Age, height and weight raw data

Dataset

This dataset was created by Anu geeta

Released under Apache 2.0

Contents

The Student Weight Status Category Reporting System (SWSCR) collects weight status category data (underweight, healthy weight, overweight or obese, based on BMI-for-age percentile). The dataset includes separate estimates of the percent of students overweight, obese and overweight or obese for all reportable grades within the county and/or region and by grade groups (elementary and middle/high). The rates of overweight and obesity reported are percentages based on counts of students in selected grades (Pre-K, K, 2, 4, 7, 10) reported to the NYSDOH. Because these rates reflect a broad range of factors that vary by school district, to make comparisons about observed differences in the rates of obesity and overweight between school districts requires the use of multivariate statistics. For more information check out http://www.health.ny.gov/prevention/obesity/, see our Instruction Guide on How to Create Visualizations https://health.data.ny.gov/api/assets/6490BDA9-AE4D-406F-BA5A-703793526B9F or go to the "About" tab.

This dataset consists of truck size and weight enforcement data including number of trucks weighed, number of violations, and number of oversize/overweight permits, as reported by the States in their annual certification to FHWA.

The low birth-weight rate measures the percentage of live births with weights below 2500 grams. A low birth-weight can affect health outcomes later in life, and is an illustrative indicator for the overall health of the measured population.

The low birth-weight rate in Champaign County has been above 8 percent since 2011, the earliest Reporting Year available in the dataset. This is close to the statewide rate, which returned to 8.4 percent from Reporting Year 2021 through present after a slight decrease in recent years. The lowest county low birth-weight rate in the state is 5.6 percent (Carroll County in the northwest corner of the state), while the highest county low birth-weight rate in the state is 11.9 percent (Pulaski County in southernmost Illinois).

This data was sourced from the University of Wisconsin's Population Health Institute's and the Robert Wood Johnson Foundation’s County Health Rankings & Roadmaps. Each year’s County Health Rankings uses data from years prior. Therefore, the 2023 County Health Rankings (“Reporting Year” in the table) uses data from 2014-2020 (“Data Years” in the table).

Source: University of Wisconsin Population Health Institute. County Health Rankings & Roadmaps 2023.

This table contains data on total vehicle kilometres of goods vehicles in the Netherlands (broken down by Dutch and foreign vehicles) and data of total kilometres and average annual kilometres of Dutch goods vehicles (broken down by Dutch and foreign territory). All figures are further broken down by lorries and road tractors, by years of construction of the vehicle and by gross vehicle weight. The vehicle population used to estimate the kilometres is based on the vehicle fleet statistics. The population of the figures in this table is based on the old selection method of the vehicle fleet. The difference between the old and the new selection method is described in a methodological report, see paragraph 4. The data series of vehicle kilometres estimated for the old population ends with 2020. The data series based on the new population is available starting from 2018. The way in which the vehicle kilometres are estimated has not changed, only the population.

For the 2020 data a correction factor was implemented to correct for the “smoothing effect” caused by the method. The smoothing effect Smoothes out yearly variation in the data and these results in a distorted picture of periods of time when mobility patterns suddenly change Drastically, like happened in 2020 due to COVID-19.

Data available from: 2001 to 2020

Status of the figures: Definite data are available for 2001 to 2018, and provisional data for 2019 and 2020.

Changes as of 10 November 2022: None, this table has been discontinued. This table is followed by the table Vehicle kilometres goods vehicles. kilometres, vehicle weight, territory, see paragraph 3.

When will new figures become available? No longer applicable.

Dataset

This dataset was created by Mohammad Hafiz Ismail (mypapit)

Released under CC0: Public Domain

Contents

Data on normal weight, overweight, and obesity among adults aged 20 and over by selected population characteristics. Please refer to the PDF or Excel version of this table in the HUS 2019 Data Finder (https://www.cdc.gov/nchs/hus/contents2019.htm) for critical information about measures, definitions, and changes over time.

SOURCE: NCHS, National Health and Nutrition Examination Survey. For more information on the National Health and Nutrition Examination Survey, see the corresponding Appendix entry at https://www.cdc.gov/nchs/data/hus/hus19-appendix-508.pdf.

United States US: Prevalence of Wasting: Weight for Height: Female: % of Children Under 5 data was reported at 0.700 % in 2012. This records an increase from the previous number of 0.500 % for 2009. United States US: Prevalence of Wasting: Weight for Height: Female: % of Children Under 5 data is updated yearly, averaging 0.550 % from Dec 1991 (Median) to 2012, with 6 observations. The data reached an all-time high of 0.800 % in 2005 and a record low of 0.100 % in 2001. United States US: Prevalence of Wasting: Weight for Height: Female: % of Children Under 5 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Health Statistics. Prevalence of wasting, female, is the proportion of girls under age 5 whose weight for height is more than two standard deviations below the median for the international reference population ages 0-59.; ; World Health Organization, Global Database on Child Growth and Malnutrition. Country-level data are unadjusted data from national surveys, and thus may not be comparable across countries.; Linear mixed-effect model estimates; Undernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF, www.childinfo.org). Estimates of child malnutrition, based on prevalence of underweight and stunting, are from national survey data. The proportion of underweight children is the most common malnutrition indicator. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition.

Stata module that implements Potter's (1990) weight distribution approach to trim extreme sampling weights. The basic idea is that the sampling weights are assumed to follow a beta distribution. The parameters of the distribution are estimated from the moments of the observed sampling weights and the resulting quantiles are used as cut-off points for extreme sampling weights. The process is repeated a specified number of times (10 by default) or until no sampling weights are more extreme than the specified quantiles.

A collection of data on flying objects, from the book by henk tennekes

Test sequences of two indoor scenes used to evaluate semantic visual SLAM (Simultaneous Localization And Mapping). This repository also contains Yolo v5 weights for object detections, either pretrained on COCO dataset or fine-tuned on statues and museum objects. This data can be used to run OA-SLAM (Object-Aided SLAM), available at https://gitlab.inria.fr/tangram/oa-slam. OA-SLAM, 1.0

Somalia SO: Prevalence of Overweight: Weight for Height: Female: % of Children Under 5 data was reported at 3.100 % in 2009. This records a decrease from the previous number of 4.500 % for 2006. Somalia SO: Prevalence of Overweight: Weight for Height: Female: % of Children Under 5 data is updated yearly, averaging 3.800 % from Dec 2006 (Median) to 2009, with 2 observations. The data reached an all-time high of 4.500 % in 2006 and a record low of 3.100 % in 2009. Somalia SO: Prevalence of Overweight: Weight for Height: Female: % of Children Under 5 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Somalia – Table SO.World Bank: Health Statistics. Prevalence of overweight, female, is the percentage of girls under age 5 whose weight for height is more than two standard deviations above the median for the international reference population of the corresponding age as established by the WHO's new child growth standards released in 2006.; ; World Health Organization, Global Database on Child Growth and Malnutrition. Country-level data are unadjusted data from national surveys, and thus may not be comparable across countries.; Linear mixed-effect model estimates; Estimates of overweight children are also from national survey data. Once considered only a high-income economy problem, overweight children have become a growing concern in developing countries. Research shows an association between childhood obesity and a high prevalence of diabetes, respiratory disease, high blood pressure, and psychosocial and orthopedic disorders (de Onis and Blössner 2003). Childhood obesity is associated with a higher chance of obesity, premature death, and disability in adulthood. In addition to increased future risks, obese children experience breathing difficulties and increased risk of fractures, hypertension, early markers of cardiovascular disease, insulin resistance, and psychological effects. Children in low- and middle-income countries are more vulnerable to inadequate nutrition before birth and in infancy and early childhood. Many of these children are exposed to high-fat, high-sugar, high-salt, calorie-dense, micronutrient-poor foods, which tend be lower in cost than more nutritious foods. These dietary patterns, in conjunction with low levels of physical activity, result in sharp increases in childhood obesity, while under-nutrition continues

Length and weight measurements for individual fish collected as part of the Commonwealth Environmental Water Holder (CEWH) Flow-MER program in the Murray-Darling Basin.\r \r Abundance and diversity of riverine fish populations are monitored annually at fixed sites within six Selected Areas using a standardised sampling regime involving boat or backpack electrofishing and fine mesh fyke nets (referred to as Category 1 sampling). These methods target large-bodied and small-bodied fish species respectively. A sample of measured individuals (length and weight) were collected for otolith sectioning and age determination to construct age vs size relationships.\r \r The CEWH’s Flow-MER program examines the contribution of Commonwealth environmental water to the environmental objectives of the Basin Plan 2012 (Basin Plan) and is assisting the CEWH to demonstrate environmental outcomes and adaptively manage the water holdings. Monitoring and evaluation is focused in seven Selected Areas: the Junction of the Warrego and Darling rivers, Gwydir river system, Lachlan river system, Murrumbidgee river system, Edward/Kolety-Wakool river system, Goulburn River and Lower Murray River. \r \r This Flow-MER data set includes and extends the long-term data collected at the same sites during the Long Term Intervention Monitoring (LTIM) project (2014-2019).\r \r

Acknowledgement\r

\r The Commonwealth Environmental Water Holder and Flow-MER program acknowledge the First Nations peoples as the Traditional Owners and Custodians of the lands, waterways and skies of the Murray-Darling Basin. We respect their continuing connection to culture and Country, and we thank them for their knowledge and science and the values reflected in these data.\r \r

Citation\r

\r CEWH (2024) Fish length weight. Flow-MER Program. Commonwealth Environmental Water Holder, Australian Government Department of Climate Change, Energy, the Environment and Water. Sourced from https://data.gov.au/data/dataset/flow-mer-fish-length-weight on [date-sourced].\r \r

The dataset is an open data from the Sistema de Informação de Nascidos Vivos (SINASC), which is a system implemented by the Brazilian federal government in the 1990s, with the purpose of collecting data on all live births in the national territory. The system makes it possible to provide information on birth rates for all levels of the Brazilian health system, as well as the development of relevant indicators in the strategic planning of management to support the planning of actions, activities, public policies and programs aimed at health.

The dataset is related to three years (2018, 2019 and 2020) of SINASC referring only to the state of Pernambuco, and it is composed of routine prenatal data, gestational history, sociodemographic data and data of newborns. born, including their weight. The pre-processed dataset has 10 attributes plus the target attribute ‘WEIGHT’, with 351,253 records, 29,625 low birth weight records and 321,628 adequate weight records. This dataset contains two CSV files: the first file “Dataset.csv” is the pre-processed dataset and the second “Attributes.csv” contains the description of each attribute.

The USDA-Agricultural Research Service Central Plains Experimental Range (CPER) is a Long-Term Agroecosystem Research (LTAR) network site located ~20 km northeast of Nunn, in north-central Colorado, USA. In 1939, scientists established the Long-term Grazing Intensity study (LTGI) with four replications of light, moderate, and heavy grazing. Each replication had three 129.5 ha pastures with the grazing intensity treatment randomly assigned. Today, one replication remains. Light grazing occurs in pasture 23W (9.3 Animal Unit Days (AUD)/ha, targeted for 20% utilization of peak growing-season biomass), moderate grazing in pasture 15E (12.5 AUD/ha, 40% utilization), and heavy grazing in pasture 23E (18.6 AUD/ha, 60% utilization). British- and continental-breed yearling cattle graze the pastures season-long from mid-May to October except when forage limitations shorten the grazing season. Individual raw data on cattle entry and exit weights, as well as weights every 28-days during the grazing season are available from 2000 to 2019. Cattle entry and exit weights are included in this dataset. Weight outliers (± 2 SD) are flagged for calculating summary statistics or performing statistical analysis. Resources in this dataset:Resource Title: Data Dictionary for LTGI Cattle weights on CPER (2000-2019). File Name: LTGI_2000-2019_data_dictionary.csvResource Description: Data dictionary for data from USDA ARS Central Plains Experimental Range (CPER) near Nunn, CO cattle weight gains managed with light, moderate and heavy grazing intensities Resource Title: LTGI Cattle weights on CPER (2000-2019). File Name: LTGI_2000-2019_all_weights_published.csvResource Description: Data from USDA ARS Central Plains Experimental Range (CPER) near Nunn, CO cattle weight gains managed with light, moderate and heavy grazing intensities

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

Inspections conducted by the Allegheny County Bureau of Weights and Measures. The Bureau inspects weighing and timing devices such as gas pumps, laundromat timers, parking meters, and produce scales. The Bureau also conducts price scan verifications on a regular basis to ensure consumers are being charged fairly.