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187 results for “digital imaging”
Leveraging Digital Transformation for Effective Issue Management as a Strategy to Strengthen Corporate Image
<p>Leveraging Digital Transformation for Effective Issue Management as a Strategy to Strengthen Corporate Image by Wiwi Lestari, Adhi Murti, Nisrin Husna</p>
Dietary Assessment Study Via Digital Images
ClinicalTrials.gov study NCT03267004. IPD Sharing: NO. Countries: 1. Publications: 2.
LeaData: a novel reference data of digital microscopic leather images for automatic species identification
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Bridging the analog divide: A comparison of printed X-ray films and digital images when using computer-aided detection software for tuberculosis screening
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A method to evaluate body length of live aquatic vertebrates using digital images
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Comparative analysis of autofocus criteria in reconstructed images of individual plankton for digital in line holography - Supplementary Material
<p>Supplementary Material belonging to the manuscript entitled "Comparative analysis of autofocus criteria in reconstructed images of individual plankton for digital in line holography", by Moreno et al., submitted to the journal OPTIK.</p>
Using Delaunay triangulation to sample whole-specimen color from digital images
<p>1. Color variation is one of the most obvious examples of variation in nature, but biologically meaningful quantification and interpretation of variation in color and complex patterns is challenging. Many current methods for assessing variation in color patterns classify color patterns using categorical measures, provide aggregate measures that ignore spatial pattern, or both, losing potentially important aspects of color pattern.</p> <p>2. Here, we present Colormesh, a novel method for analyzing complex color patterns that offers unique capabilities. Our approach is based on unsupervised color quantification combined with geometric morphometrics to identify regions of putative spatial homology across samples, from histology sections to whole organisms. Colormesh quantifies color at individual sampling points across the whole sample.</p> <p>3. We demonstrate the utility of Colormesh using digital images of Trinidadian guppies (Poecilia reticulata), for which the evolution of color has been frequently studied. Guppies have repeatedly evolved in response to ecological differences between up- and downstream locations in Trinidadian rivers, resulting in extensive parallel evolution of many phenotypes. Previous studies have, for example, compared the area and quantity of discrete color (e.g., area of orange, number of black spots) between these up- and downstream locations neglecting spatial placement of these areas. Using the Colormesh pipeline, we show that patterns of whole-animal color variation do not match expectations suggested by previous work.</p> <p>4. Colormesh can be deployed to address a much wider range of questions about color pattern variation than previous approaches. Colormesh is thus especially suited for analyses that seek to identify the biologically important aspects of color pattern when there are multiple competing hypotheses, or even no a priori hypotheses at all.</p>
FIGURE. Disc ovary in a taxon of Callilepis with imbricate involucral bracts. A. Digital image of the disc ovary of C. normae (Koekemoer 4573, PRE) showing the entire surface twin hairy. B. Scanning electron micrograph of the surface of the disc ovary of C. normae (Koekemoer 4573, PRE) showing the twin hairs on the surface. in A taxonomic revision of the genus Callilepis (Asteraceae) in South Africa
FIGURE. Disc ovary in a taxon of Callilepis with imbricate involucral bracts. A. Digital image of the disc ovary of C. normae (Koekemoer 4573, PRE) showing the entire surface twin hairy. B. Scanning electron micrograph of the surface of the disc ovary of C. normae (Koekemoer 4573, PRE) showing the twin hairs on the surface.
FIGURE. Disc cypselae and pappus in Callilepis taxa. A. Scanning electron micrograph of the laterally compressed disc cypsela of C. laureola var. laureola with one long and one short awn (Bester 13813, PRE). B. Digital image of the inner ray floret of C. leptophylla with laterally compressed ovary, one long awn and one short awn (Van Vuuren 1307, PRE). C. and D. Scanning electron micrographs of the disc cypselae of C. lancifolia: C, with one pappus awn and scales and D, no pappus awns, only scales (Koekemoer 5555, PRE). in A taxonomic revision of the genus Callilepis (Asteraceae) in South Africa
FIGURE. Disc cypselae and pappus in Callilepis taxa. A. Scanning electron micrograph of the laterally compressed disc cypsela of C. laureola var. laureola with one long and one short awn (Bester 13813, PRE). B. Digital image of the inner ray floret of C. leptophylla with laterally compressed ovary, one long awn and one short awn (Van Vuuren 1307, PRE). C. and D. Scanning electron micrographs of the disc cypselae of C. lancifolia: C, with one pappus awn and scales and D, no pappus awns, only scales (Koekemoer 5555, PRE).
FIGURE. The receptacle and paleae in the genus Callilepis. A. Digital image of the conical receptacle in the solitary capitulum of C. lancifolia (Galpin 12433, PRE). B. Digital image of the conical receptacle in the capitulum from the corymbose synflorescence in C. normae (Theron 3568, PRE). C. Digital image of the palea clasping the disc ovary of C. leptophylla (Hobson 1970, PRE). D. Digital image of the palea enveloping the disc ovary of C normae (Koekemoer 4573, PRE). Arrows in C and D indicate the centre of the capitulum. in A taxonomic revision of the genus Callilepis (Asteraceae) in South Africa
FIGURE. The receptacle and paleae in the genus Callilepis. A. Digital image of the conical receptacle in the solitary capitulum of C. lancifolia (Galpin 12433, PRE). B. Digital image of the conical receptacle in the capitulum from the corymbose synflorescence in C. normae (Theron 3568, PRE). C. Digital image of the palea clasping the disc ovary of C. leptophylla (Hobson 1970, PRE). D. Digital image of the palea enveloping the disc ovary of C normae (Koekemoer 4573, PRE). Arrows in C and D indicate the centre of the capitulum.
WebMicroscope's Deep Learning AI platform automates image analyses with an approach that is faster and able to understand tissue context, which reduces steps needed for accurate results. Researchers can gain access to digitized samples, such as this image of breast-cancer tissue (left), and analyze results through the cloud platform anywhere, anytime. This is a whole slide image of a tissue section of an adrenal gland (right). Fimmic's WebMicroscope cloud platform allows researchers to manage, share, and view digital gigapixel images with any modern browser. Researchers can rapidly pan, zoom, and analyze a digital sample. Photographs: Courtesy of Fimmic Oy. in Deep learning brings speed, accuracy to the life sciences.
WebMicroscope's Deep Learning AI platform automates image analyses with an approach that is faster and able to understand tissue context, which reduces steps needed for accurate results. Researchers can gain access to digitized samples, such as this image of breast-cancer tissue (left), and analyze results through the cloud platform anywhere, anytime. This is a whole slide image of a tissue section of an adrenal gland (right). Fimmic's WebMicroscope cloud platform allows researchers to manage, share, and view digital gigapixel images with any modern browser. Researchers can rapidly pan, zoom, and analyze a digital sample. Photographs: Courtesy of Fimmic Oy.
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>
JDIAZ - Navigating Digital Transformation and Technology Adoption: Data & High RES Images
<p>JDIAZ - Navigating Digital Transformation and Technology Adoption: Data & High RES Images</p> <p><strong>Navigating Digital Transformation and Technology Adoption: A Literature Review from Small and Medium Enterprises in Developing Countries</strong></p>
Supplementary material 2 from: Ströbel B, Schmelzle S, Blüthgen N, Heethoff M (2018) An automated device for the digitization and 3D modelling of insects, combining extended-depth-of-field and all-side multi-view imaging. ZooKeys 759: 1-27. https://doi.org/10.3897/zookeys.759.24584
SV1: EDOF imaging : Explanation note: This video demonstrates the effect of the registered EDOF-calculation.
Supplementary material 1 from: Ströbel B, Schmelzle S, Blüthgen N, Heethoff M (2018) An automated device for the digitization and 3D modelling of insects, combining extended-depth-of-field and all-side multi-view imaging. ZooKeys 759: 1-27. https://doi.org/10.3897/zookeys.759.24584
Technical information : Explanation note: Detailed technical information and additional theoretical background.
Supplementary material 3 from: Ströbel B, Schmelzle S, Blüthgen N, Heethoff M (2018) An automated device for the digitization and 3D modelling of insects, combining extended-depth-of-field and all-side multi-view imaging. ZooKeys 759: 1-27. https://doi.org/10.3897/zookeys.759.24584
SV2: Illustrative examples : Explanation note: Illustrative examples of insects and snail shell models generated with DISC3D.
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>
PMcardio ECG Image Database (PM-ECG-ID): A Diverse ECG Database for Evaluating Digitization Solutions
<p>The dataset presents the collection of a diverse electrocardiogram (ECG) database for testing and evaluating ECG digitization solutions. The Powerful Medical ECG image database was curated using 100 ECG waveforms selected from the PTB-XL Digital Waveform Database and various images generated from the base waveforms with varying lead visibility and real-world paper deformations, including the use of different mobile phones, bends, crumbles, scans, and photos of computer screens with ECGs. The ECG waveforms were augmented using various techniques, including changes in contrast, brightness, perspective transformation, rotation, image blur, JPEG compression, and resolution change. This extensive approach yielded 6,000 unique entries, which provides a wide range of data variance and extreme cases to evaluate the limitations of ECG digitization solutions and improve their performance, and serves as a benchmark to evaluate ECG digitization solutions.<br><br>PM-ECG-ID database contains electrocardiogram (ECG) images and their corresponding ECG information. The data records are organized in a hierarchical folder structure, which includes metadata, waveform data, and visual data folders. The contents of each folder are described below:<br><br></p> <ul> <li><strong>metadata.csv:</strong> <br>This file serves as a key-to-key bridge between the image data and the corresponding ECG information. It contains the following columns: <ul> <li><strong>Image name: </strong>image name with extension,</li> <li><strong>ECG ID:</strong> this ID corresponds to the specific ECG identifier from the original PTB-XL dataset. Under this ID you can find a cutout array in the <em>leads.npz </em>and <em>rhythms.npz,</em></li> <li><strong>Image relative path: </strong>relative path to the image in question,</li> <li><strong>Image page: </strong>page number of the particular image (starting from 0),</li> <li><strong>ECG number of pages: </strong>number of pages in the whole ECG,</li> <li><strong>ECG number of columns per page: </strong>number of columns per page in the ECG,</li> <li><strong>ECG number of rows per page: </strong>number of rows in the ECG,</li> <li><strong>ECG number of rhythm leads: </strong>number of rhythms in the ECG,</li> <li><strong>ECG format: </strong>short version of the ECG format.</li> </ul> </li> <li><strong>data </strong>folder: <ul> <li><strong>leads.npz: </strong>NPZ file containing all underlying cutout lead signals; each signal is there under its ECG ID.</li> <li><strong>rhythms.npz:</strong> NPZ file containing all underlying rhythm signals; each signal is there under its ECG ID. If no rhythm lead is in the ECG, you will find an empty array in the NPZ.</li> </ul> </li> <li><strong>visual_data</strong> folder: <br>This folder contains subfolders for various image data, including augmented photos and visualization and different types of photos of ECG printouts. The subfolders are organized based on the specific augmentation or type of photograph. These folders contain images with various augmentation settings, such as different levels of blur, brightness, contrast, padding, perspective transformation, resolution scaling, and rotation. The database is organized in a way that allows for easy navigation and understanding of the different augmentations applied to the image data. Each of these subfolders contains images relevant to the specific augmentation or type of photograph. The <em>metadata.csv</em> file provides a direct link to each image and its associated ECG information.</li> </ul>
ScienceDex guides
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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.