This statistic shows the unitl sales of kitchen scales in the United States from 2012 to 2016. In 2016, U.S. unit sales of kitchen scales amounted to approximately 4.6 million.

In the United States in 2022, the majority of diagnostic vendors only shared data to health information exchanges (HIE) on a regional or state level. While around 30 percent said they contributed data to a private HIE.

The MNIST Large Scale dataset is based on the classic MNIST dataset, but contains large scale variations up to a factor of 16. The motivation behind creating this dataset was to enable testing the ability of different algorithms to learn in the presence of large scale variability and specifically the ability to generalise to new scales not present in the training set over wide scale ranges.

The dataset contains training data for each one of the relative size factors 1, 2 and 4 relative to the original MNIST dataset and testing data for relative scaling factors between 1/2 and 8, with a ratio of $\sqrt[4]{2}$ between adjacent scales.

About this dataset

Context

The dataset tabulates the population of Scales Mound by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Scales Mound. The dataset can be utilized to understand the population distribution of Scales Mound by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Scales Mound. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Scales Mound.

Key observations

Largest age group (population): Male # 10-14 years (33) | Female # 70-74 years (27). Source: U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

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.

Variables / Data Columns

• Age Group: This column displays the age group for the Scales Mound population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Scales Mound is shown in the following column.
• Population (Female): The female population in the Scales Mound is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Scales Mound for each age group.

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 Scales Mound Population by Gender. You can refer the same here

Abstract

Sustainable Development Goal (SDG) target 2.1 commits countries to end hunger, ensure access by all people to safe, nutritious and sufficient food all year around. Indicator 2.1.2, “Prevalence of moderate or severe food insecurity based on the Food Insecurity Experience Scale (FIES)”, provides internationally-comparable estimates of the proportion of the population facing difficulties in accessing food. More detailed background information is available at http://www.fao.org/in-action/voices-of-the-hungry/fies/en/ .

The FIES-based indicators are compiled using the FIES survey module, containing 8 questions. Two indicators can be computed:
1. The proportion of the population experiencing moderate or severe food insecurity (SDG indicator 2.1.2), 2. The proportion of the population experiencing severe food insecurity.

These data were collected by FAO through the Gallup World Poll. General information on the methodology can be found here: https://www.gallup.com/178667/gallup-world-poll-work.aspx. National institutions can also collect FIES data by including the FIES survey module in nationally representative surveys.

Microdata can be used to calculate the indicator 2.1.2 at national level. Instructions for computing this indicator are described in the methodological document available in the documentations tab. Disaggregating results at sub-national level is not encouraged because estimates will suffer from substantial sampling and measurement error.

Geographic coverage

National

Analysis unit

Individuals

Universe

Individuals of 15 years or older with access to landline and/or mobile phones.

Kind of data

Sample survey data [ssd]

Sampling procedure

NA Exclusions: NA Design effect: 1.5

Mode of data collection

Computer-Assisted Telephone Interviewing [CATI]

Cleaning operations

Statistical validation assesses the quality of the FIES data collected by testing their consistency with the assumptions of the Rasch model. This analysis involves the interpretation of several statistics that reveal 1) items that do not perform well in a given context, 2) cases with highly erratic response patterns, 3) pairs of items that may be redundant, and 4) the proportion of total variance in the population that is accounted for by the measurement model.

Sampling error estimates

The margin of error is estimated as 3.8. This is calculated around a proportion at the 95% confidence level. The maximum margin of error was calculated assuming a reported percentage of 50% and takes into account the design effect.

The Q-Herilearn scale is a probabilistic scale of summative estimates that measures different aspects of the learning process in Heritage Education. It consists of seven factors (Knowing, Understanding, Respecting, Valuing, Caring, Enjoying and Transmitting). Each dimension is measured by means of seven indicators scored on a 4-point frequency response scale (1 = Never or almost never; 2 = Sometimes; 3 = Quite often; 4 = Always or almost always). Sufficient evidence of content validity has been obtained through a concordance analysis —which employed multi-facet logistic models (Many Facet Rasch Model MFRM)— of the scores of 40 judges, who estimated the relevance, adequacy, and clarity of each item. The metric properties of the scores were determined using ESEM —Exploratory Structural Equation Modeling—, EGA Exploratory Graph Analysis and Network Analysis. The scale was calibrated using Item Response Theory models: the Nominal Response Model and the Graded Response Model.

Abstract

Sustainable Development Goal (SDG) target 2.1 commits countries to end hunger, ensure access by all people to safe, nutritious and sufficient food all year around. Indicator 2.1.2, “Prevalence of moderate or severe food insecurity based on the Food Insecurity Experience Scale (FIES)”, provides internationally-comparable estimates of the proportion of the population facing difficulties in accessing food. More detailed background information is available at http://www.fao.org/in-action/voices-of-the-hungry/fies/en/ .

The FIES-based indicators are compiled using the FIES survey module, containing 8 questions. Two indicators can be computed:
1. The proportion of the population experiencing moderate or severe food insecurity (SDG indicator 2.1.2), 2. The proportion of the population experiencing severe food insecurity.

These data were collected by FAO through the Gallup World Poll. General information on the methodology can be found here: https://www.gallup.com/178667/gallup-world-poll-work.aspx. National institutions can also collect FIES data by including the FIES survey module in nationally representative surveys.

Microdata can be used to calculate the indicator 2.1.2 at national level. Instructions for computing this indicator are described in the methodological document available in the documentations tab. Disaggregating results at sub-national level is not encouraged because estimates will suffer from substantial sampling and measurement error.

Geographic coverage

National coverage

Analysis unit

Individuals

Universe

Individuals of 15 years or older with access to landline and/or mobile phones.

Kind of data

Sample survey data [ssd]

Sampling procedure

A simple stratified sample design was used for selection of mobile phone samples. Within each explicit stratum (service provider), sample of specified size was drawn using pure Random Digit Dial (RDD) procedures. Sampling was done independently within each stratum. All sampled numbers were pre-screened for working status. For respondents contacted by mobile phone, there was no respondent selection other than verification of the eligibility of the respondent that they were 15 years of age or older. For the purpose of data collection, the total initial sample was split into random subsamples (replicate samples) and released sequentially based on the progress of interviewing in different strata. The goal was to release an optimum amount of sample each time to achieve a high response rate while completing the targeted number of interviews within the field period. Exclusions: NA Design effect: 2.48

Mode of data collection

Computer Assisted Telephone Interview [cati]

Cleaning operations

Statistical validation assesses the quality of the FIES data collected by testing their consistency with the assumptions of the Rasch model. This analysis involves the interpretation of several statistics that reveal 1) items that do not perform well in a given context, 2) cases with highly erratic response patterns, 3) pairs of items that may be redundant, and 4) the proportion of total variance in the population that is accounted for by the measurement model.

Sampling error estimates

The margin of error is estimated as 4.9. This is calculated around a proportion at the 95% confidence level. The maximum margin of error was calculated assuming a reported percentage of 50% and takes into account the design effect.

Data appraisal

The variable WORRIED was not considered in the computation of the published FAO food insecurity indicator based on FIES due to the results of the validation process.

AbstractHumans have elevated global extinction rates and thus lowered global-scale species richness. However, there is no a priori reason to expect that losses of global species richness should always, or even often, trickle down to losses of species richness at regional and local scales, even though this relationship is often assumed. Here, we show that scale can modulate our estimates of species richness change through time in the face of anthropogenic pressures, but not in a unidirectional way. Instead, the magnitude of species richness change through time can increase, decrease, reverse, or be unimodal across spatial scales. Using several case studies, we show different forms of scale-dependent richness change through time in the face of anthropogenic pressures. For example, Central American corals show a homogenization pattern, where small scale richness is largely unchanged through time, while larger scale richness change is highly negative. Alternatively, birds in North America showed a differentiation effect, where species richness was again largely unchanged through time at small scales, but was more positive at larger scales. Finally, we collated data from a heterogeneous set of studies of different taxa measured through time from sites ranging from small plots to entire continents, and found highly variable patterns that nevertheless imply complex scale-dependence in several taxa. In summary, understanding how biodiversity is changing in the Anthropocene requires an explicit recognition of the influence of spatial scale, and we conclude with some recommendations for how to better incorporate scale into our estimates of change. Usage notesdata_for_dryadThis file contains all data associated with the manuscript. A metadata file is included in the zip folder.

Source data assessment of statistical capacity (scale 0 - 100) in Dominica was reported at 40 in 2020, according to the World Bank collection of development indicators, compiled from officially recognized sources. Dominica - Source data assessment of statistical capacity (scale 0 - 100) - actual values, historical data, forecasts and projections were sourced from the World Bank on March of 2025.

1712 Global import shipment records of Scales Electronic with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

About this dataset

Context

The dataset tabulates the Scales Mound population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Scales Mound. The dataset can be utilized to understand the population distribution of Scales Mound by age. For example, using this dataset, we can identify the largest age group in Scales Mound.

Key observations

The largest age group in Scales Mound, IL was for the group of age 10 to 14 years years with a population of 54 (12.24%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Scales Mound, IL was the 85 years and over years with a population of 7 (1.59%). 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

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Scales Mound is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Scales Mound 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 Scales Mound Population by Age. You can refer the same here

Contained within the 3rd Edition (1957) of the Atlas of Canada is a plate that shows six maps of cities or towns at different scales. The portions of all, but the first and last of the maps, illustrate the principal national map series produced by the Surveys and Mapping Branch of the Department of Mines and Technical Surveys [now Natural Resources Canada], circa 1958.

Methodology assessment of statistical capacity (scale 0 - 100) in Serbia was reported at 70 in 2020, according to the World Bank collection of development indicators, compiled from officially recognized sources. Serbia - Methodology assessment of statistical capacity (scale 0 - 100) - actual values, historical data, forecasts and projections were sourced from the World Bank on March of 2025.

Methodology assessment of statistical capacity (scale 0 - 100) in Georgia was reported at 90 in 2020, according to the World Bank collection of development indicators, compiled from officially recognized sources. Georgia - Methodology assessment of statistical capacity (scale 0 - 100) - actual values, historical data, forecasts and projections were sourced from the World Bank on March of 2025.

The natural amenities scale is a measure of the physical characteristics of a county area that enhance the location as a place to live. The scale was constructed by combining six measures of climate, topography, and water area that reflect environmental qualities most people prefer. These measures are warm winter, winter sun, temperate summer, low summer humidity, topographic variation, and water area. The data are available for counties in the lower 48 States. The file contains the original measures and standardized scores for each county as well as the amenities scale.

Methodology assessment of statistical capacity (scale 0 - 100) in Ukraine was reported at 100 in 2020, according to the World Bank collection of development indicators, compiled from officially recognized sources. Ukraine - Methodology assessment of statistical capacity (scale 0 - 100) - actual values, historical data, forecasts and projections were sourced from the World Bank on March of 2025.

The raw dataset used in the study (in SPSS format). Data has been anonymized to comply with confidentiality principles. (SAV)

Descriptive statistics from the survey assessing participant's impression of the ImPACT program on scales ranging from 0 to 10.

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.

This repository contains all of the data used in the manuscript "Linearizing the vertical scale of an interferometric microscope and its effect on step-height measurement," by Thomas A. Germer, T. Brian Renegar, Ulf Griesmann, and Johannes A. Soons, which has been published in Surface Topography: Metrology and Properties volume 12, number 2, article 025012 on 8 May 2024. The repository also contains a Python Jupyter notebook that performs the analysis of the data and generates the figures in the manuscript.