We show that the expected value of the largest order statistic in Gaussian samples can be accurately approximated as (0.2069 ln (ln (n))+0.942)4, where n∈[2,108] is the sample size, while the standard deviation of the largest order statistic can be approximated as −0.4205arctan(0.5556[ln(ln (n))−0.9148])+0.5675. We also provide an approximation of the probability density function of the largest order statistic which in turn can be used to approximate its higher order moments. The proposed approximations are computationally efficient, and improve previous approximations of the mean and standard deviation given by Chen and Tyler (1999).

Liner shipping connectivity index (maximum value in 2004 = 100) in Denmark was reported at 45.82 in 2021, according to the World Bank collection of development indicators, compiled from officially recognized sources. Denmark - Liner shipping connectivity index (maximum value in 2004 = 100) - actual values, historical data, forecasts and projections were sourced from the World Bank on November of 2025.

This dataset contains the predicted prices of the asset Maximum Extractable Value over the next 16 years. This data is calculated initially using a default 5 percent annual growth rate, and after page load, it features a sliding scale component where the user can then further adjust the growth rate to their own positive or negative projections. The maximum positive adjustable growth rate is 100 percent, and the minimum adjustable growth rate is -100 percent.

Liner shipping connectivity index (maximum value in 2004 = 100) in Dominican Republic was reported at 42.23 in 2021, according to the World Bank collection of development indicators, compiled from officially recognized sources. Dominican Republic - Liner shipping connectivity index (maximum value in 2004 = 100) - actual values, historical data, forecasts and projections were sourced from the World Bank on November of 2025.

Trinidad and Tobago TT: Liner Shipping Connectivity Index: Maximum Value In 2004 = 100 data was reported at 17.390 NA in 2016. This records a decrease from the previous number of 17.890 NA for 2015. Trinidad and Tobago TT: Liner Shipping Connectivity Index: Maximum Value In 2004 = 100 data is updated yearly, averaging 15.880 NA from Dec 2004 (Median) to 2016, with 13 observations. The data reached an all-time high of 18.900 NA in 2012 and a record low of 10.610 NA in 2005. Trinidad and Tobago TT: Liner Shipping Connectivity Index: Maximum Value In 2004 = 100 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Trinidad and Tobago – Table TT.World Bank.WDI: Transportation. The Liner Shipping Connectivity Index captures how well countries are connected to global shipping networks. It is computed by the United Nations Conference on Trade and Development (UNCTAD) based on five components of the maritime transport sector: number of ships, their container-carrying capacity, maximum vessel size, number of services, and number of companies that deploy container ships in a country's ports. For each component a country's value is divided by the maximum value of each component in 2004, the five components are averaged for each country, and the average is divided by the maximum average for 2004 and multiplied by 100. The index generates a value of 100 for the country with the highest average index in 2004. . The underlying data come from Containerisation International Online.; ; United Nations Conference on Trade and Development, Review of Maritime Transport 2010.; ;

Libya LY: Liner Shipping Connectivity Index: Maximum Value In 2004 = 100 data was reported at 4.860 NA in 2016. This records a decrease from the previous number of 5.930 NA for 2015. Libya LY: Liner Shipping Connectivity Index: Maximum Value In 2004 = 100 data is updated yearly, averaging 5.930 NA from Dec 2004 (Median) to 2016, with 13 observations. The data reached an all-time high of 9.430 NA in 2009 and a record low of 4.710 NA in 2006. Libya LY: Liner Shipping Connectivity Index: Maximum Value In 2004 = 100 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Libya – Table LY.World Bank.WDI: Transportation. The Liner Shipping Connectivity Index captures how well countries are connected to global shipping networks. It is computed by the United Nations Conference on Trade and Development (UNCTAD) based on five components of the maritime transport sector: number of ships, their container-carrying capacity, maximum vessel size, number of services, and number of companies that deploy container ships in a country's ports. For each component a country's value is divided by the maximum value of each component in 2004, the five components are averaged for each country, and the average is divided by the maximum average for 2004 and multiplied by 100. The index generates a value of 100 for the country with the highest average index in 2004. . The underlying data come from Containerisation International Online.; ; United Nations Conference on Trade and Development, Review of Maritime Transport 2010.; ;

Pre-processed ML input data for 4 Roseworthy paddocks, B4, B3, E2, E5. Files suffixed with paddock names, includes read me file for all paddock dataThe collection includes 4 paddocks with data including paddock boundaries, crop yield, EM38 geophysics, elevation, yield associated moisture percentage. The data accessible from the paddocks and has been acquired between 2005 and 2020. Pre-processed data for machine learning analytics. Pre-processed data was converted to standard csv machine-readable format with CRS included for all measurements. Includes processed paddock measurements, pre-processed Remote Sensing time-series data (Landsat, resampled to 5-m resolution using bilinear interpolation) and pre-processed climate time-series data (SILO database). Readme metadata documents of processed files to assist for ML purposes. Measurements re-scaled and spatially aligned using ordinary block kriging method using locally estimated variograms. The value at each grid point represents an average interpolated value within a 5-m block, centred at the grid point.

A. UDS data files -------------------- Eight files are provided that conform to the UDS conventions regarding the naming of files and the format of the data. The eight files are divided into 4 pairs of files with each pair consisting of a file containing data averaged over a 10 minute period and a file containing the maximum data value during the same 10 minute period. The 4 pairs of file contain data for the RAR, the PFR, WFA - magnetic field, and WFA - magnetic field.

Brazil Social Security: Maximum Benefits Value data was reported at 8,157.410 BRL in Oct 2025. This stayed constant from the previous number of 8,157.410 BRL for Sep 2025. Brazil Social Security: Maximum Benefits Value data is updated monthly, averaging 5,189.820 BRL from Jan 2008 (Median) to Oct 2025, with 214 observations. The data reached an all-time high of 8,157.410 BRL in Oct 2025 and a record low of 2,894.280 BRL in Feb 2008. Brazil Social Security: Maximum Benefits Value data remains active status in CEIC and is reported by Ministry of Labor and Social Security. The data is categorized under Global Database’s Brazil – Table BR.GBE: Social Security: Summary.

Liner shipping connectivity index (maximum value in 2004 = 100) in Ivory Coast was reported at 19.28 in 2021, according to the World Bank collection of development indicators, compiled from officially recognized sources. Ivory Coast - Liner shipping connectivity index (maximum value in 2004 = 100) - actual values, historical data, forecasts and projections were sourced from the World Bank on November of 2025.

A. UDS data files ------------------- Eight files are provided that conform to the UDS conventions regarding the naming of files and the format of the data. The eight files are divided into 4 pairs of files with each pair consisting of a file containing data averaged over a 10 minute period and a file containing the maximum data value during the same 10 minute period. The 4 pairs of file contain data for the RAR, the PFR, WFA - magnetic field, and WFA - magnetic field.

The Liner Shipping Connectivity Index captures how well countries are connected to global shipping networks. It is computed by the United Nations Conference on Trade and Development (UNCTAD) based on five components of the maritime transport sector: number of ships, their container-carrying capacity, maximum vessel size, number of services, and number of companies that deploy container ships in a country's ports. For each component a country's value is divided by the maximum value of each component in 2004, the five components are averaged for each country, and the average is divided by the maximum average for 2004 and multiplied by 100. The index generates a value of 100 for the country with the highest average index in 2004. . The underlying data come from Containerisation International Online.

A. UDS data files --------------------- Eight files are provided that conform to the UDS conventions regarding the naming of files and the format of the data. The eight files are divided into 4 pairs of files with each pair consisting of a file containing data averaged over a 10 minute period and a file containing the maximum data value during the same 10 minute period. The 4 pairs of file contain data for the RAR, the PFR, WFA - magnetic field, and WFA - magnetic field.

Debris Flow, Precipitation, and Volume Measurements in the Grizzly Creek Burn Perimeter June 2021-September 2022 https://doi.org/10.5066/P9Z7RROL This data release contains data summarizing observations within and adjacent to the Grizzly Creek Fire, which burned from 10 August to 18 December 2020. This monitoring data summarizes precipitation, observations of debris flows, and the volume of sediment eroded during debris flows triggered during the summer monsoonal period in 2021 and 2022. Summary rainfall data 2021 (1a_Storm_matrix_2021_gr1mmhr.csv) are provided in a comma-separated value (CSV) file. These data represent the maximum measured rainfall intensities during the monsoon months of 2021 (June-Sept). The columns in the csv file are: Date (m/dd/yy), Name (11 columns have unique gage names), Max 15 min (this is the maximum 15-minute rainfall intensity in mm/h for the unique gauge), Maximum Value for All Gages (this is the maximum rainfall intensity for all of the gauges in units of either mm/h or in/15 min), Peak 15-minute Intensity (in/15 min) (this is the total inches of rainfall in 15 minutes), Debris Flow (this can be 0 indicating no debris flow response, or 1 indicating a debris flow response). Note that we only display gauges that record data sufficient to produce a 15-minute rainfall intensity. Gauges with longer recording rates (e.g., 1 hour) cannot be used to compute the 15-minute rainfall intensity and are not displayed in this table. A null value (‘n/a’) populates the entries where the rain gauge did not measure a 15-minute rainfall intensity greater than 1 mm/hr. Time series rainfall data from the gauges are provided in the child item: Precipitation Data Grizzly Creek Burn Perimeter. Summary rainfall data 2022 (1b_Storm_matrix_2022_gr1mmhr.csv) are provided in a comma-separated value (CSV) file. These data represent the maximum measured rainfall intensities during the monsoon months of 2022 (June-Sept). The columns in the csv file are: Date (m/dd/yy), Name (7 columns have unique gage names), Max 15 min (this is the maximum 15-minute rainfall intensity in mm/h), Peak 15-minute Intensity (in/15 min) (this is the total inches of rainfall in 15 minutes), Debris Flow (this can be 0 indicating no debris flow response, or 1 indicating a debris flow response). Note that we only display gauges that record data sufficient to produce a 15-minute rainfall intensity. Gauges with longer recording rates (e.g., 1 hour) cannot be used to compute the 15-minute rainfall intensity and are not displayed in this table. A null value (‘n/a’) populates the entries where the rain gauge did not measure a 15-minute rainfall intensity greater than 1 mm/hr. Time series rainfall data from the gauges are provided in the child item: Precipitation Data Grizzly Creek Burn Perimeter. Debris Flow Observation data 2021 (2a_All_Verification_2021.csv) are provided in a comma-separated value (CSV) file. The columns in the csv file are: Year (yyyy), State, Fire Name, Fire_ID (index for the fire developed during the USGS debris flow hazard assessment), Fire_SegID (a specific index assigned by the USGS debris flow hazard assessment to the channel segment that produced the debris flow), Site Name (the name of the nearest milemarker on interstate 70), ObservationDate_mmddyyyy, ObservationLatitude_DD, ObservationLongitude_DD, DebrisFlowResponse (this can be 0 indicating no debris flow response, or 1 indicating a debris flow response), SourceOfObservation (name of the observer), StormDate_mmddyyyy, GaugeName, GaugeLatitude_DD, GaugeLongitude_DD, GaugeDist_km (distance from watershed of the debris flow observation to the nearest rain gage in km), StormAccum_mm (the total rainfall during a storm in millimeters), StormDuration_hr (the total duration of a storm in hours), Peak_I15_mm/h (the maximum 15 minute rainfall intensity in mm/h), Peak_I30_mm/h (the maximum 30 minute rainfall intensity in mm/h), Peak_I60_mm/h (the maximum 60 minute rainfall intensity in mm/h), Peak_I15_in in 15min (the total inches of rainfall in 15 minutes), Peak_I30_in in 30min (the total inches of rainfall in 30 minutes), Peak_I60_in in 60min (the total inches of rainfall in 60 minutes). In the event that no rain gage within 6.1 km of an observation measured a 15-minute rainfall intensity greater than 1 mm/hr, no rain gage information is displayed. Debris Flow Observation data 2022 (2b_All_Verification_2022.csv) are provided in a comma-separated value (CSV) file. The columns in the csv file are: Year (yyyy), State, Fire Name, Fire_ID (index for the fire developed during the USGS debris flow hazard assessment), Fire_SegID (a specific index assigned by the USGS debris flow hazard assessment to the channel segment that produced the debris flow), Site Name (the name of the nearest milemarker on interstate 70), ObservationDate_mmddyyyy, ObservationLatitude_DD, ObservationLongitude_DD, DebrisFlowResponse (this can be 0 indicating no debris flow response, or 1 indicating a debris flow response), SourceOfObservation (name of the observer), StormDate_mmddyyyy, GaugeName, GaugeLatitude_DD, GaugeLongitude_DD, GaugeDist_km (distance from watershed of the debris flow observation to the nearest rain gage), StormAccum_mm (the total rainfall during a storm in millimeters), StormDuration_hr (the total duration of a storm in hours), Peak_I15_mm/h (the maximum 15 minute rainfall intensity in mm/h), Peak_I30_mm/h (the maximum 30 minute rainfall intensity in mm/h), Peak_I60_mm/h (the maximum 60 minute rainfall intensity in mm/h), Peak_I15_in in 15min (the total inches of rainfall in 15 minutes), Peak_I30_in in 30min (the total inches of rainfall in 30 minutes), Peak_I60_in in 60min (the total inches of rainfall in 60 minutes). In the event that no rain gage within 6.1 km of an observation measured a 15-minute rainfall intensity greater than 1 mm/hr, no rain gage information is displayed. Lidar Derived Volume data (3_Glenwood_Volume_table_final.csv) are provided in a comma-separated value (CSV) file. The columns in the csv file are: Site Name (the name of the nearest milemarker on interstate 70), Debris Flow Observation Date (mm/dd/yy), Digitized Channel Area (m^2) (the area of a polygon delineating the channel contributing sediment), Net Channel Volume (m^3) (the net volume of erosion (negative values) and deposition (positive values) within the digitized channel area), Net Depositional Fan Volume (m^3) (the net volume of erosion (negative values) and deposition (positive values) within the digitized depositional fan), Upstream Drainage Area at Erosion/Deposition Transition (m^2) (the drainage area where channels switch from primarily erosional to primarily depositional), Latitude of erosion/deposition transition (Decimal Degrees), Longitude of erosion/deposition transition (Decimal Degrees), Geographic Name Reference (This is a geographic name if the location of the channel has a known name). Note that the Latitude and Longitude indicate the most downstream point of the channel areas, where erosion transitions to deposition. Some debris flows contribute to a single depositional fan, and in those cases the depositional volume represents the full volume of the fan. Sometimes debris flows deposited on a road and were cleaned up, therefore no depositional volume was measured by the lidar and the depositional value is left blank. Furthermore, the locations are repeated if there was more than one observed debris flow at the same spot, however, the volume does not change because the volume difference could only be calculated between the available lidar flights on 10 June 2016 and 24 August 2021. Rain Gauge Locations (4_Gauge_Location.csv) are provided in a comma-separated value (CSV) file. The columns in the csv file are: Gauge Name, Latitude (Decimal Degrees), and Longitude (Decimal Degrees). Time series rainfall data from the gauges are provided in the child item: Precipitation Data Grizzly Creek Burn Perimeter. Acknowledgements: We gratefully acknowledge the rainfall data obtained from the U.S. Geological Survey Colorado Water Science Center, Colorado Department of Transportation, and the Bureau of Land Management. Lidar data was generously provided by the Colorado Department of Transportation and the U.S. Geological Survey 3DEP program. Adjustments to the 3DEP lidar were provided by the original vendor, Merrick and Company.

Graph and download economic data for Federal Old-Age and Survivors Insurance and Disability Insurance Program; Present Value of Maximum Entitlements, Other Changes in Volume (BOGZ1FV354190005A) from 1946 to 2023 about maximum entitlements, present value, old-age, survivors, program, volume, change, disability, insurance, federal, and USA.

View details of Perfumery Products Import Data of Max Value Bv Supplier from Belgium to US at New Yorknewark Area Newark Nj Port with product description, price, date, quantity and more.

Ozone pollution (PM2.5): number of days on which a sliding 8-hour average exceeded 120 µg/m3 (micrograms per cubic metre). Average 2021-22-23 for each station Data sources: ATMO Grand Est, Agence Wallonne de l'Air et du Climat - AWAC, Landesamt für Umwelt- und Arbeitsschutz Saarland - IMMESA, Landesamt für Umwelt Rheinland-Pfalz - ZIMEN, Administration de l'environnement Luxembourg. Harmonization: ATMO Grand Est and GIS-GR 2024 Link to interactive map: https://map.gis-gr.eu/theme/main?version=3&zoom=8&X=708580&Y=6429642&lang=fr&rotation=0&layers=2399&opacities=1&bgLayer=basemap_2015_global Link to Geocatalog: https://geocatalogue.gis-gr.eu/geonetwork/srv/eng/catalog.search#/metadata/b3a7f209-dc7c-4661-828b-a97f8a9b2635 WMS link: https://ws.geoportail.lu/wss/service/GR_Air_Quality_WMS/guest with layer name(s): -O3_max_daily_running_8-hour_mean_over_120_ugm_2021-22-23

Each pixel value corresponds to the best quality maximum NDVI recorded within that pixel over the week specified. Poor quality pixel observations are removed from this product. Observations whose quality is degraded by snow cover, shadow, cloud, aerosols, and/or low sensor zenith angles are removed (and are assigned a value of “missing data”). In addition, negative Max-NDVI values, occurring where R reflectance > NIR reflectance, are considered non-vegetated and assigned a value of 0. This results in a Max-NDVI product that should (mostly) contain vegetation-covered pixels. Max-NDVI values are considered high quality and span a biomass gradient ranging from 0 (no/low biomass) to 1 (high biomass).

Nicaragua NI: Liner Shipping Connectivity Index: Maximum Value In 2004 = 100 data was reported at 8.840 NA in 2016. This records an increase from the previous number of 8.820 NA for 2015. Nicaragua NI: Liner Shipping Connectivity Index: Maximum Value In 2004 = 100 data is updated yearly, averaging 8.410 NA from Dec 2004 (Median) to 2016, with 13 observations. The data reached an all-time high of 10.580 NA in 2009 and a record low of 4.750 NA in 2004. Nicaragua NI: Liner Shipping Connectivity Index: Maximum Value In 2004 = 100 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Nicaragua – Table NI.World Bank.WDI: Transportation. The Liner Shipping Connectivity Index captures how well countries are connected to global shipping networks. It is computed by the United Nations Conference on Trade and Development (UNCTAD) based on five components of the maritime transport sector: number of ships, their container-carrying capacity, maximum vessel size, number of services, and number of companies that deploy container ships in a country's ports. For each component a country's value is divided by the maximum value of each component in 2004, the five components are averaged for each country, and the average is divided by the maximum average for 2004 and multiplied by 100. The index generates a value of 100 for the country with the highest average index in 2004. . The underlying data come from Containerisation International Online.; ; United Nations Conference on Trade and Development, Review of Maritime Transport 2010.; ;

The annual maximum and minimum daily data are the maximum and minimum daily mean values for a given year.