Summary statistics for all diagnostic groups.

Descriptive statistics for group 2 using the standard CT detector.

--orthoMCL_ENS.html: R script that (1) generate a data frame for each species with raw reads and read per million (RPMs) for genes assigned to an OGG, and (2) identify the number of genes that overlap within a clade and the number of OGGs that overlap between clades. --MaxFinder_ParalogTests.html: R script that finds the gene with the largest log2fold change for each clade (positive or negative)using the normalized RPM, filters the name of that gene and generates a data frame with OrthoGroup, MaxLFC, and raw counts for these representative genes/OGGs for each species. In addition, it generates paralog numbers and expression concordance summary statistics and related plots for each clade. --OGGmeans_variance_CladeComparisons.html: R script that calculates and compares overall patterns of relative expression of OGGs in monogamous and non-monogamous species across clades.

This data release contains the input-data files and R scripts associated with the analysis presented in [citation of manuscript]. The spatial extent of the data is the contiguous U.S. The input-data files include one comma separated value (csv) file of county-level data, and one csv file of city-level data. The county-level csv (“county_data.csv”) contains data for 3,109 counties. This data includes two measures of water use, descriptive information about each county, three grouping variables (climate region, urban class, and economic dependency), and contains 18 explanatory variables: proportion of population growth from 2000-2010, fraction of withdrawals from surface water, average daily water yield, mean annual maximum temperature from 1970-2010, 2005-2010 maximum temperature departure from the 40-year maximum, mean annual precipitation from 1970-2010, 2005-2010 mean precipitation departure from the 40-year mean, Gini income disparity index, percent of county population with at least some college education, Cook Partisan Voting Index, housing density, median household income, average number of people per household, median age of structures, percent of renters, percent of single family homes, percent apartments, and a numeric version of urban class. The city-level csv (city_data.csv) contains data for 83 cities. This data includes descriptive information for each city, water-use measures, one grouping variable (climate region), and 6 explanatory variables: type of water bill (increasing block rate, decreasing block rate, or uniform), average price of water bill, number of requirement-oriented water conservation policies, number of rebate-oriented water conservation policies, aridity index, and regional price parity. The R scripts construct fixed-effects and Bayesian Hierarchical regression models. The primary difference between these models relates to how they handle possible clustering in the observations that define unique water-use settings. Fixed-effects models address possible clustering in one of two ways. In a "fully pooled" fixed-effects model, any clustering by group is ignored, and a single, fixed estimate of the coefficient for each covariate is developed using all of the observations. Conversely, in an unpooled fixed-effects model, separate coefficient estimates are developed only using the observations in each group. A hierarchical model provides a compromise between these two extremes. Hierarchical models extend single-level regression to data with a nested structure, whereby the model parameters vary at different levels in the model, including a lower level that describes the actual data and an upper level that influences the values taken by parameters in the lower level. The county-level models were compared using the Watanabe-Akaike information criterion (WAIC) which is derived from the log pointwise predictive density of the models and can be shown to approximate out-of-sample predictive performance. All script files are intended to be used with R statistical software (R Core Team (2017). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org) and Stan probabilistic modeling software (Stan Development Team. 2017. RStan: the R interface to Stan. R package version 2.16.2. http://mc-stan.org).

Descriptive statistics for group 2 using the low-dose CT detector.

Descriptive statistics for group 1 using the standard CT detector.

Abstract \r

\r The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement.\r \r \r \r Receptor impact models (RIMs) are developed for specific landscape classes. The prediction of Receptor Impact Variables is a multi-stage process. It relies on the runs from surface water and groundwater models at nodes within the analysis extent. These outputs derive directly from the hydrological model. For a given node, there is a value for each combination of hydrological response variable, future, and replicate or run number. Not all variables may be available or appropriate at every node. This differs to the quantile summary information that is otherwise used to summarise the HRV output and is also registered.\r \r

Dataset History \r

\r There is a key look up table (Excel file) that lists the assessment units (AUIDs) by landscape class (or landscape group if appropriate) and notes that groundwater modelling node and runs, and the surface water modelling node and runs, that should be used for that AUID. In some cases the AUID is only mapped to one set of hydrological modelling output. This look up table represent the AUIDs that require RIV predictions. For NAM and GAL there is a single look up table. For GLO and HUN surface and GW are provided separately. \r \r Receptor impact models (RIMs) are developed for specific landscape classes. The hydrological response variables that a RIM within a landscape class requires are organised by the R script RIM_Prediction_CreateArray.R into an array. The formatted data is available as an R data file format called RDS and can be read directly into R.\r \r The R script IMIA_XXX_RIM_predictions.R applies the receptor model functions (RDS object as part of Data set 1: Ecological expert elicitation and receptor impact models for the XXX subregion) to the HRV array for each landscape class (or landscape group) to make predictions of receptor impact varibles (RIVs). Predictions of a receptor impact from a RIM for a landscape class are summarised at relevant AUIDs by the 5th through to the 95th percentiles (in 5% increments) for baseline and CRDP futures. These are available in the XXX_RIV_quantiles_IMIA.csv data set. RIV predictions are further summarised and compared as boxplots (using the R script boxplotsbyfutureperiod.R) and as (aggregated) spatial risk maps using GIS.\r \r

Dataset Citation \r

\r Bioregional Assessment Programme (2018) GAL Predictions of receptor impact variables v01. Bioregional Assessment Derived Dataset. Viewed 10 December 2018, http://data.bioregionalassessments.gov.au/dataset/67e0aec1-be25-46f5-badc-b4d895a934aa.\r \r

Dataset Ancestors \r

\r * Derived From Queensland wetland data version 3 - wetland areas.\r \r * Derived From Geofabric Surface Cartography - V2.1\r \r * Derived From Landscape classification of the Galilee preliminary assessment extent\r \r * Derived From Geofabric Surface Cartography - V2.1.1\r \r * Derived From GAL Landscape Class Reclassification for impact and risk analysis 20170601\r \r * Derived From GEODATA TOPO 250K Series 3, File Geodatabase format (.gdb)\r \r * Derived From Queensland groundwater dependent ecosystems\r \r * Derived From GEODATA TOPO 250K Series 3\r \r * Derived From Multi-resolution Valley Bottom Flatness MrVBF at three second resolution CSIRO 20000211\r \r * Derived From Landscape classification of the Galilee preliminary assessment extent\r \r * Derived From Biodiversity status of pre-clearing and remnant regional ecosystems - South East Qld\r \r

UPDATED on October 15 2020 After some mistakes in some of the data were found, we updated this data set. The changes to the data are detailed on Zenodo (http://doi.org/10.5281/zenodo.4061807), and an Erratum has been submitted. This data set under CC-BY license contains time series of total abundance and/or biomass of assemblages of insect, arachnid and Entognatha assemblages (grouped at the family level or higher taxonomic resolution), monitored by standardized means for ten or more years. The data were derived from 165 data sources, representing a total of 1668 sites from 41 countries. The time series for abundance and biomass represent the aggregated number of all individuals of all taxa monitored at each site. The data set consists of four linked tables, representing information on the study level, the plot level, about sampling, and the measured assemblage sizes. all references to the original data sources can be found in the pdf with references, and a Google Earth file (kml) file presents the locations (including metadata) of all datasets. When using (parts of) this data set, please respect the original open access licenses. This data set underlies all analyses performed in the paper 'Meta-analysis reveals declines in terrestrial, but increases in freshwater insect abundances', a meta-analysis of changes in insect assemblage sizes, and is accompanied by a data paper entitled 'InsectChange – a global database of temporal changes in insect and arachnid assemblages'. Consulting the data paper before use is recommended. Tables that can be used to calculate trends of specific taxa and for species richness will be added as they become available. The data set consists of four tables that are linked by the columns 'DataSource_ID'. and 'Plot_ID', and a table with references to original research. In the table 'DataSources', descriptive data is provided at the dataset level: Links are provided to online repositories where the original data can be found, it describes whether the dataset provides data on biomass, abundance or both, the invertebrate group under study, the realm, and describes the location of sampling at different geographic scales (continent to state). This table also contains a reference column. The full reference to the original data is found in the file 'References_to_original_data_sources.pdf'. In the table 'PlotData' more details on each site within each dataset are provided: there is data on the exact location of each plot, whether the plots were experimentally manipulated, and if there was any spatial grouping of sites (column 'Location'). Additionally, this table contains all explanatory variables used for analysis, e.g. climate change variables, land-use variables, protection status. The table 'SampleData' describes the exact source of the data (table X, figure X, etc), the extraction methods, as well as the sampling methods (derived from the original publications). This includes the sampling method, sampling area, sample size, and how the aggregation of samples was done, if reported. Also, any calculations we did on the original data (e.g. reverse log transformations) are detailed here, but more details are provided in the data paper. This table links to the table 'DataSources' by the column 'DataSource_ID'. Note that each datasource may contain multiple entries in the 'SampleData' table if the data were presented in different figures or tables, or if there was any other necessity to split information on sampling details. The table 'InsectAbundanceBiomassData' provides the insect abundance or biomass numbers as analysed in the paper. It contains columns matching to the tables 'DataSources' and 'PlotData', as well as year of sampling, a descriptor of the period within the year of sampling (this was used as a random effect), the unit in which the number is reported (abundance or biomass), and the estimated abundance or biomass. In the column for Number, missing data are included (NA). The years with missing data were added because this was essential for the analysis performed, and retained here because they are easier to remove than to add. Linking the table 'InsectAbundanceBiomassData.csv' with 'PlotData.csv' by column 'Plot_ID', and with 'DataSources.csv' by column 'DataSource_ID' will provide the full dataframe used for all analyses. Detailed explanations of all column headers and terms are available in the ReadMe file, and more details will be available in the forthcoming data paper. WARNING: Because of the disparate sampling methods and various spatial and temporal scales used to collect the original data, this dataset should never be used to test for differences in insect abundance/biomass among locations (i.e. differences in intercept). The data can only be used to study temporal trends, by testing for differences in slopes. The data are standardized within plots to allow the temporal comparison, but not necessarily among plots (even within one dataset).

Example data sets and computer code for the book chapter titled "Missing Data in the Analysis of Multilevel and Dependent Data" submitted for publication in the second edition of "Dependent Data in Social Science Research" (Stemmler et al., 2015). This repository includes the computer code (".R") and the data sets from both example analyses (Examples 1 and 2). The data sets are available in two file formats (binary ".rda" for use in R; plain-text ".dat").

The data sets contain simulated data from 23,376 (Example 1) and 23,072 (Example 2) individuals from 2,000 groups on four variables:

ID = group identifier (1-2000) x = numeric (Level 1) y = numeric (Level 1) w = binary (Level 2)

In all data sets, missing values are coded as "NA".

Summary statistics of scientist networks.

Descriptive statistics obtained for the variables referring to the evaluation of the strategies used by group.

Summary statistics of mortality by colon cancer according to age groups.

Descriptive statistics for advertisement calls parameters of species of the D. leucophyllatus-triangulum complex.

b. Descriptive statistics and p-values for biodemographics, lifestyle, self-reported stress and anxiety comparisons by age group, by stress group for Study 2A. c. Descriptive statistics and p-values for biodemographics, lifestyle, self-reported stress and anxiety comparisons by age group, by stress group for Study 2B. d. Means and standard errors of total cumulative stress scores for each study by age group by stress group.

Notes: SA, sleep apnea; NAR, narcolepsy; PSYC, psychiatric sleep disorder; PLM, periodic limb movement; Mdn, median; MAD, median absolute deviation from the median; MRank, mean of ranks; W, test statistic for the Brunner-Munzel rank test. Cut-off scores are from Douglass et al. (1994) [29].*p

Descriptive statistics for the “Early neuroimaging” and “No early neuroimaging” groups (matched).

Note: N = 62 (Uncomplicated MTBI, n = 31; Complicated MTBI, n = 31)*Cohen’s [74] effect size (d): small (.20), medium (.50), large (.80).Descriptive statistics, group comparisons, and effect sizes for individual NAB tests (demographically-adjusted T scores).

ANN sensitivity analysis.

Descriptive statistics and ANT-I performance by sport type, N = 97.

ImportanceDementia is an “overdetermined” syndrome. Few individuals are demented by any single biomarker, while several may independently explain small fractions of dementia severity. It may be advantageous to identify individuals afflicted by a specific biomarker to guide individualized treatment.ObjectiveWe aim to validate a psychometric classifier to identify persons adversely impacted by inflammation and replicate it in a second cohort.DesignSecondary analyses of data collected by the Texas Alzheimer’s Research and Care Consortium (TARCC) (N = 3497) and the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (N = 1737).SettingTwo large, well-characterized multi-center convenience samples.ParticipantsVolunteers with normal cognition (NC), Mild Cognitive Impairment (MCI) or clinical “Alzheimer’s Disease (AD)”.ExposureParticipants were assigned to “Afflicted” or “Resilient” classes on the basis of a psychometric classifier derived by confirmatory factor analysis.Main outcome(s) and measure(s)The groups were contrasted on multiple assessments and biomarkers. The groups were also contrasted regarding 4-year prospective conversions to “AD” from non-demented baseline diagnoses (controls and MCI). The Afflicted groups were predicted to have adverse levels of inflammation-related blood-based biomarkers, greater dementia severity and greater risk of prospective conversion.ResultsIn ADNI /plasma, 47.1% of subjects were assigned to the Afflicted class. 44.6% of TARCC’s subjects were afflicted, 49.5% of non-Hispanic Whites (NHW) and 37.2% of Mexican Americans (MA). There was greater dementia severity in the Afflicted class [by ANOVA: ADNI /F(1) = 686.99, p