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.

DH represents 100% for the relative measure. Differences between medians and distributions were significant between all disciplines if indicated with * and were significantly different between GS and SG when marked with 1, significantly different between GS and DH if marked with 2 and significantly different between SG and DH if marked with 3. If no parameter was significantly different the column is empty. Columns marked with—indicate that the measure was not calculated.Median, interquartile range (IQR) and significance level of the difference between discipline medians and distributions for all parameters, and percentage of DH for GS and SG.

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/

The median (and interquartile range) of the individuals’ median and inter quartile range (range) of both the time on the stone and the number of steps on the stone in the standardized and nonstandardized configuration.

Measurement of network speed, specifically bandwidth, has long been used as a key performance indicator for home broadband. Not only has it become a tool for detecting and diagnosing poor performance, but also for making investment decisions and measuring the quality of experience. However, current tools employ traditional techniques that consider wired measurements as the most accurate. Unfortunately, home users rarely have the capability to conduct reliable wired tests, instead being only able to measure using Wi-Fi. In particular, home wireless is often viewed as an unreliable indicator of network speed, leaving home users with little recourse to challenge the quality of broadband speed that is actually delivered.

In this thesis, we investigate the extent to which Wi-Fi-based tests are actually unreliable, and more importantly, to understand if one can accurately determine if the result was indicative of broadband as a bottleneck or if the measurement was limited by Wi-Fi. We also examine whether the accuracy of the tool is determined by the congestion control algorithm (CCA) and robust against specific use cases.

The results demonstrate that such a determination is eminently possible regardless of the CCA, and that it can be done drawing only on the feature and groups of features already reported by iPerf. We show through extensive experiments that goodness (the test was indicative of broadband speeds) or badness (the test was not `indicative of broadband speeds) can be captured with a precision of 92.4%, drawing only the median throughput and interquartile range with second-by-second windowing reported by iPerf. Finally, we illustrate that the classifier is robust against cross-traffic.

This dataset includes elevation-based probability and depth statistics for estimating inundation under various sea-level rise and high tide flooding scenarios in and around the National Park Service’s Biscayne National Park. For information on the digital elevation model (DEM) source used to develop these datasets refer to the corresponding spatial metadata file (Danielson and others, 2023). This data release includes results from analyses of two local sea-level rise scenarios for two-time steps — the Intermediate-Low and Intermediate-High for 2040 and 2080 from Sweet and others (2022). Additionally, this data release includes maps of inundation probability under the minor, moderate, and major hight tide flooding thresholds. We estimated the probability of an area being inundated under a given scenario using Monte Carlo simulations with 1,000 iterations. For an individual iteration, each pixel of the DEM was randomly propagated based on the lidar data uncertainty, while the sea-level rise and high tide flooding water level estimates were also propagated based on uncertainty in the estimate (Sweet and others, 2022) and tidal datum transformation. Moreover, the probability of a pixel being inundated was calculated by summing the binary simulation outputs and dividing by 1,000. Following, probability was binned into the following classes: 1) Unlikely, probability ≤0.33; 2) Likely as not, probability >0.33 and ≤0.66; and 3) Likely, probability >0.66. Finally, depth statistics were only recorded when depth was equal to or greater than 0. We calculated the median depth, 25th percentile, 75th percentile, and interquartile range using all the pixels that met this criterion. When utilizing the depth statistics, it is important to also consider the probability of this pixel being flooded. In other words, the depth layers may show some depth returns, but the pixel may have rarely been inundated for the 1,000 iterations.

Distribution of Body Mass Index (BMI) (mean, standard deviation (SD), median and inter-quartile range (IQR)) and frequencies of major BMI categories across year of birth and contextual variables of Swiss conscripts.

The objective was to identify horn fly-susceptible and horn fly-resistant animals in a Sindhi herd by two different methods. The number of horn flies on 25 adult cows from a Sindhi herd was counted every 14 days. As it was an open herd, the trial period was divided into three stages based on cow composition, with the same cows maintained within each period: 2011-2012 (36 biweekly observations); 2012-2013 (26 biweekly observations); and 2013-2014 (22 biweekly observations). Only ten cows were present in the herd throughout the entire period from 2011-2014 (84 biweekly observations). The variables evaluated were the number of horn flies on the cows, the sampling date and a binary variable for rainy or dry season. Descriptive statistics were calculated, including the median, the interquartile range, and the minimum and maximum number of horn flies, for each observation day. For the present analysis, fly-susceptible cows were identified as those for which the infestation of flies appeared in the upper quartile for more than 50% of the weeks and in the lower quartile for less than 20% of the weeks. In contrast, fly-resistant cows were defined as those for which the fly counts appeared in the lower quartile for more than 50% of the weeks and in the upper quartile for less than 20% of the weeks. To identify resistant and susceptible cows for the best linear unbiased predictions analysis, three repeated measures linear mixed models (one for each period) were constructed with cow as a random effect intercept. The response variable was the log ten transformed counts of horn flies per cow, and the explanatory variable were the observation date and season. As the trail took place in a semiarid region with two seasons well stablished the season was evaluated monthly as a binary outcome, considering a rainy season if it rained more or equal than 50mm or dry season if the rain was less than 50mm. The Standardized residuals and the BLUPs of the random effects were obtained and assessed for normality, heteroscedasticity and outlying observations. Each cow’s BLUPs were plotted against the average quantile rank values that were determined as the difference between the number of weeks in the high-risk quartile group and the number of weeks in the low risk quartile group, averaged by the total number of weeks in each of the observation periods. A linear model fit for the values of BLUPS against the average rank values and the correlation between the two methods was tested using Spearman’s correlation coefficient. The animal effect values (BLUPs) were evaluated by percentiles, with 0 representing the lowest counts (or more resistant cows) and 10 representing the highest counts (or more susceptible cows). These BLUPs represented only the effect of cow and not the effect of day, season or other unmeasured counfounders.

This dataset includes elevation-based probability and depth statistics for estimating inundation under various sea-level rise and high tide flooding scenarios in and around the National Park Service’s De Soto National Memorial. These datasets were developed using 1-m digital elevation model (DEM) from the 3D Elevation program. This data release includes results from analyses of two local sea-level rise scenarios for two-time steps — the Intermediate-Low and Intermediate-High for 2050 and 2100 from Sweet and others (2022). Additionally, this data release includes maps of inundation probability under the minor, moderate, and major high tide flooding thresholds defined by the National oceanic and Atmospheric Administration (NOAA). We estimated the probability of an area being inundated under a given scenario using Monte Carlo simulations with 1,000 iterations. For an individual iteration, each pixel of the DEM was randomly propagated based on the lidar data uncertainty, while the sea-level rise and high tide flooding water level estimates were also propagated based on uncertainty in the estimate (Sweet and others, 2022) and tidal datum transformation, respectively. Moreover, the probability of a pixel being inundated was calculated by summing the binary simulation outputs and dividing by 1,000. Following, probability was binned into the following classes: 1) Unlikely, probability ≤0.33; 2) Likely as not, probability >0.33 and ≤0.66; and 3) Likely, probability >0.66. Finally, depth statistics were only recorded when depth was equal to or greater than 0. We calculated the median depth, 25th percentile, 75th percentile, and interquartile range using all the pixels that met this criterion. When utilizing the depth statistics, it is important to also consider the probability of this pixel being flooded. In other words, the depth layers may show some depth returns, but the pixel may have rarely been inundated for the 1,000 iterations.

This dataset includes elevation-based probability and depth statistics for estimating inundation under various sea-level rise and high tide flooding scenarios in and around the National Park Service’s Dry Tortugas National Park. These datasets were developed using digital elevation model (DEM) from National Oceanic and Atmospheric Administration (NOAA). This data release includes results from analyses of two local sea-level rise scenarios for two-time steps — the Intermediate-Low and Intermediate-High for 2050 and 2100 from Sweet and others (2022). Additionally, this data release includes maps of inundation probability under the minor, moderate, and major high tide flooding thresholds defined by NOAA. We estimated the probability of an area being inundated under a given scenario using Monte Carlo simulations with 1,000 iterations. For an individual iteration, each pixel of the DEM was randomly propagated based on the lidar data uncertainty, while the sea-level rise and high tide flooding water level estimates were also propagated based on uncertainty in the sea-level rise estimate (Sweet and others, 2022) and tidal datum transformation, respectively. Moreover, the probability of a pixel being inundated was calculated by summing the binary simulation outputs and dividing by 1,000. Following, probability was binned into the following classes: 1) Unlikely, probability ≤0.33; 2) Likely as not, probability >0.33 and ≤0.66; and 3) Likely, probability >0.66. Finally, depth statistics were only recorded when depth was equal to or greater than 0. We calculated the median depth, 25th percentile, 75th percentile, and interquartile range using all the pixels that met this criterion. When utilizing the depth statistics, it is important to also consider the probability of this pixel being flooded. In other words, the depth layers may show some depth returns, but the pixel may have rarely been inundated for the 1,000 iterations.

Larval life history traits and geographic distribution for each thoracican barnacle species used in the study

The table "finalmergeddata.csv" contains life history and enironmental data as well as the calculated variance (IQR = interquartile range, se = standard error) summarized per species. The table "lifehistory.xls" contains the species-specific larval life history data we extracted from the literature. The first tab, "Taxonomy + larval mode" has one row per species. The taxonomy is taken from WoRMS (www.marinespecies.org). The following two tabs contain information on other larval traits and the known geographic distribution of the barnacle species. In these tabs, each species can occur several times, as we chose to give each reference a separate row. The references are detailed in the datatable_references file. The meaning of all columns is explained in the last tab "METADATA". Detailed references for the data sources are available in the last tab "Data sourc...

This dataset includes elevation-based probability and depth statistics for estimating inundation under various sea-level rise and high tide flooding scenarios in and around the National Park Service’s Big Cypress National Preserve. For information on the digital elevation model (DEM) source used to develop these datasets refer to the corresponding spatial metadata file (Danielson and others, 2023). This data release includes results from analyses of two local sea-level rise scenarios for two-time steps — the Intermediate-Low and Intermediate-High for 2050 and 2100 from Sweet and others (2022). Additionally, this data release includes maps of inundation probability under the minor, moderate, and major high tide flooding thresholds defined by the National Oceanic and Atmospheric Administration (NOAA). We estimated the probability of an area being inundated under a given scenario using Monte Carlo simulations with 1,000 iterations. For an individual iteration, each pixel of the DEM was randomly propagated based on the lidar data uncertainty, while the sea-level rise and high tide flooding water level estimates were also propagated based on uncertainty in the sea-level rise estimate (Sweet and others, 2022) and tidal datum transformation, respectively. Moreover, the probability of a pixel being inundated was calculated by summing the binary simulation outputs and dividing by 1,000. Following, probability was binned into the following classes: 1) Unlikely, probability ≤0.33; 2) Likely as not, probability >0.33 and ≤0.66; and 3) Likely, probability >0.66. Finally, depth statistics were only recorded when depth was equal to or greater than 0. We calculated the median depth, 25th percentile, 75th percentile, and interquartile range using all the pixels that met this criterion. When utilizing the depth statistics, it is important to also consider the probability of this pixel being flooded. In other words, the depth layers may show some depth returns, but the pixel may have rarely been inundated for the 1,000 iterations.

The ranges of the plasma lactate quartiles were determined using specimens from the weighted random cohort sample.†Represents the maximum number of participants in each category. Actual number may vary due to missing data.‡Plasma lactate mg/dL may be converted to mmol/L by multiplying by 0.111.§P-trend evaluated with linear or logistic regression using the median lactate value for each quartile as an ordinal variable.∧There were no participants with coronary heart disease in quartile 1. SE not calculated due to small sample size.*Represents geometric mean and interquartile range.Note: LDL represents low density lipoprotein. HDL represents high density lipoprotein.

a : Median absolute percent bias of σ2u was calculated for each simulation scenario, then summarized across scenarios.b : This is the number of simulation scenarios used to calculate the information.

This dataset consists of over 2000 snow depth and position (GNSS) measurements collected on Samoylov and Kurungnakh Islands in the Lena Delta, Russia, between March 31st and April 12th, 2019.These data were collected using an in-house (AWI) build magnetic snow depth probe (mSDP). It allows snow depth measurements to be quickly collected along transects and in grids. The device consists of a Magnetostrictive Transducer contained within a 1.5 m long stainless steel rod (the probe) which can very accurately measure the position of a magnet mounted on a plastic disk that slides up and down the rod. As the rod is inserted into the snow, the plastic disk remains on the snow surface. When the tip of the rod reaches the ground surface, a button is pressed and the device measures and records the snow depth along with the GPS position. The accuracy of the magnetic depth measurement is approximately 0.1 cm; however, the overall accuracy of the depth measurement is significantly less (2 - 4 cm) due to uncertainties in the positioning of the tip in loose vegetation at the base of the snowpack. The minimum measurable snow depth is 5 cm, due to the construction of the device.The mSDP takes 10 samples per measurement point. A Matlab script was used to process the raw data into means for each measurement point. For each measurement, outliers more than 1.5 interquartile ranges above the upper quartile or below the lower quartile were removed. If the deviation between samples was still greater than 1 cm, then all samples with a difference of more than 2 cm from the maximum were removed. This occurred only when the mSDP was removed from the snowpack before the 10 samples had been collected. Otherwise, the 10 samples from a single measurement position varied only by 1 or 2 mm.Missing data in the files are indicated by an empty value. Snow depths of less than 5 cm, below the detection limit of the mSDP, have been assigned a Dsn of "<5".

This record contains raw data related to the article "Novel risk calculator performance in athletes with arrhythmogenic right ventricular cardiomyopathy."

Abstract

Background: Disease progression and ventricular arrhythmias (VAs) in arrhythmogenic right ventricular cardiomyopathy (ARVC) are correlated with physical exercise, and clinical detraining and avoidance of competitive sport practice are suggested for ARVC patients. An algorithm assessing primary arrhythmic risk in ARVC patients was recently developed by Cadrin-Tourigny et al. Data regarding its transferability to athletes are lacking.

Objective: The purpose of this study was to assess the reliability of the Cadrin-Tourigny risk prediction algorithm in a cohort of athletes with ARVC and to describe the impact of clinical detraining on disease progression.

Methods: All athletes undergoing clinical detraining after ARVC diagnosis at our institution were enrolled. Baseline and follow-up clinical characteristics and data on VA events occurring during follow-up were collected. The Cadrin-Tourigny algorithm was used to calculate the a priori predicted VA risk, which was compared with the observed outcomes.

Results: Twenty-five athletes (age 36.1 ± 14.0 years; 80% male) with definite ARVC who were undergoing clinical detraining were enrolled. Over median (interquartile range) follow-up of 5.3 (3.2-6.6) years, a reduction in premature ventricular complex (PVC) burden (P = .001) was assessed, and 10 VA events (40%) were recorded. The a priori algorithm-predicted risk seemed to fit with the observed cohort arrhythmic risk [mean observed-predicted risk difference over 5 years -0.85% (interquartile range -4.8% to +3.1%); P = .85]. At 1-year follow-up, 11 patients (44%) had an improved stress ECG response, and no significant changes in right ventricular ejection fraction were observed.

Conclusion: Clinical detraining is associated with PVC burden reduction in athletes with ARVC. The novel risk prediction algorithm does not seem to require any correction for its application to ARVC athletes.

Keywords: Arrhythmogenic right ventricular cardiomyopathy; Athletes; Clinical detraining; Physical exercise; Risk calculator; Ventricular arrhythmia.

This dataset includes elevation-based probability and depth statistics for estimating inundation under various sea-level rise and high tide flooding scenarios in and around the National Park Service’s San Juan National Historic Site. These datasets were developed using 1-m digital elevation model (DEM) from the 3D Elevation program. This data release includes results from analyses of two local sea-level rise scenarios for two-time steps — the Intermediate-Low and Intermediate-High for 2050 and 2100 from Sweet and others (2022). Additionally, this data release includes maps of inundation probability under the minor, moderate, and major high tide flooding thresholds defined by the National Oceanic and Atmospheric Administration (NOAA). We estimated the probability of an area being inundated under a given scenario using Monte Carlo simulations with 1,000 iterations. For an individual iteration, each pixel of the DEM was randomly propagated based on the lidar data uncertainty, while the sea-level rise and high tide flooding water level estimates were also propagated based on uncertainty in the sea-level rise estimate (Sweet and others, 2022) and tidal datum transformation, respectively. Moreover, the probability of a pixel being inundated was calculated by summing the binary simulation outputs and dividing by 1,000. Following, probability was binned into the following classes: 1) Unlikely, probability ≤0.33; 2) Likely as not, probability >0.33 and ≤0.66; and 3) Likely, probability >0.66. Finally, depth statistics were only recorded when depth was equal to or greater than 0. We calculated the median depth, 25th percentile, 75th percentile, and interquartile range using all the pixels that met this criterion. When utilizing the depth statistics, it is important to also consider the probability of this pixel being flooded. In other words, the depth layers may show some depth returns, but the pixel may have rarely been inundated for the 1,000 iterations.

P-values are for test of difference in quantile normalised characteristic between diagnostic groups, adjusted for age and gender and relatedness.n = Number; Interquartile range is lower quartile (LQ) and upper quartile (UQ). BMI  =  body mass index; SBP  =  systolic blood pressure; DBP  =  diastolic blood pressure; FBG  =  fasting blood glucose; TG  =  triglyceride; HDL-c  =  high density lipoprotein cholesterol Scr  =  serum creatinine; eGFR  =  estimated glomerular filtration rate; UACR  =  urinary albumin-to-creatinine ratio.

AL refers to the axial length, CCT to the central corneal thickness, ACD to the external phakic anterior chamber depth measured from the corneal front apex to the front apex of the crystalline lens, LT to the central thickness of the crystalline lens, R1 and R2 to the corneal radii of curvature for the flat and steep meridians, Rmean to the average of R1 and R2, PIOL to the refractive power of the intraocular lens implant, and SEQ to the spherical equivalent power achieved 5 to 12 weeks after cataract surgery.

Parameters are shown separately for the three different datasets (1, 2, pathological gamblers [PG]).