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109
datasets available to search
ShareScore release 0.9.0
Dataset results
109 results for “prediction accuracy”
The Accuracy of the Performance and Placement Test for Predicting Supraglottic Airway Device (SAD) Position in the Hypopharynx as Confirmed With Video Laryngoscopy
ClinicalTrials.gov study NCT03643029. IPD Sharing: YES. Countries: 0. Publications: 2.
Randomized Trial Comparing Prediction Accuracy of Two Swept Source Optical Coherence Tomography Biometers
ClinicalTrials.gov study NCT05748275. IPD Sharing: NO. Countries: 1. Publications: 0.
Feasibility and Predictive Accuracy of an In-Home Computer Controlled Mandibular Positioner in Identifying Favorable Candidates for Oral Appliance Therapy
ClinicalTrials.gov study NCT03011762. IPD Sharing: NO. Countries: 0. Publications: 2.
Data from: An equation to predict the accuracy of genomic values by combining data from multiple traits, populations, or environments
Open the record for dataset details and reuse information.
Data from: An upper bound for accuracy of prediction using GBLUP
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Data from: Modelled three-dimensional suction accuracy predicts prey capture success in three species of centrarchid fishes
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Effects of sample size on differential gene expression, rank order and prediction accuracy of a gene signature
GEO Series GSE41726. Homo sapiens. 134 samples. Type: Expression profiling by array.
Data of "Accuracy of predicting chemical body composition of growing pigs by dual-energy X-ray absorptiometry"
<p>Data set for article "Accuracy of predicting chemical body composition of growing pigs by dual-energy X-ray absorptiometry" (DOI). Data set of Swiss Large White entire male pigs for nutrient composition (water, lipid, N, ash, Ca and P) determined by wet-chemistry and body composition (lean mass, body mineral content and fat tissue mass) by dual-energy X-ray absorptiometry (DXA) in the empty body of live pigs and (N=61) in pig carcasses (N=68) within a body weight range from 20 to 100 kg.</p> <p><strong>metadata.xlsx</strong>: description of variables in the data sets</p> <p><strong>emptybody.txt</strong>: corresponds to contents of the empty body at slaughter (three days after DXA live scans) and DXA live scans</p> <p><strong>Carcass.txt</strong>: corresponds to carcass contents and DXA carcass scans on the day of slaughter</p> <p> </p>
Data from: Improving accuracies of genomic predictions for drought tolerance in maize by joint modeling of additive and dominance effects in multi-environment trials
Breeding for drought tolerance is a challenging task that requires costly, extensive and precise phenotyping. Genomic selection (GS) can be used to maximize selection efficiency and the genetic gains in maize (Zea mays L.) breeding programs for drought tolerance. Here we evaluated the accuracy of genomic selection of additive (A) against additive+dominance (AD) models to predict the performance of untested maize single-cross hybrids for drought tolerance in multi-environment trials. Phenotypic data of five drought-tolerance traits were measured in 308 hybrids in eight trials under water-stressed (WS) and well-watered (WW) conditions over two years and two locations in Brazil. Hybrids' genotypes were inferred based on their parents' genotypes (inbred lines) using single nucleotide polymorphism data obtained via genotyping-by-sequencing. GS analyses were performed using genomic best linear unbiased prediction by fitting a factor analytic (FA) multiplicative mixed model. Results showed differences in the predictive accuracy between A and AD models for the five traits under consideration in both water conditions. For grain yield (GY), the AD model doubled the predictive accuracy in comparison to the A model. FA framework allowed for investigating the stability of additive and dominance effects across environments, as well as the additive- and dominance-by-environment interactions, with interesting applications for parental and hybrid selection. Prediction performance of untested hybrids using GS that benefit from borrowing information from correlated trials increased 40% and 9% for A and AD models, respectively. These results highlighted the importance of multi-environment trial analysis with GS that incorporate dominance effects into genomic predictions of GY in maize single-cross hybrids.
Accuracy of Endoscopists in Predicting Polyp Pathology
ClinicalTrials.gov study NCT03477318. IPD Sharing: NO. Countries: 1. Publications: 0.
Comparison of the Accuracy and Reliability of AMH, FSH and AFC in Predicting Ovarian Response
ClinicalTrials.gov study NCT02173444. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Accuracy of Hemoglobin A1C to Predict Glycemia in HIV
ClinicalTrials.gov study NCT00433628. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Accuracy of TCD Monitoring in Predicting Cerebral Hyperperfusion Syndrome After Carotid Endarterectomy
ClinicalTrials.gov study NCT01799070. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Accuracy of Pulse Pressure Variations Measured by a Non Invasive Digital Device to Predict Fluid Responsiveness
ClinicalTrials.gov study NCT03066388. IPD Sharing: NO. Countries: 1. Publications: 0.
The Accuracy of Endometrial Ultrasound to Predict Implantation
ClinicalTrials.gov study NCT03860636. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Accuracy of Ultrasound Markers Versus Biochemical Markers in Prediction of Ovarian Response in Obese Women Undergoing IVF/ICSI Treatment
ClinicalTrials.gov study NCT03684824. IPD Sharing: Not stated. Countries: 1. Publications: 0.
The Accuracy of Targeted Lymph Node Dissection of Non-small Cell Lung Cancer Patients According to Predictive Models
ClinicalTrials.gov study NCT06768853. IPD Sharing: NO. Countries: 1. Publications: 0.
Analysis and Accuracy of Mortality Prediction Scores
ClinicalTrials.gov study NCT04737148. IPD Sharing: NO. Countries: 1. Publications: 0.
Using Surveys to Examine the Association of Exposure to ML Mortality Risk Predictions With Medical Oncologists' Prognostic Accuracy and Decision-making
ClinicalTrials.gov study NCT06463977. IPD Sharing: NO. Countries: 1. Publications: 0.
PRedictive Accuracy of Initial Stone Burden Evaluation.
ClinicalTrials.gov study NCT04746378. IPD Sharing: UNDECIDED. Countries: 1. 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)
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.