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Found 35 dataset(s) matching "Uncertainty Quantification".
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In this work, we introduce a new method for uncertainty quantification in ToxCast data. We explore how unavoidable uncertainties in the data result in uncertainties in concentration-response...
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The data set is composed of inputs and outputs of the DST demonstration and application to risk-based TMDLs and water quality risk assessment in Midwest river basins (Upper Mississippi River, Ohio...
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This article presented a discussion on uncertainty representation and management for model-based prog- nostics methodologies based on the Bayesian tracking framework and specifically for a Kalman...
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Note: This dataset has been superseded by the dataset found at "End-Use Load Profiles for the U.S. Building Stock" (submission 4520; linked in the submission resources), which is a comprehensive...
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This data publication contains the raw, derived, and processed data of active pharmaceutical ingredients dispensed by drop-on-demand. Gravimetric and chemical data from this study includes UV-Vis...
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A numerical groundwater flow model of the Hualapai Valley Basin, using MODFLOW-NWT, was developed to assist water-resource managers in understanding the potential effects of projected...
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Data sets include 1. Excel file with Hershberger assay protocols and data and summaries of in vivo antiandrogen studies 2. Figures of in vitro AR assay results from contract work and in house...
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This software package performs joint quantum state and measurement tomography. The software is provided as Python source code. A description of the algorithms used is in "Joint Quantum State and...
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The purpose of this code and data is to enable reproductionand facilitate extension of the computationalresults associated with following workDeJaco, R. F.; Roberts, M. J.; Romsos, E. L.; Vallone,...
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This document details the data registration process for the previously published datasets from Additive Manufacturing Metrology Testbed (AMMT) parts, "Overhang Part X4," generated at the National...
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Source Code for the manuscript "Characterizing Variability and Uncertainty for Parameter Subset Selection in PBPK Models" -- This R code generates the results presented in this manuscript; the...
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Included here are figures and relevant data for the work "Estimating Uncertainty in Robot Kinematics and Pose Measurements with Expectation-Maximization". We present a method to validate the...
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The Method of Uncertainty Minimization using Polynomial Chaos Expansions (MUM-PCE) was developed as a software tool to constrain physical models against experimental measurements. These models...
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ml_uncertainty: A Python module for estimating uncertainty in predictions of machine learning models
This software is a Python module for estimating uncertainty in predictions of machine learning models. It is a Python package that calculates uncertainties in machine learning models using...
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Closely related species of Salmonidae, including Pacific and Atlantic salmon, can be distinguished from one another based on nucleotide sequences from the cytochrome c oxidase sub-unit 1...
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The NIST/SEMATECH e-Handbook of Statistical Methods is a Web-based book written to help scientists and engineers incorporate statistical methods into their work as efficiently as possible....
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The data used to generate the graphs in figures 1-9 of the paper "Evaluating Uncertainty of Microwave Calibrations from Regression Residuals".The full citation is D. F. Williams, B. F. Jamroz, J....
