Elderly Dependency Ratio(Sample Survey): Shanghai data was reported at 23.990 % in 2021. This records an increase from the previous number of 22.020 % for 2020. Elderly Dependency Ratio(Sample Survey): Shanghai data is updated yearly, averaging 17.850 % from Dec 2002 (Median) to 2021, with 20 observations. The data reached an all-time high of 23.990 % in 2021 and a record low of 9.400 % in 2011. Elderly Dependency Ratio(Sample Survey): Shanghai data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Socio-Demographic – Table CN.GA: Population: Sample Survey: Elderly Dependency Ratio: By Region.

Data Statistics of example 2.

Gross Dependency Ratio(Sample Survey): Jiangxi data was reported at 47.780 % in 2023. This records a decrease from the previous number of 49.010 % for 2022. Gross Dependency Ratio(Sample Survey): Jiangxi data is updated yearly, averaging 44.300 % from Dec 2002 (Median) to 2023, with 22 observations. The data reached an all-time high of 51.160 % in 2020 and a record low of 40.300 % in 2013. Gross Dependency Ratio(Sample Survey): Jiangxi data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Socio-Demographic – Table CN.GA: Population: Sample Survey: Gross Dependency Ratio: By Region.

Data Statistics of example 1.

China Gross Dependency Ratio data was reported at 46.600 % in 2022. This records an increase from the previous number of 46.300 % for 2021. China Gross Dependency Ratio data is updated yearly, averaging 42.000 % from Dec 1982 (Median) to 2022, with 35 observations. The data reached an all-time high of 62.600 % in 1982 and a record low of 34.200 % in 2010. China Gross Dependency Ratio data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Socio-Demographic – Table CN.GA: Population: Sample Survey: Gross Dependency Ratio: By Region.

Gross Dependency Ratio(Sample Survey): Guangdong data was reported at 38.780 % in 2023. This records a decrease from the previous number of 39.010 % for 2022. Gross Dependency Ratio(Sample Survey): Guangdong data is updated yearly, averaging 34.400 % from Dec 2002 (Median) to 2023, with 22 observations. The data reached an all-time high of 52.900 % in 2003 and a record low of 30.500 % in 2015. Gross Dependency Ratio(Sample Survey): Guangdong data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Socio-Demographic – Table CN.GA: Population: Sample Survey: Gross Dependency Ratio: By Region.

High-resolution transmission electron microscopy (HRTEM) is an important technique for investigating nanoparticles at atomic resolution. One drawback is that the intense electron beam required for sufficient electron signal can be harmful for the sample. Lowering the electron beam intensity (electron dose rate) can cause less damage to the sample. However, the latter can result in images drowning in noise. Novel techniques applying neural networks in machine learning can be applied to detect events during a series of HRTEM images recorded at low electron dose rate.To assist the machine learning approach, novel signal-to-noise ratio (SNR) models are applied to series of HRTEM images at varied electron dose rates. Furthermore, a novel approach of structural similarity index measurement (SSIM), where each frame is compared to an adjacent frame, is applied to the HRTEM image series.The HRTEM image series consist of .dm4 files for each point in time. All files are compressed into a folder (.zip). The dataset provided is an example of 50 frames, each recorded at 0.2 s exposure time, at a fixed dose rate.Obtaining SNR and SSIM values of the HRTEM image series is done using Jupyter notebooks. An example of such is obtainable at a GitLab repository: https://gitlab.com/wibang_dtu_91dk/doserate-snr-ssim/The Jupyther notebook loads the individual .dm4 files into a stack. Using a specific package called HyperSpy, the user can browse through the series and subsequently extract data from selected areas used for the SNR and SSIM. The Jupyter notebook exports the data into data sheets (.csv), which can be loaded into other scripts for further treatments. Finally, the script can also export the data as a video (.mp4 and .gif) at a specified frame rate.The main work referring to the Jupyter notebook and dataset is a recently accepted article: W. B. Lomholdt, M. H. L. Larsen, C. N. Valencia, J. Schiøtz and T. W. Hansen, "Interpretability of high-resolution transmission electron microscopy images", Ultramicroscopy, vol. 263, 2024, pp. 113997, https://doi.org/10.1016/j.ultramic.2024.113997

About this dataset

Context

The dataset tabulates the population of Weston by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Weston. The dataset can be utilized to understand the population distribution of Weston by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Weston. 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 Weston.

Key observations

Largest age group (population): Male # 55-59 years (36) | Female # 55-59 years (28). 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

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 Weston population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Weston is shown in the following column.
• Population (Female): The female population in the Weston 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 Weston 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 Weston Population by Gender. You can refer the same here

Supplementary material 1: Supplementary Code (variable totals). Supplementary Code (fixed totals).

The model contains a fixed effect for treatment and a random effect for pens (n = 24) nested within rooms (n = 2).

In recent years, research has increasingly identified changes in animal body shape occurring concomitantly with climate change. Shape changes, or ‘shape-shifting’, are believed to reflect a temporal extension of Allen’s rule, wherein appendages increase in size in response to warmer temperatures. Shape-shifting responses are considered in terms of increases in appendage size relative to body size, however, the statistical methods of analyzing relative appendage size differ between studies. There have been two primary statistical methods by which changes in relative appendage size have predominantly been investigated: 1) a multiple regression approach, wherein appendage size is made relative to body size by inclusion of body size as a covariate in the model, and 2) a ratio approach, wherein a ratio of appendage size to body size is calculated prior to modelling changes in the ratio over time. In this paper, we use simulated and real-world data to test both statistical approaches and how they impact assessments of shape-shifting. We demonstrate that the two approaches can yield different results across a range of body and appendage size change scenarios. We discuss the implications of this, and suggest that the multiple regression approach is most suitable for detecting changes in relative appendage size because it properly accounts for allometric scaling. We further suggest that the ratio approach does not adequately disentangle changes in ratio that are caused exclusively by variation in body size. We conclude that the multiple regression approach is more appropriate for investigations of shape-shifting, especially when other factors may also be changing through time. While we demonstrate these principles using examples and data of changes through time, the same would apply for Allen’s rule and appendage size changes over spatial scales.

R code shows data simulation and testing of statistical approaches. Data is taken from McQueen et al. 2022 (Nat Comms).

This dataset provides calculated age of air (AoA) and the argon/nitrogen (Ar/N2) ratio (per meg) from stratospheric flask samples and simultaneous high-frequency measurements of nitrous oxide (N2O), carbon dioxide (CO2), ozone (O3), methane (CH4), and carbon monoxide (CO) compiled from three airborne projects. The trace gases were used to identify 235 flask samples with stratospheric influence collected by the Medusa Whole Air Sampler and to calculate AoA using a new N2O-AoA relationship developed using a Markov Chain Monte Carlo algorithm. The data span a wide range of latitudes poleward of 40 degrees in both the Northern and Southern Hemispheres and cover the period 2009-01-10 to 2018-05-21.

About this dataset

Context

The dataset tabulates the population of Ottawa County by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Ottawa County. The dataset can be utilized to understand the population distribution of Ottawa County by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Ottawa County. 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 Ottawa County.

Key observations

Largest age group (population): Male # 15-19 years (1,202) | Female # 5-9 years (1,090). 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

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 Ottawa County population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Ottawa County is shown in the following column.
• Population (Female): The female population in the Ottawa County 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 Ottawa County 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 Ottawa County Population by Gender. You can refer the same here

The model contains a fixed effect for period and a random effect for district (n = 15).

a All numbers are weighted unless otherwise specified.b The INCAM report provides an estimate of the MMR among women age 15–49 of 489.3 per 100,000 live births (Table 32) but this estimate is based on the 2007 census data not on the INCAM data [16].Maternal mortality statistics by country and survey platform.a

About this dataset

Context

The dataset tabulates the population of Seward by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Seward. The dataset can be utilized to understand the population distribution of Seward by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Seward. 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 Seward.

Key observations

Largest age group (population): Male # 45-49 years (183) | Female # 10-14 years (162). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 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 Seward population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Seward is shown in the following column.
• Population (Female): The female population in the Seward 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 Seward 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 Seward Population by Gender. You can refer the same here

Of the major developed economies, Japan had the highest debt to equity ratio for financial corporations, reaching *** in 2022. The United Kingdom had the second-highest debt to equity ratio with *** percent while the United States had the lowest with only *** percent. The debt to equity ratio is a measure of whether companies finance their activities with equity or debt. It is calculated by dividing the total outstanding debt of all financial corporations by the market value of those companies' shares. A ratio of 2.5, for example, means that outstanding debt is 2.5 times larger than the market value of the financial sector's equity.

Estimation of the Log odds ratio and its standard error comparison corresponding to the observational study data reported in Table 4.

Data and code for "Valid t-ratio Inference for IV"AbstractIn the single-IV model, researchers commonly rely on t-ratio-based inference, even though the literature has quantified its potentially severe large-sample distortions. Building on Stock and Yogo (2005), we introduce the tF critical value function, leading to a standard error adjustment that is a smooth function of the first-stage F-statistic. For one-quarter of specifications in 61 AER papers, corrected standard errors are at least 49 and 136 percent larger than conventional 2SLS standard errors at the 5-percent and 1-percent significance levels, respectively. tF confidence intervals have shorter expected length than those of Anderson and Rubin (1949), whenever both are bounded.

Gross Dependency Ratio(Sample Survey): Zhejiang data was reported at 38.920 % in 2023. This records an increase from the previous number of 38.620 % for 2022. Gross Dependency Ratio(Sample Survey): Zhejiang data is updated yearly, averaging 33.650 % from Dec 2002 (Median) to 2023, with 22 observations. The data reached an all-time high of 38.920 % in 2023 and a record low of 26.700 % in 2012. Gross Dependency Ratio(Sample Survey): Zhejiang data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Socio-Demographic – Table CN.GA: Population: Sample Survey: Gross Dependency Ratio: By Region.