How many people use social media?

              Social media usage is one of the most popular online activities. In 2024, over five billion people were using social media worldwide, a number projected to increase to over six billion in 2028.

              Who uses social media?
              Social networking is one of the most popular digital activities worldwide and it is no surprise that social networking penetration across all regions is constantly increasing. As of January 2023, the global social media usage rate stood at 59 percent. This figure is anticipated to grow as lesser developed digital markets catch up with other regions
              when it comes to infrastructure development and the availability of cheap mobile devices. In fact, most of social media’s global growth is driven by the increasing usage of mobile devices. Mobile-first market Eastern Asia topped the global ranking of mobile social networking penetration, followed by established digital powerhouses such as the Americas and Northern Europe.

              How much time do people spend on social media?
              Social media is an integral part of daily internet usage. On average, internet users spend 151 minutes per day on social media and messaging apps, an increase of 40 minutes since 2015. On average, internet users in Latin America had the highest average time spent per day on social media.

              What are the most popular social media platforms?
              Market leader Facebook was the first social network to surpass one billion registered accounts and currently boasts approximately 2.9 billion monthly active users, making it the most popular social network worldwide. In June 2023, the top social media apps in the Apple App Store included mobile messaging apps WhatsApp and Telegram Messenger, as well as the ever-popular app version of Facebook.

US Age-Standardized Stroke Mortality Rates (2013-15) by State/County/Gender/Race

Investigating Variations in Rates

By US Open Data Portal, data.gov [source]

About this dataset

This dataset contains the age-standardized stroke mortality rate in the United States from 2013 to 2015, by state/territory, county, gender and race/ethnicity. The data source is the highly respected National Vital Statistics System. The rates are reported as a 3-year average and have been age-standardized. Moreover, county rates are spatially smoothed for further accuracy. The interactive map of heart disease and stroke produced by this dataset provides invaluable information about the geographic disparities in stroke mortality across America at different scales - county, state/territory and national. By using the adjustable filter settings provided in this interactive map, you can quickly explore demographic details such as gender (Male/Female) or race/ethnicity (e.g Non-Hispanic White). Conquer your fear of unknown with evidence! Investigate these locations now to inform meaningful action plans for greater public health resilience in America and find out if strokes remain a threat to our millions of citizens every day! Updated regularly since 2020-02-26, so check it out now!

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How to use the dataset

The US Age-Standardized Stroke Mortality Rates (2013-2015) by State/County/Gender/Race dataset provides valuable insights into stroke mortality rates among adults ages 35 and over in the USA between 2013 and 2015. This dataset contains age-standardized data from the National Vital Statistics System at the state, county, gender, and race level. Use this guide to learn how best use this dataset for your purposes!

Understand the Data

This dataset provides information about stroke mortality rates among adult Americans aged 35+. The data is collected from 2013 to 2015 in three year averages. Even though it is possible to view county level data, spatial smoothing techniques have been applied here. The following columns of data are provided: - Year – The year of the data collection - LocationAbbr – The abbreviation of location where the data was collected
- LocationDesc – A description of this location
- GeographicLevel – Geographic level of granularity where these numbers are recorded * DataSource - source of these statistics * Class - class or group into which these stats fall * Topic - overall topic on which we have stats * Data_Value - age standardized value associated with each row * Data_Value_Unit - units associated with each value * Stratification1– First stratification defined for a given row * Stratification2– Second stratification defined for a given row

Additionally, several other footnotes fields such as ‘Data_value_Type’; ‘Data_Value_Footnote _Symbol’; ‘StratificationCategory1’ & ‘StratificatoinCategory2’ etc may be present accordingly .  

## Exploring Correlations

Now that you understand what individual columns mean it should take no time to analyze correlations within different categories using standard statistical methods like linear regressions or boxplots etc. If you want to compare different regions , then you can use LocationAbbr column with locations reduced geographical levels such as State or Region. Alternatively if one wants comparisons across genders then they can refer column labelled Stratifacation1 alongwith their desired values within this

Research Ideas

• Creating a visualization to show the relationship between stroke mortality and specific variations in race/ethnicity, gender, and geography.
• Comparing two or more states based on their average stroke mortality rate over time.
• Building a predictive model that disregards temporal biases to anticipate further changes in stroke mortality for certain communities or entire states across the US

Acknowledgements

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

License

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

Columns

File: csv-1.csv | Column name | Description | |:--...

How much time do people spend on social media?

              As of 2024, the average daily social media usage of internet users worldwide amounted to 143 minutes per day, down from 151 minutes in the previous year. Currently, the country with the most time spent on social media per day is Brazil, with online users spending an average of three hours and 49 minutes on social media each day. In comparison, the daily time spent with social media in
              the U.S. was just two hours and 16 minutes. Global social media usageCurrently, the global social network penetration rate is 62.3 percent. Northern Europe had an 81.7 percent social media penetration rate, topping the ranking of global social media usage by region. Eastern and Middle Africa closed the ranking with 10.1 and 9.6 percent usage reach, respectively.
              People access social media for a variety of reasons. Users like to find funny or entertaining content and enjoy sharing photos and videos with friends, but mainly use social media to stay in touch with current events friends. Global impact of social mediaSocial media has a wide-reaching and significant impact on not only online activities but also offline behavior and life in general.
              During a global online user survey in February 2019, a significant share of respondents stated that social media had increased their access to information, ease of communication, and freedom of expression. On the flip side, respondents also felt that social media had worsened their personal privacy, increased a polarization in politics and heightened everyday distractions.

The city, through the American Rescue Plan Act (ARPA), is the direct recipient of $154,141,050 in State and Local Fiscal Recovery Funds (SLFRF). This dataset contains information on the status of City Council approved projects that were made possible by SLFRF. The data comes from the city’s financial system AFMS and city staff. The financial data will be updated daily based on activity in the city’s financial system, AFMS. The remaining data will be updated monthly.

For data about this dataset, please click on the below link: https://data.norfolk.gov/Government/American-Rescue-Plan-Act-State-and-Local-Recovery-/kekx-9mip/about_data

analyze the health and retirement study (hrs) with r the hrs is the one and only longitudinal survey of american seniors. with a panel starting its third decade, the current pool of respondents includes older folks who have been interviewed every two years as far back as 1992. unlike cross-sectional or shorter panel surveys, respondents keep responding until, well, death d o us part. paid for by the national institute on aging and administered by the university of michigan's institute for social research, if you apply for an interviewer job with them, i hope you like werther's original. figuring out how to analyze this data set might trigger your fight-or-flight synapses if you just start clicking arou nd on michigan's website. instead, read pages numbered 10-17 (pdf pages 12-19) of this introduction pdf and don't touch the data until you understand figure a-3 on that last page. if you start enjoying yourself, here's the whole book. after that, it's time to register for access to the (free) data. keep your username and password handy, you'll need it for the top of the download automation r script. next, look at this data flowchart to get an idea of why the data download page is such a righteous jungle. but wait, good news: umich recently farmed out its data management to the rand corporation, who promptly constructed a giant consolidated file with one record per respondent across the whole panel. oh so beautiful. the rand hrs files make much of the older data and syntax examples obsolete, so when you come across stuff like instructions on how to merge years, you can happily ignore them - rand has done it for you. the health and retirement study only includes noninstitutionalized adults when new respondents get added to the panel (as they were in 1992, 1993, 1998, 2004, and 2010) but once they're in, they're in - respondents have a weight of zero for interview waves when they were nursing home residents; but they're still responding and will continue to contribute to your statistics so long as you're generalizing about a population from a previous wave (for example: it's possible to compute "among all americans who were 50+ years old in 1998, x% lived in nursing homes by 2010"). my source for that 411? page 13 of the design doc. wicked. this new github repository contains five scripts: 1992 - 2010 download HRS microdata.R loop through every year and every file, download, then unzip everything in one big party impor t longitudinal RAND contributed files.R create a SQLite database (.db) on the local disk load the rand, rand-cams, and both rand-family files into the database (.db) in chunks (to prevent overloading ram) longitudinal RAND - analysis examples.R connect to the sql database created by the 'import longitudinal RAND contributed files' program create tw o database-backed complex sample survey object, using a taylor-series linearization design perform a mountain of analysis examples with wave weights from two different points in the panel import example HRS file.R load a fixed-width file using only the sas importation script directly into ram with < a href="http://blog.revolutionanalytics.com/2012/07/importing-public-data-with-sas-instructions-into-r.html">SAScii parse through the IF block at the bottom of the sas importation script, blank out a number of variables save the file as an R data file (.rda) for fast loading later replicate 2002 regression.R connect to the sql database created by the 'import longitudinal RAND contributed files' program create a database-backed complex sample survey object, using a taylor-series linearization design exactly match the final regression shown in this document provided by analysts at RAND as an update of the regression on pdf page B76 of this document . click here to view these five scripts for more detail about the health and retirement study (hrs), visit: michigan's hrs homepage rand's hrs homepage the hrs wikipedia page a running list of publications using hrs notes: exemplary work making it this far. as a reward, here's the detailed codebook for the main rand hrs file. note that rand also creates 'flat files' for every survey wave, but really, most every analysis you c an think of is possible using just the four files imported with the rand importation script above. if you must work with the non-rand files, there's an example of how to import a single hrs (umich-created) file, but if you wish to import more than one, you'll have to write some for loops yourself. confidential to sas, spss, stata, and sudaan users: a tidal wave is coming. you can get water up your nose and be dragged out to sea, or you can grab a surf board. time to transition to r. :D

The global number of Facebook users was forecast to continuously increase between 2023 and 2027 by in total 391 million users (+14.36 percent). After the fourth consecutive increasing year, the Facebook user base is estimated to reach 3.1 billion users and therefore a new peak in 2027. Notably, the number of Facebook users was continuously increasing over the past years. User figures, shown here regarding the platform Facebook, 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 and count multiple accounts by persons only once.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).

This dataset contains the American Community Survey (ACS) five-year estimates for Norfolk, Virginia. According to the United States Census Bureau, the ACS is the premier source for detailed population and housing information about communities and the nation. Every year, the Census Bureau conducts a survey and creates estimates for demographic categories such as income, employment, poverty, race, ethnicity, housing, age, gender, internet access, vehicle access, and other topics. For census tracts, 5-year estimates are generated and released to the public. This dataset includes five-year estimates released in 2020 for census tracts in Norfolk, VA and will be updated annually with each new release of five-year estimates

For data about this dataset, please click on the below link: https://data.norfolk.gov/Government/Norfolk-2020-American-Community-Survey-Five-Year-E/q552-bpmw/about_data

Key Table Information.Table Title.Annual Business Survey: Statistics for Employer Firms by Race for the U.S.: 2023.Table ID.ABSCS2023.AB00MYCSA01C.Survey/Program.Economic Surveys.Year.2023.Dataset.ECNSVY Annual Business Survey Company Summary.Source.U.S. Census Bureau, 2023 Economic Surveys, Annual Business Survey.Release Date.2025-11-20.Release Schedule.The Annual Business Survey (ABS) occurs every year, beginning in reference year 2017.For more information about ABS planned data product releases, see Tentative ABS Schedule..Dataset Universe.The dataset universe consists of employer firms that are in operation for at least some part of the reference year, are located in one of the 50 U.S. states, associated offshore areas, or the District of Columbia, have paid employees and annual receipts of $1,000 or more, and are classified in one of nineteen in-scope sectors defined by the 2022 North American Industry Classification System (NAICS), except for NAICS 111, 112, 482, 491, 521, 525, 813, 814, and 92 which are not covered..Sponsor.National Center for Science and Engineering Statistics, U.S. National Science Foundation.Methodology.Data Items and Other Identifying Records.Number of employer firms (firms with paid employees)Sales and receipts of employer firms (reported in $1,000s of dollars)Number of employees (during the March 12 pay period)Annual payroll (reported in $1,000s of dollars)These data are aggregated by the following demographic classifications of firm for:All firms Classifiable (firms classifiable by sex, ethnicity, race, and veteran status) Race White Black or African American American Indian and Alaska Native Asian Native Hawaiian and Other Pacific Islander Minority (Firms classified as any race and ethnicity combination other than non-Hispanic and White) Equally minority/nonminority Nonminority (Firms classified as non-Hispanic and White) Unclassifiable (firms not classifiable by sex, ethnicity, race, and veteran status) Definitions can be found by clicking on the column header in the table or by accessing the Economic Census Glossary..Unit(s) of Observation.The reporting units for the ABS are employer companies or firms rather than establishments. A company or firm is comprised of one or more in-scope establishments that operate under the ownership or control of a single organization..Geography Coverage.The data are shown for the U.S. only.For information about geographies, see Geographies..Industry Coverage.The data are shown for the total of all sectors ("00") NAICS code. Sector "00" is not an official NAICS sector but is rather a way to indicate a total for multiple sectors. Note: Other programs outside of ABS may use sector 00 to indicate when multiple NAICS sectors are being displayed within the same table and/or dataset.The following are excluded from the total of all sectors:Crop and Animal Production (NAICS 111 and 112)Rail Transportation (NAICS 482)Postal Service (NAICS 491)Monetary Authorities-Central Bank (NAICS 521)Funds, Trusts, and Other Financial Vehicles (NAICS 525)Office of Notaries (NAICS 541120)Religious, Grantmaking, Civic, Professional, and Similar Organizations (NAICS 813)Private Households (NAICS 814)Public Administration (NAICS 92)For information about NAICS, see North American Industry Classification System..Sampling.The ABS sample includes firms that are selected with certainty if they have known research and development activities, were included in the 2023 BERD sample, or have high receipts, payroll, or employment. Total sample size is 330,000 firms. The universe is stratified by state, industry group, and expected demographic group. Firms selected to the sample receive a questionnaire. For all data on this table, firms not selected into the sample are represented with administrative, 2022 Economic Census, or other economic surveys records.For more information about the sample design, see Annual Business Survey Methodology..Confidentiality.The Census Bureau has reviewed this data product to ensure appropriate access, use, and disclosure avoidance protection of the confidential source data (Project No. P-7504866, Disclosure Review Board (DRB) approval numbers: CBDRB-FY25-0115 and CBDRB-FY25-0410).To protect confidentiality, the U.S. Census Bureau suppresses cell values to minimize the risk of identifying a particular business' data or identity.To comply with data quality standards, data rows with high relative standard errors (RSE) are not presented. Additionally, firm counts are suppressed when other select statistics in the same row are suppressed. More information on disclosure avoidance is available in the Annual Business Survey Methodology..Technical Documentation/Methodology.For detailed information about the methods used to collect data and produce statistics, survey questionnaires, Primary Business Activity/NAICS codes, and more, see Technical Documentation..Weights.For more information about weighting, see Annual Business Survey Methodology..Table Inf...

How Much Sleep Do Americans Really Get?

The Consequences of Sleep Deprivation

By Makeover Monday [source]

About this dataset

Do you find yourself tossing and turning at night, struggling to fall asleep? You're not alone. A recent study found that the average American adult gets just under seven hours of sleep per night.

But how does this compare to other countries? And what factors contribute to our sleeplessness?

This dataset contains data on the average amount of time Americans spend sleeping, broken down by age group, sex, and activity. The data includes both weekdays and weekends, so you can see how our sleep habits change depending on the day of the week.

So take a look and see if you can find any patterns in the data. Why do you think some groups get more (or less) sleep than others? And what can we do to improve our sleep habits as a nation?

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How to use the dataset

This dataset includes data on the average number of hours per day Americans spend sleeping, broken down by age group, sex, and activity. This can be used to understand patterns in sleep habits among different groups of people, as well as how these patterns may change over time.

To use this dataset effectively, it is important to understand the different variables that are included. The 'Age Group' variable indicates the age group that the data applies to, while the 'Sex' variable indicates whether the data is for male or female respondents. The 'Activity' variable indicates what activity was being undertaken when the respondent was asked about their sleep habits (e.g. 'sleeping', 'working', 'watching TV', etc.), while the 'Type of Days' variable indicates whether the data was collected for weekdays, weekends, or holidays.

Finally, the 'Avg hrs per day sleeping' and 'Standard Error' variables give information on the average amount of time spent sleeping per day, along with a measure of how accurate this estimate is

Research Ideas

• To study the effect of sleep deprivation on health
• To understand the role of sleep in regulating mood and behavior
• To examine the relationship between sleep and cognitive function

Acknowledgements

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

Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: Time Americans Spend Sleeping.csv | Column name | Description | |:-----------------------------|:---------------------------------------------------------------------------------------------| | Year | The year the data was collected. (Integer) | | Period | The period of the day the data was collected. (String) | | Avg hrs per day sleeping | The average number of hours per day Americans spend sleeping. (Float) | | Standard Error | The standard error for the average number of hours per day Americans spend sleeping. (Float) | | Type of Days | The type of day the data was collected. (String) | | Age Group | The age group of the Americans surveyed. (String) | | Activity | The activity the Americans surveyed were engaged in. (String) | | Sex | The sex of the Americans surveyed. (String) |

Acknowledgements

If you use this dataset in your research, please credit Makeover Monday [source]

The New York Times is releasing a series of data files with cumulative counts of coronavirus cases in the United States, at the state and county level, over time. We are compiling this time series data from state and local governments and health departments in an attempt to provide a complete record of the ongoing outbreak.

Since late January, The Times has tracked cases of coronavirus in real time as they were identified after testing. Because of the widespread shortage of testing, however, the data is necessarily limited in the picture it presents of the outbreak.

We have used this data to power our maps and reporting tracking the outbreak, and it is now being made available to the public in response to requests from researchers, scientists and government officials who would like access to the data to better understand the outbreak.

The data begins with the first reported coronavirus case in Washington State on Jan. 21, 2020. We will publish regular updates to the data in this repository.

Hollywood Movies Domestic Lifetime Gross and Ranking

An Opportunity for Investigating Box Office Performance

By Elias Dabbas [source]

About this dataset

This dataset contains the details about Hollywood's all-time domestic box office records. It includes data scraped from Box Office Mojo, which breakdowns every movie's lifetime gross, ranking and production year. Domestic gross (adjusted to inflation) has been used as the benchmark to determine what movies were the most successful at the box office in America. This dataset allows you to explore an extensive, comprehensive list of Hollywood all-time biggest hits. Analyze examples of previously unprecedented blockbusters and observe current market trends with this comprehensive overview of domestic box office history - only here at this treasury of motion picture insights!

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How to use the dataset

This dataset contains comprehensive information about Hollywood movies and their domestic performance at the box office. It includes data on films' production year, lifetime gross, ranking and the studio that produced them. By using this dataset, you can analyze the financial successes and failures of films produced by different studios to gain insights into the Hollywood movie market over time.

The 'rank' column shows each film's ranking compared to other Hollywood movies released in its year of release based on its box office revenue from theaters (not including other sources such as DVD sales or streaming services). The higher the number for a film’s rank means it was more successful financially than other films released in its date window when ticket prices were taken into account; lower numbers equate to less success at that time frame's box office.

The ‘title’ column features all movies analyzed here with links provided which direct users to articles giving background information about those projects - directorial credentials or management history -- as well as full reviews with ratings given by critics while they were screened theatricallly across North America (U.S., Canada).

The ‘studio’ outlines which media conglomerate is credited with distribution/marketing rights for each featured motion picture during their original domestic theatrical runs; these name-brands represent umbrella-corporations comprising multiple divisions specializing in creative development/financing of cinematic works along with doorways engineered around technical know-how -- ie: visual effects shops used by filmmakers during post-production responsibilities their respective productions entailed) -- maintained throughout various industrial regions across entertainment media outlets extending well beyond motion pictures proper... including music/television sector domains defined under respective company flags like Warner Bros., Disney(ABC), NBCUniversal(Comcast) ++ et al mirroring segmentations off any parent brand cited within this database under said label; pertaining solely toward big screen celluloid matters examined herein because charter established assumptions indicate only valid commercially viable feature length fare delivering both titles & collections contained below adheres relevant criterion set forth specifications that warrant inclusion alongside applicable vertical peers made front % center terms established formulating current entries visible within page iteration whilst conforming platform protocols designed enable public

Research Ideas

• Creating a recommendation engine to suggest similar movies based on lifetime gross and year of release.
• Data analysis and visualization of box office trends over time for major Hollywood studios.
• Utilizing the data to recommend alternative ways for movie marketers to invest their advertising budgets in order to maximize their return on investment

Acknowledgements

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

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - **Keep i...

Which county has the most Facebook users?

              There are more than 378 million Facebook users in India alone, making it the leading country in terms of Facebook audience size. To put this into context, if India’s Facebook audience were a country then it would be ranked third in terms of largest population worldwide. Apart from India, there are several other markets with more than 100 million Facebook users each: The United States, Indonesia, and Brazil with 193.8 million, 119.05 million, and 112.55 million Facebook users respectively.

              Facebook – the most used social media

              Meta, the company that was previously called Facebook, owns four of the most popular social media platforms worldwide, WhatsApp, Facebook Messenger, Facebook, and Instagram. As of the third quarter of 2021, there were around 3,5 billion cumulative monthly users of the company’s products worldwide. With around 2.9 billion monthly active users, Facebook is the most popular social media worldwide. With an audience of this scale, it is no surprise that the vast majority of Facebook’s revenue is generated through advertising.

              Facebook usage by device
              As of July 2021, it was found that 98.5 percent of active users accessed their Facebook account from mobile devices. In fact, almost 81.8 percent of Facebook audiences worldwide access the platform only via mobile phone. Facebook is not only available through mobile browser as the company has published several mobile apps for users to access their products and services. As of the third quarter 2021, the four core Meta products were leading the ranking of most downloaded mobile apps worldwide, with WhatsApp amassing approximately six billion downloads.

Inflation Rate in the United States increased to 3 percent in September from 2.90 percent in August of 2025. This dataset provides - United States Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Bank of America (BAC) Historical Stock Prices (1978-2025)

Description:

This dataset provides daily historical stock price data for Bank of America (ticker: BAC) from March 1, 1978, to January 31, 2025. The data includes opening, high, low, closing, and adjusted closing prices, along with trading volume.

Columns:

Date: Trading date (1978-03-01).

Open: Opening price of the stock.

High: Highest price during the trading day.

Low: Lowest price during the trading day.

Close: Closing price of the stock.

Adj Close: Adjusted closing price, accounting for corporate actions (e.g., splits, dividends).

Volume: Number of shares traded during the day.

Key Notes:

Date Range Anomaly: The dataset includes dates up to January 31, 2025, which appears to be a placeholder for future data. Users should verify the latest entries.

Price Format: Prices are recorded in fractions (e.g., 1.453125), reflecting historical stock price conventions.

Missing/Zero Values: Some Open values are listed as 0.0, likely indicating non-trading days (e.g., weekends, holidays) or data gaps.

Adjusted Close: Adjusted for splits and dividends to reflect accurate historical performance.

Potential Use Cases:

Technical Analysis: Study trends, moving averages, or volatility.

Machine Learning: Train models to predict stock movements.

Historical Research: Analyze long-term performance and market cycles.

Backtesting: Validate trading strategies using historical data.

Dataset Source:

Data is compiled from historical market records. Adjusted close prices are calculated retroactively to ensure consistency.

License: Public Domain (CC0).

Suggested Citation: "Bank of America (BAC) Historical Stock Prices, 1978-2025."

Kaggle Tags:

finance, stocks, historical-data, banking, time-series-analysis

Acknowledgments:

This dataset is intended for educational and research purposes. Always verify data accuracy before making financial decisions.

More Dataset

This dataset is scrape by Muhammad Atif Latif.

If you want to explore more datasets then CLICK HERE

Temperature Over Time by State (Starts: 1895)

State and County Temperature Changes

By Environmental Data [source]

About this dataset

Do you want to know how rising temperatures are changing the contiguous United States? The Washington Post has used National Oceanic and Atmospheric Administration's Climate Divisional Database (nClimDiv) and Gridded 5km GHCN-Daily Temperature and Precipitation Dataset (nClimGrid) data sets to help analyze warming temperatures in all of the Lower 48 states from 1895-2019. To provide this analysis, we calculated annual mean temperature trends in each state and county in the Lower 48 states. Our results can be found within several datasets now available on this repository.

We are offering: Annual average temperatures for counties and states, temperature change estimates for each of the Lower 48-states, temperature change estimates for counties in the contiguous U.S., county temperature change data joined to a shapefile in GeoJSON format, gridded temperature change data for the contiguous U.S. in GeoTiff format - all contained with our dataset! We invite those curious about climate change to explore these data sets based on our analysis over multiple stories published by The Washington Post such as Extreme climate change has arrived in America, Fires, floods and free parking: California’s unending fight against climate change, In fast-warming Minnesota, scientists are trying to plant the forests of the future, This giant climate hot spot is robbing West of its water ,and more!

By accessing our dataset containing columns such as fips code, year range from 1895-2019, three season temperatures (Fall/Spring/Summer/Winter), max warming season temps plus temp recorded total yearly - you can become an active citizen scientist! If publishing a story or graphic work based off this data set please credit The Washington Post with a link back to this repository while sending us an email so that we can track its usage as well - 2cdatawashpost.com.

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How to use the dataset

The main files provided by this dataset are climdiv_state_year, climdiv_county_year, model_state, model_county , climdiv_national_year ,and model county .geojson . Each file contains different information capturing climate change across different geographies of the United States over time spans from 1895.

Research Ideas

• Investigating and mapping the temperatures for all US states over the past 120 years, to observe long-term changes in temperature patterns.
• Examining regional biases in warming trends across different US counties and states to help inform resource allocation decisions for climate change mitigation and adaption initiatives.
• Utilizing the ClimDiv National Dataset to understand continental-level average annual temperature changes, allowing comparison of global average temperatures with US averages over a long period of time

Acknowledgements

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

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: climdiv_state_year.csv | Column name | Description | |:--------------|:------------------------------------------------------------------------| | fips | Federal Information Processing Standard code for each county. (Integer) | | year | Year of the temperature data. (Integer) | | tempc | Temperature change from the previous year. (Float) |

File: climdiv_county_year.csv | Column name | Description | |:--------------|:------------------------------------------------------------------------| | fips | Federal Information Processing Standard code for each county. (Integer) | | year | Year of the temperature data. (Integer) | | tempc | Temperature change from the previous year. (Float) |

File: model_state.csv | Column name | Description | |:------------------...

The global social media penetration rate in was forecast to continuously increase between 2024 and 2028 by in total 11.6 (+18.19 percent). After the ninth consecutive increasing year, the penetration rate is estimated to reach 75.31 and therefore a new peak in 2028. Notably, the social media penetration rate of was continuously increasing over the past years.

Unemployment Rate in the United States increased to 4.40 percent in September from 4.30 percent in August of 2025. This dataset provides the latest reported value for - United States Unemployment Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Context

In 2006, global concern was raised over the rapid decline in the honeybee population, an integral component to American honey agriculture. Large numbers of hives were lost to Colony Collapse Disorder, a phenomenon of disappearing worker bees causing the remaining hive colony to collapse. Speculation to the cause of this disorder points to hive diseases and pesticides harming the pollinators, though no overall consensus has been reached. Twelve years later, some industries are observing recovery but the American honey industry is still largely struggling. The U.S. used to locally produce over half the honey it consumes per year. Now, honey mostly comes from overseas, with 350 of the 400 million pounds of honey consumed every year originating from imports. This dataset provides insight into honey production supply and demand in America by state from 1998 to 2012.

Content

The National Agricultural Statistics Service (NASS) is the primary data reporting body for the US Department of Agriculture (USDA). NASS's mission is to "provide timely, accurate, and useful statistics in service to U.S. agriculture". From datasets to census surveys, their data covers virtually all aspects of U.S. agriculture. Honey production is one of the datasets offered. Click here for the original page containing the data along with related datasets such as Honey Bee Colonies and Cost of Pollination. Data wrangling was performed in order to clean the dataset. honeyproduction.csv is the final tidy dataset suitable for analysis. The three other datasets (which include "honeyraw" in the title) are the original raw data downloaded from the site. They are uploaded to this page along with the "**Wrangling The Honey Production Dataset**" kernel as an example to show users how data can be wrangled into a cleaner format. Useful metadata on certain variables of the honeyproduction dataset is provided below:

• numcol: Number of honey producing colonies. Honey producing colonies are the maximum number of colonies from which honey was taken during the year. It is possible to take honey from colonies which did not survive the entire year
• yieldpercol: Honey yield per colony. Unit is pounds
• totalprod: Total production (numcol x yieldpercol). Unit is pounds
• stocks: Refers to stocks held by producers. Unit is pounds
• priceperlb: Refers to average price per pound based on expanded sales. Unit is dollars.
• prodvalue: Value of production (totalprod x priceperlb). Unit is dollars.
• Other useful information: Certain states are excluded every year (ex. CT) to avoid disclosing data for individual operations. Due to rounding, total colonies multiplied by total yield may not equal production. Also, summation of states will not equal U.S. level value of production.

Acknowledgements

Honey production data was published by the National Agricultural Statistics Service (NASS) of the U.S. Department of Agriculture. The beautiful banner photo was by Eric Ward on Unsplash.

Inspiration

• How has honey production yield changed from 1998 to 2012?
• Over time, which states produce the most honey? Which produce the least? Which have experienced the most change in honey yield?
• Does the data show any trends in terms of the number of honey producing colonies and yield per colony before 2006, which was when concern over Colony Collapse Disorder spread nationwide?
• Are there any patterns that can be observed between total honey production and value of production every year? How has value of production, which in some sense could be tied to demand, changed every year?

Key Table Information.Table Title.Annual Business Survey: Statistics for Employer Firms by Race for the U.S.: 2022.Table ID.ABSCS2022.AB00MYCSA01C.Survey/Program.Economic Surveys.Year.2022.Dataset.ECNSVY Annual Business Survey Company Summary.Release Date.2024-12-19.Release Schedule.The Annual Business Survey (ABS) occurs every year, beginning in reference year 2017.For more information about ABS planned data product releases, see Tentative ABS Schedule..Dataset Universe.The dataset universe consists of employer firms that are in operation for at least some part of the reference year, are located in one of the 50 U.S. states, associated offshore areas, or the District of Columbia, have paid employees and annual receipts of $1,000 or more, and are classified in one of nineteen in-scope sectors defined by the 2022 North American Industry Classification System (NAICS), except for NAICS 111, 112, 482, 491, 521, 525, 813, 814, and 92 which are not covered..Sponsor.National Center for Science and Engineering Statistics, U.S. National Science Foundation.Methodology.Data Items and Other Identifying Records.Number of employer firms (firms with paid employees)Sales and receipts of employer firms (reported in $1,000s of dollars)Number of employees (during the March 12 pay period)Annual payroll (reported in $1,000s of dollars)These data are aggregated by the following demographic classifications of firm for:All firms Classifiable (firms classifiable by sex, ethnicity, race, and veteran status) Race White Black or African American American Indian and Alaska Native Asian Asian Indian Chinese Filipino Japanese Korean Vietnamese Other Asian Native Hawaiian and Other Pacific Islander Native Hawaiian Guamanian or Chamorro Samoan Other Pacific Islander Minority (Firms classified as any race and ethnicity combination other than non-Hispanic and White) Equally minority/nonminority Nonminority (Firms classified as non-Hispanic and White) Unclassifiable (firms not classifiable by sex, ethnicity, race, and veteran status) Definitions can be found by clicking on the column header in the table or by accessing the Economic Census Glossary..Unit(s) of Observation.The reporting units for the ABS are employer companies or firms rather than establishments. A company or firm is comprised of one or more in-scope establishments that operate under the ownership or control of a single organization..Geography Coverage.The data are shown for the U.S. only.For information about geographies, see Geographies..Industry Coverage.The data are shown for the total of all sectors ("00") NAICS code. Sector "00" is not an official NAICS sector but is rather a way to indicate a total for multiple sectors. Note: Other programs outside of ABS may use sector 00 to indicate when multiple NAICS sectors are being displayed within the same table and/or dataset.The following are excluded from the total of all sectors:Crop and Animal Production (NAICS 111 and 112)Rail Transportation (NAICS 482)Postal Service (NAICS 491)Monetary Authorities-Central Bank (NAICS 521)Funds, Trusts, and Other Financial Vehicles (NAICS 525)Office of Notaries (NAICS 541120)Religious, Grantmaking, Civic, Professional, and Similar Organizations (NAICS 813)Private Households (NAICS 814)Public Administration (NAICS 92)For information about NAICS, see North American Industry Classification System..Sampling.The ABS sample includes firms that are selected with certainty if they have known research and development activities, were included in the 2022 BERD sample, or have high receipts, payroll, or employment. Total sample size is 850,000 firms. The universe is stratified by state, industry group, and expected demographic group. Firms selected to the sample receive a questionnaire. For all data on this table, firms not selected into the sample are represented with administrative, 2022 Economic Census, or other economic surveys records.For more information about the sample design, see Annual Business Survey Methodology..Confidentiality.The Census Bureau has reviewed this data product to ensure appropriate access, use, and disclosure avoidance protection of the confidential source data (Project No. P-7504866, Disclosure Review Board (DRB) approval number: CBDRB-FY24-0351).To protect confidentiality, the U.S. Census Bureau suppresses cell values to minimize the risk of identifying a particular business' data or identity.To comply with data quality standards, data rows with high relative standard errors (RSE) are not presented. Additionally, firm counts are suppressed when other select statistics in the same row are suppressed. More information on disclosure avoidance is available in the Annual Business Survey Methodology..Technical Documentation/Methodology.For detailed information about the methods used to collect data and produce statistics, survey questionnaires, Primary Business Activity/NAICS codes, and more, see Technical Documentation..Weights.For more information about weighting, see An...

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              Social media: trust and consumption

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