Inflation was the most worrying topic worldwide as of May 2025, with ********* of the respondents choosing that option. Crime and violence, as well as poverty and social inequality, followed behind. Moreover, following Russia's invasion of Ukraine and the war in Gaza, *** percent of the respondents were worried about military conflict between nations. Only *** percent were worried about the COVID-19 pandemic, which dominated the world after its outbreak in 2020. Global inflation and rising prices Inflation rates have spiked substantially since the beginning of the COVID-19 pandemic in 2020. From 2020 to 2021, the worldwide inflation rate increased from *** percent to *** percent, and from 2021 to 2022, the rate increased sharply from *** percent to *** percent. While rates are predicted to fall by 2025, many are continuing to struggle with price increases on basic necessities. Poverty and global development Poverty and social inequality were the third most worrying issues for respondents. While poverty and inequality are still prominent, global poverty rates have been on a steady decline over the years. In 1994, ** percent of people in low-income countries and around one percent of people in high-income countries lived on less than 2.15 U.S. dollars per day. By 2018, this had fallen to almost ** percent of people in low-income countries and 0.6 percent in high-income countries. Moreover, fewer people globally are dying of preventable diseases, and people are living longer lives. Despite these aspects, issues such as wealth inequality have global prominence.

The Global Attitudes Project (Spring 2022) is a cross-national survey of attitudes on global issues. There are 18 countries included in the survey. Topics include domestic society, economy, and politics, as well as social issues, globalization, national identity, life satisfaction, and religion. Respondents were also surveyed on their views of the roles of the United States and the other major global powers, as well as their impressions of well-known leaders (Joe Biden, Xi Jinping, Vladimir Putin, Emmanuel Macron, Olaf Scholz, and Kamala Harris). Items about the COVID-19 pandemic were also included.

A survey of people from 31 different countries around the world found that mental health was the biggest health problem respondents said was facing their country in 2024. Other health problems reported by respondents included cancer, stress, and obesity. The COVID-19 pandemic The COVID-19 pandemic impacted almost every country in the world and was the biggest global health crisis in recent history. It resulted in hundreds of millions of cases and millions of deaths, causing unprecedented disruption in health care systems. Lockdowns imposed in many countries to halt the spread of the virus also resulted in a rise of mental health issues as feelings of stress, isolation, and hopelessness arose. However, vaccines to combat the virus were developed at record speed, and many countries have now vaccinated large shares of their population. Nevertheless, in 2024, ** percent of respondents still stated that COVID-19 was the biggest health problem facing their country. Mental health issues One side effect of the COVID-19 pandemic has been a focus on mental health around the world. The two most common mental health issues worldwide are anxiety disorders and depression. In 2021, it was estimated that around *** percent of the global population had an anxiety disorder, while **** percent suffered from depression. Rates of depression are higher among females than males, with some *** percent of females suffering from depression, compared to *** percent of men. However, rates of suicide in most countries are higher among men than women. One positive outcome of the COVID-19 pandemic and the spotlight it shined on mental health may be a decrease in stigma surrounding mental health issues and seeking help for such issues. This would be a positive development as many people around the world do not or cannot receive the necessary treatment they need for their mental health.

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

The average for 2023 based on 193 countries was -0.07 points. The highest value was in Liechtenstein: 1.61 points and the lowest value was in Syria: -2.75 points. The indicator is available from 1996 to 2023. Below is a chart for all countries where data are available.

Inflation is generally defined as the continued increase in the average prices of goods and services in a given region. Following the extremely high global inflation experienced in the 1980s and 1990s, global inflation has been relatively stable since the turn of the millennium, usually hovering between three and five percent per year. There was a sharp increase in 2008 due to the global financial crisis now known as the Great Recession, but inflation was fairly stable throughout the 2010s, before the current inflation crisis began in 2021. Recent years Despite the economic impact of the coronavirus pandemic, the global inflation rate fell to 3.26 percent in the pandemic's first year, before rising to 4.66 percent in 2021. This increase came as the impact of supply chain delays began to take more of an effect on consumer prices, before the Russia-Ukraine war exacerbated this further. A series of compounding issues such as rising energy and food prices, fiscal instability in the wake of the pandemic, and consumer insecurity have created a new global recession, and global inflation in 2024 is estimated to have reached 5.76 percent. This is the highest annual increase in inflation since 1996. Venezuela Venezuela is the country with the highest individual inflation rate in the world, forecast at around 200 percent in 2022. While this is figure is over 100 times larger than the global average in most years, it actually marks a decrease in Venezuela's inflation rate, which had peaked at over 65,000 percent in 2018. Between 2016 and 2021, Venezuela experienced hyperinflation due to the government's excessive spending and printing of money in an attempt to curve its already-high inflation rate, and the wave of migrants that left the country resulted in one of the largest refugee crises in recent years. In addition to its economic problems, political instability and foreign sanctions pose further long-term problems for Venezuela. While hyperinflation may be coming to an end, it remains to be seen how much of an impact this will have on the economy, how living standards will change, and how many refugees may return in the coming years.

The Cline Center Global News Index is a searchable database of textual features extracted from millions of news stories, specifically designed to provide comprehensive coverage of events around the world. In addition to searching documents for keywords, users can query metadata and features such as named entities extracted using Natural Language Processing (NLP) methods and variables that measure sentiment and emotional valence. Archer is a web application purpose-built by the Cline Center to enable researchers to access data from the Global News Index. Archer provides a user-friendly interface for querying the Global News Index (with the back-end indexing still handled by Solr). By default, queries are built using icons and drop-down menus. More technically-savvy users can use Lucene/Solr query syntax via a ‘raw query’ option. Archer allows users to save and iterate on their queries, and to visualize faceted query results, which can be helpful for users as they refine their queries. Additional Resources: - Access to Archer and the Global News Index is limited to account-holders. If you are interested in signing up for an account, please fill out the Archer Access Request Form so we can determine if you are eligible for access or not. - Current users who would like to provide feedback, such as reporting a bug or requesting a feature, can fill out the Archer User Feedback Form. - The Cline Center sends out periodic email newsletters to the Archer Users Group. Please fill out this form to subscribe to it. Citation Guidelines: 1) To cite the GNI codebook (or any other documentation associated with the Global News Index and Archer) please use the following citation: Cline Center for Advanced Social Research. 2022. Global News Index and Extracted Features Repository [codebook], v1.1.0. Champaign, IL: University of Illinois. Dec. 16. doi:10.13012/B2IDB-5649852_V3 2) To cite data from the Global News Index (accessed via Archer or otherwise) please use the following citation (filling in the correct date of access): Cline Center for Advanced Social Research. 2022. Global News Index and Extracted Features Repository [database], v1.1.0. Champaign, IL: University of Illinois. Dec. 16. Accessed Month, DD, YYYY. doi:10.13012/B2IDB-5649852_V3

During a 2024 survey, 77 percent of respondents from Nigeria stated that they used social media as a source of news. In comparison, just 23 percent of Japanese respondents said the same. Large portions of social media users around the world admit that they do not trust social platforms either as media sources or as a way to get news, and yet they continue to access such networks on a daily basis.

              Social media: trust and consumption

              Despite the majority of adults surveyed in each country reporting that they used social networks to keep up to date with news and current affairs, a 2018 study showed that social media is the least trusted news source in the world. Less than 35 percent of adults in Europe considered social networks to be trustworthy in this respect, yet more than 50 percent of adults in Portugal, Poland, Romania, Hungary, Bulgaria, Slovakia and Croatia said that they got their news on social media.

              What is clear is that we live in an era where social media is such an enormous part of daily life that consumers will still use it in spite of their doubts or reservations. Concerns about fake news and propaganda on social media have not stopped billions of users accessing their favorite networks on a daily basis.
              Most Millennials in the United States use social media for news every day, and younger consumers in European countries are much more likely to use social networks for national political news than their older peers.
              Like it or not, reading news on social is fast becoming the norm for younger generations, and this form of news consumption will likely increase further regardless of whether consumers fully trust their chosen network or not.

The Global Investment Report 2023 revealed that after a sharp decline in 2020 and a strong rebound in 2021, global foreign direct investment (FDI) declined by 12 percent to $1.3 trillion in 2022. However, in developing countries, FDI increased by 4% to $916 billion, a record share of more than 70% of global flows. The number of greenfield investment projects in developing countries increased by 37 percent and international project finance transactions by 5 percent. Foreign investment from China, the second largest recipient of foreign investment globally, increased by 5 percent. The service industry has become the mainstream industry in the global FDI structure. The global industry is accelerating its transformation to a "service-based economy," international FDI in productive service industries has become an essential means of industrial transfer in developed countries and a meaningful way to upgrade the industrial structure and high-quality development in emerging economies. As a representative province in central China, Hubei Province has unique advantages in human capital, factor cost, and market potential, which provide preferential conditions to attract foreign investment. This paper first introduced the concept of the productive service industry, based on the relevant statistical data from 2011 to 2022, focused on the current situation of foreign investment utilization in five major sub-sectors of the productive service industry in Hubei Province in the past ten years, and empirically investigated the impact of foreign investment utilization in five major sub-sectors of the productive service industry on the economic growth of Hubei Province, and obtained that the level of foreign investment attraction varied significantly among the regions in Hubei Province. The three productive service industries, namely transportation, storage and postal services, information transmission, software and information technology services, and financial services, played a significant role in the active attraction and optimal utilization of foreign capital and the economic development of Hubei Province. Based on this, it was proposed to build a market-oriented rule of law and internationalized business environment, improve the infrastructure construction in different regions of the province, focus on the training of professional talents for the development of productive service industries, and pay attention to the improvement of independent innovation capacity.

This 6MB download is a zip file containing 5 pdf documents and 2 xlsx spreadsheets. Presentation on COVID-19 and the potential impacts on employment

May 2020Waka Kotahi wants to better understand the potential implications of the COVID-19 downturn on the land transport system, particularly the potential impacts on regional economies and communities.

To do this, in May 2020 Waka Kotahi commissioned Martin Jenkins and Infometrics to consider the potential impacts of COVID-19 on New Zealand’s economy and demographics, as these are two key drivers of transport demand. In addition to providing a scan of national and international COVID-19 trends, the research involved modelling the economic impacts of three of the Treasury’s COVID-19 scenarios, to a regional scale, to help us understand where the impacts might be greatest.

Waka Kotahi studied this modelling by comparing the percentage difference in employment forecasts from the Treasury’s three COVID-19 scenarios compared to the business as usual scenario.

The source tables from the modelling (Tables 1-40), and the percentage difference in employment forecasts (Tables 41-43), are available as spreadsheets.

Arataki - potential impacts of COVID-19 Final Report

Employment modelling - interactive dashboard

The modelling produced employment forecasts for each region and district over three time periods – 2021, 2025 and 2031. In May 2020, the forecasts for 2021 carried greater certainty as they reflected the impacts of current events, such as border restrictions, reduction in international visitors and students etc. The 2025 and 2031 forecasts were less certain because of the potential for significant shifts in the socio-economic situation over the intervening years. While these later forecasts were useful in helping to understand the relative scale and duration of potential COVID-19 related impacts around the country, they needed to be treated with care recognising the higher levels of uncertainty.

The May 2020 research suggested that the ‘slow recovery scenario’ (Treasury’s scenario 5) was the most likely due to continuing high levels of uncertainty regarding global efforts to manage the pandemic (and the duration and scale of the resulting economic downturn).

The updates to Arataki V2 were framed around the ‘Slower Recovery Scenario’, as that scenario remained the most closely aligned with the unfolding impacts of COVID-19 in New Zealand and globally at that time.

Find out more about Arataki, our 10-year plan for the land transport system

May 2021The May 2021 update to employment modelling used to inform Arataki Version 2 is now available. Employment modelling dashboard - updated 2021Arataki used the May 2020 information to compare how various regions and industries might be impacted by COVID-19. Almost a year later, it is clear that New Zealand fared better than forecast in May 2020.Waka Kotahi therefore commissioned an update to the projections through a high-level review of:the original projections for 2020/21 against performancethe implications of the most recent global (eg International monetary fund world economic Outlook) and national economic forecasts (eg Treasury half year economic and fiscal update)The treasury updated its scenarios in its December half year fiscal and economic update (HYEFU) and these new scenarios have been used for the revised projections.Considerable uncertainty remains about the potential scale and duration of the COVID-19 downturn, for example with regards to the duration of border restrictions, update of immunisation programmes. The updated analysis provides us with additional information regarding which sectors and parts of the country are likely to be most impacted. We continue to monitor the situation and keep up to date with other cross-Government scenario development and COVID-19 related work. The updated modelling has produced employment forecasts for each region and district over three time periods - 2022, 2025, 2031.The 2022 forecasts carry greater certainty as they reflect the impacts of current events. The 2025 and 2031 forecasts are less certain because of the potential for significant shifts over that time.

Data reuse caveats: as per license.

Additionally, please read / use this data in conjunction with the Infometrics and Martin Jenkins reports, to understand the uncertainties and assumptions involved in modelling the potential impacts of COVID-19.

COVID-19’s effect on industry and regional economic outcomes for NZ Transport Agency [PDF 620 KB]

Data quality statement: while the modelling undertaken is high quality, it represents two point-in-time analyses undertaken during a period of considerable uncertainty. This uncertainty comes from several factors relating to the COVID-19 pandemic, including:

a lack of clarity about the size of the global downturn and how quickly the international economy might recover differing views about the ability of the New Zealand economy to bounce back from the significant job losses that are occurring and how much of a structural change in the economy is required the possibility of a further wave of COVID-19 cases within New Zealand that might require a return to Alert Levels 3 or 4.

While high levels of uncertainty remain around the scale of impacts from the pandemic, particularly in coming years, the modelling is useful in indicating the direction of travel and the relative scale of impacts in different parts of the country.

Data quality caveats: as noted above, there is considerable uncertainty about the potential scale and duration of the COVID-19 downturn. Please treat the specific results of the modelling carefully, particularly in the forecasts to later years (2025, 2031), given the potential for significant shifts in New Zealand's socio-economic situation before then.

As such, please use the modelling results as a guide to the potential scale of the impacts of the downturn in different locations, rather than as a precise assessment of impacts over the coming decade.

The survey on social and political attitudes was conducted by forsa on behalf of the Press and Information Office of the German Federal Government. In the first quarter of 2022, 19542 persons aged 14 and older were surveyed in telephone interviews (CATI) on the following topics: assessment of one´s own financial situation, assessments of the general living situation and perceptions of the federal government´s policies, and assessment of the world or European political situation. Respondents were selected by a multi-stage random sample. Assessments of one´s own financial situation: assessment of one´s own financial situation compared with that of a year ago; expected change in one´s own financial situation in a year´s time; currently favorable time for major purchases vs. rather reluctant; assessment of how most people from the social environment assess their economic situation: rather optimistic or rather pessimistic. 2. Assessments of the general living situation and perceptions of the federal government´s policies: development of things in the country in the right direction; perceived issues of the federal government (e.g. debates or legislative proposals) in recent weeks (open); satisfaction in selected areas of life and problems (situation in the labor market, protection against violence and crime, extent of social justice, quality of life in Germany, financial situation of public budgets, school and education system in Germany, integration of migrants and foreigners, with the reception or handling of refugees and asylum seekers, securing old-age pensions, care for those in need of long-term care, protection of the environment and climate, health care in Germany). 3. Assessment of the world situation resp. the European political situation: concerns about world peace; worldwide crises with threat potential for Germany (open); opinion on Germany´s foreign policy role in the world with regard to the world political situation (take more vs. less responsibility, already does enough); opinion on Germany´s role in the EU (takes too little vs. too much consideration for other member states, just right). Demography: sex; age; employment; education; net household income (grouped); Federal election voting intention; federal election voting behavior. Additionally coded: Quarter; region East/West; weighting factor.

Shutdowns: Economies Affected: North: Acre data was reported at 71.790 Unit in 2022. This records a decrease from the previous number of 7,446.720 Unit for 2021. Shutdowns: Economies Affected: North: Acre data is updated yearly, averaging 2,631.785 Unit from Dec 2014 (Median) to 2022, with 8 observations. The data reached an all-time high of 9,048.780 Unit in 2020 and a record low of 71.790 Unit in 2022. Shutdowns: Economies Affected: North: Acre data remains active status in CEIC and is reported by Ministry of Cities. The data is categorized under Brazil Premium Database’s Environmental, Social and Governance Sector – Table BR.EVB011: Quality Indicators: Issues: Shutdowns.

Please cite the following paper when using this dataset: N. Thakur, “MonkeyPox2022Tweets: The first public Twitter dataset on the 2022 MonkeyPox outbreak,” Preprints, 2022, DOI: 10.20944/preprints202206.0172.v2

Abstract The world is currently facing an outbreak of the monkeypox virus, and confirmed cases have been reported from 28 countries. Following a recent “emergency meeting”, the World Health Organization just declared monkeypox a global health emergency. As a result, people from all over the world are using social media platforms, such as Twitter, for information seeking and sharing related to the outbreak, as well as for familiarizing themselves with the guidelines and protocols that are being recommended by various policy-making bodies to reduce the spread of the virus. This is resulting in the generation of tremendous amounts of Big Data related to such paradigms of social media behavior. Mining this Big Data and compiling it in the form of a dataset can serve a wide range of use-cases and applications such as analysis of public opinions, interests, views, perspectives, attitudes, and sentiment towards this outbreak. Therefore, this work presents MonkeyPox2022Tweets, an open-access dataset of Tweets related to the 2022 monkeypox outbreak that were posted on Twitter since the first detected case of this outbreak on May 7, 2022. The dataset is compliant with the privacy policy, developer agreement, and guidelines for content redistribution of Twitter, as well as with the FAIR principles (Findability, Accessibility, Interoperability, and Reusability) principles for scientific data management.

Data Description The dataset consists of a total of 255,363 Tweet IDs of the same number of tweets about monkeypox that were posted on Twitter from 7th May 2022 to 23rd July 2022 (the most recent date at the time of dataset upload). The Tweet IDs are presented in 6 different .txt files based on the timelines of the associated tweets. The following provides the details of these dataset files. • Filename: TweetIDs_Part1.txt (No. of Tweet IDs: 13926, Date Range of the Tweet IDs: May 7, 2022 to May 21, 2022) • Filename: TweetIDs_Part2.txt (No. of Tweet IDs: 17705, Date Range of the Tweet IDs: May 21, 2022 to May 27, 2022) • Filename: TweetIDs_Part3.txt (No. of Tweet IDs: 17585, Date Range of the Tweet IDs: May 27, 2022 to June 5, 2022) • Filename: TweetIDs_Part4.txt (No. of Tweet IDs: 19718, Date Range of the Tweet IDs: June 5, 2022 to June 11, 2022) • Filename: TweetIDs_Part5.txt (No. of Tweet IDs: 47718, Date Range of the Tweet IDs: June 12, 2022 to June 30, 2022) • Filename: TweetIDs_Part6.txt (No. of Tweet IDs: 138711, Date Range of the Tweet IDs: July 1, 2022 to July 23, 2022)

The dataset contains only Tweet IDs in compliance with the terms and conditions mentioned in the privacy policy, developer agreement, and guidelines for content redistribution of Twitter. The Tweet IDs need to be hydrated to be used.

Brazil BR: Gini Coefficient (GINI Index): World Bank Estimate data was reported at 52.000 % in 2022. This records a decrease from the previous number of 52.900 % for 2021. Brazil BR: Gini Coefficient (GINI Index): World Bank Estimate data is updated yearly, averaging 56.400 % from Dec 1981 (Median) to 2022, with 38 observations. The data reached an all-time high of 63.300 % in 1989 and a record low of 48.900 % in 2020. Brazil BR: Gini Coefficient (GINI Index): World Bank Estimate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Brazil – Table BR.World Bank.WDI: Social: Poverty and Inequality. Gini index measures the extent to which the distribution of income (or, in some cases, consumption expenditure) among individuals or households within an economy deviates from a perfectly equal distribution. A Lorenz curve plots the cumulative percentages of total income received against the cumulative number of recipients, starting with the poorest individual or household. The Gini index measures the area between the Lorenz curve and a hypothetical line of absolute equality, expressed as a percentage of the maximum area under the line. Thus a Gini index of 0 represents perfect equality, while an index of 100 implies perfect inequality.;World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org.;;The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).

Abstract

Like the rest of the world, Sudan has been experiencing the unprecedented social and economic impact of the COVID-19 pandemic. From restrictions on movement to school closures and lockdowns, the economic situation worsened, and commodity prices soared across the country. Results from the first six rounds of the High-Frequency Phone survey indicated that household welfare was negatively affected. The situation led to the loss of employment and income, decreased access to essential commodities and services, and food insecurity, particularly among the poor and vulnerable Sudanese. Moreover, the inability to access food and medicine degraded in July/August 2021 despite a slight amelioration in February/April 2021.

After COVID-19 in 2020, Sudan experienced situations that are more likely to compromise the recovery process. Political instability, unrest, and protests occurred before and after the military takeover in October 2021. Meanwhile, Sudan Central Bank devalued the currency, which may increase the already high commodities price. Besides, Sudan encountered historic flooding since the onset of the rainy season between May and June 2022. To monitor and assess the dynamics of the impacts of the country's economic and political situation (high inflation, social unrest, food shortages, asset loss, displacement, etc.) on households' welfare, another round of the Sudan High-Frequency Phone survey took place in June to August 2022.

Similar to the six previous rounds, the survey was conducted using mobile phones and covered all 18 states of Sudan. Round 7 sample is composed of 2816 Households from both urban and rural areas of Sudan. This sample allows us to draw statistical inferences about the Sudanese population at the national and rural/urban levels. The risk of nonresponse was a concern, so efforts were made to minimize this risk, including follow-up with respondents who failed to respond and keep the interviews short (15–20 minutes) to reduce respondent fatigue.

The questions are similar to the previous six rounds of the High-Frequency Phone survey but with added context. Households are asked about the key channels through which individuals and households are expected to be affected by the exchange rate distortions, country political instability, or flooding that occurred in May/June 2022, as well as how they have recovered from the COVID-19 pandemic impacts. Furthermore, questions cover a range of topics/themes including, but not limited to, health conditions, access to health facilities, access to other social services, availability of common food and non-food items (including medicines), nutrition and food security, employment/labor, income, assets, coping strategies, remittances, subjective welfare, climate/weather events, and the safety nets assistance.

Geographic coverage

National

Analysis unit

• Households
• Individuals

Sampling procedure

The sampling methodology adopted for the implementation of this survey is probabilistic. Each of the units in the targeted population of the study must have a nonzero and known probability of selection. The sample was stratified by rural/urban for all 18 states. The distribution of the sub-sample between states and rural/urban is proportional to the size of the individuals owning mobile phones, i.e., not equal allocation. The selection of the individual phones (the households) is random, i.e., with equal probability, using a systematic sample procedure in the list (frame) of phones. This allows for extrapolating the results of the sample to the target population and estimating the precision of the results obtained. However, the implementation of this approach requires the availability of an adequate sampling frame containing all the units of the population without omissions or duplications.

In this survey, the sampling frame is provided by the phone lists. Considerable efforts were made to compile the frame using multiple lists of phone numbers collected during the implementation of various projects/surveys during the last few years at the household level across the country. This reduces the chances of having more than one phone number per household. Moreover, the interviewers double-checked during data collection that only one number was called for each selected surveyed household. Therefore, selecting individual phone numbers is the same as selecting households. It is worth noting that for West Kordofan and Central Darfur, the proportionality of rural/urban cannot be done according to the size of phones since there are no details for rural/urban. So, the size of the rural and urban populations (projection 2020) was used instead.

In Sudan, under the present federal system, the state is considered a semiautonomous entity mandated to take care of the affairs of the citizen, provide governance, and be responsible for planning, policy formulation, and implementation of the annual program. Consequently, the sample needed to cover all 18 states of the country. The sample is conceived to provide reliable estimates for the country (urban and rural) and to give statistically meaningful results at the national level.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

BASELINE (ROUND 1): One questionnaire, the Household Questionnaire, was administered to all households in the sample. The Household Questionnaire provides information on: - Demographics - Knowledge regarding the spread of COVID-19 - Behavior and social distancing - Access to basic goods and services (medicines, staple food, health, education, financial services) - Employment - Income loss - Food insecurity experience - Welfare - Shocks and Coping strategies - Social safety nets

ROUND 2: One questionnaire, the Household Questionnaire, was administered to all households in the sample. The Household Questionnaire provides information on: - Demographics - Knowledge regarding the spread of COVID-19 - Behavior and social distancing - Access to basic goods and services (medicines, staple food, health, education, financial services, water, transportation, housing, internet, energy) - Employment - Income loss - Food insecurity experience - Welfare - Shocks and Coping strategies - Social safety nets ROUND 3: One questionnaire, the Household Questionnaire, was administered to all households in the sample. The Household Questionnaire provides information on: - Demographics - Behavior and social distancing - Access to basic goods and services (medicines, staple food, health, education, financial services) - Employment - Income loss - Food insecurity experience - Welfare - Shocks and Coping strategies - Social safety nets ROUND 4: One questionnaire, the Household Questionnaire, was administered to all households in the sample. The Household Questionnaire provides information on: - Demographics - Youth module screening - Behavior and social distancing - Access to basic goods and services (medicines, staple food, health, education, transportation, fuel) - Employment - Income loss - Food insecurity experience - Welfare - Shocks and Coping strategies - Social safety nets ROUND 5: One questionnaire, the Household Questionnaire, was administered to all households in the sample. Respondent were asked to think about each child in their household for the education question. The Household Questionnaire provides information on: - Demographics - Mental health of the respondent - Children education.

ROUND 6: One questionnaire, the Household Questionnaire, was administered to all households in the sample. One youth per household is interviewed in the youth section of the questionnaire. The Questionnaire provides information on: - Demographics - Access to basic goods (medicines, staple food) - Youth employment - Youth job search - Youth aspirations and expectations - Youth skills and mental health.

ROUND 7: One questionnaire, the Household Questionnaire, was administered to all households in the sample. The Household Questionnaire provides information on: - Geography - Access to basic goods and services (medicines, staple food, health, education, water, housing, electricity) - Employment - Income loss - Food insecurity experience - Welfare - Experience of Climate/Weather events - Shocks and Coping strategies

Response rate

BASELINE (ROUND 1): A total of 4,032 households were successfully interviewed during the first round of data collection (conducted during June 16–July 5, 2020). Selected households from each state include both rural and urban households, with the representation of each state in the final sample being proportional to the state’s population relative to the overall population. Households who refused to tell their location (mode of living and state) were dropped to minimize bias. The final sample size accounts 4,027 households.

ROUND 2: Interviewers attempted to contact and interview all 4,032 households that were successfully interviewed in the baseline of the Sudan HFS on COVID-19. 2,989 households were successfully interviewed in the second round. However, households who refused to tell their location (mode of living and state) were dropped to minimize bias. The final sample size accounts 2,987 households.

ROUND 3: Interviewers attempted to contact and interview all 4,032 households that were successfully interviewed in the Baseline of the Sudan HFS on COVID-19. 2,990 households were successfully interviewed in the third round. Households who refused to tell their location (mode of living and state) were dropped to minimize bias. The final sample size accounts 2,987 households.

ROUND 4: Interviewers attempted to contact and interview all 4,032 households that were successfully interviewed in the Baseline of the Sudan

Coups d'Ètat are important events in the life of a country. They constitute an important subset of irregular transfers of political power that can have significant and enduring consequences for national well-being. There are only a limited number of datasets available to study these events (Powell and Thyne 2011, Marshall and Marshall 2019). Seeking to facilitate research on post-WWII coups by compiling a more comprehensive list and categorization of these events, the Cline Center for Advanced Social Research (previously the Cline Center for Democracy) initiated the Coup d’État Project as part of its Societal Infrastructures and Development (SID) project. More specifically, this dataset identifies the outcomes of coup events (i.e., realized, unrealized, or conspiracy) the type of actor(s) who initiated the coup (i.e., military, rebels, etc.), as well as the fate of the deposed leader. Version 2.2.0 adds 94 additional coup events. 66 of these came from examining Powell and Thyne’s “discarded” events and 28 of these events were added to the data set in the normal annual review of potential new coup events. This version also updates the coding to events in Brazil in 1945 and the Congo in 1968. Version 2.1.3 adds 19 additional coup events to the data set, corrects the date of a coup in Tunisia, and reclassifies an attempted coup in Brazil in December 2022 as a conspiracy. Version 2.1.2 added 6 additional coup events that occurred in 2022 and updated the coding of an attempted coup event in Kazakhstan in January 2022. Version 2.1.1 corrected a mistake in version 2.1.0, where the designation of “dissident coup” had been dropped in error for coup_id: 00201062021. Version 2.1.1 fixed this omission by marking the case as both a dissident coup and an auto-coup. Version 2.1.0 added 36 cases to the data set and removed two cases from the v2.0.0 data. This update also added actor coding for 46 coup events and added executive outcomes to 18 events from version 2.0.0. A few other changes were made to correct inconsistencies in the coup ID variable and the date of the event. Version 2.0.0 improved several aspects of the previous version (v1.0.0) and incorporated additional source material to include: • Reconciling missing event data • Removing events with irreconcilable event dates • Removing events with insufficient sourcing (each event needs at least two sources) • Removing events that were inaccurately coded as coup events • Removing variables that fell below the threshold of inter-coder reliability required by the project • Removing the spreadsheet ‘CoupInventory.xls’ because of inadequate attribution and citations in the event summaries • Extending the period covered from 1945-2005 to 1945-2019 • Adding events from Powell and Thyne’s Coup Data (Powell and Thyne, 2011) Version 1.0.0 was released in 2013. This version consolidated coup data taken from the following sources: • The Center for Systemic Peace (Marshall and Marshall, 2007) • The World Handbook of Political and Social Indicators (Taylor and Jodice, 1983) • Coup d’Ètat: A Practical Handbook (Luttwak, 1979) • The Cline Center’s Social, Political and Economic Event Database (SPEED) Project (Nardulli, Althaus and Hayes, 2015) • Government Change in Authoritarian Regimes – 2010 Update (Svolik and Akcinaroglu, 2006)
Items in this Dataset 1. Cline Center Coup d'État Codebook v.2.2.0 Codebook.pdf - This 17-page document describes the Cline Center Coup d’État Project dataset. The first section of this codebook provides a summary of the different versions of the data. The second section provides a succinct definition of a coup d’état used by the Coup d'État Project and an overview of the categories used to differentiate the wide array of events that meet the project's definition. It also defines coup outcomes. The third section describes the methodology used to produce the data. Revised January 2025 2. Coup Data v2.2.0.csv - This CSV (Comma Separated Values) file contains all of the coup event data from the Cline Center Coup d’État Project. It contains 29 variables and 1094 observations. Revised January 2025 3. Source Document v2.2.0.pdf - This 347-page document provides the sources used for each of the coup events identified in this dataset. Please use the value in the coup_id variable to identify the sources used to identify that particular event. Revised January 2025 4. README.md - This file contains useful information for the user about the dataset. It is a text file written in markdown language. Revised January 2025
Citation Guidelines 1. To cite the codebook (or any other documentation associated with the Cline Center Coup d’État Project Dataset) please use the following citation: Peyton, Buddy, Joseph Bajjalieh, Dan Shalmon, Michael Martin, Jonathan Bonaguro, and Scott Althaus. 2025. “Cline Center Coup d’État Project Dataset Codebook”. Cline Center Coup d’État Project Dataset. Cline Center for Advanced Social Research. V.2.2.0. Janurary 30. University of Illinois Urbana-Champaign. doi: 10.13012/B2IDB-9651987_V8 2. To cite data from the Cline Center Coup d’État Project Dataset please use the following citation (filling in the correct date of access): Peyton, Buddy, Joseph Bajjalieh, Michael Martin, Sam Alahi, Norah Fadell, and Maddie Jeralds. 2025. Cline Center Coup d’État Project Dataset. Cline Center for Advanced Social Research. V.2.2.0. Janurary 30. University of Illinois Urbana-Champaign. doi: 10.13012/B2IDB-9651987_V8

Brazil Shutdowns: Economies Affected data was reported at 3,465.500 Unit in 2022. This records an increase from the previous number of 3,108.320 Unit for 2021. Brazil Shutdowns: Economies Affected data is updated yearly, averaging 2,631.180 Unit from Dec 2012 (Median) to 2022, with 11 observations. The data reached an all-time high of 3,465.500 Unit in 2022 and a record low of 540.700 Unit in 2012. Brazil Shutdowns: Economies Affected data remains active status in CEIC and is reported by Ministry of Cities. The data is categorized under Brazil Premium Database’s Environmental, Social and Governance Sector – Table BR.EVB011: Quality Indicators: Issues: Shutdowns.

Imports of environmental goods comprise all environmental goods entering the national territory. A relatively high share of environmental goods imports indicates that an economy purchases a significant share of environmental goods from other economies. Exports of environmental goods comprise all environmental goods leaving the national territory. A relatively high share of environmental goods exports indicates that an economy produces and sells a significant share of environmental goods to other economies. An economy’s environmental goods trade balance is the difference between its exports and imports of environmental goods.Comparative advantage is a measure of the relative advantage or disadvantage a particular economy has in a certain class of goods (in this case, environmental goods), and can be used to evaluate export potential in that class of goods. A value greater than one indicates a relative advantage in environmental goods, while a value of less than one indicates a relative disadvantage.Sources: Department of Economic and Social Affairs/United Nations. 2022. United Nations Comtrade database. https://comtrade.un.org. Accessed on 2023-06-28; International Monetary Fund (IMF) Direction of Trade Statistics (DOTS). https://data.imf.org/dot. Accessed on 2023-06-28. World Economic Outlook (WEO) Database. https://www.imf.org/en/Publications/WEO/weo-database/2022/April. Accessed on 2023-06-28; IMF staff calculations.Category: Cross-Border IndicatorsData series: Comparative advantage in environmental goodsEnvironmental goods exportsEnvironmental goods exports as percent of GDPEnvironmental goods exports as share of total exportsEnvironmental goods importsEnvironmental goods imports as percent of GDPEnvironmental goods imports as share of total importsEnvironmental goods trade balanceEnvironmental goods trade balance as percent of GDPTotal trade in environmental goodsTotal trade in environmental goods as percent of GDPMetadata:Sources: Trade data from UN Comtrade Database (https://comtrade.un.org/). Harmonized Commodity Description and Coding System (HS) 2017. Trade aggregates from IMF Direction of Trade Statistics (DOTS) (data.imf.org/dot). GDP data from World Economic Outlook.Methodology:Environmental goods imports and exports are estimated by aggregating HS 6-digit commodities identified as environmental goods based on OECD and Eurostat, The Environmental Goods & Services Industry: Manual for Data Collection and Analysis, 1999, and IMF research. Total goods imports and exports are estimated by aggregating all commodities. Environmental goods trade balance is calculated as environmental goods exports less environmental goods imports. A positive trade balance means an economy has a surplus in environmental goods, while a negative trade balance means an economy has a deficit in environmental goods.Total goods are estimated by aggregating all commodities. Comparative advantage is calculated as the proportion of an economy’s exports that are environmental goods to the proportion of global exports that are environmental goods. Total trade in environmental goods is calculated as the sum of environmental goods exports and environmental goods imports. This measure provides an indication of an economy’s involvement (openness) to trade in environmental goods.National-accounts basis GDP at current prices from the World Economic Outlook is used to calculate the percent of GDP. This measure provides an indication of an economy’s involvement (openness) to trade in environmental goods.Methodology Attachment Environmental Goods Harmonized System Codes

Shutdowns: Economies Affected: Southeast: Rio de Janeiro data was reported at 3,793.800 Unit in 2022. This records a decrease from the previous number of 34,161.190 Unit for 2021. Shutdowns: Economies Affected: Southeast: Rio de Janeiro data is updated yearly, averaging 1,015.980 Unit from Dec 2012 (Median) to 2022, with 11 observations. The data reached an all-time high of 34,161.190 Unit in 2021 and a record low of 592.720 Unit in 2017. Shutdowns: Economies Affected: Southeast: Rio de Janeiro data remains active status in CEIC and is reported by Ministry of Cities. The data is categorized under Brazil Premium Database’s Environmental, Social and Governance Sector – Table BR.EVB011: Quality Indicators: Issues: Shutdowns.

John Ioannidis and co-authors [1] created a publicly available database of top-cited scientists in the world. This database, intended to address the misuse of citation metrics, has generated a lot of interest among the scientific community, institutions, and media. Many institutions used this as a yardstick to assess the quality of researchers. At the same time, some people look at this list with skepticism citing problems with the methodology used. Two separate databases are created based on career-long and, single recent year impact. This database is created using Scopus data from Elsevier[1-3]. The Scientists included in this database are classified into 22 scientific fields and 174 sub-fields. The parameters considered for this analysis are total citations from 1996 to 2022 (nc9622), h index in 2022 (h22), c-score, and world rank based on c-score (Rank ns). Citations without self-cites are considered in all cases (indicated as ns). In the case of a single-year case, citations during 2022 (nc2222) instead of Nc9622 are considered.

To evaluate the robustness of c-score-based ranking, I have done a detailed analysis of the matrix parameters of the last 25 years (1998-2022) of Nobel laureates of Physics, chemistry, and medicine, and compared them with the top 100 rank holders in the list. The latest career-long and single-year-based databases (2022) were used for this analysis. The details of the analysis are presented below: Though the article says the selection is based on the top 100,000 scientists by c-score (with and without self-citations) or a percentile rank of 2% or above in the sub-field, the actual career-based ranking list has 204644 names[1]. The single-year database contains 210199 names. So, the list published contains ~ the top 4% of scientists. In the career-based rank list, for the person with the lowest rank of 4809825, the nc9622, h22, and c-score were 41, 3, and 1.3632, respectively. Whereas for the person with the No.1 rank in the list, the nc9622, h22, and c-score were 345061, 264, and 5.5927, respectively. Three people on the list had less than 100 citations during 96-2022, 1155 people had an h22 less than 10, and 6 people had a C-score less than 2.
In the single year-based rank list, for the person with the lowest rank (6547764), the nc2222, h22, and c-score were 1, 1, and 0. 6, respectively. Whereas for the person with the No.1 rank, the nc9622, h22, and c-score were 34582, 68, and 5.3368, respectively. 4463 people on the list had less than 100 citations in 2022, 71512 people had an h22 less than 10, and 313 people had a C-score less than 2. The entry of many authors having single digit H index and a very meager total number of citations indicates serious shortcomings of the c-score-based ranking methodology. These results indicate shortcomings in the ranking methodology.