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35 results for “optimal estimation”
Figure 2 in Estimating optimal sample size for tardigrade morphometry
Figure 2. The relationship between estimation error and sample size has an asymptotic character both for means (A) and for ranges (B), meaning that the larger the sample size, the smaller the estimation error, but the reduction in error diminishes with the increasing sampling effort. Graphs show means ƚSD calculated from six species means (see Table 3). Dashed horizontal lines represent acceptable error rates: A, for means, errors under the dashed line are acceptable (i.e. sample sizes n ≥ 20; B, for ranges, errors above the dashed line are acceptable (i.e. none of the shown sample sizes returns an acceptable range estimation).
Figure 1 in Estimating optimal sample size for tardigrade morphometry
Figure 1. Exemplary graphs illustrating the results of simulations for mean (A) and range estimations (B) of the buccal tube length in the experimental population of Diphascon higginsi (N = 404; global mean = 22.2 µm; global range = 15.1– 25.6 µm = 10.5 µm). A, mean estimation graph: the solid horizontal line represents the global mean whereas the two dashed lines delimit the acceptable error zone, i.e. ƚ10% of the global range (21.2–23.3 µm); dots below and above the global mean delimit the zone within which 95% of computed means fell; the arrow points to the sample size at which 95% of computed means fell within the acceptable error around the global mean (n = 15). B, range estimation graph: the solid horizontal line represents the global range whereas the dashed line delimits the acceptable error zone, i.e. –20% of the global range (8.4 µm); dots at each sample size delimit the zone within which 95% of computed ranges fell; the arrow points to the sample size at which 95% of computed ranges fell within the acceptable error below the global range (n = 130). Graphs for all traits and species are available in the Supplementary Materials.
BP-C1 Monotherapy in Patients With Metastatic Breast Cancer Cancer: Estimation of Optimal Duration of Treatment
ClinicalTrials.gov study NCT03789019. IPD Sharing: NO. Countries: 2. Publications: 2.
Estimated Oxygen Extraction Versus Dynamic Parameters for Perioperative Hemodynamic Optimization
ClinicalTrials.gov study NCT04053595. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Data from: Reproducibility of volumetric computed tomography of stable small pulmonary nodules with implications on estimated growth rate and optimal scan interval
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Data from: How to optimize the precision of allele and haplotype frequency estimates using pooled-sequencing data
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Data from: Marginal likelihood estimate comparisons to obtain optimal species delimitations in Silene sect. Cryptoneurae (Caryophyllaceae)
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Meshes from Efficient estimation of personalized biventricular mechanical function employing gradient-based optimization
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Data from: The metabolic cost of changing walking speeds is significant, implies lower optimal speeds for shorter distances, and increases daily energy estimates
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Perfusion Estimation For Optimal Treatment Strategy in Chronic Coronary Syndrome
ClinicalTrials.gov study NCT05865600. IPD Sharing: NO. Countries: 1. Publications: 0.
A Trial Estimating The Optimal Radiation Volume Of Postsurgical Radiation For Patients With Esophageal Cancer
ClinicalTrials.gov study NCT01391572. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Visual Field Defect Estimation Using Sequentially Optimized Reconstruction Strategy on Healthy and Glaucoma Subjects
ClinicalTrials.gov study NCT03325751. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Data from: A new framework for analysing automated acoustic species detection data: occupancy estimation and optimization of recordings post-processing
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Dataset related to article "Knowledge-based DVH estimation and optimization for breast VMAT plans with and without avoidance sectors"
<p>This record contains raw data related to article "Knowledge-based DVH estimation and optimization for breast VMAT plans with and without avoidance sectors"</p><p><strong> Abstract</strong></p><p>Background: To analyze RapidPlan knowledge-based models for DVH estimation of organs at risk from breast cancer VMAT plans presenting arc sectors en-face to the breast with zero dose rate, feature imposed during the optimization phase (avoidance sectors AS).</p><p>Methods: CT datasets of twenty left breast patients in deep-inspiration breath-hold were selected. Two VMAT plans, PartArc and AvoidArc, were manually generated with double arcs from ~ 300 to ~ 160°, with the second having an AS en-face to the breast to avoid contralateral breast and lung direct irradiation. Two RapidPlan models were generated from the two plan sets. The two models were evaluated in a closed loop to assess the model performance on plans where the AS were selected or not in the optimization.</p><p>Results: The PartArc plans model estimated DVHs comparable with the original plans. The AvoidArc plans model estimated a DVH pattern with two steps for the contralateral structures when the plan does not contain the AS selected in the optimization phase. This feature produced mean doses of the contralateral breast, averaged over all patients, of 0.4 ± 0.1 Gy, 0.6 ± 0.2 Gy, and 1.1 ± 0.2 Gy for the AvoidArc plan, AvoidArc model estimation, RapidPlan generated plan, respectively. The same figures for the contralateral lung were 0.3 ± 0.1 Gy, 1.6 ± 0.6 Gy, and 1.2 ± 0.5 Gy. The reason was found in the possible incorrect information extracted from the model training plans due to the lack of knowledge about the AS. Conversely, in the case of plans with AS set in the optimization generated with the same AvoidArc model, the estimated and resulting DVHs were comparable. Whenever the AvoidArc model was used to generate DVH estimation for a plan with AS, while the optimization was made on the plan without the AS, the optimizer evidentiated the limitation of a minimum dose rate of 0.2 MU/°, resulting in an increased dose to the contralateral structures respect to the estimation.</p><p>Conclusions: The RapidPlan models for breast planning with VMAT can properly estimate organ at risk DVH. Attention has to be paid to the plan selection and usage for model training in the presence of avoidance sectors.</p><p> </p>
Optimizing Radar-Based Rainfall Estimators Using Machine Learning Modles
<p>Weather radar research has produced numerous radar-based rainfall estimators based on climate, rainfall intensity, a variety of ground-truthing instruments and sensors (e.g., rain gauges, disdrometers), and techniques. Although each research direction gives improvement, their collective application in an operational sense still yields uncertainty in rainfall estimation at different times. This study aims to explore the concept of implementing Machine Learning (ML) models in choosing the optimal radar-based rainfall estimator from a group of estimators at each bin of a radar scan. </p> <p>The Canadian King City C-Band radar was used with a GEONOR T-200B rain gauge, a total of 263 sample points, to establish a group of polarimetric-based rainfall estimators (R(Z), R(Z, ZDR), R(KDP)). The estimators were used to train three ML models, namely Decision Tree, Random Forest, and Gradient Boost, to choose the optimal rainfall estimators based on radar variables (Z, ZDR, KDP). Data from the Canadian Exeter C-Band radar and a Texas Electronics TE525 tipping bucket gauge at a different location were used to verify the ML models and compare their results to the classic Marshall-Gunn (1952) Z-R relation and the composite estimator produced by Bringi et al. (2011). The results show promising results for the ML models, specifically the Gradient Boost model. These encouraging results need to be further explored with more sample points to further refine the ML mod</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.