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51 results for “uncertainty quantification”
Data from systematic audit for paper: Insights into the quantification and reporting of model-related uncertainty across different disciplines
<p>This upload contains 7 data files (each contains cleaned and compiled data for a given scientific field) and 2 R scripts. These files support the paper: Insights into the quantification and reporting of model-related uncertainty across different disciplines.</p> <p> </p> <p><strong>Description of the data</strong></p> <p>Compiled data files for each field contain all reviewers audit answers for eligible papers. All papers that met exclusion criteria have been removed.</p> <p>Data checks have been performed and formatting errors corrected either in R or manually, following steps detailed in the STAR methods.</p> <p>Column names and description:</p> <ul> <li>Number: number of question from 1 to 9</li> <li>Questions: question text – question to be answered by the reviewer</li> <li>QuestionCode: shortened code for each question</li> <li>Paper: paper code - first author surname/initial and surname and year</li> <li>Initials: initials of reviewer</li> <li>Answer: answer to the question</li> <li>Details: extra details to support the answer</li> <li>Location: where in the text the uncertainty was presented</li> <li>Presentation: how the uncertainty was presented</li> <li>ModelType: type of model (focal model)</li> <li>Comments: any other comments from the reviewer</li> <li>Checks: checks of whether NA or no have been included in correct places e.g. if answers to questions 1:4 are no then question 9 is NA, if question 7 is no then 8 is NA</li> <li>Check 1 = when Answer = No, Location is NA</li> <li>Check 2 = when Answer to Number 1-4, 6 or 8-9 is Yes that Details are not NA</li> <li>Check 3 = when Answer = No, Presentation = NA</li> <li>Check 4 = when Location is not NA, presentation is not NA</li> <li>Check 5 = if the Answer to 5 or 7 is "No" then Answer to 6 and 8 = "NA"</li> <li>Check 6 = if Answer for 1-4 is "No", then Answer for 9 = "NA"</li> </ul> <p><strong>Code description</strong></p> <p>Two scripts are included, the first is theme_script.R, this includes code to set up a ggplot theme for the figures. The second is Figure_code.R, this script contains all code to plot and save the three figures from the paper.</p>
GrainLearning: A Bayesian uncertainty quantification toolbox for discrete and continuum numerical models of granular materials
GrainLearning is a Bayesian uncertainty quantification and propagation toolbox for computer simulations of granular materials. The software is primarily used to infer and quantify parameter uncertainties in computational models of granular materials from observation data, also known as inverse analyses or data assimilation. Implemented in Python, GrainLearning can be loaded into a Python environment to process the simulation and observation data, or alternatively, as an independent tool where simulation runs are done separately, e.g., via a shell script.
Data to publication: Fibre optic measurements and model uncertainty quantification for Fe-SMA strengthened concrete structures
<p>This dataset contains the results of an experimental campaign, presented in the publication "Fibre optic measurements and model uncertainty quantification for Fe-SMA strengthened concrete structures". The publication covers fibre optic measurements inside large-scale specimens subjected to external load. The specimens comprised reinforced concrete slabs, strengthened with reinforcement bars made from iron-based shape memory alloy.</p>
Dataset of "An end-to-end KNN-based PTV approach for high-resolution measurements and uncertainty quantification"
<p>Dataset of the article "An end-to-end KNN-based PTV approach for high-resolution measurements and uncertainty quantification" (https://doi.org/10.1016/j.expthermflusci.2022.110756). Local similarity between non-time-resolved snapshots is enforced by KNN to extract high-resolution velocity fields and estimate the uncertainty of the measurements.</p> <p>The codes processing data here are on https://github.com/erc-nextflow/KNN-PTV.</p> <p>This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant agreement No 949085, NEXTFLOW).</p>
Uncertainty quantification of parenchymal tracer distribution using random diffusion and convective velocity fields (data sets)
<p>Supplementary dataset for Uncertainty quantification of parenchymal tracer distribution using random diffusion and convective velocity fields. Output functionals of interest from finite element simulations together with postprocessing source code. </p>
Simulation data for eddy current rail testing - simulation accuracy and evaluation uncertainty quantification
<p>This dataset serve to quantify the simulation error and the evaluation uncertainties in the context of eddy current rail testing. It was obtained during the AIFRI project (Artificial Intelligence for Rail Inspection) with the Faraday software by INTEGRATED Engineering Software, using its BEM Solver. For an analysis see the article below.</p>
Dataset of Machine Learning forecasted VTEC from paper: Uncertainty Quantification for Machine Learning-based Ionosphere and Space Weather Forecasting
<p>The *csv files contain forecasted one-day-ahead Vertical Total Electron Content (VTEC), consisting of the mean/median VTEC values and the upper and lower VTEC bounds of the 95% confidence intervals of 4 models based on machine learning for test data.</p> <p>The first part of the *csv file name corresponds to the type of model: SE stands for the super-ensemble VTEC model, QGB stands for the quantile gradient boosting VTEC model, BNN1 stands for the Bayesian neural network VTEC model, and BNN2 stands for the Bayesian neural network with negative log-likelihood (NLL) loss VTEC model. The second part of the file name refers to the geographic location of the VTEC points for which the forecast is performed, i.e., 10E70N for 10 degree of longitude and 70 degree of latitude, 10E40N for 10 degree of longitude and 40 degree of latitude, and 10E10N for 10 degree of longitude and 10 degree of latitude. The last part of the file name corresponds to the test year, i.e., year 2017.</p> <p>The SE_*_2017.csv file consists of 14 columns. The index column ("Date-time") is expressed in Coordinated Universal Time (UTC) as YYYY-MM-DD. Columns 1-3 contain the VTEC forecast results of Random Forest (RF) trained on three data subsets; columns 4-6 contain the VTEC forecast results of Adaptive Boosting (AB) trained on three data subsets; columns 7-9 contain the VTEC forecast results of Gradient Boosting (XGBoost) trained on three data subsets. Column 10 ("Mean") represents the mean of columns 1-9, i.e., the ensemble mean; column 11 ("Std") represents the standard deviation of columns 1-9, i.e., the ensemble spread; columns 12 ("UB") and 13 ("LB") contain the upper and lower bounds of the 95% confidence interval of VTEC, respectively; and column 14 contains the Global Ionosphere Maps (GIM) values of CODE, i.e., the ground-truth in this study.</p> <p>The QGB_*_2017.csv file consists of 4 columns. The index column ("Date-time") is expressed in UTC as YYYY-MM-DD. Column 1 ("Median") contains the median VTEC forecast, column 2 ("LB") contains the lower VTEC bound of the 95% confidence interval, column 3 ("UB") contains the upper VTEC bound of the 95% confidence interval, and column 4 contains the GIM values of CODE, i.e., the ground-truth in this study.</p> <p>The BNN*_2017.csv file consists of 5 columns. The index column ("Date-time") is expressed in UTC as YYYY-MM-DD. Column 1 ("Mean") contains the mean VTEC forecast, column 2 ("Std") contains the standard deviation, column 3 contains GIM values of CODE, i.e., ground-truth in this study; column 4 ("UB") contains the upper VTEC bound of the 95% confidence interval, and column 5 ("LB") contains the lower VTEC bound of the 95% confidence interval.</p> <p>----------------------------------------------------------------------------------------------------------------------------------------</p> <p>Contact</p> <p>----------------------------------------------------------------------------------------------------------------------------------------</p> <p>If you have any questions regarding these data, please contact:</p> <p>Randa Natras</p> <p>Deutsches Geodätisches Forschungsinstitut (DGFI-TUM)</p> <p>Technical University of Munich</p> <p>Arcisstraße 21</p> <p>80333 München</p> <p>randa.natras@tum.de</p>
A Tool for Uncertainty Quantification in Reconstructing Sparse Water Quality Time Series Data to Assess Risk Metrics for Watershed Health and TMDL Analysis
<p>The uploaded file contains the input and output data which can be used to reproduce the results in the research article 'Uncertainty Quantification in Reconstruction of Sparse Water Quality Time Series: Implications for Watershed Health and Risk-Based TMDL Assessment'. Please refer to the file '<a href="https://zenodo.org/api/files/31b59cce-8eb2-4ee7-93aa-61474c6f6359/dst_2019_SJRW_TP_TDS.zip?versionId=2af2b54d-d5fb-4720-919d-de2e827595e2">dst_2019_SJRW_TP_TDS.zip'</a> for updated files..</p>
Dataset - Controlled release experiment to investigate uncertainties in UAV-based emission quantification for methane point sources
<p>This dataset was created by Randulph Morales (randulph.morales@empa.ch) and was used for Morales et al. (2021) AMT publication (amt-2021-314). A short description of the files is written in <strong>readme.txt</strong></p> <p>The dataset contains:</p> <ul> <li>QCLAS methane measurement</li> <li>Active AirCore methane measurement</li> <li>Meteorology files</li> </ul>
Citation count error data for "Data inaccuracy quantification and uncertainty propagation for bibliometric indicators"
<p>This is the original collected data on citation count errors resulting from citation matching errors in Web of Science data for the publication "Data inaccuracy quantification and uncertainty propagation for<br>bibliometric indicators". The first column, <code>CITCOUNT_ALL</code>, gives the total (corrected) citation count for a publication, which is the citation count according to WoS plus the additionally manually identified citations (missed by WoS's algorithm). The second column, <code>CITCOUNT_WOS</code>, is the WoS citation count. The numeric difference between the two column values in one row is the number of additionally manually identified citations.</p>
A new sampling capability for uncertainty quantification in the Ice-sheet and Sea-level System Model v4.19 using Gaussian Markov random fields -- Datasets and results
<p>Data archives for test experiments (Section 3) and Pine Island Glacier application (Section 4) from the manuscript "Kevin Bulthuis and Eric Larour, A new sampling capability for uncertainty quantification in the Ice-sheet and Sea-Level System Model v4.19 using Gaussian Markov random fields"</p> <p>Source code is available at https://doi.org/10.5281/zenodo.5532775.</p>
Parameter uncertainty quantification of wake models to analyze effects of wake superposition: data and code
<p>Codebase for wake deficit, wake superposition, and wake-added turbulence modeling within Markov-chain Monte Carlo framework. Data for results and figures in associated paper is also included.</p>
Quantification of uncertainties introduced by data-processing procedures of sap flow measurements using the cut-tree method on a large mature tree
<p>Motivation: Sap flow sensors are crucial instruments to understand whole-tree water use. The lack of direct calibration of the available methods on large trees and the application of several data-processing procedures may jeopardize our understanding of water uptake dynamics by increasing the uncertainties around sensor-based estimates. We directly compared the heat ratio method (HRM) sap flow measurements to water uptake measured gravimetrically using the cut-tree method on a large mature aspen tree to quantify those uncertainties for ten consecutive days.</p> <p>Dataset: In this dataset, we provide sap flux density (ten-minutes intervals; g.cm-2.hr-1; corrected for wounding and sapwood thermal diffusivity) obtained from four HRM sap flow sensors installed at 2.5 m high on the focus tree (20 m tall, 60 years old trembling aspen in the boreal mixedwood region of Alberta) between July 18th and August 22nd 2017. We present the code and data (weather data from neighboring weather station) used to calculate whole-tree sap flux (L.hr-1) from each of the individual sensors using different methods of radial integration of sap flux density across the sapwood area estimated via different calculations, as well as different zero-flow corrections used. The cut-tree procedure was applied to the focus tree, and gravimetric measurements of water uptake (ten-minutes intervals) were made using a recording scale. We directly compared the different estimates of hourly, daily and cumulative sap flows obtained with gravimetric measurement of water uptake. We present the code providing the statistical analysis and results reported in the associated publication (Merlin, M., Solarik, K.A., Landhäusser, S.M. Quantification of uncertainties introduced by data-processing procedures of sap flow measurements using the cut-tree method on a large mature tree. 2020. Agricultural and Forest Meteorology, http://dx.doi.org/10.1016/j.agrformet.2020.107926)</p>
Intrusive and Non-Intrusive Uncertainty Quantification Methodologies for Pyrolysis Modeling
<p>This repository contains python scripts and results for uncertainty analysis of Arrhenius equation with kinetic parameters as uncertain. The data set contains folders for each PMMA variant (1,2 and 3) used for uncertainty quantification (UQ) and 'Misc' folder containing miscellaneous files and scripts used in the study. </p> <p>Each PMMA variant folder has further sub-folders for the UQ methods implemented:</p> <ol> <li>Intrusive polynomial chaos (IPC)</li> <li>Monte Carlo (MC)</li> <li>Non-intrusive polynomial chaos (NIPC)</li> </ol> <p>Additionally convergence and comparison for the UQ methods is available in sub-folders of the same name respectively.</p> <p>The python file 'UoWu_noLatex.mplstyle' for plotting style used by us is attached to ease the rerunning of the scripts.</p>
Bi-fidelity Variational Auto-encoder for Uncertainty Quantification: Experimental Data
<p>Quantifying the uncertainty of quantities of interest (QoIs) from physical systems is a primary objective in model validation. However, achieving this goal entails balancing the need for computational efficiency with the requirement for numerical accuracy. To address this trade-off, we propose a novel bi-fidelity formulation of variational auto-encoders (BF-VAE) designed to estimate the uncertainty associated with a QoI from low-fidelity (LF) and high-fidelity (HF) samples of the QoI. This model allows for the approximation of the statistics of the HF QoI by leveraging information derived from its LF counterpart. Specifically, we design a bi-fidelity auto-regressive model in the latent space that is integrated within the VAE's probabilistic encoder-decoder structure. An effective algorithm is proposed to maximize the variational lower bound of the HF log-likelihood in the presence of limited HF data, resulting in the synthesis of HF realizations with a reduced computational cost. Additionally, we introduce the concept of the bi-fidelity information bottleneck (BF-IB) to provide an information-theoretic interpretation of the proposed BF-VAE model. Our numerical results demonstrate that BF-VAE leads to considerably improved accuracy, as compared to a VAE trained using only HF data, when limited HF data is available.</p>
Data Set "Systematic QM Region Construction in QM/MM Calculations Based on Uncertainty Quantification"
<p>Data set accompanying the publication "Systematic QM Region Construction in QM/MM Calculations Based on Uncertainty Quantification"</p> <p>This dataset contains:</p> <p>- PDB files of the reactant and product starting structure</p> <p>- modified AMBER95 force field file</p> <p>- AMS fragment files for the ligands and ions</p> <p>- AMS input files for all geometry optimizations and single point calculations</p>
Multi-Level Monte Carlo Models for Flood Inundation Uncertainty Quantification - Dataset
<p>Dataset used for the analysis of Multi-level Monte Carlo methods for flood inundation uncertainty quantification. This includes:</p> <ol> <li>Flood model simulations for Dyce, Glasgow and Inverurie across three resolutions (5m/10m/20m).</li> <li>Data for violin plot figures with code.</li> </ol>
Code and Data for "Probabilistic Eddy Identification with Uncertainty Quantification"
<p>The code and data used in the paper "Probabilistic Eddy Identification with Uncertainty Quantification".</p>
Inferring Surface NO2 over Western Europe: A Machine Learning Approach with Uncertainty Quantification
<p>The data that serves to substantiate the analysis presented in the article.</p>
Data for "Uncertainty quantification in geochemical mapping: a review and recommendations"
<p>Data for "Uncertainty quantification in geochemical mapping: a review and recommendations".</p>
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