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.

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.

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.

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.

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

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 weighted depeding on the importance of the role in each use case and the final prioritization. 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 fuzzy merging of the critical sets) Finally, the differences between the mean and standard deviations of the scores between two consecutive rounds are provided.

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.

This dataset is a collection of 1, 2, or 3 images from: BIPED, BSDS500, BSDS300, DIV2K, WIRE-FRAME, CID, CITYSCAPES, ADE20K, MDBD, NYUD, THANGKA, PASCAL-Context, SET14, URBAN10, and the camera-man image. The image selection process consists on computing the Inter-Quartile Range (IQR) intensity value on all the images, images larger than 720×720 pixels were not considered. In dataset whose images are in HR, they were cut. We thank all the datasets owners to make them public. This dataset is just for Edge Detection not contour nor Boundary tasks.

This layer gives the results of a detailed investigation into the background concentrations of selected trace elements in Canterbury's major soil groups from samples taken between 28/2/2006 and 16/3/2006. Canterbury soil groups identified by the Land Resource Inventory (LRI) and Canterbury Soils (CS) datasets were used in this investigation and are retained in this layer. A total of 90 sample sites were distributed across these soil groups; 17 in the Christchurch urban area and 73 through-out the rest of Canterbury. From these samples concentrations of; Arsenic, Boron, Cadmium, Chromium, Copper, Lead, Manganese, Mercury, Nickel, and Zinc were measured in mg/kg. Level 2 gives the maximum concentration values of the above trace elements measured in each soil group plus half the interquartile range (buffer). It is recommended new soil sample results be compared against both "Trace Elements Level 1" and "Level 2" to assess whether the site is contaminated. For a detailed account of the site selection and sampling method employed in this investigation and recommend user guidelines please see Report No. R07/1 "Background concentrations of selected trace elements in Canterbury soils" prepared for Environment Canterbury by Tonkin and Taylor Ltd, July 2006.

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.

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.

A live version of the data record, which will be kept up-to-date with new estimates, can be downloaded from the Humanitarian Data Exchange: https://data.humdata.org/dataset/covid-19-mobility-italy.

If you find the data helpful or you use the data for your research, please cite our work:

Pepe, E., Bajardi, P., Gauvin, L., Privitera, F., Lake, B., Cattuto, C., & Tizzoni, M. (2020). COVID-19 outbreak response, a dataset to assess mobility changes in Italy following national lockdown. Scientific Data 7, 230 (2020).

The data record is structured into 4 comma-separated value (CSV) files, as follows:

id_provinces_IT.csv. Table of the administrative codes of the 107 Italian provinces. The fields of the table are:

COD_PROV is an integer field that is used to identify a province in all other data records;

SIGLA is a two-letters code that identifies the province according to the ISO_3166-2 standard (https://en.wikipedia.org/wiki/ISO_3166-2:IT);

DEN_PCM is the full name of the province.

OD_Matrix_daily_flows_norm_full_2020_01_18_2020_04_17.csv. The file contains the daily fraction of users’ moving between Italian provinces. Each line corresponds to an entry of matrix (i, j). The fields of the table are:

p1: COD_PROV of origin,

p2: COD_PROV of destination,

day: in the format yyyy-mm-dd.

median_q1_q3_rog_2020_01_18_2020_04_17.csv. The file contains median and interquartile range (IQR) of users’ radius of gyration in a province by week. Each entry of the table fields of the table are:

COD_PROV of the province;

SIGLA of the province;

DEN_PCM of the province;

week: median value of the radius of gyration on week week, with week in the format dd/mm-DD/MM where dd/mm and DD/MM are the first and the last day of the week, respectively.

week Q1 first quartile (Q1) of the distribution of the radius of gyration on week week,

week Q3 third quartile (Q3) of the distribution of the radius of gyration on week week,

average_network_degree_2020_01_18_2020_04_17.csv. The file contains daily time-series of the average degree 〈k〉 of the proximity network. Each entry of the table is a value of 〈k〉 on a given day. The fields of the table are:

COD_PROV of the province;

SIGLA of the province;

DEN_PCM of the province;

day in the format yyyy-mm-dd.

ESRI shapefiles of the Italian provinces updated to the most recent definition are available from the website of the Italian National Office of Statistics (ISTAT): https://www.istat.it/it/archivio/222527.

This dataset provides geospatial location data and scripts used to analyze the relationship between MODIS-derived NDVI and solar and sensor angles in a pinyon-juniper ecosystem in Grand Canyon National Park. The data are provided in support of the following publication: "Solar and sensor geometry, not vegetation response, drive satellite NDVI phenology in widespread ecosystems of the western United States". The data and scripts allow users to replicate, test, or further explore results. The file GrcaScpnModisCellCenters.csv contains locations (latitude-longitude) of all the 250-m MODIS (MOD09GQ) cell centers associated with the Grand Canyon pinyon-juniper ecosystem that the Southern Colorado Plateau Network (SCPN) is monitoring through its land surface phenology and integrated upland monitoring programs. The file SolarSensorAngles.csv contains MODIS angle measurements for the pixel at the phenocam location plus a random 100 point subset of pixels within the GRCA-PJ ecosystem. The script files (folder: 'Code') consist of 1) a Google Earth Engine (GEE) script used to download MODIS data through the GEE javascript interface, and 2) a script used to calculate derived variables and to test relationships between solar and sensor angles and NDVI using the statistical software package 'R'. The file Fig_8_NdviSolarSensor.JPG shows NDVI dependence on solar and sensor geometry demonstrated for both a single pixel/year and for multiple pixels over time. (Left) MODIS NDVI versus solar-to-sensor angle for the Grand Canyon phenocam location in 2018, the year for which there is corresponding phenocam data. (Right) Modeled r-squared values by year for 100 randomly selected MODIS pixels in the SCPN-monitored Grand Canyon pinyon-juniper ecosystem. The model for forward-scatter MODIS-NDVI is log(NDVI) ~ solar-to-sensor angle. The model for back-scatter MODIS-NDVI is log(NDVI) ~ solar-to-sensor angle + sensor zenith angle. Boxplots show interquartile ranges; whiskers extend to 10th and 90th percentiles. The horizontal line marking the average median value for forward-scatter r-squared (0.835) is nearly indistinguishable from the back-scatter line (0.833). The dataset folder also includes supplemental R-project and packrat files that allow the user to apply the workflow by opening a project that will use the same package versions used in this study (eg, .folders Rproj.user, and packrat, and files .RData, and PhenocamPR.Rproj). The empty folder GEE_DataAngles is included so that the user can save the data files from the Google Earth Engine scripts to this location, where they can then be incorporated into the r-processing scripts without needing to change folder names. To successfully use the packrat information to replicate the exact processing steps that were used, the user should refer to packrat documentation available at https://cran.r-project.org/web/packages/packrat/index.html and at https://www.rdocumentation.org/packages/packrat/versions/0.5.0. Alternatively, the user may also use the descriptive documentation phenopix package documentation, and description/references provided in the associated journal article to process the data to achieve the same results using newer packages or other software programs.

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

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):[...]

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.

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.

About Datasets:

This dataset contains features extracted from voice recordings of male and female speakers using the Python Librosa library. The recordings consist of 3682 samples from male voices and 2311 samples from female voices, totaling 5993 samples. Each sample is represented by a set of features derived from frequency analysis using the Librosa library.

Features:

1. mean_freq: The average frequency of the voice signal. 2.sd_freq: The standard deviation of the frequency distribution. 3.median_freq: The median frequency of the voice signal. 4.q25_freq: The 25th percentile of the frequency distribution. 5.q75_freq: The 75th percentile of the frequency distribution. 6.iqr_freq: The interquartile range of the frequency distribution. 7.skewness: Skewness of the frequency distribution. 8.kurtosis_val: Kurtosis of the frequency distribution. 9.spectral_flatness: Flatness of the power spectrum. 10.tempogram: Temporal rhythm analysis feature. 11.mode_freq: The mode frequency of the voice signal. 12.centroid_freq: Centroid frequency of the voice signal. 13.peak_freq: The peak frequency of the voice signal. 14.mean_fun: Mean fundamental frequency of the voice signal. 15.min_fun: Minimum fundamental frequency of the voice signal. 16.max_fun: Maximum fundamental frequency of the voice signal. 17.mean_dom: Mean dominant frequency of the voice signal. 18.min_dom: Minimum dominant frequency of the voice signal. 19.max_dom: Maximum dominant frequency of the voice signal. 20.fund_freq: Fundamental frequency of the voice signal. 21.mod_index: Modulation index of the voice signal. 22.label: The gender label of the voice sample (male, female).

Purpose:

This dataset is suitable for various machine learning tasks, such as gender classification based on voice characteristics, analysis of frequency features in male and female voices, and exploring differences in vocal patterns between genders.

This table contains a source catalog based on 90-cm (324-MHz) Very Large Array (VLA) imaging of the COSMOS field, comprising a circular area of 3.14 square degrees centered on 10^h 00^m 28.6^s, 02^o 12' 21" (J2000.0 RA and Dec). The image from the merger of 3 nights of observations using all 27 VLA antennas had an effective total integration time of ~ 12 hours, an 8.0 arcsecond x 6.0 arcsecond angular resolution, and an average rms of 0.5 mJy beam^-1. The extracted catalog contains 182 sources (down to 5.5 sigma), 30 of which are multi-component sources. Using Monte Carlo artificial source simulations, the authors derive the completeness of the catalog, and show that their 90-cm source counts agree very well with those from previous studies. In their paper, the authors use X-ray, NUV-NIR and radio COSMOS data to investigate the population mix of this 90-cm radio sample, and find that the sample is dominated by active galactic nuclei. The average 90-20 cm spectral index (S_nu~ nu^alpha, where S_nu is the flux density at frequency nu and alpha the spectral index) of the 90-cm selected sources is -0.70, with an interquartile range from -0.90 to -0.53. Only a few ultra-steep-spectrum sources are present in this sample, consistent with results in the literature for similar fields. These data do not show clear steepening of the spectral index with redshift. Nevertheless, this sample suggests that sources with spectral indices steeper than -1 all lie at z >~ 1, in agreement with the idea that ultra-steep-spectrum radio sources may trace intermediate-redshift galaxies (z >~ 1). Using both the signal and rms maps (see Figs. 1 and 2 in the reference paper) as input data, the authors ran the AIPS task SAD to obtain a catalog of candidate components above a given local signal-to-noise ratio (S/N) threshold. The task SAD was run four times with search S/N levels of 10, 8, 6 and 5, using the resulting residual image each time. They recovered all the radio components with a local S/N > 5.00. Subsequently, all the selected components were visually inspected, in order to check their reliability, especially for the components near strong side-lobes. After a careful analysis, a S/N threshold of 5.50 was adopted as the best compromise between a deep and a reliable catalog. The procedure yielded a total of 246 components with a local S/N > 5.50. More than one component, identified in the 90-cm map sometimes belongs to a single radio source (e.g. large radio galaxies consist of multiple components). Using the 90-cm COSMOS radio map, the authors combined the various components into single sources based on visual inspection. The final catalog (contained in this HEASARC table) lists 182 radio sources, 30 of which have been classified as multiple, i.e. they are better described by more than a single component. Moreover, in order to ensure a more precise classification, all sources identified as multi-component sources have been also double-checked using the 20-cm radio map. The authors found that all the 26 multiple 90-cm radio sources within the 20-cm map have 20-cm counterpart sources already classified as multiple. The authors have made use of the VLA-COSMOS Large and Deep Projects over 2 square degrees, reaching down to an rms of ~15 µJy beam¹ ^ at 1.4 GHz and 1.5 arcsec resolution (Schinnerer et al. 2007, ApJS, 172, 46: the VLACOSMOS table in the HEASARC database). The 90-cm COSMOS radio catalog has, however, been extracted from a larger region of 3.14 square degrees (see Fig. 1 and Section 3.1 of the reference paper). This implies that a certain number of 90-cm sources (48) lie outside the area of the 20-cm COSMOS map used to select the radio catalog. Thus, to identify the 20-cm counterparts of the 90-cm radio sources, the authors used the joint VLA-COSMOS catalog (Schinnerer et al. 2010, ApJS, 188, 384: the VLACOSMJSC table in the HEASARC database) for the 134 sources within the 20-cm VLA-COSMOS area and the VLA- FIRST survey (White et al. 1997, ApJ, 475, 479: the FIRST table in the HEASARC database) for the remaining 48 sources. The 90-cm sources were cross-matched with the 20-cm VLA-COSMOS sources using a search radius of 2.5 arcseconds, while the cross-match with the VLA-FIRST sources has been done using a search radius of 4 arcseconds in order to take into account the larger synthesized beam of the VLA-FIRST survey of ~5 arcseconds. Finally, all the 90 cm - 20 cm associations were visually inspected in order to ensure also the association of the multiple 90-cm radio sources for which the value of the search radius used during the cross-match could be too restrictive. In summary, out of the total of 182 sources in the 90-cm catalog, 168 have counterparts at 20 cm. This table was created by the HEASARC in October 2014 based on an electronic version of Table 1 from the reference paper which was obtained from the COSMOS web site at IRSA, specifically the file vla-cosmos_327_sources_published_version.tbl at http://irsa.ipac.caltech.edu/data/COSMOS/tables/vla/. This is a service provided by NASA HEASARC .