Summary statistics for study behavior for the Self-Regulated and Yoked groups and t-tests statistics comparing differences between the two groups.

Descriptive statistics for group 1 using the standard CT detector.

Summary statistics of available studies by age grouping.

Descriptive statistics for group 1 using the low-dose detector.

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

We used a Pearson’s correlation test to identify CpG sites where DNA methylation was significantly correlated between whole blood and sperm This analysis was restricted to the 155,269 sites that showed met minimum variability criteria in both tissues (range of middle 80% > 5%). Summary statistics are reported for all sites in the discovery dataset. Summary statistics from the replication groups are reported for the sites that also passed quality control in our replication dataset. IlmnID = Illumina CpG identifier, chr = chromosome, location = position on chromosome in hg19 reference, P = p-value in the discovery data, r = correlation coefficient in the discovery data, P_rep = p-value in the lean replication group, r_lean = correlation coefficient in the lean replication group, P_ob = p-value in the obese replication group, r_ob = correlation coefficient in the obese replication group. (ZIP)

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 standard CT detector.

In the last decade, a plethora of algorithms have been developed for spatial ecology studies. In our case, we use some of these codes for underwater research work in applied ecology analysis of threatened endemic fishes and their natural habitat. For this, we developed codes in Rstudio® script environment to run spatial and statistical analyses for ecological response and spatial distribution models (e.g., Hijmans & Elith, 2017; Den Burg et al., 2020). The employed R packages are as follows: caret (Kuhn et al., 2020), corrplot (Wei & Simko, 2017), devtools (Wickham, 2015), dismo (Hijmans & Elith, 2017), gbm (Freund & Schapire, 1997; Friedman, 2002), ggplot2 (Wickham et al., 2019), lattice (Sarkar, 2008), lattice (Musa & Mansor, 2021), maptools (Hijmans & Elith, 2017), modelmetrics (Hvitfeldt & Silge, 2021), pander (Wickham, 2015), plyr (Wickham & Wickham, 2015), pROC (Robin et al., 2011), raster (Hijmans & Elith, 2017), RColorBrewer (Neuwirth, 2014), Rcpp (Eddelbeuttel & Balamura, 2018), rgdal (Verzani, 2011), sdm (Naimi & Araujo, 2016), sf (e.g., Zainuddin, 2023), sp (Pebesma, 2020) and usethis (Gladstone, 2022).

It is important to follow all the codes in order to obtain results from the ecological response and spatial distribution models. In particular, for the ecological scenario, we selected the Generalized Linear Model (GLM) and for the geographic scenario we selected DOMAIN, also known as Gower's metric (Carpenter et al., 1993). We selected this regression method and this distance similarity metric because of its adequacy and robustness for studies with endemic or threatened species (e.g., Naoki et al., 2006). Next, we explain the statistical parameterization for the codes immersed in the GLM and DOMAIN running:

In the first instance, we generated the background points and extracted the values of the variables (Code2_Extract_values_DWp_SC.R). Barbet-Massin et al. (2012) recommend the use of 10,000 background points when using regression methods (e.g., Generalized Linear Model) or distance-based models (e.g., DOMAIN). However, we considered important some factors such as the extent of the area and the type of study species for the correct selection of the number of points (Pers. Obs.). Then, we extracted the values of predictor variables (e.g., bioclimatic, topographic, demographic, habitat) in function of presence and background points (e.g., Hijmans and Elith, 2017).

Subsequently, we subdivide both the presence and background point groups into 75% training data and 25% test data, each group, following the method of Soberón & Nakamura (2009) and Hijmans & Elith (2017). For a training control, the 10-fold (cross-validation) method is selected, where the response variable presence is assigned as a factor. In case that some other variable would be important for the study species, it should also be assigned as a factor (Kim, 2009).

After that, we ran the code for the GBM method (Gradient Boost Machine; Code3_GBM_Relative_contribution.R and Code4_Relative_contribution.R), where we obtained the relative contribution of the variables used in the model. We parameterized the code with a Gaussian distribution and cross iteration of 5,000 repetitions (e.g., Friedman, 2002; kim, 2009; Hijmans and Elith, 2017). In addition, we considered selecting a validation interval of 4 random training points (Personal test). The obtained plots were the partial dependence blocks, in function of each predictor variable.

Subsequently, the correlation of the variables is run by Pearson's method (Code5_Pearson_Correlation.R) to evaluate multicollinearity between variables (Guisan & Hofer, 2003). It is recommended to consider a bivariate correlation ± 0.70 to discard highly correlated variables (e.g., Awan et al., 2021).

Once the above codes were run, we uploaded the same subgroups (i.e., presence and background groups with 75% training and 25% testing) (Code6_Presence&backgrounds.R) for the GLM method code (Code7_GLM_model.R). Here, we first ran the GLM models per variable to obtain the p-significance value of each variable (alpha ≤ 0.05); we selected the value one (i.e., presence) as the likelihood factor. The generated models are of polynomial degree to obtain linear and quadratic response (e.g., Fielding and Bell, 1997; Allouche et al., 2006). From these results, we ran ecological response curve models, where the resulting plots included the probability of occurrence and values for continuous variables or categories for discrete variables. The points of the presence and background training group are also included.

On the other hand, a global GLM was also run, from which the generalized model is evaluated by means of a 2 x 2 contingency matrix, including both observed and predicted records. A representation of this is shown in Table 1 (adapted from Allouche et al., 2006). In this process we select an arbitrary boundary of 0.5 to obtain better modeling performance and avoid high percentage of bias in type I (omission) or II (commission) errors (e.g., Carpenter et al., 1993; Fielding and Bell, 1997; Allouche et al., 2006; Kim, 2009; Hijmans and Elith, 2017).

Table 1. Example of 2 x 2 contingency matrix for calculating performance metrics for GLM models. A represents true presence records (true positives), B represents false presence records (false positives - error of commission), C represents true background points (true negatives) and D represents false backgrounds (false negatives - errors of omission).

Validation set

Model

True

False

Presence

A

B

Background

C

D

We then calculated the Overall and True Skill Statistics (TSS) metrics. The first is used to assess the proportion of correctly predicted cases, while the second metric assesses the prevalence of correctly predicted cases (Olden and Jackson, 2002). This metric also gives equal importance to the prevalence of presence prediction as to the random performance correction (Fielding and Bell, 1997; Allouche et al., 2006).

The last code (i.e., Code8_DOMAIN_SuitHab_model.R) is for species distribution modelling using the DOMAIN algorithm (Carpenter et al., 1993). Here, we loaded the variable stack and the presence and background group subdivided into 75% training and 25% test, each. We only included the presence training subset and the predictor variables stack in the calculation of the DOMAIN metric, as well as in the evaluation and validation of the model.

Regarding the model evaluation and estimation, we selected the following estimators:

1) partial ROC, which evaluates the approach between the curves of positive (i.e., correctly predicted presence) and negative (i.e., correctly predicted absence) cases. As farther apart these curves are, the model has a better prediction performance for the correct spatial distribution of the species (Manzanilla-Quiñones, 2020).

2) ROC/AUC curve for model validation, where an optimal performance threshold is estimated to have an expected confidence of 75% to 99% probability (De Long et al., 1988).

Resource availability largely determines the distribution and behaviour of organisms. In plant‐pollinator communities, availability of floral resources may change so rapidly that pollinator individuals can benefit from switching between multiple resources, i.e. different flowering plant species. Insect pollinator individuals of a given generation often occur in different time windows during the reproductive season. This temporal variation in individual occurrences, together with the rapidly changing resource availability, may lead individuals of the same population to encounter and use different resources, resulting in an apparent individual specialisation. We hypothesized, that 1) individual pollinators change their resource use (flower visitation) during their lifetime according to the changing availability of floral resources, and that 2) temporal variation in individual occurrences of pollinators and in resource availability will partly explain individual specialisation. To test the...

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

Because of the COVID-19 pandemic, presentation of public health data to the public has increased without much of the public having the knowledge to understand what these statistics mean or why some populations are at higher risk of adverse outcomes. Recognizing that those most impacted by COVID-19 are from vulnerable populations, we developed a training program called "The quantitative public health data literacy training program", aimed at increasing the data literacy of towards high school and college students from such vulnerable groups that introduces the basics of public health, data literacy, statistical software, descriptive statistics, and data ethics. The instructors taught eight synchronous sessions (five were also offered asynchronously), consisting of lectures and experiential group exercises. The program recruited, engaged, and retained a large cohort (n > 100) of underrepresented students in biostatistics and data science for a virtual data literacy training. The course provides a framework for developing and implementing similar public health training programs designed to increase diversity in the field.This project provides de-identified data for program's baseline/final assessment , program feedback as well as grades for certain portion of the program. The "Data-files" folder contains all the data collected during program. Along with the deidentified data, code is also provided (in R language) to analyze the data as presented in tables in potential publications.

Summary statistics of scientist networks.

Cognitive measure descriptive statistics, independent sample t-tests evaluating group differences in sex (female vs. male), ethnicity (Hispanic vs. Non-Hispanic), and respiratory distress (ARDS/AHRF vs. SOB/None), as well as Pearson’s r correlations testing associations with COVID-19 clinical severity (NEWS2), depression (PHQ-9), anxiety (GAD 7), sleep disturbance (PSQI), and brain fog (BFQ) total scores.

This file contains data dictionaries for the following datasets within LMIP (http://lmip.gov.au/):\r \r Summary Data\r Employment by Industry\r Employment by Industry Time Series\r Employment Projections by Industry\r Employment by occupation\r Unemployment Rate, Participation Rate & Employment Rate Time Series for States/Territories\r Unemployment Duration\r Population by Age Group\r Population by Age Group Time Series\r Population by Labour Force Status

Diffusion tensor imaging (DTI) descriptive statistics of axial diffusivity, fractional anisotropy and mean diffusivity per tract, per group.

Extracted in machine readable form from the AIHW Australian Cancer Incidence and Mortality books\r \r These files contain summary statistics by age, year and sex for major cancers.\r \r Users are advised to read the Data Quality Statement for the 2010 version of the ACD. In particular, please note that the 2010 data contained in the ACIM books include estimates for NSW and ACT because the real data are not yet available.\r

Accumulating evidence indicates that unpredictable signals in early life represent a unique form of adverse childhood experiences (ACEs) associated with disrupted neurodevelopmental trajectories in children and adolescents. The Questionnaire of Unpredictability in Childhood (QUIC) was developed to assess early life unpredictability [1], encompassing social, emotional, and physical unpredictability in a child’s environment, and has been validated in three independent cohorts. However, the importance of identifying ACEs in diverse populations, including non-English speaking groups, necessitates translation of the QUIC. The current study aims to translate and validate a Spanish language version of the QUIC (QUIC-SP) and assess its associations with mental and physical health. Spanish-speaking participants (N = 285) were recruited via the online market crowdsourcing platform, Amazon Mechanical Turk (MTurk), and completed an online survey that included the QUIC-SP and validated Spanish language assessments of physical and mental health. The QUIC-SP demonstrated excellent psychometric properties and similar mean scores, endorsement rates, and internal reliability to the English language version, thus establishing its validity among Spanish-speaking adults. Higher QUIC-SP scores, indicating greater unpredictability in early life, predicted increased symptoms of anxiety, anhedonia, depression, and poorer physical health. Given significant racial and ethnic disparities in health, the QUIC-SP may serve as a valuable tool to address the public health consequences of ACEs among Spanish-speaking populations.

Descriptive statistics for the three genotype groups studied.

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