The total population in the United States was estimated at 341.2 million people in 2024, according to the latest census figures and projections from Trading Economics. This dataset provides - United States Population - actual values, historical data, forecast, chart, statistics, economic calendar and news.

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

Changelog:

Update September 20, 2021: Data and overview updated to reflect data used in the September 15 story Over Half of States Have Rolled Back Public Health Powers in Pandemic. It includes 303 state or local public health leaders who resigned, retired or were fired between April 1, 2020 and Sept. 12, 2021. Previous versions of this dataset reflected data used in the Dec. 2020 and April 2021 stories.

Overview

Across the U.S., state and local public health officials have found themselves at the center of a political storm as they combat the worst pandemic in a century. Amid a fractured federal response, the usually invisible army of workers charged with preventing the spread of infectious disease has become a public punching bag.

In the midst of the coronavirus pandemic, at least 303 state or local public health leaders in 41 states have resigned, retired or been fired since April 1, 2020, according to an ongoing investigation by The Associated Press and KHN.

According to experts, that is the largest exodus of public health leaders in American history.

Many left due to political blowback or pandemic pressure, as they became the target of groups that have coalesced around a common goal — fighting and even threatening officials over mask orders and well-established public health activities like quarantines and contact tracing. Some left to take higher profile positions, or due to health concerns. Others were fired for poor performance. Dozens retired. An untold number of lower level staffers have also left.

The result is a further erosion of the nation’s already fragile public health infrastructure, which KHN and the AP documented beginning in 2020 in the Underfunded and Under Threat project.

Findings

The AP and KHN found that:

• One in five Americans live in a community that has lost its local public health department leader during the pandemic
• Top public health officials in 28 states have left state-level departments ## Using this data To filter for data specific to your state, use this query

To get total numbers of exits by state, broken down by state and local departments, use this query

Methodology

KHN and AP counted how many state and local public health leaders have left their jobs between April 1, 2020 and Sept. 12, 2021.

The government tasks public health workers with improving the health of the general population, through their work to encourage healthy living and prevent infectious disease. To that end, public health officials do everything from inspecting water and food safety to testing the nation’s babies for metabolic diseases and contact tracing cases of syphilis.

Many parts of the country have a health officer and a health director/administrator by statute. The analysis counted both of those positions if they existed. For state-level departments, the count tracks people in the top and second-highest-ranking job.

The analysis includes exits of top department officials regardless of reason, because no matter the reason, each left a vacancy at the top of a health agency during the pandemic. Reasons for departures include political pressure, health concerns and poor performance. Others left to take higher profile positions or to retire. Some departments had multiple top officials exit over the course of the pandemic; each is included in the analysis.

Reporters compiled the exit list by reaching out to public health associations and experts in every state and interviewing hundreds of public health employees. They also received information from the National Association of City and County Health Officials, and combed news reports and records.

Public health departments can be found at multiple levels of government. Each state has a department that handles these tasks, but most states also have local departments that either operate under local or state control. The population served by each local health department is calculated using the U.S. Census Bureau 2019 Population Estimates based on each department’s jurisdiction.

KHN and the AP have worked since the spring on a series of stories documenting the funding, staffing and problems around public health. A previous data distribution detailed a decade's worth of cuts to state and local spending and staffing on public health. That data can be found here.

Attribution

Findings and the data should be cited as: "According to a KHN and Associated Press report."

Is Data Missing?

If you know of a public health official in your state or area who has left that position between April 1, 2020 and Sept. 12, 2021 and isn't currently in our dataset, please contact authors Anna Maria Barry-Jester annab@kff.org, Hannah Recht hrecht@kff.org, Michelle Smith mrsmith@ap.org and Lauren Weber laurenw@kff.org.

Context

As the world is fighting against this invisible enemy a lot of data-driven students like me want to study it as well as we can. There is an enormous number of data set available on covid19 today but as a beginner, in this field, I wanted to find some more simple data. So here I come up with this covid19 data set which I scrapped from "https://www.worldometers.info/coronavirus". It is my way of learning by doing. This data is till 5/17/2020. I will keep on updating it.

Content

The dataset contains 194 rows and 12 columns which are described below:-

Country: Contains the name of all Countries. Total_Cases: It contains the total number of cases the country has till 5/17/2020. Total_Deaths: Total number of deaths in that country till 5/17/2020. Total_Recovered: Total number of individuals recovered from covid19. Active_Cases: Total active cases in the country on 5/17/2020. Critical_Cases: Number of patients in critical condition. Cases/Million_Population: Number of cases per million population of that country. Deaths/Million_Population: Number of deaths per million population of that country. Total_Tests: Total number of tests performed 5/17/2020 Tests/Million_Population: Number of tests performed per million population. Population: Population of the country Continent: Continent in which the country lies.

Acknowledgements

"https://www.worldometers.info/coronavirus/"

Data for CDC’s COVID Data Tracker site on Rates of COVID-19 Cases and Deaths by Vaccination Status. Click 'More' for important dataset description and footnotes

Dataset and data visualization details: These data were posted on October 21, 2022, archived on November 18, 2022, and revised on February 22, 2023. These data reflect cases among persons with a positive specimen collection date through September 24, 2022, and deaths among persons with a positive specimen collection date through September 3, 2022.

Vaccination status: A person vaccinated with a primary series had SARS-CoV-2 RNA or antigen detected on a respiratory specimen collected ≥14 days after verifiably completing the primary series of an FDA-authorized or approved COVID-19 vaccine. An unvaccinated person had SARS-CoV-2 RNA or antigen detected on a respiratory specimen and has not been verified to have received COVID-19 vaccine. Excluded were partially vaccinated people who received at least one FDA-authorized vaccine dose but did not complete a primary series ≥14 days before collection of a specimen where SARS-CoV-2 RNA or antigen was detected. Additional or booster dose: A person vaccinated with a primary series and an additional or booster dose had SARS-CoV-2 RNA or antigen detected on a respiratory specimen collected ≥14 days after receipt of an additional or booster dose of any COVID-19 vaccine on or after August 13, 2021. For people ages 18 years and older, data are graphed starting the week including September 24, 2021, when a COVID-19 booster dose was first recommended by CDC for adults 65+ years old and people in certain populations and high risk occupational and institutional settings. For people ages 12-17 years, data are graphed starting the week of December 26, 2021, 2 weeks after the first recommendation for a booster dose for adolescents ages 16-17 years. For people ages 5-11 years, data are included starting the week of June 5, 2022, 2 weeks after the first recommendation for a booster dose for children aged 5-11 years. For people ages 50 years and older, data on second booster doses are graphed starting the week including March 29, 2022, when the recommendation was made for second boosters. Vertical lines represent dates when changes occurred in U.S. policy for COVID-19 vaccination (details provided above). Reporting is by primary series vaccine type rather than additional or booster dose vaccine type. The booster dose vaccine type may be different than the primary series vaccine type. ** Because data on the immune status of cases and associated deaths are unavailable, an additional dose in an immunocompromised person cannot be distinguished from a booster dose. This is a relevant consideration because vaccines can be less effective in this group. Deaths: A COVID-19–associated death occurred in a person with a documented COVID-19 diagnosis who died; health department staff reviewed to make a determination using vital records, public health investigation, or other data sources. Rates of COVID-19 deaths by vaccination status are reported based on when the patient was tested for COVID-19, not the date they died. Deaths usually occur up to 30 days after COVID-19 diagnosis. Participating jurisdictions: Currently, these 31 health departments that regularly link their case surveillance to immunization information system data are included in these incidence rate estimates: Alabama, Arizona, Arkansas, California, Colorado, Connecticut, District of Columbia, Florida, Georgia, Idaho, Indiana, Kansas, Kentucky, Louisiana, Massachusetts, Michigan, Minnesota, Nebraska, New Jersey, New Mexico, New York, New York City (New York), North Carolina, Philadelphia (Pennsylvania), Rhode Island, South Dakota, Tennessee, Texas, Utah, Washington, and West Virginia; 30 jurisdictions also report deaths among vaccinated and unvaccinated people. These jurisdictions represent 72% of the total U.S. population and all ten of the Health and Human Services Regions. Data on cases among people who received additional or booster doses were reported from 31 jurisdictions; 30 jurisdictions also reported data on deaths among people who received one or more additional or booster dose; 28 jurisdictions reported cases among people who received two or more additional or booster doses; and 26 jurisdictions reported deaths among people who received two or more additional or booster doses. This list will be updated as more jurisdictions participate. Incidence rate estimates: Weekly age-specific incidence rates by vaccination status were calculated as the number of cases or deaths divided by the number of people vaccinated with a primary series, overall or with/without a booster dose (cumulative) or unvaccinated (obtained by subtracting the cumulative number of people vaccinated with a primary series and partially vaccinated people from the 2019 U.S. intercensal population estimates) and multiplied by 100,000. Overall incidence rates were age-standardized using the 2000 U.S. Census standard population. To estimate population counts for ages 6 months through 1 year, half of the single-year population counts for ages 0 through 1 year were used. All rates are plotted by positive specimen collection date to reflect when incident infections occurred. For the primary series analysis, age-standardized rates include ages 12 years and older from April 4, 2021 through December 4, 2021, ages 5 years and older from December 5, 2021 through July 30, 2022 and ages 6 months and older from July 31, 2022 onwards. For the booster dose analysis, age-standardized rates include ages 18 years and older from September 19, 2021 through December 25, 2021, ages 12 years and older from December 26, 2021, and ages 5 years and older from June 5, 2022 onwards. Small numbers could contribute to less precision when calculating death rates among some groups. Continuity correction: A continuity correction has been applied to the denominators by capping the percent population coverage at 95%. To do this, we assumed that at least 5% of each age group would always be unvaccinated in each jurisdiction. Adding this correction ensures that there is always a reasonable denominator for the unvaccinated population that would prevent incidence and death rates from growing unrealistically large due to potential overestimates of vaccination coverage. Incidence rate ratios (IRRs): IRRs for the past one month were calculated by dividing the average weekly incidence rates among unvaccinated people by that among people vaccinated with a primary series either overall or with a booster dose. Publications: Scobie HM, Johnson AG, Suthar AB, et al. Monitoring Incidence of COVID-19 Cases, Hospitalizations, and Deaths, by Vaccination Status — 13 U.S. Jurisdictions, April 4–July 17, 2021. MMWR Morb Mortal Wkly Rep 2021;70:1284–1290. Johnson AG, Amin AB, Ali AR, et al. COVID-19 Incidence and Death Rates Among Unvaccinated and Fully Vaccinated Adults with and Without Booster Doses During Periods of Delta and Omicron Variant Emergence — 25 U.S. Jurisdictions, April 4–December 25, 2021. MMWR Morb Mortal Wkly Rep 2022;71:132–138. Johnson AG, Linde L, Ali AR, et al. COVID-19 Incidence and Mortality Among Unvaccinated and Vaccinated Persons Aged ≥12 Years by Receipt of Bivalent Booster Doses and Time Since Vaccination — 24 U.S. Jurisdictions, October 3, 2021–December 24, 2022. MMWR Morb Mortal Wkly Rep 2023;72:145–152. Johnson AG, Linde L, Payne AB, et al. Notes from the Field: Comparison of COVID-19 Mortality Rates Among Adults Aged ≥65 Years Who Were Unvaccinated and Those Who Received a Bivalent Booster Dose Within the Preceding 6 Months — 20 U.S. Jurisdictions, September 18, 2022–April 1, 2023. MMWR Morb Mortal Wkly Rep 2023;72:667–669.

Updates

• Notice of data discontinuation: Since the start of the pandemic, AP has reported case and death counts from data provided by Johns Hopkins University. Johns Hopkins University has announced that they will stop their daily data collection efforts after March 10. As Johns Hopkins stops providing data, the AP will also stop collecting daily numbers for COVID cases and deaths. The HHS and CDC now collect and visualize key metrics for the pandemic. AP advises using those resources when reporting on the pandemic going forward.

  • CDC Weekly case and death counts (national and state level)
  • CDC County level cases and deaths
  • HHS New hospital admissions
  • CDC NowCast COVID variant proportions (national and regional level)

• April 9, 2020

  • The population estimate data for New York County, NY has been updated to include all five New York City counties (Kings County, Queens County, Bronx County, Richmond County and New York County). This has been done to match the Johns Hopkins COVID-19 data, which aggregates counts for the five New York City counties to New York County.

• April 20, 2020

  • Johns Hopkins death totals in the US now include confirmed and probable deaths in accordance with CDC guidelines as of April 14. One significant result of this change was an increase of more than 3,700 deaths in the New York City count. This change will likely result in increases for death counts elsewhere as well. The AP does not alter the Johns Hopkins source data, so probable deaths are included in this dataset as well.

• April 29, 2020

  • The AP is now providing timeseries data for counts of COVID-19 cases and deaths. The raw counts are provided here unaltered, along with a population column with Census ACS-5 estimates and calculated daily case and death rates per 100,000 people. Please read the updated caveats section for more information.

• September 1st, 2020

  • Johns Hopkins is now providing counts for the five New York City counties individually.

• February 12, 2021

  • The Ohio Department of Health recently announced that as many as 4,000 COVID-19 deaths may have been underreported through the state’s reporting system, and that the "daily reported death counts will be high for a two to three-day period."
  • Because deaths data will be anomalous for consecutive days, we have chosen to freeze Ohio's rolling average for daily deaths at the last valid measure until Johns Hopkins is able to back-distribute the data. The raw daily death counts, as reported by Johns Hopkins and including the backlogged death data, will still be present in the new_deaths column.

• February 16, 2021

  - Johns Hopkins has reconciled Ohio's historical deaths data with the state.

  Overview

The AP is using data collected by the Johns Hopkins University Center for Systems Science and Engineering as our source for outbreak caseloads and death counts for the United States and globally.

The Hopkins data is available at the county level in the United States. The AP has paired this data with population figures and county rural/urban designations, and has calculated caseload and death rates per 100,000 people. Be aware that caseloads may reflect the availability of tests -- and the ability to turn around test results quickly -- rather than actual disease spread or true infection rates.

This data is from the Hopkins dashboard that is updated regularly throughout the day. Like all organizations dealing with data, Hopkins is constantly refining and cleaning up their feed, so there may be brief moments where data does not appear correctly. At this link, you’ll find the Hopkins daily data reports, and a clean version of their feed.

The AP is updating this dataset hourly at 45 minutes past the hour.

To learn more about AP's data journalism capabilities for publishers, corporations and financial institutions, go here or email kromano@ap.org.

Queries

Use AP's queries to filter the data or to join to other datasets we've made available to help cover the coronavirus pandemic

• Filter cases by state here

• Rank states by their status as current hotspots. Calculates the 7-day rolling average of new cases per capita in each state: https://data.world/associatedpress/johns-hopkins-coronavirus-case-tracker/workspace/query?queryid=481e82a4-1b2f-41c2-9ea1-d91aa4b3b1ac

• Find recent hotspots within your state by running a query to calculate the 7-day rolling average of new cases by capita in each county: https://data.world/associatedpress/johns-hopkins-coronavirus-case-tracker/workspace/query?queryid=b566f1db-3231-40fe-8099-311909b7b687&showTemplatePreview=true

• Join county-level case data to an earlier dataset released by AP on local hospital capacity here. To find out more about the hospital capacity dataset, see the full details.

• Pull the 100 counties with the highest per-capita confirmed cases here

• Rank all the counties by the highest per-capita rate of new cases in the past 7 days here. Be aware that because this ranks per-capita caseloads, very small counties may rise to the very top, so take into account raw caseload figures as well.

Interactive

The AP has designed an interactive map to track COVID-19 cases reported by Johns Hopkins.

@(https://datawrapper.dwcdn.net/nRyaf/15/)

Interactive Embed Code

<iframe title="USA counties (2018) choropleth map Mapping COVID-19 cases by county" aria-describedby="" id="datawrapper-chart-nRyaf" src="https://datawrapper.dwcdn.net/nRyaf/10/" scrolling="no" frameborder="0" style="width: 0; min-width: 100% !important;" height="400"></iframe><script type="text/javascript">(function() {'use strict';window.addEventListener('message', function(event) {if (typeof event.data['datawrapper-height'] !== 'undefined') {for (var chartId in event.data['datawrapper-height']) {var iframe = document.getElementById('datawrapper-chart-' + chartId) || document.querySelector("iframe[src*='" + chartId + "']");if (!iframe) {continue;}iframe.style.height = event.data['datawrapper-height'][chartId] + 'px';}}});})();</script>

Caveats

• This data represents the number of cases and deaths reported by each state and has been collected by Johns Hopkins from a number of sources cited on their website.
• In some cases, deaths or cases of people who've crossed state lines -- either to receive treatment or because they became sick and couldn't return home while traveling -- are reported in a state they aren't currently in, because of state reporting rules.
• In some states, there are a number of cases not assigned to a specific county -- for those cases, the county name is "unassigned to a single county"
• This data should be credited to Johns Hopkins University's COVID-19 tracking project. The AP is simply making it available here for ease of use for reporters and members.
• Caseloads may reflect the availability of tests -- and the ability to turn around test results quickly -- rather than actual disease spread or true infection rates.
• Population estimates at the county level are drawn from 2014-18 5-year estimates from the American Community Survey.
• The Urban/Rural classification scheme is from the Center for Disease Control and Preventions's National Center for Health Statistics. It puts each county into one of six categories -- from Large Central Metro to Non-Core -- according to population and other characteristics. More details about the classifications can be found here.

Johns Hopkins timeseries data - Johns Hopkins pulls data regularly to update their dashboard. Once a day, around 8pm EDT, Johns Hopkins adds the counts for all areas they cover to the timeseries file. These counts are snapshots of the latest cumulative counts provided by the source on that day. This can lead to inconsistencies if a source updates their historical data for accuracy, either increasing or decreasing the latest cumulative count. - Johns Hopkins periodically edits their historical timeseries data for accuracy. They provide a file documenting all errors in their timeseries files that they have identified and fixed here

Attribution

This data should be credited to Johns Hopkins University COVID-19 tracking project

This dataset contains counts of deaths for California as a whole based on information entered on death certificates. Final counts are derived from static data and include out-of-state deaths to California residents, whereas provisional counts are derived from incomplete and dynamic data. Provisional counts are based on the records available when the data was retrieved and may not represent all deaths that occurred during the time period. Deaths involving injuries from external or environmental forces, such as accidents, homicide and suicide, often require additional investigation that tends to delay certification of the cause and manner of death. This can result in significant under-reporting of these deaths in provisional data.

The final data tables include both deaths that occurred in California regardless of the place of residence (by occurrence) and deaths to California residents (by residence), whereas the provisional data table only includes deaths that occurred in California regardless of the place of residence (by occurrence). The data are reported as totals, as well as stratified by age, gender, race-ethnicity, and death place type. Deaths due to all causes (ALL) and selected underlying cause of death categories are provided. See temporal coverage for more information on which combinations are available for which years.

The cause of death categories are based solely on the underlying cause of death as coded by the International Classification of Diseases. The underlying cause of death is defined by the World Health Organization (WHO) as "the disease or injury which initiated the train of events leading directly to death, or the circumstances of the accident or violence which produced the fatal injury." It is a single value assigned to each death based on the details as entered on the death certificate. When more than one cause is listed, the order in which they are listed can affect which cause is coded as the underlying cause. This means that similar events could be coded with different underlying causes of death depending on variations in how they were entered. Consequently, while underlying cause of death provides a convenient comparison between cause of death categories, it may not capture the full impact of each cause of death as it does not always take into account all conditions contributing to the death.

The Generalized Analog Regression Downscaling method Large Ensemble (GARD-LENS) dataset is comprised of daily precipitation, mean temperature, and temperature range over the Contiguous U.S., Alaska, and Hawaii at 12 km, 4 km, and 1 km resolutions, respectively. GARD-LENS downscales three CMIP6 global climate model large ensembles, CESM2, CanESM5, and EC-Earth3, totaling 200 ensemble members. GARD-LENS is the first downscaled SMILE (single model initial-condition large ensemble), providing information about the role of internal climate variability at high resolutions. GARD LENS uses GMET as a training dataset for the period 1980-2014, although Hawaii GMET data is only available for 1990-2014. The total dataset consists of 200 ensemble member files per region per variable (e.g., 200 files for t_mean for CONUS), for a total of 1800 files and a total dataset size of roughly 12 TB.

The 150-year record of this large ensemble dataset provides ample data for assessing trends and extremes and allows users to robustly assess internal variability, forced climate signals, and time of emergence at high resolutions. As the need for high resolution, robust climate datasets continues to grow, GARD-LENS will be a valuable tool for scientists and practitioners who wish to account for internal variability in their future climate analyses and adaptation plans.

Content

Financial Intermediary Funds (FIFs) are multilateral financing arrangements for which the World Bank provides Trustee services that include committing and transferring funds to project implementers (generally international organizations such as multilateral development banks or UN agencies). In all cases the World Bank as Trustee is required to act in accordance with instructions of independent governing bodies.

In fulfilling its responsibilities, the World Bank as Trustee complies with all sanctions applicable to World Bank transactions.

The innovative financing and governance arrangements of FIFs enable funds to be raised from multiple sources, including from sovereign and private sources. FIF structures are customizable. For instance FIFs have been customized to receive contributions in the form of concessional loans in addition to traditional grant funds, and can provide funding to recipients using customized financial products.

Data is provided as of 12/31/2013. No further updates are planned for this particular dataset.

Context

This is a dataset hosted by the World Bank. The organization has an open data platform found here and they update their information according the amount of data that is brought in. Explore World Bank's Financial Data using Kaggle and all of the data sources available through the World Bank organization page!

• Update Frequency: This dataset is updated daily.

Acknowledgements

This dataset is maintained using Socrata's API and Kaggle's API. Socrata has assisted countless organizations with hosting their open data and has been an integral part of the process of bringing more data to the public.

This dataset is distributed under a Creative Commons Attribution 3.0 IGO license.

Cover photo by Jared Erondu on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

This dataset is distributed under Creative Commons Attribution 3.0 IGO

This dataset contains counts of deaths for California counties based on information entered on death certificates. Final counts are derived from static data and include out-of-state deaths to California residents, whereas provisional counts are derived from incomplete and dynamic data. Provisional counts are based on the records available when the data was retrieved and may not represent all deaths that occurred during the time period. Deaths involving injuries from external or environmental forces, such as accidents, homicide and suicide, often require additional investigation that tends to delay certification of the cause and manner of death. This can result in significant under-reporting of these deaths in provisional data.

The final data tables include both deaths that occurred in each California county regardless of the place of residence (by occurrence) and deaths to residents of each California county (by residence), whereas the provisional data table only includes deaths that occurred in each county regardless of the place of residence (by occurrence). The data are reported as totals, as well as stratified by age, gender, race-ethnicity, and death place type. Deaths due to all causes (ALL) and selected underlying cause of death categories are provided. See temporal coverage for more information on which combinations are available for which years.

The cause of death categories are based solely on the underlying cause of death as coded by the International Classification of Diseases. The underlying cause of death is defined by the World Health Organization (WHO) as "the disease or injury which initiated the train of events leading directly to death, or the circumstances of the accident or violence which produced the fatal injury." It is a single value assigned to each death based on the details as entered on the death certificate. When more than one cause is listed, the order in which they are listed can affect which cause is coded as the underlying cause. This means that similar events could be coded with different underlying causes of death depending on variations in how they were entered. Consequently, while underlying cause of death provides a convenient comparison between cause of death categories, it may not capture the full impact of each cause of death as it does not always take into account all conditions contributing to the death.

The Our World in Data COVID vaccination data

To bring this pandemic to an end, a large share of the world needs to be immune to the virus. The safest way to achieve this is with a vaccine. Vaccines are a technology that humanity has often relied on in the past to bring down the death toll of infectious diseases.

Within less than 12 months after the beginning of the COVID-19 pandemic, several research teams rose to the challenge and developed vaccines that protect from SARS-CoV-2, the virus that causes COVID-19.

Now the challenge is to make these vaccines available to people around the world. It will be key that people in all countries — not just in rich countries — receive the required protection. To track this effort we at Our World in Data are building the international COVID-19 vaccination dataset that we make available on this page.

This indicator is defined as the percentage of the population living in an overcrowded household (excluding the single-person households). A person is considered as living in an overcrowded household if the household does not have at its disposal a minimum of rooms equal to: - one room for the household; - one room by couple in the household; - one room for each single person aged 18 and more; - one room by pair of single people of the same sex between 12 and 17 years of age; - one room for each single person between 12 and 17 years of age and not included in the previous category; - one room by pair of children under 12 years of age. The indicator is presented by poverty status.

Context

This dataset contains 2082016 headlines from usetoday.com from 2012 to 2020 year. This dataset can be used to: 1. classify tags for untracked news articles 2. identify the type of language used in different news articles.

Content

Each news headline has a corresponding categories in the "level2" column. Categories and corresponding article counts are as follows:

sports 977004 news 518083 life 193051 money 127934 tech 75241 travel 54649 opinion 45101 entertainment 21839 weather 10214 college 9390 gameon 7746 happyeverafter 7528 theoval 6962 todayinthesky 5344 nletter 5006 popcandy 4624 cruiselog 2630 onpolitics 2308 driveon 1292 experience 1221 dispatches 992 your-take 746 nation 546 idolchatter 542 cybertruth 496 hotelcheckin 314 interactives 230 ondeadline 226 technologylive 144 lightpost 120 virtual-reality 104 community-hub 55 entertainthis 52 augmented-reality 47 home 40 graphics 31 aerial-journalism 26 bistro 18 some 18 gamehunters 12 grateful 10 world 8 ncaaf 6 ncaaw 6 mlb 6 system 6 fantasy 4 movies 4 ...

Acknowledgements

This dataset was collected from usetoday.com from 2012 to 2018 year.

Inspiration

How do you map the categories of "level2" and "level3" to your own categories?

This dataset contains estimates of the socioeconomic status (SES) position of each of 149 countries covering the period 1880-2010. Measures of SES, which are in decades, allow for a 130 year time-series analysis of the changing position of countries in the global status hierarchy. SES scores are the average of each country’s income and education ranking and are reported as percentile rankings ranging from 1-99. As such, they can be interpreted similarly to other percentile rankings, such has high school standardized test scores. If country A has an SES score of 55, for example, it indicates that 55 percent of the countries in this dataset have a lower average income and education ranking than country A. ISO alpha and numeric country codes are included to allow users to merge these data with other variables, such as those found in the World Bank’s World Development Indicators Database and the United Nations Common Database.

See here for a working example of how the data might be used to better understand how the world came to look the way it does, at least in terms of status position of countries.

VARIABLE DESCRIPTIONS:

unid: ISO numeric country code (used by the United Nations)

wbid: ISO alpha country code (used by the World Bank)

SES: Country socioeconomic status score (percentile) based on GDP per capita and educational attainment (n=174)

country: Short country name

year: Survey year

gdppc: GDP per capita: Single time-series (imputed)

yrseduc: Completed years of education in the adult (15+) population

region5: Five category regional coding schema

regionUN: United Nations regional coding schema

DATA SOURCES:

The dataset was compiled by Shawn Dorius (sdorius@iastate.edu) from a large number of data sources, listed below. GDP per Capita:

1. Maddison, Angus. 2004. 'The World Economy: Historical Statistics'. Organization for Economic Co-operation and Development: Paris. GDP & GDP per capita data in (1990 Geary-Khamis dollars, PPPs of currencies and average prices of commodities). Maddison data collected from: http://www.ggdc.net/MADDISON/Historical_Statistics/horizontal-file_02-2010.xls.

2. World Development Indicators Database Years of Education 1. Morrisson and Murtin.2009. 'The Century of Education'. Journal of Human Capital(3)1:1-42. Data downloaded from http://www.fabricemurtin.com/ 2. Cohen, Daniel & Marcelo Cohen. 2007. 'Growth and human capital: Good data, good results' Journal of economic growth 12(1):51-76. Data downloaded from http://soto.iae-csic.org/Data.htm

3. Barro, Robert and Jong-Wha Lee, 2013, "A New Data Set of Educational Attainment in the World, 1950-2010." Journal of Development Economics, vol 104, pp.184-198. Data downloaded from http://www.barrolee.com/

4. Maddison, Angus. 2004. 'The World Economy: Historical Statistics'. Organization for Economic Co-operation and Development: Paris. 13.

5. United Nations Population Division. 2009.

Historical chart and dataset showing World population growth rate by year from 1961 to 2023.

Global Sanction Screening and Access Options

Our Global Sanction Lists Database is a powerful tool designed for quick and easy global sanction screening and verification of both individuals and organizations listed on international sanction lists. This service emphasizes the fight against money laundering and terrorism financing (AML-CFT), ensuring your business stays in line with global regulations. We keep our database up to date every day, processed into a professional and secure system, giving you access to the most current information.

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EU-Level PEP Screening and Access Options

Our service provides exclusive access to a database for EU-level PEP screening of Politically Exposed Persons at the European Union level. It empowers obligated entities to efficiently identify individuals with significant public roles within EU institutions and bodies. This database provides insights into persons currently in or those who have held significant public positions in Brussels and other EU institutions in the last 12 months. It spans not only individuals in key positions but also their relatives, broadening the scope for risk assessment. With daily updates from diverse public sources and careful manual processing, our database aids organizations in effectively navigating compliance and mitigating PEP-related risks.

PEP Group 1: Significant Public Functions

Includes individuals currently in or who have in the last 12 months held function of significant public role as defined by the Directive of the European Parliament and of the Council EU 2015/849 and further detailed in the Commission Decision C/2002/3105. Profiles generally include the exact date of birth and usually the domicile. In cases where the full date of birth is not available, the indication "Partially Identified PEP" is displayed. Individuals with enduring risks are recorded for up to five years after ending their function, especially for positions of pan-European significance or extended duration.

Specific Positions within PEP Group 1: Executive Authority Leaders Legislative Members Judges Members of the European Central Bank Bodies Members of the Court of Auditors Ambassadors and Chargés d’Affaires

PEP Groups 2 - 4 and 7: Family and Close Associates

Includes spouses/partners, children (including sons-in-law and daughters-in-law), and parents of individuals in Group PEP1, as well as individuals in a familial or similar relationship.

Specification PEP2: Spouse/partner PEP3: Child/son-in-law/daughter-in-law PEP4: Parent PEP7: Individuals in a long-term familial or similar relationship

There was no emotional speech dataset available in Kannada. This was a limiting factor for research in the Kannada-speaking world. I introduce a Kannada emotional speech dataset and give details about its design and content. This dataset contains six different sentences, pronounced by thirteen people (four male and nine female), in five basic emotions plus one neutral emotion. They are all Kannada speakers. The dataset has been contributed by volunteers and the recordings were not made in a controlled environment. The dataset contains a total of 468 audio samples, each one in a separate audio file. The file naming convention is as follows: AA-EE-SS.wav where AA is a two-character field that gives the actor number (01 to 13), EE is a two-character field that indicates the emotion number (01 to 06), and SS is a two-character field that gives the sentence number (01 to 06). This dataset is freely available under a Creative Commons license.

Gender and age of each of the 13 people who contributed to the dataset

01, F, 45

02, F, 20

03, F, 21

04, M, 47

05, F, 48

06, M, 20

07, F, 20

08, F, 45

09, F, 21

10, F, 12

11, F, 12

12, M, 17

13, M, 26

Identification characters for the emotions in the dataset

01, Anger

02, Sadness

03, Surprise

04, Happiness

05, Fear

06, Neutral

Identification characters for the sentences in the dataset

01, ರೋಗಿಗಳಿಗೆ ಚಿಕಿತ್ಸೆ ನೀಡಿ ಉಪಚರಿಸುವುದು

02, ಈ ಕಾದಂಬರಿಯು ಎರಡು ಪಾತ್ರಗಳನ್ನು ಒಳಗೊಂಡಿದೆ

03, ಖಾಸಗಿ ವಿಮಾನಗಳೆಂದೂ ಸಾರ್ವಜನಿಕ ವಿಮಾನಗಳೆಂದೂ ವಿಂಗಡಿಸಿದ್ದಾರೆ

04, ನಿಮ್ಮನ್ನು ಬೀಟಿಯಾಗಿ ಬಹಳ ಸಂತೋಶ ಆಯಿತು

05, ರಾಮನ ಎಡಬಲ ದಲ್ಲಿ ಸೀತಾ ಲಕ್ಷ್ಮಣ ರಿದ್ದಾರೆ

06, ಕನ್ನಡವನ್ನು ಕಲಿಯಬೆಕು

The global number of internet users in was forecast to continuously increase between 2024 and 2029 by in total 1.3 billion users (+23.66 percent). After the fifteenth consecutive increasing year, the number of users is estimated to reach 7 billion users and therefore a new peak in 2029. Notably, the number of internet users of was continuously increasing over the past years.Depicted is the estimated number of individuals in the country or region at hand, that use the internet. As the datasource clarifies, connection quality and usage frequency are distinct aspects, not taken into account here.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of internet users in countries like the Americas and Asia.

This is an hourly future weather dataset for energy modeling applications. The dataset is primarily based on the output of a regional climate model (RCM), i.e., the Weather Research and Forecasting (WRF) model version 3.3.1. The WRF simulations are driven by the output of a general circulation model (GCM), i.e., the Community Climate System Model version 4 (CCSM4). This dataset is in the EPW format, which can be read or translated by more than 25 building energy modeling programs (e.g., EnergyPlus, ESP-r, and IESVE), energy system modeling programs (e.g., System Advisor Model (SAM)), indoor air quality analysis programs (e.g., CONTAM), and hygrothermal analysis programs (e.g., WUFI). It contains 13 weather variables, which are the Dry-Bulb Temperature, Dew Point Temperature, Relative Humidity, Atmospheric Pressure, Horizontal Infrared Radiation Intensity from Sky, Global Horizontal Irradiation, Direct Normal Irradiation, Diffuse Horizontal Irradiation, Wind Speed, Wind Direction, Sky Cover, Albedo, and Liquid Precipitation Depth. This dataset provides future weather data under two emissions scenarios - RCP4.5 and RCP8.5 - across two 10-year periods (2045-2054 and 2085-2094). It also includes simulated historical weather data for 1995-2004 to serve as the baseline for climate impact assessments. We strongly recommend using this built-in baseline rather than external sources (e.g., TMY3) for two key reasons: (1) it shares the same model grid as the future projections, thereby minimizing geographic-averaging bias, and (2) both historical and future datasets were generated by the same RCM, so their differences yield anomalies largely free of residual model bias. This dataset offers a spatial resolution of 12 km by 12 km with extensive coverage across most of North America. Due to the enormous size of the entire dataset, in the first stage of its distribution, we provide weather data for the centroid of each Public Use Microdata Area (PUMA), excluding Hawaii. PUMAs are non-overlapping, statistical geographic areas that partition each state or equivalent entity into geographic areas containing no fewer than 100,000 people each. The 2,378 PUMAs as a whole cover the entirety of the U.S. The weather data can be utilized alongside the large-scale energy analysis tools, ResStock and ComStock, developed by National Renewable Energy Laboratory, whose smallest resolution is at the PUMA scale. The authors observed an anomalous warming signal over the Great Plains in the end-of-century (2085 - 2094) RCP4.5 time slice. This anomaly is absent in the mid-century slice (2045 - 2054) under RCP4.5 and in both the mid- (2045 - 2054) and end-of-century (2085 - 2094) slices under RCP8.5. Consequently, we recommend that users exercise particular caution when using the RCP4.5 2085-2094 data, especially for analyses involving the Great Plains region.