Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
82
datasets available to search
ShareScore release 0.9.0
Dataset results
82 results for “Denoising”
Supplementary material - Fingerprint multiplex CARS at high speed based on supercontinuum generation in bulk media and deep learning spectral denoising
<p>Supplementary material</p>
CARE 2D denoising and upsampling example data
<p>Example data for 2D CARE denoising/upsampling consisting of pairs of low and high signal-to-noise ratio (SNR) 2D images of cells. The high SNR images are acquistions of Human U2OS cells taken from the <a href="https://bbbc.broadinstitute.org/BBBC006/">Broad Bioimage Benchmark Collection</a> and the low SNR images were created by synthetically adding strong read-out and shot-noise and applying pixel binning of 2x2, thus mimicking acquisitions at a very low light level.</p>
Denoised Earthquake Data of Cook Inlet-DAS and Phase Picks
Open the record for dataset details and reuse information.
A Deep Learning Approach for TEM Data Denoising, Inversion and Uncertainty Analysis with Monte Carlo Dropout
<p>This dataset includes the code and data for training the inversion network used in the study. The provided files cover data loading, preprocessing, and network training for transient electromagnetic (TEM) data inversion. For details on the included files and instructions on usage, please refer to the README.txt file.</p>
Noise to void model and results of denoising + segmentation using VollSeg
<p>Noise to void model and results of denoising + segmentation using VollSeg</p>
Dataset and trained models for video denoising in fluorescence guided surgery
Open the record for dataset details and reuse information.
Stacked Dense Denoise-Segmentation FBP synthetic reconstruction from 3601 projection without ring artefacts
<p>The FBP recontruction without ring artefacts from the 3601 projection of the synthetic dataset used in the Stacked Dense Denoise-Segmentation Network. For the reconstruction the TomoPhantom software was used (<a href="https://doi.org/10.5281/zenodo.2546856">https://doi.org/10.5281/zenodo.2546856</a>). We acknowledge Diamond Light Source for the time on I13-2 under proposal mt9396.</p> <div> </div>
Denoising Autoencoders for Phenotype Stratification (DAPS) Sample Simulated Patient Data
<p>Generated with https://github.com/greenelab/DAPS/</p>
data and code for Machine Learning-based Denoising of Surface Solar Irradiance simulated with Monte Carlo Ray Tracing
<p>Data and radiative transfer code used for the manuscript "Machine Learning-based Denoising of Surface Solar Irradiance simulated with Monte Carlo Ray Tracing". See Readme for details</p>
DSMBind: SE(3) denoising score matching for unsupervised binding energy prediction and nanobody design
<p>This record provides the training and evaluation dataset for DSMBind</p> <p>Paper link: https://www.biorxiv.org/content/10.1101/2023.12.10.570461v1.abstract</p> <p>Github repo: https://github.com/wengong-jin/DSMBind</p>
Source data for manuscript(De novo protein design with a denoising diffusion network independent of pre-trained structure prediction models)
<p>This respository contains the source data for figure and supplementary figure in manuscript(SCUBA-D).</p>
Source data for manuscript(De novo protein design with a denoising diffusion network independent of pre-trained structure prediction models)
<p>This respository contains the source data for figure and supplementary figure in manuscript(SCUBA-D).</p>
Noisy nuclei dataset for testing deep learning-based denoising tools
<p><strong>Description</strong>: Contains a denoising training and test dataset for deep learning applications. </p> <p><strong>Training dataset</strong>: 20 paired matching noisy and high signal to noise images</p> <p><strong>Test dataset</strong>: 5 paired matching noisy and high signal to noise images</p> <p><strong>Microscopy data type</strong>: Fluorescence microscopy (SiR-DNA) images.</p> <p><strong>Microscope</strong>: Spinning disk confocal microscope with a 20x 0.8 NA objective</p> <p><strong>Cell type</strong>: DCIS.COM Lifeact-RFP cells</p> <p><strong>File format</strong>: .tif (16-bit for fluorescence)</p> <p><strong>Image size</strong>: 1024x1024 (Pixel size: 634 nm)</p>
Functional connectomes denoised with various strategies
<p>Dataset denoised with the following strategies:</p> <p>https://github.com/SIMEXP/fmriprep-denoise-benchmark/code/benchmark_strategies.json</p> <p>Atlas used:</p> <p>Schaefer 7 network 400 ROIs<br> <br> Preprocessing:</p> <p>fMRIPrep LTS 20.2.1</p> <pre><code class="language-bash">singularity run --cleanenv \ -B $SLURM_TMPDIR:/DATA \ -B /PATH/TO/templateflow:/templateflow \ -B /etc/pki:/etc/pki/ \ -B /PATH/TO/OUTPUT:/OUTPUT \ /PATH/TO/fmriprep-20.2.1lts.sif \ -w /DATA/fmriprep_work \ --participant-label pixar001 \ --bids-filter-file /OUTPUT/bids_filters.json \ --cifti-output 91k \ --use-aroma \ --output-spaces MNI152NLin2009cAsym MNI152NLin6Asym fsaverage5 \ --output-layout bids \ --notrack \ --skip_bids_validation \ --write-graph \ --omp-nthreads 8 \ --nprocs 16 \ --mem_mb 65536 \ --resource-monitor \ /DATA/ds000288 /DATA/ds000288/derivatives/fmriprep participant </code></pre> <p> </p>
Synthetic dataset and prediction files for the paper "Denoising of Geodetic Time Series Using Spatiotemporal Graph Neural Networks: Application to Slow Slip Event Extraction", by Costantino et al. (2024)
<p>Synthetic database used for training and evaluation of SSEdenoiser</p>
EPS_parameter-free_denoising
<p>This is a open-sources code and data for An Adaptive Parameter-free Seismic Denoising Method Combining General Cross-Validation Thresholding and Pixel Connectivity in Synchrosqueezed Domain</p>
Denoised tomograms and filament traces for F-actin under myosin directed forces
<p>This dataset contains the neural networks used to denoise cryo-electron tomograms of F-actin filaments under myosin directed forces as well as the denoised tomograms and filament traces.</p>
Stacked Dense Denoise-Segmentation Volumetric Annotations
<p>The volumetric annotations of https://doi.org/10.5281/zenodo.2645838 and https://doi.org/10.5281/zenodo.2645965 used in the Stacked Dense Denoise-Segmentation Network. For the annotation the SuRVoS workbench was used (https://doi.org/10.5281/10.5281/zenodo.247547). We acknowledge Diamond Light Source for the time on I13-2 under proposal mt9396.</p>
Stacked Dense Denoise-Segmentation FBP reconstruction from 3601 projection
<p>The FBP recontruction from 3601 projection of the dataset used in the Stacked Dense Denoise-Segmentation Network. For the reconstruction the Savu Python package was used (https://doi.org/10.5281/zenodo.32840). We acknowledge Diamond Light Source for the time on I13-2 under proposal mt9396.</p>
Stacked Dense Denoise-Segmentation CGLS reconstruction from 91 projection
<p>The CGLS recontruction from 91 projection of the dataset used in the Stacked Dense Denoise-Segmentation Network. For the reconstruction the Savu Python package was used (https://doi.org/10.5281/zenodo.32840). We acknowledge Diamond Light Source for the time on I13-2 under proposal mt9396.</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.