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

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.

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.

YouTube Video and Channel Analytics

YouTube Video and Channel Analytics: Statistics and Features

By VISHWANATH SESHAGIRI [source]

About this dataset

The YouTube Video and Channel Metadata dataset is a comprehensive collection of data related to YouTube videos and channels. It consists of various features and statistics that provide insights into the performance and engagement of videos, as well as the overall popularity and success of channels.

The dataset includes both direct features, such as total views, channel elapsed time, channel ID, video category ID, channel view count, likes per subscriber, dislikes per subscriber, comments per subscriber, and more. Additionally, there are indirect features derived from YouTube's API that provide additional metrics for analysis.

One important aspect covered in this dataset is the ratio between certain metrics. For example: - The totalviews/channelelapsedtime ratio represents the average number of views a video has received relative to the elapsed time since the channel was created. - The likes/dislikes ratio indicates the proportion of likes on a video compared to dislikes. - The views/subscribers ratio showcases how engaged subscribers are by measuring the number of views relative to the number of subscribers.

Other metrics explored in this dataset include comments/views ratio (representing viewer engagement), dislikes/views ratio (measuring viewer sentiment), comments/subscriber ratio (indicating community participation), likes/subscriber ratio (reflecting audience loyalty), dislikes/subscriber ratio (highlighting dissatisfaction levels), total number of subscribers for a channel (subscriberCount), total views on a channel (channelViewCount), total number of comments on a channel (channelCommentCount), among others.

By analyzing these features and statistics within this dataset, researchers or data analysts can gain valuable insights into various aspects related to YouTube videos and channels. Furthermore, it may be possible to build statistical relationships between videos based on their performance characteristics or even develop topic trees based on similarities between different content categories. This dataset serves as an excellent resource for studying YouTube's ecosystem comprehensively.

For accessing additional resources related to this dataset or exploring code repositories associated with it, users can refer to the provided GitHub repository

How to use the dataset

Introduction:

Step 1: Understanding the Dataset Start by familiarizing yourself with the columns in the dataset. Here are some key features to pay attention to:

• 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 for a channel.
• dislikes/views: The ratio of dislikes on a video to its total views.
• comments/subscriber: The ratio comments on a video receive per subscriber count.

Step 2: Determining Data Analysis Objectives Define your objectives or research questions before diving into data analysis using this dataset. For example, you may want to explore relationships between viewership, engagement metrics, and various attributes such as category ID or elapsed time.

Step 3: Analyzing Relationships between Variables Use statistical techniques like correlation analysis or visualization tools like scatter plots, bar graphs, or heatmaps to understand relationships between variables in this dataset.

For example: - Plotting totalviews/channelelapsedtime against channelViewCount can help identify patterns between overall video popularity and channels' view count growth over time. - Comparing likes/dislikes with comments/views can give insights into viewer engagement levels across different videos.

Step 4: Building Machine Learning Models (Optional) If your objective includes predictive analysis or building machine learning models, select relevant features as predictors and the target variable (e.g., totalviews/channelelapsedtime) for training and evaluation.

You can use various algorithms such as linear regression, decision trees, or neural networks to predict video performance or channel growth based on available attributes.

Step 5: Evaluating Model Performance Assess the predictive model's performance using appropriate evaluation metrics like mean square...

Data Statistics of example 2.

Children Dependency Ratio(Sample Survey): Ningxia data was reported at 27.330 % in 2023. This records a decrease from the previous number of 27.990 % for 2022. Children Dependency Ratio(Sample Survey): Ningxia data is updated yearly, averaging 29.370 % from Dec 2002 (Median) to 2023, with 22 observations. The data reached an all-time high of 39.000 % in 2002 and a record low of 25.100 % in 2017. Children 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: Children Dependency Ratio: By Region.

We compare percentages present in the true labels of the real data and the predicted labels. Analogously, we measure the ratio of samples with positive label present in the synthetic generated data and predicted labels for datasets generated using distinct synthesizer techniques. Predictions(R) represents ratio of positive prediction labels of an experiment where model trained on synthetic data was evaluated on real data, and Predictions(S) ratio of positive prediction labels of an experiment where model trained on synthetic data was evaluated on synthetic data.

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.

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.

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

Children Dependency Ratio(Sample Survey): Hainan data was reported at 28.130 % in 2021. This records a decrease from the previous number of 28.700 % for 2020. Children Dependency Ratio(Sample Survey): Hainan data is updated yearly, averaging 27.865 % from Dec 2002 (Median) to 2021, with 20 observations. The data reached an all-time high of 38.600 % in 2002 and a record low of 25.600 % in 2013. Children Dependency Ratio(Sample Survey): Hainan 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: Children 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

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

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).

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.

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.

NOTE. No positively selected codon sites were detected for the wild grass samples “S1” and “S2”. Asterisks denote significant positive sites for the Iran samples on wheat.aWith the exception of site 48, the same positively selected sites were detected using the program PAML (Table S3).bBecause of the large number of negatively selected sites in some enzymes, the number of selected codons per total number of codons is given.

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.

YouTube Videos and Channels Metadata

Analyze the statistical relation between videos and form a topic tree

By VISHWANATH SESHAGIRI [source]

About this dataset

This dataset contains YouTube video and channel metadata to analyze the statistical relation between videos and form a topic tree. With 9 direct features, 13 more indirect features, it has all that you need to build a deep understanding of how videos are related – including information like total views per unit time, channel views, likes/subscribers ratio, comments/views ratio, dislikes/subscribers ratio etc. This data provides us with a unique opportunity to gain insights on topics such as subscriber count trends over time or calculating the impact of trends on subscriber engagement. We can develop powerful models that show us how different types of content drive viewership and identify the most popular styles or topics within YouTube's vast catalogue. Additionally this data offers an intriguing look into consumer behaviour as we can explore what drives people to watch specific videos at certain times or appreciate certain channels more than others - by analyzing things like likes per subscribers and dislikes per views ratios for example! Finally this dataset is completely open source with an easy-to-understand Github repo making it an invaluable resource for anyone looking to gain better insights into how their audience interacts with their content and how they might improve it in the future

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

How to Use This Dataset

In general, it is important to understand each parameter in the data set before proceeding with analysis. The parameters included are totalviews/channelelapsedtime, channelViewCount, likes/subscriber, views/subscribers, subscriberCounts, dislikes/views comments/subscriberchannelCommentCounts,, likes/dislikes comments/views dislikes/ subscribers totviewes /totsubsvews /elapsedtime.

To use this dataset for your own analysis:1) Review each parameter’s meaning and purpose in our dataset; 2) Get familiar with basic descriptive statistics such as mean median mode range; 3) Create visualizations or tables based on subsets of our data; 4) Understand correlations between different sets of variables or parameters; 5) Generate meaningful conclusions about specific channels or topics based on organized graph hierarchies or tables.; 6) Analyze trends over time for individual parameters as well as an aggregate reaction from all users when videos are released

Research Ideas

• Predicting the Relative Popularity of Videos: This dataset can be used to build a statistical model that can predict the relative popularity of videos based on various factors such as total views, channel viewers, likes/dislikes ratio, and comments/views ratio. This model could then be used to make recommendations and predict which videos are likely to become popular or go viral.

• Creating Topic Trees: The dataset can also be used to create topic trees or taxonomies by analyzing the content of videos and looking at what topics they cover. For example, one could analyze the most popular YouTube channels in a specific subject area, group together those that discuss similar topics, and then build an organized tree structure around those topics in order to better understand viewer interests in that area.

• Viewer Engagement Analysis: This dataset could also be used for viewer engagement analysis purposes by analyzing factors such as subscriber count, average time spent watching a video per user (elapsed time), comments made per view etc., so as to gain insights into how engaged viewers are with specific content or channels on YouTube. From this information it would be possible to optimize content strategy accordingly in order improve overall engagement rates across various types of video content and channel types

Acknowledgements

If you use this dataset in your research, please credit the original authors.

Data Source

License

Unknown License - Please check the dataset description for more information.

Columns

File: YouTubeDataset_withChannelElapsed.csv | Column name | Description | |:----------------------------------|:-------------------------------------------------------| | totalviews/channelelapsedtime | Ratio of total views to channel elapsed time. (Ratio) | | channelViewCount | Total number of views for the channel. (Integer) | | likes/subscriber ...