An Open Context "predicates" dataset item. Open Context publishes structured data as granular, URL identified Web resources. This "Variables" record is part of the "Domuztepe Excavations" data publication.

A survey revealed that clothing was the most popular product for digital purchases among U.S. consumers in the month leading up to December 2023. a total 67.1 percent of shoppers in the U.S. bought clothing in this period. Shoes was the second-most popular category, with 44 percent of shoppers having bought them online in the same time frame.

During the second quarter of 2022, comics apps hosted and distributed in the Google Play Store registered higher engagement among global users aged between 18 and 24 years. Younger users were the most active demographic across all examined app categories, with approximately half of all downloads in the music and audio category generated in the Google Play Store coming from users in the 18 to 24 demographic group. Approximately three in 10 users downloading news and magazines apps were aged between 50 and 64 years, while 22 percent of parenting apps were downloaded by users aged 25 and 34 years.

France Job Seekers: Category A: Male: Age: 25 to 49 data was reported at 1,019.600 Person th in Oct 2018. This records an increase from the previous number of 999.000 Person th for Sep 2018. France Job Seekers: Category A: Male: Age: 25 to 49 data is updated monthly, averaging 899.500 Person th from Jan 1996 (Median) to Oct 2018, with 274 observations. The data reached an all-time high of 1,200.600 Person th in Jan 2016 and a record low of 597.700 Person th in Jun 2008. France Job Seekers: Category A: Male: Age: 25 to 49 data remains active status in CEIC and is reported by Ministry of Labour, Employment and Health. The data is categorized under Global Database’s France – Table FR.G044: Labour Statistics: Job Seekers.

An Open Context "predicates" dataset item. Open Context publishes structured data as granular, URL identified Web resources. This "Variables" record is part of the "Faunal Data from Neolithic Menteşe" data publication.

This dataset provides information about different age categories along with population and percentage in the town of Dumfries in the year 2020

The data covers different aspects of the school workforce in Wales, using the data collected from the School Workforce Annual Census (SWAC).

During the second quarter of 2022, sticker apps hosted and distributed in the Apple App Store registered higher engagement among global users aged between 18 and 24 years. Approximately 35 percent of weather iOS apps users were aged between 50 and 64 years, while 24 percent of newsstand iOS app users were aged between 25 and 34 years. Additionally, three percent of book app users in the Apple App Store were aged over 65 years in the examined quarter.

The global forest age dataset (GFAD v.1.1) provides a correction to GFAD v1.0, as well as its uncertainties. GFAD describes the age distributions of plant functional types (PFT) on a 0.5-degree grid. Each grid cell contains information on the fraction of each PFT within an age class. The four PFTs, needleaf evergreen (NEEV), needleleaf deciduous (NEDE), broadleaf evergreen (BREV) and broadleaf deciduous (BRDC) are mapped from the MODIS Collection 5.1 land cover dataset, crosswalking land cover types to PFT fractions. The source of data for the age distributions is from country-level forest inventory for temperate and high-latitude countries, and from biomass for tropical countries. The inventory and biomass data are related to fifteen age classes defined in ten-year intervals, from 1-10 up to a class greater than 150 years old. […]

HRST by category, age and NUTS 1 region

France Job Seekers: Category A: Age: Under 25 data was reported at 506.000 Person th in Oct 2018. This records an increase from the previous number of 490.900 Person th for Sep 2018. France Job Seekers: Category A: Age: Under 25 data is updated monthly, averaging 477.950 Person th from Jan 1996 (Median) to Oct 2018, with 274 observations. The data reached an all-time high of 708.000 Person th in Oct 1996 and a record low of 307.400 Person th in Jun 2001. France Job Seekers: Category A: Age: Under 25 data remains active status in CEIC and is reported by Ministry of Labour, Employment and Health. The data is categorized under Global Database’s France – Table FR.G044: Labour Statistics: Job Seekers.

This shapefile represents habitat suitability categories (High, Moderate, Low, and Non-Habitat) derived from a composite, continuous surface of sage-grouse habitat suitability index (HSI) values for Nevada and northeastern California during spring, which is a surrogate for habitat conditions during the sage-grouse breeding and nesting period. Summary of steps to create Habitat Categories: HABITAT SUITABILITY INDEX: The HSI was derived from a generalized linear mixed model (specified by binomial distribution) that contrasted data from multiple environmental factors at used sites (telemetry locations) and available sites (random locations). Predictor variables for the model represented vegetation communities at multiple spatial scales, water resources, habitat configuration, urbanization, roads, elevation, ruggedness, and slope. Vegetation data was derived from various mapping products, which included NV SynthMap (Petersen 2008, SageStitch (Comer et al. 2002, LANDFIRE (Landfire 2010), and the CA Fire and Resource Assessment Program (CFRAP 2006). The analysis was updated to include high resolution percent cover within 30 x 30 m pixels for Sagebrush, non-sagebrush, herbaceous vegetation, and bare ground (C. Homer, unpublished; based on the methods of Homer et al. 2014, Xian et al. 2015 ) and conifer (primarily pinyon-juniper, P. Coates, unpublished). The pool of telemetry data included the same data from 1998 - 2013 used by Coates et al. (2014); additional telemetry location data from field sites in 2014 were added to the dataset. The dataset was then split according calendar date into three seasons (spring, summer, winter). Spring included telemetry locations (n = 14,058) from mid-March to June, and is a surrogate for habitat conditions during the sage-grouse breeding and nesting period. All age and sex classes of marked grouse were used in the analysis. Sufficient data (i.e., a minimum of 100 locations from at least 20 marked Sage-grouse) for modeling existed in 10 subregions for spring and summer, and seven subregions in winter, using all age and sex classes of marked grouse. It is important to note that although this map is composed of HSI values derived from the seasonal data, it does not explicitly represent habitat suitability for reproductive females (i.e., nesting). Insufficient data were available to allow for estimation of this habitat type for all seasons throughout the study area extent. A Resource Selection Function (RSF) was calculated for each subregion and using generalized linear models to derive model-averaged parameter estimates for each covariate across a set of additive models. Subregional RSFs were transformed into Habitat Suitability Indices, and averaged together to produce an overall statewide HSI whereby a relative probability of occurrence was calculated for each raster cell during the spring season. In order to account for discrepancies in HSI values caused by varying ecoregions within Nevada, the HSI was divided into north and south extents using a slightly modified flood region boundary (Mason 1999) that was designed to represent respective mesic and xeric regions of the state. North and south HSI rasters were each relativized according to their maximum value to rescale between zero and one, then mosaicked once more into a state-wide extent. HABITAT CATEGORIZATION: Using the same ecoregion boundaries described above, the habitat classification dataset (an independent data set comprising 10% of the total telemetry location sample) was split into locations falling within respective north and south regions. HSI values from the composite and relativized statewide HSI surface were then extracted to each classification dataset location within the north and south region. The distribution of these values were used to identify class break values corresponding to 0.5 (high), 1.0 (moderate), and 1.5 (low) standard deviations (SD) from the mean HSI. These class breaks were used to classify the HSI surface into four discrete categories of habitat suitability: High, Moderate, Low, and Non-Habitat. In terms of percentiles, High habitat comprised greater than 30.9 % of the HSI values, Moderate comprised 15 – 30.9%, Low comprised 6.7 – 15%, and Non-Habitat comprised less than 6.7%.The classified north and south regions were then clipped by the boundary layer and mosaicked to create a statewide categorical surface for habitat selection. Each habitat suitability category was converted to a vector output where gaps within polygons less than 1.2 million square meters were eliminated, polygons within 500 meters of each other were connected to create corridors and polygons less than 1.2 million square meters in one category were incorporated to the adjacent category. The final step was to mask major roads that were buffered by 50m (Census, 2014), lakes (Peterson, 2008) and urban areas, and place those masked areas into the non-habitat category. The existing urban layer (Census 2010) was not sufficient for our needs because it excluded towns with a population lower than 1,500. Hence, we masked smaller towns (populations of 100 to 1500) and development with Census Block polygons (Census 2015) that had at least 50% urban development within their boundaries when viewed with reference imagery (ArcGIS World Imagery Service Layer). REFERENCES: California Forest and Resource Assessment Program (CFRAP). 2006. Statewide Land Use / Land Cover Mosaic. [Geospatial data.] California Department of Forestry and Fire Protection, http://frap.cdf.ca.gov/data/frapgisdata-sw-rangeland-assessment_data.php Census 2010. TIGER/Line Shapefiles. Urban Areas [Geospatial data.] U.S. Census Bureau, Washington D.C., https://www.census.gov/geo/maps-data/data/tiger-line.html Census 2014. TIGER/Line Shapefiles. Roads [Geospatial data.] U.S. Census Bureau, Washington D.C., https://www.census.gov/geo/maps-data/data/tiger-line.html Census 2015. TIGER/Line Shapefiles. Blocks [Geospatial data.] U.S. Census Bureau, Washington D.C., https://www.census.gov/geo/maps-data/data/tiger-line.html Coates, P.S., Casazza, M.L., Brussee, B.E., Ricca, M.A., Gustafson, K.B., Overton, C.T., Sanchez-Chopitea, E., Kroger, T., Mauch, K., Niell, L., Howe, K., Gardner, S., Espinosa, S., and Delehanty, D.J. 2014, Spatially explicit modeling of greater sage-grouse (Centrocercus urophasianus) habitat in Nevada and northeastern California—A decision-support tool for management: U.S. Geological Survey Open-File Report 2014-1163, 83 p., http://dx.doi.org/10.3133/ofr20141163. ISSN 2331-1258 (online) Comer, P., Kagen, J., Heiner, M., and Tobalske, C. 2002. Current distribution of sagebrush and associated vegetation in the western United States (excluding NM). [Geospatial data.] Interagency Sagebrush Working Group, http://sagemap.wr.usgs.gov Homer, C.G., Aldridge, C.L., Meyer, D.K., and Schell, S.J. 2014. Multi-Scale Remote Sensing Sagebrush Characterization with Regression Trees over Wyoming, USA; Laying a Foundation for Monitoring. International Journal of Applied Earth Observation and Geoinformation 14, Elsevier, US. LANDFIRE. 2010. 1.2.0 Existing Vegetation Type Layer. [Geospatial data.] U.S. Department of the Interior, Geological Survey, http://landfire.cr.usgs.gov/viewer/ Mason, R.R. 1999. The National Flood-Frequency Program—Methods For Estimating Flood Magnitude And Frequency In Rural Areas In Nevada U.S. Geological Survey Fact Sheet 123-98 September, 1999, Prepared by Robert R. Mason, Jr. and Kernell G. Ries III, of the U.S. Geological Survey; and Jeffrey N. King and Wilbert O. Thomas, Jr., of Michael Baker, Jr., Inc. http://pubs.usgs.gov/fs/fs-123-98/ Peterson, E. B. 2008. A Synthesis of Vegetation Maps for Nevada (Initiating a 'Living' Vegetation Map). Documentation and geospatial data, Nevada Natural Heritage Program, Carson City, Nevada, http://www.heritage.nv.gov/gis Xian, G., Homer, C., Rigge, M., Shi, H., and Meyer, D. 2015. Characterization of shrubland ecosystem components as continuous fields in the northwest United States. Remote Sensing of Environment 168:286-300. NOTE: This file does not include habitat areas for the Bi-State management area and the spatial extent is modified in comparison to Coates et al. 2014

According to data collected across 1,272 mobile applications in 16 different industries in South Korea in 2023, 42 percent of education-related application users were people in their forties. People aged between 20 and 49 made up the large majority of users in every category of mobile applications.

Unemployed persons by HRST category and age

This layer shows age and sex demographics. Data is from US Census American Community Survey (ACS) 5-year estimates.To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right (in ArcGIS Online). Layer includes:Key demographicsTotal populationMale total populationFemale total populationPercent male total population (calculated)Percent female total population (calculated)Age and other indicatorsTotal population by AGE (various ranges)Total population by SELECTED AGE CATEGORIES (various ranges)Total population by SUMMARY INDICATORS (including median age, sex ratio, age dependency ratio, old age dependency ratio, child dependency ratio)Percent total population by AGE (various ranges)Percent total population by SELECTED AGE CATEGORIES (various ranges)Male by ageMale total population by AGE (various ranges)Male total population by SELECTED AGE CATEGORIES (various ranges)Male total population Median age (years)Percent male total population by AGE (various ranges)Percent male total population by SELECTED AGE CATEGORIES (various ranges)Female by ageFemale total population by AGE (various ranges)Female total population by SELECTED AGE CATEGORIES (various ranges)Female total population Median age (years)Percent female total population by AGE (various ranges)Percent female total population by SELECTED AGE CATEGORIES (various ranges)A ‘Null’ entry in the estimate indicates that data for this geographic area cannot be displayed because the number of sample cases is too small (per the U.S. Census).Current Vintage: 2016-2020ACS Table(s): S0101 (Not all lines of this ACS table are available in this feature layer.)Data downloaded from: Census Bureau's API for American Community Survey Date of Census update: March 17, 2022Data Preparation: Data table downloaded and joined with Zip Code boundaries in the City of Tempe.National Figures: data.census.gov

France Job Seekers: Category ABC: Male: Age: 25 to 49 data was reported at 1,671.200 Person th in Oct 2018. This records an increase from the previous number of 1,660.500 Person th for Sep 2018. France Job Seekers: Category ABC: Male: Age: 25 to 49 data is updated monthly, averaging 1,216.300 Person th from Jan 1996 (Median) to Oct 2018, with 274 observations. The data reached an all-time high of 1,733.500 Person th in Jan 2016 and a record low of 921.200 Person th in Jun 2001. France Job Seekers: Category ABC: Male: Age: 25 to 49 data remains active status in CEIC and is reported by Ministry of Labour, Employment and Health. The data is categorized under Global Database’s France – Table FR.G044: Labour Statistics: Job Seekers.

HRST by category, age and NUTS 1 region

This dataset shows the number of new cases of Persons With Disabilities registered by category of disabilities and age group, Malaysia, 2018

These data examine the effects on total crime rates of changes in the demographic composition of the population and changes in criminality of specific age and race groups. The collection contains estimates from national data of annual age-by-race specific arrest rates and crime rates for murder, robbery, and burglary over the 21-year period 1965-1985. The data address the following questions: (1) Are the crime rates reported by the Uniform Crime Reports (UCR) data series valid indicators of national crime trends? (2) How much of the change between 1965 and 1985 in total crime rates for murder, robbery, and burglary is attributable to changes in the age and race composition of the population, and how much is accounted for by changes in crime rates within age-by-race specific subgroups? (3) What are the effects of age and race on subgroup crime rates for murder, robbery, and burglary? (4) What is the effect of time period on subgroup crime rates for murder, robbery, and burglary? (5) What is the effect of birth cohort, particularly the effect of the very large (baby-boom) cohorts following World War II, on subgroup crime rates for murder, robbery, and burglary? (6) What is the effect of interactions among age, race, time period, and cohort on subgroup crime rates for murder, robbery, and burglary? (7) How do patterns of age-by-race specific crime rates for murder, robbery, and burglary compare for different demographic subgroups? The variables in this study fall into four categories. The first category includes variables that define the race-age cohort of the unit of observation. The values of these variables are directly available from UCR and include year of observation (from 1965-1985), age group, and race. The second category of variables were computed using UCR data pertaining to the first category of variables. These are period, birth cohort of age group in each year, and average cohort size for each single age within each single group. The third category includes variables that describe the annual age-by-race specific arrest rates for the different crime types. These variables were estimated for race, age, group, crime type, and year using data directly available from UCR and population estimates from Census publications. The fourth category includes variables similar to the third group. Data for estimating these variables were derived from available UCR data on the total number of offenses known to the police and total arrests in combination with the age-by-race specific arrest rates for the different crime types.

60-64 years old 65 and above This dataset has been released by the municipality of Milan through two different csv files, one in tabular format and one in pivot format.