Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
14
datasets available to search
ShareScore release 0.9.0
Dataset results
14 results for “high-throughput microscopy”
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>
Data for: "A high-throughput microscopy method for single-cell analysis of event-time correlations in nanoparticle-induced cell death"
<p>Data related to the publication Murschhauser <em>et al.</em>: <a href="https://doi.org/10.1038/s42003-019-0282-0">A high-throughput microscopy method for single-cell analysis of event-time correlations in nanoparticle-induced cell death</a>. It contains fluorescence time traces of single cells marked with cell-event markers and observed by time-lapse microscopy. The cells were treated with nanoparticles at different doses (NP25 and NP100), with staurosporine (sts) or were left untreated for control (ctrl). See the above-mentioned publication for more details.</p> <p>The format of the data is described below.</p> <p>The file <code>Data_A549.zip</code> contains data measured with A549 cells, and the file <code>Data_Huh7.zip</code> contains data measured with Huh7 cells. Both files have the same structure. Each file contains the directories <code>Raw</code> and <code>Fitted</code> as well as a checksum file. The <code>Raw</code> directory contains single-cell fluorescence time courses as obtained by time-lapse microscopy. The <code>Fitted</code> directory contains the results of fitting model functions as well as properties of identified events, such as event times. The checksum file contains SHA256 checksums of all files within these directories and can be used to check file integrity.</p> <p>Both directories contain measurement directories. Each measurement directory contains the data corresponding to one experiment. The name of the measurement directory is the measurement identifier. Each measurement directory contains condition directories. Each condition directory contains data corresponding to one condition measured in the measurement and is named after the condition. Each condition directory contains marker directories. They are named after the fluorescence markers measured and contain files with single-cell data corresponding to the respective markers.</p> <p>The names of those files consist of multiple parts separated by underscores. The first two parts identify a position of the microscope. Since pairs of markers were measured, each position is present in two marker directories. The third part is the measurement identifier. The other parts will be described below.</p> <p>The <code>Raw</code> directory contains only CSV files with the raw fluorescence time courses. The filenames contain no other parts and have the suffix “.txt”. The first row of each CSV file is the time (in units of 10 minutes), and the other rows are the fluorescence time courses of the cells observed at the corresponding position (in arbitrary units). Each file in the <code>Raw</code> directory corresponds to a group of files in the <code>Fitted</code> directory.</p> <p>The <code>Fitted</code> directory contains three types of CSV files. Their names have “ALL” as fourth part, a session identifier as sixth part and the suffix “.csv”. The fifth part indicates the type of file and is one of the following:</p> <ul> <li>“PARAMS” indicates the estimated values for the model parameters. Each row stands for one cell and each column for a parameter of the model function fitted to the data. The model functions are published with the <a href="https://doi.org/10.5281/zenodo.1418465">fitting software</a>.</li> <li>“SIMULATED” indicates the fitted traces. The traces are calculated using the model functions and the estimated parameters. The format is the same as for the raw traces, but the time is in units of hours and has a higher resolution.</li> <li>“STATE” indicates additional information extracted from the fitted traces. Each row stands for a cell and each column for a property. The first column is the number of the cell. The second column is the event time found (in hours); non-finite values indicate that no event time was found. The third and fourth columns contain the absolute and relative amplitude of the trace, respectively. The fifth column is the logarithmic likelihood of the best fit. The sixth column indicates an algorithm used for postprocessing, and the seventh column indicates the trace slope at the event. See the fitting software for details.</li> </ul> <p> </p>
Dataset of confocal microscopy stacks from plant samples - ImageJ SurfCut: a user-friendly, high-throughput pipeline for extracting cell contours from 3D confocal stacks
<p>This data set contains confocal stacks from <em>Arabidopsis thaliana </em><em>35S::GFP-MBD</em> light grown hypocotyl as well as propidium iodide stained cotyledon pavement cells and shoot apical meristem. This is the test dataset for the Fiji macro SurfCut (https://github.com/sverger/SurfCut; 10.5281/zenodo.2635737)</p> <p> </p> <p><strong>Material and methods:</strong></p> <p>Plant material and growth conditions</p> <p><em>Arabidopsis thaliana </em>wild type Col-0 and the microtubule reporter line <em>GFP-MBD</em> (WS-4, (Marc et al. 1998) were used. Seeds were cold treated for 48 hr to synchronize germination. Plants were then grown in a phytotron at 20°C, in a 16 hr light/8 hr dark cycle on solid Murashige and Skoog medium (MS medium, Duchefa, Haarlem, the Netherlands) with 0.8% agar, 1% sucrose, and no vitamin.</p> <p> </p> <p>Confocal microscopy</p> <p>Cell contour staining in the case of PC_PI_Col0_(1-8).tif and SAM_PI_Col-0.tif was performed by staining the cell wall with Propidium Iodide (PI). Plants were immersed in 0.2 mg/ml propidium iodide (PI, Sigma-Aldrich) for 10 min and washed with water prior to imaging. For imaging, samples were either placed on a solid agar medium and immersed in water, or placed between glass slide and coverslip separated by 400 μm spacers to prevent tissue crushing. Images were acquired using a Leica TCS SP8 confocal microscope, equipped with a water immersion objective (HCX IRAPO L 25x/0.95 W). PI excitation was performed using a 552 nm solid-state laser and fluorescence was detected at 600–650 nm. GFP excitation was performed using a 488 nm solid-state laser and fluorescence was detected at 495–535 nm. Stacks of 1024x1024 pixels (pixel size of 0.363 x 0.363 micron) optical section were generated with a Z interval of 0.5 μm.</p> <p> </p> <p><strong>File list:</strong></p> <p>Light grown hypocotyl, <em>GFP-MBD</em> reporter line:</p> <p>- Hypocotyl_GFP-MBD.tif</p> <p>Cotyledon’s pavement cells, PI staining:</p> <p>- PC_PI_Col0_1.tif</p> <p>- PC_PI_Col0_2.tif</p> <p>- PC_PI_Col0_3.tif</p> <p>- PC_PI_Col0_4.tif</p> <p>- PC_PI_Col0_5.tif</p> <p>- PC_PI_Col0_6.tif</p> <p>- PC_PI_Col0_7.tif</p> <p>- PC_PI_Col0_8.tif</p> <p>Shoot apical meristem, PI staining:</p> <p>- SAM_PI_Col-0.tif</p> <p> </p> <p><strong>Reference:</strong></p> <p>Marc, Jan, Cheryl L. Granger, Jennifer Brincat, Deborah D. Fisher, Teh-hui Kao, Andrew G. McCubbin, and Richard J. Cyr. 1998. “A GFP–MAP4 Reporter Gene for Visualizing Cortical Microtubule Rearrangements in Living Epidermal Cells.” <em>The Plant Cell</em> 10 (11): 1927–39. https://doi.org/10.1105/tpc.10.11.1927.</p>
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>
Data for: High-throughput expansion microscopy enables scalable super-resolution imaging
Open the record for dataset details and reuse information.
Datasets for "Depth-enhanced high-throughput microscopy by compact PSF engineering"
<p>Datasets and code accompanying the paper: "Depth-enhanced high-throughput microscopy by compact PSF engineering". The data is split by two PSF types: Extended-Depth-Of-Field (EDOF) PSF, and the Tetrapod PSF. For EDOF imaging, 3 datasets are included: sparse and dense beads embedded in a gel, and a cellular spheroid imaged with/without the EDOF PSF. For 3D imaging with the Tetrapod PSF, the training/testing data of CellSnap is provided, in addition to a sample of diffusing beads exemplifying the application of nanoparticle tracking analysis. </p>
Data from: Flow imaging microscopy as a novel tool for high-throughput evaluation of elastin-like polymer coacervates
Biological and bioinspired polymer microparticles have broad biomedical and industrial applications, including drug delivery, tissue engineering, surface modification, environmental remediation, imaging, and sensing. Full realization of the potential of biopolymer microparticles will require methods for rigorous characterization of particle sizes, morphologies, and dynamics, so that researchers may correlate particle characteristics with synthesis methods and desired functions. Toward this end, we evaluated biopolymer microparticles using flow imaging microscopy. This technology is widely used in the biopharmaceutical industry but is not yet well-known among the materials community. Our polymer, a genetically engineered elastin-like polypeptide (ELP), self-assembles into micron-scale coacervates. We performed flow imaging of ELP coacervates using two different instruments, one with a lower size limit of approximately 2 microns, the other with a lower size limit of approximately 300 nanometers. We validated flow imaging results by comparison with dynamic light scattering and atomic force microscopy analyses. We explored the effects of various solvent conditions on ELP coacervate size, morphology, and behavior, such as the dispersion of single particles versus aggregates. We found that flow imaging is a superior tool for rapid and thorough particle analysis of ELP coacervates in solution. We anticipate that researchers studying many types of microscale protein or polymer assemblies will be interested in flow imaging as a tool for quantitative, solution-based characterization.
Fig. 3 in Do we similarly assess diversity with microscopy and high-throughput sequencing? Case of microalgae in lakes
Fig. 3 Correlation between the samples positions obtained on the first axes of the PCA based on microscopy and HTS diatom composition of the samples. There is a highly significant correlation (p <0.001, R 2 = 31%) between both axes
Fig. 4 in Do we similarly assess diversity with microscopy and high-throughput sequencing? Case of microalgae in lakes
Fig. 4 Comparison of the diatom assemblages heterogeneity inside each lake, obtained with HTS and microscopy. Inside lake assemblage heterogeneity is the sum of the Bray-Curtis distances between the three samples of a lake. Correlation is significant (Pearson correlation p <0.001) and follows a linear model (p <0.001, R 2 = 50.8%) (see black line)
Fig. 6 in Do we similarly assess diversity with microscopy and high-throughput sequencing? Case of microalgae in lakes
Fig. 6 Correlations of diversity indices obtained with microscopy and HTS. All correlations are significant and follow linear models (see Table 1)
Fig. 2 in Do we similarly assess diversity with microscopy and high-throughput sequencing? Case of microalgae in lakes
Fig. 2 Correlation between both distance matrices (Bray-Curtis distances) calculated between diatom compositions of samples obtained with microscopy and HTS
Fig. 5 in Do we similarly assess diversity with microscopy and high-throughput sequencing? Case of microalgae in lakes
Fig. 5 Comparison of diversity indices obtained with microscopy and HTS. Classes boundaries for α diversity: c1 <0.675 ≤ c2 <1.100 ≤ c3 <1.525 ≤ c4 <1.950 ≤ c5 <2.375 ≤ c6 <2.800 ≤ c7 <3.225 ≤ c8 <3.650 ≤ c9 <4.075 ≤ c10. For β diversity: c1 <0.7 ≤ c2 <0.8 ≤ c3 <0.9 ≤ c4 <1.0 ≤ c5 <1.1 ≤ c6 <1.2 ≤ c7 <1.3 ≤ c8 <1.4 ≤ c9 <1.5 ≤ c10. For ϒ diversity: c1 <1.04 ≤ c2 <1.48 ≤ c3 <1.92 ≤ c4 <2.36 ≤ c5 <2.80 ≤ c6 <3.24 ≤ c7 <3.68 ≤ c8 <4.12 ≤ c9 <4.56 ≤ c10
Data from: Flow imaging microscopy as a novel tool for high-throughput evaluation of elastin-like polymer coacervates
Open the record for dataset details and reuse information.
Fig. 1 in Do we similarly assess diversity with microscopy and high-throughput sequencing? Case of microalgae in lakes
Fig. 1 Location of the sampled lakes in the French Northern Alps
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.