Abstract copyright UK Data Service and data collection copyright owner. To provide information on the effect of travel publicity material and attitudes of visitors to the USA towards their experiences. Main Topics: Attitudinal/Behavioural Questions Publicity and information material: what first interested respondent in considering a trip to the USA (e.g. travel movie, television), views on USTS, promotional material (i.e. whether text informative, whether illustrations appealing, whether additional material needed, and if so, what kind), where travel posters and advertisements were seen, whether they were the product of the USTS. The appeal of such material is recorded (i.e. whether they stimulated interest in a trip to USA), whether respondent has actually recently read an article, heard or seen a radio or television programme on travel to or within the USA; finally, whether respondent plans to visit the USA in the next 12 months. The second part of the survey is concerned with the attitudes towards their trips of those respondents who have already visited the USA. Information includes: approximate date and length of most recent visit, whether this was first trip, main purpose of trip (7 categories), total expenditure incurred, whether it was an inclusive tour, number of other people in party, who organised the trip (11 categories), whether travel arrangements were made through a travel agent, modes of inter-city transportation used (6 categories), public accommodation used. Attitudinal data include: degrees of satisfaction with the 99 day - dollar 99 unlimited bus travel plan (if used), degree of satisfaction with the special local service airline fare (if used), degree of enjoyment of USA visit (4 point scale). Respondents are asked to state their general opinion, according to a 3 point scale for both quality and price, on public accommodation used and food eaten in public eating places. They are also asked to state what sightseeing highlighted their trip, and the things they least liked and most liked about the USA. Background Variables Occupation, magazine and newspaper readership, whether a holiday or business trip has been taken outside the UK within the last 12 months, and, if so, countries visited.

This United States Environmental Protection Agency (US EPA) feature layer represents monitoring site data, updated hourly concentrations and Air Quality Index (AQI) values for the latest hour received from monitoring sites that report to AirNow.Map and forecast data are collected using federal reference or equivalent monitoring techniques or techniques approved by the state, local or tribal monitoring agencies. To maintain "real-time" maps, the data are displayed after the end of each hour. Although preliminary data quality assessments are performed, the data in AirNow are not fully verified and validated through the quality assurance procedures monitoring organizations used to officially submit and certify data on the EPA Air Quality System (AQS).This data sharing, and centralization creates a one-stop source for real-time and forecast air quality data. The benefits include quality control, national reporting consistency, access to automated mapping methods, and data distribution to the public and other data systems. The U.S. Environmental Protection Agency, National Oceanic and Atmospheric Administration, National Park Service, tribal, state, and local agencies developed the AirNow system to provide the public with easy access to national air quality information. State and local agencies report the Air Quality Index (AQI) for cities across the US and parts of Canada and Mexico. AirNow data are used only to report the AQI, not to formulate or support regulation, guidance or any other EPA decision or position.About the AQIThe Air Quality Index (AQI) is an index for reporting daily air quality. It tells you how clean or polluted your air is, and what associated health effects might be a concern for you. The AQI focuses on health effects you may experience within a few hours or days after breathing polluted air. EPA calculates the AQI for five major air pollutants regulated by the Clean Air Act: ground-level ozone, particle pollution (also known as particulate matter), carbon monoxide, sulfur dioxide, and nitrogen dioxide. For each of these pollutants, EPA has established national air quality standards to protect public health. Ground-level ozone and airborne particles (often referred to as "particulate matter") are the two pollutants that pose the greatest threat to human health in this country.A number of factors influence ozone formation, including emissions from cars, trucks, buses, power plants, and industries, along with weather conditions. Weather is especially favorable for ozone formation when it’s hot, dry and sunny, and winds are calm and light. Federal and state regulations, including regulations for power plants, vehicles and fuels, are helping reduce ozone pollution nationwide.Fine particle pollution (or "particulate matter") can be emitted directly from cars, trucks, buses, power plants and industries, along with wildfires and woodstoves. But it also forms from chemical reactions of other pollutants in the air. Particle pollution can be high at different times of year, depending on where you live. In some areas, for example, colder winters can lead to increased particle pollution emissions from woodstove use, and stagnant weather conditions with calm and light winds can trap PM2.5 pollution near emission sources. Federal and state rules are helping reduce fine particle pollution, including clean diesel rules for vehicles and fuels, and rules to reduce pollution from power plants, industries, locomotives, and marine vessels, among others.How Does the AQI Work?Think of the AQI as a yardstick that runs from 0 to 500. The higher the AQI value, the greater the level of air pollution and the greater the health concern. For example, an AQI value of 50 represents good air quality with little potential to affect public health, while an AQI value over 300 represents hazardous air quality.An AQI value of 100 generally corresponds to the national air quality standard for the pollutant, which is the level EPA has set to protect public health. AQI values below 100 are generally thought of as satisfactory. When AQI values are above 100, air quality is considered to be unhealthy-at first for certain sensitive groups of people, then for everyone as AQI values get higher.Understanding the AQIThe purpose of the AQI is to help you understand what local air quality means to your health. To make it easier to understand, the AQI is divided into six categories:Air Quality Index(AQI) ValuesLevels of Health ConcernColorsWhen the AQI is in this range:..air quality conditions are:...as symbolized by this color:0 to 50GoodGreen51 to 100ModerateYellow101 to 150Unhealthy for Sensitive GroupsOrange151 to 200UnhealthyRed201 to 300Very UnhealthyPurple301 to 500HazardousMaroonNote: Values above 500 are considered Beyond the AQI. Follow recommendations for the Hazardous category. Additional information on reducing exposure to extremely high levels of particle pollution is available here.Each category corresponds to a different level of health concern. The six levels of health concern and what they mean are:"Good" AQI is 0 to 50. Air quality is considered satisfactory, and air pollution poses little or no risk."Moderate" AQI is 51 to 100. Air quality is acceptable; however, for some pollutants there may be a moderate health concern for a very small number of people. For example, people who are unusually sensitive to ozone may experience respiratory symptoms."Unhealthy for Sensitive Groups" AQI is 101 to 150. Although general public is not likely to be affected at this AQI range, people with lung disease, older adults and children are at a greater risk from exposure to ozone, whereas persons with heart and lung disease, older adults and children are at greater risk from the presence of particles in the air."Unhealthy" AQI is 151 to 200. Everyone may begin to experience some adverse health effects, and members of the sensitive groups may experience more serious effects."Very Unhealthy" AQI is 201 to 300. This would trigger a health alert signifying that everyone may experience more serious health effects."Hazardous" AQI greater than 300. This would trigger a health warnings of emergency conditions. The entire population is more likely to be affected.AQI colorsEPA has assigned a specific color to each AQI category to make it easier for people to understand quickly whether air pollution is reaching unhealthy levels in their communities. For example, the color orange means that conditions are "unhealthy for sensitive groups," while red means that conditions may be "unhealthy for everyone," and so on.Air Quality Index Levels of Health ConcernNumericalValueMeaningGood0 to 50Air quality is considered satisfactory, and air pollution poses little or no risk.Moderate51 to 100Air quality is acceptable; however, for some pollutants there may be a moderate health concern for a very small number of people who are unusually sensitive to air pollution.Unhealthy for Sensitive Groups101 to 150Members of sensitive groups may experience health effects. The general public is not likely to be affected.Unhealthy151 to 200Everyone may begin to experience health effects; members of sensitive groups may experience more serious health effects.Very Unhealthy201 to 300Health alert: everyone may experience more serious health effects.Hazardous301 to 500Health warnings of emergency conditions. The entire population is more likely to be affected.Note: Values above 500 are considered Beyond the AQI. Follow recommendations for the "Hazardous category." Additional information on reducing exposure to extremely high levels of particle pollution is available here.

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.

Overview

This is the data repository for the 2019 Novel Coronavirus Visual Dashboard operated by the Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE). This database was created in response to the Coronavirus public health emergency to track reported cases in real-time. The data include the location and number of confirmed COVID-19 cases, deaths and recoveries for all affected countries, aggregated at the appropriate province or state. It was developed to enable researchers, public health authorities and the general public to track the outbreak as it unfolds. Additional information is available in the blog post, Mapping 2019-nCoV (https://systems.jhu.edu/research/public-health/ncov/), and included data sources are listed here: https://github.com/CSSEGISandData/COVID-19

Sample Query 1

How many confirmed COVID-19 cases were there in the US, by state? This query determines the total number of cases by province in February. A "province_state" can refer to any subset of the US in this particular dataset, including a county or state. SELECT province_state, confirmed AS feb_confirmed_cases, FROM bigquery-public-data.covid19_jhu_csse.summary WHERE country_region = "US" AND date = '2020-02-29' ORDER BY feb_confirmed_cases desc

Sample Query 2

Which countries with the highest number of confirmed cases have the most per capita? This query joins the Johns Hopkins dataset with the World Bank's global population data to determine which countries among those with the highest total number of confirmed cases have the most confirmed cases per capita.

with country_pop AS( SELECT IF(country = "United States","US",IF(country="Iran, Islamic Rep.","Iran",country)) AS country, year_2018 FROM bigquery-public-data.world_bank_global_population.population_by_country)

SELECT cases.date AS date, cases.country_region AS country_region, SUM(cases.confirmed) AS total_confirmed_cases, SUM(cases.confirmed)/AVG(country_pop.year_2018) * 100000 AS confirmed_cases_per_100000 FROM bigquery-public-data.covid19_jhu_csse.summary cases JOIN country_pop ON cases.country_region LIKE CONCAT('%',country_pop.country,'%') WHERE cases.country_region = "US" AND country_pop.country = "US" AND cases.date = DATE_SUB(current_date(),INTERVAL 1 day) GROUP BY country_region, date

UNION ALL

SELECT cases.date AS date, cases.country_region AS country_region, SUM(cases.confirmed) AS total_confirmed_cases, SUM(cases.confirmed)/AVG(country_pop.year_2018) * 100000 AS confirmed_cases_per_100000 FROM bigquery-public-data.covid19_jhu_csse.summary cases JOIN country_pop ON cases.country_region LIKE CONCAT('%',country_pop.country,'%') WHERE cases.country_region = "France" AND country_pop.country = "France" AND cases.date = DATE_SUB(current_date(),INTERVAL 1 day) GROUP BY country_region, date

UNION ALL

SELECT cases.date AS date, cases.country_region AS country_region, SUM(cases.confirmed) AS total_confirmed_cases, SUM(cases.confirmed)/AVG(country_pop.year_2018) * 100000 AS confirmed_cases_per_100000 FROM bigquery-public-data.covid19_jhu_csse.summary cases JOIN country_pop ON cases.country_region LIKE CONCAT('%',country_pop.country,'%') WHERE cases.country_region = "China" AND country_pop.country = "China" AND cases.date = DATE_SUB(current_date(),INTERVAL 1 day)

GROUP BY country_region, date

UNION ALL

SELECT cases.date AS date, cases.country_region AS country_region, cases.confirmed AS total_confirmed_cases, cases.confirmed/country_pop.year_2018 * 100000 AS confirmed_cases_per_100000 FROM bigquery-public-data.covid19_jhu_csse.summary cases JOIN country_pop ON cases.country_region LIKE CONCAT('%',country_pop.country,'%') WHERE cases.country_region IN ("Italy", "Spain", "Germany", "Iran") AND cases.date = DATE_SUB(current_date(),INTERVAL 1 day) ORDER BY confirmed_cases_per_100000 desc

Dataset source

JHU CSSE

Update frequency

Daily

📦 Global Financial and Macroeconomic Market Dataset

This dataset contains structured historical financial market data across various countries and time intervals. It is sourced from publicly available metastock-formatted and ASCII-formatted files used in economic and trading research.

📁 Directory Overview

financial_dataset/ ├── 5 min/ │ ├── hk/ # Hong Kong 5-min market data │ ├── hu/ # Hungary 5-min market data │ ├── pl/ # Poland 5-min market data │ ├── uk/ # United Kingdom 5-min market data │ ├── us/ # United States 5-min market data │ └── world/ # Global economic/market data (5-min) │ ├── daily/ │ ├── hk/ # Hong Kong daily data │ ├── hu/ # Hungary daily data │ └── jp/ # Japan daily market data

ℹ️ Note: Some hidden macOS metadata files (like ._filename) may appear; they can be safely ignored or removed before analysis.

⸻

🌍 Country & Region Codes

Code Country/Region hk Hong Kong hu Hungary pl Poland uk United Kingdom us United States jp Japan world Global aggregation (macroeconomic indices)

⸻

🕒 Timeframes

Folder Name Frequency Description daily/ Daily End-of-day market summaries (open, high, low, close, volume) 5 min/ 5-minute Intraday data for high-frequency market modeling or backtesting

⸻

📊 File Format & Content • All files are in ASCII format. • Columns typically include: • date • open • high • low • close • volume • There may be slight variations based on country/source.

⸻

📌 Source

Data retrieved from Sqoot/Metastock economic archives as of May 7, 2025.

⸻

🔍 Use Cases • Backtesting trading algorithms on intraday data • Macroeconomic trend modeling • Comparative analysis between developed and emerging markets • Visualization of international market movement patterns

This data set is supplement to this Scientific Reports article.

The data set provides estimates of country-level daily mobility metrics (uncertainty included) for 17 countries from March 11, 2020 to present. Estimates are based on more than 3.8 million smartphone trajectories.

Metrics:

Estimated daily average travelled distance by people.

Estimated percentage of people who did not move during the 24 hours of the day.

Countries: Argentina (ARG), Chile (CHL), Colombia (COL), Costa Rica (CRI), Ecuador (ECU), Greece (GRC), Guatemala (GTM), Italy (ITA), Mexico (MEX), Nicaragua (NIC), Panama (PAN), Peru (PER), Philippines (PHL), Slovenia (SVN), Turkey (TUR), United States (USA) and Venezuela (VEN).

Covered period: from March 11, 2020 to present.

Temporal resolution: daily.

Temporal smoothing:

No smoothing.

7-day moving average.

14-day moving average.

21-day moving average.

28-day moving average.

Uncertainty: 95% bootstrap confidence interval.

Data ownership

Anonymized data on smartphone trajectories are collected, owned and managed by Futura Innovation SRL. Smartphone trajectories are stored and analyzed on servers owned by Futura Innovation SRL and not shared with third parties, including the author of this repository and his organization (University of Bergamo).

Contribution

Ilaria Cremonesi of Futura Innovation SRL is the data owner and data manager.

Francesco Finazzi of University of Bergamo developed the statistical methodology for the data analysis and the algorithms implemented on Futura Innovation SRL servers.

Repository update

CSV files of this repository are regularly produced by Futura Innovation SRL and published by the repository's author after validation.

Judgement on American and Soviet foreign policy as well as the competition between the great powers. Topics: Most important domestic and foreign policy problems; perceived changes in the relations between the great powers; attitude to selected countries and politicians; preferred East-West orientation of one´s own country; the peace efforts of China; danger of war; assessment of the credibility of the foreign policy of the USSR and the western powers as well as the seriousness of the disarmament efforts of the great powers; principle agreement of one´s own country with the interests of the USA, the USSR, Great Britain, France and China; expected development of agreement between the USSR and China; expected development of the economic and military competition between the great powers; contribution of NATO to European security; NATO contribution of one´s own country; trust in NATO; judgement on the result of the Paris summit conference and assessment of the readiness of the participants to make concessions; attitude to concessions by the western powers in the Berlin question; comparison of current status and future development of science, the military, the standard of living, industrial and agricultural production, welfare, technology, medicine and space flight in the USA and the USSR; assessment of the steadfastness of the American as well as Soviet population in the respective basic ideas and assessment of the readiness of the peoples to make an effort for this conviction; judgement on the prospects for the future of the two economic systems; frequency of watching television in the evening hours; TV possession; number of adults watching television in the afternoon as well as in the evening; going to the movies; assessment of the influence of foreign films on one´s own country; impression of Americans (tourists, students, business people, musicians, politicians) who have been in one´s country; assessment of the influence of American magazines, books, films, television programs, the Voice of America and Jazz on one´s own country; attitude to stationing of American troops in the country and judgement on their conduct; most important sources of information about the USA; perceived differences between American and British broadcast of news and information; most trustworthy source of news; attitude to construction of nuclear weapons by France and the atomic bomb test in the Sahara; the significance of the visit by Khruschev in France for world peace. The following questions were posed except in Great Britain: media usage in form of a detailed recording of the frequency of listening to foreign radio stations (BBC, BFN, AFN) as well as the Voice of America; self-assessment of knowledge of English and judgement on the understandability of radio announcers; union membership; length of interview. The following questions were posed in France: possession of a motor vehicle; possession of a radio; house ownership. The following questions were posed in Germany: number of contact attempts; willingness of respondent to cooperate. The following questions were posed in Italy: place of interview; day of interview. Beurteilung der amerikanischen und sowjetischen Außenpolitik sowie des Wettstreits zwischen den Großmächten. Themen: Wichtigste innen- und außenpolitische Probleme; empfundene Veränderungen in den Beziehungen zwischen den Großmächten; Einstellung zu ausgewählten Ländern und Politikern; präferierte Ost-West-Orientierung des eigenen Landes; die Friedensbemühungen Chinas; Kriegsgefahr; Einschätzung der Glaubhaftigkeit der Außenpolitik der UdSSR und der Westmächte sowie der Ernsthaftigkeit der Abrüstungsbemühungen der Großmächte; grundsätzliche Übereinstimmung des eigenen Landes mit den Interessen der USA, der UdSSR, Großbritanniens, Frankreichs und Chinas; erwartete Entwicklung der Übereinstimmung von UdSSR und China; erwartete Entwicklung des wirtschaftlichen und militärischen Wettstreits zwischen den Großmächten; Beitrag der Nato zur europäischen Sicherheit; Nato-Beitrag des eigenen Landes; Vertrauen in die Nato; Beurteilung des Ausgangs der Pariser Gipfelkonferenz und Einschätzung der Konzessionsbereitschaft der Teilnehmer; Einstellung zu Zugeständnissen der Westmächte in der Berlin-Frage; Vergleich des derzeitigen Stands und der zukünftigen Entwicklung der Wissenschaft, des Militärs, des Lebensstandards, der industriellen und agrarischen Produktion, der Wohlfahrt, der Technik, der Medizin und der Raumfahrt in den USA und der UdSSR; Einschätzung der Verhaftetheit der amerikanischen sowie der sowjetischen Bevölkerung in den jeweiligen Grundideen und Einschätzung der Bereitschaft der Völker, sich für diese Überzeugung einzusetzen; Beurteilung der Zukunftsaussichten der beiden Wirtschaftssysteme; Fernsehhäufigkeit in den Abendstunden; TV-Besitz; Anzahl der fernsehenden Erwachsenen am Nachmittag sowie am Abend; Kinobesuch; Einschätzung des Einflusses ausländischer Filme auf das eigene Land; Eindruck von Amerikanern (Touristen, Studenten, Geschäftsleuten, Musikern, Politikern), die im eigenen Land aufgetreten sind; Einschätzung des Einflusses amerikanischer Zeitschriften, Bücher, Filme, Fernsehprogramme, der Stimme Amerikas und des Jazz auf das eigene Land; Einstellung zur Stationierung amerikanischer Truppen im Lande und Beurteilung deren Verhaltens; wichtigste Informationsquellen über die USA; wahrgenommene Differenzen zwischen amerikanischer und britischer Übermittlung von Nachrichten und Informationen; vertrauenvollste Nachrichtenquelle; Einstellung zum Bau von Atomwaffen durch Frankreich und zum Atombombenversuch in der Sahara; die Bedeutung des Chruschtschowsbesuchs in Frankreich für den Weltfrieden. Außer in Großbritannien wurde gefragt: Mediennutzung in Form einer detaillierten Erfassung der Häufigkeit des Hörens ausländischer Radiosender (BBC, BFN, AFN) sowie der Stimme Amerikas; Selbsteinschätzung der Englischkenntnisse und Beurteilung der Verständlichkeit der Rundfunksprecher; Gewerkschaftsmitgliedschaft; Interviewdauer. In Frankreich wurde zusätzlich gefragt: Kraftfahrzeugbesitz; Radiobesitz; Hausbesitz. In Deutschland wurde zusätzlich gefragt: Anzahl der Kontaktversuche; Kooperationsbereitschaft des Befragten. In Italien wurde zusätzlich gefragt: Interviewort; Interviewtag.

Context

This is a dataset of the most highly populated city (if applicable) in a form easy to join with the COVID19 Global Forecasting (Week 1) dataset. You can see how to use it in this kernel

Content

There are four columns. The first two correspond to the columns from the original COVID19 Global Forecasting (Week 1) dataset. The other two is the highest population density, at city level, for the given country/state. Note that some countries are very small and in those cases the population density reflects the entire country. Since the original dataset has a few cruise ships as well, I've added them there.

Acknowledgements

Thanks a lot to Kaggle for this competition that gave me the opportunity to look closely at some data and understand this problem better.

Inspiration

Summary: I believe that the square root of the population density should relate to the logistic growth factor of the SIR model. I think the SEIR model isn't applicable due to any intervention being too late for a fast-spreading virus like this, especially in places with dense populations.

After playing with the data provided in COVID19 Global Forecasting (Week 1) (and everything else online or media) a bit, one thing becomes clear. They have nothing to do with epidemiology. They reflect sociopolitical characteristics of a country/state and, more specifically, the reactivity and attitude towards testing.

The testing method used (PCR tests) means that what we measure could potentially be a proxy for the number of people infected during the last 3 weeks, i.e the growth (with lag). It's not how many people have been infected and recovered. Antibody or serology tests would measure that, and by using them, we could go back to normality faster... but those will arrive too late. Way earlier, China will have experimentally shown that it's safe to go back to normal as soon as your number of newly infected per day is close to zero.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F197482%2F429e0fdd7f1ce86eba882857ac7a735e%2Fcovid-summary.png?generation=1585072438685236&alt=media" alt="">

My view, as a person living in NYC, about this virus, is that by the time governments react to media pressure, to lockdown or even test, it's too late. In dense areas, everyone susceptible has already amble opportunities to be infected. Especially for a virus with 5-14 days lag between infections and symptoms, a period during which hosts spread it all over on subway, the conditions are hopeless. Active populations have already been exposed, mostly asymptomatic and recovered. Sensitive/older populations are more self-isolated/careful in affluent societies (maybe this isn't the case in North Italy). As the virus finishes exploring the active population, it starts penetrating the more isolated ones. At this point in time, the first fatalities happen. Then testing starts. Then the media and the lockdown. Lockdown seems overly effective because it coincides with the tail of the disease spread. It helps slow down the virus exploring the long-tail of sensitive population, and we should all contribute by doing it, but it doesn't cause the end of the disease. If it did, then as soon as people were back in the streets (see China), there would be repeated outbreaks.

Smart politicians will test a lot because it will make their condition look worse. It helps them demand more resources. At the same time, they will have a low rate of fatalities due to large denominator. They can take credit for managing well a disproportionally major crisis - in contrast to people who didn't test.

We were lucky this time. We, Westerners, have woken up to the potential of a pandemic. I'm sure we will give further resources for prevention. Additionally, we will be more open-minded, helping politicians to have more direct responses. We will also require them to be more responsible in their messages and reactions.

Project Tycho datasets contain case counts for reported disease conditions for countries around the world. The Project Tycho data curation team extracts these case counts from various reputable sources, typically from national or international health authorities, such as the US Centers for Disease Control or the World Health Organization. These original data sources include both open- and restricted-access sources. For restricted-access sources, the Project Tycho team has obtained permission for redistribution from data contributors. All datasets contain case count data that are identical to counts published in the original source and no counts have been modified in any way by the Project Tycho team. The Project Tycho team has pre-processed datasets by adding new variables, such as standard disease and location identifiers, that improve data interpretability. We also formatted the data into a standard data format.

Each Project Tycho dataset contains case counts for a specific condition (e.g. measles) and for a specific country (e.g. The United States). Case counts are reported per time interval. In addition to case counts, datasets include information about these counts (attributes), such as the location, age group, subpopulation, diagnostic certainty, place of acquisition, and the source from which we extracted case counts. One dataset can include many series of case count time intervals, such as "US measles cases as reported by CDC", or "US measles cases reported by WHO", or "US measles cases that originated abroad", etc.

Depending on the intended use of a dataset, we recommend a few data processing steps before analysis: - Analyze missing data: Project Tycho datasets do not include time intervals for which no case count was reported (for many datasets, time series of case counts are incomplete, due to incompleteness of source documents) and users will need to add time intervals for which no count value is available. Project Tycho datasets do include time intervals for which a case count value of zero was reported. - Separate cumulative from non-cumulative time interval series. Case count time series in Project Tycho datasets can be "cumulative" or "fixed-intervals". Cumulative case count time series consist of overlapping case count intervals starting on the same date, but ending on different dates. For example, each interval in a cumulative count time series can start on January 1st, but end on January 7th, 14th, 21st, etc. It is common practice among public health agencies to report cases for cumulative time intervals. Case count series with fixed time intervals consist of mutually exclusive time intervals that all start and end on different dates and all have identical length (day, week, month, year). Given the different nature of these two types of case count data, we indicated this with an attribute for each count value, named "PartOfCumulativeCountSeries".

Time use data derived from the Multinational Time Use Study (MTUS) for France, Netherlands, Norway, UK and USA with 11 timepoints in a pooled format for the period 1971 - 2000. This dataset was composed to analyse time use and family expenditure to understanding consumption practices and the dynamics of change across time and space. The data derive from national time use surveys, which record how people allocate their time during the day. This dataset allows detailed decomposition of activities and identification of the behaviour of different groups and categories of people. The dataset contains records for 55,496 individuals. The countries and time points included are France 1974, 1998; Netherlands 1975, 1985, 1995; Norway 1971, 2000; UK 1975, 2000; USA 1975, 1985, 1998.This project examined trends in patterns of consumption since the 1970s in five countries: USA, UK, France, Italy and Norway. This comparative analysis was designed to examine systematically whether there was an overall tendency in developed consumer societies towards international convergence in consumption behaviour. This general overarching question allows the exploration of many controversial issues in the understanding of consumer culture, for example, globalization, national differences, social divisions, commodification, formation of demand, diversity of taste, individualization, changing lifestyles and classifications of the consumer. Mapping of patterns and trends in consumption were accomplished by reconstructing and transforming existing national data sets on household expenditure and aligning them with the time use surveys which have been conducted intermittently since the 1960s. Investigation involved identifying trends within each country separately in terms of expenditure and time use, comparing the five cases for signs of convergence and difference and, finally, contrasting the evidence of time-use with that of spending. The project also analysed changes in the categories used by different states statistical offices when classifying expenditures. Dataset derived from the Multinational Time Use Study, which in turn compiles data from a range of national time use surveys. Please refer to the MTUS surveys website (reference in Related resources) for details of the time-use diary methods used in each country. The survey sources used are France 1974, 1998; Netherlands 1975, 1985, 1995; Norway 1971, 2000; UK 1975, 2000; and USA 1975, 1985, 1998. The persons in the sample are part of households which are identifiable in the original MTUS data for some countries. The unit of analysis of this dataset however is the person. Each person’s time-use diaries for between 2 and 7 days have been averaged into the ‘average daily minutes’ figures which appear in the data as AV numbers.

Project Tycho datasets contain case counts for reported disease conditions for countries around the world. The Project Tycho data curation team extracts these case counts from various reputable sources, typically from national or international health authorities, such as the US Centers for Disease Control or the World Health Organization. These original data sources include both open- and restricted-access sources. For restricted-access sources, the Project Tycho team has obtained permission for redistribution from data contributors. All datasets contain case count data that are identical to counts published in the original source and no counts have been modified in any way by the Project Tycho team. The Project Tycho team has pre-processed datasets by adding new variables, such as standard disease and location identifiers, that improve data interpretabilty. We also formatted the data into a standard data format. Each Project Tycho dataset contains case counts for a specific condition (e.g. measles) and for a specific country (e.g. The United States). Case counts are reported per time interval. In addition to case counts, datsets include information about these counts (attributes), such as the location, age group, subpopulation, diagnostic certainty, place of aquisition, and the source from which we extracted case counts. One dataset can include many series of case count time intervals, such as "US measles cases as reported by CDC", or "US measles cases reported by WHO", or "US measles cases that originated abroad", etc. Depending on the intended use of a dataset, we recommend a few data processing steps before analysis:

Analyze missing data: Project Tycho datasets do not inlcude time intervals for which no case count was reported (for many datasets, time series of case counts are incomplete, due to incompleteness of source documents) and users will need to add time intervals for which no count value is available. Project Tycho datasets do include time intervals for which a case count value of zero was reported. Separate cumulative from non-cumulative time interval series. Case count time series in Project Tycho datasets can be "cumulative" or "fixed-intervals". Cumulative case count time series consist of overlapping case count intervals starting on the same date, but ending on different dates. For example, each interval in a cumulative count time series can start on January 1st, but end on January 7th, 14th, 21st, etc. It is common practice among public health agencies to report cases for cumulative time intervals. Case count series with fixed time intervals consist of mutually exxclusive time intervals that all start and end on different dates and all have identical length (day, week, month, year). Given the different nature of these two types of case count data, we indicated this with an attribute for each count value, named "PartOfCumulativeCountSeries".

Abstract: Jasper and Spijker arrive at Kosovo´s Hospital, and Roman and Lode find them. Roman and Jasper must testify to the police, who believe that Jasper is part of a mafia. Andy pretends to sign a contract with the German businessmen for the absolute automation of the packing area before Roman arrives in Vosselaar. Jasper and Roman find Tina and Katarina, and Jasper decides to stay in Kosovo for a while. Details: The man that Jasper finds on the road decides to help both friends and brings them in his car boot through a secret path. They illegally pass the frontier and go to the Hospital of Kosovo. Lode and Roman arrive at the official border control in Kosovo, but there is a long waiting queue. They recognize the signal of the golden truffle in Kosovo’s Hospital, and they begin to worry. When Jasper and Spijker arrive at the Hospital, the doctors take Spijker to a private room for treatment, and the Hospital's administrative staff notify the police that Jasper and Spijker do not have a passport. Spijker gets better, and when he wakes up, he has to say goodbye to Jasper, who must go for police questioning. In Vosselaar, Andy is complaining about how Rachel appropriated the meeting with the German businessmen. His grandfather reveals to him that in 1982, when Roman´s sister Indra died, he obtained legal permission to make decisions and sign documents related to the management of Tytgat Chocolat. This permission was not reversed, and this fact enables Andy to access executive decision-making as long as the executive director is absent. Lately, Andy and his grandfather have dinner with Mr. Müller and his business partner, and Andy proposes that they sign the needed documents for the automatization of the packing area. However, Mr. Müller states that they prefer to draw up a contract in Stuttgart and come back in two days for signing. In the police station, Jasper is being interrogated by two policemen, but the communication is not good due to a lack of understanding of Jasper´s language. At this moment, Roman shows up there with a translator. In the beginning, the policemen think that Jasper is part of a mafia, but Roman explains that he is only a worker in his factory, and Jasper starts to recreate all the true stories that got them to Kosovo. After a long questioning and obtaining an X-ray of Spijker proving that the gold truffle is still inside him, the policemen decide that they have to find Tina. Nevertheless, due to the fact that Jasper and Spijker are illegally in the country, they all have to leave it. Back at the Tytgat Chocolat factory, Andy is preparing a public event to announce the modifications in the firm. He asks Marieke, Roman´s secretary, not to be on the day of the event since she is a distraction for him. Marieke gets really angry with that and tells him that he is only a pathetic little man. After that, she goes to Rachel's house and explains to her that she wants to resign from her job, but Rachel encourages her to stay. Marieke explains to Rachel Andy´s plan and the use that he is making of the permission of 1982 to decide by himself the future of Tytgat Chocolat. Immediately after that, Rachel calls Roman and alerts him about the plan of his nephew and asks him to be back in Vosselaar as soon as possible. Roman and Jasper meet Lode and Spijker outside the police station. Jasper establishes that he is not leaving Kosovo without finding Tina, and suddenly, the translator shows up and informs Roman that he should go back inside: the Police have some news about Tina´s current status. The police officers say that Tina and Katarina appear as dead people. The death was registered two weeks ago, a fact that confuses Roman and Jasper. They go out to the police station again, but before they leave in the caravan, Jasper hears a group of children singing the same song that Tina taught him when they met for the first time. Jasper starts following them, and Roman, who sees that Jasper is walking away, ignoring his calls, goes after him. The children arrive at one school. Jasper recognizes it because it coincides with one that Tina once showed him in a photograph. Jasper and Roman decide to enter the school, and they ask a teacher about Tina. The teacher accompanies Jasper and Roman to an archive of yearbooks, and they look together for her. Jasper recognises Tina in a photo, but she has another name: Sani. After that, Jasper and Roman explain to the teacher that they are looking for two deported women, and the teacher tells them that he knows a place for returning people, a temporary shelter place. The teacher brings them to a building in a peripheral area. The three start looking for Tina and Katarina, but they do not find them. Before giving up the search, Jasper sees that a group of children are playing with a Tytgat Chocolat box of truffles and alerts Roman and the teacher. One of the children brings them to the house of the person who gave it to them. There, they know an old woman who tells them that she has a lot of these boxes because her daughter sends money from Belgium through them. When they are talking, Tina appears behind them, runs to Jasper, and hugs him. Then Katarina arrives and sees them: “My name here is Monica, and Tina is Sani”, she says. Monica prepares a party to celebrate the re-encounter, and Spijker and Lode arrive there. Katarina tells Lode that she and her daughter were living in the previous years with a false identity of two people who died in the Kosovo War. Jasper gives Sani the golden truffle, and Roman promises her the prize. The celebration goes on, and suddenly, Raoul arrives with the police at the party, and he and his son meet again. After the party, Roman has to arrive as soon as possible in Vosselaar in order to avoid Andy signing the contract with the German businessmen. Lode suggests that they could go back by plane in order to shorten the time. Spijker, Roman, and Lode go to the airport. Lode asks Roman if they could form an executive team in the company. Roman is pleased with his brother´s proposal. When both brothers arrive at the factory, Andy and his grandfather are in the middle of a meeting with the German businessmen. Roman and Lode break into the meeting, and they sit at the negotiating table in front of Andy´s astonished view. Andy gets nervous and goes out of the room. After the meeting with the German businessmen, Roman goes with the journalist, who was also in the factory, to respond honestly to some questions about the golden truffle contest and Jasper´s journey. Jasper stays in Kosovo with Sani and his family. There, he has a conversation with Raoul in which his father says that he and his mother want to give Jasper more freedom and self-determination in his life. Jasper expresses his desire to live with Sani, and he establishes that he would live in Kosovo for half the year and the other half in Belgium with his parents. A photo of Tina and Jasper is posted on the hall TV of Tytgat Chocolat.

The overall ACE project is motivated by the need to better understand the behaviour of non-state armed groups in perpetrating atrocity crimes such as crimes against humanity, ethnic cleansing and war crimes. The data collection is based on six countries Central African Republic, the Democratic Republic of Congo, Iraq, Nigeria, Syria, and Somalia with a focus on non-state actor perpetrated atrocity events. The aim is to have a fine-grained event data collection of different types of atrocity crimes in these countries. These event types are derived from the Rome Statute. More specifically, the unit of observation in ACE is the event where a named state or non-state actor is involved on a given day in a specific location. Each individual event is covered with the best precision regarding the type of event, location, perpetrator and victims.Since 2010, there has been a 'dramatic resurgence' of violent conflict around the world (United Nations, 2018, p. v). As part of this trend, mass atrocity crimes, defined as genocide, war crimes, crimes against humanity, and ethnic cleansing (GWCE), have become 'the new normal' (Human Rights Watch 2018). At this time of writing, the Global Centre for the Responsibility to Protect (GCR2P) identifies seven countries that are 'currently' experiencing GWCE, three at 'imminent risk', seven of 'serious concern', and thirteen being 'monitored' because they have experienced GWCE in the recent past (Global Centre for the Responsibility to Protect 2019). These crises have seen millions of people killed, tens of thousands raped, and underpin an unprecedented refugee crisis. Although mass violence is not a new phenomenon, non-state armed groups such as Al Qaeda, Islamic State, Boko Haram, Lord's Resistance Army, and Al-Shabaab are increasingly playing a critical role in the perpetration of atrocity crimes leading to key policymakers calling for urgent research on this specific threat (see case for support). Responding to this new reality, the project answers the following primary research question: under what conditions do non-state armed groups perpetrate GWCE? The funding will enable us to develop the first dataset in the world that collects systematic evidence on non-state armed groups perpetrating GWCE, which we call 'Atrocity Crime Events' (ACE) dataset. To do this, we will analyse six countries and three themes. The former refers to the Central African Republic, the Democratic Republic of Congo, Iraq, Nigeria, Syria and Somalia. The latter focuses on i) interactions, for example, between the non-state armed group[s] themselves, other actors such as the government, and external actors such as UN peacekeepers, ii) local factors, for instance, geography, economics, population density, as well as natural resources, and iii) group characteristics, such as age, ideology, and external support. The scientific impact develops in three stages. First, the unique dataset 'ACE' will provide the necessary information to run statistical analysis to explain why, when, and where mass atrocities happen in our six chosen countries. Second, we will develop hypothesis based on our three themes that can be tested through future academic inquiry. Third, the project seeks to drive forward quantitative research into the causes of non-state armed groups perpetrating mass violence. This advance in knowledge will allow us to provide policy recommendations in order to improve international, regional, and national strategies toward mass atrocity prevention with a specific focus on policymakers in the United Nations (UN), the European Union (EU), the United Kingdom (UK), and Africa (the four case study governments and organisations such as the African Union). We will work with three project partners, GCR2P (New York and Geneva), Aegis Trust (Kigali), and Protection Approaches (London), as well as an advisory board consisting of Alex Bellamy, Gyorgy Tatar, Ivan Simonovic, Karen E. Smith, and Kristian Skrede Gleditsch. As part of our impact strategy, we will hold end of project workshops in London, New York, and Kigali. Outputs will include i) publicly available dataset and codebook, ii) six articles in high ranking journals, iii) an Analysis Framework for the United Nations Office on Genocide Prevention and the RtoP, iv) co-created policy reports with each project partner focusing on the UN, the UK, the EU, and African mass atrocity prevention strategies, v) blogposts, vi) infographics, and vii) presentations at conferences and policy-orientated meetings. The data collection methodology is based on coding news reports extracted from LexisNexis. The extraction of news reports from LexisNexis has been narrowed down by using specific search terms for each event type, including the countries in this project. The focus is primarily on English language sources and where necessary, the geography filter is used to narrow down results based on the location of the event. Once a set of news reports have been identified from Lexis Nexis, the coders skim through the reports based on headlines/short descriptions and select to read through the ones that seem to constitute an event (as opposed to, for example, reports about UN meetings to discuss atrocities etc.). The coders then write a short description of the event on the dataset and code the rest of the variables in the dataset with best precision possible. The coding of the events is based on ACE codebook and is conducted by human coders, each specialising in one of the countries in question.

Abstract copyright UK Data Service and data collection copyright owner. Carried out every four years, the European Quality of Life Survey (EQLS) examines both the objective circumstances of European citizens' lives and how they feel about those circumstances and their lives in general. It collects data on a range of issues, such as employment, income, education, housing, family, health and work-life balance. It also looks at subjective topics, such as people's levels of happiness, life satisfaction, and perceived quality of society. By running the survey regularly, it has also become possible to track key trends in the quality of people's lives over time. Previous surveys have shown, for instance, that people are having greater difficulty making ends meet since the economic crisis began. In many countries, they also feel that there is now more tension between people from different ethnic groups. And across Europe, people now trust their governments less than they did before. However, people still continue to get the greatest satisfaction from their family life and personal relationships. Over the years, the EQLS has developed into a valuable set of indicators which complements traditional indicators of economic growth and living standard such as GDP or income. The EQLS indicators are more inclusive of environmental and social aspects of progress and therefore are easily integrated into the decision-making process and taken up by public debate at EU and national levels in the European Union. In each wave a sample of adult population has been selected randomly for a face to face interview. In view of the prospective European enlargements the geographical coverage of the survey has expanded over time from 28 countries in 2003 to 34 countries in 2011-12. Further information about the survey can be found on the European Foundation for the Improvement of Living and Working Conditions (Eurofound) EQLS web pages. Main Topics: The survey examines a range of issues, such as employment, income, education, housing, family, health, work-life balance, life satisfaction and perceived quality of society. Multi-stage stratified random sample See documentation for details Face-to-face interview 2007 AGE ATTITUDES Austria BASIC NEEDS Belgium Bulgaria CARE OF DEPENDANTS CHARITABLE ORGANIZA... CHIEF INCOME EARNERS CHILD CARE CHILDREN CHRONIC ILLNESS Croatia Cyprus Czech Republic DEBILITATIVE ILLNESS DEBTS DISADVANTAGED GROUPS DOMESTIC RESPONSIBI... Denmark ECONOMIC ACTIVITY EDUCATIONAL BACKGROUND EDUCATIONAL LEVELS EMOTIONAL STATES EMPLOYMENT ENGLISH LANGUAGE ETHNIC GROUPS EVERYDAY LIFE EXPECTATION EXPOSURE TO NOISE Estonia European Union Coun... FAMILY LIFE FATHER S PLACE OF B... FINANCIAL DIFFICULTIES FINANCIAL RESOURCES FURNITURE Finland France GENDER GENERAL PRACTITIONERS GROUPS Germany October 1990 Greece HAPPINESS HEALTH HEALTH CONSULTATIONS HEALTH SERVICES HOBBIES HOME OWNERSHIP HOURS OF WORK HOUSEHOLD BUDGETS HOUSEHOLD HEAD S EC... HOUSEHOLD HEAD S OC... HOUSEHOLD INCOME HOUSEHOLDS HOUSEWORK HOUSING CONDITIONS HOUSING TENURE Hungary INCOME INTERGROUP CONFLICT INTERNET USE Ireland Italy JOB SATISFACTION JOB SECURITY LEISURE GOODS LIFE EXPECTANCY LIFE SATISFACTION LIFE STYLES LIVING CONDITIONS LOCAL COMMUNITY FAC... Latvia Lithuania Luxembourg MARITAL STATUS MENTAL HEALTH MIGRANTS MOTHER S PLACE OF B... MOTOR VEHICLES Macedonia Malta NEIGHBOURHOODS Netherlands Norway OCCUPATIONAL SAFETY OCCUPATIONAL STATUS OCCUPATIONS PARENTS PERSONAL CONTACT PLACE OF BIRTH POLITICAL PARTICIPA... POLLUTION POVERTY PUBLIC SERVICES Poland Portugal QUALITY OF LIFE RECREATIONAL FACILI... RELIGIOUS ATTENDANCE RELIGIOUS GROUPS ROOMS RURAL AREAS Romania SATISFACTION SOCIAL ATTITUDES SOCIAL CAPITAL SOCIAL DISADVANTAGE SOCIAL EXCLUSION SOCIAL INDICATORS SOCIAL LIFE SOCIAL SECURITY BEN... SOCIAL SUPPORT STANDARD OF LIVING STATE RETIREMENT PE... STATUS IN EMPLOYMENT STRESS PSYCHOLOGICAL SUBSIDIARY EMPLOYMENT SUPERVISORY STATUS Slovakia Slovenia Social behaviour an... Social conditions a... Spain Sweden TIME TRUST TRUST IN GOVERNMENT Turkey URBAN AREAS United Kingdom VOLUNTARY WORK WAGES WATER PROPERTIES WORK ATTITUDE WORKING CONDITIONS

Context

The United States have recently become the country with the most reported cases of 2019 Novel Coronavirus (COVID-19). This dataset contains daily updated number of reported cases & deaths in the US on the state and county level, as provided by the Johns Hopkins University. In addition, I provide matching demographic information for US counties.

Content

The dataset consists of two main csv files: covid_us_county.csv and us_county.csv. See the column descriptions below for more detailed information. In addition, I've added US county shape files for geospatial plots: us_county.shp/dbf/prj/shx.

• covid_us_county.csv: COVID-19 cases and deaths which will be updated daily. The data is provided by the Johns Hopkins University through their excellent github repo. I combined the separate "confirmed cases" and "deaths" files into a single table, removed a few (I think to be) redundant geo identifier columns, and reshaped the data into long format with a single date column. The earliest recorded cases are from 2020-01-22.

• us_counties.csv: Demographic information on the US county level based on the (most recent) 2014-18 release of the Amercian Community Survey. Derived via the great tidycensus package.

Column Description

COVID-19 dataset covid_us_county.csv:

• fips: County code in numeric format (i.e. no leading zeros). A small number of cases have NA values here, but can still be used for state-wise aggregation. Currently, this only affect the states of Massachusetts and Missouri.

• county: Name of the US county. This is NA for the (aggregated counts of the) territories of American Samoa, Guam, Northern Mariana Islands, Puerto Rico, and Virgin Islands.

• state: Name of US state or territory.

• state_code: Two letter abbreviation of US state (e.g. "CA" for "California"). This feature has NA values for the territories listed above.

• lat and long: coordinates of the county or territory.

• date: Reporting date.

• cases & deaths: Cumulative numbers for cases & deaths.

Demographic dataset us_counties.csv:

• fips, county, state, state_code: same as above. The county names are slightly different, but mostly the difference is that this dataset has the word "County" added. I recommend to join on fips.

• male & female: Population numbers for male and female.

• population: Total population for the county. Provided as convenience feature; is always the sum of male + female.

• female_percentage: Another convenience feature: female / population in percent.

• median_age: Overall median age for the county.

Acknowledgements

Data provided for educational and academic research purposes by the Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE).

Licence

The github repo states that:

This GitHub repo and its contents herein, including all data, mapping, and analysis, copyright 2020 Johns Hopkins University, all rights reserved, is provided to the public strictly for educational and academic research purposes. The Website relies upon publicly available data from multiple sources, that do not always agree. The Johns Hopkins University hereby disclaims any and all representations and warranties with respect to the Website, including accuracy, fitness for use, and merchantability. Reliance on the Website for medical guidance or use of the Website in commerce is strictly prohibited.

Version history

• In version 1, a small number of cases had values of `county == "Unassigned". Those have been superseded.
• Version 5: added US county shape files

Context

Coronavirus infection is currently the most important health topic. It surely tested and continues to test to the fullest extent the healthcare systems around the world. Although big progress is made in handling this pandemic, a tremendous number of questions are needed to be answered. I hereby present to you the local Bulgarian COVID-19 dataset with some context. It could be used as a comparator because it stands out compared to other countries and deserves analysis.

Context for Bulgarian population: Population - 6 948 445 Median age - 44.7 years Aged >65 - 20.801 % Aged >70 - 13.272%

Summary of the results: - first pandemic wave was weak, probably because of the early state of emergency (5 days after the first confirmed case). Whether this was a good decision or it was too early and just postpone the inevitable is debatable. -healthcare system collapses (probably due to delayed measures) in the second and third waves which resulted in Bulgaria gaining the top ranks for mortality and morbidity tables worldwide and in the EU. - low percentage of vaccinated people results in a prolonged epidemic and delaying the lifting of the preventive measures.

Some of the important moments that should be considered when interpreting the data: 08.03.2020 - Bulgaria confirmed its first two cases. The government issued a nationwide ban on closed-door public events (first lockdown); 13.03.2020- after 16 reported cases in one day, Bulgaria declared a state of emergency for one month until 13.04.2020. Schools, shopping centres, cinemas, restaurants, and other places of business were closed. All sports events were suspended. Only supermarkets, food markets, pharmacies, banks, and gas stations remain open. 03.04.2020 - The National Assembly approved the government's proposal to extend the state of emergency by one month until 13.05.2020; 14.05.2020 - the national emergency was lifted, and in its place was declared a state of an emergency epidemic situation. Schools and daycares remain closed, as well as shopping centers and indoor restaurants; 18.05.2020 - Shopping malls and fitness centers opened; 01.06.2020 - Restaurants and gaming halls opened; 10.07.2020 - discos and bars are closed, the sports events are without an audience; 29.10.2020 - High school and college students are transitioning to online learning; 27.11.2020 - the whole education is online, restaurants, nightclubs, bars, and discos are closed (second lockdown 27.11 - 21.12); 05.12.2020 - the 14-day mortality rate is the highest in the world; 16.01.2021 - some of the students went back to school; 01.03.2021 - restaurants and casinos opened; 22.03.2021 - restaurants, shopping malls, fitness centers, and schools are closed (third lockdown for 10 days - 22.03 - 31.03); 19.04.2021 - children daycare facilities, fitness centers, and nightclubs are opened;

Content

This dataset consists of 447 rows with 29 columns and covers the period 08.03.2020 - 28.05.2021. In the beginning, there are some missing values until the proper statistical report was established.

Inspiration

A publication proposal is sent to anyone who wishes to collaborate. Based on the results and the value of the findings and the relevance of the topic it is expected to publish: - in a local journal (guaranteed); - in a SCOPUS journal (highly probable); - in an IF journal (if the results are really insightful).

The topics could be, but not limited to: - descriptive analysis of the pandemic outbreak in the country; - prediction of the pandemic or the vaccination rate; - discussion about the numbers compared to other countries/world; - discussion about the government decisions; - estimating cut-off values for step-down or step-up of the restrictions.

Error or query reporting

If you find an error, have a question, or wish to make a suggestion, I encourage you to reach me.

Context

This is a Covid 19 data set for India. The data set is updated frequently and is analysed using tableau. Click on the link to visit the tableau story. Click each of the caption in the story to unveil its content.

https://public.tableau.com/profile/ambili.nair#!/vizhome/COVID19Indiastory/Indiastory?publish=yes

The first Covid 19 case in India was reported on 30th January 2020 in South Indian state of Kerala on a medical student who was pursuing the studies at Wuhan University, China. Two more students were found to be infected in Kerala in the consecutive days. The Kerala government was successful in containing the disease with its proactive measures back then. The second outbreak of Covid 19 in India started in the first week of March from various parts of India in various people who visited the foreign countries and in some of the tourists from different countries.

The tableau story consists of the following data analysis : 1. State-wise number of infected and number of death count in India map. Hover the mouse on each state in the India map to know the count. 2. Click on the next caption to know the state-wise number of confirmed, active, recovered and deceased cases in the form of bar chart. 3. The next caption takes you to the bar chart which shows the number of cases getting confirmed in India each day starting from January 30, 2020. 4. Next caption takes us to an analysis of the Mortality rate and the Recovery rate (in percentage) of each of the Indian state. We get an idea how hard each of the state is hit by the pandemic. 5. Next caption gives a detailed analysis of the state Kerala which has the mortality rate of 0.806% and the recovery rate of 74.4% as of now. Hover the mouse to know the count in each district. Don't forget to have a look at the line graph of 'number of active cases' in Kerala. It looks almost flattened ! As everyday we hear the increasing number of cases and deaths across the country, this graph may make you feel better...! 6. Finally the caption takes you to the statistics from the topmost district of Kerala - Kasaragod. The total number of cases reported is 179 at Kasaragod. The active number of cases is just 12 as of now... !!! Have a look at the statistics from Kasaragod and the story of 'Kasaragod model' as some of the national media in India call it !!!

Content

This data set consists of the following data: 1. state-wise statistics - Confirmed, Active, Recovered, Deceased cases 2. day-wise count of infected and deceased from various states 3. Statistics from Kerala - day-wise count of confirmed, Active, Recovered, Deceased cases 4. Statistics from Kasaragod district, Kerala - day-wise count of confirmed, Active, Recovered, Deceased cases 5. Count of confirmed cases from various districts of India

Acknowledgements

Ministry of Health and Family Welfare - India covid19india.org Wikipedia page - Covid 19 Pandemic India Govt. of Kerala dashboard - official Kerala Covid 19 statistics

Inspiration

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

Context

COVID-19 has infected many people in Indonesia, and the number of confirmed cases is increasing exponentially. Indonesia has raised its coronavirus alert to the "Darurat Nasional (National Emergency)" until 29 May 2020. The Java island, especially Jakarta, the capital city of Indonesia, is the most affected region by the coronavirus.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F2849532%2Ff46e130bad5d4e74a8835ca057dd05ca%2Facc.png?generation=1584939612835429&alt=media" alt="">

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F2849532%2F93b53d1b6601da74041f41ea4ba227f6%2Fcases.png?generation=1584938551413887&alt=media" alt="">

Following are the list of available online portals announce the information of COVID-19, from the public community and provincial (regional) government website in Indonesia.

1. https://www.covid19.go.id/situasi-virus-corona/, by Indonesian National Board for Disaster Management.
2. https://kawalcovid19.id/, by Kawal Covid-19 Indonesia community.
3. https://corona.jakarta.go.id/, Jakarta tanggap Covid-19 by Pemda DKI Jakarta.
4. https://pikobar.jabarprov.go.id/, Pusat Informasi & Koordinasi COVID-19, by Pemprov Jawa Barat.
5. https://corona.jatengprov.go.id/, Jawa Tengah Tanggap COVID-19, by Pemprov Jawa Tengah.
6. https://corona.sumbarprov.go.id/, Sumbar Tanggap Corona, by Pemprov Sumatera Barat.
7. http://corona.jogjaprov.go.id/, Yogyakarta Tanggap Covid-19, by Pemprov DIY.
8. https://covid19.bandung.go.id/. Pusat Informasi & Koordinasi COVID-19 Kota Bandung.

We make a structured dataset based on the report materials in these portals. Thus, the research community can apply recent AI and statistical techniques to generate new insights in support of the ongoing fight against this infectious disease in Indonesia.

Current State

Dataset 1) Total Confirmed Positive Cases 2) Google Trend Related keywords 3) Patient Epidemiological Data 4) Daily Case Statistics 5) Case per Province 6) Case in Jakarta Capital City 7) Daily New Confirmed Cases in Each Province (Timeline)

Kernel 1) Predicting Coronavirus Positive Cases in Indonesia 2) Visualization & Analysis of Covid-19 in Indonesia 3) Logistic Model for Indonesia COVID-19 4) DataSet Characteristics of Corona patients in several countries, including Indonesia 5) Novel Corona Virus (Covid-19) Indonesia EDA 6) Simple Visualization and Forecasting 7) Characteristics of Corona patients DS

Related Publication 1) Response to Covid-19: Data Analytics and Transparency, Koderea Talks, 18 March 2020, https://www.researchgate.net/publication/340003505_Response_to_Covid-19_Data_Analytics_and_Transparency 2) Covid-19 Data Science, ID Institute Obrolin Data Coronavirus, 24 March 2020, https://www.researchgate.net/publication/340116231_IDInstitute_Covid-19_Data_Science

Other Country Level Datasets

• South Korea - https://www.kaggle.com/kimjihoo/coronavirusdataset
• Italy - https://www.kaggle.com/sudalairajkumar/covid19-in-italy
• Brazil - https://www.kaggle.com/unanimad/corona-virus-brazil
• USA - https://www.kaggle.com/sudalairajkumar/covid19-in-usa
• France - https://www.kaggle.com/lperez/coronavirus-france-dataset
• Tunisia - https://www.kaggle.com/ghassen1302/coronavirus-tunisia
• Japan - https://www.kaggle.com/tsubasatwi/close-contact-status-of-corona-in-japan

Acknowledgements

Thanks sincerely to all the members of the DSCI Team, KawalCovid19.id, Pemda DKI Jakarta, Pemprov Jawa Barat, Pemprov Jawa Tengah, Pemprov Sumatera Barat, and Pemprov DIY.

DSCI Team

1. Ardiansyah (ardisragen)
2. Tri A Sundara (trilabs)
3. Thomhert (thomhert)
4. Epsi Sayidina (epsisayidina)
5. Teuku Hashrul (hahasrul)
6. Naufal Hakim (hakimbazol)

Invitation

We welcome anyone to join us as collaborators! Join WAG Chat: https://s.id/fgPoP For more information please contact ardi@ejnu.net or WA +8210-4297-0504

Working with https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F2849532%2Fd56eaf0a5d770d756a54cec0d09c87ff%2Fkoderea.png?generation=1584539195622597&alt=media" alt="">

Context

On January 30, 2020, the International Health Regulations Emergency Committee of the World Health Organization declared the outbreak a public health emergency of international concern (PHEIC). On January 31, 2020, Health and Human Services Secretary Alex M. Azar II declared a public health emergency (PHE) for the United States to aid the nation’s healthcare community in responding to COVID-19. On March 11, 2020 WHO publicly characterized COVID-19 as a pandemic.

Content

The data files present the total confirmed cases, total deaths and daily new cases and deaths by country. This data is sourced from the World Health Organization (WHO) Situation Reports (which you find here). The WHO Situation Reports are published daily [reporting data as of 10am (CET; Geneva time)]. The main section of the Situations Reports are long tables of the latest number of confirmed cases and confirmed deaths by country.

This dataset has five files : - total_cases.csv : Total confirmed cases - total_deaths.csv : Total deaths - new_cases.csv : New confirmed cases - new_deathes.csv : New deaths - full_data.csv : put it all files together

Acknowledgements

This dataset is sourced from WHO and confirmed by OurworldInData Special Thank to Hannah Ritchie that did a great reports explaining those datasets.

Inspiration

Insights on - Confirmed cases is what we do know - Confirmed COVID-19 cases by country - How we can make preventive measures - Growth of cases: How long did it take for the number of confirmed cases to double? - Understanding exponential growth - Try to predict the spread of COVID-19 ahead of time .

Context

This dataset was collected from data received via this APi.

Content

“[Recovered cases are a] more important metric to track than Confirmed cases.”— Researchers for the University of Virginia’s COVID-19 dashboard

If the number of total cases were accurately known for every country then the number of cases per million people would be a good indicator as to how well various countries are handling the pandemic.

Countries:

Datasets contend data about covid_19 from 232 countries - Afghanistan - Albania - Algeria - Andorra - Angola - Anguilla - Antigua-and-Barbuda - Argentina - Armenia - Aruba - Australia - Austria - Azerbaijan - Bahamas - Bahrain - Bangladesh - Barbados - Belarus - Belgium - Belize - Benin - Bermuda - Bhutan - Bolivia - Bosnia-and-Herzegovina - Botswana - Brazil - British-Virgin-Islands - Brunei - Bulgaria - Burkina-Faso - Burundi - Cabo-Verde - Cambodia - Cameroon - Canada - CAR - Caribbean-Netherlands - Cayman-Islands - Chad - Channel-Islands - Chile - China - Colombia - Comoros - Congo - Cook-Islands - Costa-Rica - Croatia - Cuba - Curaçao - Cyprus - Czechia - Denmark - Diamond-Princess - Diamond-Princess- - Djibouti - Dominica - Dominican-Republic - DRC - Ecuador - Egypt - El-Salvador - Equatorial-Guinea - Eritrea - Estonia - Eswatini - Ethiopia - Faeroe-Islands - Falkland-Islands - Fiji - Finland - France - French-Guiana - French-Polynesia - Gabon - Gambia - Georgia - Germany - Ghana - Gibraltar - Greece - Greenland - Grenada - Guadeloupe - Guam - Guatemala - Guinea - Guinea-Bissau - Guyana - Haiti - Honduras - Hong-Kong - Hungary - Iceland - India - Indonesia - Iran - Iraq - Ireland - Isle-of-Man - Israel - Italy - Ivory-Coast - Jamaica - Japan - Jordan - Kazakhstan - Kenya - Kiribati - Kuwait - Kyrgyzstan - Laos - Latvia - Lebanon - Lesotho - Liberia - Libya - Liechtenstein - Lithuania - Luxembourg - Macao - Madagascar - Malawi - Malaysia - Maldives - Mali - Malta - Marshall-Islands - Martinique - Mauritania - Mauritius - Mayotte - Mexico - Micronesia - Moldova - Monaco - Mongolia - Montenegro - Montserrat - Morocco - Mozambique - MS-Zaandam - MS-Zaandam- - Myanmar - Namibia - Nepal - Netherlands - New-Caledonia - New-Zealand - Nicaragua - Niger - Nigeria - Niue - North-Macedonia - Norway - Oman - Pakistan - Palau - Palestine - Panama - Papua-New-Guinea - Paraguay - Peru - Philippines - Poland - Portugal - Puerto-Rico - Qatar - Réunion - Romania - Russia - Rwanda - S-Korea - Saint-Helena - Saint-Kitts-and-Nevis - Saint-Lucia - Saint-Martin - Saint-Pierre-Miquelon - Samoa - San-Marino - Sao-Tome-and-Principe - Saudi-Arabia - Senegal - Serbia - Seychelles - Sierra-Leone - Singapore - Sint-Maarten - Slovakia - Slovenia - Solomon-Islands - Somalia - South-Africa - South-Sudan - Spain - Sri-Lanka - St-Barth - St-Vincent-Grenadines - Sudan - Suriname - Sweden - Switzerland - Syria - Taiwan - Tajikistan - Tanzania - Thailand - Timor-Leste - Togo - Tonga - Trinidad-and-Tobago - Tunisia - Turkey - Turks-and-Caicos - UAE - Uganda - UK - Ukraine - Uruguay - US-Virgin-Islands - USA - Uzbekistan - Vanuatu - Vatican-City - Venezuela - Vietnam - Wallis-and-Futuna - Western-Sahara - Yemen - Zambia - Zimbabw-