The total amount of data created, captured, copied, and consumed globally is forecast to increase rapidly. While it was estimated at ***** zettabytes in 2025, the forecast for 2029 stands at ***** zettabytes. Thus, global data generation will triple between 2025 and 2029. Data creation has been expanding continuously over the past decade. In 2020, the growth was higher than previously expected, caused by the increased demand due to the coronavirus (COVID-19) pandemic, as more people worked and learned from home and used home entertainment options more often.

IT spending worldwide is projected to reach over 5.7 trillion U.S. dollars in 2025, over a nine percent increase on 2024 spending. Smaller companies spending a greater share on hardware According to the results of a survey, hardware projects account for a fifth of IT budgets across North America and Europe. Larger companies tend to allocate a smaller share of their budget to hardware projects. Companies employing between one and 99 people allocated 31 percent of the budget to hardware, compared with 29 percent in companies of five thousand people or more. This could be explained by the greater need to spend money on managed services in larger companies. Not all companies can reduce their spending While COVID-19 has the overall effect of reducing IT spending, not all companies will face the same experiences. Setting up employees to comfortably work from home can result in unexpected costs, as can adapting to new operational requirements. In a recent survey of IT buyers, 18 percent of the respondents said they expected their IT budgets to increase in 2020. For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

Daily Global Trends - Insights on Popularity

Analyzing Crowd Behaviour and Buzz Worldwide

By Jeffrey Mvutu Mabilama [source]

About this dataset

This dataset provides a comprehensive look into 2020’s top trends worldwide, with information on the hottest topics and conversations happening all around the globe. With details such as trending type, country origin, dates of interest, URLs to find further information, keywords related to the trend and more - it's an invaluable insight into what's driving society today.

You can use this data in conjunction with other sources to get ideas for businesses or products tailored to popular desires or opinions. If you are interested in international business perspectives then this is also your go-to source; you can adjust how best to interact with people from certain countries upon learning what they hold important in terms of search engine activity.

It also gives key insights into buzz formation by monitoring trends over many countries over different periods of time then analysing whether events tend to last longer or if their effect is short-lived and how much impact it made in terms column ‘traffic’ – number of searches for an individual topic – for the duration of its period affecting higher positions and opinion polls. In addition, marketing / advertising professionals can anticipate what content is likely best received by audiences based off previous trends related images/snippets provided with each trend/topic as well as URL links tracking users who have shown interest.. This way they become better prepared when rolling out campaigns targeted at specific regions/areas taking cultural perspective into consideration rather than just raw numbers.

Last but not least it serves perfectly as great starting material when getting acquainted foreigners online (at least we know what conversation starters won't be awkward mentioned!) before deepening our empathetic understanding like terms used largely solely within cultures such as TV program titles… So…… question is: What will be next big thing? See for yourself.

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How to use the dataset

How to use this dataset for Insights on Popularity?

This Daily Global Trends 2020 dataset provides valuable information about trends around the world, including insights on their popularity. It can be used to identify popular topics and find ways to capitalize on them through marketing, business ideas and more. Below are some tips for how to use this data in order to gain insight into global trends and the level of popularity they have.

• For Business Ideas: Use the URL information provided in order to research each individual trend, analyzing both when it gained traction as well as when its popularity faded away (if at all). This will give insight into transforming a brief trend into a long-lived one or making use of an existing but brief surge in interest – think new apps related to a trending topic! Combining the geographic region listed with these timeframes gives even more granular insight that could be used for product localization or regional target marketing.

• To study Crowd Behaviour & Dynamics: Explore both country-wise and globally trending topics by looking at which countries similarly exhibit interest levels for said topics. Go further by understanding what drives people’s interest in particular subjects from different countries; here web scraping techniques can be employed using the URLs provided accompanied with basic text analysis techniques such as word clouds! This allows researchers/marketers get better feedback from customers from multiple regions, enabling smarter decisions based upon real behaviour rather than assumptions.

• For **Building Better Products & Selling Techniques: Utilize combine Category (Business, Social etc.), Country and Related keywords mentioned with traffic figures so that you can obtain granular information about what excites people across cultures i.e ‘Food’ is popular everywhere but certain variations depending upon geo-location may not sell due need catering towards local taste buds.-For example selling frozen food that requires little preparation via supermarket chains showing parallels between nutritional requirements vs expenses incurred while shopping will drive effective sales strategy using this data set . Further combining date information also helps make predictions based upon buyers behaviour over seasons i.e buying seedless watermelons during winter season would be futile .

• For Social & Small Talk opportunities - Incorporating recently descr...

Across industries, organizations are increasing their hiring efforts to build larger data science arsenals: from 2020 to 2021, the percentage of surveyed organizations that employed ** data scientists or more increased from ** percent to almost ** percent. On average, the number of data scientists employed in a organization grew from ** to **.

This dataset provides a comprehensive view of global economic trends, combining multiple essential indicators for analysis and research. The data focuses on the period from 2020 to 2023 and includes two key components:

1. GDP Per Capita and Inflation (2020–2023)

Scope: Yearly GDP per capita (in USD) and inflation rates per countries over the four-year period.

1. Population (2023)

Scope: The total population of each country at the end of 2023.

The dataset is meticulously compiled from trusted sources:

GDP per capita and inflation data are sourced from the World Bank national accounts data and OECD National Accounts data files.

Population data is derived from the World Bank Data Catalog (Population Ranking).

Potential Applications

Analyze the impact of inflation on economic growth during and after the pandemic.

Examine relationships between GDP per capita and population size.

Compare economic indicators across countries and regions.

Key Features: Clean, structured, and ready-to-use format.

Country-level granularity for detailed comparisons.

Suitable for trend analysis, visualizations, and predictive modeling.

Licensing: This dataset is licensed under the Creative Commons Attribution 4.0 International (CC-BY 4.0) license. You are free to copy, modify, and distribute the data for any purpose, including commercial use, as long as appropriate credit is given to the World Bank.

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

United States SBP: COVID-19 Impact: Little or Number Effect data was reported at 5.000 % in 04 Oct 2020. This records a decrease from the previous number of 5.100 % for 27 Sep 2020. United States SBP: COVID-19 Impact: Little or Number Effect data is updated weekly, averaging 3.900 % from Apr 2020 (Median) to 04 Oct 2020, with 18 observations. The data reached an all-time high of 5.900 % in 06 Sep 2020 and a record low of 1.600 % in 26 Apr 2020. United States SBP: COVID-19 Impact: Little or Number Effect data remains active status in CEIC and is reported by U.S. Census Bureau. The data is categorized under Global Database’s United States – Table US.S036: Small Business Pulse Survey: by Sector: Weekly, Beg Sunday (Discontinued).

China Number of Region at County Level: City at County Level data was reported at 388.000 Unit in 2020. This records an increase from the previous number of 387.000 Unit for 2019. China Number of Region at County Level: City at County Level data is updated yearly, averaging 368.000 Unit from Dec 1978 (Median) to 2020, with 43 observations. The data reached an all-time high of 445.000 Unit in 1996 and a record low of 92.000 Unit in 1978. China Number of Region at County Level: City at County Level 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.GJ: No of Region at County Level.

TL;DR

Our free COVID-19 Stats and New API lets you send a web-based query to Smartable AI and get back details about global and regional coronavirus data, including latest numbers, historic values, and geo-breakdowns. It is the same API that powers our popular COVID-19 stats pages. Developers can take the returned information and display it in their own tools, apps and visualizations. Different from other coronavirus data sources that produce breaking changes from time to time, the data from our API are more stable, **detailed **and close to real-time, as we leverage AI to gather information from many credible sources. With a few clicks in our API try-it experience, developers can get it running quickly and unleash their creativity.

Click here to get started

Our Mission

“We’re not just fighting an epidemic; we’re fighting an infodemic” – WHO Director-General Tedros Adhanom Ghebreyesus

In Smartable AI, our mission is to use AI to help you be smart in this infodemic world. Information is exploded, and mis-information has impacted the decisions of governments, businesses, and citizens around the world, as well as individuals’ lives. In 2018, The World Economic Forum identified it as one of the top 10 global risks. In a recent study, the economic impact has been estimated to be upwards of 80-100 Billion Dollars. Everything we do is focused on fighting misinformation, curating quality content, putting information in order and leveraging technology to bring clean, organized information through our APIs. Everyone wins when they can make sense of the world around them.

Our Free API and Daily Data Dump

The coronavirus stats and news API offers the latest and historic COVID-19 stats and news information per country or state. The stats are refreshed every hour using credible data sources, including the country/state’s official government websites, data available on wikipedia pages, latest news reports, Johns Hopkins University CSSE 2019-nCoV Dashboard, WHO Situation Reports, CDC Situation Updates, and DXY.cn.

The API takes the location ISO code as input (e.g. US, US-MA), and returns the latest numbers (confirmed, deaths, recovered), the delta from yesterday, the full history in that location, and geo-breakdown when applicable. We offer detailed API documentation, a try-it experience, and code examples in many different programming languages.

https://smartable.azureedge.net/media/2020/03/coronavirus-api-documentation.webp" alt="API Documentation">

We upload a daily dump of the data in the csv format here.

We want it to be a collaborative effort. If you have any additional requirements for the API or observe anything wrong with the data, we welcome you to report issues in our GitHub account. The team will jump in right away. All our team members are ex-Microsoft employees, so you can trust the quality of support, I guess 🙂

Our Example Apps powered by the API

We have developed two example apps by using the API.

• COVID-19 Live Stats+News
• COVID-19 Android app

On March 10, 2023, the Johns Hopkins Coronavirus Resource Center ceased its collecting and reporting of global COVID-19 data. For updated cases, deaths, and vaccine data please visit: World Health Organization (WHO)For more information, visit the Johns Hopkins Coronavirus Resource Center.COVID-19 Trends MethodologyOur goal is to analyze and present daily updates in the form of recent trends within countries, states, or counties during the COVID-19 global pandemic. The data we are analyzing is taken directly from the Johns Hopkins University Coronavirus COVID-19 Global Cases Dashboard, though we expect to be one day behind the dashboard’s live feeds to allow for quality assurance of the data.DOI: https://doi.org/10.6084/m9.figshare.125529863/7/2022 - Adjusted the rate of active cases calculation in the U.S. to reflect the rates of serious and severe cases due nearly completely dominant Omicron variant.6/24/2020 - Expanded Case Rates discussion to include fix on 6/23 for calculating active cases.6/22/2020 - Added Executive Summary and Subsequent Outbreaks sectionsRevisions on 6/10/2020 based on updated CDC reporting. This affects the estimate of active cases by revising the average duration of cases with hospital stays downward from 30 days to 25 days. The result shifted 76 U.S. counties out of Epidemic to Spreading trend and no change for national level trends.Methodology update on 6/2/2020: This sets the length of the tail of new cases to 6 to a maximum of 14 days, rather than 21 days as determined by the last 1/3 of cases. This was done to align trends and criteria for them with U.S. CDC guidance. The impact is areas transition into Controlled trend sooner for not bearing the burden of new case 15-21 days earlier.Correction on 6/1/2020Discussion of our assertion of an abundance of caution in assigning trends in rural counties added 5/7/2020. Revisions added on 4/30/2020 are highlighted.Revisions added on 4/23/2020 are highlighted.Executive SummaryCOVID-19 Trends is a methodology for characterizing the current trend for places during the COVID-19 global pandemic. Each day we assign one of five trends: Emergent, Spreading, Epidemic, Controlled, or End Stage to geographic areas to geographic areas based on the number of new cases, the number of active cases, the total population, and an algorithm (described below) that contextualize the most recent fourteen days with the overall COVID-19 case history. Currently we analyze the countries of the world and the U.S. Counties. The purpose is to give policymakers, citizens, and analysts a fact-based data driven sense for the direction each place is currently going. When a place has the initial cases, they are assigned Emergent, and if that place controls the rate of new cases, they can move directly to Controlled, and even to End Stage in a short time. However, if the reporting or measures to curtail spread are not adequate and significant numbers of new cases continue, they are assigned to Spreading, and in cases where the spread is clearly uncontrolled, Epidemic trend.We analyze the data reported by Johns Hopkins University to produce the trends, and we report the rates of cases, spikes of new cases, the number of days since the last reported case, and number of deaths. We also make adjustments to the assignments based on population so rural areas are not assigned trends based solely on case rates, which can be quite high relative to local populations.Two key factors are not consistently known or available and should be taken into consideration with the assigned trend. First is the amount of resources, e.g., hospital beds, physicians, etc.that are currently available in each area. Second is the number of recoveries, which are often not tested or reported. On the latter, we provide a probable number of active cases based on CDC guidance for the typical duration of mild to severe cases.Reasons for undertaking this work in March of 2020:The popular online maps and dashboards show counts of confirmed cases, deaths, and recoveries by country or administrative sub-region. Comparing the counts of one country to another can only provide a basis for comparison during the initial stages of the outbreak when counts were low and the number of local outbreaks in each country was low. By late March 2020, countries with small populations were being left out of the mainstream news because it was not easy to recognize they had high per capita rates of cases (Switzerland, Luxembourg, Iceland, etc.). Additionally, comparing countries that have had confirmed COVID-19 cases for high numbers of days to countries where the outbreak occurred recently is also a poor basis for comparison.The graphs of confirmed cases and daily increases in cases were fit into a standard size rectangle, though the Y-axis for one country had a maximum value of 50, and for another country 100,000, which potentially misled people interpreting the slope of the curve. Such misleading circumstances affected comparing large population countries to small population counties or countries with low numbers of cases to China which had a large count of cases in the early part of the outbreak. These challenges for interpreting and comparing these graphs represent work each reader must do based on their experience and ability. Thus, we felt it would be a service to attempt to automate the thought process experts would use when visually analyzing these graphs, particularly the most recent tail of the graph, and provide readers with an a resulting synthesis to characterize the state of the pandemic in that country, state, or county.The lack of reliable data for confirmed recoveries and therefore active cases. Merely subtracting deaths from total cases to arrive at this figure progressively loses accuracy after two weeks. The reason is 81% of cases recover after experiencing mild symptoms in 10 to 14 days. Severe cases are 14% and last 15-30 days (based on average days with symptoms of 11 when admitted to hospital plus 12 days median stay, and plus of one week to include a full range of severely affected people who recover). Critical cases are 5% and last 31-56 days. Sources:U.S. CDC. April 3, 2020 Interim Clinical Guidance for Management of Patients with Confirmed Coronavirus Disease (COVID-19). Accessed online. Initial older guidance was also obtained online. Additionally, many people who recover may not be tested, and many who are, may not be tracked due to privacy laws. Thus, the formula used to compute an estimate of active cases is: Active Cases = 100% of new cases in past 14 days + 19% from past 15-25 days + 5% from past 26-49 days - total deaths. On 3/17/2022, the U.S. calculation was adjusted to: Active Cases = 100% of new cases in past 14 days + 6% from past 15-25 days + 3% from past 26-49 days - total deaths. Sources: https://www.cdc.gov/mmwr/volumes/71/wr/mm7104e4.htm https://covid.cdc.gov/covid-data-tracker/#variant-proportions If a new variant arrives and appears to cause higher rates of serious cases, we will roll back this adjustment. We’ve never been inside a pandemic with the ability to learn of new cases as they are confirmed anywhere in the world. After reviewing epidemiological and pandemic scientific literature, three needs arose. We need to specify which portions of the pandemic lifecycle this map cover. The World Health Organization (WHO) specifies six phases. The source data for this map begins just after the beginning of Phase 5: human to human spread and encompasses Phase 6: pandemic phase. Phase six is only characterized in terms of pre- and post-peak. However, these two phases are after-the-fact analyses and cannot ascertained during the event. Instead, we describe (below) a series of five trends for Phase 6 of the COVID-19 pandemic.Choosing terms to describe the five trends was informed by the scientific literature, particularly the use of epidemic, which signifies uncontrolled spread. The five trends are: Emergent, Spreading, Epidemic, Controlled, and End Stage. Not every locale will experience all five, but all will experience at least three: emergent, controlled, and end stage.This layer presents the current trends for the COVID-19 pandemic by country (or appropriate level). There are five trends:Emergent: Early stages of outbreak. Spreading: Early stages and depending on an administrative area’s capacity, this may represent a manageable rate of spread. Epidemic: Uncontrolled spread. Controlled: Very low levels of new casesEnd Stage: No New cases These trends can be applied at several levels of administration: Local: Ex., City, District or County – a.k.a. Admin level 2State: Ex., State or Province – a.k.a. Admin level 1National: Country – a.k.a. Admin level 0Recommend that at least 100,000 persons be represented by a unit; granted this may not be possible, and then the case rate per 100,000 will become more important.Key Concepts and Basis for Methodology: 10 Total Cases minimum threshold: Empirically, there must be enough cases to constitute an outbreak. Ideally, this would be 5.0 per 100,000, but not every area has a population of 100,000 or more. Ten, or fewer, cases are also relatively less difficult to track and trace to sources. 21 Days of Cases minimum threshold: Empirically based on COVID-19 and would need to be adjusted for any other event. 21 days is also the minimum threshold for analyzing the “tail” of the new cases curve, providing seven cases as the basis for a likely trend (note that 21 days in the tail is preferred). This is the minimum needed to encompass the onset and duration of a normal case (5-7 days plus 10-14 days). Specifically, a median of 5.1 days incubation time, and 11.2 days for 97.5% of cases to incubate. This is also driven by pressure to understand trends and could easily be adjusted to 28 days. Source

Collected COVID-19 datasets from various sources as part of DAAN-888 course, Penn State, Spring 2022. Collaborators: Mohamed Abdelgayed, Heather Beckwith, Mayank Sharma, Suradech Kongkiatpaiboon, and Alex Stroud

**1 - COVID-19 Data in the United States ** Source: The data is collected from multiple public health official sources by NY Times journalists and compiled in one single file. Description: Daily count of new COVID-19 cases and deaths for each state. Data is updated daily and runs from 1/21/2020 to 2/4/2022. URL: https://github.com/nytimes/covid-19-data/blob/master/us-states.csv Data size: 38,814 row and 5 columns.

**2 - Mask-Wearing Survey Data ** Source: The New York Times is releasing estimates of mask usage by county in the United States. Description: This data comes from a large number of interviews conducted online by the global data and survey firm Dynata, at the request of The New York Times. The firm asked a question about mask usage to obtain 250,000 survey responses between July 2 and July 14, enough data to provide estimates more detailed than the state level. URL: https://github.com/nytimes/covid-19-data/blob/master/mask-use/mask-use-by-county.csv Data size: 3,142 rows and 6 columns

**3a - Vaccine Data – Global ** Source: This data comes from the US Centers for Disease Control and Prevention (CDC), Our World in Data (OWiD) and the World Health Organization (WHO). Description: Time series data of vaccine doses administered and the number of fully and partially vaccinated people by country. This data was last updated on February 3, 2022 URL: https://github.com/govex/COVID-19/blob/master/data_tables/vaccine_data/global_data/time_series_covid19_vaccine_global.csv
Data Size: 162,521 rows and 8 columns

**3b -Vaccine Data – United States ** Source: The data is comprised of individual State's public dashboards and data from the US Centers for Disease Control and Prevention (CDC). Description: Time series data of the total vaccine doses shipped and administered by manufacturer, the dose number (first or second) by state. This data was last updated on February 3, 2022. URL: https://github.com/govex/COVID-19/blob/master/data_tables/vaccine_data/us_data/time_series/vaccine_data_us_timeline.csv
Data Size: 141,503 rows and 13 columns

**4 - Testing Data ** Source: The data is comprised of individual State's public dashboards and data from the U.S. Department of Health & Human Services. Description: Time series data of total tests administered by county and state. This data was last updated on January 25, 2022. URL: https://github.com/govex/COVID-19/blob/master/data_tables/testing_data/county_time_series_covid19_US.csv
Data size: 322,154 rows and 8 columns

**5 – US State and Territorial Public Mask Mandates ** Source: Data from state and territory executive orders, administrative orders, resolutions, and proclamations is gathered from government websites and cataloged and coded by one coder using Microsoft Excel, with quality checking provided by one or more other coders. Description: US State and Territorial Public Mask Mandates from April 10, 2020 through August 15, 2021 by County by Day URL: https://data.cdc.gov/Policy-Surveillance/U-S-State-and-Territorial-Public-Mask-Mandates-Fro/62d6-pm5i Data Size: 1,593,869 rows and 10 columns

**6 – Case Counts & Transmission Level ** Source: This open-source dataset contains seven data items that describe community transmission levels across all counties. This dataset provides the same numbers used to show transmission maps on the COVID Data Tracker and contains reported daily transmission levels at the county level. The dataset is updated every day to include the most current day's data. The calculating procedures below are used to adjust the transmission level to low, moderate, considerable, or high.
Description: US State and County case counts and transmission level from 16-Aug-2021 to 03-Feb-2022 URL: https://data.cdc.gov/Public-Health-Surveillance/United-States-COVID-19-County-Level-of-Community-T/8396-v7yb Data Size: 550,702 rows and 7 columns

**7 - World Cases & Vaccination Counts ** Source: This is an open-source dataset collected and maintained by Our World in Data. OWID provides research and data to help against the world’s largest problems.
Description: This dataset includes vaccinations, tests & positivity, hospital & ICU, confirmed cases, confirmed deaths, reproduction rate, policy responses and other variables of interest. URL: https://github.com/owid/covid-19-data/tree/master/public/data Data Size: 67 columns and 157,000 rows

**8 - COVID-19 Data in the European Union ** Source: This is an open-source dataset collected and maintained by ECDC. It is an EU agency aimed at strengthening Europe's defenses against infectious diseases.
Description: This dataset co...

GEDI Version 1 data products were decommissioned on February 15, 2022. Users are advised to use the improved GEDI02_A Version 2 data product.The Global Ecosystem Dynamics Investigation (GEDI) mission aims to characterize ecosystem structure and dynamics to enable radically improved quantification and understanding of the Earth’s carbon cycle and biodiversity. The GEDI instrument produces high resolution laser ranging observations of the 3-dimensional structure of the Earth. GEDI is attached to the International Space Station and collects data globally between 51.6 degrees N and 51.6 degrees S latitudes at the highest resolution and densest sampling of any light detection and ranging (lidar) instrument in orbit to date.The purpose of the GEDI Level 2A Geolocated Elevation and Height Metrics product (GEDI02_A) is to provide waveform interpretation and extracted products from each GEDI01_B received waveform, including ground elevation, canopy top height, and relative height (RH) metrics. The methodology for generating the GEDI02_A product datasets is adapted from the Land, Vegetation, and Ice Sensor (LVIS) algorithm. The GEDI01_B product is provided in HDF5 format and has a spatial resolution (average footprint) of 25 meters. The GEDI02_A data product contains 156 variables for each of the eight beams, including ground elevation, canopy top height, relative return energy metrics (describing canopy vertical structure, for example), and many other interpreted products from the return waveforms. Additional information for the variables can be found in the GEDI Level 2A Dictionary.Known Issues Known Issues: Section 7 of the User Guide provides additional information on known issues. Data acquisition gaps: GEDI data acquisitions were suspended on December 19, 2019 (2019 Day 353) and resumed on January 8, 2020 (2020 Day 8).

Actual value and historical data chart for World Population Female Percent Of Total

Number of departures of United Kingdom sank by 74.40% from 93,086,000 number in 2019 to 23,827,000 number in 2020. Since the 3.82% upward trend in 2018, number of departures plummeted by 73.69% in 2020. International outbound tourists are the number of departures that people make from their country of usual residence to any other country for any purpose other than a remunerated activity in the country visited. The data on outbound tourists refer to the number of departures, not to the number of people traveling. Thus a person who makes several trips from a country during a given period is counted each time as a new departure.

The global number of Youtube users in was forecast to continuously increase between 2024 and 2029 by in total ***** million users (+***** percent). After the ninth consecutive increasing year, the Youtube user base is estimated to reach *** billion users and therefore a new peak in 2029. Notably, the number of Youtube users of was continuously increasing over the past years.User figures, shown here regarding the platform youtube, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period.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 *** 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 Youtube users in countries like Africa and South America.

United States US: Total Business Enterprise R&D Personnel: Per Thousand Employment In Industry data was reported at 17.169 Per 1000 in 2020. This records an increase from the previous number of 15.152 Per 1000 for 2019. United States US: Total Business Enterprise R&D Personnel: Per Thousand Employment In Industry data is updated yearly, averaging 13.282 Per 1000 from Dec 2011 (Median) to 2020, with 10 observations. The data reached an all-time high of 17.169 Per 1000 in 2020 and a record low of 12.478 Per 1000 in 2012. United States US: Total Business Enterprise R&D Personnel: Per Thousand Employment In Industry data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s United States – Table US.OECD.MSTI: Number of Researchers and Personnel on Research and Development: OECD Member: Annual.

Definition of MSTI variables 'Value Added of Industry' and 'Industrial Employment':

R&D data are typically expressed as a percentage of GDP to allow cross-country comparisons. When compiling such indicators for the business enterprise sector, one may wish to exclude, from GDP measures, economic activities for which the Business R&D (BERD) is null or negligible by definition. By doing so, the adjusted denominator (GDP, or Value Added, excluding non-relevant industries) better correspond to the numerator (BERD) with which it is compared to.

The MSTI variable 'Value added in industry' is used to this end:

It is calculated as the total Gross Value Added (GVA) excluding 'real estate activities' (ISIC rev.4 68) where the 'imputed rent of owner-occupied dwellings', specific to the framework of the System of National Accounts, represents a significant share of total GVA and has no R&D counterpart. Moreover, the R&D performed by the community, social and personal services is mainly driven by R&D performers other than businesses.

Consequently, the following service industries are also excluded: ISIC rev.4 84 to 88 and 97 to 98. GVA data are presented at basic prices except for the People's Republic of China, Japan and New Zealand (expressed at producers' prices).In the same way, some indicators on R&D personnel in the business sector are expressed as a percentage of industrial employment. The latter corresponds to total employment excluding ISIC rev.4 68, 84 to 88 and 97 to 98.

Number of researchers in R&D of Indonesia rose by 2.11% from 387.2 per million people in 2019 to 395.3 per million people in 2020. Since the 55.01% slump in 2009, number of researchers in R&D soared by 347.56% in 2020. Researchers in R&D are professionals engaged in the conception or creation of new knowledge, products, processes, methods, or systems and in the management of the projects concerned. Postgraduate PhD students (ISCED97 level 6) engaged in R&D are included.

The Gridded Population of the World, Version 4 (GPWv4): Population Count, Revision 11 consists of estimates of human population (number of persons per pixel), consistent with national censuses and population registers, for the years 2000, 2005, 2010, 2015, and 2020. A proportional allocation gridding algorithm, utilizing approximately 13.5 million national and sub-national administrative Units, was used to assign population counts to 30 arc-second grid cells. The data files were produced as global rasters at 30 arc-second (~1 km at the equator) resolution. To enable faster global processing, and in support of research commUnities, the 30 arc-second data were aggregated to 2.5 arc-minute, 15 arc-minute, 30 arc-minute and 1 degree resolutions.

The Gross Domestic Product (GDP) in the United States was worth 29184.89 billion US dollars in 2024, according to official data from the World Bank. The GDP value of the United States represents 27.49 percent of the world economy. This dataset provides - United States GDP - actual values, historical data, forecast, chart, statistics, economic calendar and news.

United States SBP: Didn't Adopted Number Dine-in Policy data was reported at 78.400 % in 21 Jun 2020. This records an increase from the previous number of 75.600 % for 14 Jun 2020. United States SBP: Didn't Adopted Number Dine-in Policy data is updated weekly, averaging 61.600 % from Apr 2020 (Median) to 21 Jun 2020, with 9 observations. The data reached an all-time high of 78.400 % in 21 Jun 2020 and a record low of 56.500 % in 10 May 2020. United States SBP: Didn't Adopted Number Dine-in Policy data remains active status in CEIC and is reported by U.S. Census Bureau. The data is categorized under Global Database’s United States – Table US.S036: Small Business Pulse Survey: by Sector: Weekly, Beg Sunday (Discontinued).