Sheet 1 (Raw-Data): The raw data of the study is provided, presenting the tagging results for the used measures described in the paper. For each subject, it includes multiple columns: A. a sequential student ID B an ID that defines a random group label and the notation C. the used notation: user Story or use Cases D. the case they were assigned to: IFA, Sim, or Hos E. the subject's exam grade (total points out of 100). Empty cells mean that the subject did not take the first exam F. a categorical representation of the grade L/M/H, where H is greater or equal to 80, M is between 65 included and 80 excluded, L otherwise G. the total number of classes in the student's conceptual model H. the total number of relationships in the student's conceptual model I. the total number of classes in the expert's conceptual model J. the total number of relationships in the expert's conceptual model K-O. the total number of encountered situations of alignment, wrong representation, system-oriented, omitted, missing (see tagging scheme below) P. the researchers' judgement on how well the derivation process explanation was explained by the student: well explained (a systematic mapping that can be easily reproduced), partially explained (vague indication of the mapping ), or not present.

Tagging scheme:
Aligned (AL) - A concept is represented as a class in both models, either

with the same name or using synonyms or clearly linkable names; Wrongly represented (WR) - A class in the domain expert model is incorrectly represented in the student model, either (i) via an attribute, method, or relationship rather than class, or (ii) using a generic term (e.g., user'' instead ofurban planner''); System-oriented (SO) - A class in CM-Stud that denotes a technical implementation aspect, e.g., access control. Classes that represent legacy system or the system under design (portal, simulator) are legitimate; Omitted (OM) - A class in CM-Expert that does not appear in any way in CM-Stud; Missing (MI) - A class in CM-Stud that does not appear in any way in CM-Expert.

All the calculations and information provided in the following sheets

originate from that raw data.

Sheet 2 (Descriptive-Stats): Shows a summary of statistics from the data collection,

including the number of subjects per case, per notation, per process derivation rigor category, and per exam grade category.

Sheet 3 (Size-Ratio):

The number of classes within the student model divided by the number of classes within the expert model is calculated (describing the size ratio). We provide box plots to allow a visual comparison of the shape of the distribution, its central value, and its variability for each group (by case, notation, process, and exam grade) . The primary focus in this study is on the number of classes. However, we also provided the size ratio for the number of relationships between student and expert model.

Sheet 4 (Overall):

Provides an overview of all subjects regarding the encountered situations, completeness, and correctness, respectively. Correctness is defined as the ratio of classes in a student model that is fully aligned with the classes in the corresponding expert model. It is calculated by dividing the number of aligned concepts (AL) by the sum of the number of aligned concepts (AL), omitted concepts (OM), system-oriented concepts (SO), and wrong representations (WR). Completeness on the other hand, is defined as the ratio of classes in a student model that are correctly or incorrectly represented over the number of classes in the expert model. Completeness is calculated by dividing the sum of aligned concepts (AL) and wrong representations (WR) by the sum of the number of aligned concepts (AL), wrong representations (WR) and omitted concepts (OM). The overview is complemented with general diverging stacked bar charts that illustrate correctness and completeness.

For sheet 4 as well as for the following four sheets, diverging stacked bar

charts are provided to visualize the effect of each of the independent and mediated variables. The charts are based on the relative numbers of encountered situations for each student. In addition, a "Buffer" is calculated witch solely serves the purpose of constructing the diverging stacked bar charts in Excel. Finally, at the bottom of each sheet, the significance (T-test) and effect size (Hedges' g) for both completeness and correctness are provided. Hedges' g was calculated with an online tool: https://www.psychometrica.de/effect_size.html. The independent and moderating variables can be found as follows:

Sheet 5 (By-Notation):

Model correctness and model completeness is compared by notation - UC, US.

Sheet 6 (By-Case):

Model correctness and model completeness is compared by case - SIM, HOS, IFA.

Sheet 7 (By-Process):

Model correctness and model completeness is compared by how well the derivation process is explained - well explained, partially explained, not present.

Sheet 8 (By-Grade):

Model correctness and model completeness is compared by the exam grades, converted to categorical values High, Low , and Medium.

R Scripts contain statistical data analisys for streamflow and sediment data, including Flow Duration Curves, Double Mass Analysis, Nonlinear Regression Analysis for Suspended Sediment Rating Curves, Stationarity Tests and include several plots.

All data associated with this data entry are the simulations related storm surge in three case study locations. These simulated water height, wind and other physical parameters are used for analysis to construct all the figures presented herein. This dataset is associated with the following publication: Liang, M., Z. Dong, S. Julius, J. Neal, and J. Yang. Storm Surge Projection for Objective-based Risk Management for Climate Change Adaptation along the US Atlantic Coast. JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT. American Society of Civil Engineers (ASCE), Reston, VA, USA, 150(6): e04024014-1, (2024).

Summary tables that contain the details for the statistical analyses throughout the manuscript.

BackgroundIndividual participant data (IPD) meta-analyses that obtain “raw” data from studies rather than summary data typically adopt a “two-stage” approach to analysis whereby IPD within trials generate summary measures, which are combined using standard meta-analytical methods. Recently, a range of “one-stage” approaches which combine all individual participant data in a single meta-analysis have been suggested as providing a more powerful and flexible approach. However, they are more complex to implement and require statistical support. This study uses a dataset to compare “two-stage” and “one-stage” models of varying complexity, to ascertain whether results obtained from the approaches differ in a clinically meaningful way. Methods and FindingsWe included data from 24 randomised controlled trials, evaluating antiplatelet agents, for the prevention of pre-eclampsia in pregnancy. We performed two-stage and one-stage IPD meta-analyses to estimate overall treatment effect and to explore potential treatment interactions whereby particular types of women and their babies might benefit differentially from receiving antiplatelets. Two-stage and one-stage approaches gave similar results, showing a benefit of using anti-platelets (Relative risk 0.90, 95% CI 0.84 to 0.97). Neither approach suggested that any particular type of women benefited more or less from antiplatelets. There were no material differences in results between different types of one-stage model. ConclusionsFor these data, two-stage and one-stage approaches to analysis produce similar results. Although one-stage models offer a flexible environment for exploring model structure and are useful where across study patterns relating to types of participant, intervention and outcome mask similar relationships within trials, the additional insights provided by their usage may not outweigh the costs of statistical support for routine application in syntheses of randomised controlled trials. Researchers considering undertaking an IPD meta-analysis should not necessarily be deterred by a perceived need for sophisticated statistical methods when combining information from large randomised trials.

Summary of results of statistical analysis and model equations for the measured responses.

For each main and supporting figures, the linear mixed models, statistical inference tests, and p-values are shown. (XLSX)

Spatial analysis and statistical summaries of the Protected Areas Database of the United States (PAD-US) provide land managers and decision makers with a general assessment of management intent for biodiversity protection, natural resource management, and recreation access across the nation. The PAD-US 3.0 Combined Fee, Designation, Easement feature class (with Military Lands and Tribal Areas from the Proclamation and Other Planning Boundaries feature class) was modified to remove overlaps, avoiding overestimation in protected area statistics and to support user needs. A Python scripted process ("PADUS3_0_CreateVectorAnalysisFileScript.zip") associated with this data release prioritized overlapping designations (e.g. Wilderness within a National Forest) based upon their relative biodiversity conservation status (e.g. GAP Status Code 1 over 2), public access values (in the order of Closed, Restricted, Open, Unknown), and geodatabase load order (records are deliberately organized in the PAD-US full inventory with fee owned lands loaded before overlapping management designations, and easements). The Vector Analysis File ("PADUS3_0VectorAnalysisFile_ClipCensus.zip") associated item of PAD-US 3.0 Spatial Analysis and Statistics ( https://doi.org/10.5066/P9KLBB5D ) was clipped to the Census state boundary file to define the extent and serve as a common denominator for statistical summaries. Boundaries of interest to stakeholders (State, Department of the Interior Region, Congressional District, County, EcoRegions I-IV, Urban Areas, Landscape Conservation Cooperative) were incorporated into separate geodatabase feature classes to support various data summaries ("PADUS3_0VectorAnalysisFileOtherExtents_Clip_Census.zip") and Comma-separated Value (CSV) tables ("PADUS3_0SummaryStatistics_TabularData_CSV.zip") summarizing "PADUS3_0VectorAnalysisFileOtherExtents_Clip_Census.zip" are provided as an alternative format and enable users to explore and download summary statistics of interest (Comma-separated Table [CSV], Microsoft Excel Workbook [.XLSX], Portable Document Format [.PDF] Report) from the PAD-US Lands and Inland Water Statistics Dashboard ( https://www.usgs.gov/programs/gap-analysis-project/science/pad-us-statistics ). In addition, a "flattened" version of the PAD-US 3.0 combined file without other extent boundaries ("PADUS3_0VectorAnalysisFile_ClipCensus.zip") allow for other applications that require a representation of overall protection status without overlapping designation boundaries. The "PADUS3_0VectorAnalysis_State_Clip_CENSUS2020" feature class ("PADUS3_0VectorAnalysisFileOtherExtents_Clip_Census.gdb") is the source of the PAD-US 3.0 raster files (associated item of PAD-US 3.0 Spatial Analysis and Statistics, https://doi.org/10.5066/P9KLBB5D ). Note, the PAD-US inventory is now considered functionally complete with the vast majority of land protection types represented in some manner, while work continues to maintain updates and improve data quality (see inventory completeness estimates at: http://www.protectedlands.net/data-stewards/ ). In addition, changes in protected area status between versions of the PAD-US may be attributed to improving the completeness and accuracy of the spatial data more than actual management actions or new acquisitions. USGS provides no legal warranty for the use of this data. While PAD-US is the official aggregation of protected areas ( https://www.fgdc.gov/ngda-reports/NGDA_Datasets.html ), agencies are the best source of their lands data.

IntroductionThe Fragility Index (FI) and the FI family are statistical tools that measure the robustness of randomized controlled trials (RCT) by examining how many patients would need a different outcome to change the statistical significance of the main results of a trial. These tools have recently gained popularity in assessing the robustness or fragility of clinical trials in many clinical areas and analyzing the strength of the trial outcomes underpinning guideline recommendations. However, it has not been applied to perioperative care Clinical Practice Guidelines (CPG).ObjectivesThis study aims to survey clinical practice guidelines in anesthesiology to determine the Fragility Index of RCTs supporting the recommendations, and to explore trial characteristics associated with fragility.Methods and analysisA methodological survey will be conducted using the targeted population of RCT referenced in the recommendations of the CPG of the North American and European societies from 2012 to 2022. FI will be assessed for statistically significant and non-significant trial results. A Poisson regression analysis will be used to explore factors associated with fragility.DiscussionThis methodological survey aims to estimate the Fragility Index of RCTs supporting perioperative care guidelines published by North American and European societies of anesthesiology between 2012 and 2022. The results of this study will inform the methodological quality of RCTs included in perioperative care guidelines and identify areas for improvement.

The objective of this study was to identify the patterns of juvenile salmonid distribution and relative abundance in relation to habitat correlates. It is the first dataset of its kind because the entire river was snorkeled by one person in multiple years. During two consecutive summers, we completed a census of juvenile salmonids and stream habitat across a stream network. We used the data to test the ability of habitat models to explain the distribution of juvenile coho salmon (Oncorhynchus kisutch), young-of-the-year (age 0) steelhead (Oncorhynchus mykiss), and steelhead parr (= age 1) for a network consisting of several different sized streams. Our network-scale models, which included five stream habitat variables, explained 27%, 11%, and 19% of the variation in the density of juvenile coho salmon, age 0 steelhead, and steelhead parr, respectively. We found weak to strong levels of spatial auto-correlation in the model residuals (Moran's I values ranging from 0.25 - 0.71). Explanatory power of base habitat models increased substantially and the level of spatial auto-correlation decreased with sequential inclusion of variables accounting for stream size, year, stream, and reach location. The models for specific streams underscored the variability that was implied in the network-scale models. Associations between juvenile salmonids and individual habitat variables were rarely linear and ranged from negative to positive, and the variable accounting for location of the habitat within a stream was often more important than any individual habitat variable. The limited success in predicting the summer distribution and density of juvenile coho salmon and steelhead with our network-scale models was apparently related to variation in the strength and shape of fish-habitat associations across and within streams and years. Summary of statistical analysis of the Calawah Riverscape data. NOAA was not involved and did not pay for the collection of this data. This data represents the statistical analysis carried out by Martin Liermann as a NOAA employee.

Summary of independent variables used in the statistical analysis: aesthetic judgment of species and informant attributes (personal and household) (n = 191).

*AUC values included in the analysis are AUC(0-∞) on Day 1 and AUC(0-τ) on Day 28.Results obtained from a mixed model ANOVA on log-transformed data with fixed effects of study day and gender and a random effect of subject.

Number of detected SNPs which are associated to the following seven diseases from WTCCC: Bipolar disorder (BD), coronary artery disease (CAD), hypertension (HT), Crohn's disease (IBD), rheumatoid arthritis (RA), type 1 diabetes (T1D) and type 2 diabetes (T2D). WTCCC refers to the regions reported by the original publication [41] in their Table 3, abbreviations for the other algorithms are just like in Table 2. In brackets we give the number of DNA regions which are covered by the detected SNPs. The whole HLA region on chromosome 6 is counted as only one region.

Regression ranks among the most popular statistical analysis methods across many research areas, including psychology. Typically, regression coefficients are displayed in tables. While this mode of presentation is information-dense, extensive tables can be cumbersome to read and difficult to interpret. Here, we introduce three novel visualizations for reporting regression results. Our methods allow researchers to arrange large numbers of regression models in a single plot. Using regression results from real-world as well as simulated data, we demonstrate the transformations which are necessary to produce the required data structure and how to subsequently plot the results. The proposed methods provide visually appealing ways to report regression results efficiently and intuitively. Potential applications range from visual screening in the model selection stage to formal reporting in research papers. The procedure is fully reproducible using the provided code and can be executed via free-of-charge, open-source software routines in R.

INTRODUCTION As part of its responsibilities, the BC Ministry of Environment monitors water quality in the province’s streams, rivers, and lakes. Often, it is necessary to compile statistics involving concentrations of contaminants or other compounds. Quite often the instruments used cannot measure concentrations below certain values. These observations are called non-detects or less thans. However, non-detects pose a difficulty when it is necessary to compute statistical measurements such as the mean, the median, and the standard deviation for a data set. The way non-detects are handled can affect the quality of any statistics generated. Non-detects, or censored data are found in many fields such as medicine, engineering, biology, and environmetrics. In such fields, it is often the case that the measurements of interest are below some threshold. Dealing with non-detects is a significant issue and statistical tools using survival or reliability methods have been developed. Basically, there are three approaches for treating data containing censored values: 1. substitution, which gives poor results and therefore, is not recommended in the literature; 2. maximum likelihood estimation, which requires an assumption of some distributional form; and 3. and nonparametric methods which assess the shape of the data based on observed percentiles rather than a strict distributional form. This document provides guidance on how to record censor data, and on when and how to use certain analysis methods when the percentage of censored observations is less than 50%. The methods presented in this document are:1. substitution; 2. Kaplan-Meier, as part of nonparametric methods; 3. lognormal model based on maximum likelihood estimation; 4. and robust regression on order statistics, which is a semiparametric method. Statistical software suitable for survival or reliability analysis is available for dealing with censored data. This software has been widely used in medical and engineering environments. In this document, methods are illustrated with both R and JMP software packages, when possible. JMP often requires some intermediate steps to obtain summary statistics with most of the methods described in this document. R, with the NADA package is usually straightforward. The package NADA was developed specifically for computing statistics with non-detects in environmental data based on Helsel (2005b). The data used to illustrate the methods described for computing summary statistics for non-detects are either simulated or based on information acquired from the B.C. Ministry of Environment. This document is strongly based on the book Nondetects And Data Analysis written by Dennis R. Helsel in 2005 (Helsel, 2005b).

Public health-related decision-making on policies aimed at controlling the COVID-19 pandemic outbreak depends on complex epidemiological models that are compelled to be robust and use all relevant available data. This data article provides a new combined worldwide COVID-19 dataset obtained from official data sources with improved systematic measurement errors and a dedicated dashboard for online data visualization and summary. The dataset adds new measures and attributes to the normal attributes of official data sources, such as daily mortality, and fatality rates. We used comparative statistical analysis to evaluate the measurement errors of COVID-19 official data collections from the Chinese Center for Disease Control and Prevention (Chinese CDC), World Health Organization (WHO) and European Centre for Disease Prevention and Control (ECDC). The data is collected by using text mining techniques and reviewing pdf reports, metadata, and reference data. The combined dataset includes complete spatial data such as countries area, international number of countries, Alpha-2 code, Alpha-3 code, latitude, longitude, and some additional attributes such as population. The improved dataset benefits from major corrections on the referenced data sets and official reports such as adjustments in the reporting dates, which suffered from a one to two days lag, removing negative values, detecting unreasonable changes in historical data in new reports and corrections on systematic measurement errors, which have been increasing as the pandemic outbreak spreads and more countries contribute data for the official repositories. Additionally, the root mean square error of attributes in the paired comparison of datasets was used to identify the main data problems. The data for China is presented separately and in more detail, and it has been extracted from the attached reports available on the main page of the CCDC website. This dataset is a comprehensive and reliable source of worldwide COVID-19 data that can be used in epidemiological models assessing the magnitude and timeline for confirmed cases, long-term predictions of deaths or hospital utilization, the effects of quarantine, stay-at-home orders and other social distancing measures, the pandemic’s turning point or in economic and social impact analysis, helping to inform national and local authorities on how to implement an adaptive response approach to re-opening the economy, re-open schools, alleviate business and social distancing restrictions, design economic programs or allow sports events to resume.

Normal Q−Q plot from ELISA data

*Systemic Rapidity 2 measured onset to moderate or worst systemic disease (score ≥2)N/A: not applicable.