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Ditrect STORM imaging and image reconstruction of the transcription initiation marker P-S5 and nuclear PI(3,4)P2 indirectly immunolabeled with AF647 (red) and AF555 (green) in DRB treated cells.

U-2 OS cells were grown in DMEM with 10% FBS at 37°C and 5% CO2. Cells were plated one day before staining in ~50% confluence on the high-precision 12 mm round coverslips treated with Hellmanex, sonicated, washed, dried and sterilized. Cells were treated for 2h with 100 µM DRB (Sigma D1916) added to the cell culture media. U2OS cells were washed twice with PBS (pH 7.4) and fixed for 30 min in 2% PFA in PBS, washed 3-times for 5 min with PBS, then permeabilized in 0.1% Triton X-100 in PBS for 20 min, washed 3-times for 5 min by PBS and blocked in filtered 5% BSA in PBS for 30 min. Cells were incubated for 45 min with rabbit polyclonal IgG anti-RNAPII CTD P-S5 (Abcam ab5131) 3 µg/mL and mouse monoclonal IgG2 anti-PI(3,4)P2 (Z-P034; Echelon Biosci. Inc., USA) 5 µg/mL  in 5% BSA in PBS, washed 3-times for 5 min in PBS and incubated for 30 min with donkey anti-mouse IgG AF555 (Invitrogen A31570) 10 µg/mL and goat anti-rabbit IgG AF647 (Invitrogen A21245) 10 µg/mL diluted in 5% BSA in PBS. Then the cells were washed 3-times for 5 min in PBS, post-fixed for 15 min in 2% PFA in PBS and washed 3-times for 5 min in PBS. All procedures were performed at RT and the cells were stored in PBS in the fridge overnight prior imaging. Coverslips with cells were mounted in the Chamlide chamber (Live Cell Instrument, Korea) and covered with imaging buffer (PBS pH 7.4, 50 mM MEA). Single-molecule localizations (SMLs) data were acquired by Zeiss Elyra PS.1 equipped with HR Diode 642-150 and HR DPSS 561-200 lasers, Alpha Plan-Apochromat 100x/1.46 oil DIC M27 Elyra objective and Andor EM CCD iXon DU 897 camera and Zeiss ZEN Black 2.1 SP3 software (Zeiss). AF647 and AF555 photo-switching was achieved by HiLo illumination and TIRF HP FOV with 100% power of 642nm or 561nm laser, and the signal was acquired via MBS 642 + EF LP 655 and MBS 561 + EF BP 570-620 / LP 750 filters, respectively. Exposure time was 40 ms and EM gain was 300 for both channels. SMLs were calculated in 2D by Zeiss ZEN Black 2.1 SP3 software using x,y 2D Gauss fit with point spread function (PSF) half width 177.9 nm, peak mask size 9 pixels and peak intensity to noise 6 and accounted for overlap in 2D with max cluster size 10. SMLs were rendered in ZEN software with 10 nm/px resolution and 1x PSF expansion factor. The data were model-based drift corrected in ZEN. Two channels were aligned using tetraspec beads fiducial markers for affine calibration. Drift-corrected and aligned localization coordinates were exported as text files. Text files were converted into csv files and imported using self-written macro (Hoboth et al., 2021a) into the ImageJ2 (Rueden et al., 2017) plug-in ThunderSTORM, visualized by normalized Gaussian method (Ovesny et al., 2014).

Ditrect STORM imaging and image reconstruction of the transcription elongation marker P-S2 and nuclear PI(4,5)P2 indirectly immunolabeled with AF647 (red) and AF555 (green) in control cells.

U-2 OS cells were grown in DMEM with 10% FBS at 37°C and 5% CO2. Cells were plated one day before staining in ~50% confluence on the high-precision 12 mm round coverslips treated with Hellmanex, sonicated, washed, dried and sterilized. Cells were control treated with 1:000 DMSO in the culture media. U2OS cells were washed twice with PBS (pH 7.4) and fixed for 30 min in 2% PFA in PBS, washed 3-times for 5 min with PBS, then permeabilized in 0.1% Triton X-100 in PBS for 20 min, washed 3-times for 5 min by PBS and blocked in filtered 5% BSA in PBS for 30 min. Cells were incubated for 45 min with rabbit polyclonal IgG anti-RNAPII CTD P-S2 (Abcam ab5095) 3 µg/mL and mouse ascites IgM anti-PI(4,5)P2 2C11 (Z-A045; Echelon Biosci. Inc., USA) 5 µg/mL  in 5% BSA in PBS, washed 3-times for 5 min in PBS and incubated for 30 min with goat anti-mouse IgM (µ-chain) AF555 (Jackson ImmunoRes. A24126) 10 µg/mL; goat anti-rabbit IgG AF647 (Invitrogen A21245) 10 µg/mL diluted in 5% BSA in PBS. Then the cells were washed 3-times for 5 min in PBS, post-fixed for 15 min in 2% PFA in PBS and washed 3-times for 5 min in PBS. All procedures were performed at RT and the cells were stored in PBS in the fridge overnight prior imaging. Coverslips with cells were mounted in the Chamlide chamber (Live Cell Instrument, Korea) and covered with imaging buffer (PBS pH 7.4, 50 mM MEA). Single-molecule localizations (SMLs) data were acquired by Zeiss Elyra PS.1 equipped with HR Diode 642-150 and HR DPSS 561-200 lasers, Alpha Plan-Apochromat 100x/1.46 oil DIC M27 Elyra objective and Andor EM CCD iXon DU 897 camera and Zeiss ZEN Black 2.1 SP3 software (Zeiss). AF647 and AF555 photo-switching was achieved by HiLo illumination and TIRF HP FOV with 100% power of 642nm or 561nm laser, and the signal was acquired via MBS 642 + EF LP 655 and MBS 561 + EF BP 570-620 / LP 750 filters, respectively. Exposure time was 40 ms and EM gain was 300 for both channels. SMLs were calculated in 2D by Zeiss ZEN Black 2.1 SP3 software using x,y 2D Gauss fit with point spread function (PSF) half width 177.9 nm, peak mask size 9 pixels and peak intensity to noise 6 and accounted for overlap in 2D with max cluster size 10. SMLs were rendered in ZEN software with 10 nm/px resolution and 1x PSF expansion factor. The data were model-based drift corrected in ZEN. Two channels were aligned using tetraspec beads fiducial markers for affine calibration. Drift-corrected and aligned localization coordinates were exported as text files. Text files were converted into csv files and imported using self-written macro (Hoboth et al., 2021a) into the ImageJ2 (Rueden et al., 2017) plug-in ThunderSTORM, visualized by normalized Gaussian method (Ovesny et al., 2014).

Ditrect STORM imaging and image reconstruction of the transcription elongation marker P-S2 and nuclear PI(4,5)P2 indirectly immunolabeled with AF647 (red) and AF555 (green) in DRB treated cells.

U-2 OS cells were grown in DMEM with 10% FBS at 37°C and 5% CO2. Cells were plated one day before staining in ~50% confluence on the high-precision 12 mm round coverslips treated with Hellmanex, sonicated, washed, dried and sterilized. Cells were treated for 2h with 100 µM DRB (Sigma D1916) added to the cell culture media. U2OS cells were washed twice with PBS (pH 7.4) and fixed for 30 min in 2% PFA in PBS, washed 3-times for 5 min with PBS, then permeabilized in 0.1% Triton X-100 in PBS for 20 min, washed 3-times for 5 min by PBS and blocked in filtered 5% BSA in PBS for 30 min. Cells were incubated for 45 min with rabbit polyclonal IgG anti-RNAPII CTD P-S2 (Abcam ab5095) 3 µg/mL and mouse ascites IgM anti-PI(4,5)P2 2C11 (Z-A045; Echelon Biosci. Inc., USA) 5 µg/mL  in 5% BSA in PBS, washed 3-times for 5 min in PBS and incubated for 30 min with goat anti-mouse IgM (µ-chain) AF555 (Jackson ImmunoRes. A24126) 10 µg/mL; goat anti-rabbit IgG AF647 (Invitrogen A21245) 10 µg/mL diluted in 5% BSA in PBS. Then the cells were washed 3-times for 5 min in PBS, post-fixed for 15 min in 2% PFA in PBS and washed 3-times for 5 min in PBS. All procedures were performed at RT and the cells were stored in PBS in the fridge overnight prior imaging. Coverslips with cells were mounted in the Chamlide chamber (Live Cell Instrument, Korea) and covered with imaging buffer (PBS pH 7.4, 50 mM MEA). Single-molecule localizations (SMLs) data were acquired by Zeiss Elyra PS.1 equipped with HR Diode 642-150 and HR DPSS 561-200 lasers, Alpha Plan-Apochromat 100x/1.46 oil DIC M27 Elyra objective and Andor EM CCD iXon DU 897 camera and Zeiss ZEN Black 2.1 SP3 software (Zeiss). AF647 and AF555 photo-switching was achieved by HiLo illumination and TIRF HP FOV with 100% power of 642nm or 561nm laser, and the signal was acquired via MBS 642 + EF LP 655 and MBS 561 + EF BP 570-620 / LP 750 filters, respectively. Exposure time was 40 ms and EM gain was 300 for both channels. SMLs were calculated in 2D by Zeiss ZEN Black 2.1 SP3 software using x,y 2D Gauss fit with point spread function (PSF) half width 177.9 nm, peak mask size 9 pixels and peak intensity to noise 6 and accounted for overlap in 2D with max cluster size 10. SMLs were rendered in ZEN software with 10 nm/px resolution and 1x PSF expansion factor. The data were model-based drift corrected in ZEN. Two channels were aligned using tetraspec beads fiducial markers for affine calibration. Drift-corrected and aligned localization coordinates were exported as text files. Text files were converted into csv files and imported using self-written macro (Hoboth et al., 2021a) into the ImageJ2 (Rueden et al., 2017) plug-in ThunderSTORM, visualized by normalized Gaussian method (Ovesny et al., 2014).

Ditrect STORM imaging and image reconstruction of the transcription initiation marker P-S5 and nuclear PI(3,4)P2 indirectly immunolabeled with AF647 (red) and AF555 (green) in control cells.

U-2 OS cells were grown in DMEM with 10% FBS at 37°C and 5% CO2. Cells were plated one day before staining in ~50% confluence on the high-precision 12 mm round coverslips treated with Hellmanex, sonicated, washed, dried and sterilized. Cells were control treated with 1:000 DMSO in the culture media. U2OS cells were washed twice with PBS (pH 7.4) and fixed for 30 min in 2% PFA in PBS, washed 3-times for 5 min with PBS, then permeabilized in 0.1% Triton X-100 in PBS for 20 min, washed 3-times for 5 min by PBS and blocked in filtered 5% BSA in PBS for 30 min. Cells were incubated for 45 min with rabbit polyclonal IgG anti-RNAPII CTD P-S5 (Abcam ab5131) 3 µg/mL and mouse monoclonal IgG2 anti-PI(3,4)P2 (Z-P034; Echelon Biosci. Inc., USA) 5 µg/mL  in 5% BSA in PBS, washed 3-times for 5 min in PBS and incubated for 30 min with donkey anti-mouse IgG AF555 (Invitrogen A31570) 10 µg/mL and goat anti-rabbit IgG AF647 (Invitrogen A21245) 10 µg/mL diluted in 5% BSA in PBS. Then the cells were washed 3-times for 5 min in PBS, post-fixed for 15 min in 2% PFA in PBS and washed 3-times for 5 min in PBS. All procedures were performed at RT and the cells were stored in PBS in the fridge overnight prior imaging. Coverslips with cells were mounted in the Chamlide chamber (Live Cell Instrument, Korea) and covered with imaging buffer (PBS pH 7.4, 50 mM MEA). Single-molecule localizations (SMLs) data were acquired by Zeiss Elyra PS.1 equipped with HR Diode 642-150 and HR DPSS 561-200 lasers, Alpha Plan-Apochromat 100x/1.46 oil DIC M27 Elyra objective and Andor EM CCD iXon DU 897 camera and Zeiss ZEN Black 2.1 SP3 software (Zeiss). AF647 and AF555 photo-switching was achieved by HiLo illumination and TIRF HP FOV with 100% power of 642nm or 561nm laser, and the signal was acquired via MBS 642 + EF LP 655 and MBS 561 + EF BP 570-620 / LP 750 filters, respectively. Exposure time was 40 ms and EM gain was 300 for both channels. SMLs were calculated in 2D by Zeiss ZEN Black 2.1 SP3 software using x,y 2D Gauss fit with point spread function (PSF) half width 177.9 nm, peak mask size 9 pixels and peak intensity to noise 6 and accounted for overlap in 2D with max cluster size 10. SMLs were rendered in ZEN software with 10 nm/px resolution and 1x PSF expansion factor. The data were model-based drift corrected in ZEN. Two channels were aligned using tetraspec beads fiducial markers for affine calibration. Drift-corrected and aligned localization coordinates were exported as text files. Text files were converted into csv files and imported using self-written macro (Hoboth et al., 2021a) into the ImageJ2 (Rueden et al., 2017) plug-in ThunderSTORM, visualized by normalized Gaussian method (Ovesny et al., 2014).

opencc-by-4.0Oct 2022View details →

3D reconstruction of an NIH/3T3 mouse fibroblast cell imaged by the SXT-100.

This dataset (*.mrc file) is a reconstructed 3D volume obtained by soft X-ray tomography on NIH/3T3 cells. The cells were grown on a 200 mesh 3.05 mm EM finder grid with a Quantifoil Holey Carbon support. The sample was vitrified by plunge-freezing in liquid ethane. The tomogram was collected with a pixel size of 28.85 nm over the tilt range from -53 to 53.5 degrees with 1.5 deg step size and 116 s exposure per tilt.

opencc-by-4.0Jun 2024View details →

A Neural Reconstruction Method for Non-Line-of-Sight Imaging

Dataset for results in work "A Neural Reconstruction Method for Non-Line-of-Sight Imaging"  Author: Salvador Rodríguez-Sanz Supervisors: Diego Gutiérrez, Albert Redó-Sánchez

opencc-by-4.0May 2024View details →

X-ray image reconstruction for continuous acquisitions with a generalized motion model: Data

This dataset contains two experimentally measured X-ray scans, one reference scan and one scan in which the object translates while rotating. The reference scan consists of 3600 projection images, with one flat field and dark field image. The roto-translational scan consists of 360 projections, also with a flat field and dark field image. The acquisition files containing all relevant specifications of the scanner and the acquisition settings are also supplied. The code for reconstruction is available at <a href="https://github.com/BenHuyge/RACE">GitHub.</a>

opencc-by-4.0Apr 2018View details →

A Genetic Algorithm Approach to Regenerate Image from a Reduce Scaled Image Using Bit Data Count-Figure 3. Data compression and image reconstruction (55:148 Digital Image Processing, 2017)

There are several techniques which are normally divided into two categories lossy and lossless image compressions. In lossy compression, after recovery there are negligible difference present where lossless gives accurate image. Huffman encoding is very well known, which can provide optimal compression and decompression without error (55:148 Digital Image Processing, 2017). The basic idea of Huffman coding is to represent data by number of variable size, where more frequent info being represented by shorter number (55:148 Digital Image Processing, 2017). Currently the Lempel-Ziv (or Lempel-Ziv-Welch, LZW) algorithm for dictionary-based coding has got attention as a better compression algorithm (55:148 Digital Image Processing, 2017).

opencc-by-4.0Feb 2019View details →

Data support for: "CCPi-Regularisation Toolkit for computed tomographic image reconstruction with proximal splitting algorithms"

Provided tomographic projection data supports the publication in SoftwareX journal "CCPi-Regularisation Toolkit for computed tomographic image reconstruction with proximal splitting algorithms" published in 2019. <ul> <li>TomoSim_data1550671417.h5 - is a simulated 3D tomographic projection data with noise and artifacts. The simulation is implemented using <a href="https://github.com/dkazanc/TomoPhantom">TomoPhantom</a> software.</li> <li>DendrData_3D.h5 - is a real dataset obtained at I13 branchline of Diamond Light Source. It features a selected time frame out of dynamically collected tomographic data. Data shows a <a href="https://www.sciencedirect.com/science/article/pii/S1359645418302994?via%3Dihub">dendritic grain growth in Mg alloys</a>.</li> </ul> The scripts to replicate the results shown in the paper are available at the Github page of the project: <a href="https://github.com/vais-ral/CCPi-Regularisation-Toolkit">CCPi-Regularisation-Toolkit</a>

opencc-by-4.0Aug 2017View details →

Text-fig. 4. Distribution of arsinoitheres in Africa. Reconstruction of Arsinoitherium is adapted from Pomerol (1973) (the body in the image is probably too similar to that of an elephant, but the reconstruction gives an idea of the dimensions and possible body plan of Arsinoitherium). in Arsinoitherium (Embrithopoda) And Other Large Mammals And Plants From The Oligocene Of Tunisia

Text-fig. 4. Distribution of arsinoitheres in Africa. Reconstruction of Arsinoitherium is adapted from Pomerol (1973) (the body in the image is probably too similar to that of an elephant, but the reconstruction gives an idea of the dimensions and possible body plan of Arsinoitherium).

opencc-by-4.0Mar 2023View details →

Data Supplement: GIRFReco.jl: An Open-Source Pipeline for Spiral Magnetic Resonance Image (MRI) Reconstruction in Julia

Dataset for GIRFReco.jl Paper Please download this and extract to an appropriate location prior to running the demonstration code in GIRFReco.jl. The extracted folder will serve as the root directory in the demo code.

opencc-by-4.0Aug 2023View details →

Test Dataset of AI Altered Images for Photogrammetric Reconstruction

As a part of a test of the capability of using AI altered images in photogrammetry, I generated these image sets using the Prisma AI app. All of these datasets will result in a model in Agisoft Metashape. Prisma AI acts as a filter, changing an input image to the style of another artwork.  Original images were collected using a Samsung S22 camera on the 1x zoom rear lens. The subject is a rock decoration in the Utrecht University Botanical Garden. Results can be seen on Sketchfab: https://sketchfab.com/gspeed0689/collections/prisma-photogrammetry-2e334a36c6e942798370d594de5e6f0c  Included datasets: <ul> <li>true-color.zip - The original images from the Samsung S22</li> <li>aviator-lowres - Processed images at normal (low resolution) quality with the filter labelled Aviator</li> <li>bubblegum-lowres - Processed images at normal (low resolution) quality with the filter labelled Bubblegum</li> <li>gothic-lowres - Processed images at normal (low resolution) quality with the filter labelled Gothic</li> <li>gothic-hd.zip - Processed images at HD (paid subscription required) quality with the filter labelled Gothic</li> <li>true-color.fbx - 3D model created with Agisoft Metashape using the parameters listed below and the images from true-color.zip</li> <li>aviator-lowres.fbx - 3D model created with Agisoft Metashape using the parameters listed below and the images from aviator-lowres.zip</li> <li>bubblegum-lowres.fbx - 3D model created with Agisoft Metashape using the parameters listed below and the images from bubblegum-lowres.zip</li> <li>gothic-lowres.fbx - 3D model created with Agisoft Metashape using the parameters listed below and the images from gothic-lowres.zip</li> <li>gothic-hd.fbx - 3D model created with Agisoft Metashape using the parameters listed below and the images from gothic-hd.zip</li> </ul> Processing parameters in Agisoft Metashape v 1.7: <ul> <li>Alignment defaults</li> <li>Dense cloud creation at "High" quality</li> <li>Mesh defaults</li> </ul>

opencc-by-4.0Sep 2020View details →

Dataset: Object condensation: one-stage grid-free multi-object reconstruction in physics detectors, graph, and image data

This dataset is the dataset used to train and test the object condensation particle flow approach described in <a href="https://arxiv.org/abs/2002.03605">arxiv:2002.03605</a>. The data can be read with DeepJetCore 3.1 (https://github.com/DL4Jets/DeepJetCore) The entries in the truth array are of dimension (batch, 200, N_truth). The truth inputs are: isElectron, isGamma, isPositron, true_energy, true_x, true_y The entries in the feature array are of dimension (batch, 200, N_features), with the features being: rechit_energy, rechit_x, rechit_y, rechit_z, rechit_layer, rechit_detid The "train.zip" file contains the training sample The "test.zip" file the test sample The main test sample is identical to the training sample in composition, but statistically independent. Other samples can be found in subfolders: test/flatNpart: sample with flat distribution of additional particles in the event w.r.t. each individual particle Test/hiNPart: sample with up to 15 particles per event

opencc-by-4.0Apr 2021View details →

SiEUGreen - Data for 'Deep Learning in Hyperspectral Image Reconstruction from Single RGB images—A Case Study on Tomato Quality Parameters'

Dataset used in the scientific publication <a href="https://zenodo.org/record/4671852">'Deep Learning in Hyperspectral Image Reconstruction from Single RGB images—A Case Study on Tomato Quality Parameters'</a>. The data includes chemical contents of tomatoes that was measured, images and scripts used in the paper. The scripts here aim to predict tomato quality parameters, sugar content, acidity, sugar acid ratio and lycopene, of automatically segmented tomato through hyperspectral image reconstruction from single RGB image. The same data can also be found at the <a href="https://github.com/ZJiangsan/TomatoQualityPredictionOnAutomaticallySegmentedTomato">Github repository</a>. The data collection and scientific paper was produced by SiEUGreen partners at Norwegian Institute of Bioeconomy Research (NIBIO).

opencc-by-4.0Aug 2016View details →

Structure Assisted Compressed Sensing Reconstruction of Undersampled AFM Images Dataset 2

This deposition contains the results from a simulation of reconstructions of undersampled atomic force microscopy (AFM) images. The reconstructions were obtained using weighted iterative thresholding compressed sensing algorithms. The deposition consists of: <ol> <li>An  HDF5 database containing the results from simulations of reconstructions of undersampled atomic force microscopy images (weighted_it_reconstructions.hdf5).</li> <li>The Python script which was used to create the database (weighted_it_reconstructions.py).</li> <li>MD5 and SHA256 checksums of the database and Python script files (weighted_it_reconstructions.MD5SUMS / weighted_it_reconstructions.SHA256SUMS).</li> </ol> The HDF5 database is licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/) . Since the CC BY 4.0 license is not well suited for source code, the Python script is licensed under the BSD 2-Clause license (http://opensource.org/licenses/BSD-2-Clause) . The files are provided as-is with no warranty as detailed in the above mentioned licenses. The database is split into ten parts: <ol> <li>weighted_it_reconstructions.hdf5.tar.xz.part-00</li> <li>weighted_it_reconstructions.hdf5.tar.xz.part-01</li> <li>weighted_it_reconstructions.hdf5.tar.xz.part-02</li> <li>weighted_it_reconstructions.hdf5.tar.xz.part-03</li> <li>weighted_it_reconstructions.hdf5.tar.xz.part-04</li> <li>weighted_it_reconstructions.hdf5.tar.xz.part-05</li> <li>weighted_it_reconstructions.hdf5.tar.xz.part-06</li> <li>weighted_it_reconstructions.hdf5.tar.xz.part-07</li> <li>weighted_it_reconstructions.hdf5.tar.xz.part-08</li> <li>weighted_it_reconstructions.hdf5.tar.xz.part-09</li> </ol> These tem parts must be concatenated before the database can be extracted from the tar.xz archive. On Unix-like systems this may be done using: $ cat weighted_it_reconstructions.hdf5.tar.xz.part-* > weighted_it_reconstructions.hdf5.tar.xz after which the archive may be extracted, e.g., using: $ tar xfJ weighted_it_reconstructions.hdf5.tar.xz WARNING: The extracted HDF5 database has a size of 114 GiB. The simulation results in the database are based on "Atomic Force Microscopy Images of Cell Specimens" and "Atomic Force Microscopy Images of Various Specimens" by Christian Rankl licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). The original images are available at http://dx.doi.org/10.5281/zenodo.17573 and http://dx.doi.org/10.5281/zenodo.60434. The original images are provided as-is without warranty of any kind. Both the original images as well as adapted images are part of the dataset.

opencc-by-4.0Jun 2015View details →

Structure Assisted Compressed Sensing Reconstruction of Undersampled AFM Images Dataset

This deposition contains the results from a simulation of reconstructions of undersampled atomic force microscopy (AFM) images. The reconstructions were obtained using weighted iterative thresholding compressed sensing algorithms. The deposition consists of: <ol> <li>An  HDF5 database containing the results from simulations of reconstructions of undersampled atomic force microscopy images (weighted_it_reconstructions.hdf5).</li> <li>The Python script which was used to create the database (weighted_it_reconstructions.py).</li> <li>MD5 and SHA256 checksums of the database and Python script files (weighted_it_reconstructions.MD5SUMS / weighted_it_reconstructions.SHA256SUMS).</li> </ol> The HDF5 database is licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/) . Since the CC BY 4.0 license is not well suited for source code, the Python script is licensed under the BSD 2-Clause license (http://opensource.org/licenses/BSD-2-Clause) . The files are provided as-is with no warranty as detailed in the above mentioned licenses. The database is split into four parts: <ol> <li>weighted_it_reconstructions.hdf5.tar.xz.part-00</li> <li>weighted_it_reconstructions.hdf5.tar.xz.part-01</li> <li>weighted_it_reconstructions.hdf5.tar.xz.part-02</li> <li>weighted_it_reconstructions.hdf5.tar.xz.part-03</li> </ol> These four parts must be concatenated before the database can be extracted from the tar.xz archive. On Unix-like systems this may be done using: cat weighted_it_reconstructions.hdf5.tar.xz.part-* > weighted_it_reconstructions.hdf5.tar.xz after which the archive may be extracted, e.g., using: tar xfJ weighted_it_reconstructions.hdf5.tar.xz WARNING: The extracted HDF5 database has a size of 70 GiB. The simulation results in the database are based on "Atomic Force Microscopy Images of Cell Specimens" by Christian Rankl licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). The original images are available at http://dx.doi.org/10.5281/zenodo.17573. The original images are provided as-is without warranty of any kind. Both the original images as well as adapted images are part of the dataset.

opencc-by-4.0Nov 2023View details →

Thermal conductivity analysis of polymer-derived nano-composite via image-base structure reconstruction, computational homogenization and machine learning

This dataset includes supplementary data and utilities for validating simulation results and training machine learning models as outlined in the publication titled "Thermal Conductivity Analysis of Polymer-Derived Nanocomposite via Image-Based Structure Reconstruction, Computational Homogenization, and Machine Learning" (<a href="https://doi.org/10.1002/adem.202302021">Fathidoost, 2024</a>). This dataset containes the microstructure images (identified by particle diameters size $D_1$ and $D_2$ volume fraction $V_\mathrm{f}$ and aspect ratio $A_\mathrm{r}$) (see Table 1) and their corresponding homogenized thermal conductivity. these images resemble the microstructure of the monolithic $\mathrm{(Hf,Ta)C/SiC}$ ceramic following FAST sintering, the material system of this work (<a href="https://doi.org/10.1002/adem.202302021">Fathidoost, 2024</a>). White and black colors within the images represent distinct regions of the material system,  respectively referring to former powder particles (FPPs) and sinter necks (SNs), which is explained in this work. Table 1. Parameterized descriptors extracted from the mesoscale SEM image analysis <table> <tbody> <tr> <td>Param.</td> <td>Mean [unit]</td> <td>Std.</td> </tr> <tr> <td>$D_{1}$</td> <td>40, 50, 60 [μm]</td> <td>20%</td> </tr> <tr> <td>$D_{2}$</td> <td>20, 25, 26, 30, 33, 40 [μm]</td> <td>30%</td> </tr> <tr> <td>$V_\mathrm{f}$</td> <td>1.5, 2.0</td> <td>-</td> </tr> <tr> <td>$A_\mathrm{r}$</td> <td>35, 40, 45, 55, 60 [%]</td> <td>-</td> </tr> </tbody> </table> This dataset contains: <ul> <li>dataset.csv: containing a summary of data including the names of microstructure images, their corresponding geometric details, as well as the first and third principal components of two-point statistics for all images, along with the effective thermal conductivity of the corresponding microstructures. Further details can be found in the associated publication.</li> <li>microstructures_images.zip: containing binary cross-section images of the RVEs from synthetic microstructures。</li> <li>results.zip: contains all the simulation results based on digitized diffuse-interface microstructures, which can be opened by the post-processing software, such as ParaView.</li> </ul>

opencc-by-4.0Aug 2024View details →

Dataset for Super-Resolution Image Reconstruction based on Random-coupled Neural Network and EDSR

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. 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.

opencc-by-4.0Jan 2022View details →

Human ISMRMRD datasets for "MaxGIRF: Image Reconstruction Incorporating Concomitant Field and Gradient Impulse Response Function Effects"

Human axial and sagittal ISMRMRD datasets for "MaxGIRF: Image Reconstruction Incorporating Concomitant Field and Gradient Impulse Response Function Effects". Code to process and reconstruct the data is available here: <a href="https://github.com/usc-mrel/lowfield_maxgirf">https://github.com/usc-mrel/lowfield_maxgirf</a>