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

Gross Dependency Ratio(Sample Survey): Beijing data was reported at 38.630 % in 2023. This records an increase from the previous number of 37.330 % for 2022. Gross Dependency Ratio(Sample Survey): Beijing data is updated yearly, averaging 26.800 % from Dec 2002 (Median) to 2023, with 22 observations. The data reached an all-time high of 38.630 % in 2023 and a record low of 20.950 % in 2010. Gross Dependency Ratio(Sample Survey): Beijing 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.

This is a sample dataset for the Biodemography Workshop. Within this dataset, input files related to demographic statistics will be considered, specifically population by gender and by Nuts2 in Italy, as well as shapefiles for map creation. The variables to be analyzed include the ratio between male and female, and vice versa. The final output consists of two maps. The data source is Istat, which provides these with a CC BY license: 1-https://demo.istat.it/app/?i=POS&l=it 2-https://www.istat.it/it/archivio/222527 To conduct the analysis, the open-source software R-Studio was used. The data management methodology will also be outlined in a Data Management Plan, written using Overleaf, in which we will provide more detailed information.

Data Statistics of example 2.

Elderly Dependency Ratio(Sample Survey): Guangdong data was reported at 13.830 % in 2023. This records an increase from the previous number of 13.340 % for 2022. Elderly Dependency Ratio(Sample Survey): Guangdong data is updated yearly, averaging 10.350 % from Dec 2002 (Median) to 2023, with 22 observations. The data reached an all-time high of 13.830 % in 2023 and a record low of 8.600 % in 2011. Elderly 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: Elderly Dependency Ratio: By Region.

Fermilab-Tevatron. Measurement of the ratios of multijet (3/2) cross sections produced in proton-antiproton interactions at a centre of mass energy of 1.96 TeV. The data sample used has an integrated luminosity of 0.7 fb-1 with the results being presented as a function of different maximum and minumum jet transverse momentum requirements.

This data set contains directly determined complex Poisson's ratio from axial and transversal strain measurements. Here, the axial and transverse strains were measured locally with strain gauges (K-CXY3-0015-3-350-O, HBK, Darmstadt, Germany) on cylindric polymethyl methacrylate (PMMA, EH-Design, Wörrstadt, Germany) samples with a diameter of d = 5 mm. Frequency measurements were performed with a rheometer (MCR 702, linear motor, Anton-Paar, Graz, Austria) in the range of 1 Hz to 100 Hz with an axial strain of 0.01 % at constant temperatures in the range of 15 °C to 105 °C. 500 periods were measured per frequency and recorded using a measuring amplifier (Universal Amplifier MX1615B, HBK, Darmstadt, Germany). Transversal and axial strain is then measured on the PMMA sample with strain gauges in tension mode. The material response in the time domain is transformed to the frequency domain using the Fast Fourier Transform. This gives the axial and transverse amplitude as well as the axial and transverse phase shift. With the variable from the frequency domain, the complex Poisson's ratio is calculated in post-processing. The data set contains the calculated complex Poisson's ratio of three measured PMMA samples.

Marine particulate organic carbon-13 stable isotope ratios (δ13C-POC) provide additional constraints and insights into the cycling of carbon from dissolved pools to marine ecosystems including anthropogenic contributions. For such purposes, a robust spatio-temporal coverage of δ13C-POC observations is essential. In this data product, we collected and merged two large data compilations (Close and Henderson, 2020; St John Glew et al., 2021) into our previous version (Verwega et al., 2021) to provide the largest available marine δ13C-POC data set. Additionally, we have incorporated more meta information including if the samples were acidified before measuring the isotope ratio. The data set consists of 6952 data points covering the global ocean from year 1966 to 2019. We provide the data in the following two formats for best application on specific research purposes: (1) A spreadsheet file including all collected individual data and meta-information; (2) Network Common Data Form (NetCDF) files that only include acidified samples (6633 total data points) interpolated onto a global ocean grid (1°x1° horizontal resolution, 33 vertical levels based on World Ocean Atlas 2009) for each month individually and all months combined, with each file covering the temporal range from year 1966 to 2019.

CN: Elderly Dependency Ratio(Sample Survey): Ningxia data was reported at 14.370 % in 2021. This records an increase from the previous number of 13.740 % for 2020. CN: Elderly Dependency Ratio(Sample Survey): Ningxia data is updated yearly, averaging 9.200 % from Dec 2002 (Median) to 2021, with 20 observations. The data reached an all-time high of 14.370 % in 2021 and a record low of 7.000 % in 2002. CN: Elderly Dependency Ratio(Sample Survey): Ningxia 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 set from peer-reviewed publication: A. J. Fleisher et al., Absolute 13C/12C Isotope Amount Ratio for Vienna Pee Dee Belemnite from Infrared Absorption Spectroscopy, Nature Physics. Measurements of isotope ratios are predominantly made with reference to standard specimens that have been characterized in the past. In the 1950s, the carbon isotope ratio was referenced to a belemnite sample collected by Heinz Lowenstam and Harold Urey in South Carolina?s Pee Dee region. Due to the exhaustion of the sample since then, reference materials that are traceable to the origin artefact are used to define the Vienna Pee Dee Belemnite (VPDB) scale for stable carbon isotope analysis. However, these reference materials have also become exhausted or proven unstable over time, mirroring issues with the international prototype of the kilogram that led to a revised International System of Units. A campaign to elucidate the stable carbon isotope ratio of VPDB is underway, but independent measurement techniques are required to support it. Here we report an accurate value for the stable carbon isotope ratio inferred from infrared absorption spectroscopy, fulfilling the promise of this fundamentally accurate approach. Our results agree with a value recently derived from mass spectrometry, and therefore advance the prospects of SI-traceable isotope analysis. Further, our calibration-free method could improve mass balance calculations and enhance isotopic tracer studies in CO2 source apportionment.

About this dataset

Context

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

Key observations

Largest age group (population): Male # 50-54 years (128) | Female # 60-64 years (102). 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 Nashville population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Nashville is shown in the following column.
• Population (Female): The female population in the Nashville 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 Nashville 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 Nashville Population by Gender. You can refer the same here

BIC of one-class versus two-class models (of 500 samples) for all models by sample size and distributional conditions.

the use of stable isotopes as ecological tracers in deep-sea ecosystems has a long history, dating back to the late 1970’s. stable isotopes have been instrumental to many key-findings about ecosystem functioning, particularly in chemosynthesis-based habitats (hydrothermal vents, cold seeps). however, constraining sampling logistics commonly limit the scope, extent, and therefore insights drawn from isotope-based deep-sea studies. overall, much is left to discover about factors globally influencing food web structure in deep-sea ecosystems. in this context, deep-sea ecologists have to ensure that no sample is left unexploited, and that all generated data are easily discoverable, available and reusable.deepiso is a collaborative effort to produce a global compilation of stable isotope ratios and elemental contents in organisms from deep-sea ecosystems. in doing so, it aims to provide the deep-sea community with an open data analysis tool that can be used in the context of future ecological research, and to help deep-sea researchers to use stable isotope markers at their full efficiency. more info about the project can be found at https://loicnmichel.com/deepiso/as of v2 (2021/08/12), the database contains 18 distinct datasets, for a total of 38335 fully documented measurements. archived parameters currently include δ13c (n = 7690), δ15n (n = 7491), δ34s (n = 3266), %c (n = 5753), %n (n = 5614), %s (n = 3342) and c/n ratio (n = 5719). those measurements pertain to 7248 distinct samples belonging to 881 taxa, plus sediments, suspended particulate organic matter, plankton, and detritus. samples were taken between 1989 and 2018 in multiple environments (hydrothermal vents, cold seeps, cold water coral reefs, and other benthic or pelagic environments) and at depths ranging up to 5338 meters.the database consists of two files: one containing the data itself, and one describing all used terms (measurements or metadata, derived from darwin core standards, https://dwc.tdwg.org/terms/). version log : v002 - 2021/08/12. annual update. 18 datasets, 38335 measurements in 8041 unique entries pertaining to 7248 distinct samples from 881 taxons. 7 parameters archived: d13c (n = 7690), d15n (n = 7491), d34s (n = 3266), %c (n = 5753), %n (n = 5614), %s (n = 3342), c/n ratio (n = 5719). temporal coverage: 1989-2018. spatial coverage: -76.7148° to 66.98283° latitude, -177.18503° to 162.2009° longitude. max depth: 5338 m.v001 - 2020/10/22. initial release of the database. 15 datasets, 18677 measurements in 4938 unique entries pertaining to 4378 distinct samples from 493 taxons. 7 parameters archived: d13c (n = 4587), d15n (n = 4388), d34s (n = 951), %c (n = 2740), %n (n = 2741), %s (n = 752), c/n ratio (n = 2518). temporal coverage: 1985-2018. spatial coverage: -62.1924° to 66.98283° latitude, -177.18503° to 152.105227° longitude. max depth: 5029 m.

These data include Nitrogen Isotope Ratios (δ15N) in amino acid standards and in four field-collected samples. Certified δ15N values are either EA-IRMS values (Glutamic acid [Glu], USGS) or produced by the persulfate oxidization method (Phenylalanine [Phe], Knapp et al., 2005) or provided by McCarthy Lab (M-std and Cyano). Mixtures of 16 amino acids were also evaluated.

The newly-developed method used here will help promote the use of δ15N-AA in important studies of nitrogen cycling and trophic ecology in a wide range of research areas. The Phe isotopic standards are available to the community for inter-lab method comparisons. These data were collected by PhD student Wingman (Charlotte) Lee and Dr. Lin Zhang (PI ) at the Texas A&M University-Corpus Christi.

In the last quarter of 2023, the debt to equity ratio in the United States amounted to 84.24 percent. Debt to equity ratio explained The debt to equity financial ratio indicates the relationship between shareholders' equity and debt used to finance the assets of a company. In order to make the calculation the data of the two required components are taken from the firm’s balance sheet. If the company is a publicly traded company then it is possible to make the calculation by taking the market value for both.The composition of debt and equity of an enterprise is much debated as is the influence that it is able to exert on the value of the firm. Nevertheless, it is important in helping investors such as banks to identify companies that are highly leveraged and therefore pose a higher risk. It is best explained by taking the example of an entrepreneur wishing to expand their operation and going to the bank for a loan. If this small business owner had total assets amounting to 120,000 U.S. dollars and liabilities (mostly loans) amounting to 100,000 U.S. dollars the bank to which the request is being made would first have to deduce the business owner’s equity; 20,000 dollars (total assets minus liabilities). With this figure the bank would proceed to divide total liabilities by equity, which gives the ratio of 500 percent. In other terms, this means that for every one dollar of equity the small business owner has 5 dollars of debt. He is highly leveraged and therefore represents high risk to the bank.

Modularity describes the case where patterns of trait covariation are unevenly dispersed across traits. Specifically, trait correlations are high and concentrated within subsets of variables (modules), but the correlations between traits across modules are relatively weaker. For morphometric data sets, hypotheses of modularity are commonly evaluated using the RV coefficient, an association statistic used in a wide variety of fields. In this article, I explore the properties of the RV coefficient using simulated data sets. Using data drawn from a normal distribution where the data were neither modular nor integrated in structure, I show that the RV coefficient is adversely affected by attributes of the data (sample size and the number of variables) that do not characterize the covariance structure between sets of variables. Thus, with the RV coefficient, patterns of modularity or integration in data are confounded with trends generated by sample size and the number of variables, which limits biological interpretations and renders comparisons of RV coefficients across data sets uninformative. As an alternative, I propose the covariance ratio (CR) for quantifying modular structure and show that it is unaffected by sample size or the number of variables. Further, statistical tests based on the CR exhibit appropriate type I error rates and display higher statistical power relative to the RV coefficient when evaluating modular data. Overall, these findings demonstrate that the RV coefficient does not display statistical characteristics suitable for reliable assessment of hypotheses of modular or integrated structure and therefore should not be used to evaluate these patterns in morphological data sets. By contrast, the covariance ratio meets these criteria and provides a useful alternative method for assessing the degree of modular structure in morphological data.

No description is available. Visit https://dataone.org/datasets/8bfd013a5017213a00beb6a73f9e1b08 for complete metadata about this dataset.

About this dataset

Context

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

Key observations

Largest age group (population): Male # 5-9 years (735) | Female # 60-64 years (636). 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 Troy population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Troy is shown in the following column.
• Population (Female): The female population in the Troy 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 Troy 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 Troy Population by Gender. You can refer the same here

For those of "fighting age" during the Second World War, gender ratios changed significantly as a result of the conflict. In nature, gender ratios at birth are generally between 103 and 107 boys per 100 girls, with these numbers balancing in early adulthood due to the disproportionate impact of conflict and childhood diseases on male populations. However, the scale of conflicts in the early twentieth century meant that gender ratios became even more imbalanced than typically expected, with countries most-heavily involved in the World Wars feeling these effects the most.

Additionally, of the listed European countries involved in the First World War and other European conflicts of the early-twentieth century, another large decline can be observed among those aged over 50 (for example, those aged 50-54 would have been in their late teens during the First World War).

About this dataset

Context

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

Key observations

Largest age group (population): Male # 0-4 years (529) | Female # 15-19 years (560). 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 Monroe population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Monroe is shown in the following column.
• Population (Female): The female population in the Monroe 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 Monroe 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 Monroe Population by Gender. You can refer the same here