Formula for converting median and interquartile range (IQR) into mean and standard deviation (SD).

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

Geoscience Australias GEOMACS model was utilised to produce hindcast hourly time series of continental shelf (~20 to 300 m depth) bed shear stress (unit of measure: Pascal, Pa) on a 0.1 degree grid covering the period March 1997 to February 2008 (inclusive). The hindcast data represents the combined contribution to the bed shear stress by waves, tides, wind and density-driven circulation. Included in the parameters that will be calculated to represent the magnitude of the bulk of the data are the quartiles of the distribution; Q25, Q50 and Q75 (i.e. the values for which 25, 50 and 75 percent of the observations fall below). The interquartile range, , of the GEOMACS output takes the observations from between Q25 and Q75 to provide an accurate representation of the spread of observations. The interquartile range was shown to provide a more robust representation of the observations than the standard deviation, which produced highly skewed observations (Hughes and Harris 2008). This dataset is a contribution to the CERF Marine Biodiversity Hub and is hosted temporarily by CMAR on behalf of Geoscience Australia.

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.

The U.S. Climate Reference Network (USCRN) was designed to monitor the climate of the United States using research quality instrumentation located within representative pristine environments. This Standardized Soil Moisture (SSM) and Soil Moisture Climatology (SMC) product set is derived using the soil moisture observations from the USCRN. The hourly soil moisture anomaly (SMANOM) is derived by subtracting the MEDIAN from the soil moisture volumetric water content (SMVWC) and dividing the difference by the interquartile range (IQR = 75th percentile - 25th percentile) for that hour: SMANOM = (SMVWC - MEDIAN) / (IQR). The soil moisture percentile (SMPERC) is derived by taking all the values that were used to create the empirical cumulative distribution function (ECDF) that yielded the hourly MEDIAN and adding the current observation to the set, recalculating the ECDF, and determining the percentile value of the current observation. Finally, the soil temperature for the individual layers is provided for the dataset user convenience. The SMC files contain the MEAN, MEDIAN, IQR, and decimal fraction of available data that are valid for each hour of the year at 5, 10, 20, 50, and 100 cm depth soil layers as well as for a top soil layer (TOP) and column soil layer (COLUMN). The TOP layer consists of an average of the 5 and 10 cm depths, while the COLUMN layer includes all available depths at a location, either two layers or five layers depending on soil depth. The SSM files contain the mean VWC, SMANOM, SMPERC, and TEMPERATURE for each of the depth layers described above. File names are structured as CRNSSM0101-STATIONNAME.csv and CRNSMC0101-STATIONNAME.csv. SSM stands for Standardized Soil Moisture and SCM represent Soil Moisture Climatology. The first two digits of the trailing integer indicate major version and the second two digits minor version of the product.

Median and interquartile range (IQR) for each numeric variable of the dataset, stratified by Survival (S: Survived, NS: Not survived, T: Total cohort), and for the SIRS and SEPSIS cohorts.

Abstract

The dataset was derived by the Bioregional Assessment Programme from multiple datasets. The source dataset is 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.

Hydrological Response Variables (HRVs) are the hydrological characteristics of the system that potentially change due to coal resource development. These data refer to the HRVs related to the AWRA-R model for the Namoi subregion for the 54 simulation nodes. The nine hydrological response variables (AF, P99, FD, IQR, ZFD, P01, LFD, LFS, LLFS) were computed under CRDP and Baseline conditions, respectively and the ACRD is the difference between the Baseline and CRDP.

Abbreviation meaning

AF - the annual streamflow volume (GL/year)

P01 - the daily streamflow rate at the first percentile (ML/day)

P01 - the daily streamflow rate at the first percentile (ML/day)

IQR - the inter-quartile range in daily streamflow (ML/day). That is, the difference between the daily streamflow rate at the 75th percentile and at the 25th percentile.

LFD - the number of low streamflow days per year. The threshold for low streamflow days is the 10th percentile from the simulated 90-year period (2013 to 2102)

LFS - the number of low streamflow spells per year (perennial streams only). A spell is defined as a period of contiguous days of streamflow below the 10th percentile threshold

LLFS - the length (days) of the longest low streamflow spell each year

P99 - the daily streamflow rate at the 99th percentile (ML/day)

FD - flood days, the number of days with streamflow greater than the 90th percentile from the simulated 90-year period (2013 to 2102)

ZFD - Zero flow days

Purpose

This is the dataset used for the Namoi 2.6.1 product to evaluate additional coal mine and coal resource development impacts on hydrological response variables at 54 simulation nodes.

Dataset History

The Namoi AWRA-R model outputs were used to determine the impacts on the HRVs to produce these data. Readme files within the folders in the dataset provide an explanation on how the resource was created. The nine HRVs (AF, P99, FD, IQR, ZFD, P01, LFD, LFS, LLFS) were computed under CRDP and Baseline conditions, respectively. The difference between CRDP and Baseline is used for predicting ACRD impacts on hydrological response variables at 54 simulation nodes.

Dataset Citation

Bioregional Assessment Programme (2017) Namoi standard Hydrological Response Variables (HRVs). Bioregional Assessment Derived Dataset. Viewed 11 December 2018, http://data.bioregionalassessments.gov.au/dataset/189f4c7a-29e1-41f9-868d-b7f5184d829f.

Dataset Ancestors

• Derived From Historical Mining Footprints DTIRIS NAM 20150914

• Derived From Namoi AWRA-R (restricted input data implementation)

• Derived From River Styles Spatial Layer for New South Wales

• Derived From Namoi Surface Water Mine Footprints - digitised

• Derived From Namoi AWRA-R model implementation (post groundwater input)

• Derived From National Surface Water sites Hydstra

• Derived From Namoi AWRA-L model

• Derived From Namoi Hydstra surface water time series v1 extracted 140814

• Derived From GEODATA 9 second DEM and D8: Digital Elevation Model Version 3 and Flow Direction Grid 2008

• Derived From Namoi Environmental Impact Statements - Mine footprints

• Derived From Namoi Existing Mine Development Surface Water Footprints

####### # Data description #

This dataset have been constructed and used for scientific purpose, available in the paper "Detecting the effects of inter-annual and seasonal changes of environmental factors on the the striped red mullet population in the Bay of Biscay" authored by Kermorvant C., Caill-Milly N., Sous D., Paradinas I., Lissardy M. and Liquet B. and published in Journal of Sea Research. This file is an extraction from the SACROIS fisheries database created by Ifremer (for more information see https://sextant.ifremer.fr/record/3e177f76-96b0-42e2-8007-62210767dc07/) and from the Copernicus database. Biochemestry comes from the product GLOBAL_ANALYSIS_FORECAST_BIO_001_028 (https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=GLOBAL_ANALYSIS_FORECAST_BIO_001_028). Temperature and salinity comes from GLOBAL_ANALYSIS_FORECAST_PHY_001_024 product (https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=GLOBAL_ANALYSIS_FORECAST_PHY_001_024). As fisheries landing per unit of effort is only available per ICES rectangle and by month, environmental data have been aggregated accordingly.

######### # Colomns description # ############### rectangle - The 6 ICES statistical rectangles used in the study. time_m - Time in months, from the beginning to the end of the study. annee = year mois = month (from 1 to 12) Poids = Weight of red mullet landed valeur = Temps_peche = fishing time Nb_sequence = number of fishing sequences Moy / Med / Var / StD Quartil_1 / Quartil_3 / min / max / CV / IQR = statistical descriptors of landing by rectangle and by month log_cpue = log of Med colomn mean_surface_s = mean of surface salinity by month and by rectangle median_surface_s = median of surface salinity by month and by rectangle mean_surface_t = mean of surface temperature by month and by rectangle median_surface_t = median of surface temperature by month and by rectangle si / zeu /po4 / pyc / o2/ nppv / no3 and nh4 mean and median concentration by rectangle and by month pc3 / pc2 / pc1 - projections of previous biochemestry variables on the three first axes of a PCA

Mean, median, and interquartile range (IQR) scores for each concern according to gender, type of diagnosis, language, and age.

This dataset contains the prioritization provided by a panel of 15 experts to a set of 28 barriers categories for 8 different roles of the future energy system. A Delphi method was followed and the scores provided in the three rounds carried out are included. The dataset also contains the scripts used to assess the results and the output of this assessment.

A list of the information contained in this file is:

• data folder: this folders includes the scores given by the 15 experts in the 3 rounds. Every round is in an individual folder. There is a file per expert that has the scores between -5 (not relevant at all) to 5 (completely relevant) per barrier (rows) and actor (columns). There is also a file with the description of the experts in terms of their position in the company, the type of company and the country.

• fig folder: this folder includes the figures created to assess the information provided by the experts. For each round, the following figures are created (in each respective folder):

  • Boxplot with the distribution of scores per barriers and roles.

  • Heatmap with the mean scores per barriers and roles.

  • Boxplots with the comparison of the different distributions provided by the experts of each group (depending on the keywords) per barrier and role.

  • Heatmap with the mean score per barrier and use case and with the prioritization per barrier and use case.

Finally, bar plots with the mean scores differences between rounds and boxplot with comparisons of the scores distributions are also provided.

• stat folder: this folder includes the files with the results of the different statistical assessment carried out. For each round, the following figures are created (in each respective folder):

  • The statistics used to assess the scores (Intraclass correlation coefficient, Inter-rater agreement, Inter-rater agreement p-value, Homogeneity of Variances, Average interquartile range, Standard Deviation of interquartile ranges, Friedman test p-value Average power post hoc) per barrier and per role.

  • The results of the post hoc of the Friedman Test per berries and per roles.

  • The average score per barrier and per role.

  • The mean value of the scores provided by the experts grouped by the keywords per barrier and role. P-value of the comparison of these two values.

  • The end prioritization of the barrier for the use case (averaging the scores or merging the critical sets)

Finally, the differences between the mean and standard deviations of the scores between two consecutive rounds are provided.

*p values, all sepsis vs controls, Mann Whitney test.†Performed in a subset of patients representative of the entire cohort, as described in methods and results. Severe sepsis n = 11, non-severe sepsis n = 12, control n = 4.

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...

*Abnormal values were defined by the clinical laboratory or manufacturer's assay. These abnormalities included: D-dimer >256 ng/ml, PAI-1 activity >31 IU/ml, antithrombin activity

IQR, interquartile range; no, number; SD, standard deviation; In total, 103 patients used systemic immunomodulating therapy after starting dupilumab. Eighteen of these patients were using systemic immunomodulating therapy from start dupilumab till study cut-off point: ciclosporin (n=6), azathioprine (n=2), methotrexate (n=4), mycophenolic acid/mycophenolate mofetil (n=3), systemic corticosteroids (n=3). Two patients stopped for respectively 14 and 33 days with systemic corticosteroids treatment and then restarted with systemic corticosteroids which they were still using at the end of study. This table displays the 83 patients that discontinued systemic concomitant immunomodulating therapy during the study.

Median (interquartile range) of percentage of children under 5 years old with access to prevention, need for and access to curative care in 53 countries.

Abbreviations: GFR, glomerular filtration rate; MDRD, Modification of Diet in Renal Disease; CKD-EPI, Chronic Kidney Disease Epidemiology Collaboration; CI, confidence interval; IQR, interquartile range.Performance of bias, precision and accuracy between measured GFR and estimated GFR in the validation data set.

BackgroundTuberculosis (TB) remains a significant public health challenge, particularly among vulnerable populations like children. This is especially true in Sub-Saharan Africa, where the burden of TB in children is substantial. Zambia ranks 21st among the top 30 high TB endemic countries globally. While studies have explored TB in adults in Zambia, the prevalence and associated factors in children are not well documented. This study aimed to determine the prevalence and sociodemographic, and clinical factors associated with active TB disease in hospitalized children under the age of 15 years at Livingstone University Teaching Hospital (LUTH), the largest referral center in Zambia’s Southern Province.MethodsThis retrospective cross-sectional study of 700 pediatric patients under 15 years old, utilized programmatic data from the Pediatrics Department at LUTH. A systematic sampling method was used to select participants from medical records. Data on demographics, medical conditions, anthropometric measurements, and blood tests were collected. Data analysis included descriptive statistics, chi-square tests, and multivariable logistic regression to identify factors associated with TB.ResultsThe median age was 24 months (interquartile range (IQR): 11, 60) and majority were male (56.7%, n = 397/700). Most participants were from urban areas (59.9%, n = 419/700), and 9.2% (n = 62/675) were living with HIV. Malnutrition and comorbidities were present in a significant portion of the participants (19.0% and 25.1%, respectively). The prevalence of active TB cases was 9.4% (n = 66/700) among hospitalized children. Persons living with HIV (Adjusted odds ratio (AOR) of 6.30; 95% confidence interval (CI) of 2.85, 13.89, p< 0.001), and those who were malnourished (AOR: 10.38, 95% CI: 4.78, 22.55, p< 0.001) had a significantly higher likelihood of developing active TB disease.ConclusionThis study revealed a prevalence 9.4% active TB among hospitalized children under 15 years at LUTH. HIV status and malnutrition emerged as significant factors associated with active TB disease. These findings emphasize the need for pediatric TB control strategies that prioritize addressing associated factors to effectively reduce the burden of tuberculosis in Zambian children.

BackgroundAn increase in the prevalence of HIV drug resistance (HIVDR) has been reported in recent years, especially in persons on non-nucleoside reverse transcriptase inhibitors (NNRTIs) due to their low genetic barrier to mutations. However, there is a paucity of epidemiological data quantifying HIVDR in the era of new drugs like dolutegravir (DTG) in sub-Saharan Africa. We, therefore, sought to determine the prevalence and correlates of viral load (VL) suppression in adult people with HIV (PWH) on a fixed-dose combination of tenofovir disoproxil fumarate/lamivudine/dolutegravir (TLD) or tenofovir alafenamide/emtricitabine/dolutegravir (TAFED) and describe patterns of mutations in individuals failing treatment.MethodsWe conducted a cross-sectional study among 384 adults living with HIV aged ≥15 years between 5th June 2023 and 10th August 2023. Demographic, laboratory and clinical data were collected from electronic health records using a data collection form. Viral load suppression was defined as plasma HIV-1 RNA VL of

Medians and interquartile range (IQR).

BackgroundEvidence is mounting that the biopsychosocial paradigm is more accurate and useful than the biomedical paradigm of care. Habits of thought can hinder the implementation of this knowledge into daily care strategies. To understand and lessen these potential barriers, we asked: 1) What is the relative implicit and explicit attitudes of musculoskeletal surgeons towards the biomedical or biopsychosocial paradigms of medicine? 2) What surgeon factors are associated with these attitudes?MethodsAn online survey-based experiment was distributed to members of the Science of Variation Group (SOVG) with a total of 163 respondents. Implicit bias towards the biomedical or biopsychosocial paradigms was measured using an Implicit Association Test (IAT) designed by our team using open-source software; explicit preferences were measured using ordinal scales.ResultsOn average, surgeons demonstrated a moderate implicit bias towards the biomedical paradigm (d-score: -0.21; Interquartile range [IQR]: -0.56 to 0.19) and a moderate explicit preference towards the biopsychosocial paradigm (mean: 14; standard deviation: 14). A greater implicit bias towards the biomedical paradigm was associated with male surgeons (d-score: -0.30; IQR: -0.57 to 0.14; P = 0.005). A greater explicit preference towards the biomedical paradigm was independently associated with a European practice location (Regression coefficient: -9.1; 95% CI: -14 to -4.4; P