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107 results for “Image correlation”
Digital image correlation measurement of linear elastic steel specimen
<p>The dataset comprises the axial and lateral displacements on the surface of a plate with a hole subjected to tensile load. The displacement data are measured by digital image correlation and the material is assumed to behave linear elastic. The material under investigation is a common low-carbon steel alloy of type S235. The displacement data are used for calibration of a linear elastic constitutive model using parametric physics-informed neural networks and finite elements. For that purpose, the dataset comprises both the raw experimental displacement data and displacement data interpolated onto a regular grid using linear interpolation, where the interpolation routine is provided as well.</p>
Bridging the gap between single nanoparticle imaging and global electrochemical response by correlative microscopy assisted by machine vision
<p>The data in this repository corresponds to experimental data: linear sweep voltammetry, optical movie and the database of the SEM images. They support the findings of a study discussed in the article by Godeffroy et al. published in Small Methods with the doi: http:/doi.org/10.1002/smtd.202200659. The data analysis to reproduce the results presented in the article has been carried out by homemade Python program routines also provided in this repository. The descirption of each routine is also provided in a text file.</p>
Correlating NanoSIMS images with ultra-high resolution EM images in Look@NanoSIMS
<p>Supplement of the study by Spataro et al. (2022) describes how to perform correlative image analysis in Look@NanoSIMS. This repository contains files used as an example in that analysis.</p> <p>* Rat7SNR_21.tif = ultra-high resolution EM image (4096 x 3072 pixels)<br> * Spataro-Sept-2018_3.im.zip = zipped raw data produced by NanoSIMS 50L (256 x 256 pixels)<br> * Spataro-Sept-2018_3.zip = zipped folder containing data generated by Look@NanoSIMS</p> <p>To start the analysis, download all files to a folder on your computer (preferably in the same folder) and unzip the file Spataro-Sept-2018_3.zip. The latter step will create a folder Spataro-Sept-2018_3 containing files generated by Look@NanoSIMS when analysing data in Spataro-Sept-2018_3.im.zip and Rat7SNR_21.tif. The files include information about the alignment of individual planes (xyalign.mat), coordinates of the pairs of reference points in the EM and NanoSIMS images (points_10x.mat), regions of interest defined for the resampled (cells_10x.mat) and original NanoSIMS data (cells_1x.mat), and Look@NanoSIMS preferences saved for the resampled (prefs_10x.mat) and original (prefs_1x.mat and prefs.mat) NanoSIMS data. You can use these files to reproduce the analysis described in the Supplement of Spataro et al. (2022).</p> <p>Reference:</p> <p>S. Spataro, B. Maco, S. Escrig, L. Jensen, L. Polerecky, G. Knott, A. Meibom, and B. L. Schneider (2022). Alpha-synuclein-induced changes to neuronal metabolism revealed by stable isotope labeling and ultra-high-resolution imaging.</p>
Correlative microscopy of mice cerebellar Purkinje cells from 20x confocal tissue imaging to super-resolution 93x 3D STED of dendritic spines
<p>This Dataset concerns the paper entitled "<em>From tissues to segmentation: a modular framework for multi-scale neuron isolation</em>" by Cauzzo et al. <strong>Nature Comm (2024).</strong></p> <p>S.Cauzzo<sup>$</sup>, E. Bruno, D. Boulet, P. Nazac, M. Basile, A. L. Callara, F. Tozzi, A. Ahluwalia, C. Magliaro, L. Danglot<sup>$</sup><sup>*</sup>, N. Vanello<sup>$</sup><sup>*</sup> *shared senior authorship: Lydia.danglot@inserm.fr ; nicola.vanello@unipi.it</p> <p><sup>$</sup> corresponding authors : cauzzo.simone@gmail.com ; Lydia.danglot@inserm.fr ; nicola.vanello@unipi.it</p> <p> </p>
Surface deformation of the Mw 6.4 and Mw 7.1 Ridgecrest earthquakes measured from subpixel correlation of Copernicus Sentinel-2 optical images
<p>Surface deformation of the Mw 6.4 and Mw 7.1 Ridgecrest earthquakes measured from subpixel correlation of Copernicus Sentinel-2 optical images </p>
Dataset accompanying manuscript "Correlative imaging of spatio-angular dynamics of biological systems with multimodal instant polarization microscope"
<p>Raw images and microscope calibration metadata for reconstruction of datasets presented in Fig. 1 and Fig. 3 of "Correlative imaging of spatio-angular dynamics of biological systems with multimodal instant polarization microscope". Notebooks demonstrating steps in the label-free and fluorescence anisotropy reconstruction pipelines can be found at https://github.com/mehta-lab/miPolScope.</p>
High-resolution images of 1550 Ordovician to Silurian graptolite specimens for global correlation and shale gas exploration
<p>A unique graptolite image dataset consists of key graptolite species used for dating rocks, global correlation, and “gold caliper” for locating shale gas favourable exploration beds (FEBs) in China. <br> All images were taken from 1,550 carefully curated graptolite specimens, taxonomically belong to 113 graptolite species or subspecies. They were collected from the Ordovician to Silurian sediments of China and published in 1958-2020. These specimens are preserved as shale and were collected from 154 representative geological sections of China. All specimens are housed at the Nanjing Institute of Geology and Palaeontology (NIGP), Chinese Academy of Sciences (CAS).</p> <p>My working group spent over two years to complete photographing every specimen using a single-lens reflex camera Nikon D800E with Nikkor 60 mm macro-lens and Leica M125 and M205C microscopes equipped with Leica cameras. Every image is well focused and better shows the morphology of graptolite bodies.</p> <p>In total, we took 40,597 images, including 20,644 camera photos (each with a resolution of 4,912 × 7,360) and 19,953 microscope photos (each with a resolution of 2,720 × 2,048). Photos of low contrast or bad focus were removed from the whole collection. We only kept and selected the photos that show the visual morphology of every specimen and the diagnostic character of each graptolite species that the specimens represent. We selected one image for each specimen as the present final dataset, uploaded to and stored in our cloud server.</p> <p>We incorporated revision suggestions from distinguished palaeontologists to generate the ground-truth labels, providing a taxonomical authority of the dataset. The dataset potentially contributes to a range of scientific activities and provides 1) easy access to high-resolution images of 2951 specimens of 113 graptolite species for teaching and training in palaeontology and geologic survey; 2) Global bio-stratigraphic correlation using graptolites, especially with those bio-zone species; 3) A standard fossil specimen image dataset used in shale gas industry to improve exploration efficiency, and 4) The potential aid of developing image-based automated classification model.</p> <p>Every specimen has two photos, one is original, another shows specimen with a scale bar. Occasionally in some large image the scale bar is embedded and beside the fossil specimen.</p> <p>All in JPG format. Single JPG file ranges from 822 KB to 7.055 MB. </p> <p>Total :10.4 GB.</p>
Data and code for Coherent Correlation Imaging: Resolving fluctuating states of matter
<p>Data and code to generate the figures in the main text and the Extended data figures.</p>
Raster Image Correlation Spectroscopy and Brightness Measurements of AtLEA proteins from Arabidopsis thaliana
<p>Temporal sequences of various fluorescent leaves were captured using a confocal scanning microscope (Olympus FV1000 inverted microscope), equipped with a 1.3 NA oil immersion 60X objective and the photon counting detection mode. Utilizing a 488 nm laser at 0.1% power and GFP filters/cubes, each temporal sequence involved the acquisition of 100 frames of 64x64 pixels, with a dwell time of 10 μs (1.76 ms per line, 130.24 ms per frame) and a pixel size of 66 nm (50X digital zoom). The interval between frames was set at 131.6 ms.</p> <p>Five plants were analyzed, each expressing one of four distinct genetic constructs fused to complementary fragments of Yellow Fluorescent Protein: pYFN-4-/5pYFC-4-5 (representing the complete AtLEA4-5 protein), pYFN-4-51-77/pYFC-4-51-77 (associated with the N-terminal region of AtLEA4-5), pYFN-4-578-158/pYFC-4-578-158 (relating to the C-terminal region of AtLEA4-5), and pYFN-pYFC (serving as the control). The raw data (*.oib files) were collected during three imaging sessions within a one-week period:</p> <p>- 220618 raw oib dataset.zip</p> <p>- 220622 raw oib dataset.zip</p> <p>- 220623 raw oib dataset.zip</p> <p>Images were converted to *.tif format using FIJI/ImageJ for further analysis and were archived in "tif dataset RICS NB LEAs.zip," excluding files with excessive movement of biological specimens. These images were then subjected to "Raster Image Correlation Spectroscopy" and "Number and Brightness" techniques for analysis.</p> <p>Notation:</p> <p>- h1, h2, h3, h4, h5: Replicates (plants) expressing one of four specific genetic constructs fused to complementary fragments of Yellow Fluorescent Protein.</p> <p>- 45: Fused to the full-length AtLEA4-5 protein (pYFN-4-/5pYFC-4-5).</p> <p>- 4h: Fused to the N-terminal region of AtLEA4-5 (pYFN-4-51-77/pYFC-4-51-77).</p> <p>- rc: Fused to the C-terminal region of AtLEA4-5 (pYFN-4-578-158/pYFC-4-578-158).</p> <p>- ct: The control condition (pYFN-pYFC).</p>
Correlative microscopy of rat cultured hippocampal pyramidal cell from 40x confocal imaging to super-resolution 93x 3D STED of dendritic spines
<p>This dataset contain multi-scale image of rat hippocampal pyramidal cell related to our paper "<em>From tissues to segmentation: a modular framework for multi-scale neuron isolation</em>" by Cauzzo et al. <strong>Nature Comm (2024).</strong></p>
Dataset for the publication "Identification of plasticity-induced crack closure by using high-resolution digital image correlation"
<p>This repository publishes the data generated in the article "Identification of plasticity-induced crack closure by using high-resolution digital image correlation" (see arxiv preprint <a href="https://arxiv.org/html/2409.02560v1">Plasticity-induced crack closure identification during fatigue crack growth in AA2024-T3 by using high-resolution digital image correlation (arxiv.org)</a>)</p> <p>This repository is structured with the following subfolders:</p> <ul> <li><strong>0_fe_data: </strong>contains the displacement field of the free surface of the 3D finite element model that were used to determine the crack opening curves and, in the following, the crack opening value Kop</li> <li><strong>1_hrdic_data: </strong>contains the high-resolution DIC displacement field data at a crack length of 27.8 mm at different load levels, starting from minimum load 1.5 kN to maximum load 15 kN</li> </ul>
Optical Image Correlation Data for 2023 Kahramanmaras Earthquakes
<p>Horizontal surface deformation maps generated from pixel tracking of Sentinel-2 optical satellite images using COSI-Corr.</p>
Noise Resistant Phase Imaging with Intensity Correlation
<p>Raw data for the arXiv:2301.11969 "Noise Resistant Phase Imaging with Intensity Correlation"</p> <p>Supported by the National Science Centre, Poland, grant number 2022/45/N/ST2/04249</p>
Digital Image Correlation Workshop at the University of Manchester, September 2016
<p>Enabling Process Technologies sponsored a Digital Image Correlation (DIC) workshop on 8 September, 2016 at the School of Mechanical, Aerospace and Civil Engineering at the University of Manchester. Alistair Tofts, Director of Sales & Marketing at Correlated Solutions attended and provided a series of presentations covering DIC theory, and the working principles of the VIC lineup of DIC software.</p> <p>Afterwards, three discrete tests were conducted with the same specimen and speckle on three different frames and setups:</p> <ul> <li>High speed: Zwick HTM-5030 test frame, Photron SA.1 high speed cameras</li> <li>Quasi-static 2D: Instron 5969 test frame, Point Grey Research machine vision camera</li> <li>Quasi-static 3D: Instron 5659 test frame, 2 Point Grey Research machine vision cameras</li> </ul> <p>The archives included in this dataset are:</p> <ul> <li>Presentations: contains the slides from the presentations given by Alistair Tofts, as well as an outline/agenda given by Matthew Roy.</li> <li>3D_fast, 2D_slow and 3D_slow: correspond to the tests/setup described above, including all calibration and test images, along with VIC metadata for replaying analyses.</li> </ul> <p>Specimen geometry is shown on slide 6 of ~/Presentation/DICWorkshopPresSept2016_MJR.pptx, and markdown readme files are included to describe the test and data.</p>
Residual strains estimations in the annulus fibrosus through digital image correlation
<p>This upload correspond to the experimental data and all the python and LMGC software pipeline to process the data.</p> <p>The raw data correspond to images of the stress/strain relaxation process within the annulus fibrosus following a radial cut.</p> <p>The corresponding scientific article has been published in the following Diamond Open Access Journal : Journal of Theoretical, Computational and Applied Mechanics.</p>
Full-field displacements and strains obtained by digital image correlation during fatigue crack growth experiments
<p>This data publication contains full-field displacements and strains obtained by 3D digital image correlation (DIC) using a GOM Aramis 12M system including three fatigue crack propagation (fcp) experiments of AA2024-T3 aluminium sheet material.</p> <p>The repository consists of three datasets of different experiments named </p> <ul> <li>S<sub>950,1.6 </sub>- MT950 specimen, 1.6 mm sheet thickness, load ratios R=0.3, 1.0</li> <li>S<sub>160,2.0 </sub>- MT160 specimen, 2.0 mm sheet thickness, load ratios R=0.1, 0.25, 0.5, 0.75, 1.0</li> <li>S<sub>160,4.7 </sub>- MT160 specimen, 4.7 mm sheet thickness, load ratios R=0.1, 0.25, 0.5, 0.75, 1.0</li> </ul> <p>where S<sub>w,t</sub> denotes a middle tension (MT) specimen with width w and thickness t. The nodal DIC measurements at different times during the experiments are provided as .txt files we call <em>"nodemaps" </em>and stored in subfolders "<strong>Nodemaps</strong>". Each <em>nodemap</em> consists of a header containing meta data information like a running number (current stage index) or the applied force, followed by the nodal displacements and strains in tabular form. Additionally, the dataset S<sub>160,4.7</sub> contains crack path and crack tip labels for each nodemap in the subfolder "<strong>GroundTruth</strong>". The ground truth is provided as arrays of size 256x256. Each pixel of the array contains the label "2" for the class "crack tip", "1" for the class "crack path", or "0" for the class "background". These labels were created in a semi-manual fashion and can be used for machine learned crack detection using supervised training.</p> <p>These datasets were recently used to evaluate neural attention of convolutional neural networks trained on fatigue crack tip detection in <a href="https://www.nature.com/articles/s41598-022-13275-1">Melching et al. (Sci Rep, 2022)</a>. Additional guidance on data loading and usage can also be found at <a href="https://github.com/dlr-wf/explainable-crack-tip-detection">https://github.com/dlr-wf/explainable-crack-tip-detection</a>.</p> <p>The experiments S<sub>160,2.0</sub> and S<sub>160,4.7 </sub>were conducted and analysed by <a href="https://doi.org/10.1111/ffe.13433">Strohmann et al. (FFEMS, 2021)</a>.</p> <p>The experiment S<sub>950,1.6</sub> was conducted and analysed by <a href="https://doi.org/10.1111/ffe.13335">Breitbarth et al. (FFEMS, 2020)</a>.</p>
Correlative Raman Imaging and Scanning Electron Microscopy: The Role of Single Ga Islands in Surface-Enhanced Raman Spectroscopy of Graphene_experimental dataset
<p>This dataset contains the raw unprocessed data for Piastek et al., Correlative Raman Imaging and Scanning Electron Microscopy: The Role of Single Ga Islands in Surface-Enhanced Raman Spectroscopy of Graphene, <em>J. Phys. Chem. C</em> 2022, 126, 9, 4508–4514. </p>
archive_image_correlation_robust_geometrical_design_of_2D_sequential_interlocking_assemblies
<p>Archive containing the X and Y deformation, obtained by image correlation, of several laser cut samples of assemblies generated using the method described in the article Robust geometrical design of 2D sequential interlocking assemblies (not published yet)</p>
Phase III Study of the Correlation Between Florbetapir F18 PET Imaging and Amyloid Pathology in the Brain
ClinicalTrials.gov study NCT00857415. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Data from: Macro-to-nanoscale investigation of wall-plate joints in the acorn barnacle Semibalanus balanoides: correlative imaging, biological form and function, and bioinspiration
Correlative imaging combines information from multiple modalities (physical–chemical–mechanical properties) at various length scales (centimetre to nanometre) to understand the complex biological materials across dimensions (2D–3D). Here, we have used numerous coupled systems: X-ray microscopy (XRM), scanning electron microscopy (SEM), electron backscatter diffraction (EBSD), optical light microscopy (LM) and focused ion beam (FIB-SEM) microscopy to ascertain the microstructural and crystallographic properties of the wall-plate joints in the barnacle Semibalanus balanoides. The exoskeleton is composed of six interlocking wall plates, and the interlocks between neighbouring plates (alae) allow barnacles to expand and grow while remaining sealed and structurally strong. Our results indicate that the ala contain functionally graded orientations and microstructures in their crystallography, which has implications for naturally functioning microstructures, potential natural strengthening and preferred oriented biomineralization. Elongated grains at the outer edge of the ala are oriented perpendicularly to the contact surface, and the c-axis rotates with the radius of the ala. Additionally, we identify for the first time three-dimensional nanoscale ala pore networks revealing that the pores are only visible at the tip of the ala and that pore thickening occurs on the inside (soft bodied) edge of the plates. The pore networks appear to have the same orientation as the oriented crystallography, and we deduce that the pore networks are probably organic channels and pockets, which are involved with the biomineralization process. Understanding these multiscale features contributes towards an understanding of the structural architecture in barnacles, but also their consideration for bioinspiration of human-made materials. The work demonstrates that correlative methods spanning different length scales, dimensions and modes enable the extension of the structure–property relationships in materials to form and function of organisms.
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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.