files produced using the <a href="https://wu-bimac.github.io/MicroMetaApp.github.io/">Micro-Meta App</a> documenting an example raw-image file acquired using the custom-built TIRF Epifluorescence Structured Illumination Microscope.</strong></p> <p>For this use case, which is presented in Figure 5 of <a href="http://doi: https://doi.org/10.1101/2021.05.31.446382">Rigano et al., 2021</a>, Micro-Meta App was utilized to document:</p> <p>1) The <strong>Hardware Specifications</strong> of the custom build TIRF Epifluorescence Structured light Microscope (TESM; <a href="https://www.pnas.org/content/109/8/E471.long">Navaroli et al., 2010</a>) developed, built on the basis of the based on Olympus IX71 microscope stand, and owned by the Biomedical Imaging Group (http://big.umassmed.edu/) at the Program in Molecular Medicine of the University of Massachusetts Medical School. Because TESM was custom-built the most appropriate documentation level is <strong>Tier 3</strong> (<em>Manufacturing/Technical Development/Full Documentation</em>) as specified by the <a href="https://doi.org/10.5281/zenodo.4710731">4DN-BINA-OME</a> Microscopy Metadata model (<a href="https://doi.org/10.1101/2021.04.25.441198">Hammer et al., 2021</a>).</p> <p>The TESM Hardware Specifications are stored in: <strong>Rigano et al._Figure 5_UseCase_Biomedical Imaging Group_TESM.JSON</strong></p> <p>2) The <strong>Image Acquisition Settings</strong> that were applied to the TESM microscope for the acquisition of an example image (FSWT-6hVirus-10minFIX-stk_4-EPI.tif.ome.tif) obtained by Nicholas Vecchietti and Caterina Strambio-De-Castillia. For this image, TZM-bl human cells were infected with HIV-1 retroviral three-part vector (FSWT+PAX2+pMD2.G). Six hours post-infection cells were fixed for 10 min with 1% formaldehyde in PBS, and permeabilized. Cells were stained with mouse anti-p24 primary antibody followed by DyLight488-anti-Mouse secondary antibody, to detect HIV-1 viral Capsid. In addition, cells were counterstained using rabbit anti-Lamin B1 primary antibody followed by DyLight649-anti-Rabbit secondary antibody, to visualize the nuclear envelope and with DAPI to visualize the nuclear chromosomal DNA.</p> <p>The Image Acquisition Settings used to acquire the FSWT-6hVirus-10minFIX-stk_4-EPI.tif.ome.tif image are stored in: <strong>Rigano et al._Figure 5_UseCase_AS_fswt-6hvirus-10minfix-stk_4-epi.tif.JSON</strong></p> <p><em><strong>Instructional video tutorials on how to use these example data files:</strong></em><br> Use these videos to get started with using Micro-Meta App after downloading the example data files available here.</p> <ul> <li><a href="https://vimeo.com/562022222">Part 1/2</a></li> <li><a href="https://vimeo.com/562022281">Part 2/2</a></li> </ul>
Microscopic trip chains for Brunswick (Germany) region
<p>The data set contains microscopic trip chains for the Brunswick (Braunschweig) area in Germany on an average day. All synthetic persons within Braunschweig are shown, as well as all households outside Braunschweig where at least one synthetic person had an activity in Braunschweig.</p> <p>The generation of this data set is based on a two-stage process. The starting point is the macroscopic transport demand model DEMO (Winkler and Mocanu, 2020: https://doi.org/10.1016/j.trd.2020.102476) and a population upscaled from the MiD 2017 ("Mobilität in Deutschland") for Germany, which was spatially distributed according to the BKG household dataset (households, inhabitants, federal government). In the first step of the process, the trip chains between the DEMO traffic cells were generated based on the daily schedules of the MiD population (Mocanu and Joshi, 2022: https://elib.dlr.de/188443/). In the second step of the process, corresponding locations were assigned within the target traffic cells. The locations were previously extracted from OpenStreeMap and attributed with activities according to their attributes/metadata (key/value pairs) (Malkus et al., 2024: https://doi.org/10.1016/j.procs.2024.06.043).</p>
Low-voltage Secondary Electron Emission Spectromicroscopy using a Scanning Auger Microscope
<p>Secondary electron emission is considered a well-established nano-scale probe for mapping the surface morphology of materials. It has also been demonstrated that secondary electrons (SE) emitted from materials can provide additional information on the local work function, bulk density of state (DOS), surface potential, charging/discharging characteristics, and elemental/chemical properties of bulk materials. The nano-scale lateral resolution and surface sensitivity of low-voltage scanning microscopes give them a unique advantage for the investigation of surfaces. However, the surface contamination caused by exposure to electron beams has always been a limiting factor for this purpose. Since the yield of SE emission is higher than that of Auger emission, the secondary electron emission spectromicroscopy (SEES) performed in an ultra-high vacuum chamber using a scanning Auger microscope (SAM) can be a very powerful tool for surface characterization, especially in the case of ultra-thin materials.</p> <p>We adapt our scanning auger microscope (SAM), equipped with a cylindrical mirror analyzer (CMA) and operated in an ultra-high vacuum, to SEES by tilting the sample holder and applying a negative bias to the sample. We also presented SEES signals of Chromium thin film at low voltages of 500 and 1000 V.</p>
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>
Dataset supporting the paper "Superconducting Scanning Tunneling Microscope Tip to Reveal Sub-millielectronvolt Magnetic Energy Variations on Surfaces. J. Phys. Chem Lett. 12, 2983 (2021)"
<p>Dataset corresponding to theoretical calculations in the supporting information of the paper "Superconducting Scanning Tunneling Microscope Tip to Reveal Sub-millielectronvolt Magnetic Energy Variations on Surfaces" J. Phys. Chem Lett. 12, 2983 (2021), <a href="https://doi.org/10.1021/acs.jpclett.1c00328">https://doi.org/10.1021/acs.jpclett.1c00328</a></p> <p>List of files:</p> <p>Several folders corresponding to the figures of the supporting information. They contain:</p> <ul> <li>.siesta files: STM images in WsXM format (http://www.wsxm.eu/) simulated using STMpw (<a href="https://doi.org/10.5281/zenodo.3581159">https://doi.org/10.5281/zenodo.3581159</a>).</li> <li>CONTCAR files: relaxed structures in VASP format. They can be visualized with VESTA (<a href="https://jp-minerals.org/vesta/en/">https://jp-minerals.org/vesta/en/</a>).</li> <li>.agr files: grace files (<a href="https://plasma-gate.weizmann.ac.il/Grace/">https://plasma-gate.weizmann.ac.il/Grace/</a>).</li> </ul>
Seeing nanoscale electrocatalytic reactions at individual MoS2 particles under an optical microscope: probing sub-mM oxygen reduction reaction
<p><span>Data in this repository include raw iSCAT optical microscopy movies for the operando monitoring of oxygen reduction reaction at bare ITO and MoS2-coated ITO electrodes in KCl solution in the presence or absence of La<sup>3+</sup> with their respective electrochemical data (voltammograms). </span></p>
Data for the manuscript "Enhanced microscopic dynamics in mucus gels under a mechanical load in the linear viscoelastic regime" (PNAS).
<p>Data files for the figures published in</p> <p>D. Larobina, A. Pommella, A.-M. Philippe, M. Y. Nagazi, and L. Cipelletti, <em>Enhanced Microscopic Dynamics in Mucus Gels under a Mechanical Load in the Linear Viscoelastic Regime</em>, Proc Natl Acad Sci USA <strong>118</strong>, e2103995118 (2021).</p> <p>DOI: 10.1073/pnas.2103995118</p> <p>Each data set is available as a plain text file (description in the file __README__DataDescription.txt), and as an Excel file.<br> The Excel files typically contain the data sets of several panels of a given figure, as separated sheets. See the description<br> provided in the "GeneralInfo" sheet of each Excel file.</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>
Data supporting: Microscopic observation of two-level systems in a metallic glass model
<p>Dataset of double well potentials sampled from energy landscape exploration of a ternary Lennard-Jones model supporting: "Microscopic observation of two-level systems in a metallic glass model"</p> <p>Thermalised configurations of the ternary Lennard-Jones model are given in the archive (configs.zip) of 1200 atoms at <span class="math-tex">\(T_f\)</span> 0.488, 0.509, 0.558 and 0.617 in the lammps (https://www.lammps.org/) data file format (https://docs.lammps.org/read_data.html).</p> <p>The two datasets each provided as (.zip) archives named dataset1.zip and dataset2.zip</p> <p>Datafiles (.csv) are named nebdf_{:3.3f}_{:05d}.csv where the float is <span class="math-tex">\(T_f\)</span> and the integer is <span class="math-tex">\(\tilde{m}\)</span>. </p> <p>columns of each .csv file are:</p> <p>'transitions', 'forward barriers', 'reverse barriers', 'asymmetry', 'barrier', 'euclidean distance', 'distance along string', 'n_intermediates', 'deltas', 'splittings', 'delta_zeroes', 'gammas', 'PR', 'glass', 'omegas1', 'omegas2', 'omegasts', 'Index 1', 'Index 2', 'Frequency 1>2', 'Frequency 2>1', 'e_1', 'e_2', 'dc', 'Tprep'</p> <p>'glass' is the index of the glassy metabasin sampled</p> <p>omegas1', 'omegas2', 'omegasts' are the curvatures of the minimum energy oaths near the first minimum, second minimum and transition state</p> <p>'e_1', 'e_2' are the energy per atom of the two glass minima </p> <p>'dc' is the typical particle displacement corresponding to <span class="math-tex">\(\sqrt{\dfrac{d^2}{PR}}\)</span></p> <p> </p>
Raw dataset for: High-throughput multimodal wide-field Fourier-transform Raman microscope
<p>This is the Raw spectral dataset of the data published in 10.1364/OPTICA.488860</p> <p>Data are arranged as follows:</p> <p>wavenumber [Nx1]</p> <p>Hyperspectrum_cube [Nx2, A, B]: hyperspectral datacube, where: Hyperspectrum_cube (1:N, :, :) is the real part; Hyperspectrum_cube (N+1:2N, :, :) is the imaginary part</p> <p>maximum [1x1]</p> <p>minimum [1x1].</p> <p>N: number of spectral bands</p> <p>A and B: size of the spatial coordinates</p> <p>Spectral amplitudes are obtained by: Hyperspectrum_cube=double(Hyperspectrum_cube)./(2.^16-1).*maximum+minimum</p>
Data bundle for "Advancing characterisation with statistics from correlative electron diffraction and X-ray spectroscopy, in the scanning electron microscope"
<p>Prepared by Tom McAuliffe (t.mcauliffe17@imperial.ac.uk)</p> <p>This repository is a release of the raw data and analysis results for: 'Advancing characterisation with statistics from correlative <br> electron diffraction and X-ray spectroscopy, in the scanning electron microscope' <br> https://doi.org/10.1016/j.ultramic.2020.112944</p> <p>The raw data is given as 'RawData.h5' - this contains patterns, spectra, and metadata in the Bruker-exported format.</p> <p>Outputs of our analysis code (which will be made available via AstroEBSD) are contained in 'PCA_Outputs' subfolders. Exported plots and <br> .mat results files are contained within. These are organised by Figure number in the paper.</p> <p>The provided results are divided into two major sections:<br> (1) Variation in the variance tolerance limit (and corresponding numbers of retained components), and the weighting of the PCA in favour of EBSD or EDS information.<br> RCCs are validated by cross-correlation with the corresponding raw data point pattern and/or spectrum. <br> (2) Full outputs of PCA analysis having varied the weighting parameter. This contains IPF maps, quantified chemical maps, PC scores, and label maps. <br> </p>
Evaluating the microscopic effect of brushing stone tools as a cleaning procedure [Python analysis]
<p>This upload includes the following files related to the Python analysis:</p> <ol> <li>Raw data as a XLSX table (brushing_v2.xlsx), i.e. results from R Script #1 (see <a href="https://doi.org/10.5281/zenodo.3632517">https://doi.org/10.5281/zenodo.3632517</a>)</li> <li>Python script of the whole analysis (RunEveryParameter.py)</li> <li>Convenience script for running RunEveryParameter.py in background and logging all output (RunSingleParametesBash.sh)</li> <li>Log file for output of sampling from the model for each parameter in a loop (logAll.txt)</li> <li>Jupyter notebooks of the analysis run on <em>epLsar</em> as an example (Notebook_SingleParameter.inpyb) and of a summary of the whole analysis (Notebook_Overview.ipynb), plus associated HTML output files (*.html)</li> <li>For each parameter:</li> </ol> <ul> <li>Full samples of parameter values (*.pkl)</li> <li>Energy plots of Hamiltonian Monte Carlo (*_Energy.pdf)</li> <li>Contrast plots between each treatment (BrushDirt = Is_Is, BrushNoDirt = Is_No, RubDirt = No_Is) and the control (No_No) (*_Contrasts.pdf)</li> <li>Trace plots for each parameter (*_Trace.pdf)</li> <li>Distribution of posteriors for each parameter (*_Posterior.pdf)</li> <li>Prior and posterior predictive distributions for each parameter (*_PriorPosterior.pdf)</li> </ul> <p>Instructions to download all files at once are given here: <a href="https://doi.org/10.5281/zenodo.4011952">https://doi.org/10.5281/zenodo.4011952</a></p>
Addressable Nanoantennas with Cleared Hotspots for Single-Molecule Detection on a Portable Smartphone Microscope
<p>The advent of highly sensitive photodetectors and the development of photostabilization strategies made detecting the fluorescence of single molecules a routine task in many labs around the world. However, to this day, this process requires cost-intensive optical instruments due to the truly nanoscopic signal of a single emitter. Simplifying single-molecule detection would enable many exciting applications, <em>e.g.</em> in point-of-care diagnostic settings, where costly equipment would be prohibitive. Here, we introduce addressable NanoAntennas with Cleared HOtSpots (NACHOS) that are scaffolded by DNA origami nanostructures and can be specifically tailored for the incorporation of bioassays. Single emitters placed in the NACHOS emit up to 461-fold (average of 89±7-fold) brighter enabling their detection with a customary smartphone camera and an 8-US-dollar objective lens. To prove the applicability of our system, we built a portable, battery-powered smartphone microscope and successfully carried out an exemplary single-molecule detection assay for DNA specific to antibiotic-resistant <em>Klebsiella pneumonia</em> „on the road “. Here we demonstrate the raw data on which our findings based on.</p>
Microscopic vehicular mobility trace of Europarc roundabout, Creteil, France (vehicular-mobility-trace.github.io: v1.0)
<p>First release of the Europarc roundabout micro mobility dataset, Creteil, France.</p> <p>http://vehicular-mobility-trace.github.io/</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>
Using the traditional microscope for mineral grain orientation determination: A prototype image analysis pipeline for optic-axis mapping (POAM). Original dataset.
<p>The data repository contains data obtained with the microscope Nikon Eclipse LV100ND that was stitched with <a href="https://imagej.net/plugins/trakem2/">TrakEM2 software</a>. The files allow reproducing the results obtained and plot in <a href="https://doi.org/10.1111/jmi.13284">Acevedo et al. (2024)</a> <strong>"Using the traditional microscope for mineral grain orientation determination: A prototype image analysis pipeline for optic-axis mapping (POAM)."</strong> by Acevedo Zamora, M. A., Schrank, C. E., & Kamber, B. S.</p> <p>The prototype uses MatLab scripts (<a href="https://github.com/marcoaaz/AcevedoEtAl._2024a_POAM">AcevedoEtAl._2024a_POAM</a>) that were documented in the paper Supplementary Material 1. The metadata can be found in Supplementary Material 3 and follows the structure of this data repository. The user needs downloading and changing the paths to run the same scripts and reproduce the results.</p> <p>Note: After download, unzip and merge (copy-paste) the folders (parts 1, 2 and 3). Before merging, the containing folder should be re-named to 'paper 2_datasets' to match exactly the MatLab scripts and reproduce our work.</p> <p>The remaining questions should be addressed to Marco Acevedo (maaz.geologia@gmail.com ; marco.acevedozamora@qut.edu.au)</p> <p>Thanks.</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.
Constraining Neutron-Star Matter with Microscopic and Macroscopic Collisions
<p>Data release associated with the preprint "<em>Constraining Neutron-Star Matter with Microscopic and Macroscopic Collisions</em>'' (2021; <a href="https://arxiv.org/abs/2107.06229">arxiv:2107.06229[nucl-th]</a>)</p> <p>Data includes:</p> <p>EOS files:</p> <ol> <li>chiral effective field theory (CEFT) up to 1nsat and extended with speed-of-sound extension (cse)</li> <li>CEFT up to 1.5 nsat and cse</li> <li>CEFT up to 1.5 nsat and extended with piecewise-polytrope</li> <li>CEFT up to 1.0 nsat, cse and enforced a uniform distribution on a radius for 1.4 solar mass neutron star (R14)</li> <li>CEFT up to 1.5 nsat, cse and enforced a uniform distribution on R14</li> </ol> <p>Posterior probability files: details to be found in README.txt<br> <br> Data used in Fig.1 and Fig.2 are included</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
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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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.