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Evaluating Uncertainty of Nonlinear Microwave Calibration Models from Regression Residuals
The data used to generate the graphs in figures 2, 4, 5 and 6 of the paper "Evaluating Uncertainty of Nonlinear Microwave Calibrations from Regression Residuals". The full reference is D. F. Williams, B. Jamroz and J. D. Rezac, "Evaluating Uncertainty of Nonlinear Microwave Calibration Models With Regression Residuals," in IEEE Transactions on Microwave Theory and Techniques, vol. 68, no. 9, pp. 3776-3782, Sept. 2020, doi: 10.1109/TMTT.2020.3005170. 0. The file with data for Fig. X is named FigX.zip. 1. The file Fig X guide.txt in the top directory of each zip file describes which EasyPlot file was used to create the graph(s) in the figure. 2. The EasyPlot files were made with EasyPlot V 4.0.4, and document the data locations, legends, etc. EasyPlot column-selection format is as follows: "xyiiyy" or "xy..yy" means that column 1 was used for x axis, column 2 for first curve y values, column 5 for second curve y values, column 6 for third curve y values
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Reference Measurements of Error Vector Magnitude
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The experiment here was to demonstrate that we can reliably measure the Reference Waveforms designed in the IEEE P1765 proposed standard and calculate EVM along with the associated uncertainties. The measurements were performed using NIST's calibrated sampling oscilloscope and were traceable to the primary standards.We have uploaded the following two datasets. (1) Table 3 contains the EVM values (in %) for the Reference Waveforms 1--7 after performing the uncertainty analyses. The Monte Carlo means are also compared with the ideal values from the calculations in the IEEE P1765 standard.(2) Figure 3 shows the complete EVM distribution upon performing uncertainty analysis for Reference Waveform 3 as an example. Each of the entries in Table 3 is associated with an EVM distribution similar to that shown in Fig. 3.
NeXLUncertainties.jl - A Julia library implementing uncertainty propagation for multi-variate measurement models.
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NeXLUncertainties.jl is a Julia package implementing algorithms for the propagation of uncertainties in multi-variate measurement models.
Rumsey and Walker AMT 2016 Figure 2.xlsx
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Figure summarizes uncertainty (error) in hourly gradient flux measurements by individual analyte. Flux uncertainty is derived from estimates of uncertainty in chemical gradients and turbulent transfer velocity. This dataset is associated with the following publication: Rumsey, I. Application of an online ion chromatography-based instrument for gradient flux measurements of speciated nitrogen and sulfur. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 9(6): 2581-2592, (2016).
ml uncertainty: A Python module for estimating uncertainty in predictions of machine learning models
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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 bootstrapping and residual bootstrapping. It is intended to interface with scikit-learn but any Python package that uses a similar interface should work.
Rumsey and Walker AMT 2016 Figure 1.xlsx
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Figure summarizes diurnal profiles of uncertainty in the chemical gradient and transfer velocity measurements from which fluxes are calculated. This dataset is associated with the following publication: Rumsey, I. Application of an online ion chromatography-based instrument for gradient flux measurements of speciated nitrogen and sulfur. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 9(6): 2581-2592, (2016).
Data release for Assessing the Uncertainties in Climatic Estimates Based on Vegetation Assemblages: Examples from Modern Vegetation Assemblages in the American Southwest
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This data release includes climatic variables and associated descriptive material created for the purpose of assessing uncertainties associated with climatic estimates based on vegetation assemblages (Thompson and others, 2021). The data are from the interior of the western United States, including all of Arizona, and portions of California, Colorado, Nevada, New Mexico, Texas, and Utah. The data are observed, interpolated, and estimated values for the mean temperature of the coldest month (MTCO, degrees C), mean temperature of the warmest month (MTWA, degrees C), and mean annual total precipitation (MAP, mm).
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