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36 results for “Measurement error”
Energy-Dependent, Self-Adaptive Mesh h(p)-Refinement of a Constraint-Based Continuous Bubnov-Galerkin Isogeometric Analysis Spatial Discretisation of the Multi-Group Neutron Diffusion Equation with Dual-Weighted Residual Error Measures
<p>This repository holds all of the raw data generated by my (Modern) Fortran code for a paper "Energy-Dependent, Self-Adaptive Mesh h(p)-Refinement of a Constraint-Based Continuous Bubnov-Galerkin Isogeometric Analysis Spatial Discretisation of the Multi-Group Neutron Diffusion Equation with Dual-Weighted Residual Error Measures".</p><p>The (Modern) Fortran code solves the multi-group neutron diffusion equation using a novel IGA-based spatial discretisations.</p>
Energy-Dependent, Self-Adaptive Mesh h(p)-Refinement of an Interior-Penalty Scheme for a Discontinuous Galerkin Isogeometric Analysis Spatial Discretisation of the Multi-Group Neutron Diffusion Equation with Dual-Weighted Residual Error Measures
<p>This repository holds all of the raw data generated by my (Modern) Fortran code for a paper "Energy-Dependent, Self-Adaptive Mesh h(p)-Refinement of an Interior-Penalty Scheme for a Discontinuous Galerkin Isogeometric Analysis Spatial Discretisation of the Multi-Group Neutron Diffusion Equation with Dual-Weighted Residual Error Measures".</p><p>The (Modern) Fortran code solves the multi-group neutron diffusion equation using a novel IGA-based spatial discretisations.</p>
Correlation Between Insulation Resistance and Temperature Measurement Error in Type K and Type N Mineral Insulated, Metal Sheathed Thermocouples
<p>Mineral insulated, metal sheathed (MI) Type K and Type N thermocouples are<br> widely used in industry for process monitoring and control. One factor that limits<br> their accuracy is the dramatic decrease in the insulation resistance at temperatures<br> above about 600 °C which results in temperature measurement errors due to electrical<br> shunting. In this work the insulation resistance of a cohort of representative MI<br> thermocouples was characterised at temperatures up to 1160 °C, with simultaneous<br> measurements of the error in indicated temperature by in situ comparison with a reference<br> Type R thermocouple. Intriguingly, there appears to be a systematic relationship<br> between the insulation resistance and the error in the indicated temperature. At<br> a given temperature, as the insulation resistance decreases, there is a corresponding<br> increasingly negative error in the temperature measurement. Although the measurements<br> have a relatively large uncertainty (up to about 1 °C in temperature error and<br> up to about 10 % in insulation resistance measurement), the trend is apparent at all<br> temperatures above 600 °C, which suggests that it is real. Furthermore, the correlation<br> disappears at temperatures below about 600 °C, which is consistent with the<br> well-established diminution of insulation resistance breakdown effects below that<br> temperature. This raises the intriguing possibility of using the as-new MI thermocouple<br> calibration as an indicator of insulation resistance breakdown: large deviations<br> of the electromotive force (emf) in the negative direction could indicate a correspondingly<br> low insulation resistance.</p>
Online Supplemental Materials for: "Total Error and Variability Measures for the Quarterly Workforce Indicators and LEHD Origin Destination Employment Statistics in OnTheMap"
<p>This archive contains supplementary materials for the published manuscript.</p> <p>We report results from the first comprehensive total quality evaluation of five major indicators in the U.S. Census Bureau's Longitudinal Employer-Household Dynamics (LEHD) Program Quarterly Workforce Indicators (QWI): total flow-employment, beginning-of-quarter employment, full-quarter employment, average monthly earnings of full-quarter employees, and total quarterly payroll. Beginning-of-quarter employment is also the main tabulation variable in the LEHD Origin-Destination Employment Statistics (LODES) workplace reports as displayed in OnTheMap (OTM), including OnTheMap for Emergency Management. We account for errors due to coverage; record-level non-response; edit and imputation of item missing data; and statistical disclosure limitation. The analysis reveals that the five publication variables under study are estimated very accurately for tabulations involving at least 10 jobs. Tabulations involving three to nine jobs are a transition zone, where cells may be fit for use with caution. Tabulations involving one or two jobs, which are generally suppressed on fitness-for-use criteria in the QWI and synthesized in LODES, have substantial total variability but can still be used to estimate statistics for untabulated aggregates as long as the job count in the aggregate is more than 10.</p>
Code and data archive for Nettle et al. 'Consequences of measurement error in qPCR telomere data: A simulation study'
<p>Code and data for Nettle et al. 'Consequences of measurement error in qPCR telomere data: A simulation study'</p> <p>Updated version of March 2019</p> <p>Main simulation functions are contained in the script ‘simulation.functions.r’. When called, these functions (listed below) return datasets with requested properties containing both the ideal values of the quantities (Cqs, TS, etc.), and their post-error measured values. This allows the user to determine the differences between ideal and measured values, and perform other analyses. All simulation parameter values are user-specifiable. The script ‘paper.results.r’ reproduces all the figures and simulation results from the main paper. 'paper.results.r' also reads in the two .csv files of empirical data (dataset1 and dataset2).</p> <p>Datasets consist of observations from <em>n</em> individuals. The steps common to all of the simulation functions are as follows:</p> <ul> <li>A vector of <em>n </em>true single copy gene abundances, <em>true.dna.scg</em> is defined, drawn from a normal distribution with mean <em>b</em> and standard deviation <em>var.sample.size</em> (<em>b </em>is a constant).</li> <li>A vector of <em>n </em>relative telomere lengths, <em>true.telo.var</em> is defined, drawn from a normal distribution with mean 1 and standard deviation <em>telomere.var.</em></li> <li>Hence, the true abundance of the telomere sequence is defined, as <em>a*true.dna.scg*true.telo.var</em>. Here, <em>a</em> is a scaling constant representing how many copies of the telomeric sequence there are per single copy gene in the average sample.</li> <li>Ideal Cq values for both reactions are defined as <em>f – log<sub>2</sub>(true.dna.scg)</em> and <em>f – log<sub>2</sub>(true.dna.telo),</em> where <em>f</em> is a constant representing the chosen fluorescence threshold.</li> <li>Measurement errors in the Cqs are generated from a normal distribution with mean 0; standard deviations given by <em>error.scg</em> and <em>error.telo</em>; and a correlation between <em>error.scg</em> and <em>error.telo</em> given by <em>error.cor</em>.</li> <li>Hence, measured Cqs are generated, which can be compared to the ideal Cq values.</li> <li>TS ratios are calculated both on the measured Cqs, and the ideal ones.</li> </ul> <p>The following functions are available. Specify desired parameter values in the parenthesis, e.g. <em>generate.one.dataset(n=10000, error.telo=0.1, error.scg=0.1, error.cor=0</em>). Default values in the simulation functions are generally those given in table 1 of the main paper.</p> <ul> <li><em>generate.one.dataset()</em> returns a simple dataset (one telomere measurement per individual) for chosen values of all the variables described in section 1. As well as ideal and measured Cqs, it returns ideal and measured TS ratios. It also returns the difference between the ideal and measured TS ratio, calculated two ways, computed (<em>error.computed</em>), and using equation (11) of online supplement 1 (<em>error.analytic</em>). Both methods produce the same number. This was included as an additional check of correctness of the simulation.</li> <li><em>generate.repeated.measure()</em> returns a dataset where telomere lengths from the same individuals are measured twice, via two independent biological samples, and the true telomere length of each individual is assumed not to have changed at all. The data frame it returns is as for <em>generate.one.dataset()</em>, except that there are two of each variable (e.g. <em>true.ts.1, true.ts.2, measured.ts.1, measured.ts.2</em>, etc.).</li> <li><em>calculate.repeatability()</em> calculates the repeatability of the measured T/S ratio (intra-class correlation coefficient) when <em>generate.repeated.measure()</em> is implemented using the given values for all the parameters. It requires prior installation of R package ‘irr’.</li> <li><em>compare.repeatability()</em> returns the repeatability of the T/S ratio and the repeatability calculated on the raw Cq for the telomere reaction, for the given parameter values.</li> </ul> <p> </p>
Gradual Pressure Error in an HPLC-HRMS Measurement Run
<p>This dataset contains files of a reverse-phase HPLC-HRMS orbitrap measurement run during which the pump pressure gradually got more unstable. While a run was being recorded, the pump for the organic eluent ceased functioning slowly. This resulted in a high amount of new signals appearing in the form of "stripes" at specific retention times.</p> <p>It is intended as a control group for assigned data quality scores, with the reasoning that a highly unstable pressure in the chromatography will lead to more purely noise-induced signals. The relevant pressure profiles are also included. The mzML files were produced from the raw data using MSconvert and only contain the MS1 spectra.</p> <p>The last file in the series (<a href="https://zenodo.org/api/records/14046512/draft/files/20240805_AA_DK_Ibu_pH6_1min_20240808161549.raw/content" target="_blank" rel="noopener noreferrer">20240805_AA_DK_Ibu_pH6_1min_20240808161549</a>) remains incomplete since the instrument shut off halfway through the measurement.</p>
Data for "Qubit fractionalization and emergent majorana liquid in the honeycomb floquet code induced by coherent errors and weak measurements"
<p>From the perspective of quantum many-body physics, the Floquet code of Hastings and Haah can be thought of as a measurement-only version of the Kitaev honeycomb model where a periodic sequence of two-qubit XX, YY, and ZZ measurements dynamically stabilizes a toric code state with two logical qubits. However, the most striking feature of the Kitaev model is its intrinsic fractionalization of quantum spins into an emergent gauge field and itinerant Majorana fermions that form a Dirac liquid, which is absent in the Floquet code. Here we demonstrate that by varying the measurement strength of the honeycomb Floquet code one can observe features akin to the fractionalization physics of the Kitaev model at finite temperature. Introducing coherent errors to weaken the measurements we observe three consecutive stages that reveal qubit fractionalization (for weak measurements), the formation of a Majorana liquid (for intermediate measurement strength), and Majorana pairing together with gauge ordering (for strong measurements). Our analysis is based on a mapping of the imperfect Floquet code to random Gaussian fermionic circuits (networks) that can be Monte Carlo sampled, exposing two crossover peaks. With an eye on circuit implementations, our analysis demonstrates that the Floquet code, in contrast to the toric code, does not immediately break down to a trivial state under weak measurements, but instead gives way to a long-range entangled Majorana liquid state.</p>
Energy-Dependent, Self-Adaptive Mesh h(p)-Refinement of a Constraint-Based Continuous Bubnov-Galerkin Isogeometric Analysis Spatial Discretisation of the Self-Adjoint Angular Flux Form of the Multi-Group Neutron Transport Equation with Dual-Weighted Residual Error Measures
<p>This repository holds all of the raw data generated by my (Modern) Fortran code for a paper "Energy-Dependent, Self-Adaptive Mesh h(p)-Refinement of a Constraint-Based Continuous Bubnov-Galerkin Isogeometric Analysis Spatial Discretisation of the Self-Adjoint Angular Flux Form of the Multi-Group Neutron Transport Equation with Dual-Weighted Residual Error Measures".</p> <p>The (Modern) Fortran code solves the SAAF form of the multi-group neutron transport equation using novel NURBS-based, IGA spatial discretisations.</p>
Quantification of Error Sources with Inertial Measurement Units in Sports - Data and Matlab Scripts
<p>Inertial measurement units (IMUs) offer the possibility to capture the lower body motions of players of outdoor team sports. However, various sources of error are present when using IMUs: the definition of the body frames, the soft tissue artefact (STA) and the orientation filer. Methods to minimize these errors are currently being used without knowing their exact influence on the various sources of errors. The goal of this study was to quantify each of the sources of error of an IMU separately. An optoelectronic system was used as a golden standard. Rigid marker clusters (RMCs) were designed to construct a rigid connection between the IMU and four markers. This allowed for the separate quantification of each of the sources of error. Ten subjects performed nine different trials, varying both in type of movement and in movement intensity. The error of the definition of the body frames (10.9-18.1 deg RMSD), the STA (3.6-9.4 deg RMSD) and the error of the orientation filter (2.8- 13.1 deg RMSD) were all quantified separately. The data and code to process the data can be found in this publication.</p> <p> </p>
Beyond Confounders: Addressing Source of Measurement Variability and Error in Shear Wave Elastography
ClinicalTrials.gov study NCT03342560. IPD Sharing: NO. Countries: 1. Publications: 30.
Are geometric morphometric analyses replicable? Evaluating landmark measurement error and its impact on extant and fossil Microtus classification
Open the record for dataset details and reuse information.
Supplementary dataset for journal article "High-accuracy current measurement with low-cost shunts by means of dynamic error correction"
<p>Supplementary material for the journal article "High-accuracy current measurement with low-cost shunts by means of dynamic error correction".</p> <p>Measurements include</p> <ul> <li>Temperature dependence of a single 10mOhm shunt resistor in TO-247 package.</li> <li>Calibration sequence measurements for automated 600A test equipment for studies on automotive Li-Ion cells.</li> <li>Obtained dynamic and steady-state calibration parameters</li> <li>Parameter distribution for 100 test systems.</li> <li>Parameter drift for a single tester after one year of operation</li> </ul> <p>All data has been combined into a single binary Matlab-File.</p>
FIGURE 3 in Distinguishing between Leptidea sinapis and L. reali (Lepidoptera: Pieridae) using a morphometric approach: impact of measurement error on the discriminative characters
FIGURE 3. Schematic drawings of characters used in morphometric analysis: A) male genitalia (lateral view), B) female genitalia (lateral view) showing both papille analis and anterior apophyses.
FIGURE 2 in Distinguishing between Leptidea sinapis and L. reali (Lepidoptera: Pieridae) using a morphometric approach: impact of measurement error on the discriminative characters
FIGURE 2. Male specimen bivariate scatter plot using aedoeagus (≈ aedeagus ≈ phallus) and saccus length as discriminative characters.
FIGURE 4 in Distinguishing between Leptidea sinapis and L. reali (Lepidoptera: Pieridae) using a morphometric approach: impact of measurement error on the discriminative characters
FIGURE 4. PC1 and PC2 scatter plot of male specimens: A) "size-and-shape" PCA, B) "size-adjusted" PCA.
FIGURE 5 in Distinguishing between Leptidea sinapis and L. reali (Lepidoptera: Pieridae) using a morphometric approach: impact of measurement error on the discriminative characters
FIGURE 5. PC1 and PC2 scatter plot of female specimens: A) "size-and-shape" PCA, B) "size-adjusted" PCA.
Characterizing quantum instruments: from non-demolition measurements to quantum error correction
<p>Source data underlying the graphical representations used in the figures.</p>
The Effects of Differential Tropospheric Error on the Measurement of Wide-swath Interferometric Altimetry
<p>Related dataset and codes for the paper "The Effects of Differential Tropospheric Error on the Measurement of Wide-swath Interferometric Altimetry" submitted to IEEE TGRS.</p>
Patient and Observer Reported Outcome Measurements in Inborn Errors of Metabolism
ClinicalTrials.gov study NCT04248062. IPD Sharing: NO. Countries: 1. Publications: 4.
Manuscript code & data: Foster et al. (2020) "Satellite-based monitoring of irrigation water use: assessing measurement errors and their implications for agricultural water management policy"
<p>Data to reproduce results presented in Foster, T., Mieno, T. and Brozovic, N. (2020). <em>Satellite-based monitoring of irrigation water use: assessing measurement errors and their implications for agricultural water management policy.</em> Water Resources Research. In Review. Files provide include:</p> <ul> <li>"ReviewMetadata_FosterWRR_2020.xlsx" - data needed to reproduce meta-analysis presented in the paper</li> <li>"MeasurementErrorCode_FosterWRR_2020.m" and "Foster2018_ProductionFunction.mat" - Matlab code and data needed to reproduce welfare loss analysis presented in the paper.</li> </ul>
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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.