Additional file 2: Supplemental Figure 1. Flowcharts of the analytic samples for FOCUS 1.0 and FOCUS 2.0 survey waves. Supplemental Figure 2A. Box and whisker plots comparing FOCUS safety climate scores by size variables for FOCUSv.1.0 departments. Supplemental Figure 2B. Box and whisker plots comparing FOCUS safety climate scores by size variables for FOCUSv.2.0 departments.

This data release contains time series and plots summarizing mean monthly temperature (TAVE) and total monthly precipitation (PPT), and runoff (RO) from the U.S. Geological Survey Monthly Water Balance Model at 115 National Wildlife Refuges within the U.S. Fish and Wildlife Service Mountain-Prairie Region (CO, KS, MT, NE, ND, SD, UT, and WY). These three variables are derived from two sets of statistically-downscaled general circulation models from 1951 through 2099. Three variables (TAVE, PPT, and RO for refuge areas) were summarized for comparison across four 19-year periods: historic (1951-1969), baseline (1981-1999), 2050 (2041-2059), and 2080 (2071-2089). For each refuge, mean monthly plots, seasonal box plots, and annual envelope plots were produced for each of the four periods.

Comparison experiments by using IF.

WIDEa is R-based software aiming to provide users with a range of functionalities to explore, manage, clean and analyse "big" environmental and (in/ex situ) experimental data. These functionalities are the following, 1. Loading/reading different data types: basic (called normal), temporal, infrared spectra of mid/near region (called IR) with frequency (wavenumber) used as unit (in cm-1); 2. Interactive data visualization from a multitude of graph representations: 2D/3D scatter-plot, box-plot, hist-plot, bar-plot, correlation matrix; 3. Manipulation of variables: concatenation of qualitative variables, transformation of quantitative variables by generic functions in R; 4. Application of mathematical/statistical methods; 5. Creation/management of data (named flag data) considered as atypical; 6. Study of normal distribution model results for different strategies: calibration (checking assumptions on residuals), validation (comparison between measured and fitted values). The model form can be more or less complex: mixed effects, main/interaction effects, weighted residuals.

WIDEa is R-based software aiming to provide users with a range of functionalities to explore, manage, clean and analyse "big" environmental and (in/ex situ) experimental data. These functionalities are the following, 1. Loading/reading different data types: basic (called normal), temporal, infrared spectra of mid/near region (called IR) with frequency (wavenumber) used as unit (in cm-1); 2. Interactive data visualization from a multitude of graph representations: 2D/3D scatter-plot, box-plot, hist-plot, bar-plot, correlation matrix; 3. Manipulation of variables: concatenation of qualitative variables, transformation of quantitative variables by generic functions in R; 4. Application of mathematical/statistical methods; 5. Creation/management of data (named flag data) considered as atypical; 6. Study of normal distribution model results for different strategies: calibration (checking assumptions on residuals), validation (comparison between measured and fitted values). The model form can be more or less complex: mixed effects, main/interaction effects, weighted residuals. R, 3.5 (minimal) This software is ranked by IN-SYLVA FRANCE Research Infrastructure. https://www6.inrae.fr/in-sylva-france_eng/Services/In-Silico/Analysis-software https://doi.org/10.15454/1A0P-HE21 Ce logiciel est référencé au sein de l'Infrastructure de Recherche In-Sylva France https://www6.inrae.fr/in-sylva-france/Services/In-Silico/Logiciels-d-analyse. https://doi.org/10.15454/1A0P-HE21

Supplementary Figure 1A: Box and Whisker Plots of log Aldosterone to Renin Ratio, additionally adjusted for body mass index Supplementary Figure 1B. Box and Whisker Plots of log Renin, additionally adjusted for body mass index Supplementary Figure 1C. Box and Whisker Plots of log Aldosterone, additionally adjusted for body mass index Supplementary Figure 2. Box and Whisker Plots of log ACE activity, additionally adjusted for body mass index

Supporting information. R code and test data. (DOCX)

Comparison of result on welded beam design problem.

The comparison results of different algorithms on CEC2017 functions with D=30.

Comparison of result on three-bar truss design problem.

Comparison of result on speed reducer design problem.

The comparison results of different algorithms on CEC2019 functions.

Comparison of result on pressure vessel design problem.

ObjectivesThis study aimed to explore the correlation between the sentiment of nursing notes and the one-year mortality of sepsis patients.MethodsThe box plot was used to compare the differences in sentiment polarity/sentiment subjectivity between different groups. Multivariate logistic regression was used to explore the correlation between sentiment polarity/sentiment subjectivity and one-year mortality of elderly sepsis patients. Ridge regression, XGBoost regression, and random forest were used to explore the importance of sentiment polarity and subjectivity in the one-year mortality of elderly sepsis patients. Restricted cubic spline (RCS) was used to explore whether there was a linear relationship between sentiment polarity, sentiment subjectivity and the one-year mortality of elderly sepsis patients. Kaplan-Meier (KM) curve was used to explore the relationship between the sentiment polarity (or sentiment subjectivity) and the 1-year death of the patient.ResultsCompared with the control group, the one-year mortality group year had lower sentiment polarity and higher sentiment subjectivity. Sentiment polarity and sentiment subjectivity were independently related to the one-year mortality of elderly sepsis patients. There was a linear relationship between sentiment polarity and the one-year mortality of elderly sepsis patients. At the same time, there was a nonlinear relationship between sentiment subjectivity and the one-year mortality of elderly sepsis patients.KM.ConclusionsThe sentiment of nursing notes was correlated with the one-year mortality of elderly sepsis patients.

The comparison results of different algorithms on 23 benchmark functions with D=30.

Source: This image was made exclusively for the paper from data collected in Tropical Medicine Center, Belém, Pará, Brazil.* Chi-square test.

BackgroundArtificial intelligence (AI) methods have established themselves in cardiovascular magnetic resonance (CMR) as automated quantification tools for ventricular volumes, function, and myocardial tissue characterization. Quality assurance approaches focus on measuring and controlling AI-expert differences but there is a need for tools that better communicate reliability and agreement. This study introduces the Verity plot, a novel statistical visualization that communicates the reliability of quantitative parameters (QP) with clear agreement criteria and descriptive statistics.MethodsTolerance ranges for the acceptability of the bias and variance of AI-expert differences were derived from intra- and interreader evaluations. AI-expert agreement was defined by bias confidence and variance tolerance intervals being within bias and variance tolerance ranges. A reliability plot was designed to communicate this statistical test for agreement. Verity plots merge reliability plots with density and a scatter plot to illustrate AI-expert differences. Their utility was compared against Correlation, Box and Bland-Altman plots.ResultsBias and variance tolerance ranges were established for volume, function, and myocardial tissue characterization QPs. Verity plots provided insights into statstistcal properties, outlier detection, and parametric test assumptions, outperforming Correlation, Box and Bland-Altman plots. Additionally, they offered a framework for determining the acceptability of AI-expert bias and variance.ConclusionVerity plots offer markers for bias, variance, trends and outliers, in addition to deciding AI quantification acceptability. The plots were successfully applied to various AI methods in CMR and decisively communicated AI-expert agreement.