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ShareScore release 0.9.0
Dataset results
164 results for “image quality”
Validity and Reliability of the Turkish Version of the Pectus Carinatum Body Image Quality of Life Questionnaire for Patients With Pectus Carinatum
ClinicalTrials.gov study NCT04782362. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Artificial Intelligence System for Assessing Image Quality of Slit-Lamp Images and Its Effects on Diagnosis
ClinicalTrials.gov study NCT04314180. IPD Sharing: NO. Countries: 1. Publications: 0.
Evaluation of On-Couch CBCT Image Quality
ClinicalTrials.gov study NCT06576908. IPD Sharing: NO. Countries: 1. Publications: 0.
Evaluation of Image Quality in Obstetrical Ultrasonography: Comparison Between Subjective Assessment and Contrast-to-noise Ratio
ClinicalTrials.gov study NCT06265974. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
PET/MRI Artificial Intelligence Reconstruction Algorithm AIR Recon DL Image Quality Evaluation and Clinical Study
ClinicalTrials.gov study NCT06856096. IPD Sharing: NO. Countries: 1. Publications: 0.
Maternal and Fetal Characteristics Influencing Image Quality in Prenatal Ultrasonography
ClinicalTrials.gov study NCT06265987. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Evaluation of ExacTrac® Imaging Device for Repositioning Quality of Patients Undergoing an External ENT Radiotherapy
ClinicalTrials.gov study NCT04670991. IPD Sharing: Not stated. Countries: 1. Publications: 0.
A Pilot Study Investigating the Impact of Different IOL Designs on Subjective 2D and 3D Image Quality
ClinicalTrials.gov study NCT02409641. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Evaluation of Image Quality and Safety of the MyVeo Surgical Visualization Headset During Standard Neurosurgical and Reconstructive Procedures Using Compatible Microscopes.
ClinicalTrials.gov study NCT07164053. IPD Sharing: YES. Countries: 2. Publications: 0.
Subjective Image Quality in Stereoscopic Image Modifications
ClinicalTrials.gov study NCT01624415. IPD Sharing: Not stated. Countries: 1. Publications: 0.
The New Combined PET-MR System NextGen PET-MR With Syngo MR XA80A: Evaluation of Diagnostic Image Quality and Usability (Software and Hardware) in the Clinical Setting.
ClinicalTrials.gov study NCT06666387. IPD Sharing: NO. Countries: 1. Publications: 0.
Effect of Laryngeal Mask Airway on Image Quality n Pediatric Patients Undergoing Magnetic Resonant Imaging
ClinicalTrials.gov study NCT04730362. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Outcome After Selective Dorsal Rhizothomy Concerning Life Quality, Cerebral Imaging and Cognition
ClinicalTrials.gov study NCT03179241. IPD Sharing: NO. Countries: 1. Publications: 0.
Defining, Evaluating, and Sharing Methodologies for Quality Control in Diagnostic Imaging
ClinicalTrials.gov study NCT05770024. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Quality Controlled Lightning Imaging Sensor (LIS) on International Space Station (ISS) Science Data V2
The Quality Controlled Lightning Imaging Sensor (LIS) on International Space Station (ISS) Science Data dataset was collected by the LIS instrument mounted on the ISS and are used to detect the distribution and variability of total lightning occurring in the Earth’s tropical and subtropical regions. This dataset consists of quality controlled science data. This data collection can be used for severe storm detection and analysis, as well as for lightning-atmosphere interaction studies. The LIS instrument makes measurements during both day and night with high detection efficiency. The data are available in both HDF-4 and netCDF-4 formats, with corresponding browse images in GIF format.
Quality Controlled Lightning Imaging Sensor (LIS) on International Space Station (ISS) Backgrounds V2
The Quality Controlled Lightning Imaging Sensor (LIS) on International Space Station (ISS) Backgrounds dataset was collected by the LIS instrument mounted on the ISS and are used to detect the distribution and variability of total lightning occurring in the Earth’s tropical and subtropical regions. This dataset consists of quality controlled science data. This data collection can be used for severe storm detection and analysis, as well as for lightning-atmosphere interaction studies. The LIS instrument makes measurements during both day and night with high detection efficiency. The data are available in both HDF-4 and netCDF-4 formats.
Machine Learning-Based Estimation of Experimental Artifacts and Image Quality in Fluorescence Microscopy - Supporting Data
<p>Supporting data for the reproduction of the results reported in Corbetta, E., Bocklitz, T., Machine learning based estimation of experimental artifacts and image quality in fluorescence microscopy (2024) [1].</p> <h2><strong>MM-IQA_Images_png and </strong><strong>MM-IQA_Images_tif</strong></h2> <p>Folder containing all the supporting datasets of the publication.</p> <ul> <li>images_manual_inspection: semisynthetic dataset used for the manual inspection of the quality metrics.</li> <li>images_lda_training: semisynthetic dataset used to train the Linear Discriminant Analysis (LDA) model.</li> <li>images_lda_prediction: datasets predicted by the LDA model. <ul> <li>images_experimental: every subfolder is a dataset composed of measurements of a different sample. Images are from publicly available datasets from [2] and [3].</li> <li>images_known_semisynthetic: knwon semisynthetic dataset used for prediction, assessment and interpretation of the trained model.</li> </ul> </li> </ul> <p>Tif files are the original data used for the study.</p> <h2><strong>MM-IQA_Source_data</strong></h2> <p>Folder containing all the supporting metadata of the publication.</p> <ul> <li>manual_inspection: quality metrics computed for the semisynthetic dataset used for the manual inspection. <ul> <li>manual_inspection_bg: indices for the selection of the background region in each sample.</li> <li>manual_inspection_free_parameters: parameters used for the generation of the simulated artifacts.</li> <li>manual_inspection_metrics: quality metrics computed for the dataset, used for the manual inspection.</li> <li>manual_inspection_samples: free parameters associated to each image of the dataset for manual inspection.</li> </ul> </li> <li>LDA_training: semisynthetic dataset used to train the Linear Discriminant Analysis (LDA) model. <ul> <li>lda_metrics_synthetic+semisynthetic_uniform_max: quality metrics computed for the training dataset, with maximum normalization of the images. (Not used in the manuscript)</li> <li>lda_metrics_synthetic+semisynthetic_uniform_rescale01: quality metrics computed for the training dataset, with image values rescaled between 0 and 1.</li> <li>parameters_all_degradations: parameters used for the generation of the simulated artifacts.</li> </ul> </li> <li>LDA_prediction: datasets predicted by the LDA model. <ul> <li>experimental: quality metrics computed for measurements of different samples. Images are from publicly available datasets from [2] and [3].</li> <li>known_semisynthetic: metadata for the knwon semi-synthetic dataset used for prediction, assessment and interpretation of the trained model: <ul> <li>known_semisynthetic_free_parameters: parameters used for the generation of the simulated artifacts.</li> <li>known_semisynthetic_metrics_rescale01: quality metrics computed for the training dataset, with image values rescaled between 0 and 1.</li> <li>known_semisynthetic_maxnorm_lda_results: lda prediction results, when metrics are maximum normalized to the training dataset.</li> <li>known_semisynthetic_znorm_lda_results: lda prediction results, when metrics are z-score normalized to the training dataset.</li> </ul> </li> </ul> </li> </ul> <p>Source data can be used to reproduce the results of the manuscript, using the codes shared in the public GitLab repository <em><a href="https://git.photonicdata.science/elena.corbetta/multi-marker-iqa" target="_blank" rel="noopener">multi-marker-IQA</a>.</em></p> <h3><em>How to use the source data</em></h3> <p>The following table describes which data can be used in the scripts provided in the public GitLab repository <em><a href="https://git.photonicdata.science/elena.corbetta/multi-marker-iqa" target="_blank" rel="noopener">multi-marker-IQA</a>.</em></p> <table> <tbody> <tr> <td><strong>Script</strong></td> <td><strong>Data to use</strong></td> <td><strong>Details</strong></td> </tr> <tr> <td>01_quality_metrics</td> <td>Subfolders of MM-IQA_Images_png</td> <td>Include all the images to evaluate in a single subfolder in <code>/test_images</code></td> </tr> <tr> <td> </td> <td>background_idx.xlsx</td> <td>The indices for the samples to evaluate must be included in the table</td> </tr> <tr> <td> <p>01_quality_metrics_visualization</p> <p>01_quality_metrics_visualization_notebook</p> </td> <td>manual_inspection_metrics.xlsx</td> <td> </td> </tr> <tr> <td> </td> <td>known_semisynthetic_metrics_rescale01</td> <td> </td> </tr> <tr> <td> </td> <td>Every metadata included in LDA_predcition/experimental/</td> <td> </td> </tr> <tr> <td> <p>02_lda_training+prediction</p> </td> <td>lda_metrics_synthetic+semisynthetic_uniform_rescale01</td> <td>As training dataset</td> </tr> <tr> <td> </td> <td>known_semisynthetic_metrics_rescale01</td> <td>As prediction dataset</td> </tr> <tr> <td> </td> <td>Every metadata included in LDA_predcition/experimental/</td> <td>As prediction dataset</td> </tr> <tr> <td> <p>02_lda_visualization</p> <p>02_lda_visualization_notebook</p> </td> <td>known_semisynthetic_maxnorm_lda_results</td> <td> </td> </tr> <tr> <td> </td> <td>known_semisynthetic_znorm_lda_results</td> <td> </td> </tr> <tr> <td>Notebook_test-iqa</td> <td>A small dataset with image data and the relative background index, if available.</td> <td>Use a limited number of images.</td> </tr> <tr> <td>Notebook_mm-iqa_workflow</td> <td>Any image dataset with the relative background indices</td> <td>For quality assessment and as prediction dataset</td> </tr> <tr> <td> </td> <td>lda_metrics_synthetic+semisynthetic_uniform_rescale01</td> <td>As training dataset</td> </tr> </tbody> </table> <p> </p> <h2>MM-IQA_Scripts</h2> <ul> <li><strong>multi-marker-iqa-main</strong>: original GitLab repository for MM-IQA, version available at the date of manuscript publication.</li> <li><strong>Notebooks_peer_review</strong>: additional notebooks generated during the peer-review process with the computation of metrics for natural images and correlation measures.</li> </ul>
Camera trap data accompanying paper titled: "Deep learning-based ecological analysis of camera trap images is impacted by training data quality and size"
<p>ZIp file that contains two camera trap datasets that support the experiments of the paper title: "Deep learning-based ecological analysis of camera trap images is impacted by training data quality and size". The paper is currently under submission.</p>
Evaluation of the Quality of Life of Patients Requiring Intestinal Cleansing Using Oral Medications to Imaging Procedure by Patient Reported Outcome
ClinicalTrials.gov study NCT02536729. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Image Quality and Radiation Dose Associated With Cardiac Scans in Modern CT Scanners
ClinicalTrials.gov study NCT05245149. IPD Sharing: YES. Countries: 0. Publications: 0.
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)
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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.