These datasets contains statements about demographic factors and outstanding members from Wiki-based knowledge (i.e., Wikipedia and Wikidata).

Group-centric dataset (sample of what is it about):

Demographic factors of winners of Nobel Prize in Physics include: male, physicist, american, university teacher, and researcher. Outstanding members in this group include Maria Curie (who isn't male but female) and Wilhelm Röntgen (who isn't a citizen of the U.S. but Germany).

Subject-centric dataset (sample of what is it about):

Fun trivia about Max Planck include: unlike 93% of winners of Liebig Medal (an award by Society of German Chemists), Planck was not a chemist, but a physicist.

This data can be also browsed at: https://wikiknowledge.onrender.com/demographics/

PopFacts Premier Demographic Flat File.

Context

The United States Census is a decennial census mandated by Article I, Section 2 of the United States Constitution, which states: "Representatives and direct Taxes shall be apportioned among the several States ... according to their respective Numbers."
Source: https://en.wikipedia.org/wiki/United_States_Census

Content

The United States census count (also known as the Decennial Census of Population and Housing) is a count of every resident of the US. The census occurs every 10 years and is conducted by the United States Census Bureau. Census data is publicly available through the census website, but much of the data is available in summarized data and graphs. The raw data is often difficult to obtain, is typically divided by region, and it must be processed and combined to provide information about the nation as a whole.

The United States census dataset includes nationwide population counts from the 2000 and 2010 censuses. Data is broken out by gender, age and location using zip code tabular areas (ZCTAs) and GEOIDs. ZCTAs are generalized representations of zip codes, and often, though not always, are the same as the zip code for an area. GEOIDs are numeric codes that uniquely identify all administrative, legal, and statistical geographic areas for which the Census Bureau tabulates data. GEOIDs are useful for correlating census data with other censuses and surveys.

Fork this kernel to get started.

Acknowledgements

https://bigquery.cloud.google.com/dataset/bigquery-public-data:census_bureau_usa

https://cloud.google.com/bigquery/public-data/us-census

Dataset Source: United States Census Bureau

Use: This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source - http://www.data.gov/privacy-policy#data_policy - and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

Banner Photo by Steve Richey from Unsplash.

Inspiration

What are the ten most populous zip codes in the US in the 2010 census?

What are the top 10 zip codes that experienced the greatest change in population between the 2000 and 2010 censuses?

https://cloud.google.com/bigquery/images/census-population-map.png" alt="https://cloud.google.com/bigquery/images/census-population-map.png"> https://cloud.google.com/bigquery/images/census-population-map.png

This dataset provides comparisons of demographic group prevalence in AmeriCorps Member/Volunteers populations to that of the greater U.S. population. The odds ratio analysis was completed by the Office of the Chief Data Officer. Population estimates were obtained from U.S. Census Bureau data reported in American Community Survey 5-Year tables DP05 (total U.S. populations) and S1701 (U.S. populations below poverty line), and socioeconomic status-related microdata maintained by IPUMS USA. See Attached Document 'AmeriCorps Demographic Analysis Procedure.pdf' for a full technical documentation of the analysis.

This dataset is extracted from https://en.wikipedia.org/wiki/List_of_countries_by_population_in_1800. Context: There s a story behind every dataset and heres your opportunity to share yours.Content: What s inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too. Acknowledgements:We wouldn t be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.Inspiration: Your data will be in front of the world s largest data science community. What questions do you want to see answered?

All cities with a population > 1000 or seats of adm div (ca 80.000)Sources and ContributionsSources : GeoNames is aggregating over hundred different data sources. Ambassadors : GeoNames Ambassadors help in many countries. Wiki : A wiki allows to view the data and quickly fix error and add missing places. Donations and Sponsoring : Costs for running GeoNames are covered by donations and sponsoring.Enrichment:add country name

The Current Population Survey Civic Engagement and Volunteering (CEV) Supplement is the most robust longitudinal survey about volunteerism and other forms of civic engagement in the United States. Produced by AmeriCorps in partnership with the U.S. Census Bureau, the CEV takes the pulse of our nation’s civic health every two years. The CEV can support evidence-based decision making and efforts to understand how people make a difference in communities across the country.

The findings on this page are based on data collected in September of 2017, 2019, 2021, and 2023. All figures are weighted to account for the random selection of eligible respondents and missing data due to nonresponse. They reflect national rates of 17 measures of civic engagement for key demographic subgroups. Please see column descriptions for details.

A spreadsheet with all of these figures is provided as an attachment along with additional resources about the CEV data. Click on "Show More" to view and download.

To explore CEV findings in an interactive dashboard, please see https://data.americorps.gov/stories/s/AmeriCorps-Civic-Engagement-and-Volunteering-CEV-D/62w6-z7xa

For the full CEV datasets, please see https://data.americorps.gov/browse?q=cev&sortBy=last_modified&utf8=%E2%9C%93

Analysis of ‘Population by Country - 2020’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/tanuprabhu/population-by-country-2020 on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

I always wanted to access a data set that was related to the world’s population (Country wise). But I could not find a properly documented data set. Rather, I just created one manually.

Content

Now I knew I wanted to create a dataset but I did not know how to do so. So, I started to search for the content (Population of countries) on the internet. Obviously, Wikipedia was my first search. But I don't know why the results were not acceptable. And also there were only I think 190 or more countries. So then I surfed the internet for quite some time until then I stumbled upon a great website. I think you probably have heard about this. The name of the website is Worldometer. This is exactly the website I was looking for. This website had more details than Wikipedia. Also, this website had more rows I mean more countries with their population.

Once I got the data, now my next hard task was to download it. Of course, I could not get the raw form of data. I did not mail them regarding the data. Now I learned a new skill which is very important for a data scientist. I read somewhere that to obtain the data from websites you need to use this technique. Any guesses, keep reading you will come to know in the next paragraph.

https://fiverr-res.cloudinary.com/images/t_main1,q_auto,f_auto/gigs/119580480/original/68088c5f588ec32a6b3a3a67ec0d1b5a8a70648d/do-web-scraping-and-data-mining-with-python.png" alt="alt text">

You are right its, Web Scraping. Now I learned this so that I could convert the data into a CSV format. Now I will give you the scraper code that I wrote and also I somehow found a way to directly convert the pandas data frame to a CSV(Comma-separated fo format) and store it on my computer. Now just go through my code and you will know what I'm talking about.

Below is the code that I used to scrape the code from the website

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3200273%2Fe814c2739b99d221de328c72a0b2571e%2FCapture.PNG?generation=1581314967227445&alt=media" alt="">

Acknowledgements

Now I couldn't have got the data without Worldometer. So special thanks to the website. It is because of them I was able to get the data.

Inspiration

As far as I know, I don't have any questions to ask. You guys can let me know by finding your ways to use the data and let me know via kernel if you find something interesting

--- Original source retains full ownership of the source dataset ---

This spreadsheet contains links to archived Wikipedia pages and categorization of their content, which can be used to replicate Table 1 from "Rule Ambiguity, Institutional Clashes and Population Loss: How Wikipedia Became the Last Good Place on the Internet" by Sverrir Steinsson.

Contains demographic information of participating households. All respondents, regardless of whether they reported a household property crime victimization, are included in this file.

This dataset contains population, property tax rate and income tax rate for the top 20 cities in Indiana by population minus Indianapolis and Ft. Wayne. Population information came from Wikipedia: https://en.wikipedia.org/wiki/List_of_cities_in_Indiana Property tax rate information came from: https://www.stats.indiana.edu/dms4/propertytaxes.asp

Context

This dataset simply combines publicly available data to characterise a country based on healthcare factors, economy, government and demographics.

Content

All data are given per 100.000 inhabitants where this is appropriate scores are given as absolute values and so are spending and demographics. Each row represents one country. Data that is included covers the following topics:

Healthcare: - Staff including: Nurses and Physicians per 100.000 inhabitants - Infrastructure including: Beds, Chnage of beds between 2018 and 2019 and the change of bed numbers since 2013, Intensive Care Unit (ICU) beds, ventilators and Extra Corporal Membrane Oxygenation (ECMO), machines per 100.000 inhabitants - Total spending on healthcare in US dollars per capita.

Demographics: - The median age for entire population and each gender - The percentage of the population within age brackets - Total population - Population per km2 - Population change between 2018 and 2019

Government The used scores are from the Economist intelligence unit and describe how democratic a country is and how the government works. These can be used to compare countries based on their government type.

Acknowledgements

All data is publicly available and just has been brought together in one place. The sources are:

• Population densities

• Populations

• ECMO machines

• Hospital beds

• Number of physicians

• Health expenditure per capita

• Democracy indexes

• Median age

• OECD nurses per capita

Inspiration

These data are meant as metadata to decide which countries are comparable. I am working on healthcare data so the inspiration is to compare health statistics between countries and make an informed decision about how comparable they are. Could be used for any non healthcare related task as well.

This table contains forecasts (including intervals) of the population of The Netherlands on 1 January by age groups (three age-groups) and population dynamics: live births, deaths and external migration. Furthermore, the table contains information about the total fertility rate, demographic pressure and (period) life expectancy at birth and at age 65 by sex.

Data available from: 2023-2070

Status of the figures: The figures in this table are calculated forecasts.

Changes as of 15 December 2023: In this new table, the previous forecast is adjusted based on the most recent insights, the forecast period now runs from 2023 to 2070.

When will new figures be published? New figures will appear December 2026.

This table contains forecasts (including intervals) of the population of The Netherlands on 1 January by age groups (three age-groups) and population dynamics: live births, deaths and external migration. Furthermore, the table contains information about the total fertility rate, demographic pressure and (period) life expectancy at birth and at age 65 by sex.

Data available from: 2017-2060

Status of the figures: The figures in this table are calculated forecasts.

Changes as of 16 December 2020: None, this table has been published once-only. See 3. for the successor of this table.

Changes as of 19 December 2017: In this new table, the previous forecast is adjusted based on the most recent insights, the forecast period now runs from 2017 to 2060.

When will new figures be published? New figures will appear December 2020.

Population data (population, household, breakdown by age) at village level (admin 4), with total 2650 villages and the admin code has been adjusted into BPS code.

This data is extracted from the latest version 2017 - SIAK database (Population Information Administration System - https://id.wikipedia.org/wiki/Sistem_informasi_administrasi_kependudukan) of the Ministry of Home Affairs - MoHA. The data is served as GIS REST Services and is available publicly.

Data cleaning and analysis was done by the World Food Programme (WFP)

Population data (population, household, breakdown by age) at village level (admin 4), with total 1551 villages and the admin code has been adjusted into BPS code.

This data is extracted from the latest version 2017 - SIAK database (Population Information Administration System - https://id.wikipedia.org/wiki/Sistem_informasi_administrasi_kependudukan) of the Ministry of Home Affairs - MoHA. The data is served as GIS REST Services and is available publicly.

Data cleaning and analysis was done by the World Food Programme (WFP)

Population density is a measurement of population per unit area or unit volume. It is frequently applied to living organisms, and particularly to humans. It is a key geographic term. (Wikipedia)

This dashboard provides visual representation for comparisons of demographic group prevalence in AmeriCorps Member/Volunteers populations to that of the greater U.S. population. The odds ratio analysis was completed by the Office of the Chief Data Officer. Note: Toggle between dashboard pages with the arrows at the bottom of the dashboard. Pages: 1) State Results, 2) National Results, 3) Key Terms and Conditions

Contains property crime victimizations. Property crimes include burglary, theft, motor vehicle theft, and vandalism. Households that did not report a property crime victimization are not included on this file. Victimizations that took place outside of the United States are excluded from this file.

This dataset contains data about obesity, suicides and unemployment segregated by Country. The sources of data are wikipedia tables as updated on 11/04/2022. More information can be found in project's github: https://github.com/martinsanc/wikipedia_scraper

Países (List of countries by population (United Nations) - Wikipedia)

Country

UN continental region

UN statistical subregion

Population 1 July 2018

Population 1 July 2019

Change

Desempleo (List of countries by unemployment rate - Wikipedia)

Unemployment Rate

Sourcedate of information

Suicidios (List of countries by suicide rate - Wikipedia)

All

Male

Female

Tasa de obesidad por país (List of countries by suicide rate - Wikipedia)

Rank

Obesity rate