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Dataset results
23 results for “super-resolution microscopy”
Temporal super-resolution microscopy using a hue-encoded shutter
<p>This dataset contains the data to reproduce the figures in our paper called "Temporal super-resolution microscopy using a hue-encoded shutter", <em>Biomedical Optics Express, 2019</em>.</p> <p>Together with the data, the code is available on <a href="https://github.com/idiap/hesm_distrib">Idiap's GitHub page</a>.</p>
Laser-free super-resolution microscopy
<p>Raw data for "Laser-free super-resolution microscopy"</p> <p>https://www.biorxiv.org/content/10.1101/121061v3.full.pdf</p>
BioSR+: Dataset Extension of biological images for super-resolution microscopy
<p>BioSR+ dataset is an extension of our pre-published BioSR dataset of biological images for super-resolution microscopy, currently including image pairs of low-and-high resolution images of five biology structures (CCPs, ER, MTs, F-actin, Myosin-IIA) and 8 signal levels for each ROI. The BioSR+ dataset is related to our Nature Methods paper "Evaluation and development of deep neural networks for image super-resolution in optical microscopy" (DOI: 10.1038/s41592-020-01048-5) and Nature Biotechnology paper "Rationalized deep learning super-resolution <br> microscopy for sustained live imaging of rapid subcellular processes" (DOI:10.1038/s41587-022-01471-3). Both BioSR and BioSR+ are freely available and can be used for non-commercial purposes with proper citations of above two papers.</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>
Source Data and Scripts - MultiMatch: Geometry-Informed Colocalization in Multi-Color Super-Resolution Microscopy
<p>Experimental and simulated STED data and scripts associated with Naas et al. "<em>MultiMatch: Geometry-Informed Colocalization in Multi-Color Super-Resolution Microscopy.</em>" <em>bioRxiv</em> (2024): 2024-02. </p> <p>The MultiMatch Python package and further illustrative examples are available on GitHub repository <a href="https://github.com/gnies/multi_match">https://github.com/gnies/multi_match</a>.</p>
BioTISR: a time-lapse biological image dataset for super-resolution microscopy
<p>BioTISR is a biological image dataset for super-resolution microscopy, currently including 2D and 3D time-lapse image pairs of low-and-high resolution images of a variety of biology structures, aiming to provide a high-quality dataset of time-lapse biological SR images for the community to spark more developments of computational SR methods.</p> <p>At present, 2D dataset includes five specimens (clathrin-coated pits, lysosomes, outer mitochondrial membrane, microtubules, and F-actin) acquired with the GI/TIRF-SIM mode and nonlinear SIM mode of our Multi-SIM system, and 3D dataset includes three specimens (outer mitochondrial membrane, microtubules, and F-actin) acquired with 3D-SIM mode of the Multi-SIM system. For each type of specimen and each imaging modality, we acquired the raw data from at least 50 distinct regions-of-interest (ROI). For each ROI, we acquired two (3D data) or three (2D data) groups of N-phase × M-orientation × T-timepoint raw images with a constant exposure time but increasing the excitation light intensity, where (N, M, T) are (3, 3, 20) for TIRF-SIM and GI-SIM, (5, 5, 10) for nonlinear SIM, and (3, 5, 10) for 3D-SIM. Specific imaging conditions and scripts for reading MRC file are provided in Supplement Files.</p> <p>The BioTISR dataset is related to the following paper:<a href="https://www.nature.com/articles/s41587-025-02553-8#citeas">Qiao, C., Liu, S., Wang, Y. <em>et al.</em> A neural network for long-term super-resolution imaging of live cells with reliable confidence quantification. <em>Nat Biotechnol</em> (2025). https://doi.org/10.1038/s41587-025-02553-8</a>, which is an extension of our previously published <a href="https://doi.org/10.6084/m9.figshare.13264793.v9">BioSR dataset</a> (https://www.nature.com/articles/s41592-020-01048-5).</p> <p>Limited by quota, the original images uploaded in the current 3D dataset are wide-field images obtained after averaging 15 images, where (N, M, T) are (1, 1, 10). We will update them to raw SIM images after the quota is expanded.</p> <p>2D dataset's url:</p> <p><a href="https://doi.org/10.5281/zenodo.13843670" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.13843670</a></p> <p>3D dataset's urls:</p> <p>F-actin:</p> <p>WF input: <a href="https://doi.org/10.5281/zenodo.13843673">https://doi.org/10.5281/zenodo.13843673</a></p> <p>Raw SIM input:<a href="https://doi.org/10.5281/zenodo.13994464" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.13994464</a></p> <p>Microtubules:</p> <p>WF input: <a href="https://doi.org/10.5281/zenodo.13932988" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.13932988</a></p> <p>Raw SIM input: <a href="https://doi.org/10.5281/zenodo.13989327" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.13989327</a></p> <p>Mitochondria:</p> <p>WF input: <a href="https://doi.org/10.5281/zenodo.13843183" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.13843183</a></p> <p>Raw SIM input: <a href="https://doi.org/10.5281/zenodo.14000502" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.14000502</a></p> <p> </p> <p>Update 2025.5.6</p> <p>Add optical transfer function(OTF) of the microscopy system and the pixel size of each data to the supplementary files.</p>
Smart 3D super-resolution microscopy reveals the architecture of the RNA scaffold in a nuclear body
<p>Data associated with the article "Smart 3D super-resolution microscopy reveals the architecture of the RNA scaffold in a nuclear body". A README.txt is provided that explains the data provided.</p>
Dataset for Reference-free Isotropic Super-resolution For Volumetric Fluorescence Microscopy
<p>Dataset for a research paper titled "Deep learning enables reference-free isotropic super-resolution for volumetric fluorescence microscopy". The images were acquired using two modalities: confocal fluorescence microscopy (CFM) and open-top light-sheet microscopy (OT-LSM). For details about the imaging, please refer to the paper (Link to be uploaded later). </p> <p>A. CFM</p> <ul> <li>CFM image of a cortical region of a Thy 1-eYFP mouse brain. </li> <li>Lateral resolution estimated as 1.24 micron and Z-depth interval of 3 micron</li> </ul> <ol> <li>Input image ["CFM_input_xy-view.tif] [Figure 2] </li> <li>Reference image acquired by rotating the sample by 90 degrees ["CFM_rotated-and-registered_xz-view.tif'] [Figure 2] </li> </ol> <p>B. OT-LSM </p> <ul> <li>OT-LSM image of a cortical region of a Thy 1-eYFP mouse brain.</li> <li>Lateral resolution estimated as 0.5 micron and axial resolution estimated as 4.6 micron. </li> <li>For testing of artifact correction, the microscope was poorly calibrated on purpose. </li> </ul> <ol> <li>Input image for artifact correction ["OT-LSM_artifact-correction_input_volume_xy-view.tif"] [Figure 4]</li> <li>Ground-truth image for artificial blurring ["OT-LSM_artificial-blurring_GT.tif"][Supplementary Figure 14]</li> <li>Input image for artificial blurring ["OT-LSM_artificial-blurring_gau-z-blurred-std-10.tif"][Supplementary Figure 14]</li> <li>Input image for PSF deconvolution ["input_volume_PSF-deconvolution.tif"][Figure 3]</li> </ol> <p>C. Simulation </p> <ul> <li>Jupyter notebook to generate a 3D image volume for simulation ["Data Generator for Simulation.ipynb"] [Figure 1] </li> </ul>
Flat-Field Super-Resolution Localization Microscopy with a Low-Cost Refractive Beam-Shaping Element
<p>Raw data for the article "Flat-Field Super-Resolution Localization Microscopy with a Low-Cost Refractive Beam-Shaping Element". Each set of three files is a set of dSTORM images, taken using either top-hat illumination or a Gaussian illumination. For Sample 0, the top-hat illumination was performed first. For Sample 1, the Gaussian illumination was performed first.</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>
Fully-Automated Multicolour Structured Illumination Module for Super-resolution Microscopy
<p> </p> <p>In the rapidly advancing field of biological imaging, high-resolution techniques that are cost-effective and accessible are essential for observing and understanding intracellular dynamics. Structured illumination microscopy (SIM) is a preferred method for achieving high axial and lateral resolution in living samples due to its optical sectioning and minimal phototoxicity. However, the high cost and complexity of conventional SIM systems limit their widespread use. In our work, we present an open-source, fully-automated, two-color structured illumination module that is compatible with commercially available microscope stands. The compact design, which includes low-cost single-mode fiber-coupled lasers and a digital micromirror device (DMD), is integrated into the open-source acquisition and control software ImSwitch to facilitate real-time super-resolution imaging. This system achieves up to a 1.55-fold improvement in lateral resolution compared to conventional wide-field microscopy. </p> <p>To ensure optimal DMD diffraction performance, we developed a model using tilt and roll pixels, enabling the use of low-cost video projectors in coherent SIM setups. Our aim is to democratize SIM-based super-resolution microscopy by providing comprehensive open-source documentation and a modular software framework compatible with various hardware components (e.g., cameras, stages) and reconstruction algorithms. </p> <p>All datasets generated and analyzed during this study are openly available and can be accessed through our public repository <a href="https://opensimmo.github.io/">[repository link]</a>. The datasets include raw and processed images, calibration files, and software scripts, enabling replication and further innovation. This approach will help upgrade as many devices as possible to the super-resolution realm, fostering greater accessibility and collaboration in the scientific community</p>
Corrected super-resolution microscopy enables nanoscale imaging of auto-fluorescent lung macrophages
<p>Observing the cell surface and underlying cytoskeleton at nanoscale resolution using super-resolution microscopy has enabled many insights into cell signalling and function. However, the nanoscale dynamics of tissue-specific immune cells have been relatively little studied. Tissue macrophages, for example, are highly auto-fluorescent, severely limiting the utility of light microscopy. Here, we report a correction technique to remove auto-fluorescent noise from Stochastic Optical Reconstruction Microscopy (STORM) datasets. Simulations identified a moving median filter as an accurate and robust correction technique. Using this, we were able to visualise lung macrophages activated through Fc receptors by antibody-coated glass slides. Accurate, nanoscale quantification of macrophage morphology revealed that activation induced the formation of cellular protrusions tipped with MHC class I protein. These data are consistent with a role for lung macrophage protrusions in antigen presentation. We further show that the tetraspanin and extracellular vesicle (EV) marker CD81 appears in ring-shaped structures (mean diameter 93 ± 50 nm) at the surface of activated lung macrophages, likely marking the secretion of extracellular vesicles. Moreover, this correction method for super-resolution microscopy is widely applicable to other challenging biological samples.</p>
Data from: Structured Detection for Simultaneous Super-Resolution and Optical Sectioning in Laser Scanning Microscopy
<p>This repository contains the raw data of the experimental ISM dataset used to make the figures and supplementary figures for the paper entitled <em>Structured Detection for Simultaneous Super-Resolution and Optical Sectioning in Laser Scanning Microscopy.<br></em></p>
Long-wave infrared super-resolution wide-field microscopy by sum-frequency generation - experimental data
<p>Experimental Data for "Long-wave infrared super-resolution wide-field microscopy by sum-frequency generation", under consideration at APL, preprint: <a href="https://doi.org/10.48550/arXiv.2112.08112">https://doi.org/10.48550/arXiv.2112.08112</a></p> <p>Files:</p> <p>readme.txt: explanation of the content<br> SFGmicroscope.h5: microscope data<br> APL_test_script.m: matlab test script generating the relevant figures from the data</p> <p>For more information, please contact Richarda Niemann (niemann@fhi-berlin.mpg.de) or Alex Paarmann (alexander.paarmann@fhi-berlin.mpg.de).</p>
Advances in volumetric super-resolution microscopy and single-particle tracking (associated codes and datasets)
<h2>Overview</h2> <p>This Zenodo repository contains datasets and code relating to the thesis entitled "Advances in volumetric super-resolution microscopy and single-particle tracking" by <a href="https://www.ch.cam.ac.uk/person/sgd46">Sam G. Daly</a> (Yusuf Hamied Department of Chemistry, University of Cambridge).</p> <p>Managed/updated versions my be avalible at <a href="https://github.com/TheLeeLab">https://github.com/TheLeeLab</a>.</p> <p>The Excel Workbook 'MicrolensRelayCalculator' is designed to help in the design of MLAs for SMLFM.</p> <h2>Available Datasets</h2> <h3>Chapter 4</h3> <ol> <li>Simulated localisation data for various PSFs: standard, astigmatism, double helix, SMLFM, and tetrapod; 4000 detected photons, 20 emitters per frame, 200 frames.</li> <li>Microtubule imaging in a fixed HeLa cell (dSTORM); 30 ms exposure, 640 nm excitation, 200 frames.</li> </ol> <h3>Chapter 5</h3> <ol> <li>B cell receptor imaging on a fixed B cell (dSTORM);<em> 30 ms exposure, 640 nm excitation, 200 frames, fiducial: nanodiamonds.</em></li> <li>SPT of the B cell receptor on a live B cell (PALM); <em>20 ms exposure, 640 nm excitation, 200 frames, fiducial: nanodiamonds.</em></li> <li>Membrane imaging on a fixed Jurkat T cell embedded in agarose (resPAINT); <em>20 ms exposure, 640 nm excitation, 200 frames, fiducial: nanodiamonds.</em></li> <li>PD-1 imaging on a fixed T cell (dSTORM);<em> 30 ms exposure, 640 nm excitation, 200 frames, fiducial: nanodiamonds.</em></li> <li>Membrane imaging on a fixed T cell (resPAINT); <em>20 ms exposure, 640 nm excitation, 200 frames, fiducial: nanodiamonds.</em></li> </ol> <h3>Chapter 6</h3> <ol> <li>SPT of ACBD3 in a live HeLa cell (PALM); <em>20 ms exposure, 640 and 405 nm excitation, 200 frames.</em></li> <li>SPT of TMD mutant (length: 27) in a live HeLa cell (PALM); <em>20 ms exposure, 640 and 405 nm excitation, 200 frames.</em></li> </ol> <h2>Available Code</h2> <ol> <li><strong>Autofocus (BeanShell):</strong> Counteracts axial drift in SMLFM experiments.</li> <li><strong>Calibration (BeanShell):</strong> Controls the piezo scanner for axial calibrations in 3D-SMLM.</li> <li><strong>3D Reconstruction (Matlab):</strong> Reconstructs 2D-localised SMLFM data in 3D. Maintained version available on GitHub.</li> <li><strong>Fiducial correction (Matlab):</strong> Removes focal drift artifacts from 3D localisation data.</li> <li><strong>Temporal grouping (Python):</strong> Removes multiple single-molecule blinking events.</li> <li><strong>3D tracking (Matlab):</strong> Converts 3D localisations into tracks and calculates diffusion quantities.</li> <li><strong>Matching (Matlab):</strong> Determines PPV, sensitivity, and Jaccard index from localisation data.</li> <li><strong>Membrane curvature (Python):</strong> Determines the frequency of 3D localisations at a given membrane curvature.</li> </ol> <h3><em>Supported by The Royal Society (RGF\EA\181021) </em></h3>
High-speed TIRF and 2D super-resolution structured illumination microscopy with large field of view based on fiber optic components
<p>Super-resolved structured illumination microscopy (SR-SIM) is among the most flexible, fast, and least perturbing fluorescence microscopy techniques capable of surpassing the optical diffraction limit. Current custom-built instruments are easily able to deliver two-fold resolution enhancement at video-rate frame rates, but the cost of the instruments is still relatively high, and the physical size of the instruments based on the implementation of their optics is still rather large. Here, we present our latest results towards realizing a new generation of compact, cost-efficient, and high-speed SR-SIM instruments. Tight integration of the fiber-based structured illumination microscope capable of multi-color 2D- and TIRF-SIM imaging, allows us to demonstrate SR-SIM with a field of view of up to 150 × 150 μm<sup>2</sup> and imaging rates of up to 44 Hz while maintaining highest spatiotemporal resolution of less than 100 nm. We discuss the overall integration of optics, electronics, and software that allowed us to achieve this, and then present the fiberSIM imaging capabilities by visualizing the intracellular structure of rat liver sinusoidal endothelial cells, in particular by resolving the structure of their trans-cellular nanopores called fenestrations.</p>
Data for: High-throughput expansion microscopy enables scalable super-resolution imaging
Open the record for dataset details and reuse information.
Raw data accompanying the manuscript "Super-resolution fluorescence microscopy by line-scanning with an unmodified two-photon microscope"
<p>Raw data sets (.tif files) of data utilized to demonstrate 2D SIM with an unmodified multi photon intravital fluorescence microscope.</p>
Transthoracic contrast echocardiography data of a patient for super-resolution ultrasound localisation microscopy
<p>We provide sample contrast echocardiography datasets used for generation of super-resolution ultrasound localisation microscopy (ULM) images as reported in the following paper:</p> <div> <div>Jipeng Yan, Biao Huang, Johanna Tonko, Matthieu Toulemonde, Joseph Hansen-Shearer, Qingyuan Tan, Kai Riemer, Konstantinos Ntagiantas, Rasheda A Chowdhury, Pier D Lambiase, Roxy Senior, Meng-Xing Tang. Transthoracic Ultrasound Localization Microscopy of Myocardial Vasculature in Patients, Nature Biomedical Engineering, 2024. DOI: (<a href="https://www.nature.com/articles/s41551-024-01206-6" target="_blank" rel="noopener noreferrer">10.1038/s41551-024-01206-6</a>).</div> <div> </div> <div>The sample datasets include:</div> <div> </div> <div>"LogCompressedCEUS.mp4' is a video of CEUS images gated within one cardiac cycle after motion correction and log compression (acquistion time: 0.36s).</div> <div> </div> <div>"LinearScaleCEUS.mat" is a Matlab data file containing CEUS images after motion correction.</div> <div> </div> <div>"LinearScaleCEUSAfterNoiseReduction.mat' is a Matlab data file containing above CEUS images with noise reduced, which can be processed with our SRUS software (<a href="https://github.com/JipengYan1995/SRUSSoftware">JipengYan1995/SRUSSoftware</a>) for localisation and tracking (A brief tutorial can be found in "Usage of sample data in SRUS Software.docx").</div> <div> </div> <div>"RcvDataSample.mat' is a Matlab data file containing RF data in channels;</div> <div> </div> <div>"BFInformation.mat' is a Matlab data file containing parameters for beamforming;</div> <div> </div> <div>"Data Description.txt" contains more detailed descriptions of above data.</div> <div> </div> </div> <p><strong>Data from all the 10 cardiac cycles of this patient will be available in the future.</strong></p> <p>If you have any questions, please contact Meng-Xing Tang (email: mengxing.tang@imperial.ac.uk).</p>
HEK293T cell super-resolution images by SoRa microscopy
<p>These images contain both untreated and VPA-treated cells, stained by H3K27ac antibodies, CCCTC binding factor (CTCF) antibodies and DNA fluorescent dye Hoechst. Cells were imaged by Yokogawa CSU-W1 SoRa super-resolution spinning disc confocal system (Tokyo, Japan).</p><p>There are two tar.zg files, <a href="https://zenodo.org/uploads/10032412">original_multi-cells_SoRa.tar.gz </a>and <a href="https://zenodo.org/uploads/10032412">single-cells_segmented.tar.gz</a>. "original_multi-cells_Sora.tar.gz" is the original image data, which have multiple cells in each image. On the other hand, "single-cells_segmented.tar.gz" is single-cell image data, obtained from "original_multi-cells_Sora.tar.gz" by segmentation processing.</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.