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146 results for “Phase Imaging”
Images and Crater Data for "Crater Detection Dependence on Resolution, Incidence Angle, Emission Angle, and Phase Angle"
<p>Images are from the LROC-NAC and have been cartographically controlled to each other and the <em>Apollo 11</em> landing site as described in Supporting Information Text S1. Images are cropped so that the cover ±0.025° from the landing site when coordinates have three significant figures. The images are provided as .png files with .pgw ("PNG World"). The images are at 1 mpp (contain a "1mpp" string in the file name), 2.5 mpp (contain a 2p5mpp" string in the file name), and 6.25 mpp (contain a "6p25mpp" string in the file name). Additionally, the three <em>e</em> > 10° images are included as unprojected .cub files; these files omit the "l2" (map projected, Level-2 data) string and any "l4" (mosaicked) string from the file name, but they instead include "trim" to indicate the image has been trimmed from its full extent to the area of interest.</p> <p>Crater data are formatted as .csv (comma-separated values) files and are one file per image per researcher. File names have the exact same name as the image file that was used to map crater data, with two differences: The initials of the author are appended, and the file extension is "csv" instead of "png". The files do not have headers, but they are formatted such that the first column is latitude (decimal degrees north), second column is longitude (decimal degrees east), and diameter (kilometers). Crater data are entirely in one .zip file.</p>
Data bundle for "Spherical-angular dark field imaging and sensitive microstructural phase clustering with unsupervised machine learning"
<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: 'Spherical-angular dark field imaging and sensitive microstructural phase clustering with unsupervised machine learning' </p> <p>The raw data is given as 'yprime.h5' - this contains patterns and metadata in the Bruker-exported format.</p> <p>Scripts for dataset decomposition into latent factors are given in 'Scripts'.</p> <p>Our spherical analysis code is included in 'SphericalAngleDF'.</p> <p>Outputs of our analysis code are contained in 'Analysis'.</p> <p>Figures for the paper are included in 'Figures'.</p> <p> </p>
Diffraction images of crystals of the spectrin repeats 7 and 8 (SR7-SR8) of the plakin domain of human plectin (PDB code 5J1G): native and Hg-derivative datasets for phasing by SIRAS
<p>Diffraction images of crystals of a fragment of the plakin domain of human plectin that includes the spectrin repeats 7 to 8 (SR7-SR8).</p> <p>Images correspond to the dataset used to solve and refine the pdb entry <strong>5J1G</strong> (http://www.rcsb.org/pdb/explore/explore.do?structureId=5J1G).</p> <p> </p> <p>The structure was phase by single isomorphous replacement with anomalous scattering (SIRAS) using two datasets: one from a native crystal and another one from a crystal derivatized with the mercurial compound ethylmercurithiosalicylate (EMTS).</p> <p> </p> <p>The <strong>Native dataset</strong> was collected on a single crystal at the beamline XALOC of the ALBA Synchrotron (Barcelona, Spain) using radiation of 0.9792 Å wavelength and a PILATUS 6M detector. The dataset consists of 4 wedges of 450 images each (0.2º oscillation per image). Each wedge was collected at a different position of the same crystal. The crystals belong to the space group P2<sub>1</sub> with approximate cell dimensions <em>a</em>=45.7 Å, <em>b</em>=115.9 Å, <em>c</em>=64.8 Å, beta=97.6 º.</p> <p> </p> <p>The data from a <strong>mercurial derivative</strong> (EMTS) was collected in house using a rotating anode X-ray generator (wavelength 1.54179 Å) and a mar345 image plate detector. The dataset consists of 360 images (1º oscillation per image). The crystal was isomorphic to the native crystal.</p> <p> </p> <p>In addition to the diffraction images the following files are included:</p> <p>a) Files for indexing with the program XDS and the HKL files containing the integrated intensities.</p> <p>b) Files for scaling using the program xscale (directory XSCALE_5J1G_Native_EMTS).<br> c) The directory “phasing_shelx” contains hkl files of the intensities of the native and EMTS datasets in a format suitable for analysis with Shelx. This directory also contains the files of the phasing by SIRAS using Shelx C/D/E.</p>
Snow equi-temperature metamorphism described by a phase-field model applicable on micro-tomographic images: prediction of microstructural and transport properties
<p>This dataset provides data described and used in the article submitted to Journal of Advances in Modeling Earth Systems "Snow equi-temperature metamorphism described by a phase-field model applicable on micro-tomographic images: prediction of microstructural and transport properties".</p> <p>It contains .csv files with different properties computed on outputs of the model Snow3D simulating equi-temperature metamorphism. This micro-scale model was used here with experimental micro-tomographic snow images as input and returns series of 3-D images of snow showing features of equi-temperature metamorphism at different time steps as output.</p> <p>In this dataset, you will find two types of files:</p> <p>- the microstructural properties (density, specific surface area, covariance lengths, mean curvature) computed on the simulated images at different time steps.</p> <p>- the transport properties (effective conductivity, normalizes effective vapor diffusion coefficient, permeability) of the simulated images at different time steps.</p> <p>Finally, metadata_simulations.csv gather the information relative to the simulations.</p>
Exoplanet imaging data challenge, phase 2
<p><strong>Datasets for the second phase of the Exoplanet Imaging Data Challenge</strong> (<a href="https://exoplanet-imaging-challenge.github.io/">https://exoplanet-imaging-challenge.github.io</a>). </p> <p>The second phase of the Exoplanet Imaging Data Challenge is focused on the characterisation of exoplanet signals in high-contrast imaging data. The participants must perform two tasks: provide (1) astrometry of the detected signals, and (2) spectrophotometry of the detected signals.</p> <p>For this phase, we therefore provide with <strong>8</strong> high-contrast data sets, taken with two integral field spectrographs: SPHERE-IFS installed at the Very Large Telescope (VLT, Chile) and GPI installed at the Gemini-South telescope (Chile). Each data set consists of the following files (in <em>.fits</em> format):</p> <p>(a) <em>image_cube_instxx.fits</em>: the coronagraphic multispectral image cube, acquired in pupil-stabilized mode;<br> (b) <em>parallactic_angles_instxx.fits</em>: the corresponding parallactic angle and airmass variation during the observation sequence;<br> (c) <em>wavelength_vect_instxx.fits</em>: the corresponding wavelength vector for each spectral channel;<br> (d) <em>psf_cube_instxx.fits</em>: the non-coronagraphic (point spread function) multispectral image of the target star;<br> (e) <em>first_guess_astrometry_instxx.fits</em>: a first guess position w.r.t the star of the (2 or 3) injected planetary signals.</p> <p>Each data set has 2 to 3 injected planetary signals in various locations.<br> Each data set has very different observing conditions, from very good to very bad.</p> <p><em>Additional information and ressources can be found on the <a href="https://exoplanet-imaging-challenge.github.io/">website</a> and dedicated <a href="https://github.com/exoplanet-imaging-challenge/phase2">Github repository.</a></em></p> <p><em>The results of the data challenge must be submitted directly by participants on the <a href="https://eval.ai/web/challenges/challenge-page/1717/overview">EvalAI platform</a>.</em></p>
Numerical refractive index correction for the stitching procedure in tomographic quantitative phase imaging – dataset
<p>Raw volumetric data used in the work "Numerical refractive index correction for the stitching procedure in tomographic quantitative phase imaging" (<a href="http://doi.org/10.1364/BOE.466403">doi.org/10.1364/BOE.466403</a>). The data is packaged using the FIJI BigStitcher into HDF5 file. The file is split into 89 parts in ZIP format. Additionally we provide XML file needed for opening the data with BigStitcher and the TXT file with the nominal locations of the volumes based on the readings from the X-Y translation stage. The volumes inside the HDF5 file are already registered for stitching using the BigStitcher pairwise registration and global optimization procedure. Using the BigStitcher option "Resave to TIFF" one can access the raw data that we processed in the work. The processing code which operates on TIFF files is available here: <a href="https://github.com/biopto/QPI-stitching-2D-3D">https://github.com/biopto/QPI-stitching-2D-3D</a>.</p>
Dataset for "Synchrotron-based phase contrast imaging of cardiovascular tissue in mice—grating interferometry or phase propagation?"
<p>This dataset contains images that were used in the analysis of the manuscript "Synchrotron-based phase contrast imaging of cardiovascular tissue in mice—grating interferometry or phase propagation?", that was published in Biomedical Physics and Engineering Express in 2018. Images are uploaded in .tif format. Three different synchrotron-based imaging techniques were compared on the same cardiovascular samples: grating interferometry (GI) and absorption-based phase propagation with and without phase retrieval according to Paganins method. An excel file is provided in which the nomenclature of the files is explained.</p>
High-Resolution Quantitative Phase Imaging of Plasmonic Metasurfaces with Sensitivity down to a Single Nanoantenna_experimental dataset
<p>This dataset shares the data presented in the paper "Geometric-phase microscopy for high-resolution quantitative phase imaging of plasmonic metasurfaces with sensitivity down to a single nanoantenna" available in open access under <a href="https://doi.org/10.5281/zenodo.3355170">10.5281/zenodo.3355170</a>. The archive contains experimental files titled with references to the figures as they appear in the paper. </p>
Reproduction package for the paper "Measuring the variability of directly imaged exoplanets using vector Apodizing Phase Plates combined with ground-based differential spectrophotometry"
<p>This is a basic reproduction package for the paper <a href="https://doi.org/10.1093/mnras/stad249">"Measuring the variability of directly imaged exoplanets using vector Apodizing Phase Plates combined with ground-based differential spectrophotometry" by Sutlieff et al. (2023)</a>. It aims to provide the most important data products to check and reproduce the main results of the paper.</p>
Dataset for demonstration of quantitative label-free imaging with phase and polarization
<p>The QLIPP_Reconstruction_Resources_20x.zip file contains raw images of mouse brain slice and anisotropic glass target acquired with QLIPP. The file also contains the configuration files to reconstruct the phase, retardance, and orientation from this data with the recOrder pipeline. The tutorial slides for using this dataset for reconstruction can be found here (10.5281/zenodo.5135889).</p> <p> </p> <p>v1.1.0: Upload two zip files for automated testing of recOrder and waveOrder repositories.</p> <p>v1.2.0: add pycromanager dataset for testing the reader and converter in waveOrder.</p> <p>v1.3.0: reduce the size of recOrder test dataset</p> <p>v 1.4.0: add datasets for new data schema defined for recOrder 0.4.0 </p> <p>v1.5.0: add a dataset that shows images of an embryo </p>
Confocal microscopy images of Escherichia coli cells treated with various antibiotics (LB, exponential phase)
<p>This dataset and CARE model is part of the publication "<strong>Transertion and cell geometry organize the </strong><i><strong>Escherichia coli</strong></i><strong> nucleoid during rapid growth</strong>".</p><p>It contains all CLSM images that were used for the publication, as well as the single-cell regions of interest for analyses.</p><p>Cells were grown to exponential phase in LB Lennox and antibiotics were added for 0-60 min. Cultures were then chemically fixed, immobilized and stained for DNA (DAPI) and membrane (Nile Red). The strain (NO34) expresses a MreBsw-sfGFP fusion protein from the native chromosomal locus. It was a kind gift from Zemer Gitai (<a href="http://doi:10.1016/j.bpj.2016.07.017">Ouzounov et al., 2016</a>).</p><p>More information can be found in the publication.</p>
Multi-colour SMLM images of untreated and drug-treated Escherichia coli (LB, exponential phase)
<p>This dataset and CARE model is part of the publication "<strong>Transertion and cell geometry organize the <em>Escherichia coli</em> nucleoid during rapid growth</strong>".</p> <p>It contains all SMLM images that were used for the publication, as well as the single-cell regions of interest for analyses.</p> <p>Cells were grown to exponential phase in LB Lennox and antibiotics were added for 0-60 min. Cultures were then chemically fixed, permeabilised and imaged for the nucleoid (JF<sub>646</sub>-Hoechst) and membranes (Nile Red) using PAINT. The strain (NO34) expresses a MreB<sup>sw</sup>-sfGFP fusion protein from the native chromosomal locus. It was a kind gift from Zemer Gitai (<a href="http://doi:10.1016/j.bpj.2016.07.017">Ouzounov et al., 2016</a>).</p> <p>More information can be found in the publication.</p>
Real-space imaging of phase transitions in bridged artificial kagome spin ice
<p>Open data to "Real-space imaging of phase transitions in bridged artificial kagome spin ice", published in Nature Physics (2022), https://www.nature.com/articles/s41567-022-01564-5</p>
Cellpose model for Digital Phase Contrast images
<p><strong>Name: </strong>Cellpose model for Digital Phase Contrast images</p> <p><strong>Data type: </strong>Cellpose model, trained via transfer learning from ‘cyto’ model.</p> <p><strong>Training Dataset: </strong>Light microscopy (Digital Phase Contrast) and Manual annotations (<em>10.5281/zenodo.5996883</em>)</p> <p><strong>Training Procedure: </strong>Model was trained using a Cellpose version 0.6.5 with GPU support (NVIDIA GeForce RTX 2080) using default settings as per the <a href="https://cellpose.readthedocs.io/en/latest/train.html">Cellpose documentation</a> </p> <p>python -m cellpose --train --dir <em>TRAINING/DATASET/PATH/</em>train --test_dir <em>TRAINING/DATASET/PATH/</em>test --pretrained_model cyto --chan 0 --chan2 0</p> <p>The model file (MODEL NAME) in this repository is the result of this training.</p> <p><strong>Prediction Procedure: </strong>Using this model, a label image can be obtained from new unseen images in a given folder with</p> <p>python -m cellpose --dir <em>NEW/DATASET/PATH</em> --pretrained_model <em>FULL_MODEL_PATH</em> --chan 0 --chan2 0 --save_tif --no_npy</p>
Cellpose models for Label Prediction from Brightfield and Digital Phase Contrast images
<p><strong>Name: </strong>Cellpose models for Brightfield and Digital Phase Contrast images</p> <p><strong>Data type: </strong>Cellpose models trained via transfer learning from the ‘nuclei’ and ‘cyto2’ pretrained model with additional <strong>Training Dataset . Includes</strong> corresponding csv files with 'Quality Control' metrics(§) (model.zip).</p> <p><strong>Training Dataset: </strong>Light microscopy (Digital Phase Contrast or Brightfield) and automatic annotations (nuclei or cyto) (<a href="https://doi.org/10.5281/zenodo.6140064">https://doi.org/10.5281/zenodo.6140064</a>)</p> <p><strong>Training Procedure: </strong>The cellpose models were trained using cellpose version 1.0.0 with GPU support (NVIDIA GeForce K40) using default settings as per the <a href="https://cellpose.readthedocs.io/en/latest/train.html">Cellpose documentation</a> . Training was done using a <a href="https://datascience.ch/renku/">Renku </a>environment (<a href="https://github.com/BIOP/renku-templates/tree/main/VNC-Napari-Fiji-Omero-CUDA11.4-cellpose-omnipose">renku template</a>).</p> <p> </p> <p><strong>Command Line Execution for the different trained models</strong></p> <p><strong>nuclei_from_bf: </strong></p> <pre><code class="language-python">cellpose --train --dir 'data/train/' --test_dir 'data/test/' --pretrained_model nuclei --img_filter _bf --mask_filter _nuclei --chan 0 --chan2 0 --use_gpu --verbose</code></pre> <p><strong>cyto_from_bf</strong>:</p> <pre><code class="language-python">cellpose --train --dir 'data/train/' --test_dir 'data/test/' --pretrained_model cyto2 --img_filter _bf --mask_filter _cyto --chan 0 --chan2 0 --use_gpu --verbose</code></pre> <p> </p> <p><strong>nuclei_from_dpc:</strong></p> <pre><code class="language-python">cellpose --train --dir 'data/train/' --test_dir 'data/test/' --pretrained_model nuclei --img_filter _dpc --mask_filter _nuclei --chan 0 --chan2 0 --use_gpu --verbose</code></pre> <p><strong>cyto_from_dpc</strong>:</p> <pre><code>cellpose --train --dir 'data/train/' --test_dir 'data/test/' --pretrained_model cyto2 --img_filter _dpc --mask_filter _cyto --chan 0 --chan2 0 --use_gpu --verbose</code></pre> <p> </p> <p><strong>nuclei_from_sqrdpc</strong>:</p> <pre><code class="language-python">cellpose --train --dir 'data/train/' --test_dir 'data/test/' --pretrained_model nuclei --img_filter _sqrdpc --mask_filter _nuclei --chan 0 --chan2 0 --use_gpu --verbose</code></pre> <p><strong>cyto_from_sqrdpc</strong>:</p> <pre><code class="language-python">cellpose --train --dir 'data/train/' --test_dir 'data/test/' --pretrained_model cyto2 --img_filter _sqrdpc --mask_filter _cyto --chan 0 --chan2 0 --use_gpu --verbose</code></pre> <p> </p> <p><em><strong>NOTE </strong></em>(§): We provide a notebook for Quality Control, which is an adaptation of the <a href="https://colab.research.google.com/github/HenriquesLab/ZeroCostDL4Mic/blob/master/Colab_notebooks/Beta%20notebooks/Cellpose_2D_ZeroCostDL4Mic.ipynb">"Cellpose (2D and 3D)" notebook from ZeroCostDL4Mic</a> .</p> <p><em><strong>NOTE</strong></em>: This dataset used a training dataset from the Zenodo entry(<a href="https://doi.org/10.5281/zenodo.6140064">https://doi.org/10.5281/zenodo.6140064</a>) generated from the “HeLa “Kyoto” cells under the scope” dataset Zenodo entry(<a href="https://doi.org/10.5281/zenodo.6139958">https://doi.org/10.5281/zenodo.6139958</a>) in order to automatically generate the label images.</p> <p><strong><em>NOTE</em></strong>:<strong> </strong>Make sure that you delete the “_flow” images that are auto-computed when running the training. If you do not, then the flows from previous runs will be used for the new training, which might yield confusing results.</p> <p> </p>
Phase Contrast Time-Lapse and F-actin Imaging of Mechanically Compressed or Irradiated Pseudostratified Human Bronchial Epithelial Cells
<p><strong>Overview</strong></p> <p>This dataset includes phase contrast time-lapse imaging of <em>in vitro</em> pseudostratified airway epithelial cells to visualize their collective cellular migration after exposure to mechanical compression (mimicking bronchoconstriction) or irradiation. Additionally, the cells were fixed and stained for F-actin to visualize the apical cell boundaries, basal cell boundaries, and basal cell stress fibers.</p> <p><strong>Cell Culture and Treatment</strong></p> <p>Primary human bronchial epithelial cells (from a single donor) were grown on transwells in air-liquid interface (ALI) culture for 14 days to model a well-differentiated, pseudostratified airway epithelium. Cells were then exposed to either mechanical compression (30 cmH2O for 3 hours) mimicking asthmatic bronchoconstriction or irradiation (1Gy of ionizing radiation using a RS 2000 Biological Research Irradiator (RadSource) on ALI days 7, 10, and 14).</p> <p><strong>Phase Contrast Time-Lapse Imaging</strong></p> <p>At 24 or 72 hours after final treatment, cells were imaged to visualize collective cellular migration. For each independent experimental replicate (2 transwells per treatment per timepoint), six fields of view per well were imaged every 6 minutes over 1.5 hours. The imaging chamber was supplied with 37°C, 5% CO2, humidified air on a Zeiss Axio Observer Z1 to collect phase contrast images. <em>The image resolution is 0.586 µm/pixel.</em></p> <p><strong>Immunofluorescence Imaging</strong></p> <p>Cells were fixed (4% PFA for 30 minutes) at 24 or 72 hours after final treatment (and after phase contrast time-lapse imaging). Fixed transwells were stained for F-actin (Alexa fluor 488-Phalloidin, ThermoFisher Scientific, diluted 1:40, 30 minutes). Transwell membranes were cut from the plastic support and mounted on glass slides. Slides were imaged using a Zeiss Axio Observer Z1 with an apotome module controlled using Zen Blue 2.0 software. Five random fields of view were imaged from each transwell membrane in a z-stack from substrate to apical cell surface. To visualize various planes through the pseudostratified epithelial layer (apical cell boundaries, basal cell boundaries, and basal cell stress fibers), maximum intensity projections were generated from regions of interest through the z-stack. <em>The image resolution is 0.293 µm/pixel.</em></p> <p><strong>Dataset</strong></p> <p>Phase contrast time-lapse movies are provided as *.avi files. Immunofluorescence images are provided as *.tif files. For an individual transwell, the imaging dataset includes:</p> <ul> <li>6 phase contrast time-lapse movies</li> <li>5 immunofluorescence images of apical cell boundaries</li> <li>5 immunofluorescence images of basal cell boundaries</li> <li>5 immunofluorescence images of basal cell stress fibers</li> </ul> <p>Phase contrast time-lapse filenames contain</p> <ul> <li>Donor: U13</li> <li>Timepoint: 24 or 72 hours</li> <li>Treatment & Well: control (C), mechanical compression (P), or irradiation (R); well 1 or 2</li> <li>Field of View: (1) – (6)</li> </ul> <p>Immunofluorescence image filenames contain:</p> <ul> <li>Donor: <strong>U13</strong></li> <li>Timepoint: <strong>24</strong> or <strong>72</strong> hours</li> <li>Treatment & Well: control (<strong>C</strong>), mechanical compression (<strong>P</strong>), or irradiation (<strong>R</strong>); well <strong>1</strong> or <strong>2</strong></li> <li>Field of View: <strong>1-5</strong></li> <li>Region of Interest: apical cell boundaries (<strong>ACB</strong>), basal cell boundaries (<strong>BCB</strong>), or basal stress fibers (<strong>SF</strong>)</li> </ul> <p>Phase contrast time-lapse and immunofluorescence from the same transwell will all start with the same “Donor_Timepoint_Treatment/Well...” (i.e. U13_24_C1…). <strong>Note that the images from phase contrast and immunofluorescence are not necessarily from matched locations within the transwell and are at different spatial scales.</strong></p> <p>Immunofluorescence images from the same z-stack field of view will start with the same “Donor_Timepoint_Treatment/Well_FieldofView…” (i.e. U13_24_C1_1…).</p>
The Longyearbyen all-sky camera full resolution image data (movie and four ASC data plots) used in the paper entitled "Auroral Morphological Changes to the Formation of Auroral Spiral during the Late Substorm Recovery Phase: Polar UVI and Ground All-Sky Camera Observations"
<p>The uploaded movie is an animation of the Longyearbyen all-sky camera (ASC) full resolution image data from 20:00 UT to 22:00 UT, which is including all ASC snapshots used in Figure 2. This movie file is the same as Movie S1. </p> <p>The uploaded four png files are the Longyearbyen all-sky camera (ASC) full resolution image snapshots, which were used in Figure S3.</p> <p>The numerical ASCII data to make the four ASC full resolution image snapshots are also uploaded; the count number of data detected by the ASC and associated latitude and longitude information in geographical coordinates of the ASC field of view.</p>
Dataset used in "Ocean floor imaging with Distributed Acoustic Sensing and water phases reverberations" by Spica et al. in Geophysical Research Letters
<p>earthquake #1<br> earthquake #2</p>
Data from the NASCENT campaign used in the publications: "Conditions favorable for secondary ice production in Arctic mixed-phase clouds" and "Understanding the history of two complex ice crystal habits deduced from a holographic imager"
<p>This repository contains the data from the Ny‐Ålesund AeroSol Cloud ExperimeNT campaign (NASCENT). This data were used to produce the figures in the two papers:</p> <p>(1) Pasquier, J. T., Henneberger, J., Ramelli, F., Korolev, A.,Wieder, J., Lauber, A., Li, G., David, R. O., Carlsen, T., Gierens, R., Maturilli, M., and Lohmann, U.: Understanding the history of two complex ice crystal habits deduced from a holographic imager, Geophys. Res. Lett., accepted, 2022</p> <p> </p> <p>(2) Pasquier J. T., Henneberger J., Ramelli F., Lauber A., David O. D., Wieder J., Carlsen T., Gierens R., Maturilli M., and Lohmann U.:Conditions favorable for secondary ice production in Arctic mixed-phase clouds, ACP, accepted.</p> <p>More information can be found in the README files.</p> <p> </p> <p>The scripts to reproduced the Figures are available on Zenodo</p> <p>(1) https://doi.org/10.5281/zenodo.7402296</p> <p>(2) https://doi.org/10.5281/zenodo.7407107</p>
Dual-modal imaging of two-phase flows with electromagnetic flow tomography and electrical tomography -- experimental evaluation of the state estimation approach
<p>The supplementary files included are associated with our experimental research on two-phase flow estimation. This study experimentally investigates the feasibility of a state estimation approach for dynamic image reconstruction in dual-modal tomography of two-phase oil-water flows using electromagnetic flow tomography (EMFT) and electrical tomography (ET). By approximating the process with a convection-diffusion model, the extended Kalman filter and fixed-interval Kalman smoother are applied to reconstruct temporally evolving velocity and phase fraction distributions. The results demonstrate that the Kalman smoother-based reconstructions, along with uncertainty estimates, outperform conventional methods and provide feasible volumetric flow rate estimates for oil and water phases in a laboratory setup.</p>
ScienceDex guides
Understand access before you commit
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.