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

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

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 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

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 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.

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.

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.

1. Introduction

Datasets are used to evaluate the performance of a Kalman filter approach to estimate daily discharge. This is a perturbed version of synthetic SWOT datasets consisting of 15 river sections, which are commonly agreed datasets for evaluating the performance of SWOT discharge algorithms (Frasson et al., 2020, 2021). The benchmarking manuscript entitled “A Kalman Filter Approach for Estimating Daily Discharge Using Space-based Discharge Estimates” is currently under review at Water Resources Research. Once the manuscript is accepted, its DOI will be included here.

2. File description

The datasets are generally divided into two categories: river information (River_Info) and time series data (Timeseries_Data). River information provides fundamental and general river characteristics, whereas time series data offers daily reach-averaged data for each reach. In time series data, the data mainly contains three components: true data, perturbed measurements, and true and perturbed flow law parameters (A0, an, and b). For each reach, there are 10000 realizations of perturbed measurements per time step and there are 100 realizations of time-invariant perturbed flow law parameters through a Monte Carlo simulation (Frasson et al., 2023). Moreover, to support our proposed Kalman filter approach to estimate daily discharge, the datasets provide the median of the perturbed discharge, river width, water surface slope, and change in the cross-sectional area, as well as the uncertainty of the perturbed discharge and change in the cross-sectional area based on the interquartile range (Fox, 2015).

To support reproducibility and facilitate example usage, we now include a MATLAB code package (KalmanFilter_Code.zip) that demonstrates how to run the Kalman filter approach using the Missouri Downstream case as an example.

Datasets are contained in a .mat file per river. The detailed groups and variables are in the following:

River_Info

Name: River name, data type: char

QWBM: Mean annual discharge from the water balance model WBMsed (Cohen et al., 2014)

rch_bnd: Reach boundaries measured in meters from the upstream end of the model

gdrch: Good reaches in the study. They were used to exclude small reaches defined around low-head dams and other obstacles where Manning’s equation should not be applied.

Timeseries_Data

t: Time measured in days since the first day or “0-January-0000” for cases when specific dates were available. Dimension: 1, time step.

A: Reach-averaged cross-sectional area of flow in m². Dimension: Reach, time step.

Q_true: True reach-averaged discharge (m³/s). Dimension: Reach, time step.

Q_ptb: Perturbed discharge (m³/s), including 10000 realizations for each measurement. Dimension: Good reach, time step, 10000.

med_Q_ptb: Median perturbed discharge (m³/s) across the 10000 realizations. Dimension: Good reach, time step.

sigma_Q_ptb: Uncertainty of the perturbed discharge (m³/s), calculated based on the interquartile range. Dimension: Good reach, time step.

W_true: True reach-averaged river width (m). Dimension: Reach, time step.

W_ptb: Perturbed river width (m), including 10000 realizations for each measurement. Dimension: Good reach, time step, 10000.

med_W_ptb: Median perturbed river width (m) across the 10000 realizations. Dimension: Good reach, time step.

H_true: True reach-averaged water surface elevation (m). Dimension: Reach, time step.

H_ptb: Perturbed water surface elevation (m), including 10000 realizations for each measurement. Dimension: Good reach, time step, 10000.

S_true: True reach-averaged water surface slope (m/m). Dimension: Reach, time step.

S_ptb: Perturbed water surface slope (m/m), including 10000 realizations for each measurement. Dimension: Good reach, time step, 10000.

med_S_ptb: Median perturbed water surface slope (m/m) across the 10000 realizations. Dimension: Good reach, time step.

dA_true: True reach-averaged change in the cross-sectional area (m²). Dimension: Good reach, time step.

dA_ptb: Perturbed change in the cross-sectional area (m²), including 10000 realizations for each measurement. Dimension: Good reach, time step, 10000.

med_dA_ptb: Median perturbed change in the cross-sectional area (m²) across the 10000 realizations. Dimension: Good reach, time step.

sigma_dA_ptb: Uncertainty of the perturbed change in the cross-sectional area (m²), calculated based on the interquartile range. Dimension: Good reach, time step.

A0_true: True baseline cross-sectional area (m²). Dimension: Good reach, 1.

A0: Perturbed baseline cross-sectional area (m²), including 100 realizations for each parameter. Dimension: Good reach, 100.

na_true: True friction coefficient. Dimension: Good reach, 1.

na: Perturbed friction coefficient, including 100 realizations for each parameter. Dimension: Good reach, 100.

b_true: True exponent coefficient. Dimension: Good reach, 1.

b: Perturbed exponent coefficient, including 100 realizations for each parameter. Dimension: Good reach, 100.

Linear Performance Pricing (LPP) Pipe Pricing Dataset

This synthetic dataset simulates supplier quotes for iron pipes of varying lengths. It is designed to demonstrate Linear Performance Pricing (LPP), a procurement analytics technique used to identify cost-saving opportunities by correlating product prices with performance parameters (e.g., pipe length). The dataset includes: - Real-world variations: Noise, outliers, and multiple suppliers. - Target prices: Calculated using market trends (target_price_market) and best-practice benchmarks (target_price_benchmark).

Inspired by the example from "Data-Driven Spend Management" (Chapter 3).

Suggested Analysis Tasks

1. Regression Analysis: Replicate the market line (P^M = 1.465 + 1.076L) using linear regression.
2. Outlier Detection: Identify overpriced quotes using Z-scores or IQR.
3. Savings Calculation: Compute total savings if prices are negotiated down to the benchmark.
4. Supplier Comparison: Analyze pricing strategies of S1 vs S2 vs S3 vs S4.
5. Visualization: Plot price vs. length with market/benchmark lines.

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Dataset from the article Secchi F, Monti CB, Alì M, Carbone FS, Cannaò PM, Sardanelli F. Diagnostic Value of Global Cardiac Strain in Patients With Myocarditis. J Comput Assist Tomogr. 2020 Jul/Aug;44(4):591-598. doi: 10.1097/RCT.0000000000001062. PMID: 32697530.

Abstract

Background: Cardiac strain represents an imaging biomarker of contractile dysfunction.

Purpose: The purpose of this study was to investigate the diagnostic value of cardiac strain obtained by feature-tracking cardiac magnetic resonance (MR) in acute myocarditis.

Materials and methods: Cardiac MR examinations of 46 patients with myocarditis and preserved ejection fraction at acute phase and follow-up were analyzed along with cardiac MR of 46 healthy age- and sex-matched controls. Global circumferential strain and global radial strain were calculated for each examination, along with myocardial edema and late gadolinium enhancement, and left ventricle functional parameters, through manual contouring of the myocardium. Correlations were assessed using Spearman ρ. Wilcoxon and Mann-Whitney U test were used to assess differences between data. Receiver operating characteristics curves and reproducibility were obtained to assess the diagnostic role of strain parameters.

Results: Global circumferential strain was significantly lower in controls (median, -20.4%; interquartile range [IQR], -23.4% to -18.7%) than patients in acute phase (-18.4%; IQR, -21.0% to -16.1%; P = 0.001) or at follow-up (-19.2%; IQR, -21.5% to -16.1%; P = 0.020). Global radial strain was significantly higher in controls (82.4%; IQR, 62.8%-104.9%) than in patients during the acute phase (65.8%; IQR, 52.9%-79.5%; P = 0.001). Correlations were found between global circumferential strain and global radial strain in all groups (acute, ρ = -0.580, P < 0.001; follow-up, ρ = -0.399, P = 0.006; controls, ρ = -0.609, P < 0.001), and between global circumferential strain and late gadolinium enhancement only in myocarditis patients (acute, ρ = 0.035, P = 0.024; follow-up, ρ = 0.307, P = 0.038).

Conclusions: Cardiac strain could potentially have a role in detecting acute myocarditis in low-risk acute myocarditis patients where cardiac MR is the main diagnosing technique.

this dataset gathered the trajectories of 161 lagrangian surface drifters that were deployed in the western mediterranean sea in 2023 by three campaigns of the swot adopt-a-crossover consortium: c-swot-2023, bioswot-med and fast-swot. drifter trajectories are available between march 27th 2023 and january 22th 2024. the deployment strategy involved releasing drifters to target specific mesoscale and submesoscale structures in the vicinity of selected swot passes. these structures were identified using spasso software, which combined near-real-time remote data from copernicus (duacs) and early swot data provided by cls/cnes. several drifter designs are used in these experiments : svp drifters drogued at 15m, 50m, and 100m; svp-b drifters at 15m depth; a customized bgc-svp drifter drogued at 15m and equipped with additional sensors such as a ctd (for temperature and salinity) and an optical triplet measuring biochemical properties of sea; surface drifters such as code, carthe, hereon type with drogue within the first meter depth; and spotter, melodi-eodyn devices as wave drifters. the original nominal sampling rates range from 5 minutes to 1 hour. drifters were deployed in the passes 3 and 16 of swot orbit during its fast-sampling (cal-val) phase (1-day revisit until july 10th) and some of the drifters further crossed the satellite ground-tracks afterwords, when the satellite science orbit was set to 21 days. this dataset is a collaborative effort between the swot-adac consortium and fast-swot, bioswot-med and c-swot cruises. to provide a single interoperable dataset, all drifter trajectories from the different campaigns were processed with the same scripts in a similar manner, resulting in three distinct levels of processing. l0 – harmonised and preprocessed trajectoriesall initial trajectories are merged into a single dataset with variables renamed to match database standards. the following steps are applied: removing rows with missing date/time, ordering by ascending time, trimming to valid deployment/recovery periods, dropping rows with missing values, eliminating duplicates, removing rows with repeated times but different positions, and excluding rows with erroneous latitude/longitude (e.g., outliers outside the mediterranean sea). l1 – processed trajectoriesl1 trajectories are filtered based on acceleration. velocity and acceleration are calculated at each timestep, and positions with accelerations exceeding 4 times the interquartile range (iqr) are removed. this results in irregularly spaced trajectories that retain the original gps positions and therefore the overall current dynamics signal with its multiscale components but exclude gps fix outliers as defined above.l2 – smoothed and regularly interpolated trajectoriesl2-trajectories are obtained from the l1-trajectories, that are regularly interpolated and smoothed in order to reduce noise, especially on acceleration. two methods are used: the lowess method (inspired by elipot et al. 2016) and a variational method developed by m. demol and a. ponte (inspired by yaremchuk and coelho, 2014). l2 trajectories are available with time steps of 10 minutes, 30 minutes, or 1 hour. for more details on the smoothing and interpolating processing, please refer to the attached pdf.data export in netcdf formateach drifter trajectory is stored in eight separate netcdf files, organised into eight distinct folders based on the processing stage and temporal resolution. for a given drifter, the following files are available :l0_data/bioswot_carthe_4388553.ncl1_data/bioswot_carthe_4388553.ncl2_data_variational_10min/bioswot_carthe_4388553.ncl2_data_variational_30min/bioswot_carthe_4388553.ncl2_data_variational_1hour/bioswot_carthe_4388553.ncl2_data_lowess_10min/bioswot_carthe_4388553.ncl2_data_lowess_30min/bioswot_carthe_4388553.ncl2_data_lowess_1hour/bioswot_carthe_4388553.nccontact list : maristella berta (maristella.berta@sp.ismar.cnr.it), margot demol (margot.demol@ifremer.fr), laura gómez navarro (laura.gomez@uib.es) and lloyd izard (lloyd.izard@locean.ipsl.fr)pis contact for the different involved projects: bio-swot-med andrea doglioli (andrea.doglioli@univ-amu.fr); c-swot pierre garreau (pierre.garreau@ifremer.fr), franck dumas (franck.dumas@shom.fr) and aurélien ponte (aurelien.ponte@ifremer.fr); fast-swot: ananda pascual (ananda.pascual@imedea.uib-csic.es) and baptiste mourre (bmourre@imedea.uib-csic.es).referencesdavis, russ e. “drifter observations of coastal surface currents during code: the method and descriptive view.” journal of geophysical research: oceans 90, no. c3 (1985): 4741–55. https://doi.org/10.1029/jc090ic03p04741.elipot, shane, rick lumpkin, renellys c perez, jonathan m lilly, jeffrey j early, and adam m sykulski. “a global surface drifter data set at hourly resolution.” journal of geophysical research: oceans 121, no. 5 (2016):[...]

Objectives: This study aimed to ascertain utility and vision-related quality of life in patients awaiting access to specialist eye care. A secondary aim was to evaluate the association of utility indices with demographic profile and waiting time. Methods: Consecutive patients that had been waiting for ophthalmology care answered the 25-item National Eye Institute Visual Function Questionnaire (NEI VFQ-25). The questionnaire was administered when patients arrived at the clinics for their first visit. We derived a utility index (VFQ-UI) from the patients’ responses, then calculated the correlation between this index and waiting time and compared utility across demographic subgroups stratified by age, sex, and care setting. Results: 536 individuals participated in the study (mean age 52.9±16.6 years; 370 women, 69% women). The median utility index was 0.85 (interquartile range [IQR] 0.70–0.92; minimum 0.40, maximum 0.97). The mean VFQ-25 score was 70.88±14.59. Utility correlated weakly and nonsignificantly with waiting time (-0.05, P = 0.24). It did not vary across age groups (P = 0.85) or care settings (P = 0.77). Utility was significantly lower for women (0.84, IQR 0.70–0.92) than men (0.87, IQR 0.73–0.93, P = 0.03), but the magnitude of this difference was small (Cohen’s d = 0.13). Conclusion: Patients awaiting access to ophthalmology care had a utility index of 0.85 on a scale of 0 to 1. This measurement was not previously reported in the literature. Utility measures can provide insight into patients’ perspectives and support economic health analyses and inform health policies.

eGFR: estimated glomerular filtration rate, IQR: interquartile range.

OBJECTIVE:

To evaluate safety and efficacy of image guided-hypofractionated radiation therapy (IG-HRT) in patients with thoracic nodes oligometastases.

METHODS:

The present study is a multicenter analysis. Oligometastatic patients, affected by a maximum of five active lesions in three or less different organs, treated with IG-HRT to thoracic nodes metastases between 2012 and 2017 were included in the analysis. Primary end point was local control (LC), secondary end points were overall survival (OS), progression-free survival, acute and late toxicity. Univariate and multivariate analysis were performed to identify possible prognostic factors for the survival end points.

RESULTS:

76 patients were included in the analysis. Different RT dose and fractionation schedules were prescribed according to site, number, size of the lymph node(s) and to respect dose constraints for relevant organs at risk. Median biologically effective dose delivered was 75 Gy (interquartile range: 59-86 Gy). Treatment was optimal; one G1 acute toxicity and seven G1 late toxicities of any grade were recorded. Median follow-up time was 23.16 months. 16 patients (21.05%) had a local progression, while 52 patients progressed in distant sites (68.42 %).Median local relapse free survival was not reached, LC at 6, 12 and 24 months was 96.05% [confidence interval (CI) 88.26-98.71%], 86.68% (CI 75.86-92.87) and 68.21% (CI 51.89-80.00%), respectively. Median OS was 28.3 months (interquartile range 16.1-47.2). Median progression-freesurvival was 9.2 months (interquartile range 4.1-17.93).At multivariate analysis, RT dose, colorectal histology, systemic therapies were correlated with LC. Performance status and the presence of metastatic sites other than the thoracic nodes were correlated with OS. Local response was a predictor of OS.

CONCLUSION:

IG-HRT for thoracic nodes was safe and feasible. Higher RT doses were correlated to better LC and should be taken in consideration at least in patients with isolated nodal metastases and colorectal histology.

ADVANCES IN KNOWLEDGE:

Radiotherapy is safe and effective treatment for thoracic nodes metastases, higher radiotherapy doses are correlated to better LC. Oligometastatic patients can receive IG-HRT also for thoracic nodes metastases.

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.

BackgroundRheumatic and musculoskeletal disorders (RMDs) are associated with cardiovascular diseases (CVDs), with hypertension being the most common. We aimed to determine the prevalence of high blood pressure (HBP), awareness, treatment, and blood pressure control among patients with RMDs seen in a Rheumatology clinic in Uganda.MethodsWe conducted a cross-sectional study at the Rheumatology Clinic of Mulago National Referral Hospital (MNRH), Kampala, Uganda. Socio-demographic, clinical characteristics and anthropometric data were collected. Multivariable logistic regression was performed using STATA 16 to determine factors associated with HBP in patients with RMDs.ResultsA total of 100 participants were enrolled. Of these, majority were female (84%, n = 84) with mean age of 52.1 (standard deviation: 13.8) years and median body mass index of 28 kg/m2 (interquartile range (IQR): 24.8 kg/m2–32.9 kg/m2). The prevalence of HBP was 61% (n = 61, 95% CI: 51.5–70.5), with the majority (77%, n = 47, 95% CI: 66.5–87.6) being aware they had HTN. The prevalence of HTN was 47% (n = 47, 37.2–56.8), and none had it under control. Factors independently associated with HBP were age 46-55years (adjusted prevalence ratio (aPR): 2.5, 95% confidence interval (CI): 1.06–5.95), 56–65 years (aPR: 2.6, 95% CI: 1.09–6.15), >65 years (aPR: 2.5, 95% CI: 1.02–6.00), obesity (aPR: 3.7, 95% CI: 1.79–7.52), overweight (aPR: 2.7, 95% CI: 1.29–5.77).ConclusionThere was a high burden of HBP among people with RMDs in Uganda with poor blood pressure control, associated with high BMI and increasing age. There is a need for further assessment of the RMD specific drivers of HBP and meticulous follow up of patients with RMDs.

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

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

*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.