Performance Measure Definition: Average Call Processing Interval

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

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

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).

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

Data description

This dataset is dedicated to benchmarking Machine Learning solutions to the problem of estimation of the components of the state vector in nonlinear dynamical systems.

The dataset is built using two dynamical systems, namely:

• The Electronic Throttle Controlled (ETC) system representing a technological device that controls the air flow rate in automotive motors. This is a three-states system in which only the first state and the control input are measured while the other two states are to be estimated using the previous available measurements. The system is controlled via an input signal (which is also measured) representing the electric current that acts on an electric torque generation sub-system. This torque changes acts on the angle of a device hence changing the flow-rate entering the combustion chamber.

• The Lorentz attractor representing a famous nonlinear chaotic system with no inputs (autonomous system). Here again, this is a three-states system in which only the first state is measured while the two remaining states are to be estimated using the available measurements over a past window.

Some definitions and notation to understand the context

The state vector and the control input (if any) are denoted by x and u respectively. Both systems are defined up to the knowledge of an associated vector of parameters p involved in the model's definition.

The very possibility of estimating the non measured components xi of the states, such as x2 and x3 in the data set of both systems relies on the existence of an associated maps of the form:

xi(k) = Fi(y_past(k), p)

where y_past encompasses the measurement acquired on some past moving window spanning the past time interval defined by:

(k-window, ..., k-1, k).

More precisely, the vector of features (used in the X features matrix) is built from the values of the measurements over the previously defined time interval with some under-sampling consisting in taking one value over nJump values. Namely when nJump=1 all the measurements are used while when nJump=5 only the fifth of the instantes are considered.

Based on the precious definitions, the features vector and the label to be identified are schematically shown in the figure below.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F9193311%2F595a8b464752a4e0eb31d431580b489b%2FCapture%20decran%202024-11-26%20a%2013.07.52.png?generation=1732623079125879&alt=media" alt="Description">

This is the main file containing the dictionary of the dataset the can be used as a benchmark for nonlinear state estimators design via Machine Learning.

The file contains a dictionary that can be acceded by using the pickle.load command:

import pickle 
data = pickle.load(open('data.pkl', 'rb')

The list of keys of the data dictionary is the following:

[('etc', 0.0, 'x2'),
 ('etc', 0.0, 'x3'),
 ('etc', 0.05, 'x2'),
 ('etc', 0.05, 'x3'),
 ('etc', 0.1, 'x2'),
 ('etc', 0.1, 'x3'),
 ('lorentz', 0.0, 'x2'),
 ('lorentz', 0.0, 'x3'),
 ('lorentz', 0.05, 'x2'),
 ('lorentz', 0.05, 'x3'),
 ('lorentz', 0.1, 'x2'),
 ('lorentz', 0.1, 'x3')]

Where each key is a triplet of values representing

• The system being considered: Possible values art etc or lorentz
• The relative standard deviation of the system's parameters: Possible values are 0, 0.05 or 0.1
• The state component to be estimated: Possible values are x2 or x3.

Notice that the noise level can be chosen and the corresponding noise added to the features matrices.

Once a key k is chosen among the above mentioned list, the corresponding value data[k] is again a dictionary enabling to access the (X,y) paires for training and test, namely:

data[k].Xtrain, data[k].Xtest, data[k].ytrain, data[k].ytest

Finally, in order to grasp an idea regarding the size of the datasets, the following script is used:

print(data[('etc', 0.0, 'x2')]['Xtrain'].shape)
print(data[('etc', 0.0, 'x2')]['Xtest'].shape)
print(data[('lorentz', 0.0, 'x2')]['Xtrain'].shape)
print(data[('lorentz', 0.0, 'x2')]['Xtest'].shape)

which produces the following results:

(136000, 30) (136000, 30) (44000, 5) (44000, 5)

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.

PIADE dataset contains data from five industrial packaging machines:

Machine s_1: from 2020-01-01 14:00:00 to 2021-12-31 13:00:00
Machine s_2: from 2020-06-17 08:00:00 to 2021-12-31 07:00:00
Machine s_3: from 2020-10-07 12:00:00 to 2022-01-01 23:00:00
Machine s_4: from 2020-01-01 01:00:00 to 2022-01-01 23:00:00
Machine s_5: from 2020-01-20 08:00:00 to 2022-01-01 12:00:00

Raw Data

Each row represents a production interval, with the following schema:

interval_start: start of the production interval  
equipment_ID: equipment identifier  
alarm: alarm code of the active stop reason, if it occurred   
type: idle, production, downtime, performance_loss or scheduled_downtime  
start: start of the production interval  
end: end of the production interval  
elapsed: duration of the production interval  
pi: input packages  
po: output packages  
speed: speed (packages per hour)

There are 133 different types of alerts, and 429394 rows.

Sequences (1h) data

For each piece of equipment, we define sequences of length = 1 hour and we aggregate raw interval data as follows:

'equipment_ID': machine identifier
'#changes': changes in machine state
'%downtime': time spent in 'downtime' state
'%idle': time spent in 'idle' state
'%performance_loss': time spent in 'performance loss' state
'%production': time spent in production
'%scheduled_downtime': time spent in scheduled downtime
'count_sum': sum of all alarm occurrences
'A_

This digital dataset defines the well locations, perforated intervals, and time series of hydraulic-head observations used in the calibration of the transient hydrologic model of the Central Valley flow system. The Central Valley encompasses an approximate 50,000 square-kilometer region of California. The complex hydrologic system of the Central Valley is simulated using the U.S. Geological Survey (USGS) numerical modeling code MODFLOW-FMP (Schmid and others, 2006b). This simulation is referred to here as the Central Valley Hydrologic Model (CVHM) (Faunt, 2009). Utilizing MODFLOW-FMP, the CVHM simulates groundwater and surface-water flow, irrigated agriculture, land subsidence, and other key processes in the Central Valley on a monthly basis from 1961-2003. The USGS and CA-DWR maintain databases of key wells in the Central Valley that are web-accessible (http://waterdata.usgs.gov and http://www.water.ca.gov/waterdatalibrary/, respectively). These data were combined to form a database of available water levels throughout the Central Valley from 1961 to 2003. More than 850,000 water-level altitude measurements from more than 21,400 wells have been compiled by the USGS or CA-DWR and have been entered into their respective databases. However, only a small portion of these wells have both sufficient construction information to determine the well-perforation interval and water-level measurements for the simulation period. For model calibration, water-level altitude data were needed that were (1) distributed spatially (both geographically and vertically) throughout the Central Valley; (2) distributed temporally throughout the simulation period (years 1961-2003); and (3) available during both wet and dry climatic regimes. From the available wells records, a subset of comparison wells was selected on the basis of perforation depths, completeness of record, climatic intervals, and locations throughout the Central Valley. Water-level altitude observations (19,725) for 206 wells were used as calibration targets during parameter estimation. The CVHM is the most recent regional-scale model of the Central Valley developed by the U.S. Geological Survey (USGS). The CVHM was developed as part of the USGS Groundwater Resources Program (see "Foreword", Chapter A, page iii, for details).

This parent dataset (collection of datasets) describes the general organization of data in the datasets for the 2009 and 2011 growing seasons (year) when sunflower (Helianthus annuus L.) was grown for seed 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). Sunflower was grown for seed grain on two large, precision weighing lysimeters, each in the center of a 4.44 ha square field. The two fields were contiguous, arranged along a north-south axis, and were labeled northeast (NE), and southeast (SE). See the resource titled "Geographic Coordinates, USDA, ARS, Bushland, Texas" for UTM geographic coordinates for field and lysimeter locations. The fields were irrigated by a linear move sprinkler system equipped with spray applicators. 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 neutron probe from 0.10- to 2.4-m depth in the field. The number and spacing of neutron probe reading locations changed through the years (additional sites were added), which is one reason why subsidiary datasets and data dictionaries are needed. 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-minute intervals, and the 5-minute 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-minute 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-minute intervals. Instruments used changed from season to season, which is another reason that subsidiary datasets and data dictionaries for each season are required.
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. There is a dictionary tab for every data tab. The name of the dictionary tab contains the name of the corresponding data tab. Tab names are unique so that if individual tabs were saved to CSV files, each CSV file in the entire collection would have a different name. The six datasets, according to their titles, are as follows:

Agronomic Calendars for the Bushland, Texas Sunflower Datasets Growth and Yield Data for the Bushland, Texas Sunflower Datasets Weighing Lysimeter Data for The Bushland, Texas Sunflower Datasets Soil Water Content Data for The Bushland, Texas, Large Weighing Lysimeter Experiments Evapotranspiration, Irrigation, Dew/frost - Water Balance Data for The Bushland, Texas Sunflower Datasets Standard Quality Controlled Research Weather Data – USDA-ARS, Bushland, Texas

See the README for descriptions of each dataset. The land slope is

Data from: American Community Survey, 5-year SeriesKing County, Washington census tracts with nonoverlapping vintages of the 5-year American Community Survey (ACS) estimates starting in 2010 from the U.S. Census Bureau's demographic and housing estimates (DP05). Also includes the most recent release annually with the vintage identified in the "ACS Vintage" field.The census tract boundaries match the vintage of the ACS data (currently 2010 and 2020) so please note the geographic changes between the decades. Tracts have been coded as being within the City of Seattle as well as assigned to neighborhood groups called "Community Reporting Areas". These areas were created after the 2000 census to provide geographically consistent neighborhoods through time for reporting U.S. Census Bureau data. This is not an attempt to identify neighborhood boundaries as defined by neighborhoods themselves.Vintages: 2010, 2015, 2020, 2021, 2022, 2023ACS Table(s): DP05Data downloaded from: Census Bureau's Explore Census Data The United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2020 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

This parent dataset (collection of datasets) describes the general organization of data in the datasets for the 1995, 2003, 2004, 2010 and 2019 growing seasons (years) when soybean [Glycine max (L.) Merr.] was grown for seed 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). In 1995, 2003, 2004, and 2010, soybean was grown for seed grain on two large, precision weighing lysimeters, each in the center of a 4.44 ha square field also seeded to soybean. The two fields were contiguous, arranged along a north-south axis, and were labeled northeast (NE), and southeast (SE). In 2019, soybean was grown on four large, precision weighing lysimeters, and on the 4.44 ha square fields surrounding each lysimeter, which were contiguous and labeled NE, SE, and northwest (NW), and southwest (SW). See the resource titled "Geographic Coordinates, USDA, ARS, Bushland, Texas" for UTM geographic coordinates for field and lysimeter locations. In 1995, 2003, 2004, and 2010, the fields were irrigated by a linear move sprinkler system equipped with mid elevation spray applicators (MESA). In 2019, the NW and SW fields were irrigated with the linear move sprinkler system equipped with low elevation spray applicators (LESA), while the NE and SE lysimeters and fields were irrigated by subsurface drip irrigation (SDI) with drip tape spaced at 1.52 m in the middle of every other interrow and buried at 0.30 to 0.32 m. Both full and deficit irrigations were applied to fields in 1995, 2003, and 2004. The 2010 crop was grown as a dryland crop with no irrigation other than an initial irrigation to establish the crop. In 2019, full irrigation was applied to all four lysimeters and fields. Except for 2010 and 2019, irrigations on a least one lysimeter 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 neutron probe from 0.10- to 2.4-m depth in the field. The number and spacing of neutron probe reading locations changed through the years (additional sites were added), which is one reason why subsidiary datasets and data dictionaries are needed. 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-minute intervals, and the 5-minute 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-minute 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-minute intervals. Instruments used changed from season to season, which is another reason that subsidiary datasets and data dictionaries for each season are required.
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. There is a dictionary tab for every data tab. The name of the dictionary tab contains the name of the corresponding data tab. Tab names are unique so that if individual tabs were saved to CSV files, each CSV file in the entire collection would have a different name. The six datasets, according to their titles, are as follows:

Agronomic Calendars for the Bushland, Texas Soybean Datasets Growth and Yield Data for the Bushland, Texas Soybean Datasets Weighing Lysimeter Data for The Bushland, Texas Soybean Datasets Soil Water Content Data for The Bushland, Texas, Large Weighing Lysimeter Experiments Evapotranspiration, Irrigation, Dew/frost - Water Balance Data for The Bushland, Texas Soybean 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 is

This dataset shows the distribution of fishing effort by fishing vessels according to the gear type used. Fishing effort is defined as the time spent engaged in fishing operations or time spent at sea, this time may be multiplied by a measure of fishing capacity, e.g. engine power. In this dataset fishing effort is measured as average hours spent actively fishing per kilometre square, per year. Data from years 2014 to 2018 was used to produce this data product for the Marine Institute publication the “Atlas of Commercial Fisheries around Ireland, third edition“ (https://oar.marine.ie/handle/10793/1432). Effort for offshore fisheries is based on the following 2 primary data types - data on vessel positioning and data on gear types used: Vessel Monitoring Systems (VMS) supplied by the Irish Naval Service provide geographical position and speed of vessel at intervals of two hours or less (Commission Regulation (EC) No. 2244/2003). The data are available for all EU vessels of 12m and larger, operating inside the Irish EEZ; outside this zone only Irish VMS data are routinely available. VMS do not record whether a vessel is fishing, steaming or inactive. Logbooks collected by the Sea-Fisheries Protection Authority and supplied by the Department of Agriculture, Food and the Marine were the primary data source for information on landings and gear types used by Irish vessels. EU Fleet Register obtained from the EU fleet register (http://ec.europa.eu/fisheries/fleet/index.cfm) provides information for non-Irish vessels and for Irish vessels for which the gear was not known from the logbooks. Note that if vessels use more than one gear, it is possible that the gear type assigned to them was not the one that was actually used. The fishing gear data was classified into eight main groups: demersal otter trawls; beam trawls; demersal seines; gill and trammel nets; longlines; dredges; pots and pelagic trawls. The VMS data was analysed using the approach described by Gerritsen and Lordan (IJMS 68(1)). This approach assigns effort to each of the VMS data points. The effort of a VMS data point is defined as the time interval since the previous data point. Next the data are filtered for fishing activity using speed criteria, vessels were assumed to be actively fishing if their speed fell within a certain range (depending on the fishing gear used). The points that remain are then aggregated into a spatial grid to produce a raster dataset showing fishing effort (in hours) per kilometre square per year for each gear type group. The data is available for all countries combined and for Irish vessels only. None .hidden { display: none }

Data from: American Community Survey, 5-year SeriesKing County, Washington census tracts with nonoverlapping vintages of the 5-year American Community Survey (ACS) estimates starting in 2010 of over 50 attributes of the most requested data derived from the U.S. Census Bureau's demographic profiles (DP02-DP05). Also includes the most recent release annually with the vintage identified in the "ACS Vintage" field.The census tract boundaries match the vintage of the ACS data (currently 2010 and 2020) so please note the geographic changes between the decades. Tracts have been coded as being within the City of Seattle as well as assigned to neighborhood groups called "Community Reporting Areas". These areas were created after the 2000 census to provide geographically consistent neighborhoods through time for reporting U.S. Census Bureau data. This is not an attempt to identify neighborhood boundaries as defined by neighborhoods themselves.Vintages: 2010, 2015, 2020, 2021, 2022, 2023ACS Table(s): DP02, DP03, DP04, DP05Data downloaded from: Census Bureau's Explore Census Data The United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2020 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

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.

IntroductionThe decision to delivery interval is a key indicator of the quality of obstetric care. This study assessed the decision to delivery interval for emergency cesarean sections and factors associated with delay.MethodsWe conducted a cross-sectional study between October 2022 and December 2022 in the labor ward at Mbale regional referral hospital. Our primary outcome variable was the decision to delivery interval defined as the time interval in minutes from the decision to perform the emergency caesarean section to delivery of the baby. We used an observer checklist and interviewer administered questionnaire to collect data. Stata version 14.0 (StataCorp; College Station, TX, USA) was used to analyze the data.ResultsWe enrolled 352 participants; the mean age was 25.9 years and standard deviation (SD) ±5.9 years. The median (interquartile range) decision to delivery interval was 110 minutes (80 to 145). Only 7/352 (2.0%) participants had a decision to delivery time interval of ≤30 minutes. More than three quarters 281 /352 (79.8%) had a decision to delivery interval of greater than 75 minutes. Emergency cesarean section done by intern doctors compared to specialists [Adjusted Prevalence Ratio (aPR): 1.26; 95% CI: (1.09–1.45)] was associated with a prolonged decision to delivery interval.ConclusionThe average decision to delivery interval was almost 2 hours. Delays were mostly due to health system challenges. We recommend routine monitoring of decision to delivery interval as an indicator of the quality of obstetric care.

Observational Dataset for "Constraining Global Coronal Models with Multiple Independent Observables", Badman et al. (2022). Arxiv : https://arxiv.org/abs/2201.11818

Contact : Samuel T. Badman (he/him) samuel_badman@berkeley.edu, Space Sciences Lab, UC Berkeley.

License : Creative Commons Attribution 4.0 International

Research Goal of Dataset : Data supports the above titled work in defining a framework for evaluating the magnetic structure of global coronal models via the evaluation of three single valued metrics. This repository contains observational data products used as input for the studies described in this work with the aim to allow external coronal modelers to reproduce and evaluate their own work against the same dataset we used.

Structure of files : This repository contains three subfolders each containing observational data relating to the three metrics defined in Badman et. al. (2022). These are :

1) ``Metric1_EUVCarringtonMaps''

Content :

Carrington maps of extreme ultraviolet (EUV) emission as observed by the SDO/AIA. These files contain slices of different wavelengths together, saved in hdf5 format. Maps for Carrington rotations (#### = 2210,2215,2216,2221) span the time intervals of interest in the associated work. The 193 angstrom wavelength slice from these maps were used as input into the EZSEG algorithm (see manuscript text) to generate ``observations'' of coronal hole boundaries which can then be compared via binary classification to modeled open field boundaries.

A python script which demonstrates reading in the hdf5 files and viewing the names of the different slices, then plots the 193 slice. The slice name of primary interest is 193A ('map_0193'), but slices at 171,211 angstrom, and a magnetogram are included.

2) ``Metric2_StreamerBelt''

A python script which demonstrates reading and plotting an example white light carrington map from this data set, as well as overplotting the downstream data extraction of the streamer maximum brightness (SMB) line.

2a) ``Metric2_StreamerBelt/WL_CarringtonMaps''

Content :

Carrington maps of white light intensity extracted at 5.0Rs altitude using coronagraph images taken by SOHO/LASCO, using the method described in the manuscript and Poirier et al. (2021). Maps at a daily cadence over each 60 day time interval studied in the manuscript are included here, incorporating the new data available as the sun rotated. Here saved as fits files.

Carrington maps as above but saved in .mat format (MATLAB).

2b) ``Metric2_StreamerBelt/SMB_Line_Extractions''

Downstream processed versions of the relevant White light carrington map from which the line of maximum brightness (SMB line) has been extracted, as well as the streamer belt "thickness" at each longitude. This is tabulated as a 3d coordinate gridded evenly in longitude, and each SMB grid point as a northwards and southwards thickness, tabulated in degrees. These data are described in the header of each file and the extraction process is described in detail in the manuscript.

3)Metric3_InSituTimeSeries

Content :

In situ polarity timeseries for 60 day intervals at 1 hour cadences during PSP encounters ## = [01,02,03], measured by spacecraft XYZ = [PSP,STA,OMN], Parker Solar Probe, STEREO A and OMNI (Earth-L1 dataset). Data values are +/- 1 indicating if magnetic vector is directed sunward or antisunward for each hour. This value is determined as described in the main text by finding the peak of a histogram of 1D B_R values over that hour interval and taking its sign.

A python script which demonstrates reading in the in situ timeseries for encounter 1 and plotting them.

A python script which demonstrates an open source method to produce source surface footpoints for a given spacecraft (here PSP) which can be used to sub-sample a HCS map provided by a modeler to generate a modeled time series which can be used to produce scores for metric 3 described in the associated manuscript.

Python scripts included in this dataset use python packages

astropy - https://github.com/astropy/astropy h5py - https://github.com/h5py/h5py astrospice - https://github.com/dstansby/astrospice matplotlib - https://github.com/matplotlib/matplotlib sunpy - https://github.com/sunpy/sunpy

Dataset SpecificationThe Data folder contains six subfolders (2D X-ray images, Experiment 1, Experiment 2, Experiment 3, Lunar orbit and OMNI dataset) and an instruction file called Readme.txt.Below I will explain the meaning of the data contained in each sub-file.OMNI dataset folder:The solar wind parameters observed from May 14, 2009, 00:00:00 UTC, to May 15, 2009, 22:40:00 UTC. From top to bottom, the panels display solar wind proton number density, solar wind velocities in three directions (Vx , Vy, and Vz), interplanetary magnetic field strength in three directions (Bx , By, and Bz ), and temperature (T).Lunar orbit folder:Depicts the lunar trajectory within the time interval from May 14, 2009, 00:00:00 UTC, to May 15, 2009, 22:40:00 UTC, with a sampling interval of 1 minute and an angular separation of 24.2222° between the start and end points. Experiment 1, Experiment 2 and Experiment 3 folder: Experiment 1 respectively illustrate the PSNR and SSIM calculated between the linear interpolation method and the adaptive dynamic X-ray image estimation method against the MHD model X-ray images . Experiment 2 conducted a detailed analysis of the results obtained with time intervals of 5 minutes, 10 minutes, 15 minutes, and 20 minutes for both linear interpolation and adaptive X-ray image estimation methods. Experiment 3 illustrates the continuous evolution of maxima and bow shock profiles in the original MHD X-ray images, linear interpolation results, and adaptive estimation results over the interval from May 14, 2009, 00:10:00 UTC, to May 16, 2009, 10:30:00 UTC, and the time period T of linear interpolation and adaptive dynamic X-ray estimation methods is set to 5 minutes.2D X-ray images folder:The folder contains the results of 2780 minutes of dynamic 2-D X-ray image estimation, and the algorithm's estimation interval is set to 5 minutes.This dataset can be used for related studies using 2-D X-ray estimates of Earth dynamics.The latest version adds Super-SloMo code and corresponding comparison results, and places code to calculate ssim and psnr. All the calculation results of SSIM and PSNR in this paper are completed after normalization.

DATASET: Alpha version 2008 estimates of proportion of people per grid square living in poverty, as defined by the Multidimensional Poverty Index (http://www.ophi.org.uk/policy/multidimensional-poverty-index/), and associated uncertainty metrics. REGION: Africa SPATIAL RESOLUTION: 0.00833333 decimal degrees (approx 1km at the equator) PROJECTION: Geographic, WGS84 UNITS: Proportion of residents living in MPI-defined poverty (poverty dataset); 95% credible interval (uncertainty dataset) MAPPING APPROACH: Bayesian model-based geostatistics in combination with high resolution gridded spatial covariates applied to GPS-located household survey data on poverty from the DHS and/or LSMS programs. FORMAT: Geotiff (zipped using 7-zip (open access tool): www.7-zip.org) FILENAMES: Examples - ken08povmpi.tif = Kenya (ken) MPI poverty map for 2008. ken08povmpi-uncert.tif = uncertainty dataset showing 95% credible intervals. DATE OF PRODUCTION: January 2013 CITATION: Tatem AJ, Gething PW, Bhatt S, Weiss D and Pezzulo C (2013) Pilot high resolution poverty maps, University of Southampton/Oxford.