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134 results for “Super Resolution”
Fast and accurate spectral estimation axial super resolution optical coherence tomography
<p>This depository contains the data and code underlying the results of the publication '<em>Fast and accurate spectral estimation axial super resolution optical coherence tomography</em>' in Optics Express (doi.org/<em>10.1364</em>/<em>OE</em>.<em>439761). </em>The reader is free to use the scripts and data in this depository, as long as the manuscript is correctly cited in their work. For further questions, please contact the corresponding author.</p> <p><strong>Description of the code and datasets</strong></p> <p>Table 1 describes the Matlab scripts and functions in this depository that were used in the publication. For reproducing the figures of the publication, refer to the scripts <em>SE_OCT_figure(..).m</em>. For understanding the method and applying it on other datasets from the reader, <em>Bscan_reconstruction.m </em>and <em>Cscan_reconstruction.m</em> are the most convenient scripts to start with. For simulating OCT data as presented in the publication, <em>OCT_simulations.m</em> could be applied. Details on the variables and parameters, such as number of iterations, grid interpolation factor and number of data chunks are commented on in the code itself and should be understandable with the publication as reference. </p> <p>Table 2 describes the datasets that have been used for the publication and are free for the readers to be used with their methods. Table 3 then gives a brief explanation of the variables that are contained in the dataset <em>.mat</em> files.</p> <table> <caption>Table 1. The Matlab scripts in this depository with brief description.</caption> <thead> <tr> <th scope="col">script name</th> <th scope="col">description</th> </tr> </thead> <tbody> <tr> <td>Bscan_reconstruction.m</td> <td>This script loads a B-scan .mat file and applies the four processing methods FBW-DFT, PBW-DFT, AR, RFIAA on the data. </td> </tr> <tr> <td>Cscan_resonstruction.m</td> <td>This script loads a 3Ddata .mat file and applies the four processing methods FBW-DFT, PBW-DFT, AR, RFIAA on the data. </td> </tr> <tr> <td>oct_ar.m</td> <td>This function applies the auto-regressive spectral estimation on the input data.</td> </tr> <tr> <td>oct_iaa.m</td> <td>This function applies RFIAA or FIAA on the input data.</td> </tr> <tr> <td>fiaa_oct.m</td> <td>This function is called within oct_iaa.m for applying FIAA (without the recursive scheme) and within rfiaa_oct.m for the first line. This function applies FIAA on a single A-scan.</td> </tr> <tr> <td>rfiaa_oct.m</td> <td>This function is called within oct_iaa.m for applying RFIAA (with the recursive scheme) on a chunk of data. It initializes the first line of the chunk with fiaa_oct.m, and then it applies rec_fiaa_oct.m with the initialization taken from the previous scanline.</td> </tr> <tr> <td>rec_fiaa_oct.m</td> <td>This function applies RFIAA on a single A-scan, taking the initialization from the previous scanline as extra input parameter. </td> </tr> <tr> <td>RayleighThreshold.m</td> <td>This function automatically determines the lower limit of the dynamic range for plotting an OCT image. It fits a Rayleigh distribution on the input data (preferably noise, but also a full image could be used) and returns a threshold in dB. </td> </tr> <tr> <td>morgenstemning.m</td> <td>This function defines the colormap as used in the publication.</td> </tr> <tr> <td>Bscan_reconstruction_function.m</td> <td>This function takes the interference OCT signal, reference spectra and reconstruction parameters as input and returns the reconstructed images according to the four methods in the publication. This function is used in the scripts for reproducing the figures in the publication. It follows the same structure as the script <em>Bscan_reconstruction.m.</em></td> </tr> <tr> <td>SE_OCT_figure3.m</td> <td>This script does the processing for and plots figure 3 in the manuscript. For this script, the .zip file <em>wedge_simulation_data </em>needs to be unpacked and placed as folder in the folder where this script is executed.</td> </tr> <tr> <td>SE_OCT_figure4.m</td> <td>This script reproduces figure 4 in the publication.</td> </tr> <tr> <td>SE_OCT_figure5.m</td> <td>This script reproduces figure 5 in the publication.</td> </tr> <tr> <td>SE_OCT_figure6.m</td> <td> <p>This script reproduces figure 6 in the publication</p> </td> </tr> <tr> <td>OCT_simulations.m</td> <td>This script reproduces the OCT simulations as described in the publication. As the noise is random, any new realization might slightly differ from the data in the publication.</td> </tr> </tbody> </table> <p> </p> <table> <caption>Table 2. The OCT datasets contained in this depository with a brief description. Table 3 describes the variables that are contained in each of these datasets.</caption> <thead> <tr> <th scope="col">dataset name</th> <th scope="col">description</th> </tr> </thead> <tbody> <tr> <td>wedge_Bscan_data.mat</td> <td>Experimental data from the wedge phantom as visualized in figure 3 of the publication. No spectrum averaging is applied.</td> </tr> <tr> <td>wedge_simulation_data.zip</td> <td>This zipped folder contains 16 simulation datasets with different noise levels, which form the basis of Figure 3 (f) in the publication.</td> </tr> <tr> <td>interfaces_simulation_Bscan_data.mat</td> <td>This file contains the simulation data for 8 interfaces with decreasing intensity and forms the basis of Figure 4 in the publication.</td> </tr> <tr> <td>layered_phantom_Bscan_data.mat</td> <td>This file contains the experimental data from the layered phantom, as used in Figure 4 (c-d) in the publication. No spectrum averaging is applied.</td> </tr> <tr> <td>onion_Bscan_data.mat</td> <td>This file contains the experimental data from the onion sample as used in Figure 5 in the publication. No spectrum averaging is applied.</td> </tr> <tr> <td>skin_Bscan_data.mat</td> <td>This file contains the experimental data from the skin sample as used in Figure 5 in the publication. No spectrum averaging is applied.</td> </tr> <tr> <td>intralipid_Bscan_data.mat</td> <td>This file contains the experimental data from the intralipid sample as used in Figure 6 in the publication. No spectrum averaging is applied.</td> </tr> <tr> <td>speckle_simulation_Bscan_data.mat</td> <td>This file contains simulation data for 3 speckle regions as used in Figure 6 in the publication.</td> </tr> <tr> <td>reference_spectrum.mat</td> <td>This file just contains a spectrum from the used experimental setup which is used as input for the simulations.</td> </tr> <tr> <td>onion_3Ddata.mat</td> <td>This file contains 3D data of the onion sample, which is used for visualization 1. The OCT spectra are obtained from averaging 8 spectra from the experimental setup.</td> </tr> <tr> <td>skin_3Ddata.mat</td> <td>This file contains 3D data of the skin sample, which is used for visualization 2. The OCT spectra are obtained from averaging 8 spectra from the experimental setup.</td> </tr> </tbody> </table> <table> <caption>Table 3. This table contains the variables in the .mat files and their description.</caption> <thead> <tr> <th scope="col">variable name</th> <th scope="col">description</th> </tr> </thead> <tbody> <tr> <td><em>iRawdata</em></td> <td>OCT interference spectra interpolated to a linear grid in k-domain, before subtracting the reference spectrum</td> </tr> <tr> <td><em>sk</em></td> <td>the reference spectrum, interpolated to a linear grid in k-domain</td> </tr> <tr> <td><em>phasep</em></td> <td>4 polynomial coeficients, which can be used in 'polyval' to correct for dispersion</td> </tr> <tr> <td><em>sizeX</em></td> <td>the lateral size of the scan in mm</td> </tr> <tr> <td><em>sizeY</em></td> <td>(only for 3D datasets) the lateral size in the direction perpendicular to x in mm</td> </tr> <tr> <td><em>sizeZ</em></td> <td>the axial field of view (one-sided) before range reduction in mm </td> </tr> <tr> <td><em>ROIp</em></td> <td>the best axial region of interest for this dataset to apply RFIAA on a reduced reconstruction range (in pixels of the DFT reconstruction without zero-padding)</td> </tr> </tbody> </table> <p> </p>
Deep-Learning-Based Harmonization and Super-Resolution of Near-Surface Air Temperature from CMIP6 Models (1850-2100)
<p>A long-term (1850-2100) monthly air temperature (tas) product with a spatial resolution of 0.5 degree. This is a merged product from 31 CMIP6 models using the Deep-learning model which reduce bias, spatial downscaling and data merge at the same time,. To facilitate user-friendly access and download the dataset is stored individually for each year in a separate file. These files contain one historical data (1850-2014) , four future scenarios data during 2015-2100 (SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5) and four future scenarios data in Australia . The dataset is stored in NetCDF format, containing the variable tas, representing air temperature, produced in centigrade (℃) as a unit. There are three dimensions included in the dataset: longitude, latitude, and time, with the longitude ranging from -179.75E to 179.75E, the latitude from -89.75N to 89.75N. </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>
3D super-resolution datasets associated with the paper "Whole-cell multi-target single-molecule super-resolution imaging in 3D with microfluidics and a single-objective tilted light sheet"
<p>3D single-molecule super-resolution datasets corresponding to reconstructions shown in <em>Whole-cell multi-target single-molecule super-resolution imaging in 3D with microfluidics and a single-objective tilted light sheet</em> by Saliba & Gagliano, Gustavsson et. al.</p>
RELLISUR: A Real Low-Light Image Super-Resolution Dataset
<p>The RELLISUR dataset contains real low-light low-resolution images paired with normal-light high-resolution reference image counterparts. This dataset aims to fill the gap between low-light image enhancement and low-resolution image enhancement (Super-Resolution (SR)) which is currently only being addressed separately in the literature, even though the visibility of real-world images is often limited by both low-light and low-resolution. The dataset contains 12750 paired images of different resolutions and degrees of low-light illumination, to facilitate learning of deep-learning based models that can perform a direct mapping from degraded images with low visibility to high-quality detail rich images of high resolution. The associated paper can be found here: <a title="https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/file/7ef605fc8dba5425d6965fbd4c8fbe1f-Paper-round2.pdf" href="https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/file/7ef605fc8dba5425d6965fbd4c8fbe1f-Paper-round2.pdf" target="_blank" rel="noopener">https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/file/7ef605fc8dba5425d6965fbd4c8fbe1f-Paper-round2.pdf</a></p>
Wood Buffalo Environmental Association (WBEA) Historical Monitoring Data used in "Passive Tracer Modelling at Super-Resolution with WRF-ARW to Assess Mass-Balance Schemes"
<p>Wood Buffalo Environmental Association (WBEA) Historical Monitoring Data from two monitoring stations Bertha Ganter – Fort McKay and Barge Landing for 20 August 2013 to 2 September 2013. This data was used in "Passive Tracer Modelling at Super-Resolution with WRF-ARW to Assess Mass-Balance Schemes" (Fathi et al., 2022 - egusphere-2022-1125) for model output and observational data comparisons. The same data can be accessed and downloaded from "<a href="https://wbea.org/historical-monitoring-data/">https://wbea.org/historical-monitoring-data/</a>".</p>
Super resolution enhancement of Landsat imagery and detections of high-latitude lakes
<p>This archive contains native resolution and super resolution (SR) Landsat imagery, derivative lake shorelines, and previously-published lake shorelines derived airborne remote sensing, used here for comparison. Landsat images are from 1985 (Landsat 5) and 2017 (Landsat 8) and are cropped to study areas used in the corresponding paper and converted to 8-bit format. SR images were created using the model of Lezine et al (2021a, 2021b), which outputs imagery at 10x-finer resolution, and they have the same extent and bit depth as the native resolution scenes included. Reference shoreline datasets are from Kyzivat et al. (2019a and 2019b) for the year 2017 and Walter Anthony et al. (2021a, 2021b) for Fairbanks, AK, USA in 1985. All derived and comparison shoreline datasets are cropped to the same extent, filtered to a common minimum lake size (40 m<sup>2</sup> for 2017; 13 m<sup>2</sup> for 1985), and smoothed via 10 m morphological closing. The SR-derived lakes were determined to have F-1 scores of 0.75 (2017 data) and 0.60 (1985 data) as compared to reference lakes for lakes larger than 500 m2, and accuracy is worse for smaller lakes. More details are in the forthcoming accompanying publication.</p> <p>All raster images are in cloud-optimized geotiff (COG) format (.tif) with file naming shown in <strong>Table 1</strong>. Vector shoreline datasets are in ESRI shapefile format (.shp, .dbf, etc.), and file names use the abbreviations LR for low resolution, SR for high resolution, and GT for “ground truth” comparison airborne-derived datasets.</p> <p>Landsat-5 and Landsat-8 images courtesy of the U.S. Geological Survey</p> <p>For an interactive map demo of these datasets via Google Earth Engine Apps, visit: <a href="https://ekyzivat.users.earthengine.app/view/super-resolution-demo">https://ekyzivat.users.earthengine.app/view/super-resolution-demo</a></p> <p><strong>Table 1</strong>: File naming scheme based on region, with some regions requiring two-scene mosaics.</p> <table> <tbody> <tr> <td> <p><strong>Region</strong></p> </td> <td> <p><strong>Landsat ID</strong></p> </td> <td> <p><strong>Mosaic name</strong></p> </td> </tr> <tr> <td> <p><strong>Yukon Flats Basin</strong></p> </td> <td> <p>LC08_L2SP_068014_20170708_20200903_02_T1</p> </td> <td> <p>LC08_20170708_yflats_cog.tif</p> </td> </tr> <tr> <td> <p><strong>“</strong></p> </td> <td> <p>LC08_L2SP_068013_20170708_20201015_02_T1</p> </td> <td> <p>“</p> </td> </tr> <tr> <td> <p><strong>Old Crow Flats</strong></p> </td> <td> <p>LC08_L2SP_067012_20170903_20200903_02_T1</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p><strong>Mackenzie River Delta</strong></p> </td> <td> <p>LC08_L2SP_064011_20170728_20200903_02_T1</p> </td> <td> <p>LC08_20170728_inuvik_cog.tif</p> </td> </tr> <tr> <td> <p><strong>“</strong></p> </td> <td> <p>LC08_L2SP_064012_20170728_20200903_02_T1</p> </td> <td> <p>“</p> </td> </tr> <tr> <td> <p><strong>Canadian Shield Margin</strong></p> </td> <td> <p>LC08_L2SP_050015_20170811_20200903_02_T1</p> </td> <td> <p>LC08_20170811_cshield-margin_cog.tif</p> </td> </tr> <tr> <td> <p><strong>“</strong></p> </td> <td> <p>LC08_L2SP_048016_20170829_20200903_02_T1</p> </td> <td> <p>“</p> </td> </tr> <tr> <td> <p><strong>Canadian Shield near Baker Creek</strong></p> </td> <td> <p>LC08_L2SP_046016_20170831_20200903_02_T1</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p><strong>Canadian Shield near Daring Lake</strong></p> </td> <td> <p>LC08_L2SP_045015_20170723_20201015_02_T1</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p><strong>Peace-Athabasca Delta</strong></p> </td> <td> <p>LC08_L2SP_043019_20170810_20200903_02_T1</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p><strong>Prairie Potholes North 1</strong></p> </td> <td> <p>LC08_L2SP_041021_20170812_20200903_02_T1</p> </td> <td> <p>LC08_20170812_potholes-north1_cog.tif</p> </td> </tr> <tr> <td> <p><strong>“</strong></p> </td> <td> <p>LC08_L2SP_041022_20170812_20200903_02_T1</p> </td> <td> <p>“</p> </td> </tr> <tr> <td> <p><strong>Prairie Potholes North 2</strong></p> </td> <td> <p>LC08_L2SP_038023_20170823_20200903_02_T1</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p><strong>Prairie Potholes South</strong></p> </td> <td> <p>LC08_L2SP_031027_20170907_20200903_02_T1</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p><strong>Fairbanks </strong></p> </td> <td> <p>LT05_L2SP_070014_19850831_20200918_02_T1</p> </td> <td> <p>-</p> </td> </tr> </tbody> </table> <p><strong>References:</strong></p> <p>Kyzivat, E. D., Smith, L. C., Pitcher, L. H., Fayne, J. V., Cooley, S. W., Cooper, M. G., Topp, S. N., Langhorst, T., Harlan, M. E., Horvat, C., Gleason, C. J., & Pavelsky, T. M. (2019b). A high-resolution airborne color-infrared camera water mask for the NASA ABoVE campaign. <em>Remote Sensing</em>, <em>11</em>(18), 2163. <a href="https://doi.org/10.3390/rs11182163">https://doi.org/10.3390/rs11182163</a></p> <p>Kyzivat, E.D., L.C. Smith, L.H. Pitcher, J.V. Fayne, S.W. Cooley, M.G. Cooper, S. Topp, T. Langhorst, M.E. Harlan, C.J. Gleason, and T.M. Pavelsky. 2019a. ABoVE: AirSWOT Water Masks from Color-Infrared Imagery over Alaska and Canada, 2017. ORNL DAAC, Oak Ridge, Tennessee, USA. <a href="https://doi.org/10.3334/ORNLDAAC/1707">https://doi.org/10.3334/ORNLDAAC/1707</a></p> <p>Ekaterina M. D. Lezine, Kyzivat, E. D., & Smith, L. C. (2021a). Super-resolution surface water mapping on the Canadian shield using planet CubeSat images and a generative adversarial network. <em>Canadian Journal of Remote Sensing</em>, <em>47</em>(2), 261–275. <a href="https://doi.org/10.1080/07038992.2021.1924646">https://doi.org/10.1080/07038992.2021.1924646</a></p> <p>Ekaterina M. D. Lezine, Kyzivat, E. D., & Smith, L. C. (2021b). Super-resolution surface water mapping on the canadian shield using planet CubeSat images and a generative adversarial network. <em>Canadian Journal of Remote Sensing</em>, <em>47</em>(2), 261–275. <a href="https://doi.org/10.1080/07038992.2021.1924646">https://doi.org/10.1080/07038992.2021.1924646</a></p> <p>Walter Anthony, K.., Lindgren, P., Hanke, P., Engram, M., Anthony, P., Daanen, R. P., Bondurant, A., Liljedahl, A. K., Lenz, J., Grosse, G., Jones, B. M., Brosius, L., James, S. R., Minsley, B. J., Pastick, N. J., Munk, J., Chanton, J. P., Miller, C. E., & Meyer, F. J. (2021a). Decadal-scale hotspot methane ebullition within lakes following abrupt permafrost thaw. <em>Environ. Res. Lett</em>, <em>16</em>, 35010. <a href="https://doi.org/10.1088/1748-9326/abc848">https://doi.org/10.1088/1748-9326/abc848</a></p> <p>Walter Anthony, K., and P. Lindgren. 2021b. ABoVE: Historical Lake Shorelines and Areas near Fairbanks, Alaska, 1949-2009. ORNL DAAC, Oak Ridge, Tennessee, USA. <a href="https://doi.org/10.3334/ORNLDAAC/1859">https://doi.org/10.3334/ORNLDAAC/1859</a></p>
An Agnostic Benchmark for Optical Remote Sensing Image Super-Resolution
<p>In remote sensing, image super-resolution (ISR) is a technique used to create high-resolution (HR) images from low-resolution (R) satellite images, giving a more detailed view of the Earth’s surface. However, with the constant development and introduction of new ISR algorithms, it can be challenging to stay updated on the latest advancements and evaluate their performance objectively. To address this issue, we introduce SRcheck, a Python package that provides an easy-to-use interface for comparing and benchmarking various ISR methods. SRcheck includes a range of datasets that consist of high-resolution and low-resolution image pairs, as well as a set of quantitative metrics for evaluating the performance of SISR algorithms.</p>
CBRA: The first multi-annual (2016-2021) and high-resolution (2.5 m) building rooftop area dataset in China derived with Super-resolution Segmentation from Sentinel-2 imagery
<p>Large-scale and up-to-date maps of building rooftop area (BRA) are crucial for addressing policy decisions and sustainable development. In addition, as a fine-grained indicator of human activities, BRA could contribute to urban planning and energy modeling to provide benefits to human well-being. However, existing large-scale BRA datasets, such as those from Microsoft and Google, do not include China, hence there are no full-coverage maps of BRA in China. To this end, we produce the multi-annual China building rooftop area dataset (CBRA) with 2.5 m resolution from 2016-2021 Sentinel-2 images. The CBRA is the first full-coverage and multi-annual BRA data in China. The CBRA achieves good performance with the F1 score of 62.55% (+10.61% compared with the previous BRA data in China) based on 250,000 testing samples in urban areas, and the recall of 78.94% based on 30,000 testing samples in rural areas. </p> <p>The CBRA is organized as GeoTIFF (.tif) raster file format with a single band and GCS_WGS_1984 coordinate system. The pixel values are 0 and 255, with 0 representing the background and 255 representing the building rooftop area. Furthermore, to facilitate the use of the data, the CBRA is split into 215 tiles of spatial grid, named “CBRA_year_E/W**N/S**.tif”, where “year” is the sampling year, the “E/W**N/S**” is the latitude and longitude coordinates found in the upper left corner of the tile data.</p> <p> </p> <p>Version 2.0: In version 1.0, there were empty raster images (because they didn't contain buildings). In version 2.0, these raster images were removed.</p>
Multi-colour super-resolution images of untreated and rifampicin-treated Xenorhabdus doucetiae (LB, exponential phase)
<p> This dataset and CARE model is part of the publication "<strong>Transertion and cell geometry organize the </strong><i><strong>Escherichia coli</strong></i><strong> nucleoid during rapid growth</strong>".</p><p>It contains all SMLM images that were used for the publication, as well as the single-cell regions of interest for analyses.</p><p>Xenorhabdus doucetiae chromosomally expressing MreBsw-sfGFP were grown to exponential phase in LB Lennox and antibiotics were added for 0-30 min. Cultures were then chemically fixed, permeabilised and imaged for the nucleoid (JF646-Hoechst) and membranes (Nile Red) using PAINT.</p><p>More information can be found in the publication.</p>
Comparing Lifeact and Phalloidin for super-resolution imaging of actin in fixed cells
<p>Visualizing actin filaments in fixed cells is of great interest for a variety of topics in cell biology such as cell division, cell movement, and cell signaling. We investigated the possibility of replacing phalloidin, the standard reagent for super-resolution imaging of F-actin in fixed cells, with the actin binding peptide `lifeact'. We compared the labels for use in single molecule based super-resolution microscopy, where AlexaFluor 647 labeled phalloidin was used in a (d)STORM modality and Atto 655 labeled lifeact was used in a single molecule imaging, reversible binding modality. We found that imaging with lifeact had a comparable resolution in reconstructed images and provided several advantages over phalloidin including lower costs, the ability to image multiple regions of interest on a coverslip without degradation, simplified sequential super-resolution imaging, and more continuous labeling of thin filaments.</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>
Dataset for Super-Resolution Image Reconstruction based on Random-coupled Neural Network and EDSR
<p>This dataset folder contains the DIV2K public dataset, which is utilized for model training and comprises 900 high-quality, high-resolution images along with their corresponding low-resolution versions. Additionally, all pre-trained models used in the experiment and their associated test results are publicly available.</p> <p>The main directory is organized into two subfolders: one labeled "dataset," which houses the DIV2K dataset, and another named "Model_results," which contains the pre-trained models and their corresponding test outcomes. The Dataset folder includes the original DIV2K dataset (referred to as "DIV2K") as well as a channel-expanded dataset processed by the RCNN model (designated as "DIV2K-RCNN"). Within the Model_results folder, the Model_trained subfolder contains all pre-trained models employed during the experiment, while the Test_results subfolder holds the test results for each model.</p>
Fluorogenic DNA-PAINT for faster, low-background super-resolution imaging
<p>Data associated with publication.</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>
Remote Sensing Satellite Video Dataset for Super-resolution
<p>This is a satellite video super-resolution dataset generated from "Jilin-1" video satellite.</p> <p>Training set: 189 clips; Test set: 12 clips.</p> <p>More details can be found in our paper published in IEEE TGRS: https://ieeexplore.ieee.org/document/9530280</p> <p>If you find our work helpful, please cite our paper. Thank you very much!</p>
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.