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.

YouTube Video and Channel Analysis

YouTube Video and Channel Statistics

By VISHWANATH SESHAGIRI [source]

About this dataset

This dataset contains valuable information about YouTube videos and channels, including various metrics related to views, likes, dislikes, comments, and other related statistics. The dataset consists of 9 direct features and 13 indirect features. The direct features include the ratio of comments on a video to the number of views on the video (comments/views), the total number of subscribers of the channel (subscriberCount), the ratio of likes on a video to the number of subscribers of the channel (likes/subscriber), the total number of views on the channel (channelViewCount), and several other informative ratios such as views/elapsedtime, totalviews/channelelapsedtime, comments/subscriber, views/subscribers, dislikes/subscriber.

The dataset also includes indirect features that are derived from YouTube's API. These indirect features provide additional insights into videos and channels by considering factors such as dislikes/views ratio, channelCommentCount (total number of comments on the channel), likes/dislikes ratio, totviews/totsubs ratio (total views on a video to total subscribers of a channel), and more.

The objective behind analyzing this dataset is to establish statistical relationships between videos and channels within YouTube. Furthermore, this analysis aims to form a topic tree based on these statistical relations.

For further exploration or utilization purposes beyond this dataset description document itself, you can refer to relevant repositories such as the GitHub repository associated with this dataset where you might find useful resources that complement or expand upon what is available in this dataset.

Overall,this comprehensive collection provides diverse insights into YouTube video and channel metadata for conducting statistical analyses in order to better understand viewer engagement patterns varies parameters across different channels. With its range from basic counts like subscriber counts,counting no.of viewership per minute , timing vs viewership rate ,text related user responses etc.,this detailed Youtube Dataset will assist in making informed decisions regarding channel optimization,more effective targeting and creation of content that will appeal to the target audience

How to use the dataset

This dataset provides valuable information about YouTube videos and their corresponding channels. With this data, you can perform statistical analysis to gain insights into various aspects of YouTube video and channel performance. Here is a guide on how to effectively use this dataset for your analysis:

• Understanding the Columns:
  • totalviews/channelelapsedtime: The ratio of total views of a video to the elapsed time of the channel.
  • channelViewCount: The total number of views on the channel.
  • likes/subscriber: The ratio of likes on a video to the number of subscribers of the channel.
  • views/subscribers: The ratio of views on a video to the number of subscribers of the channel.
  • subscriberCount: The total number of subscribers of the channel.
  • dislikes/views: The ratio

Research Ideas

• Predicting the popularity of YouTube videos: By analyzing the various ratios and metrics in this dataset, such as comments/views, likes/subscriber, and views/subscribers, one can build predictive models to estimate the popularity or engagement level of YouTube videos. This can help content creators or businesses understand which types of videos are likely to be successful and tailor their content accordingly.
• Analyzing channel performance: The dataset provides information about the total number of views on a channel (channelViewCount), the number of subscribers (subscriberCount), and other related statistics. By examining metrics like views/elapsedtime and totalviews/channelelapsedtime, one can assess how well a channel is performing over time. This analysis can help content creators identify trends or patterns in their viewership and make informed decisions about their video strategies.
• Understanding audience engagement: Ratios like comments/subscriber, likes/dislikes, dislikes/subscriber provide insights into how engaged a channel's subscribers are with its content. By examining these ratios across multiple videos or channels, one can identify trends in audience behavior and preferences. For example, a high ratio of comments/subscriber may indicate strong community participation and active discussion around the videos posted by a particular YouTuber or channel

Acknowledgements

If you use this dataset in y...

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.

Data Statistics of example 2.

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

The perfection ratio of a number is a concept that is related to perfect numbers and how closely a given number approximates the ideal perfection ratio, which is 2.0.

Perfect Numbers:

A perfect number is a positive integer that is equal to the sum of its proper divisors, excluding the number itself. For example: • 6 is a perfect number because its divisors are 1, 2, and 3, and 1 + 2 + 3 = 6 . • 28 is another perfect number because its divisors are 1, 2, 4, 7, and 14, and 1 + 2 + 4 + 7 + 14 = 28 .

Perfection Ratio:

The perfection ratio of a number n is a measure of how close the sum of its divisors (excluding the number itself) is to the number. It is defined as:

\text{Perfection Ratio} = \frac{\text{Sum of Proper Divisors of } n}{n}

•  If the perfection ratio is 2.0, the number is considered perfect.
•  If the perfection ratio is greater than 2.0, the number is abundant (i.e., the sum of its proper divisors exceeds the number itself).
•  If the perfection ratio is less than 2.0, the number is deficient (i.e., the sum of its proper divisors is less than the number itself).

Examples:

1. Perfect Number Example:
•  For n = 6 :
•  Proper divisors: 1, 2, 3 
•  Sum of proper divisors: 1 + 2 + 3 = 6 
•  Perfection ratio: \frac{6}{6} = 1.0 
•  Since the perfection ratio is 2.0 for a perfect number, we see the idea of perfect numbers where the sum of divisors divides evenly.

1. Near-Perfect Numbers

• Definition: A near-perfect number is a number for which the sum of its proper divisors is close to the number itself but not exactly equal.
• Example: Consider the number 24. Its proper divisors are 1, 2, 3, 4, 6, 8, and 12. The sum is 36, which is larger than 24, making it almost perfect in the sense that the sum of its divisors is significant but not equal to the number.

2. Almost-Perfect Numbers

• Definition: An almost-perfect number is a number where the sum of its proper divisors equals the number minus one.
• Example: The number 16 is an almost-perfect number. Its proper divisors are 1, 2, 4, and 8, which sum to 15 (16 - 1).

3. Abundant Numbers

• Definition: A number is abundant if the sum of its proper divisors is greater than the number itself.
• Example: The number 12 is abundant because its proper divisors (1, 2, 3, 4, and 6) sum to 16, which is greater than 12.

4. Deficient Numbers

• Definition: A number is deficient if the sum of its proper divisors is less than the number itself.
• Example: The number 8 is deficient because its proper divisors (1, 2, and 4) sum to 7, which is less than 8.

5. Semiperfect Numbers

• Definition: A semiperfect number is a number that is equal to the sum of some (or all) of its proper divisors.
• Example: The number 12 is semiperfect because 12 = 6 + 4 + 2 (some of its proper divisors).

Relevance to the Heat Map

• Density Analysis: By analyzing the heat map further, we might observe concentrations at other specific perfection ratios besides 2. These could indicate near-perfect, almost-perfect, abundant, deficient, or semiperfect numbers.
• Patterns and Trends: Identifying where these numbers cluster can help us understand the distribution and frequency of numbers with these properties within your dataset.

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.

Elderly Dependency Ratio(Sample Survey): Anhui data was reported at 23.860 % in 2022. This records an increase from the previous number of 23.430 % for 2021. Elderly Dependency Ratio(Sample Survey): Anhui data is updated yearly, averaging 15.100 % from Dec 2002 (Median) to 2022, with 21 observations. The data reached an all-time high of 23.860 % in 2022 and a record low of 11.700 % in 2003. Elderly Dependency Ratio(Sample Survey): Anhui 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.

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).

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

Elderly Dependency Ratio(Sample Survey): Beijing data was reported at 21.980 % in 2023. This records an increase from the previous number of 20.760 % for 2022. Elderly Dependency Ratio(Sample Survey): Beijing data is updated yearly, averaging 14.000 % from Dec 2002 (Median) to 2023, with 22 observations. The data reached an all-time high of 21.980 % in 2023 and a record low of 10.500 % in 2014. Elderly 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: Elderly Dependency Ratio: By Region.

About this dataset

Context

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

Key observations

Largest age group (population): Male # 10-14 years (23) | Female # 65-69 years (14). 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 Rufus population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Rufus is shown in the following column.
• Population (Female): The female population in the Rufus 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 Rufus 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 Rufus Population by Gender. You can refer the same here

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

About this dataset

Context

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

Key observations

Largest age group (population): Male # 30-34 years (299,497) | Female # 30-34 years (296,760). 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 Virginia population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Virginia is shown in the following column.
• Population (Female): The female population in the Virginia 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 Virginia 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 Virginia Population by Gender. You can refer the same here

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.

This dataset provides black carbon (BC) mass mixing ratios (in units of ng BC / kg air) measured during NASA's Atmospheric Tomography (ATom)-1 flight campaign during July and August 2016. The BC-core masses of BC-containing aerosol particles were measured using a Single Particle Soot Photometer (SP2). Conversion to mass mixing ratio (MMR) is achieved by monitoring sample flow. Influences in air mass composition were determined using the Particle Analysis by Laser Mass Spectrometry (PALMS) instruments. Also included here are data from the Cloud, Aerosol and Precipitation Spectrometer (CAPS) instrument which are used to identify measurements taken while in clouds. Finally, the associated latitude, longitude, altitude, and the timestamp of each measurement are included. All data are at ten seconds resolution. ATom-1 flights originated from the Armstrong Flight Research Center in Palmdale, California, fly north to the western Arctic, south to the South Pacific, east to the Atlantic, north to Greenland, and return to California across central North America.

About this dataset

Context

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

Key observations

Largest age group (population): Male # 30-34 years (364,068) | Female # 30-34 years (371,238). 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 New York population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the New York is shown in the following column.
• Population (Female): The female population in the New York 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 New York 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 New York Population by Gender. You can refer the same here

The repayment hypothesis predicts that reproductive females in cooperative breeding systems overproduce the helping sex. Thanks to well-documented examples of this predicted sex ratio bias, repayment has been considered an important driver of variation in sex allocation patterns. Here we test this hypothesis using data on population brood sex ratios and facultative sex allocation from 28 cooperatively breeding bird species. We find that biased sex ratios of helpers do not correlate with production biases in brood sex ratios, contrary to predictions. We also test whether females facultatively produce the helping sex in response to a deficiency of help (i.e., when they have fewer or no helpers). Although this is observed in a few species, it is not a significant trend overall, with a mean effect size close to zero. We conclude that, surprisingly, repayment does not appear to be a widespread influence on sex ratios in cooperatively breeding birds. We discuss possible explanations for our results and encourage further examination of the repayment model.

About this dataset

Context

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

Key observations

Largest age group (population): Male # 60-64 years (2,443) | Female # 60-64 years (2,412). 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 Petaluma population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Petaluma is shown in the following column.
• Population (Female): The female population in the Petaluma 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 Petaluma 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 Petaluma Population by Gender. You can refer the same here

The Second World War severely altered the demographic composition of many countries, particularly in terms of gender ratios across certain age groups. For age groups below 14 years, there is little observable impact of the war on gender ratios, however, some countries see a drastic change across older generations, particularly in the Soviet Union. For men in their twenties (i.e. those in their late-teens or early-twenties when the war began), the ratio drops from 98 men per 100 women in the 15-19 age group, to 68 men per 100 women in the 25-29 group.

In addition to the Second World War, these figures are affected by trends in nature and other historical events. For example, women tend to have higher overall life expectancies than men, which typically sees gender ratios widen among older generations. The impact of the First World War is also most-observable in France's gender ratios for those aged in their fifties. Additionally, the gap in ratios remains high for the Soviet Union across older age groups due to the impact of the First World War and the famine of the early 1930s, however the figures for Russia itself are even lower as it was disproportionately affected by the Russian Revolution and famine of the 1920s.