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91 results for “Fluorescence microscopy”
Fibered Confocal Fluorescence Microscopy Imaging in Patients With Diffuse Parenchymal Lung Diseases
ClinicalTrials.gov study NCT01624753. IPD Sharing: Not stated. Countries: 1. Publications: 4.
Data from: Learning to count: determining the stoichiometry of bio-molecular complexes using fluorescence microscopy and statistical modelling
<p>As stated in the Read Me file:</p> <p>These data and resources are associated with the manuscript:</p> <p><em>Learning to count: determining the stoichiometry of bio-molecular complexes using fluorescence microscopy and statistical modelling</em>, Mersmann et. al., as submitted to biorXiv in July 2020.</p> <p>The raw imaging data relates to Figure 5, S1, S2 and Table S1. The images are fluorescent micrographs displaying immobilised adenovirus particles bound to a monoclonal antibody 9C12.</p> <p>Each experiment folder is numbered, as in Table S1, and appended with the mixing proportion (Fl), as defined in the manuscript. Within each folder there are 6 subfolders, representing samples incubated with different concentrations of 9C12 antibody.</p> <p>Each image is a 3 channel 1024x1024 tif. Channel 1 = 9C12 Alexa Fluor 647. Channel 2 = 9C12 Biotin + QDot655. Channel 3 = Adenovirus Alexa Fluor 488. Samples were illuminated in TIRF mode using a 100X objective, images were captured on a Hamamatsu OCRA Flash 4 sCMOS camera. Further details are available in the header of each file.</p> <p>The control samples are labelled with 100% 9C12 Alexa Fluor 647 or 100% 9C12 Biotin, as described in the manuscript.</p> <p>The data analysis script is an imageJ macro. It runs on the FIJI version of ImageJ with the NanoJ package installed (https://github.com/HenriquesLab). It outputs fluorescent measurements for each identified AdV particle. Note that the script rearranges the channel order such that Channel 1 = Adenovirus Alexa Fluor 488, Channel 2 = 9C12 Alexa Fluor 647, Channel 3 = 9C12 Biotin + QDot655. </p> <p>The channels require registration due to chromatic aberration, this is achieved using the Realign Channels function in NanoJ, appropriate translation masks are provided along with the script.</p> <p>Any question about the data or script should be addressed in Joe Grove (j.grove@ucl.ac.uk)</p>
SIMToolbox: A MATLAB toolbox for structured illumination fluorescence microscopy
<p>SIMToolbox is an open-source, modular set of functions for MATLAB equipped with a user-friendly graphical interface and designed for processing two-dimensional and three-dimen- sional data acquired by structured illumination microscopy (SIM). Both optical sectioning and super-resolution applications are supported. The software is also capable of maximum a posteriori probability image estimation (MAP-SIM), an alternative method for reconstruction of structured il- lumination images. MAP-SIM can potentially reduce reconstruction artifacts, which commonly occur due to refractive index mismatch within the sample and to imperfections in the illumination.</p>
Cell Volume (3D) Correlative Microscopy Facilitated by Intra-Cellular Fluorescent Nanodiamonds as Multi-Modal Probes
<p>RAW files</p>
3D+time nuclei tracking dataset of diSPIM lightsheet fluorescence microscopy time series of C. elegans embryos
<p>The dataset consists of 3 diSPIM microscopy time series of <em>C. elegans</em> embryos, fully tracked.</p> <ul> <li>3 raw time-series and the corresponding tracks/lineage trees</li> <li>temporal resolution: 1min</li> <li>temporal extent: 350-400 frames, tracked for at least 330 frames</li> <li>spatial resolution (zyx): 0.1625 x 0.1625 x 0.1625μm</li> <li>spatial extent (zyx): 250 x 250 x 400px (average)</li> <li>Microscope: dual-view ASI diSPIM (fused and deconvolved using the MIPAV GenerateFusion plugin)</li> </ul> <p>The original raw data and annotations were part of the following publication (please also cite this if you use the dataset):</p> <p><em> </em>Moyle, M.W., Barnes, K.M., Kuchroo, M. <em>et al.</em> Structural and developmental principles of neuropil assembly in <em>C. elegans</em>. <em>Nature</em> 591<strong>, </strong>99–104 (2021). <a href="https://doi.org/10.1038/s41586-020-03169-5">https://doi.org/10.1038/s41586-020-03169-5</a></p> <p>Additionally the data was extended and slightly curated further by Peter Hirsch (MDC) and used for the development of a new tracking method in the following publication:</p> <p><em> Hirsch, P., Malin-Mayor, C., Santella, A., Preibisch, S., Kainmueller, D., Funke, J. Tracking by weakly-supervised learning and graph optimization for whole-embryo C. elegans lineages. MICCAI 2022</em></p> <p>For questions please contact Peter Hirsch (<a href="mailto:peter.hirsch@mdc-berlin.de">peterhirsch@posteo.de</a>).</p>
Fluorescence, Light-microscopy, Ultrasound Integrated / Intraoperative Diagnosis to MAXimise Resection
ClinicalTrials.gov study NCT05330559. IPD Sharing: NO. Countries: 0. Publications: 8.
Images obtained by fluorescence microscopy technique for monitoring diffusion of PI molecules into pressure-treated Listeria monocytogenes cells
Open the record for dataset details and reuse information.
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>
A Multi-Rater Benchmark for Perineuronal Nets Detection and Counting in Fluorescence Microscopy Images
<p>Dataset of fluorescence microscopy images of mice brain slices stained against perineuronal nets (PNNs). The dataset is composed of two subsets: a large single-rater subset (PNN-SR) and a smaller multi-rater subset (PNN-MR).</p> <ul> <li>PNN-SR consists of 25 images having different sizes ranging from 8184×6163 to 15120×9477 pixels. Among all the images, there are roughly 34k annotated PNNs, varying from a few dozens to some thousand per image, dot-annotated by a single human rater. <br> </li> <li>PNN-MS comprises 12 microscopic images of 2000×2000 pixels representing different portions of a mouse brain, with a total of 2,532 dot-annotated PNNs. The annotation procedure has been performed by seven different raters.</li> </ul>
iGEM Leiden 2023 - Fluorescence microscopy pictures for PHA visualization
<p>Raw data files of fluorescence microscopy of <em>Methylobacterium extorquens</em> AM1 stained with Nile Red to visualize PHA granules. </p>
Machine Learning-Based Estimation of Experimental Artifacts and Image Quality in Fluorescence Microscopy - Supporting Data
<p>Supporting data for the reproduction of the results reported in Corbetta, E., Bocklitz, T., Machine learning based estimation of experimental artifacts and image quality in fluorescence microscopy (2024) [1].</p> <h2><strong>MM-IQA_Images_png and </strong><strong>MM-IQA_Images_tif</strong></h2> <p>Folder containing all the supporting datasets of the publication.</p> <ul> <li>images_manual_inspection: semisynthetic dataset used for the manual inspection of the quality metrics.</li> <li>images_lda_training: semisynthetic dataset used to train the Linear Discriminant Analysis (LDA) model.</li> <li>images_lda_prediction: datasets predicted by the LDA model. <ul> <li>images_experimental: every subfolder is a dataset composed of measurements of a different sample. Images are from publicly available datasets from [2] and [3].</li> <li>images_known_semisynthetic: knwon semisynthetic dataset used for prediction, assessment and interpretation of the trained model.</li> </ul> </li> </ul> <p>Tif files are the original data used for the study.</p> <h2><strong>MM-IQA_Source_data</strong></h2> <p>Folder containing all the supporting metadata of the publication.</p> <ul> <li>manual_inspection: quality metrics computed for the semisynthetic dataset used for the manual inspection. <ul> <li>manual_inspection_bg: indices for the selection of the background region in each sample.</li> <li>manual_inspection_free_parameters: parameters used for the generation of the simulated artifacts.</li> <li>manual_inspection_metrics: quality metrics computed for the dataset, used for the manual inspection.</li> <li>manual_inspection_samples: free parameters associated to each image of the dataset for manual inspection.</li> </ul> </li> <li>LDA_training: semisynthetic dataset used to train the Linear Discriminant Analysis (LDA) model. <ul> <li>lda_metrics_synthetic+semisynthetic_uniform_max: quality metrics computed for the training dataset, with maximum normalization of the images. (Not used in the manuscript)</li> <li>lda_metrics_synthetic+semisynthetic_uniform_rescale01: quality metrics computed for the training dataset, with image values rescaled between 0 and 1.</li> <li>parameters_all_degradations: parameters used for the generation of the simulated artifacts.</li> </ul> </li> <li>LDA_prediction: datasets predicted by the LDA model. <ul> <li>experimental: quality metrics computed for measurements of different samples. Images are from publicly available datasets from [2] and [3].</li> <li>known_semisynthetic: metadata for the knwon semi-synthetic dataset used for prediction, assessment and interpretation of the trained model: <ul> <li>known_semisynthetic_free_parameters: parameters used for the generation of the simulated artifacts.</li> <li>known_semisynthetic_metrics_rescale01: quality metrics computed for the training dataset, with image values rescaled between 0 and 1.</li> <li>known_semisynthetic_maxnorm_lda_results: lda prediction results, when metrics are maximum normalized to the training dataset.</li> <li>known_semisynthetic_znorm_lda_results: lda prediction results, when metrics are z-score normalized to the training dataset.</li> </ul> </li> </ul> </li> </ul> <p>Source data can be used to reproduce the results of the manuscript, using the codes shared in the public GitLab repository <em><a href="https://git.photonicdata.science/elena.corbetta/multi-marker-iqa" target="_blank" rel="noopener">multi-marker-IQA</a>.</em></p> <h3><em>How to use the source data</em></h3> <p>The following table describes which data can be used in the scripts provided in the public GitLab repository <em><a href="https://git.photonicdata.science/elena.corbetta/multi-marker-iqa" target="_blank" rel="noopener">multi-marker-IQA</a>.</em></p> <table> <tbody> <tr> <td><strong>Script</strong></td> <td><strong>Data to use</strong></td> <td><strong>Details</strong></td> </tr> <tr> <td>01_quality_metrics</td> <td>Subfolders of MM-IQA_Images_png</td> <td>Include all the images to evaluate in a single subfolder in <code>/test_images</code></td> </tr> <tr> <td> </td> <td>background_idx.xlsx</td> <td>The indices for the samples to evaluate must be included in the table</td> </tr> <tr> <td> <p>01_quality_metrics_visualization</p> <p>01_quality_metrics_visualization_notebook</p> </td> <td>manual_inspection_metrics.xlsx</td> <td> </td> </tr> <tr> <td> </td> <td>known_semisynthetic_metrics_rescale01</td> <td> </td> </tr> <tr> <td> </td> <td>Every metadata included in LDA_predcition/experimental/</td> <td> </td> </tr> <tr> <td> <p>02_lda_training+prediction</p> </td> <td>lda_metrics_synthetic+semisynthetic_uniform_rescale01</td> <td>As training dataset</td> </tr> <tr> <td> </td> <td>known_semisynthetic_metrics_rescale01</td> <td>As prediction dataset</td> </tr> <tr> <td> </td> <td>Every metadata included in LDA_predcition/experimental/</td> <td>As prediction dataset</td> </tr> <tr> <td> <p>02_lda_visualization</p> <p>02_lda_visualization_notebook</p> </td> <td>known_semisynthetic_maxnorm_lda_results</td> <td> </td> </tr> <tr> <td> </td> <td>known_semisynthetic_znorm_lda_results</td> <td> </td> </tr> <tr> <td>Notebook_test-iqa</td> <td>A small dataset with image data and the relative background index, if available.</td> <td>Use a limited number of images.</td> </tr> <tr> <td>Notebook_mm-iqa_workflow</td> <td>Any image dataset with the relative background indices</td> <td>For quality assessment and as prediction dataset</td> </tr> <tr> <td> </td> <td>lda_metrics_synthetic+semisynthetic_uniform_rescale01</td> <td>As training dataset</td> </tr> </tbody> </table> <p> </p> <h2>MM-IQA_Scripts</h2> <ul> <li><strong>multi-marker-iqa-main</strong>: original GitLab repository for MM-IQA, version available at the date of manuscript publication.</li> <li><strong>Notebooks_peer_review</strong>: additional notebooks generated during the peer-review process with the computation of metrics for natural images and correlation measures.</li> </ul>
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.