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7 results for “cycleGAN”
Using CycleGANs to Generate Realistic STEM Images for Machine Learning
<p>This data set contains part of the images that were used in the manuscript "Using CycleGANs to Generate Realistic STEM<br> Images for Machine Learning", including experimental, simulated, and CycleGAN-processed monolayer WSe<sub>2</sub> images. The acquisition and simulated parameters are publicly available in the manuscript (arXiv:2301.07743).</p> <p> </p>
Data for "Global Precipitation Correction Across a Range of Climates Using CycleGAN"
<p># Data for "Global Precipitation Correction Across a Range of Climates Using CycleGAN"</p> <p>This repository contains the data used in the paper "Global Precipitation Correction Across a Range of Climates Using CycleGAN" by J. McGibbon et al. (2023, *in review*).</p> <p>`train_val` contains the model outputs for the training and validation sets used in the paper. Model spinup data is not included. The C384 runs are stored as a single series along a concatenated time axes. All C384 data has been coarsened to C384 resolution.</p> <p>`ramping_data` contains the model outputs from the 4-year, 3-month ramping simulation, including spinup data.</p> <p>`predicted` is included for convenience, and contains the model outputs from the "best" model, as was used to create figures shown in the paper. This model output was created from the CycleGAN using the validation dataset (stored in `train_val`) as input, and years 2 and 3 of the ramping simulation in `ramping_data` (following the 3-month spinup period). Ramping predictions are stored as separate datasets for the C48 (backwards) prediction and the C384 prediction. Validation data is stored alongside its respective target data in a single netCDF file, labelled "processed-agg". These filenames are intentionally left unchanged so that they correspond with the names of files used in the `process_combined_aggregate.py` script in `projects/cyclegan` of the code DOI for this paper, which was used to create its figures.</p>
CycleGAN network for photoacoustic histology
<p>An unsupervised deep learning algorithm based on cycle-consistent generative adversarial networks (CycleGAN ) converted the UV-PAM images into H&E-like pseudocolor histologic images, allowing the pathologists to readily identify the cancerous features following existing pattern-recognition parameters. Unlike supervised deep learning methods such as generational adversarial networks (GAN), the unsupervised deep learning method based on CycleGAN does not require coupled pairs of stained and unstained images. It avoids the need for well-aligned UV-PAM and H&E-stained images for neural network training, which can be challenging to acquire due to artifacts caused by sample preparation-induced morphology changes.</p>
ZeroCostDL4Mic - CycleGAN example training and test dataset
<p><strong>Name</strong>: ZeroCostDL4Mic - CycleGAN example training and test dataset</p> <p>(see <a href="https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki">our Wiki</a> for details)</p> <p> </p> <p><strong>Data type</strong>: unpaired microscopy images (fluorescence) of microtubules (Spinning-disk and SRRF reconstructed images)</p> <p><strong>Microscopy data type</strong>: Fluorescence microscopy. </p> <p><strong>Microscope</strong>: Spinning disk confocal microscope with a 100x 1.4 NA objective. SRRF reconstructions were performed using the latest generation of SRRF and 200 frames.</p> <p><strong>Cell type</strong>: U2OS cells</p> <p><strong>File format</strong>: .png (8-bit)</p> <p><strong>Image size</strong>: 1024x1024 (Pixel size: 62.5 nm)</p> <p> </p> <p><strong>Author(s)</strong>: Guillaume Jacquemet<sup>1,2,3</sup></p> <p><strong>Contact email</strong>: guillaume.jacquemet@abo.fi</p> <p><strong>Affiliation(s)</strong> : </p> <p>1) Faculty of Science and Engineering, Cell Biology, Åbo Akademi University, 20520 Turku, Finland</p> <p>2) Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520 Turku</p> <p>3) ORCID: 0000-0002-9286-920X</p> <p> </p> <p><strong>Associated publications</strong>: Jacquemet et al 2020, Journal of Cell Science DOI: https://doi.org/10.1242/jcs.240713</p> <p> </p> <p><strong>Funding bodies</strong>: G.J. was supported by grants awarded by the Academy of Finland, the Sigrid Juselius Foundation and Åbo Akademi University Research Foundation (CoE CellMech) and by Drug Discovery and Diagnostics strategic funding to Åbo Akademi University.</p>
Ramping simulations from "Global Precipitation Correction Across a Range of Climates Using CycleGAN"
<p>Four-year ramping simulations from "Global Precipitation Correction Across a Range of Climates Using CycleGAN" depicting the real input C48 and C384 precipitation and the generated C384 (ML) and C48 (ML) precipitation based on these inputs for each 3-hourly sample.</p>
Hanyin(Chinese Seal) Dataset for CycleGAN Training
<p>Hanyin(Chinese Seal) Dataset for CycleGAN Training</p>
Database to train the Polarimetric CycleGAN
<p>Database to train the polarimetric CycleGAN, article in process.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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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.