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.
217
datasets available to search
ShareScore release 0.9.0
Dataset results
217 results for “image contrast”
Baseline embeddings from the BBBC022 dataset used in "Semisupervised contrastive learning for bioactivity prediction using Cell Painting image data"
<p>3 Baseline embeddings aclculated from the BBBC022 dataset. A self-supervised contrastive learning-based model, DINO and CellProfiler were used to calculate the embeddings.</p>
Raw data for: Use the 4S (Signal-Safe Speckle Subtraction): Explainable Machine Learning reveals the Giant Exoplanet AF Lep b in High-Contrast Imaging Data from 2011
<p>This collection of data contains all raw data needed to reproduce the results in the paper:</p> <p>Use the 4S (Signal-Safe Speckle Subtraction): Explainable Machine Learning reveals the Giant Exoplanet AF Lep b in High-Contrast Imaging Data from 2011</p> <p>It can also be used as a demonstration dataset for our Python package fours.</p> <p>More details can be found in the online documentation of our python package:<br><a href="https://fours.readthedocs.io/en/latest/">https://fours.readthedocs.io/en/latest/</a></p>
Intermediate results for: Use the 4S (Signal-Safe Speckle Subtraction): Explainable Machine Learning reveals the Giant Exoplanet AF Lep b in High-Contrast Imaging Data from 2011
<p>This collection contains all intermediate results needed to reproduce the results in the paper:</p> <p>Use the 4S (Signal-Safe Speckle Subtraction): Explainable Machine Learning reveals the Giant Exoplanet AF Lep b in High-Contrast Imaging Data from 2011</p> <p>You can use these intermediate results to create all plots in our paper without the need to run all experiments on a large cluster.</p> <p>More details can be found in the online documentation of our python package:<br><a href="https://fours.readthedocs.io/en/latest/">https://fours.readthedocs.io/en/latest/</a></p>
Automatic Choroid Vascularity Index Calculation in Optical Coherence Tomography Images with Low Contrast Sclerocho-roidal Junction Using Deep Learning
<p>This project aims to calculate Choroid Vascularity Index (CVI) in optical coherenece tomography (OCT) images, using loss modified U-Net. The method is detailed in "Automatic Choroid Vascularity Index Calculation in Optical Coherence Tomography Images low contrast sclerochoroidal junction Using Deep Learning". The dataset consists of Enhanced-depth imaging optical coherence tomography images from two patient groups.</p> <p>• First dataset is including Raster OCT B-scans from patients with diabetic retinopathy.</p> <p>• Second dataset is including EDI-HD OCT B-scans from patients with pachychoroid spectrum.</p>
Data and Results for: Comparing Apples with Apples: Robust Detection Limits for Exoplanet High-Contrast Imaging in the Presence of non-Gaussian Noise
<p>This collection of data and results contains everything needed to reproduce the results in the paper:</p> <p>Comparing Apples with Apples: Robust Detection Limits for Exoplanet \\ High-Contrast Imaging in the Presence of non-Gaussian Noise</p> <p>The <a href="/api/files/53bfc05e-f632-443a-9c07-590b9bf860e1/apples_root_dir.zip?versionId=d41f6d1e-ef44-497c-ae2a-a5b291a8c9b9">apples_root_dir.zip</a> is further needed to run the examples of the python package Applefy.</p>
Spectral imaging enables contrast agent-free real-time ischemia monitoring in laparoscopic surgery
<p>Sample video showing the 'ischemia index' computed with a deep-learning model trained on multispectral images recorded during partial nephrectomy before a clamp is applied to the renal artery (perfused) and after clamping the artery (ischemic).</p>
Radiomics and Artificial Intelligence Analysis with Textural Metrics Extracted by Contrast-Enhanced Mammography and Dynamic Contrast Magnetic Resonance Imaging to detect breast malignant Lesions
<p>We uploade the dataset of the manuscript "Radiomics and Artificial Intelligence Analysis with Textural Metrics Extracted by Contrast-Enhanced Mammography and Dynamic Contrast Magnetic Resonance Imaging to detect breast malignant Lesions" by Current Oncology.</p> <p> </p>
Phase contrast images of Hierodula membranacea spermatocytes in metaphase I
<p>These are phase contrast images of <em>Hierodula membranacea</em> primary spermatocytes. For each image, 608 px=10 µm.</p>
Combining reference-star and angular differential imaging for high-contrast imaging of extended sources - Appendix
<p>This appendix presents the disk estimations obtained with RDI, ADI, and ARDI, using IPCA, for all 60 test data sets considered in Juillard et. al. (2024). Page-sized figures shows IPCA disk estimations obtained leveraging RDI, ADI and ARDI strategies for cube 1 to 4 and disks A to E.</p> <p>Details on the data sets where disks were injected can be found in Juillard et. al. (2023) and Juillard et. al. (2024), and can be found on the Zeneodo folder : <a href="../records/11442267">https://zenodo.org/records/11442267</a></p> <p>Each page-sized figure presents the results for a specific synthetic disk in a given data set. The injected disks (A→E) are shown in the left-most column of each figure. Each figure contains three pairs of rows, each corresponding to a different injected contrast level: $10^{-3}$ (top), $10^{-4}$ (middle), and $10^{-5}$ (bottom). The top row of each pair represents the estimation, while the bottom row represents the residuals. Each column displays the following in order: ground truth (left), RDI (middle left), ADI (middle right), and ARDI (right). The best method, as determined by each metric, is indicated in the bottom-left corner of each pair of rows. The color bar bounds in the residual plots is set to $\pm$ maximum of intensity of the GT, centered at 0. For the disk image estimation plots, the color bar is adjusted for each data set to have a maximum value equal to the 99th percentile of the image. The minimal value is set to 0. Additionally, the optimal IPCA parameters (rank and number of iterations) are written at the top left of the corresponding images.</p>
Data pertaining to the published article "Detection of pathological contrast enhancement with synthetic brain imaging from quantitative multiparametric MRI" by Donatelli et al., 2024
<p>Data pertaining to the published article "Detection of pathological contrast enhancement with synthetic brain imaging from quantitative multiparametric MRI" by Donatelli et al., 2024. <a href="https://doi.org/10.1111/jon.13201">https://doi.org/10.1111/jon.13201</a></p>
Differential interference contrast (DIC) image of unstained living HepG2 human liver cancer cells
<p><strong>Introduction</strong></p> <p>This dataset is associated with our submission to Computers in Biology and Medicine, titled "Accurate Detection and Instance Segmentation of Unstained Living Adherent Cells in Differential Interference Contrast Images". The submission number for this manuscript is CIBM-D-23-09623R1.</p> <p><strong>Authors</strong>: Fei Pan, Yutong Wu, Kangning Cui, Shuxun Chen, Yanfang Li, Yaofang Liu, Adnan Shakoor, Han Zhao, Beijia Lu, Shaohua Zhi, Raymond Hon-Fu Chan, Dong Sun</p> <p><strong>Dataset Description</strong></p> <p>Our dataset comprises 520 differential interference contrast (DIC) images of 12,198 unstained HepG2 human liver cancer cells, each with a corresponding fluorescence image stained with calcein acetoxymethyl (AM), ensuring high-quality ground-truth annotations. Unique in addressing the multi-state nature of adherent cells commonly seen in wet labs, it includes both healthy and unhealthy cells in a single image, providing a valuable resource for studying multi-state cell detection and instance segmentation.<br>Citation</p> <p>We kindly request that researchers who use this dataset cite both our paper and this dataset. This will help acknowledge the work and facilitate further advancements in the field.</p> <p><br><strong>Please cite as follows:</strong></p> <p><strong>Paper:</strong><br>Pan, F., Wu, Y., Cui, K., Chen, S., Li, Y., Liu, Y., Shakoor, A., Zhao, H., Lu, B., Zhi, S., Chan, R. H.-F., & Sun, D. "Accurate detection and instance segmentation of unstained living adherent cells in differential interference contrast images,” <em>Computers in Biology and Medicine</em>, vol. 182, p. 109151, Nov. 2024, doi: 10/g5p9d8.</p> <p><strong>Dataset:</strong><br>Pan, F., Chen, S., Li, Y., Shakoor, A., Zhao, H., & Sun, D. (2024). Differential interference contrast (DIC) image of unstained living HepG2 human liver cancer cells. Zenodo. </p> <p>Thank you for your interest and support in our work. We look forward to seeing the innovative research that this dataset will enable.</p> <p> </p> <p> </p>
Sample files for auto_SAR_Ocean_Contrast (autonomous Ocean Contrast Estimation for SAR Images)
<p>Sample input and output files for the open source code auto_SAR_Ocean_Contrast (autonomous Ocean Contrast Estimation for SAR Images).</p> <p>auto_SAR_Ocean_Contrast is a python module for estimating the contrast in a SAR image of the ocean surface, relative to clean water pixels. The code is primarily intended to identify oil slicks, but can be used to identify any radar-dark feature in a scene that is not entirely radar-dark. The contrast ratio is often referred to as the damping ratio in the scientific literature concerning mineral oil slicks.</p> <p>github repository:</p>
Improving magnetic STEM-differential phase contrast imaging using precession
<p>Scanning transmission electron microscopy datasets and processing files used in the journal publication "<strong>Improving Magnetic STEM-Differential Phase Contrast Imaging using Precession</strong>".</p> <p>DOI link to publication: <a href="https://doi.org/10.1093/micmic/ozad001">https://doi.org/10.1093/micmic/ozad001</a></p> <p> </p> <p><strong>Prerequisites</strong></p> <p>To run the scripts necessary to process the files, the open source packages JupyterLab, HyperSpy, pyXem, and fpd need to be installed. These notebooks were created with these package versions:</p> <ul> <li>hyperspy 1.6.4</li> <li>pyxem 0.13.3</li> <li>fpd 0.2.0</li> <li>jupyterlab 3.2.0</li> </ul> <p> </p> <p><strong>Data files and processing scripts</strong></p> <p>Data files are collected in .zip folders and have names that start with "d00..", while processing scripts are in the Jupyter Notebook .ipynb data format whose names start with "p00..". These files are divided into three main processing steps, outlined as follows:</p> <ol> <li><strong>Processing of raw data</strong>: Raw data files can be found in the d001_scans.zip folder. These are processed with the p002_get_dpc_raw.ipynb script which uses either the center of mass or phase correlation methods.</li> <li><strong>D-scan correction</strong>: The processed files from the previous step are saved in the d002_dpc_raw.zip folder. The p003_get_dpc_cor.ipynb script performs a d-scan correction on these files and saves the output in the d003_dpc_cor.zip folder. <ul> <li><strong>Virtual segmented detector algorithm</strong>: For comparison purposes to the other processing algorithms, a virtual segmented detector algorithm was developed and can be found in the p004_segmented_detector.ipynb script. This algorithm extracts a linear d-scan plane from already processed phase correlation files found in d002_dpc_raw.zip, subtracts it from the raw data files found in d001_scans.zip, and finally performs the processing algorithm.</li> </ul> </li> <li><strong>Plotting files</strong>:<strong> </strong>The p005_plot_dpc_images.ipynb script creates the figures as seen in the journal publication. The input files are those found in d003_dpc_cor.zip from the previous processing step.</li> </ol>
Datasets of "Influence of contrast and texture based image modifications on the performance and attention shift of U-Net models for brain tissue segmentation" Part 1 of 14
<p>This dataset is part of the work <a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a>. This is the first part of 14 parts of the full dataset (1/14). It contains 3 sets of simulated T1 weighted brain volumes in 3 simulated scanning sequences of spin-echo. The parameters of simulated scanning sequences are respectively repetition time (TR) = 300ms, 400ms, 500ms, and echo time (TE) = 10ms. Under <strong>each</strong> simulated scanning sequence, there are 500 brain volumes.</p> <p>Within this part, we also include the segmentation labels for each tissue.</p> <p>The simulation process of this dataset involves two processes. The first is to simulate one brain under different simulated scanning sequences. For this, we use BrainWeb <a href="https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request">https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request</a>. In the custom setting, we use spin-echo and apply image artifact the same as the default setting of this page. The second process is to transform each simulated brain from BrainWeb to different anatomical shapes. We use Human Connectome Project (HCP) 1200 subject data <a href="https://www.humanconnectome.org/study/hcp-young-adult">https://www.humanconnectome.org/study/hcp-young-adult</a> and randomly select 500 brains as anatomical references.</p> <p>Other details of this dataset can be found at <a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a> where the details of the data construction are discussed.</p> <p>All parts of the whole dataset can be found at:</p> <p>Part 1: <a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a></p> <p>Part 2: <a href="https://zenodo.org/record/7389550">https://zenodo.org/record/7389550</a></p> <p>Part 3: <a href="https://zenodo.org/record/7390382">https://zenodo.org/record/7390382</a></p> <p>Part 4: <a href="https://zenodo.org/record/7390741">https://zenodo.org/record/7390741</a></p> <p>Part 5: <a href="https://zenodo.org/record/7391205">https://zenodo.org/record/7391205</a></p> <p>Part 6: <a href="https://zenodo.org/record/7393060">https://zenodo.org/record/7393060</a></p> <p>Part 7: <a href="https://zenodo.org/record/7393174">https://zenodo.org/record/7393174</a></p> <p>Part 8: <a href="https://zenodo.org/record/7393347">https://zenodo.org/record/7393347</a></p> <p>Part 9: <a href="https://zenodo.org/record/7394250">https://zenodo.org/record/7394250</a></p> <p>Part 10: <a href="https://zenodo.org/record/7394667">https://zenodo.org/record/7394667</a></p> <p>Part 11: <a href="https://zenodo.org/record/7394939">https://zenodo.org/record/7394939</a></p> <p>Part 12: <a href="https://zenodo.org/record/7395031">https://zenodo.org/record/7395031</a></p> <p>Part 13: <a href="https://zenodo.org/record/7395620">https://zenodo.org/record/7395620</a></p> <p>Part 14: <a href="https://zenodo.org/record/7395622">https://zenodo.org/record/7395622</a></p>
Datasets of "Influence of contrast and texture based image modifications on the performance and attention shift of U-Net models for brain tissue segmentation" Part 9 of 14
<p>This dataset is part of the work <a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a>. This is the ninth part of 14 parts of the full dataset (9/14). It contains 3 sets of simulated T1 weighted brain volumes in 3 simulated scanning sequences of spin-echo. The parameters of simulated scanning sequences are respectively repetition time (TR) = 600ms, 700ms, 800ms, and echo time (TE) = 15ms. Under <strong>each</strong> simulated scanning sequence, there are 500 brain volumes.</p> <p>The segmentation labels for each tissue are contained in the first part which you may find at <a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a></p> <p>The simulation process of this dataset involves two processes. The first is to simulate one brain under different simulated scanning sequences. For this, we use BrainWeb <a href="https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request">https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request</a>. In the custom setting, we use spin-echo and apply image artifact the same as the default setting of this page. The second process is to transform each simulated brain from BrainWeb to different anatomical shapes. We use Human Connectome Project (HCP) 1200 subject data <a href="https://www.humanconnectome.org/study/hcp-young-adult">https://www.humanconnectome.org/study/hcp-young-adult</a> and randomly select 500 brains as anatomical references.</p> <p>Other details of this dataset can be found at <a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a> where the details of the data construction are discussed.</p> <p>All parts of the whole dataset can be found at:</p> <p>Part 1: <a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a></p> <p>Part 2: <a href="https://zenodo.org/record/7389550">https://zenodo.org/record/7389550</a></p> <p>Part 3: <a href="https://zenodo.org/record/7390382">https://zenodo.org/record/7390382</a></p> <p>Part 4: <a href="https://zenodo.org/record/7390741">https://zenodo.org/record/7390741</a></p> <p>Part 5: <a href="https://zenodo.org/record/7391205">https://zenodo.org/record/7391205</a></p> <p>Part 6: <a href="https://zenodo.org/record/7393060">https://zenodo.org/record/7393060</a></p> <p>Part 7: <a href="https://zenodo.org/record/7393174">https://zenodo.org/record/7393174</a></p> <p>Part 8: <a href="https://zenodo.org/record/7393347">https://zenodo.org/record/7393347</a></p> <p>Part 9: <a href="https://zenodo.org/record/7394250">https://zenodo.org/record/7394250</a></p> <p>Part 10: <a href="https://zenodo.org/record/7394667">https://zenodo.org/record/7394667</a></p> <p>Part 11: <a href="https://zenodo.org/record/7394939">https://zenodo.org/record/7394939</a></p> <p>Part 12: <a href="https://zenodo.org/record/7395031">https://zenodo.org/record/7395031</a></p> <p>Part 13: <a href="https://zenodo.org/record/7395620">https://zenodo.org/record/7395620</a></p> <p>Part 14: <a href="https://zenodo.org/record/7395622">https://zenodo.org/record/7395622</a></p> <p> </p>
Phase contrast images of bacteria and ground truth segmentations
<p><strong>Name</strong>: Phase contrast images of bacteria </p> <p><strong>Data type</strong>: Paired microscopy images and corresponding labels/masks used for model training, organized as recommended by the <a href="https://imagej.net/plugins/denoiseg">DenoiSeg documentation</a>.</p> <p><strong>Microscopy data type</strong>: Light microscopy (Phase Contrast)</p> <p><strong>Manual annotations</strong>: Labels/masks obtained via manual segmentation. For each region, all cells were annotated manually. Uncertain objects were left unannotated.</p> <p><strong>Microscope</strong>: Zeiss Axio Imager M2 epi-fluorescence microscope with a Zeiss Plan-Apochromat; 100x/1.4 oil DIC objective</p> <p><strong>File format</strong>: .tif (float 32-bits for phase contrast and 16-bit for mask images)</p> <p><strong>Image size</strong>: 256x256 pixels (Pixel size: 64.5 nm)</p> <p> </p> <p>Content: </p> <p>train - raw (33 files) </p> <p> - masks (33 files)</p> <p>test - raw (11 files) </p> <p> - masks (11 files)</p> <p> </p> <p>All images available in the raw folders were normalized by dividing the original images with a gaussian blurred version or the original image (200 pixels). A groovy code working within ImageJ/Fiji corresponding to this operation is as follow:</p> <pre><code class="language-java">ImagePlus normalize(ImagePlus input_image) { flatfield = (new Duplicator()).run(input_image) (new GaussianBlur()).blur(flatfield.getProcessor(), 200) return ImageCalculator.run(input_image, flatfield, "Divide create 32-bit") } import ij.ImagePlus import ij.plugin.Duplicator import ij.plugin.ImageCalculator import ij.plugin.filter.GaussianBlur</code></pre> <p> </p>
Edge illumination X-ray phase contrast imaging with alternative gratings: dataset
<p>This dataset contains results from Edge illumunation X-ray phase contrast simulations with alternative gratings, as shown in 'Setup.png' The simulations are performed with the monte-carlo software Gate. Postprocessing is done in Matlab. Four different grating geometries were simulated: Conventional, sheared, curved and folded gratings. As phantom, a row of Aluminum cylinders is chosen.</p> <p>The simulation parameters can be found in the excel-file 'Simulation_parameters.xlsx'.</p> <p>The folder 'gate' contains the macros that where used for the monte carlo-simulation.</p> <p>The folder 'matlab' contains the results of post-processing in matlab for each grating geometry. They can be opened with the file 'results_script.m</p> <p>The folder 'results' contains images of the results for each geometry, including, flatfield, projection, threefold contrast and fitting parameters.</p> <table> <tbody> <tr> <td>This research was supported by EU Interreg Flanders - Netherlands Smart*Light (0386), Fonds wetenschappelijk onderzoek (G090020N, G094320N), and Agentschap Innoveren \& Ondernemen (Vlaio) (HBC.2020.2159). Nathanaël Six and Ben Huyge have a PhD fellowship of the FWO (11D8319N, 1S46122N).</td> </tr> </tbody> </table> <p> </p> <p></p>
Phenotyping Interstitial Cystitis/Bladder Pain Syndrome (IC/BPS) by Intravesical Contrast Enhanced - Magnetic Resonance Imaging (ICE-MRI) Bladder Permeability Assay
ClinicalTrials.gov study NCT05811377. IPD Sharing: NO. Countries: 1. Publications: 2.
Magnetic Resonance Imaging Evaluation of Inner Ear Pathology Using Intra-Tympanic Contrast Agent
ClinicalTrials.gov study NCT02080312. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Dynamic Contrast-Enhanced Magnetic Resonance Imaging in Measuring Effects of Pazopanib Hydrochloride in Patients With Metastatic Kidney Cancer
ClinicalTrials.gov study NCT01599832. IPD Sharing: Not stated. Countries: 1. Publications: 1.
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.