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107 results for “Image correlation”
Confocal images from: Cell density, alignment, and orientation correlate with C-signal-dependent gene expression during Myxococcus xanthus development
<p>Starving <em>Myxococcus xanthus</em> bacteria use short-range C-signaling to coordinate their movements and construct multicellular mounds, which mature into fruiting bodies as rods differentiate into spherical spores. Differentiation requires efficient C-signaling to drive the expression of developmental genes, but how the arrangement of cells within nascent fruiting bodies (NFBs) affects C-signaling is not fully understood. Here, we used confocal microscopy and cell segmentation to visualize and quantify the arrangement, morphology, and gene expression of cells near the bottom of NFBs at much higher resolution than previously achieved. We discovered that "transitioning cells" (TCs), intermediate in morphology between rods and spores, comprised 10 to 15% of the total population. Spores appeared midway between the center and the edge of NFBs early in their development and near the center as maturation progressed. The developmental pattern as well as C-signal-dependent gene expression in TCs and spores were correlated with cell density, the alignment of neighboring rods, and the tangential orientation of rods early in the development of NFBs. These dynamic radial patterns support a model in which the arrangement of cells within the NFBs affects C-signaling efficiency to regulate precisely the expression of developmental genes and cellular differentiation in space and time. Developmental patterns in other bacterial biofilms may likewise rely on short-range signaling to communicate multiple aspects of cellular arrangement, analogous to juxtacrine and paracrine signaling during animal development.</p>
Correlations Images
<p>COMMSENV-20-0370B.</p>
Cross-correlation coefficient maps generated in the paper "Correlation of Venusian Mesoscale Cloud Morphology Between Images Acquired at Various Wavelengths" by Narita et al. published in Journal of Geophysical Research - Planets
<p>This data archive contains the cross-correlation coefficient maps. Unzipping the compressed file, the following directories corresponding to different wavelength pairs appear. </p> <p> IR1_IR2/ : 0.9 micron & 2.02 micron<br> UVI283_UVI365/ : 283 nm & 365 nm<br> IR2_UVI365/ : 2.02 micron &. 365 nm<br> IR2_UVI283/ : 2.02 micron & 283 nm<br> IR2_LIR/ : 2.02 micron & 10 micron</p> <p>If usual unzip tools do not work, the use of 7zip is recommended:<br> https://www.7-zip.org/download.html</p> <p>Each directory contains CSV files for the longitude-latitude distribution of the correlation coefficient. The 2880 longitude grids cover the longitude range of 0 - 360 degrees, and the 1440 latitude grids cover the latitude range of -90 - +90 degrees, with a pixel resolution of 0.125 degree/pixel. Invalid regions are filled with the value of 1.1.</p> <p>Each filename is composed of the date, the instrument (wavelength), and the time. For example, for the file "20160720_ir2_150821_hp6_IR1_150209_hp6_sb24.csv":</p> <p> 20160720 : July 20, 2016<br> ir2 : 2.02 micron filter of IR2 camera<br> 150821 : IR2 exposure at 15:08:21<br> hp6: High-pass filtering size is 6 deg x 6 deg<br> IR1 : 0.9 micron filter of IR1 camera<br> 150209 : IR1 exposure at 15:02:09<br> sb24 : Sliding box size is 24 deg x 24 deg</p>
An Ordovician to Silurian graptolite specimen image dataset for global correlation and shale gas exploration
<p>A unique high-resolution 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.</p> <p>All images were taken from 1,550 carefully curated graptolite specimens, taxonomically belong to 113 graptolite species or subspecies. These specimens were collected from 154 representative geological sections of the Ordovician to Silurian sediments of China and published in 1958-2020. All specimens are housed at the Nanjing Institute of Geology and Palaeontology (NIGP), Chinese Academy of Sciences (CAS). Detailed scientific information of every piece of fossil specimen is given in the attached spreadsheet file.</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. Example: The file name: ‘9721Cardiograptus_amplus_S.jpg’, ‘9721’ is the specimens number, ‘Cardiograptus_amplus’ means species name is ‘Cardiograptus amplus’, with ‘_S’ means it is a photo with scale bar. In all scale bar, the minimum unit is millimeter.</p> <p>Author and contact:</p> <p>Hong-He Xu</p> <p>Nanjing Institute of Geology and Palaeontology, Chinese Academy of Sciences</p> <p>39 East Beijing Road, Nanjing, 210008</p> <p>China</p> <p>E-mail: hhxu@nigpas.ac.cn</p>
Digital Image Correlation for the Structural Helth Monitoring of composite GFRP materials
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Laboratory visualization of fault asymmetry formation via acoustic emission and digital imaging correlation
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Digital image correlation displacements and strains around a growing fatigue crack in an AA2024-T3 aluminium alloy
<p>This repository contains the data used in the research article:</p> <p>Strohmann, Melching, Paysan, Dietrich, Requena, Breitbarth. Next generation fatigue crack growth experiments of aerospace materials. <em>Scientific Reports</em>, 2024, <a href="https://doi.org/10.1038/s41598-024-63915-x" target="_blank" rel="noopener">https://doi.org/10.1038/s41598-024-63915-x</a>.</p> <p> </p> <p><strong>General Description</strong><br>The dataset contains digital image correlation (DIC) data of a growing fatigue crack in an AA2024-T3 alloy. For the experiment, two independent DIC measurement devices were used – a global full-field DIC and a local microscopic DIC. Thus, the dataset consists of two main directories. One for the 3D DIC data ("global_3d_dic") and a second one for the 2D microscopic DIC data ("local_2d_dic"). Both of these are described and connected by rich metadata.<br>The 3D DIC data directory contains two subdirectories (a "Nodemaps" directory and a "Connections" directory). Both of them contain 797 '.txt'-files. The "Nodemaps" give the DIC results (i.e. coordinates, displacement and strains) for every timestep throughout the experiment. These are usually maximum, minimum, and mean load of a certain load cycle. However, for few crack lengths, we obtained DIC data for a higher number (ca. 100) of images within one load cycle. The nodemaps' format and structure is optimized for data processing in the open-source Python package <a title="CrackPy" href="https://doi.org/10.5281/zenodo.10990494" target="_blank" rel="noopener">CrackPy</a>. The last integer number of each filename can be interpreted as a 'timestep' throughout the experiment. The "Connection" files represent the connections of the DIC facet center coordinates. These are necessary to export the "Nodemap" data to any mesh like dataset, e.g. for VTK. <br>The 2D microscopic DIC directory contains 3 subdirectories ("80", "90", "95") for predefined positions with respect to the specimen coordinate system. For each location, a number of DIC data are stored, again within two subdirectories "Nodemaps" and "Connections" as '.txt'-files. For the 2D DIC data, the last integer of each name cannot be correlated to a timestep. Instead, we provide a descriptive file "local_2d_microscopic_coordinates_by_nodemaps.csv" linking each and every "Nodemap"-file to its respective coordinates and timestep (i.e. the load cycles).</p> <p>To describe the data, we distinguish between<br>1. Higher-level metadata - these data contain information about the experiment and material. The data do not change between timesteps and are given within this description.<br>2. Timestep metadata - these data contain information about one timestep of the experiment and are stored in the header of each "Nodemap"-file.</p> <p> </p> <p><strong>Higher-level Metadata</strong><br>The experiment is described in detail in the reference publication by <a title="Strohmann et al. (2024)" href="https://www.researchsquare.com/article/rs-3128435/v1" target="_blank" rel="noopener">Strohmann et al. (2024)</a> and a summary is given below. Moreover, we provide a dictionary in javascript object notation explaining terms which are used in the higher-level metadata. We use such a dictionary since no standardized ontology is currently available. This dictionary is stored in the main directory as "higher_level_metadata_dictionary.json".</p> <p><em>Material </em><br>A commercially available AA2024-T3 aluminum alloy was tested in L-T orientation, i.e. rolling direction, L, parallel to the load axis. The specimen had a width W = 160 mm cut from a rolled sheet of 2 mm.</p> <p><em>Digital image correlation</em><br>For 3D DIC, we used a GOM Aramis 12M system with a facet size of 20 x 20 pixels and a 16 pixels facet distance. One facet, therefore, covers ~0.614 x 0.614 mm². For the 2D microscopic DIC we captured images using a Zeiss STEMI 206C light optical microscope (LOM), equipped with a Basler a2A5320-23µmPro global shutter CMOS camera. One image has a size of 10.2 x 5.7 mm², 5328 x 3040 Pixels and a facet size of 40x40 pixels (distance of facet center points 30 pixels). The LOM was mounted to a robotic arm, a KUKA lbr Iiwa Cobot.</p> <p><em>Fatigue crack growth</em><br>We used a standard uniaxial servo-hydraulic testing rig. We applied a cyclic load ranging from Fmin = 4.5 kN to Fmax = 15 kN, i.e. R=Fmin/Fmax = 0.3. Throughout the experiment, we measured the crack length using direct current potential drop (DCPD).</p> <p><em>Image acquisition during fatigue crack growth</em><br>We acquired reference images for the DIC calculations before the experiment. For the global DIC, this is simply an image of the unloaded specimen. For the local microscopic DIC, the reference images are acquired in a checker board pattern with an overlap of 70 %. The depth of focus was calibrated for each image individually following (see <a title="Paysan et al. (2023)" href="https://doi.org/10.1007/s11340-023-00964-9" target="_blank" rel="noopener">Paysan et al. (2023)</a>). Images were acquired every 0.5 mm of crack extension at minimum, maximum and 0.5(Fmax- Fmin).</p> <p> </p> <p><strong>Timestep Metadata</strong><br>The timestep-wise metadata is stored in the individual DIC output files, "Nodemaps". We explain the terms used in a second dictionary, "timestep_level_metadata_dictionary.json". For all DIC data, we stored all data coming from the machine controller, i.e. number of cycles, force, displacement of the cylinder and also potential and crack length calculated from the potential as well as current values for back face strain gauges at both back faces of the MT specimen. In addition, for the local microscopic DIC data, we also store the current location of the center point of the image with respect to the global coordinate system provided by the current position of the robot carrying the LOM.</p>
Experimental dataset: stationary images for digital image correlation uncertainty quantification
<div>----------------------------------------------------------------------------------------------------------------------------------------------------------------------</div> <div>-------------------------------------------------------------------------------- <strong>SUMMARY</strong> ---------------------------------------------------------------------------</div> <div>----------------------------------------------------------------------------------------------------------------------------------------------------------------------</div> <div> </div> <div>Stereo-DIC 5 MPx system was used to capture sets of stationary images for quantification of DIC uncertainties.</div> <div> </div> <div>----------------------------------------------------------------------------------------------------------------------------------------------------------------------</div> <div>-------------------------------------------------------------------------------- <strong>FOLDERS </strong>---------------------------------------------------------------------------</div> <div>----------------------------------------------------------------------------------------------------------------------------------------------------------------------</div> <div> </div> <div><strong>Image sets:</strong> </div> <div> </div> <div><strong>Set 1: </strong>100 stationary images with cross polarisation to reduce effect of specular reflection. Test sample clamped in the clamps of a uniaxial tensile test bench.</div> <div><strong>Set 2: </strong>Same as set 1, but test sample unclamped at the bottom, displaced by 1 mm vertically. Meant to introduce rigid body motion into teh stationary images. </div> <div>For investigation of the impact of cross-polarisation: image gradients made similar as much as possible by adjusting exposure time and apetrture. </div> <div><strong>Set 3:</strong> With cross polarisation - 100 stationary images.</div> <div><strong>Set 4:</strong> Without cross polarisation - 100 stationary images.</div> <div> </div> <div>Images for stereo calibration:</div> <div> </div> <div><strong>Calib_sets_1_2: </strong>Calibration images for sets 1 and 2 mentioned above </div> <div><strong>Calib_sets_3:</strong> Calibration images for set 3 mentioned above </div> <div><strong>Calib_sets_4: </strong>Calibration images for set 4 mentioned above </div> <div> </div> <div>----------------------------------------------------------------------------------------------------------------------------------------------------------------------</div> <div>------------------------------------------------------------------------ <strong>SUPPORTING NINFORMATION </strong>--------------------------------------------------------------------</div> <div>---------------------------------------------------------------------------------------------------------------------------------------------------------------------- </div> <div> </div> <div>Image folder for each set contains an *.xaml file with image capture settings.</div> <div>Each calibration image folder contains a *.caldat file with intrinsic and extrinsic stereo camera parameters identified by MatchID 2024.2 DIC package.</div>
Digital image correlation experiments involving in-plane loading of a composite laminate
<div>----------------------------------------------------------------------------------------------------------------------------------------------------------------------</div> <div>-------------------------------------------------------------------------------- <strong>SUMMARY</strong> ---------------------------------------------------------------------------</div> <div>----------------------------------------------------------------------------------------------------------------------------------------------------------------------</div> <div> </div> <div>Stereo-DIC 5 MPx system was used to perform experiments on a composite laminate.</div> <div>Thickness = 2.48 mm</div> <div>Layup is [0, 45 -45 90]2s leading to quasi-isotropic behaviour</div> <div> </div> <div>----------------------------------------------------------------------------------------------------------------------------------------------------------------------</div> <div>-------------------------------------------------------------------------------- <strong>FOLDERS </strong>---------------------------------------------------------------------------</div> <div>----------------------------------------------------------------------------------------------------------------------------------------------------------------------</div> <div> </div> <div><strong>Image sets: </strong> </div> <div><strong>Sample 1:</strong> monotonically loaded up to 7.2 kN. <br><strong>Sample 2: </strong>monotonically loaded up to 10 kN.<br><strong>Sample 3:</strong> loaded cyclically with increasing peak load to study the presence of permanent strains.</div> <div> </div> <div><strong>Each folder contains the following subfolders:</strong></div> <div><strong>stationary: </strong>stationary images for DIC noise evaluation</div> <div><strong>calib: </strong>calibration target images for stereo-DIC calibration using MatchID software</div> <div><strong>test: </strong>images of the test sample being loaded, with force.csv including load reading from the uniaxial test bench synced with the images. </div> <div> </div> <div> </div> <div>----------------------------------------------------------------------------------------------------------------------------------------------------------------------</div> <div>------------------------------------------------------------------------ <strong>SUPPORTING NINFORMATION </strong>--------------------------------------------------------------------</div> <div>---------------------------------------------------------------------------------------------------------------------------------------------------------------------- </div> <div> </div> <div>Each calibration image folder contains a *.caldat file with intrinsic and extrinsic stereo camera parameters identified by MatchID 2024.2 DIC package.</div>
Supplementary movies and datasets of the paper: Precise targeting for 3D cryo-correlative light and electron microscopy volume imaging of tissues using a FinderTOP
<p>Imaging data supporting the paper: </p> <p>Precise targeting for 3D cryo-correlative light and electron microscopy volume imaging of tissues using a FinderTOP, containing raw and processed data from fluorescent and electron microscopy.</p> <p> </p>
Early Detection of Lung Cancer Via Bi-plane Correlation Chest Imaging
ClinicalTrials.gov study NCT00673283. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Prophylactic Mastectomy: Prospective Evaluation of the Correlation Between Skin Flap Thickness, Residual Glandular Tissue and Skin Necrosis by Imaging and Clinical Examination
ClinicalTrials.gov study NCT05162677. IPD Sharing: UNDECIDED. Countries: 1. Publications: 2.
Correlation of Clitoral Hood and Labia Minora Measurements and Female Genital Self Image Scale (FGSIS) Scores in Women
ClinicalTrials.gov study NCT06134596. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Chronic Kidney Disease and Bone - Correlation Between 18F-PET-TT Imaging and Histomorphometry of Bone in CKD Patients
ClinicalTrials.gov study NCT02967042. IPD Sharing: NO. Countries: 1. Publications: 1.
Cognitive Impairment and Imaging Correlates in End Stage Renal Disease
ClinicalTrials.gov study NCT01883349. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Digital Image Correlation in Right Ventricular Evaluation
ClinicalTrials.gov study NCT03115294. IPD Sharing: NO. Countries: 1. Publications: 2.
ParisK: Correlation of Imaging Techniques With Histology
ClinicalTrials.gov study NCT01709045. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Quantitative GLP-1 Receptor Imaging Correlated to ex Vivo Distribution of In-111-exendin
ClinicalTrials.gov study NCT03889496. IPD Sharing: NO. Countries: 1. Publications: 1.
Correlation of Metabolic Imaging With Immune Markers in NSCLC Candidate to Immunotherapy.
ClinicalTrials.gov study NCT03563482. IPD Sharing: NO. Countries: 1. Publications: 17.
Study on the Correlation Between the Quantitative Parameters of Mr Mean Cell Size Imaging and the Histopathological Characteristics of Breast Cancer
ClinicalTrials.gov study NCT05373628. IPD Sharing: Not stated. Countries: 1. Publications: 1.
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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)
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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.