Japan JP: International Liquidity: Total Reserves: Including Gold at Market Price data was reported at 902,634.286 XDR mn in Sep 2018. This records an increase from the previous number of 898,420.618 XDR mn for Aug 2018. Japan JP: International Liquidity: Total Reserves: Including Gold at Market Price data is updated monthly, averaging 55,783.498 XDR mn from Dec 1950 (Median) to Sep 2018, with 748 observations. The data reached an all-time high of 910,141.902 XDR mn in Feb 2017 and a record low of 598.000 XDR mn in Dec 1950. Japan JP: International Liquidity: Total Reserves: Including Gold at Market Price data remains active status in CEIC and is reported by International Monetary Fund. The data is categorized under Global Database’s Japan – Table JP.IMF.IFS: International Liquidity.

Russia Exports: Cuba: Articles Made of Stone, Plaster, Cement, Asbestos data was reported at 43.000 USD th in Dec 2018. This records an increase from the previous number of 34.000 USD th for Sep 2018. Russia Exports: Cuba: Articles Made of Stone, Plaster, Cement, Asbestos data is updated quarterly, averaging 22.000 USD th from Mar 2005 (Median) to Dec 2018, with 56 observations. The data reached an all-time high of 465.000 USD th in Jun 2008 and a record low of 0.000 USD th in Jun 2011. Russia Exports: Cuba: Articles Made of Stone, Plaster, Cement, Asbestos data remains active status in CEIC and is reported by Federal Customs Service. The data is categorized under Russia Premium Database’s Foreign Trade – Table RU.JAD019: Exports: by 2-Digit HS Code: Cuba.

About this dataset

Context

The dataset tabulates the population of Keytesville by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of Keytesville across both sexes and to determine which sex constitutes the majority.

Key observations

There is a slight majority of male population, with 51.52% of total population being male. Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis. No further analysis is done on the data reported from the Census Bureau.

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the Keytesville is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Keytesville total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Keytesville Population by Race & Ethnicity. You can refer the same here

Dataset Generated by Stream Engine from Ocean Observatories Initiative AssetManagementRecordLastModified=2020-08-28T17:25:09.559000 AssetUniqueID=CGINS-CTDMOG-10224 cdm_data_type=Other collection_method=recovered_host Conventions=CF-1.6, NCCSV-1.0 defaultDataQuery=practical_salinity,ctdmo_seawater_conductivity_qartod_executed,ctdmo_seawater_conductivity_qartod_results,ctd_time,conductivity,ctdmo_seawater_temperature_qartod_executed,temperature,density,ctdmo_seawater_temperature,ctdmo_seawater_temperature_qartod_results,ctdmo_seawater_conductivity,ctdmo_seawater_pressure_qartod_results,pressure,ctdmo_seawater_pressure,inductive_id,time,ctdmo_seawater_pressure_qartod_executed&time>=max(time)-1days Description=CTD Mooring (Inductive): CTDMO Series G feature_Type=point FirmwareVersion=Not specified. geospatial_lat_resolution=0.1 geospatial_lat_units=degrees_north geospatial_lon_resolution=0.1 geospatial_lon_units=degrees_east geospatial_vertical_positive=down geospatial_vertical_resolution=0.1 geospatial_vertical_units=meters history=2020-09-01T08:34:15.242525 generated from Stream Engine id=GP03FLMA-RIM01-02-CTDMOG042-recovered_host-ctdmo_ghqr_sio_mule_instrument infoUrl=http://oceanobservatories.org/ institution=Ocean Observatories Initiative lat=49.97667 lon=-144.24617 Manufacturer=Sea-Bird Electronics Metadata_Conventions=Unidata Dataset Discovery v1.0 Mobile=False ModelNumber=SBE 37-IM naming_authority=org.oceanobservatories nodc_template_version=NODC_NetCDF_TimeSeries_Orthogonal_Template_v1.1 node=RIM01 Notes=Not specified. Owner=Woods Hole Oceanographic Institution processing_level=L2 project=Ocean Observatories Initiative references=More information can be found at http://oceanobservatories.org/ RemoteResources=[] requestUUID=c2a41127-be3d-40bc-85e5-eecc2c3e2683 sensor=02-CTDMOG042 SerialNumber=37-10224 ShelfLifeExpirationDate=Not specified. SoftwareVersion=Not specified. source=GP03FLMA-RIM01-02-CTDMOG042-recovered_host-ctdmo_ghqr_sio_mule_instrument sourceUrl=http://oceanobservatories.org/ standard_name_vocabulary=NetCDF Climate and Forecast (CF) Metadata Convention Standard Name Table 29 stream=ctdmo_ghqr_sio_mule_instrument subsite=GP03FLMA time_coverage_end=2016-06-27T21:58:20Z time_coverage_resolution=P900.15S time_coverage_start=2015-06-06T22:43:10Z uuid=c2a41127-be3d-40bc-85e5-eecc2c3e2683

Customs records of are available for YONGKANG VANZ IMPORT&EXPORT CO.,LTD. Learn about its Importer, supply capabilities and the countries to which it supplies goods

Dataset

This dataset was created by Wu Bing

Contents

Just for myself.

Current meter data were collected from FIXED PLATFORMS from the Gulf of Mexico and Straits of Florida. Data were submitted by the Atlantic Oceanographic and Meteorological Laboratory (AOML) as part of the Ocean Thermal Energy Conversion (OTEC) and other projects from 24 October 1964 to 01 November 1977. Data were processed by NODC to the NODC standard F015 Current Meter Components format. The F015 format contains time series measurements of ocean currents. These data are obtained from current meter moorings and represent the Eulerian method of current measurement, i.e., the meters are deployed at a fixed point and measure flow past a sensor. Position, bottom depth, sensor depth and meter characteristics are reported for each station. The data record includes values of east-west (u) and north-south (v) current vector components at specified date and time. Current direction is defined as the direction toward which the water is flowing with positive directions east and north. Data values may be subject to averaging or filtering and are typically reported at 10 - 15 minute time intervals. Water temperature, pressure and conductivity or salinity may also be reported. A text record is available for optional comments.

🇺🇸 미국

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. The Address Ranges Relationship File (ADDR.dbf) contains the attributes of each address range. Each address range applies to a single edge and has a unique address range identifier (ARID) value. The edge to which an address range applies can be determined by linking the address range to the All Lines Shapefile (EDGES.shp) using the permanent topological edge identifier (TLID) attribute. Multiple address ranges can apply to the same edge since an edge can have multiple address ranges. Note that the most inclusive address range associated with each side of a street edge already appears in the All Lines Shapefile (EDGES.shp). The TIGER/Line Files contain potential address ranges, not individual addresses. The term "address range" refers to the collection of all possible structure numbers from the first structure number to the last structure number and all numbers of a specified parity in between along an edge side relative to the direction in which the edge is coded. The address ranges in the TIGER/Line Files are potential ranges that include the full range of possible structure numbers even though the actual structures may not exist.

'The USGS Earth Resources Observation and Science (EROS) Center archive holds data collected by the Landsat suite of satellites, beginning with Landsat 1 in 1972. All Landsat data held in the USGS EROS archive are available for download at no charge. '

Coarse-grid weather and climate models rely particularly on parameterizations of cloud fields, and coarse-grained cloud fields from a fine-grid reference model are a natural target for a machine-learned parameterization. We machine-learn the coarsened-fine cloud properties as a function of coarse-grid model state in each grid cell of NOAA's FV3GFS global atmosphere model with 200 km grid spacing, trained using a 3-km fine-grid reference simulation with a modified version of FV3GFS. The ML outputs are coarsened-fine fractional cloud cover and liquid and ice cloud condensate mixing ratios, and the inputs are coarse model temperature, pressure, relative humidity, and ice cloud condensate. The predicted fields are skillful and unbiased, but somewhat under-dispersed, resulting in too many partially-cloudy model columns. When the predicted fields are applied diagnostically (offline) in FV3GFS's radiation scheme, they lead to small biases in global-mean top-of-atmosphere (TOA) and surface radiative fluxes. An unbiased global-mean TOA net radiative flux is obtained by setting to zero any predicted cloud with grid-cell mean cloud fraction less than a threshold of 6.5%; this does not significantly degrade the ML prediction of cloud properties. The diagnostic, ML-derived radiative fluxes are far more accurate than those obtained with the existing cloud parameterization in the nudged coarse-grid model, as they leverage the accuracy of the fine-grid reference simulation's cloud properties.This dataset provides the coarsened fine-grid model outputs needed to run the nudged coarse climate model, including running with prescribed coarsened fine-grid cloud fields and to train the ML model that predicts coarsened-fine cloud fields as functions of nudged coarse model state. Methods This dataset was generated by running a 10-day simulation of NOAA GFDL's X-SHiELD global storm-resolving atmospheric model at C3072 (3km) resolution. X-SHiELD shares the same FV3 dynamical core and most of its physics parameterizations with NOAA's Global Forecast System (GFS), NOAA's operational global weather forecast model. The simulation was run on GFDL's GAEA supercomputing system and was coarse-grained online to C48 (~200km) resolution to produce the model state and diagnostic files included here. Please see Harris et al, 2020, "GFDL SHiELD: A Unified System for Weather-to-Seasonal Prediction" (JAMES) doi:10.1029/2020MS002223 for more information on X-SHiELD.

This dataset is about books. It has 1 row and is filtered where the author is Paul Rostand. It features 7 columns including author, publication date, language, and book publisher.

Graph and download economic data for Chain-Type Quantity Index for Real GDP: Real Estate and Rental and Leasing (53) in New Jersey (NJRERENTLEAQGSP) from 1997 to 2024 about quantity index, leases, NJ, finance, insurance, rent, real estate, GSP, private industries, private, industry, GDP, and USA.

This dataset is about books. It has 1 row and is filtered where the book is In love and war : a letter to my parents. It features 7 columns including author, publication date, language, and book publisher.

United States PPI: Mfg: PM: FO: NM: DF: Primary Products (PP) data was reported at 123.209 Dec2011=100 in Mar 2025. This records an increase from the previous number of 122.860 Dec2011=100 for Feb 2025. United States PPI: Mfg: PM: FO: NM: DF: Primary Products (PP) data is updated monthly, averaging 99.300 Dec2011=100 from Dec 2011 (Median) to Mar 2025, with 160 observations. The data reached an all-time high of 124.140 Dec2011=100 in Feb 2023 and a record low of 94.900 Dec2011=100 in Aug 2016. United States PPI: Mfg: PM: FO: NM: DF: Primary Products (PP) data remains active status in CEIC and is reported by U.S. Bureau of Labor Statistics. The data is categorized under Global Database’s United States – Table US.I092: Producer Price Index: by Industry: Manufacturing: Primary and Fabricated Metal Products.

Sokic Detection

## Overview

Sokic Detection is a dataset for computer vision tasks - it contains Sokic annotations for 259 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [MIT license](https://creativecommons.org/licenses/MIT).

URL: https://geoscience.data.qld.gov.au/dataset/clon263

Surface and drillhole exploration geochemistry for Block: CLON263. A whole of Queensland geochemistry dataset is available for download at https://geoscience.data.qld.gov.au/dataset/whole-of-queensland-geochemistry-databases. Instructions on how to use and analyse the geochemistry data is available at https://geoscience.data.qld.gov.au/dataset/geochemistry-data-how-to-guide

United States Months of Supply: Multi-Family: Kapaa, HI data was reported at 0.500 Month in Apr 2020. This records a decrease from the previous number of 3.000 Month for Mar 2020. United States Months of Supply: Multi-Family: Kapaa, HI data is updated monthly, averaging 4.000 Month from Dec 2012 (Median) to Apr 2020, with 34 observations. The data reached an all-time high of 9.000 Month in Oct 2017 and a record low of 0.500 Month in Apr 2020. United States Months of Supply: Multi-Family: Kapaa, HI data remains active status in CEIC and is reported by Redfin. The data is categorized under Global Database’s United States – Table US.EB029: Months of Supply: by Metropolitan Areas.

Tonga Imports from New Zealand of Flat-rolled Products of Iron/Non-alloy Steel, Clad, Plated or Coated was US$352.74 Thousand during 2014, according to the United Nations COMTRADE database on international trade.

About this dataset

Context

The dataset presents the the household distribution across 16 income brackets among four distinct age groups in Jacobs town: Under 25 years, 25-44 years, 45-64 years, and over 65 years. The dataset highlights the variation in household income, offering valuable insights into economic trends and disparities within different age categories, aiding in data analysis and decision-making..

Key observations

• Upon closer examination of the distribution of households among age brackets, it reveals that there are 0 households where the householder is under 25 years old, 20(10.36%) households with a householder aged between 25 and 44 years, 97(50.26%) households with a householder aged between 45 and 64 years, and 76(39.38%) households where the householder is over 65 years old.
• The age group of 25 to 44 years exhibits the highest median household income, while the largest number of households falls within the 45 to 64 years bracket. This distribution hints at economic disparities within the town of Jacobs town, showcasing varying income levels among different age demographics.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Income brackets:

• Less than $10,000
• $10,000 to $14,999
• $15,000 to $19,999
• $20,000 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $59,999
• $60,000 to $74,999
• $75,000 to $99,999
• $100,000 to $124,999
• $125,000 to $149,999
• $150,000 to $199,999
• $200,000 or more

Variables / Data Columns

• Household Income: This column showcases 16 income brackets ranging from Under $10,000 to $200,000+ ( As mentioned above).
• Under 25 years: The count of households led by a head of household under 25 years old with income within a specified income bracket.
• 25 to 44 years: The count of households led by a head of household 25 to 44 years old with income within a specified income bracket.
• 45 to 64 years: The count of households led by a head of household 45 to 64 years old with income within a specified income bracket.
• 65 years and over: The count of households led by a head of household 65 years and over old with income within a specified income bracket.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Jacobs town median household income by age. You can refer the same here