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.
47
datasets available to search
ShareScore release 0.9.0
Dataset results
47 results for “Texture Analysis”
Extracorporeal Shockwave Versus Phonophoresis Using Chitosan-Nanoparticles Gel on Functional and Anatomical Changes Detected With Artificial Intelligence Based Texture Analysis Algorithm in Knee Osteo
ClinicalTrials.gov study NCT06567301. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Textural Analysis and Effect of ROI Size on Infrared Thermography in Athletes With Patellar Tendinopathy
ClinicalTrials.gov study NCT07095738. IPD Sharing: YES. Countries: 1. Publications: 0.
Local Recurrence Prediction of by Texture Analysis Derived From Positron Emission Tomography (PET / CT) Imaging of Non-small Cell Lung Cancer (NSCLC) Treated With Stereotactic Radiotherapy (SBRT) (Ret
ClinicalTrials.gov study NCT06555861. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Textural Analysis of Mandibular Trabecular Bone Based on Grey Level Co-occurrence Matrix Features in Correlation With Age and Gender of an Egyptian Population Sample: A Cross-sectional Study Using Con
ClinicalTrials.gov study NCT05128565. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Integrating a genome-wide association study with a large-scale transcriptome analysis to predict genetic regions influencing the glycemic index and texture in rice
GEO Series GSE123616. Oryza sativa Indica Group. 21 samples. Type: Expression profiling by array.
Recopliación de datos cuantitativos extraídos del análisis textural de la colección de referencia experimental sobre tratamiento de superficie en cerámica a mano mediante microscopía óptica confocal / Compilation of quantitative data extracted from the textural analysis of the experimental reference collection on surface treatment of handmade pottery by confocal optical microscopy.
<p>This repository contains the quantitative data and stadistical test extracted from the textural analysis of the experimental reference collection on surface treatment of handmade pottery by confocal optical microscopy.</p>
Texture analysis and machine learning to predict water T2 and fat fraction from non-quantitative MRI of thigh muscles in Facioscapulohumeral muscular dystrophy
<p><strong>Introduction</strong>. This database includes the radiomic features used as covariates to train machine learning algorithms in the paper “ Texture analysis and machine learning to predict water T2 and fat fraction from non-quantitative MRI of thigh muscles in Facioscapulohumeral muscular dystrophy”.</p> <p><strong>Purpose</strong>. Quantitative MRI (qMRI) plays a crucial role for assessing disease progression and treatment response in neuromuscular disorders, but the required MRI sequences are not routinely available in every center. The aim of this study was to predict qMRI values of water T2 (wT2) and fat fraction (FF) from conventional MRI, using texture analysis and machine learning.</p> <p><strong>Method</strong>. Fourteen patients affected by Facioscapulohumeral muscular dystrophy were imaged at both thighs using conventional and quantitative MR sequences. Muscle FF and wT2 were calculated for each muscle of the thighs. Forty-seven texture features were extracted for each muscle on the images obtained with conventional MRI. Multiple machine learning regressors were trained to predict qMRI values from the texture analysis dataset.</p> <p><strong>Results</strong>. Eight machine learning methods (linear, ridge and lasso regression, tree, random forest (RF), generalized additive model (GAM), k-nearest-neighbor (kNN) and support vector machine (SVM) provided mean absolute errors ranging from 0.110 to 0.133 for FF and 0.068 to 0.115 for wT2. The most accurate methods were RF, SVM and kNN to predict FF, and tree, RF and kNN to predict wT2.</p> <p><strong>Conclusion</strong>. This study demonstrates that it is possible to estimate with good accuracy qMRI parameters starting from texture analysis of conventional MRI.</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.