Performance Measure Definition: Trauma Alert Scene Interval

Descriptive statistics of the dataset with mean, standard deviation (SD), median, and the lower (quantile 5%) and upper (quantile 95%) boundary of the 90% confidence interval.

Performance Measure Definition: STEMI Alert Call-to-Door Interval

In genomic study, log transformation is a common prepossessing step to adjust for skewness in data. This standard approach often assumes that log-transformed data is normally distributed, and two sample t-test (or its modifications) is used for detecting differences between two experimental conditions. However, recently it was shown that two sample t-test can lead to exaggerated false positives, and the Wilcoxon-Mann-Whitney (WMW) test was proposed as an alternative for studies with larger sample sizes. In addition, studies have demonstrated that the specific distribution used in modeling genomic data has profound impact on the interpretation and validity of results. The aim of this paper is three-fold: 1) to present the Exp-gamma distribution (exponential-gamma distribution stands for log-transformed gamma distribution) as a proper biological and statistical model for the analysis of log-transformed protein abundance data from single-cell experiments; 2) to demonstrate the inappropriateness of two sample t-test and the WMW test in analyzing log-transformed protein abundance data; 3) to propose and evaluate statistical inference methods for hypothesis testing and confidence interval estimation when comparing two independent samples under the Exp-gamma distributions. The proposed methods are applied to analyze protein abundance data from a single-cell dataset.

This dataset identifies all regions in which the full 95% confidence interval is between 4 and 18 �C for all 12 months. The sea surface temperature data includes the mean sea surface temperature per month, the standard deviation and the number of observations used to calculate the mean. Based on these values, the 95% upper and lower confidence levels about the mean for each month have been generated.

Performance Measure Definition: Stroke Alert Call-to-Door Interval

The dataset contains data collected from patients suffering from cancer-related pain. The features extracted from clinical data (including typical cancer phenomena such as breakthrough pain) and the biosignal acquisitions contributed to the definition of a multidimensional dataset. This unique database can be useful for the characterization of the patient’s pain experience from a qualitative and quantitative perspective. We implemented measurable biosignals-related indicators of the individual’s pain response and of the overall Autonomic Nervous System (ANS) functioning. The most peculiar features extracted from EDA and ECG signals can be adopted to investigate the status and complex functioning of the ANS through the study of sympatho-vagal activations. Specifically, while EDA is mainly related sympathetic activation, the Heart Rate Variability (HRV), which can be derived from ECG recordings, is strictly related to the interplay between sympathetic and parasympathetic functioning.

As far as the EDA signal, two types of analyzes have been performed: (i) the Trough-To-Peak analysis (TTP), or min-max analysis, aimed at measuring the difference between the Skin Conductance (SC) at the peak of a response and its previous minimum within pre-established time-windows; (ii) the Continuous Decomposition Analysis (CDA), aimed at performing a decomposition of SC data into continuous signals of tonic (basic level of conductance) and phasic (short-duration changes in the SC) activity. Before applying the TPP analysis or the CDA, the signal was filtered by means of a fifth-order Butterworth low-pass filter with a cutoff frequency of 1 Hz and downsampled up to 10 Hz to reducing the computational burden of the analysis. The application of TPP and CDA allowed the detection and measurement of SC Responses (SCR) and the following parameters have been calculated for both TPP and CDA methodologies:

• Total number of detected SCRs.
• Maximum value of SCRs [measured in μS].
• Minimum value of SCRs [measured in μS].
• Arithmetic mean of the SCRs [measured in μS].
• Maximum interval between SCRs [measured in ms].
• Minimum interval between SCRs [measured in ms].
• Arithmetic mean of the intervals between SCRs [measured in ms].

Concerning the ECG, the RR series of interbeat intervals (i.e., the time between successive R waves of the QRS complex on the ECG waveform) has been computed to extract time-domain parameters of the HRV. The R peak detection was carried out by adopting the Pan–Tompkins algorithm for QRS detection and R peak identification. The corresponding RR series of interbeat intervals were derived as the difference between successive R peaks.

The ECG-derived RR time series was then filtered by means of a recursive procedure to remove the intervals differing most from the mean of the surrounding RR intervals. Then, both the Time-Domain Analysis (TDA) and Frequency-Domain Analysis (FDA) of the HRV have been carried out to extract the main features characterizing the variability of the heart rhythm. Time-domain parameters are obtained from statistical analysis of the intervals between heart beats and are used to describe how much variability in the heartbeats is present at various time scales.

The parameters computed through the TDA include the following:

• Arithmetic mean of the RR time series [measured in ms].
• The standard deviation of the RR time series [measured in ms].
• Mean value of heart rate [measured in bpm].
• Standard deviation of the heart rate [measured in bpm].
• Root Mean Square of Successive Differences of RR intervals [measured in ms], which is sensitive to high-frequency heart period fluctuations in the respiratory frequency range and has been used as an index of vagal cardiac control.
• Number of successive RR intervals whose difference is higher than 50 ms.
• Percentage of successive RR intervals higher than 50 ms.
• Number of successive RR intervals whose difference is higher than 50 ms.

Frequency-domain parameters reflect the distribution of spectral power across different frequencies bands and are used to assess specific components of HRV (e.g., thermoregulation control loop, baroreflex control loop, and respiration control loop, which are regulated by both sympathetic and vagal nerves of the ANS).
The parameters computed through the FDA have been computed by adopting the Welch's Fourier periodogram method based on the Discrete Fourier Transform (DFT), which allows the expression of the RR series in the discrete frequency domain. However, due to the non-stationarity of the RR series, Welch Fourier periodogram method is used for dealing with non-stationarity. Specifically, Welch's periodogram divides the signal into specific periods of constant length appliying the Fast Fourier Transform (FFT) trasforming individually these parts of the signal. The periodogram is basically a way of estimating power spectral density of a time series.

The FDA parameters include the following:

• Peak value in the Very Low Frequency Band of the HRV power density spectrum [measured in Hz].
• Peak value in the Low Frequency Band of the HRV power density spectrum [measured in Hz].
• Peak value in the High Frequency Band of the HRV power density spectrum [measured in Hz].
• Power in the Very Low Frequency Band of the HRV power density spectrum [measured in ms^2].
• Power in the Low Frequency Band of the HRV power density spectrum [measured in ms^2].
• Power in the High Frequency Band of the HRTotal Power of the HRV power density spectrum [measured in ms^2].
• Total Power of the HRV power density spectrum [measured in ms^2].
• Percentage power in the Very Low Frequency Band of the HRV power density spectrum with respect to the total power.
• Percentage power in the Low Frequency Band of the HRV power density spectrum with respect to the total power.
• Percentage power in the High Frequency Band of the HRV power density spectrum with respect to the total power.
• Normalized power in the Low Frequency Band of the HRV power density spectrum with respect to the sum of LF and HF power.
• Normalized power in the High Frequency Band of the HRV power density spectrum with respect to the sum of LF and HF power.
• Sympathovagal balance measured as the ration between power in LF and power in the LF band.

This dataset identifies all regions in which the full 95% confidence interval is greater than 1 mg/m3 for all 12 months. The chlorophyll 2 data includes the mean chlorophyll 2 level per month, the standard deviation and the number of observations used to calculate the mean. Based on these values, the 95% upper and lower confidence levels about the mean for each month have been generated.

This data release supports the analysis of the recurrence interval of post-fire debris-flow generating rainfall in the southwestern United States. We define the recurrence interval of the peak 15-, 30-, and 60-minute rainfall intensities for 316 observations of post-fire debris-flow occurrence in 18 burn areas, 5 U.S. states, and 7 climate types (as defined by Beck, H. E., Zimmermann, N. E., McVicar, T. R., Vergopolan, N., Berg, A., & Wood, E. F. (2018). Present and future Köppen-Geiger climate classification maps at 1-km resolution. Scientific Data, 5(1), 180214. doi:10.1038/sdata.2018.214).

This data set contains estimated teen birth rates for age group 15–19 (expressed per 1,000 females aged 15–19) by county and year.

DEFINITIONS

Estimated teen birth rate: Model-based estimates of teen birth rates for age group 15–19 (expressed per 1,000 females aged 15–19) for a specific county and year. Estimated county teen birth rates were obtained using the methods described elsewhere (1,2,3,4). These annual county-level teen birth estimates “borrow strength” across counties and years to generate accurate estimates where data are sparse due to small population size (1,2,3,4). The inferential method uses information—including the estimated teen birth rates from neighboring counties across years and the associated explanatory variables—to provide a stable estimate of the county teen birth rate. Median teen birth rate: The middle value of the estimated teen birth rates for the age group 15–19 for counties in a state. Bayesian credible intervals: A range of values within which there is a 95% probability that the actual teen birth rate will fall, based on the observed teen births data and the model.

NOTES

Data on the number of live births for women aged 15–19 years were extracted from the National Center for Health Statistics’ (NCHS) National Vital Statistics System birth data files for 2003–2015 (5).

Population estimates were extracted from the files containing intercensal and postcensal bridged-race population estimates provided by NCHS. For each year, the July population estimates were used, with the exception of the year of the decennial census, 2010, for which the April estimates were used.

Hierarchical Bayesian space–time models were used to generate hierarchical Bayesian estimates of county teen birth rates for each year during 2003–2015 (1,2,3,4).

The Bayesian analogue of the frequentist confidence interval is defined as the Bayesian credible interval. A 100*(1-α)% Bayesian credible interval for an unknown parameter vector θ and observed data vector y is a subset C of parameter space Ф such that 1-α≤P({C│y})=∫p{θ │y}dθ, where integration is performed over the set and is replaced by summation for discrete components of θ. The probability that θ lies in C given the observed data y is at least (1- α) (6).

County borders in Alaska changed, and new counties were formed and others were merged, during 2003–2015. These changes were reflected in the population files but not in the natality files. For this reason, two counties in Alaska were collapsed so that the birth and population counts were comparable. Additionally, Kalawao County, a remote island county in Hawaii, recorded no births, and census estimates indicated a denominator of 0 (i.e., no females between the ages of 15 and 19 years residing in the county from 2003 through 2015). For this reason, Kalawao County was removed from the analysis. Also , Bedford City, Virginia, was added to Bedford County in 2015 and no longer appears in the mortality file in 2015. For consistency, Bedford City was merged with Bedford County, Virginia, for the entire 2003–2015 period. Final analysis was conducted on 3,137 counties for each year from 2003 through 2015. County boundaries are consistent with the vintage 2005–2007 bridged-race population file geographies (7).

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CloudCast: A large-scale dataset and baseline for forecasting clouds

The CloudCast dataset contains 70080 cloud-labeled satellite images with 10 different cloud types corresponding to multiple layers of the atmosphere. The raw satellite images come from a satellite constellation in geostationary orbit centred at zero degrees longitude and arrive in 15-minute intervals from the European Organisationfor Meteorological Satellites (EUMETSAT). The resolution of these images is 3712 x 3712 pixels for the full-disk of Earth, which implies that every pixel corresponds to a space of dimensions 3x3km. This is the highest possible resolution from European geostationary satellites when including infrared channels. Some pre- and post-processing of the raw satellite images are also being done by EUMETSAT before being exposed to the public, such as removing airplanes. We collect all the raw multispectral satellite images and annotate them individually on a pixel-level using a segmentation algorithm. The full dataset then has a spatial resolution of 928 x 1530 pixels recorded with 15-min intervals for the period 2017-2018, where each pixel represents an area of 3×3 km. To enable standardized datasets for benchmarking computer vision methods, this includes a full-resolution gray-scaled dataset centered and projected dataset over Europe (128×128).

License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Citation

If you use this dataset in your research or elsewhere, please cite/reference the following paper: CloudCast: A Satellite-Based Dataset and Baseline for Forecasting Clouds

Data dictionary

There are 24 folders in the dataset containing the following information:

| File | Definition | Note | | --- | --- | | X.npy | Numpy encoded array containing the actual 128x128 image with pixel values as labels, see below. | | | GEO.npz| Numpy array containing geo coordinates where the image was taken (latitude and longitude). | | | TIMESTAMPS.npy| Numpy array containing timestamps for each captured image. | Images are captured in 15-minute intervals. |

Cloud types

0 = No clouds or missing data 1 = Very low clouds 2 = Low clouds 3 = Mid-level clouds 4 = High opaque clouds 5 = Very high opaque clouds 6 = Fractional clouds 7 = High semitransparant thin clouds 8 = High semitransparant moderately thick clouds 9 = High semitransparant thick clouds 10 = High semitransparant above low or medium clouds

Examples

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This parent dataset (collection of datasets) describes the general organization of data in the datasets for each growing season (two-year period) when winter wheat (Triticum aestivum L.) was grown for grain at the USDA-ARS Conservation and Production Laboratory (CPRL), Soil and Water Management Research Unit (SWMRU), Bushland, Texas (Lat. 35.186714°, Long. -102.094189°, elevation 1170 m above MSL). Winter wheat was grown on two large, precision weighing lysimeters, calibrated to NIST standards (Howell et al., 1995). Each lysimeter was in the center of a 4.44 ha square field on which wheat was also grown (Evett et al., 2000). The two fields were contiguous and arranged with one directly north of the other. See the resource titled "Geographic Coordinates, USDA, ARS, Bushland, Texas" for UTM geographic coordinates for field and lysimeter locations. Wheat was planted in Autumn and grown over the winter in 1989-1990, 1991-1992, and 1992-1993. Agronomic calendar for the each of the three growing seasons list by date the agronomic practices applied, severe weather, and activities (e.g., planting, thinning, fertilization, pesticide application, lysimeter maintenance, harvest) in and on lysimeters that could influence crop growth, water use, and lysimeter data. These include fertilizer and pesticide applications. Irrigation was by linear move sprinkler system equipped with pressure regulated low pressure sprays (mid-elevation spray application, MESA). Irrigations were managed to replenish soil water used by the crop on a weekly or more frequent basis as determined by soil profile water content readings made with a field-calibrated (Evett and Steiner, 1995) neutron probe from 0.10- to 2.4-m depth in the field. The lysimeters and fields were planted to the same plant density, row spacing, tillage depth (by hand on the lysimeters and by machine in the fields), and fertilizer and pesticide applications. The weighing lysimeters were used to measure relative soil water storage to 0.05 mm accuracy at 5-min intervals, and the 5-min change in soil water storage was used along with precipitation, dew and frost accumulation, and irrigation amounts to calculate crop evapotranspiration (ET), which is reported at 15-min intervals. Each lysimeter was equipped with a suite of instruments to sense wind speed, air temperature and humidity, radiant energy (incoming and reflected, typically both shortwave and longwave), surface temperature, soil heat flux, and soil temperature, all of which are reported at 15-min intervals. Instruments used changed from season to season, which is another reason that subsidiary datasets and data dictionaries for each season are required. The Bushland weighing lysimeter research program was described by Evett et al. (2016), and lysimeter design is described by Marek et al. (1988). Important conventions concerning the data-time correspondence, sign conventions, and terminology specific to the USDA ARS, Bushland, TX, field operations are given in the resource titled "Conventions for Bushland, TX, Weighing Lysimeter Datasets". There are six datasets in this collection. Common symbols and abbreviations used in the datasets are defined in the resource titled, "Symbols and Abbreviations for Bushland, TX, Weighing Lysimeter Datasets". Datasets consist of Excel (xlsx) files. Each xlsx file contains an Introductory tab that explains the other tabs, lists the authors, describes conventions and symbols used and lists any instruments used. The remaining tabs in a file consist of dictionary and data tabs. The six datasets are as follows: Agronomic Calendars for the Bushland, Texas Winter Wheat Datasets Growth and Yield Data for the Bushland, Texas Winter Wheat Datasets Weighing Lysimeter Data for The Bushland, Texas Winter Wheat Datasets Soil Water Content Data for The Bushland, Texas, Large Weighing Lysimeter Experiments Evapotranspiration, Irrigation, Dew/frost - Water Balance Data for The Bushland, Texas Winter Wheat Datasets Standard Quality Controlled Research Weather Data – USDA-ARS, Bushland, Texas See the README for descriptions of each dataset. The soil is a Pullman series fine, mixed, superactive, thermic Torrertic Paleustoll. Soil properties are given in the resource titled "Soil Properties for the Bushland, TX, Weighing Lysimeter Datasets". The land slope in the lysimeter fields is <0.3% and topography is flat. The mean annual precipitation is ~470 mm, the 20-year pan evaporation record indicates ~2,600 mm Class A pan evaporation per year, and winds are typically from the South and Southwest. The climate is semi-arid with ~70% (350 mm) of the annual precipitation occurring from May to September, during which period the pan evaporation averages ~1520 mm. These datasets originate from research aimed at determining crop water use (ET), crop coefficients for use in ET-based irrigation scheduling based on a reference ET, crop growth, yield, harvest index, and crop water productivity as affected by irrigation method, timing, amount (full or some degree of deficit), agronomic practices, cultivar, and weather. Prior publications have described the facilities and research methods (Evett et al., 2016), and have focused on winter wheat ET (Howell et al., 1995, 1997, 1998), and crop coefficients (Howell et al., 2006; Schneider and Howell, 1997, 2001) that have been used by ET networks for irrigation management. The data have utility for developing, calibrating, and testing simulation models of crop ET, growth, and yield (Evett et al., 1994; Kang et al., 2009), and have been used by several universities and for testing, and calibrating models of ET that use satellite and/or weather data. Resources in this dataset: Resource Title: Geographic Coordinates of Experimental Assets, Weighing Lysimeter Experiments, USDA, ARS, Bushland, Texas. File Name: Geographic Coordinates, USDA, ARS, Bushland, Texas.xlsx. Resource Description: The file gives the UTM latitude and longitude of important experimental assets of the Bushland, Texas, USDA, ARS, Conservation & Production Research Laboratory (CPRL). Locations include weather stations [Soil and Water Management Research Unit (SWMRU) and CPRL], large weighing lysimeters, and corners of fields within which each lysimeter was centered. There were four fields designated NE, SE, NW, and SW, and a weighing lysimeter was centered in each field. The SWMRU weather station was adjacent to and immediately east of the NE and SE lysimeter fields. Resource Title: Conventions for Bushland, TX, Weighing Lysimeter Datasets. File Name: Conventions for Bushland, TX, Weighing Lysimeter Datasets.xlsx. Resource Description: Descriptions of conventions and terminology used in the Bushland, TX, weighing lysimeter research program. Resource Title: Symbols and Abbreviations for Bushland, TX, Weighing Lysimeter Datasets. File Name: Symbols and Abbreviations for Bushland, TX, Weighing Lysimeter Datasets.xlsx. Resource Description: Definitions of symbols and abbreviations used in the Bushland, TX, weighing lysimeter research datasets. Resource Title: Soil Properties for the Bushland, TX, Weighing Lysimeter Datasets. File Name: Bushland_TX_soil_properties.xlsx. Resource Description: Soil properties useful for simulation modeling and for describing the soil are given for the Pullman soil series at the USDA, ARS, Conservation & Production Research Laboratory, Bushland, TX, USA. For each soil layer, soil horizon designation and texture according to USDA Soil Taxonomy, bulk density, porosity, water content at field capacity (33 kPa) and permanent wilting point (1500 kPa), percent sand, percent silt, percent clay, percent organic matter, pH, and van Genuchten-Mualem characteristic curve parameters describing the soil hydraulic properties are given. A separate table describes the soil horizon thicknesses, designations, and textures according to USDA Soil Taxonomy. Another table describes important aspects of the soil hydrologic and rooting behavior. Resource Title: README - Bushland Texas Winter Wheat collection. File Name: README_Bushland_winter_wheat_collection.pdf. Resource Description: Descriptions of the datasets in the Bushland Texas Winter Wheat collection

This dataset identifies all regions in which the full 95% confidence interval is greater than 0.5 mg/m3 that were combined for the months available in each hemisphere for the blue mussel. The chlorophyll 2 data includes the mean chlorophyll 2 level per month, the standard deviation and the number of observations used to calculate the mean. Based on these values, the 95% upper and lower confidence levels about the mean for each month have been generated.

The City of Toronto's Transportation Services Division collects short-term traffic count data across the City on an ad-hoc basis to support a variety of safety initiatives and projects. The data available in this repository are a full collection of Speed, Volume and Classification Counts conducted across the City since 1993. The two most common types of short-term traffic counts are Turning Movement Counts and Speed / Volume / Classification Counts. Turning Movement Count data, comprised of motor vehicle, bicycle and pedestrian movements through intersections, can be found here. Speed / Volume / Classification Counts are collected using pneumatic rubber tubes installed across the roadway. This dataset is a critical input into transportation safety initiatives, infrastructure design and program design such as speed limit changes, signal coordination studies, traffic calming and complete street designs. Each Speed / Volume / Classification Count is comprised of motor vehicle count data collected over a continuous 24-hour to 168-hour period (1-7 days), at a single location. A handful of non-standard 2-week counts are also included. Some key notes about these counts include: Not all counts have complete speed and classification data. These data are provided for locations and dates only where they exist. Raw data are recorded in 15-minute intervals. Raw data are recorded separately for each direction of traffic movement. Some data are only available for one direction, even if the street is two-way. Within each 15 minute interval, speed data are aggregated into approximately 5 km/h increments. Within each 15 minute interval, classification data are aggregated into vehicle type bins by the number of axles, according to the FWHA classification system attached below. The following files showing different views of the data are available: Data Dictionary (svc_data_dictionary.xlsx): Provides a detailed definition of every data field in all files. Summary Data (svc_summary_data): Provides metadata about every Speed / Volume / Classification Count available, including information about the count location and count date, as well as summary data about each count (total vehicle volumes, average daily volumes, a.m. and p.m. peak hour volumes, average / 85 percentile / 95 percentile speeds, where available, and heavy vehicle percentage, where available). Most Recent Count Data (svc_most_recent_summary_data): Provides metadata about the most recent Speed / Volume / Classification Count data available at each location for which a count exists, including information about the count location and count date, as well as the summary data provided in the “Summary Data” file (see above). Raw Data: Raw data is available in 15-minute intervals, and is distributed into one of three different file types based on the count type: volume-only, speed and volume, or classification and volume. If you’re looking for 15-minute data for a specific count, identify the count type and count date, then download the raw data file associated with the count type and period. If you’re looking for volume data for all count types, you will need to download and aggregate all three file types for a given period. Volume Raw Data (svc_raw_data_volume_yyyy_yyyy): These files—grouped by 5-10 year interval—provide volume data in 15-minute intervals, for each direction separately. You will find the raw data for volume-only counts (ATR_VOLUME) here. Speed and Volume Raw Data (svc_raw_data_speed_yyyy_yyyy): These files—grouped by 5-10 year interval—provide volume data aggregated into speed bins in approximately 5 km/h increments. Speed data are not available for all counts. You will find the raw data for speed and volume counts (ATR_SPEED_VOLUME) here. Classification and Volume Raw Data (svc_raw_data_classification_yyyy_yyyy): These files—grouped by 5-10 year interval—provide volume data aggregated into vehicle type bins by the number of axles, according to the FWHA classification system. Classification data are not available for all counts. You will find the raw data for classification and volume counts (VEHICLE_CLASS) here. FWHA Classification Reference (fwha_classification.png): Provides a reference for the FWHA classification system. This dataset references the City of Toronto's Street Centreline dataset, Intersection File dataset and Street Traffic Signal dataset.

Performance Measure Definition: Average Call Processing Interval

Czech translation of WordSim353. The Czech translation of English WordSim353 word pairs were obtained from four translators. All translation variants were scored according to the lexical similarity/relatedness annotation instructions for WordSim353 annotators, by 25 Czech annotators. The resulting data set consists of two annotation files: "WordSim353-cs.csv" and "WordSim-cs-Multi.csv". Both files are encoded in UTF-8, have a header, text is enclosed in double quotes, and columns are separated by commas. The rows are numbered. The WordSim-cs-Multi data set has rows numbered from 1 to 634, whereas the row indices in the WordSim353-cs data set reflect the corresponding row numbers in the WordSim-cs-Multi data set.

The WordSim353-cs file contains a one-to-one mapping selection of 353 Czech equivalent pairs whose judgments have proven to be most similar to the judgments of their corresponding English originals (compared by the absolute value of the difference between the means over all annotators in each language counterpart). In one case ("psychology-cognition"), two Czech equivalent pairs had identical means as well as confidence intervals, so we randomly selected one.

The "WordSim-cs-Multi.csv" file contains human judgments for all translation variants.

In both data sets, we preserved all 25 individual scores. In the WordSim353-cs data set, we added a column with their Czech means as well as a column containing the original English means and 95% confidence intervals in separate columns for each mean (computed by the CI function in the Rmisc R package). The WordSim-cs-Multi data set contains only the Czech means and confidence intervals. For the most convenient lexical search, we provided separate columns with the respective Czech and English single words, entire word pairs, and eventually an English-Czech quadruple in both data sets.

The data set also contains an xls table with the four translations and a preliminary selection of the best variants performed by an adjudicator.

Performance Measure Definition: Trauma Alert Call-to-Door Interval

ERA5 is the fifth generation ECMWF reanalysis for the global climate and weather for the past 8 decades. Data is available from 1940 onwards. ERA5 replaces the ERA-Interim reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. This principle, called data assimilation, is based on the method used by numerical weather prediction centres, where every so many hours (12 hours at ECMWF) a previous forecast is combined with newly available observations in an optimal way to produce a new best estimate of the state of the atmosphere, called analysis, from which an updated, improved forecast is issued. Reanalysis works in the same way, but at reduced resolution to allow for the provision of a dataset spanning back several decades. Reanalysis does not have the constraint of issuing timely forecasts, so there is more time to collect observations, and when going further back in time, to allow for the ingestion of improved versions of the original observations, which all benefit the quality of the reanalysis product. ERA5 provides hourly estimates for a large number of atmospheric, ocean-wave and land-surface quantities. An uncertainty estimate is sampled by an underlying 10-member ensemble at three-hourly intervals. Ensemble mean and spread have been pre-computed for convenience. Such uncertainty estimates are closely related to the information content of the available observing system which has evolved considerably over time. They also indicate flow-dependent sensitive areas. To facilitate many climate applications, monthly-mean averages have been pre-calculated too, though monthly means are not available for the ensemble mean and spread. ERA5 is updated daily with a latency of about 5 days. In case that serious flaws are detected in this early release (called ERA5T), this data could be different from the final release 2 to 3 months later. In case that this occurs users are notified. The data set presented here is a regridded subset of the full ERA5 data set on native resolution. It is online on spinning disk, which should ensure fast and easy access. It should satisfy the requirements for most common applications. An overview of all ERA5 datasets can be found in this article. Information on access to ERA5 data on native resolution is provided in these guidelines. Data has been regridded to a regular lat-lon grid of 0.25 degrees for the reanalysis and 0.5 degrees for the uncertainty estimate (0.5 and 1 degree respectively for ocean waves). There are four main sub sets: hourly and monthly products, both on pressure levels (upper air fields) and single levels (atmospheric, ocean-wave and land surface quantities). The present entry is "ERA5 hourly data on single levels from 1940 to present".

Data for Figure 3.34 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6). Figure 3.34 shows attribution of observed seasonal trends in the annular modes to forcings. --------------------------------------------------- How to cite this dataset --------------------------------------------------- When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates: Eyring, V., N.P. Gillett, K.M. Achuta Rao, R. Barimalala, M. Barreiro Parrillo, N. Bellouin, C. Cassou, P.J. Durack, Y. Kosaka, S. McGregor, S. Min, O. Morgenstern, and Y. Sun, 2021: Human Influence on the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 423–552, doi:10.1017/9781009157896.005. --------------------------------------------------- Figure subpanels --------------------------------------------------- The figure has 3 panels, and all the data are provided in a single file named NAM_SAM_detection_attribution.nc. --------------------------------------------------- List of data provided --------------------------------------------------- This dataset contains - Observed and simulated DJF NAM trends for 1958-2019 - Observed and simulated JJA NAM trends for 1958-2019 - Observed and simulated DJF SAM trends for 1979-2019 - Observed and simulated JJA SAM trends for 1979-2019 - Observed and simulated DJF SAM trends for 2000-2019 - Observed and simulated JJA SAM trends for 2000-2019 Simulations are from CMIP6 historical, hist-GHG, hist-aer, hist-nat, and hist-stratO3 simulations, and from equivalent time segments from CMIP6 piControl simulations (one segment from one model). NAM: Northern Annular Mode ​​​ SAM: Southern Annular Mode GHG: greenhouse gas JJA: June, July, August DJF: December, January, February --------------------------------------------------- Data provided in relation to figure --------------------------------------------------- Panel a: - NAM_obs_DJF_1958_2019: grey horizontal lines in the left -->ERA5: obs_dataset = 0 -->JRA-55: obs_dataset = 1 - NAM_piControl_DJF_62yrs: multimodel ensemble mean and percentiles for blue open box-whisker in the left - NAM_hist_DJF_1958_2019: multimodel ensemble mean and percentiles for red open box-whisker in the left, and multimodel ensemble mean and confidence interval for red filled box, with ensemble means of individual models for black dots, in the left - NAM_GHG_DJF_1958_2019: multimodel ensemble mean and confidence interval for brown filled box, with ensemble means of individual models for black dots, in the left - NAM_aer_DJF_1958_2019: multimodel ensemble mean and confidence interval for light blue filled box, with ensemble means of individual models for black dots, in the left - NAM_stratO3_DJF_1958_2019: multimodel ensemble mean and confidence interval for purple filled box, with ensemble means of individual models for black dots, in the left - NAM_nat_DJF_1958_2019: multimodel ensemble mean and confidence interval for green filled box, with ensemble means of individual models for black dots, in the left - NAM_obs_JJA_1958_2019: grey horizontal lines in the right -->ERA5: obs_dataset = 0 -->JRA-55: obs_dataset = 1 - NAM_piControl_JJA_62yrs: multimodel ensemble mean and percentiles for blue open box-whisker in the right - NAM_hist_JJA_1958_2019: multimodel ensemble mean and percentiles for red open box-whisker in the right, and multimodel ensemble mean and confidence interval for red filled box, with ensemble means of individual models for black dots, in the right - NAM_GHG_JJA_1958_2019: multimodel ensemble mean and confidence interval for brown filled box, with ensemble means of individual models for black dots, in the right - NAM_aer_JJA_1958_2019: multimodel ensemble mean and confidence interval for light blue filled box, with ensemble means of individual models for black dots, in the right - NAM_stratO3_JJA_1958_2019: multimodel ensemble mean and confidence interval for purple filled box, with ensemble means of individual models for black dots, in the right - NAM_nat_JJA_1958_2019: multimodel ensemble mean and confidence interval for green filled box, with ensemble means of individual models for black dots, in the right Panel b: - SAM_obs_DJF_1979_2019: grey horizontal lines in the left -->ERA5: obs_dataset = 0 -->JRA-55: obs_dataset = 1 - SAM_piControl_DJF_41yrs: multimodel ensemble mean and percentiles for blue open box-whisker in the left - SAM_hist_DJF_1979_2019: multimodel ensemble mean and percentiles for red open box-whisker in the left, and multimodel ensemble mean and confidence interval for red filled box, with ensemble means of individual models for black dots, in the left - SAM_GHG_DJF_1979_2019: multimodel ensemble mean and confidence interval for brown filled box, with ensemble means of individual models for black dots, in the left - SAM_aer_DJF_1979_2019: multimodel ensemble mean and confidence interval for light blue filled box, with ensemble means of individual models for black dots, in the left - SAM_stratO3_DJF_1979_2019: multimodel ensemble mean and confidence interval for purple filled box, with ensemble means of individual models for black dots, in the left - SAM_nat_DJF_1979_2019: multimodel ensemble mean and confidence interval for green filled box, with ensemble means of individual models for black dots, in the left - SAM_obs_JJA_1979_2019: grey horizontal lines in the right -->ERA5: obs_dataset = 0 -->JRA-55: obs_dataset = 1 - SAM_piControl_JJA_41yrs: multimodel ensemble mean and percentiles for blue open box-whisker in the right - SAM_hist_JJA_1979_2019: multimodel ensemble mean and percentiles for red open box-whisker in the right, and multimodel ensemble mean and confidence interval for red filled box, with ensemble means of individual models for black dots, in the right - SAM_GHG_JJA_1979_2019: multimodel ensemble mean and confidence interval for brown filled box, with ensemble means of individual models for black dots, in the right - SAM_aer_JJA_1979_2019: multimodel ensemble mean and confidence interval for light blue filled box, with ensemble means of individual models for black dots, in the right - SAM_stratO3_JJA_1979_2019: multimodel ensemble mean and confidence interval for purple filled box, with ensemble means of individual models for black dots, in the right - SAM_nat_JJA_1979_2019: multimodel ensemble mean and confidence interval for green filled box, with ensemble means of individual models for black dots, in the right Panel c: - SAM_obs_DJF_2000_2019: grey horizontal lines in the left -->ERA5: obs_dataset = 0 -->JRA-55: obs_dataset = 1 - SAM_piControl_DJF_20yrs: multimodel ensemble mean and percentiles for blue open box-whisker in the left - SAM_hist_DJF_2000_2019: multimodel ensemble mean and percentiles for red open box-whisker in the left, and multimodel ensemble mean and confidence interval for red filled box, with ensemble means of individual models for black dots, in the left - SAM_GHG_DJF_2000_2019: multimodel ensemble mean and confidence interval for brown filled box, with ensemble means of individual models for black dots, in the left - SAM_aer_DJF_2000_2019: multimodel ensemble mean and confidence interval for light blue filled box, with ensemble means of individual models for black dots, in the left - SAM_stratO3_DJF_2000_2019: multimodel ensemble mean and confidence interval for purple filled box, with ensemble means of individual models for black dots, in the left - SAM_nat_DJF_2000_2019: multimodel ensemble mean and confidence interval for green filled box, with ensemble means of individual models for black dots, in the left - SAM_obs_JJA_2000_2019: grey horizontal lines in the right -->ERA5: obs_dataset = 0 -->JRA-55: obs_dataset = 1 - SAM_piControl_JJA_20yrs: multimodel ensemble mean and percentiles for blue open box-whisker in the right - SAM_hist_JJA_2000_2019: multimodel ensemble mean and percentiles for red open box-whisker in the right, and multimodel ensemble mean and confidence interval for red filled box, with ensemble means of individual models for black dots, in the right - SAM_GHG_JJA_2000_2019: multimodel ensemble mean and confidence interval for brown filled box, with ensemble means of individual models for black dots, in the right - SAM_aer_JJA_2000_2019: multimodel ensemble mean and confidence interval for light blue filled box, with ensemble means of individual models for black dots, in the right - SAM_stratO3_JJA_2000_2019: multimodel ensemble mean and confidence interval for purple filled box, with ensemble means of individual models for black dots, in the right - SAM_nat_JJA_2000_2019: multimodel ensemble mean and confidence interval for green filled box, with ensemble means of individual models for black dots, in the right --------------------------------------------------- Notes on reproducing the figure from the provided data --------------------------------------------------- Multimodel ensemble means, interquartile ranges and 5th and 95th percentiles of historical and hist-* simulations are calculated after weighting individual members with the inverse of the ensemble size of the same model. The weight is given as the weight attribute of each variable. The weighting is not applied to piControl simulations. Filled boxes and black dots are evaluated based on the models with minimum 3 ensemble members. ensemble_assign attribute in each

For each variable in the table, the UFA-identified threshold aligns with the known physiological bound, as established by the National Institutes of Health. The mortality rates for patients who violated these thresholds range from 52.7% to 55.9%, much higher than the 30.9% death rate in the septic population overall.