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41 results for “high throughput imaging”
Quantification of Giant Unilamellar Vesicle Fusion Products by High-Throughput Image Analysis - Imaging Flow Citometry Data
<p>Imaging flow citometry (IFC) datasets analysed in "Quantification of Giant Unilamellar Vesicle Fusion Products by High-Throughput Image Analysis" (under revision).</p> <p>The folders contain acquisitions of giant unilamellar vesicles (GUVs) for lipid exchange and content exchange controls, with file naming convention DATE_SAMPLE_REPLICATE.rif, content exchange is indicated by CE samples in the 20230228_CE.zip folder, lipid exchange by LE samples in the 20221222_LE.zip folder. 24 samples per set are included, triplicates of isolated P1 (DOPE Af488 0.6% in LE; Dex-Af488 40 uM for CE), P2 (DOPE Cy50.6% in LE; Dex-Af647 10 uM for CE), NC (P1 + P2 1:1), PC (DOPE Af488 0.3% + DOPE Cy5 0.3 in LE; Dex-Af488 20 uM + Dex-Af647 5 uM for CE), and M samples numbered 1 to 4, prepared by mixing P1, P2 and PC in different ratios (M1= 1:1:1; M2= 1:1:0.5; M3= 1:1:0.1; M4= 1:1:0.05).</p> <p>Only .rif files are provided, they have to be elaborated via compensation and application of an analysis template using the Amnis IDEAS software. Compensation matrices for lipid exchange (20230217_LEcom.ctm) and content exchange (20230217_CEcomp.ctm) are included, as well as the analysis template (Lipid_exchange_analysis_6.2.ast). Gating in the latter may have to be adjusted to analyse LE and CE experiments.</p> <p>10000 objects in the GUV population or 50000 objects in total were acquired in each file. The files were elaborated in batch mode, outputting the statistic reports (Statistics report CE.txt for CE; Statistics report LE.txt for LE) that were elaborated using an R scirpt (included, IFC_analysis.R) </p>
Image dataset for the evaluation of a low-cost high-throughput plant phenotyping system
<p>This dataset contains the raw and processed images from a low-cost high-throughput plant phenotyping (HTP) system, as well as the raw and processed images that were manually acquired for comparison. The HTP images were automatically and wirelessly acquired for entire benches of plants with a system composed of a Raspberry Pi and eight GoPro cameras. The entire file system of each GoPro camera was copied directly into a subfolder of finalGoProImages (numbered by camera). The raw HTP images were processed by correcting for lens distortion, computing the "greenness index" for each individual pixel, and filtering out extreme high and low values. These processed HTP images were then saved in the "greenness" subfolder of finalGoProImages. The manually acquired images in the finalDSLR folder each represent an individual plant from one of five time points during the same greenhouse experiment. The raw manually acquired images were processed in the same manner as the raw HTP images by computing the greenness index for each individual pixel and filtering out extreme high and low values. The two tab-delimited text files include the number of green pixels and mean greenness index for each HTP (greennessGoProTable2.txt) and manually acquired (greennessDSLRTable2.txt) image.</p>
Dataset of image processing - High-throughput characterization of cortical microtubule arrays response to anisotropic tensile stress
<p>The data set contains the analysis data files from the image analysis workflow developed to quantify cortical microtubules rearrangements in the case of tensile stress (<a href="https://github.com/VergerLab/MT_Angle2Ablation_Workflow">https://github.com/VergerLab/MT_Angle2Ablation_Workflow</a>), generated form a specific dataset (https://doi.org/10.5878/17te-jg54). The files include the intermediary images processed at each step of the image analysis workflow in imageJ, the log files produced by the imageJ macro describing the input and the output images and the text files containing the quantified values. </p>
Quantification of Giant Unilamellar Vesicle Fusion Products by High-Throughput Image Analysis - Microscopy Data
<p>Microscopy dataset of multipoint-multichannel images of giant unilamellar vesicles (GUVs) suspensions analysed in "Quantification of Giant Unilamellar Vesicle Fusion Products by High-Throughput Image Analysis" (under revision).</p> <p>Three folders concerning different sections of the work are included. "preliminary analysis.zip" contians the raw files and analysis scripts for recall computation and imaging setup optimization as described in the paper. Timelapse data was excluded due to file size restrictions (available upon request at the corresponding authors of the work). "IFC comparison.zip" contains raw files and analysis scripts used to optimize colocalization computation in lipid exchange and content exchange experiments. "GUV fusion analysis" contains raw files and analysis scripts for the quantification of lipid and content exchange upon sodium chloride-induced aggregation.</p> <p>Further details on the analysis are provided in the paper. The R scripts require files saved upon analysis of the raw files by the ImageJ macro "CE_analysis_CPU.ijm" included here. The R environment of the complete analysis are included in each folder to provide easier access to the elaborated data.</p>
High-throughput in-situ plankton imaging from the East China Sea: raw images and acantharian ROIs
<p>Vertical imaging profiles were performed at four stations (3, 10, 15, 17; closed circles on the map) during the Japan Agency for Marine-Earth Science and Technology (JAMSTEC) MR17-03C cruise from May 29 to June 13, 2017 with an ISIIS small-imager (<a href="https://www.planktonimaging.com/smaller-imagers">https://www.planktonimaging.com/smaller-imagers</a>) attached to the JAMSTEC DEEP TOW 6KCTD (<a href="https://www.jamstec.go.jp/e/about/equipment/ships/deeptow.html">https://www.jamstec.go.jp/e/about/equipment/ships/deeptow.html</a>). The ISIIS camera was programmed to take 1 photo per second coinciding with an LED flash. Each photo imaged 0.39 L (st. 3 and 10) or 0.35 L (st. 15 and 17) parcels of water in 2448 x 2050 pixel resolution, with each pixel being 22.5 µm. A Sea-Bird SBE 9 CTD was deployed with the DEEP TOW and the ISIIS internal clock was calibrated to match the CTD’s so that CTD data could be used to determine the depth at which each image was taken. Raw images are labeled with the time stamp. Acantharian ROIs are labeled with the timestamp for the raw image from which they were cropped. If more than one acantharian ROI was found in a single raw image, a letter was appended to the ROI file name. </p> <p>Accompanying data (CTD, sequencing) and analyses are available from the GitHub repository: <a href="https://github.com/maggimars/Acanth_ImageSeq">https://github.com/maggimars/Acanth_ImageSeq</a>.</p> <p> </p>
Homogeneous multifocal excitation for high-throughput super-resolution imaging - Expanded centriole particles
<p>Datasets containing the segmented expanded centriole particles. The prefix Hs is used to denote particles acquired in synchronized RPE-1 human cells. Otherwise particles were collected from expanded isolated centrioles from <em>Chlamydomoanas reinhardtii</em>. Resized datasets have uniform voxel size of 14x14x14 nm3 after expansion (56x56x56 nm3 before expansion). Non-resized datasets have 14x14x30 pixel size (56x56x120 nm3 before expansion). All files should be mirrored horizontally/vertically to account for the chirality inversion due to the imaging process</p> <p>The channels in different datasets are:</p> <ul> <li>Chlamy acetylated sample: <ul> <li>C1: acetylated tubulin-Alexa488</li> <li>C2: aTubulin-Alexa568</li> </ul> </li> <li>Chlamy MonoE sample <ul> <li>C1: aTubulin-Alexa488</li> <li>C2: GT335-Alexa568</li> </ul> </li> <li>Chlamy PolyE sample <ul> <li>C1: PolyE-Alexa488</li> <li>C2: aTubulin-Alexa568</li> </ul> </li> <li>Hs sample: <ul> <li>C1: PolyE-Alexa488</li> <li>C2: acetylated tubulin-Alexa586</li> </ul> </li> </ul>
Quantum Cascade Laser Spectral Histopathology: Breast Cancer Diagnostics Using High Throughput Chemical Imaging
<p>Fourier transform infrared (FT-IR) microscopy, coupled with machine learning approaches, has been demonstrated to be a powerful technique for identifying abnormalities in human tissue. The ability to objectively identify the prediseased state, and diagnose cancer with high levels of accuracy, has the potential to revolutionise current histopathological practice. Despite recent technological advances in FT-IR microscopy, sample throughput and speed of acquisition are key barriers to clinical translation. Wide-field quantum cascade laser (QCL) infrared imaging systems with large focal plane array detectors utilising discrete frequency imaging, have demonstrated that large tissue microarrays (TMA) can be imaged in a matter of minutes. However this ground breaking technology is still in its infancy and its applicability for routine disease diagnosis is, as yet, unproven. In light of this we report on a large study utilising a breast cancer TMA comprised of 207 different patients. We show that by using QCL imaging with continuous spectra acquired between 912 and 1800 cm<sup>-1</sup>, we can accurately differentiate between 4 different histological classes. We demonstrate that we can discriminate between malignant and non-malignant stroma spectra with high sensitivity (93.56%) and specificity (85.64%) for an independent test set. Finally, we classify each core in the TMA and achieve high diagnostic accuracy on a patient basis with 100% sensitivity and 86.67% specificity. The absence of false negatives reported here opens up the possibility of utilising high throughput chemical imaging for cancer screening, thereby reducing pathologist workload and improving patient care.</p>
Codes for "High-throughput parallel optofluidic 3D-imaging flow cytometry"
<p>Codes used in Ugawa & Ota. "High-throughput parallel optofluidic 3D-imaging flow cytometry". Small size data is also included.</p>
High-throughput Computational Screening of Hydrocarbon Molecules for Long-wavelength Infrared Imaging
<p>This repository contains datasets associated with the paper titled "High-throughput Computational Screening of Hydrocarbon Molecules for Long-wavelength Infrared Imaging," accepted at ACS Materials Letters Journal.</p> <p><strong>Contents:</strong></p> <ol> <li> <p><strong>Optimized XYZ Coordinates:</strong> The hydrocarbon molecules' XYZ coordinates, obtained using the B3LYP functional and the 6-31g(d,p) basis set in Gaussian 16 software, used to simulate the IR spectra (including transition energies and absorption intensities) of the molecules.</p> </li> <li> <p><strong>Broadened Molar Absorptivity IR Spectra:</strong> The dataset's IR spectra, broadened using a Lorentzian band shape with a gamma (half-width at half-height) value of 5 cm⁻¹. Molecules with imaginary frequencies have been excluded.</p> </li> <li> <p><strong>Related SMILES Strings:</strong> Contains SMILES strings for these hydrocarbons.</p> </li> <li> <p><strong>NUMBERS_SMILES.csv:</strong> Provides the associated SMILES string for each numerated XYZ coordinate.</p> </li> </ol> <p>For any inquiries, please contact Dr. Maliheh Shaban Tameh at malihe.shaban<a rel="noreferrer">@gmail.com</a></p>
A high-throughput multispectral imaging system for museum specimens
<p>We present an economical imaging system with integrated hardware and software to capture multispectral images of Lepidoptera with high efficiency. This method facilitates the comparison of colors and shapes among species at fine and broad taxonomic scales and may be adapted for other insect orders with greater three-dimensionality. Our system can image both the dorsal and ventral sides of pinned specimens. Together with our processing pipeline, the descriptive data can be used to systematically investigate multispectral colors and shapes based on full-wing reconstruction and a universally applicable ground plan that objectively quantifies wing patterns for species with different wing shapes (including tails) and venation systems. Basic morphological measurements, such as body length, thorax width, and antenna size are automatically generated. This system can increase exponentially the amount and quality of trait data extracted from museum specimens.</p>
Data from: Accelerated high-throughput imaging and phenotyping system for small organisms
<p>Studying the complex web of interactions in biological communities requires large multifactorial experiments with sufficient statistical power. Automation tools reduce the time and labor associated with setup, data collection, and analysis in experiments that untangle these webs. We developed tools for high-throughput experimentation (HTE) in duckweeds, small aquatic plants that are amenable to autonomous experimental preparation and image-based phenotyping. We showcase the abilities of our HTE system in a study with 6,000 experimental units grown across 2,000 treatments. These automated tools facilitated the collection and analysis of time-resolved growth data, which revealed finer dynamics of plant-microbe interactions across environmental gradients. Altogether, our HTE system can run experiments with up to 11,520 experimental units and can be adapted for other small organisms.</p>
Data for: Tools and methods for high-throughput single-cell imaging with the mother machine
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A high-throughput multispectral imaging system for museum specimens
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Data from: Accelerated high-throughput imaging and phenotyping system for small organisms
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Sample data for "Live Cell Fluorescence Microscopy – An End-to-End Workflow for High-Throughput Image and Data Analysis"
<p>This repository contains:</p> <ul> <li> <p>Sample data for the "Live Cell Fluorescence Microscopy – From Sample Preparation to Numbers and Plots" methodology paper by Zahumensky & Malinsky. The paper describes the preparation of live yeast cell samples for microscopy, the subsequent semi-automatic analysis of the microscopy images using our custom-written Fiji macros, and automatic processing of the output (Results table) from the image analys using custom-written R scripts. The data provided here are real experimental data from two publications of our group: Zahumensky et al., 2022 and Vesela et al., 2023</p> </li> <li> <p>"Results tables" from the Fiji based analysis</p> </li> <li> <p>Outputs of the processing of these Results tables using our R scripts, in the form of summary tables, graphs, and statistical analyses</p> </li> </ul>
Automated, high-throughput image calibration for parallel-laser photogrammetry
<p>This contains the data required to recreate the analyses in this paper. The code for performing the machine learning and image processing methods presented in the paper are available as supplemental files to the manuscript, and are also available at https://github.com/ejlevy/Photogrammetry_Coding_InterLaser_Distance.</p> <p>Paper abstract: <span>Parallel-laser photogrammetry is growing in popularity as a way to collect non-invasive body size data from wild mammals. Despite its many appeals, this method requires researchers to hand-measure (i) the pixel distance between the parallel laser spots (inter-laser distance) to produce a scale within the image, and (ii) the pixel distance between the study subject's body landmarks (inter-landmark distance). This manual effort is time-consuming and introduces human error: a researcher measuring the same image twice will rarely return the same values both times (resulting in within-observer error), as is the case when two researchers measure the same image (resulting in between-observer error). Here, we present two independent methods that automate the inter-laser distance measurement of parallel-laser photogrammetry images. One method uses machine learning and image processing techniques in Python, and the other uses image processing techniques in ImageJ. Both of these methods reduce labor and increase precision without sacrificing accuracy. We first introduce the workflow of the two methods. Then, using two parallel-laser datasets of wild mountain gorilla and wild savannah baboon images, we validate the precision of these two automated methods relative to manual measurements and to each other. We also estimate the reduction of variation in final body size estimates in centimeters when adopting these automated methods, as these methods have no human error. Finally, we highlight the strengths of each method, suggest best practices for adopting either of them, and propose future directions for the automation of parallel-laser photogrammetry data. </span></p>
Unidimensional Phenotypes using Concurrent Imaging Collected Via a High-Throughput Imaging System
<p>SIPID and SIMID are dataset repositories used to compute Single-Aspect Phenotypes. SAPP has 28 samples and SAPM has 12 samples. Each dataset contains:</p> <ol> <li>Two species physically different. Buckwheat is a thin plant with a variety sizes of leaves and Sunflower is a bushy plant that contains flowering;</li> <li>the most commonly used induced environments in plant phenotyping such as a control and drought-induced; </li> <li>a temporal resolution that begins with the plants vegetative stage and ends with the plant fully matured;</li> <li>modalities (infrared, visible, near infrared) that are commonly used in plant phenotyping analysis; and </li> <li>multiple perspectives that are becoming widely acquired in plant phenotyping analysis due to its potential for three dimensional analysis.</li> </ol> <p>We thank Vincent Stoeger for acquiring the dataset using LemnaTec at the University of Nebraska-Lincoln.</p> <p>If you use this dataset, please cite this paper:</p>
Spatial- and Fourier-domain ptychography for high throughput bio-imaging
<p>Title: Spatial and Fourier domain ptychography for high-throughput bio-imaging<br> Version: 1.0 <br> Copyright: Shaowei Jiang, Pengming Song, Guoan Zheng, 2023<br> License: Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International Public License</p> <p>******************************************************************************</p> <p>If you use this code, please cite the following paper:<br> Shaowei Jiang, Pengming Song, Tianbo Wang, Liming Yang,Ruihai Wang, Chengfei Guo, Bin Feng, Andrew Maiden, and Guoan Zheng,<br> "Spatial and Fourier domain ptychography for high-throughput bio-imaging", Nature Protocols, 2023. </p> <p>For algorithmic details, please refer to our paper.</p> <p>******************************************************************************</p> <p>How to use: </p> <p>Foruier-domain ptychography (FP)<br> 1. Unpack the full package and install related softwares. <br> Refer to 'Materials for Procedure 1: Fourier-domain ptychography' and 'Procedure 1: Fourier-domain ptychography' sections in our paper for additional details (including version number and instructions). <br> 2. We provide 4 different FP experimental datasets. Please refer to 'Content in this package' section for additional details. <br> 3. Run FP_Recovery.m for FP image reconstruction. The reconstruction time for a tile of 256x256 dimensions, with 4-times padding, is ~2 seconds. Please refer to our paper for additional information. </p> <p>Spatial-domain coded ptychography (CP)<br> 1. Unpack the full package and install related softwares. <br> Refer to 'Materials for Procedure 2: Spatial-domain coded ptychography' and 'Procedure 2: Spatial-domain coded ptychography' sections in our paper for additional details (including version number and instructions). <br> 2. We provide 3 different CP experimental datasets. Please refer to 'Content in this package' section for additional details. <br> 3. Run CP_Recovery.m for CP image reconstruction. The reconstruction time for raw images with 1024*1024 dimensions, with 3-times padding, is ~58 seconds. Please refer to our paper for additional information. </p> <p>******************************************************************************</p> <p>Content in this package: <br> CP_HeLaCellCulture Dataset and reconstruction code of HeLa cell sample for CP<br> CP_Immunohistochemistry Dataset and reconstruction code of IHC stained sample for CP<br> CP_UnstainedCytologySmear Dataset and reconstruction code of unstained cytology smear for CP<br> FP_Intestine_Aberrations Dataset and reconstruction code of Intestine cancer sample for FP<br> FP_H&E_RGB Dataset (RGB) and reconstruction code of H&E stained sample for FP<br> FP_Immunohistochemistry_RGB Dataset (RGB) and reconstruction code of IHC stained sample for FP<br> FP_Leukemia_RGB Dataset (RGB) and reconstruction code of Leukemia sample for FP</p> <p>******************************************************************************</p> <p>License of using this package (refer to 'license.txt'): </p> <p>Creative Commons Attribution-NonCommercial-ShareAlike 4.0<br> International Public License</p> <p>Any inclusion or other use of this package means acceptance of this license.</p>
Automated, high-throughput image calibration for parallel-laser photogrammetry
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Data for: High-throughput expansion microscopy enables scalable super-resolution imaging
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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.