In 2021, about **** million people aged 65 years or older were living in California -- the most out of any state. In that same year, Florida, Texas, New York, and Pennsylvania rounded out the top five states with the most people aged 65 and over living there.

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 the first reported coronavirus case in Washington State on Jan. 21, 2020, 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.

California Medical Fee-for-Service

Geographic, Legal and Specialty Data for In-State and Out-of-State Providers

By California Health and Human Services [source]

About this dataset

Welcome to the California Health and Human Services Agency's Open Data Portal! Here, you can explore and utilize information from one of the state's most valuable assets: the non-confidential data set of Medi-Cal Fee-for-Service (FFS) program providers.

This dataset provides insight into Medi-Cal FFS enrollment. The information was retrieved from the Provider Master File (PMF), which is maintained by the Provider Enrollment Division (PED). With this dataset, you will gain insights into provider number, legal name, type description, specialty description and other geographical data points such as county code, attention line address parts , landmark coordinate points (longitude/latitude) and more!

The goal with this Open Data Portal initiative is to empower Californians with:

• Increased public access to high quality health & human service data;
• Stemmed creativity & innovation in research;
• The ability to make informed decisions about our health & services providers;
• Transparency in government policy expenditure measures.

Our hope is that you'll use these tools for responsible data analytics exploration on not just Medi-Cal FFS provision but on any related subject matter that interest& benefit your community at large. Good luck & happy researching!

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

Research Ideas

• Creating a mobile application or website to help people easily and quickly find their nearest Medi-Cal FFS providers based on location, specialty and provider type.
• Developing analytics tools to help organizations understand the concentrations of providers across the state in order to inform decision making when considering regional expansion and improving service accessibility.
• Developing a tool that visualizes specialty diversity across the state to identify areas with low provider density while helping inform strategies aimed at increasing access to care for communities with high needs populations

Acknowledgements

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

License

License: Open Database License (ODbL) v1.0 - You are free to: - Share - copy and redistribute the material in any medium or format. - 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. - No Derivatives - If you remix, transform, or build upon the material, you may not distribute the modified material. - No additional restrictions - You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.

Columns

File: Profile_of_Enrolled_Medi-Cal_Fee-for-Service_FFS_Providers_as_of_May_1_2016.csv | Column name | Description | |:----------------------------|:---------------------------------------------------------------| | NPI | National Provider Identifier (Number) | | SERVICE LOCATION NUMBER | Unique identifier for the provider's service location (Number) | | LEGAL NAME | Legal name of the provider (Text) | | TYPE DESCRIPTION | Type of provider (Text) | | SPECIALTY DESCRIPTION | Specialty of the provider (Text) | | OUT OF STATE INDICATOR | Indicates if the provider is located out of state (Boolean) | | IN/OUT OF STATE | Indicates if the provider is located in or out of state (Text) | | COUNTY CODE | County code of the provider's service location (Number) | | COUNTY NAME | County name of the provider's service location (Text) | | ADDRESS ATTENTION | Attention line of the provider's address (Text) | | ADDRESS LINE 1 | First l...

Unemployment Rate in the United States increased to 4.20 percent in July from 4.10 percent in June 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.

This dataset is part of the Geographical repository maintained by Opendatasoft. This dataset contains data for counties and equivalent entities in United States of America. The primary legal divisions of most states are termed counties. In Louisiana, these divisions are known as parishes. In Alaska, which has no counties, the equivalent entities are the organized boroughs, city and boroughs, municipalities, and for the unorganized area, census areas. The latter are delineated cooperatively for statistical purposes by the State of Alaska and the Census Bureau. In four states (Maryland, Missouri, Nevada, and Virginia), there are one or more incorporated places that are independent of any county organization and thus constitute primary divisions of their states. These incorporated places are known as independent cities and are treated as equivalent entities for purposes of data presentation. The District of Columbia and Guam have no primary divisions, and each area is considered an equivalent entity for purposes of data presentation. The Census Bureau treats the following entities as equivalents of counties for purposes of data presentation: Municipios in Puerto Rico, Districts and Islands in American Samoa, Municipalities in the Commonwealth of the Northern Mariana Islands, and Islands in the U.S. Virgin Islands. The entire area of the United States, Puerto Rico, and the Island Areas is covered by counties or equivalent entities.Processors and tools are using this data. Enhancements Add ISO 3166-3 codes. Simplify geometries to provide better performance across the services. Add administrative hierarchy.

This physiological data was collected from pilot/copilot pairs in and out of a flight simulator. It was collected to train machine-learning models to aid in the detection of pilot attentive states. The benchmark training set is comprised of a set of controlled experiments collected in a non-flight environment, outside of a flight simulator. The test set (abbreviated LOFT = Line Oriented Flight Training) consists of a full flight (take off, flight, and landing) in a flight simulator. The pilots experienced distractions intended to induce one of the following three cognitive states: Channelized Attention (CA) is the state of being focused on one task to the exclusion of all others. This is induced in benchmarking by having the subjects play an engaging puzzle-based video game. Diverted Attention (DA) is the state of having one’s attention diverted by actions or thought processes associated with a decision. This is induced by having the subjects perform a display monitoring task. Periodically, a math problem showed up which had to be solved before returning to the monitoring task. Startle/Surprise (SS) is induced by having the subjects watch movie clips with jump scares. For each experiment, a pair of pilots (each with its own crew ID) was recorded over time and subjected to the CA, DA, or SS cognitive states. The training set contains three experiments (one for each state) in which the pilots experienced just one of the states. For example, in the experiment labelled CA, the pilots were either in a baseline state (no event) or the CA state. The test set contains a full flight simulation during which the pilots could experience any of the states (but never more than one at a time). Each sensor operated at a sample rate of 256 Hz. Please note that since this is physiological data from real people, there will be noise and artifacts in the data.

This dataset contains counts of live births for California as a whole based on information entered on birth certificates. Final counts are derived from static data and include out of state births 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 births that occurred during the time period.

The final data tables include both births that occurred in California regardless of the place of residence (by occurrence) and births to California residents (by residence), whereas the provisional data table only includes births that occurred in California regardless of the place of residence (by occurrence). The data are reported as totals, as well as stratified by parent giving birth's age, parent giving birth's race-ethnicity, and birth place type. See temporal coverage for more information on which strata are available for which years.

State and territorial executive orders, administrative orders, resolutions, and proclamations are collected from government websites and cataloged and coded using Microsoft Excel by one coder with one or more additional coders conducting quality assurance. Data were collected to determine when members of the public in states and territories were subject to state and territorial executive orders, administrative orders, resolutions, and proclamations for COVID-19 that require them to wear masks in public. “Members of the public” are defined as individuals operating in a personal capacity. “In public” is defined to mean either (1) anywhere outside the home or (2) both in retail businesses and in restaurants/food establishments. Data consists exclusively of state and territorial orders, many of which apply to specific counties within their respective state or territory; therefore, data is broken down to the county level. These data are derived from publicly available state and territorial executive orders, administrative orders, resolutions, and proclamations (“orders”) for COVID-19 that expressly require individuals to wear masks in public found by the CDC, COVID-19 Community Intervention & Critical Populations Task Force, Monitoring & Evaluation Team, Mitigation Policy Analysis Unit, Center for State, Tribal, Local, and Territorial Support, Public Health Law Program, and Max Gakh, Assistant Professor, School of Public Health, University of Nevada, Las Vegas from April 10, 2020 through August 15, 2021. These data will be updated as new orders are collected. Any orders not available through publicly accessible websites are not included in these data. Only official copies of the documents or, where official copies were unavailable, official press releases from government websites describing requirements were coded; news media reports on restrictions were excluded. Recommendations not included in an order are not included in these data. Effective and expiration dates were coded using only the dates provided; no distinction was made based on the specific time of the day the order became effective or expired. These data do not include data on counties that have opted out of their state mask mandate pursuant to state law. These data do not necessarily represent an official position of the Centers for Disease Control and Prevention.

PROBLEM AND OPPORTUNITY In the United States, voting is largely a private matter. A registered voter is given a randomized ballot form or machine to prevent linkage between their voting choices and their identity. This disconnect supports confidence in the election process, but it provides obstacles to an election's analysis. A common solution is to field exit polls, interviewing voters immediately after leaving their polling location. This method is rife with bias, however, and functionally limited in direct demographics data collected. For the 2020 general election, though, most states published their election results for each voting location. These publications were additionally supported by the geographical areas assigned to each location, the voting precincts. As a result, geographic processing can now be applied to project precinct election results onto Census block groups. While precinct have few demographic traits directly, their geographies have characteristics that make them projectable onto U.S. Census geographies. Both state voting precincts and U.S. Census block groups: are exclusive, and do not overlap are adjacent, fully covering their corresponding state and potentially county have roughly the same size in area, population and voter presence Analytically, a projection of local demographics does not allow conclusions about voters themselves. However, the dataset does allow statements related to the geographies that yield voting behavior. One could say, for example, that an area dominated by a particular voting pattern would have mean traits of age, race, income or household structure. The dataset that results from this programming provides voting results allocated by Census block groups. The block group identifier can be joined to Census Decennial and American Community Survey demographic estimates. DATA SOURCES The state election results and geographies have been compiled by Voting and Election Science team on Harvard's dataverse. State voting precincts lie within state and county boundaries. The Census Bureau, on the other hand, publishes its estimates across a variety of geographic definitions including a hierarchy of states, counties, census tracts and block groups. Their definitions can be found here. The geometric shapefiles for each block group are available here. The lowest level of this geography changes often and can obsolesce before the next census survey (Decennial or American Community Survey programs). The second to lowest census level, block groups, have the benefit of both granularity and stability however. The 2020 Decennial survey details US demographics into 217,740 block groups with between a few hundred and a few thousand people. Dataset Structure The dataset's columns include: Column Definition BLOCKGROUP_GEOID 12 digit primary key. Census GEOID of the block group row. This code concatenates: 2 digit state 3 digit county within state 6 digit Census Tract identifier 1 digit Census Block Group identifier within tract STATE State abbreviation, redundent with 2 digit state FIPS code above REP Votes for Republican party candidate for president DEM Votes for Democratic party candidate for president LIB Votes for Libertarian party candidate for president OTH Votes for presidential candidates other than Republican, Democratic or Libertarian AREA square kilometers of area associated with this block group GAP total area of the block group, net of area attributed to voting precincts PRECINCTS Number of voting precincts that intersect this block group ASSUMPTIONS, NOTES AND CONCERNS: Votes are attributed based upon the proportion of the precinct's area that intersects the corresponding block group. Alternative methods are left to the analyst's initiative. 50 states and the District of Columbia are in scope as those U.S. possessions voting in the general election for the U.S. Presidency. Three states did not report their results at the precinct level: South Dakota, Kentucky and West Virginia. A dummy block group is added for each of these states to maintain national totals. These states represent 2.1% of all votes cast. Counties are commonly coded using FIPS codes. However, each election result file may have the county field named differently. Also, three states do not share county definitions - Delaware, Massachusetts, Alaska and the District of Columbia. Block groups may be used to capture geographies that do not have population like bodies of water. As a result, block groups without intersection voting precincts are not uncommon. In the U.S., elections are administered at a state level with the Federal Elections Commission compiling state totals against the Electoral College weights. The states have liberty, though, to define and change their own voting precincts https://en.wikipedia.org/wiki/Electoral_precinct. The Census Bureau... Visit https://dataone.org/datasets/sha256%3A05707c1dc04a814129f751937a6ea56b08413546b18b351a85bc96da16a7f8b5 for complete metadata about this dataset.

Labor Force Participation Rate in the United States decreased to 62.20 percent in July from 62.30 percent in June of 2025. This dataset provides the latest reported value for - United States Labor Force Participation Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

THIS DATASET WAS LAST UPDATED AT 8:11 PM EASTERN ON AUG. 30

OVERVIEW

2019 had the most mass killings since at least the 1970s, according to the Associated Press/USA TODAY/Northeastern University Mass Killings Database.

In all, there were 45 mass killings, defined as when four or more people are killed excluding the perpetrator. Of those, 33 were mass shootings . This summer was especially violent, with three high-profile public mass shootings occurring in the span of just four weeks, leaving 38 killed and 66 injured.

A total of 229 people died in mass killings in 2019.

The AP's analysis found that more than 50% of the incidents were family annihilations, which is similar to prior years. Although they are far less common, the 9 public mass shootings during the year were the most deadly type of mass murder, resulting in 73 people's deaths, not including the assailants.

One-third of the offenders died at the scene of the killing or soon after, half from suicides.

About this Dataset

The Associated Press/USA TODAY/Northeastern University Mass Killings database tracks all U.S. homicides since 2006 involving four or more people killed (not including the offender) over a short period of time (24 hours) regardless of weapon, location, victim-offender relationship or motive. The database includes information on these and other characteristics concerning the incidents, offenders, and victims.

The AP/USA TODAY/Northeastern database represents the most complete tracking of mass murders by the above definition currently available. Other efforts, such as the Gun Violence Archive or Everytown for Gun Safety may include events that do not meet our criteria, but a review of these sites and others indicates that this database contains every event that matches the definition, including some not tracked by other organizations.

This data will be updated periodically and can be used as an ongoing resource to help cover these events.

Using this Dataset

To get basic counts of incidents of mass killings and mass shootings by year nationwide, use these queries:

Mass killings by year

Mass shootings by year

To get these counts just for your state:

Filter killings by state

Definition of "mass murder"

Mass murder is defined as the intentional killing of four or more victims by any means within a 24-hour period, excluding the deaths of unborn children and the offender(s). The standard of four or more dead was initially set by the FBI.

This definition does not exclude cases based on method (e.g., shootings only), type or motivation (e.g., public only), victim-offender relationship (e.g., strangers only), or number of locations (e.g., one). The time frame of 24 hours was chosen to eliminate conflation with spree killers, who kill multiple victims in quick succession in different locations or incidents, and to satisfy the traditional requirement of occurring in a “single incident.”

Offenders who commit mass murder during a spree (before or after committing additional homicides) are included in the database, and all victims within seven days of the mass murder are included in the victim count. Negligent homicides related to driving under the influence or accidental fires are excluded due to the lack of offender intent. Only incidents occurring within the 50 states and Washington D.C. are considered.

Methodology

Project researchers first identified potential incidents using the Federal Bureau of Investigation’s Supplementary Homicide Reports (SHR). Homicide incidents in the SHR were flagged as potential mass murder cases if four or more victims were reported on the same record, and the type of death was murder or non-negligent manslaughter.

Cases were subsequently verified utilizing media accounts, court documents, academic journal articles, books, and local law enforcement records obtained through Freedom of Information Act (FOIA) requests. Each data point was corroborated by multiple sources, which were compiled into a single document to assess the quality of information.

In case(s) of contradiction among sources, official law enforcement or court records were used, when available, followed by the most recent media or academic source.

Case information was subsequently compared with every other known mass murder database to ensure reliability and validity. Incidents listed in the SHR that could not be independently verified were excluded from the database.

Project researchers also conducted extensive searches for incidents not reported in the SHR during the time period, utilizing internet search engines, Lexis-Nexis, and Newspapers.com. Search terms include: [number] dead, [number] killed, [number] slain, [number] murdered, [number] homicide, mass murder, mass shooting, massacre, rampage, family killing, familicide, and arson murder. Offender, victim, and location names were also directly searched when available.

This project started at USA TODAY in 2012.

Contacts

Contact AP Data Editor Justin Myers with questions, suggestions or comments about this dataset at jmyers@ap.org. The Northeastern University researcher working with AP and USA TODAY is Professor James Alan Fox, who can be reached at j.fox@northeastern.edu or 617-416-4400.

Former self-employed persons were interviewed about their general living conditions, their attitudes towards self-employment, the opportunities and problems of setting up a business, the measures to promote self-employment and their political and social attitudes. Topics: 1. Professional situation: gainful employment; current occupation; occupation within the last five years; general life satisfaction; assessment of the current own economic situation; number of employees; professional activity; business start-up. 2. Attitude towards self-employment: motivation to become self-employed (e.g. self-determined work, flexible working hours, new challenges, etc.); reasons for terminating self-employment (ranking, e.g. too much bureaucracy, high workload, too low income, etc.); willingness to become self-employed again; reasons against becoming self-employed again 3. Business start-up: evaluation of the current conditions for business start-ups in Germany. 4. Advice and support: desired measures of advice and support during self-employment (open). 5. Political and social attitudes: political interest; self-perception of the self-employed (in Germany the self-employed receive too little recognition, politics takes the concerns of the self-employed seriously, Germany is an entrepreneur-friendly country); attitudes towards the state and society based on opposing positions (scale of 7: state should guarantee comprehensive social security for citizens vs. leave it to citizens to take personal responsibility, state intervention in the economy vs. free enterprise, state should support people in emergency situations through no fault of their own for a certain period of time vs. balance between rich and poor, economy must make profits vs. benefit the common good, right of the state to restrict citizens´ freedom to protect against crime vs. protection of citizens´ freedom from state intervention, preferred social system: strong political leadership vs. democratic citizen participation, performance principle vs. solidarity principle, education policy as equal opportunity vs. promotion of elites, dependence of social progress on origin and property vs. performance, lower taxes and contributions vs. more welfare benefits); party sympathy. Demography: age; sex; federal state; school education: highest general school leaving certificate; vocational training: type of vocational training qualification; self-assessment of social class; net household income; city size; size of household. Additionally coded: ID; weighting factor. Befragt wurden ehemalige Selbstständige zu ihren allgemeinen Lebensumständen, ihren Einstellungen zur Selbstständigkeit, zu Chancen und Problemen bei der Existenzgründung, zu den Fördermaßnahmen zur Selbstständigkeit sowie zu ihren politischen und gesellschaftlichen Einstellungen. Themen: 1. Berufliche Situation: Erwerbstätigkeit; derzeitige Tätigkeit; Tätigkeit innerhalb der letzten fünf Jahre; allgemeine Lebenszufriedenheit; Beurteilung der derzeitigen eigenen wirtschaftlichen Lage; Anzahl der Mitarbeiter; berufliche Tätigkeit; Existenzgründung. 2. Einstellung zur Selbstständigkeit: Motivation zur Selbstständigkeit (z.B. eigenbestimmtes Arbeiten, flexible Arbeitszeiten, neue Herausforderungen, etc.); Gründe für Beendigung der Selbstständigkeit (Rangfolge, z.B. zu hoher bürokratischer Aufwand, hohe Arbeitsbelastung, zu geringes Einkommen,, etc.); Bereitschaft zu erneuter Selbstständigkeit; Gründe gegen eine erneute Selbstständigkeit. 3. Existenzgründung: Bewertung der derzeitigen Bedingungen für Existenzgründungen in Deutschland. 4. Beratung und Förderung: gewünschte Maßnahmen der Beratung und Förderung während der Selbstständigkeit (offen). 5. Politische und gesellschaftliche Einstellungen: Politikinteresse; Selbstwahrnehmung der Selbständigen (in Deutschland bekommen Selbstständige zu wenig Anerkennung, die Politik nimmt die Sorgen der Selbstständigen ernst, Deutschland ist ein unternehmerfreundliches Land); Einstellung zu Staat und Gesellschaft anhand von gegensätzlichen Positionen (7er Skala: Staat soll umfassende soziale Absicherung der Bürger garantieren vs. der Eigenverantwortung der Bürger überlassen, staatliche Eingriffe in die Wirtschaft vs. freie Wirtschaft, Staat sollte Menschen in unverschuldeten Notsituationen eine gewisse Zeit unterstützen vs. Ausgleich zwischen Arm und Reich schaffen, Wirtschaft muss Gewinne erzielen vs. dem Gemeinwohl nützen, Recht des Staates auf Einschränkung der Freiheit der Bürger zum Schutz vor Kriminalität vs. Schutz der Freiheit der Bürger vor Eingriffen des Staates, präferiertes Gesellschaftssystem: starke politische Führung vs. demokratische Bürgerbeteiligung, Leistungsprinzip vs. Solidaritätsprinzip, Bildungspolitik als Chancengleichheit vs. Elitenförderung, Abhängigkeit des gesellschaftlichen Fortkommens von Herkunft und Besitz vs. Leistung, weniger Steuern und Abgaben vs. mehr sozialstaatliche Leistungen); Parteisympathie. Demographie: Alter; Geschlecht; Bundesland; Schulbildung: höchster allgemeinbildender Schulabschluss; berufliche Bildung: Art des beruflichen Ausbildungsabschlusses; Selbsteinschätzung soziale Schichtzugehörigkeit; Haushaltsnettoeinkommen; Ortsgröße; Haushaltsgröße. Zusätzlich verkodet wurde: ID; Gewichtungsfaktor.

Employment Rate in the United States decreased to 59.60 percent in July from 59.70 percent in June of 2025. This dataset provides - United States Employment Rate- actual values, historical data, forecast, chart, statistics, economic calendar and news.

On behalf of the Press and Information Office of the Federal Government, the opinion research institute Kantar conducted a target group survey of the ´Generation Z´. For this purpose, 1,022 people between the ages of 14 and 24 were surveyed online between 05 and 18 July 2021. The focus of the survey was on the values and orientation of the generation, their situation in the pandemic, political interest and information behaviour as well as political and social attitudes. In order to map the influence of the corona pandemic on the attitudes and social image of Generation Z, the results of this survey were compared with a survey from 2019. Current life circumstances: life satisfaction; highest school-leaving qualification of father and mother; material situation: frequency of renunciation for financial reasons; source of money (from own work, from parents, from state support, from elsewhere); primary source of money; negative effects of the Corona crisis on personal income; organisation of distance learning (communication via a digital learning platform, via video conference, via e-mail, via messenger/chats such as e.g. WhatsApp, via a cloud, by telephone, by post or by other means); agreement with statements on the situation in schools/colleges (I was able to concentrate well on my tasks at home, I missed direct contact with my classmates/ fellow students, my grades deteriorated during the pandemic, distance learning at my school/college worked well, I had insufficient equipment to follow lessons, the accessibility of teachers was very good even in times of distance learning, learning became more strenuous for me during the pandemic); opinion on the future recognition of school, university or professional degrees made during the Corona pandemic; leisure activities during the pandemic (less sport since the beginning of the pandemic than before, relationships with friends have deteriorated during the pandemic, significantly more time on the internet since the beginning of the pandemic than before, started a new hobby during the pandemic); vaccination status; likelihood of Corona vaccination. 2. Values and attitudes: personally most important life goals (e.g. self-discovery, independence, enjoying life, career, etc.); importance of various aspects for pursuing a profession (secure job, adequate income, interesting work that is fun, compatibility of private life and profession (work-life balance), career opportunities, responsibility, opportunities for further training and development); comparison of values : comparison of values Corona: extensive collection of data for infection protection vs. data protection, especially young vs. especially old people have suffered from the pandemic, pandemic as a chance for change vs. after the pandemic back to the usual normality, comparison of values State: debts in favour of education and infrastructure not a problem vs. always a burden for future generations, active role of the state for important future tasks such as climate protection and educational justice vs. leaving a passive role and shaping of the future to society and the economy, orienting politics towards future generations vs. protecting the interests of those who have already made a contribution to society, comparison of lifestyle values: conscious renunciation in favour of sustainability vs. doing what I feel like doing, doing without in favour of health vs. having fun in the foreground, self-realisation vs. putting aside one´s own needs in favour of one´s personal environment, today´s generation has completely different values than the generation before it vs. in principle very similar values as the generation before it). 3. Media and information: interest in politics; points of contact with politics in everyday life (e.g. media consumption, when using social networks, in personal conversations with friends and family, at work, at school or university, in public spaces, in leisure time/hobbies); being informed about politics; most frequently used sources of political information (media) (e.g. news programmes on TV, talk shows on TV, websites of public institutions and authorities, etc.). e.g. news programmes on TV, talk shows on TV, websites of public institutions and authorities, satire programmes on TV, etc.); change in political information behaviour in the Corona pandemic. 4. Politics and society: satisfaction with democracy; opinion on democracy as an idea; need for reform of politics in Germany; most important political problems in Germany (open); satisfaction with the work of the federal government; trust in institutions (judiciary, environmental and aid organisations such as Greenpeace or Amnesty International, public health authorities such as the Robert Koch Institute, federal government, Bundestag, police, churches, school/university); perception of social lines of conflict (between rich and poor, employers and employees, young and old, foreigners and Germans, East Germans and West Germans, women and men, people in the city and people in the countryside); attitudes towards Corona (politicians take young people´s concerns seriously, young people received sufficient financial support from the state during the pandemic, young people´s needs were not taken into account enough by politicians during the Corona pandemic, the Corona pandemic will affect my generation´s future opportunities in the long term, my generation will benefit significantly from the awakening after the Corona pandemic, the Corona crisis has changed my perspective on many things in life, young people´s career opportunities have deteriorated as a result of the pandemic); agreement with various statements on Corona vaccination (children and young people aged 12 and over should also be vaccinated against Corona, young people currently have to wait too long for a vaccination appointment, vaccination prioritisation should have been lifted earlier, vaccination of young people against Corona is not necessary, there should be compulsory vaccination for schoolchildren, I personally feel that Corona vaccinations in Germany are treated fairly); currently appropriate measures to support children and young people (open). 5. Future perspectives: assessment of personal future opportunities; assessment of the future opportunities of one´s own generation in Germany; future vision of politics: agreement with various statements (a council of randomly selected citizens should be created to draw up political recommendations for the federal government, voting in elections should be possible via app, the voting age in federal elections should be lowered to 16, the population should be represented in the Bundestag by means of quotas, the population should vote directly on important political issues by referendum). Demography: age; sex; federal state; current attendance at school, college or university; type of educational institution currently attended; highest level of education attained to date; employment; subjective class classification; housing situation; household size; party sympathies; migration background. Additionally coded was: serial number; city size; weighting factor. Im Auftrag des Presse- und Informationsamt der Bundesregierung hat das Meinungsforschungsinstitut Kantar eine Zielgruppenbefragung der „Generation Z“ durchgeführt. Dazu wurden im Zeitraum vom 05. – 18. Juli 2021 1.022 Personen zwischen 14 und 24 Jahren online befragt. Die Schwerpunkte der Befragung lagen auf den Werten und Orientierung der Generation, ihrer Situation in der Pandemie, dem politischen Interesse und Informationsverhalten sowie auf den politischen und gesellschaftlichen Einstellungen. Um den Einfluss der Coronapandemie auf die Einstellungen und das Gesellschaftsbild der Generation Z abzubilden, wurden die Ergebnisse dieser Befragung mit einer Befragung aus dem Jahr 2019 verglichen. Aktuelle Lebensumstände: Lebenszufriedenheit; höchster Schulabschluss von Vater und Mutter; materielle Situation: Häufigkeit des Verzichts aus finanziellen Gründen; Geldquelle (aus eigener Arbeit, von den Eltern, aus staatlicher Unterstützung, von woanders her); primäre Geldquelle; negative Auswirkungen der Corona-Krise auf das persönliche Einkommen; Organisation des Fernunterrichts (Kommunikation über eine digitale Lernplattform, per Videokonferenz, per E-Mail, per Messenger/Chats wie z.B. WhatsApp, über eine Cloud, per Telefon, per Post oder auf sonstige Weise); Zustimmung zu Aussagen zur Situation in Schulen/ an Hochschulen (ich konnte mich zu Hause gut auf meine Aufgaben konzentrieren, der direkte Kontakt zu meinen Mitschüler/innen/ Kommilitonen/innen hat mir gefehlt, meine Noten sind während der Pandemie schlechter geworden, der Fernunterricht an meiner Schule/ Hochschule hat gut funktioniert, ich hatte nur ungenügende Ausstattung zur Verfügung, um dem Unterricht folgen zu können, die Erreichbarkeit der Lehrkräfte war auch in Zeiten des Fernunterrichts sehr gut, das Lernen ist für mich während der Pandemie anstrengender geworden); Meinung zur künftigen Anerkennung von Schul-, Universitäts- oder Berufsabschlüssen, die während der Corona-Pandemie gemacht wurden; Freizeitgestaltung während der Pandemie (seit Beginn der Pandemie weniger Sport als davor, Beziehungen zu Freunden haben sich in der Pandemie verschlechtert, seit Beginn der Pandemie deutlich mehr Zeit im Internet als davor, in der Pandemie ein neues Hobby begonnen); Impfstatus; Wahrscheinlichkeit einer Corona-Impfung. 2. Werte und Einstellungen: persönlich wichtigste Lebensziele (z.B. Selbstfindung, Unabhängigkeit, Leben genießen, Karriere, etc.); Wichtigkeit verschiedener Aspekte für die Ausübung eines Berufs (sicherer Arbeitsplatz, angemessenes Einkommen, interessante Arbeit, die Spaß macht, Vereinbarkeit von Privatleben und Beruf (Work-Life-Balance), Karrieremöglichkeiten, Verantwortung, Weiterbildungs- und Entwicklungsmöglichkeiten); Gegenüberstellung von Werten :

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 number of Twitter users in the United States was forecast to continuously increase between 2024 and 2028 by in total 4.3 million users (+5.32 percent). After the ninth consecutive increasing year, the Twitter user base is estimated to reach 85.08 million users and therefore a new peak in 2028. Notably, the number of Twitter users of was continuously increasing over the past years.User figures, shown here regarding the platform twitter, 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 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 Twitter users in countries like Canada and Mexico.

This dataset contains counts of live births for California counties based on information entered on birth certificates. Final counts are derived from static data and include out of state births 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 births that occurred during the time period.

The final data tables include both births that occurred in California regardless of the place of residence (by occurrence) and births to California residents (by residence), whereas the provisional data table only includes births that occurred in California regardless of the place of residence (by occurrence). The data are reported as totals, as well as stratified by parent giving birth's age, parent giving birth's race-ethnicity, and birth place type. See temporal coverage for more information on which strata are available for which years.

The Occupational Safety and Health Administration (OSHA) collected work-related injury and illness data from employers within specific industry and employment size specifications from 2002 through 2011. This data collection is called the OSHA Data Initiative or ODI. The data provided is used by OSHA to calculate establishment specific injury and illness incidence rates. This searchable database contains a table with the name, address, industry, and associated Total Case Rate (TCR), Days Away, Restricted, and Transfer (DART) case rate, and the Days Away From Work (DAFWII) case rate for the establishments that provided OSHA with valid data for calendar years 2002 through 2011. This data has been sampled down from its original size to 4%. In addition, the original dataset only has data from a small portion of all private sector establishments in the United States (80,000 out of 7.5 million total establishments). Therefore, these data are not representative of all businesses and general conclusions pertaining to all US business should not be overdrawn. Data quality: While OSHA takes multiple steps to ensure the data collected is accurate, problems and errors invariably exist for a small percentage of establishments. OSHA does not believe the data for the establishments with the highest rates on this file are accurate in absolute terms. Efforts were made during the collection cycle to correct submission errors, however some remain unresolved. It would be a mistake to say establishments with the highest rates on this file are the ‘most dangerous’ or ‘worst’ establishments in the Nation. Rate Calculation: An incidence rate of injuries and illnesses is computed from the following formula: (Number of injuries and illnesses X 200,000) / Employee hours worked = Incidence rate. The Total Case Rate includes all cases recorded on the OSHA Form 300 (Column G + Column H + Column I + Column J). The Days Away/Restriced/Transfer includes cases recorded in Column H + Column I. The Days Away includes cases recorded in Column H. For further information on injury and illness incidence rates, please visit the Bureau of Labor Statistics’ webpage at http://www.bls.gov/iif/osheval.htm State Participation: Not all state plan states participate in the ODI. The following states did not participate in the 2010 ODI (collection of CY 2009 data), establishment data is not available for these states: Alaska; Oregon; Puerto Rico; South Carolina; Washington; Wyoming.

Data Dictionary

Acknowlegement

Original Data

CORGIS Dataset Project

Foto von National Cancer Institute auf Unsplash

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.

This dataset contains information about the movement of people in and out of the State Library of Queensland. More about the data set can be found in the Explanatory Information document.