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Dataset results
21 results for “correlative microscopy”
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>
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>
Dataset: Correlative Light, Electron Microscopy and Raman Spectroscopy Workflow to Detect and Observe Microplastic Interactions with Whole Jellyfish
<p>ABSTRACT</p> <p>Many researchers have turned their attention to understanding microplastic interaction with marine fauna. Efforts are being made to monitor exposure pathways and concentrations, and to assess the impact such interactions may have. To answer these questions, it is important to select appropriate experimental parameters and analytical protocols. This study focuses on medusae of <em>Cassiopea andromeda</em> jellyfish: a unique benthic jellyfish known to favor (sub-)tropical coastal regions which are potentially exposed to plastic waste from land-based sources. Juvenile medusae were exposed to fluorescent poly(ethylene terephthalate) and polypropylene microplastics (< 300 µm), resin embedded, and sectioned before analysis with confocal laser scanning microscopy as well as transmission electron microscopy and Raman Spectroscopy. Results show the fluorescent microplastics were stable enough to be detected with the optimized analytical protocol presented, and that their observed interaction with medusae occurs in a manner which is likely driven by the microplastic properties (<em>e.g.</em> density, hydrophobicity).</p>
ultraLM and miniLM: Locator tools for smart tracking of fluorescent cells in correlative light and electron microscopy
<p>Data for submission to Wellcome Open Research entitled "ultraLM and miniLM: Locator tools for smart tracking of fluorescent cells in correlative light and electron microscopy".</p> <p>Data_ultraLM.tif is an image stack from the fluorescence microscope mounted on the ultramicrotome.</p> <p>Data_miniLM.tif is an image stack from the fluorescence microscope mounted in the SBF-SEM.</p> <p>Data_miniLM_EM.tif is an image stack from the SBF-SEM while the miniLM was in-situ.</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>
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>
100 Hz ROCS microscopy correlated with fluorescence reveals cellular dynamics on different spatiotemporal scales
<p>Image Datasets to 100 Hz ROCS microscopy correlated with fluorescence reveals cellular dynamics on different spatiotemporal scales</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>
Training Data for "DeepCLEM: automated registration for correlative light and electron microscopy using deep learning"
<p><strong>This folder contains the training dataset used for the paper</strong></p> <p>"DeepCLEM: automated registration for correlative light and electron microscopy using deep learning"</p> <p><em>Rick Seifert, Sebastian M. Markert, Sebastian Britz, Veronika Perschin, Christoph Erbacher, Christian Stigloher and Philip Kollmannsberger</em></p> <p>F1000Research 9:1275 (2020), https://f1000research.com/articles/9-1275</p> <p>------------------------------------------------------------</p> <p>These are 117+4 manually aligned CLEM images of C.elegans acquired by Sebastian M. Markert, Sebastian Britz and Rick Seifert in the Electron Microscopy Facility of the Biocenter of University of Wuerzburg, Germany. For details and experimental protocols, please see the paper linked above.</p> <p>Contents:</p> <ul> <li>"fluo_training": Fluorescence microscopic channel of the 117 training images </li> <li>"sem_training": Scanning electron microscopic channel of the 117 training images</li> <li>"fluo_validation": Fluorescence microscopic channel of the 4 validation images </li> <li>"sem_validation": Scanning electron microscopic channel of the 4 validation images</li> </ul> <p>License: CC-BY 4.0</p>
Correlative study of liquid in human bone by 3D neutron microscopy and lab-based X-ray µCT
<p>X-ray (xray_data.tiff) and neutron microscopy (neutron_data.tiff) of the same piece of human cortical bone.</p> <p>Neutron data were collected at the neutron microscope at SINQ, PSI, Switzerland while the x-ray data were collected in house at an VERSA 620 X-ray microscope. </p> <p>The voxel size is in both case 2.7 µm</p>
Revealing nanoscale plasticity of metallic nanosponges with correlative and scale-bridging 3D microscopy and modelling
<p>Data for manuscript "Revealing nanoscale plasticity of metallic nanosponges with correlative and scale-bridging 3D microscopy and modelling":</p> <p>SI Movies 01-12. Movies from Supporting Information. Description in file name.</p>
Single and few cell analysis for correlative light microscopy, metabolomics, and targeted proteomics (Data)
<p>Combined data for the manuscript `Single and few cell analysis for correlative light microscopy, metabolomics, and targeted proteomics` for all manuscript and supplemental information figures.</p> <p>Every folder contains the raw data and Jupyter notebook (python) for graph creation.</p> <p>Images are not enclosed but are shown in the manuscript.</p> <p> </p>
uploaded files: Correlative microscopy approach for biology using x-ray holography, x-ray scanning diffraction and STED microscopy
<p>The data uploaded here corresponds to a manuscript on x-ray /STED correlative imaging by the same authors published under the same title in Nature Communications in 2018.</p> <p>The provided data are subdivided into three parts:<br> 1. The 01_STED_fig2a.mat file contains the main results shown in Fig.2a (main article) as variables:<br> - STED_micrograph: the STED micrograph with each pixel representing single photon counts<br> - STED_dwell_time: the dwell time at each pixel position</p> <p>2. The 02_HOLO_fig2b.mat file contains the main results shown in Fig.2b (main article) as variables:<br> - I: the emptyimage devided, but not yet filtered hologram<br> - geo: a structure including the geometrical magnification M, the fresnel-number F, the waveguide-sample-distance z01, the sample-detector-distance z12, the effective propagation distance z_eff and the effective pixelsize dxeff<br> - lambda: the wavelength used for all x-ray experiments<br> - phi_raar: the reconstructed phasemap. Note, that for depicting the phase shifts, the matlab command angle(phi_raar) has to be used</p> <p>3. The 03_SCANNING_fig2c.mat file contains the main results shown in Fig.2c (main article) and Fig.4 (inset) as variables:<br> - darkfield: the x-ray dark field map of the scan area<br> - sSAXS_dwell_time: the dwell time for each scan point<br> - mask: the dark field mask applied on the diffraction patterns<br> - single_diff_image: a single diffraction pattern</p>
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>
Reflectance Confocal Microscopy and Molecular Correlation in Atypical Melanocytic Lesions
ClinicalTrials.gov study NCT07277920. IPD Sharing: Not stated. Countries: 1. Publications: 10.
Cell Volume (3D) Correlative Microscopy Facilitated by Intra-Cellular Fluorescent Nanodiamonds as Multi-Modal Probes
<p>RAW files</p>
Spatial Transcriptomics correlated Electron Microscopy [scRNA-Seq]
GEO Series GSE202636. Mus musculus. 1332 samples. Type: Expression profiling by high throughput sequencing.
Spatial Transcriptomics correlated Electron Microscopy [MERFISH]
GEO Series GSE202623. Mus musculus; synthetic construct. 3 samples. Type: Other.
Spatial Transcriptomics correlated Electron Microscopy
GEO Series GSE202638. Mus musculus; synthetic construct. 1335 samples. Type: Expression profiling by high throughput sequencing; Other.
Supplementary information: Cell volume (3D) correlative microscopy facilitated by intracellular fluorescent nanodiamonds as multi-modal probes
<p><em>Supplementary video files for manuscript</em>:</p> <p><strong>Cell volume (3D) correlative microscopy facilitated by intracellular fluorescent nanodiamonds as multi-modal probes</strong></p> <p>Neeraj Prabhakar<sup>1,2*</sup>, Ilya Belevich<sup>3</sup>, Markus Peurla<sup>4,5,6</sup>, Xavier Heiligenstein<sup>7</sup>, Huan-Cheng Chang<sup>8</sup>, Cecilia Sahlgren<sup>2</sup>, Eija Jokitalo<sup>3</sup> and Jessica M. Rosenholm<sup>1</sup></p> <ol> <li>Pharmaceutical Sciences Laboratory, Faculty of Science and Engineering, Åbo Akademi University, Turku, 20520, Finland.</li> <li>Cell Biology, Faculty of Science and Engineering, Åbo Akademi University, Turku, 20520 Finland.</li> <li>Electron Microscopy Unit, Helsinki Institute of Life Science - Institute of Biotechnology, University of Helsinki, Helsinki, FI-00014, Finland</li> <li>Institute of Biomedicine, Faculty of Medicine, University of Turku, Turku, 20520, Finland.</li> <li>Cancer Research Laboratory FICAN West, Institute of Biomedicine, University of Turku, 20520 Turku, Finland</li> <li>Turku Bioscience Centre, University of Turku and Åbo Akademi University, 20520 Turku, Finland</li> <li>CryoCapCell, 155 Boulevard de l’Hopital, 75013 Paris, France.</li> <li>Institute of Atomic and Molecular Sciences, Academia Sinica, Taipei, 10617, Taiwan.</li> </ol>
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.