Descriptive statistics, mean ± SD, range, median and interquartile range (IQR).

Median regional Pearson correlations of QQuant values ± interquartile range in each cohort.

Median (interquartile range) of percentage of adult respondents with need for and access to care in 53 countries.

Proportion of positive results, interquartile range (IQR), minimum-maximum range, and median per diagnostic test at three different time points (baseline) of 24 S. haematobium-positive subjects.

Median (InterQuartile Range, IQR) of air polltants and adjusteda odds ratio (95% confidence intervals) of the use of spectacles associated with one Inter-Quartile Range (IQR) increase in exposure to each pollutant.

Median (interquartile range) times (in minutes) from arrival to imaging and key emergency interventions in study hospital (21,681 patients).

Median (interquartile range) of percentage of characteristics of adult respondents and their households in 53 countries.

Dietary intake of the study participants in the GUSTO study presented as median and interquartile range (IQR) (n = 511).

A Mann-Whitney U test, with post hoc Bonferroni correction were used for statistical analysis. Adjusted significance value p<0.016 (*).MoM: multiple of the median; IQR: interquartile range; PAPP-A: Pregnancy-Associated Plasma Protein-A; fβ–hCG: free β–human Chorionic Gonadotropin; ADAM12: A Disintegrin And Metalloprotease 12; PlGF: Placental Growth Factor; MAP: Mean Arterial Pressure; EO-PE: early-onset preeclampsia; LO-PE: late-onset preeclampsia.

These are simulated data without any identifying information or informative birth-level covariates. We also standardize the pollution exposures on each week by subtracting off the median exposure amount on a given week and dividing by the interquartile range (IQR) (as in the actual application to the true NC birth records data). The dataset that we provide includes weekly average pregnancy exposures that have already been standardized in this way while the medians and IQRs are not given. This further protects identifiability of the spatial locations used in the analysis. This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: File format: R workspace file; “Simulated_Dataset.RData”. Metadata (including data dictionary) • y: Vector of binary responses (1: adverse outcome, 0: control) • x: Matrix of covariates; one row for each simulated individual • z: Matrix of standardized pollution exposures • n: Number of simulated individuals • m: Number of exposure time periods (e.g., weeks of pregnancy) • p: Number of columns in the covariate design matrix • alpha_true: Vector of “true” critical window locations/magnitudes (i.e., the ground truth that we want to estimate) Code Abstract We provide R statistical software code (“CWVS_LMC.txt”) to fit the linear model of coregionalization (LMC) version of the Critical Window Variable Selection (CWVS) method developed in the manuscript. We also provide R code (“Results_Summary.txt”) to summarize/plot the estimated critical windows and posterior marginal inclusion probabilities. Description “CWVS_LMC.txt”: This code is delivered to the user in the form of a .txt file that contains R statistical software code. Once the “Simulated_Dataset.RData” workspace has been loaded into R, the text in the file can be used to identify/estimate critical windows of susceptibility and posterior marginal inclusion probabilities. “Results_Summary.txt”: This code is also delivered to the user in the form of a .txt file that contains R statistical software code. Once the “CWVS_LMC.txt” code is applied to the simulated dataset and the program has completed, this code can be used to summarize and plot the identified/estimated critical windows and posterior marginal inclusion probabilities (similar to the plots shown in the manuscript). Optional Information (complete as necessary) Required R packages: • For running “CWVS_LMC.txt”: • msm: Sampling from the truncated normal distribution • mnormt: Sampling from the multivariate normal distribution • BayesLogit: Sampling from the Polya-Gamma distribution • For running “Results_Summary.txt”: • plotrix: Plotting the posterior means and credible intervals Instructions for Use Reproducibility (Mandatory) What can be reproduced: The data and code can be used to identify/estimate critical windows from one of the actual simulated datasets generated under setting E4 from the presented simulation study. How to use the information: • Load the “Simulated_Dataset.RData” workspace • Run the code contained in “CWVS_LMC.txt” • Once the “CWVS_LMC.txt” code is complete, run “Results_Summary.txt”. Format: Below is the replication procedure for the attached data set for the portion of the analyses using a simulated data set: Data The data used in the application section of the manuscript consist of geocoded birth records from the North Carolina State Center for Health Statistics, 2005-2008. In the simulation study section of the manuscript, we simulate synthetic data that closely match some of the key features of the birth certificate data while maintaining confidentiality of any actual pregnant women. Availability Due to the highly sensitive and identifying information contained in the birth certificate data (including latitude/longitude and address of residence at delivery), we are unable to make the data from the application section publically available. However, we will make one of the simulated datasets available for any reader interested in applying the method to realistic simulated birth records data. This will also allow the user to become familiar with the required inputs of the model, how the data should be structured, and what type of output is obtained. While we cannot provide the application data here, access to the North Carolina birth records can be requested through the North Carolina State Center for Health Statistics, and requires an appropriate data use agreement. Description Permissions: These are simulated data without any identifying information or informative birth-level covariates. We also standardize the pollution exposures on each week by subtracting off the median exposure amount on a given week and dividing by the interquartile range (IQR) (as in the actual application to the true NC birth records data). The dataset that we provide includes weekly average pregnancy exposures that have already been standardized in this way while the medians and IQRs are not given. This further protects identifiability of the spatial locations used in the analysis. This dataset is associated with the following publication: Warren, J., W. Kong, T. Luben, and H. Chang. Critical Window Variable Selection: Estimating the Impact of Air Pollution on Very Preterm Birth. Biostatistics. Oxford University Press, OXFORD, UK, 1-30, (2019).

Univariate Associations between Tumor Infiltrates and Prostate Cancer Outcomes: Median counts (interquartile range) unless otherwise specified.

Median (interquartile ranges) of propofol doses (in mg kg-1 h-1) and time intervals at which the different clinical outcome scores changed (in square brackets) and intubation was performed.

aStatistical significance was determined by the Kruskal-Wallis test.bStatistical significance was determined by the Mann-Whitey U test.c The mean age of the patients is 64.8.MiR-210 expression and clinicaopathological feature in BC patients [median (interquartile range)].

*n = 1041 (35 missing data).BMI = body mass index (kg/m2); SD = standard deviation; IQR = interquartile range; EI energy intake (MJ/d); BMR = basal metabolic rate (MJ/d).

a: Chi square test;b: Kruskal-Wallis test.

The Precipitation Estimation from Remotely Sensed Information using an Artificial Neural Network-Climate Data Record (PERSIANN-CDR) is a new, retrospective satellite-based precipitation dataset, constructed as a climate data record for hydrological and climate studies. The PERSIANN-CDR is available from 1983-present making the dataset the longest satellite based precipitation data record available. The precipitation maps are available at daily temporal resolution for the latitude band 60°S–60°N at 0.25 degrees. The maps shown here represent 30-year annual and seasonal median and interquartile range (IQR) of the PERSIANN-CDR dataset from 1984 – 2014. In the median precipitation maps, the mid-point value (or 50th percentile) for each pixel in is computed and plotted for the study area. The range of the data about the median is represented by the interquartile range (IQR), and shows the variability of the dataset. For these maps, winter = December – February, spring = March – May, summer = June – August, fall = September – November

Tumor Infiltrating T Lymphocyte and Macrophage Count Associations with Obesity Status reported as median counts (interquartile range) unless otherwise specified.

Context

Our target was to predict gender, age and emotion from audio. We found audio labeled datasets on Mozilla and RAVDESS. So by using R programming language 20 statistical features were extracted and then after adding the labels these datasets were formed. Audio files were collected from "Mozilla Common Voice" and “Ryerson AudioVisual Database of Emotional Speech and Song (RAVDESS)”.

Content

Datasets contains 20 feature columns and 1 column for denoting the label. The 20 statistical features were extracted through the Frequency Spectrum Analysis using R programming Language. They are: 1) meanfreq - The mean frequency (in kHz) is a pitch measure, that assesses the center of the distribution of power across frequencies. 2) sd - The standard deviation of frequency is a statistical measure that describes a dataset’s dispersion relative to its mean and is calculated as the variance’s square root. 3) median - The median frequency (in kHz) is the middle number in the sorted, ascending, or descending list of numbers. 4) Q25 - The first quartile (in kHz), referred to as Q1, is the median of the lower half of the data set. This means that about 25 percent of the data set numbers are below Q1, and about 75 percent are above Q1. 5) Q75 - The third quartile (in kHz), referred to as Q3, is the central point between the median and the highest distributions. 6) IQR - The interquartile range (in kHz) is a measure of statistical dispersion, equal to the difference between 75th and 25th percentiles or between upper and lower quartiles. 7) skew - The skewness is the degree of distortion from the normal distribution. It measures the lack of symmetry in the data distribution. 8) kurt - The kurtosis is a statistical measure that determines how much the tails of distribution vary from the tails of a normal distribution. It is actually the measure of outliers present in the data distribution. 9) sp.ent - The spectral entropy is a measure of signal irregularity that sums up the normalized signal’s spectral power. 10) sfm - The spectral flatness or tonality coefficient, also known as Wiener entropy, is a measure used for digital signal processing to characterize an audio spectrum. Spectral flatness is usually measured in decibels, which, instead of being noise-like, offers a way to calculate how tone-like a sound is. 11) mode - The mode frequency is the most frequently observed value in a data set. 12) centroid - The spectral centroid is a metric used to describe a spectrum in digital signal processing. It means where the spectrum’s center of mass is centered. 13) meanfun - The meanfun is the average of the fundamental frequency measured across the acoustic signal. 14) minfun - The minfun is the minimum fundamental frequency measured across the acoustic signal 15) maxfun - The maxfun is the maximum fundamental frequency measured across the acoustic signal. 16) meandom - The meandom is the average of dominant frequency measured across the acoustic signal. 17) mindom - The mindom is the minimum of dominant frequency measured across the acoustic signal. 18) maxdom - The maxdom is the maximum of dominant frequency measured across the acoustic signal 19) dfrange - The dfrange is the range of dominant frequency measured across the acoustic signal. 20) modindx - the modindx is the modulation index, which calculates the degree of frequency modulation expressed numerically as the ratio of the frequency deviation to the frequency of the modulating signal for a pure tone modulation.

Acknowledgements

Gender and Age Audio Data Souce: Link: https://commonvoice.mozilla.org/en Emotion Audio Data Souce: Link : https://smartlaboratory.org/ravdess/

Left atrial volumes and ejection fraction (median [interquartile range]) assessed in 155 Cavalier King Charles Spaniels using the monoplane and biplane area length methods (ALM) and the Simpson’s methods of discs (SMOD) from the left apical 4- and 2-chamber views, according to the ACVIM classification [8].

The Precipitation Estimation from Remotely Sensed Information using an Artificial Neural Network-Climate Data Record (PERSIANN-CDR) is a satellite-based precipitation dataset for hydrological and climate studies, spanning from 1983 to present. It is the longest satellite-based precipitation record available, with daily data at 0.25° resolution for the 60°S–60°N latitude band.PERSIANN rain rate estimates are generated at 0.25° resolution and calibrated to a monthly merged in-situ and satellite product from the Global Precipitation Climatology Project (GPCP). The model uses Gridded Satellite (GridSat-B1) infrared data at 3-hourly time steps, with the raw output (PERSIANN-B1) bias-corrected and accumulated to produce the daily PERSIANN-CDR.The maps show 31 years (1984–2014) of annual and seasonal median and interquartile range (IQR) data. The median represents the 50th percentile of precipitation, and the IQR reflects the range between the 75th and 25th percentiles, showing data variability. Median and IQR are preferred over mean and standard deviation as they are less influenced by extreme values and better represent non-normally distributed data, such as precipitation, which is skewed and zero-limited.Data and Metadata: NCEIThis is a component of the Gulf Data Atlas (V1.0) for the Physical topic area.