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 3.5 percent to 4.7 percent, and from 2021 to 2022, the rate increased sharply from 4.7 percent to 8.7 percent. While rates are predicted to fall come 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 to respondents. While poverty and inequality are still prominent, global poverty rates have been on a steady decline over the years. In 1994, 64 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 44 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.

Global shoppers' primary grievance regarding purchases from social media platforms was the prolonged delivery time, as reported by a 2025 survey. Around ** percent of respondents identified this issue. Another issue was that the item received appeared significantly different from the one ordered, with a ******* of the survey respondents being of this opinion.

This dataset provides insight into how public opinion shapes the world news cycle, offering public opinion engagement data from posts on the r/worldnews subreddit. It gathers posts on various topics such as politics, current affairs, socio-economic issues, sports, and entertainment. The dataset includes engagement metrics for each post, allowing for analysis of public sentiment. It is a valuable tool for assessing discussion threads, delving into individual posts to understand prevalent perspectives on world news, and analysing how stories on foreign policy, environmental action, and social movements influence our global outlook.

Columns

The worldnews.csv dataset includes the following columns: * title: The title of the post. (String) * score: The number of upvotes the post has received. (Integer) * id: A unique identifier for the post. (String) * url: The URL of the post. (String) * comms_num: The number of comments the post has received. (Integer) * created: The date and time the post was created. (Datetime) * body: The main text content of the post. (String) * timestamp: The date and time the post was last updated. (Datetime)

Distribution

The dataset is provided in CSV format. It contains 1,871 unique post IDs. While a total row count for the entire dataset is not explicitly stated, data is available in various ranges for scores, comments, and timestamps, indicating a substantial collection of records. For instance, timestamps span from 8th December 2022 to 15th December 2022.

Usage

This dataset is ideal for: * Understanding the most popular topics on world news by correlating post engagement with their subject matter. * Analysing differences in post engagement across various geographic regions to identify trending global issues. * Tracking changes in public opinion by monitoring engagement over time, particularly concerning specific news cycles or events. * Conducting deep dives into individual posts to ascertain which perspectives on world news gain the most traction. * Analysing how global stories, from foreign policy to environmental action and social movements, shape collective global outlook.

Coverage

The dataset offers global coverage of public opinion, as it is sourced from the r/worldnews subreddit. The time range for the included posts spans from 8th December 2022 to 15th December 2022. The scope primarily focuses on posts related to general world news, politics, current affairs, and socio-economic issues.

License

CC0

Who Can Use It

This dataset is well-suited for data science and analytics professionals, researchers, and anyone interested in: * Analysing public sentiment related to world events. * Studying the dynamics of online news consumption and engagement. * Exploring the relationship between social media discussions and global outlook. * Developing Natural Language Processing (NLP) models for text analysis and sentiment detection.

Dataset Name Suggestions

• Reddit World News Engagement Data
• Global Public Opinion on News
• r/worldnews Submission & Comment Data
• World News Social Sentiment
• Reddit World News Post Analytics

Attributes

Original Data Source: Reddit: /r/worldnews (Submissions & Comments)

This study is part of a quadrennial series designed to investigate the opinions and attitudes of the general public on matters related to foreign policy, and to define the parameters of public opinion within which decision-makers must operate. This public opinion study of the United States focused on respondents' opinions of the United States' leadership role in the world and the challenges the country faces domestically and internationally. The survey covered the following international topics: relations with other countries, role in foreign affairs, possible threats to vital interests in the next ten years, foreign policy goals, benefits or drawbacks of globalization, situations that might justify the use of United States troops in other parts of the world, the number and location of United States military bases overseas, respondent feelings toward people of other countries, opinions on the influence of other countries in the world and how much influence those countries should have, whether there should be a global regulating body to prevent economic instability, international trade, United States participation in potential treaties, the United States' role in the United Nations and NATO, respondent opinions on international institutions and regulating bodies such as the United Nations, World Trade Organization, and the World Health Organization, whether the United States will continue to be the world's leading power in the next 50 years, democracy in the Middle East and South Korea, the role of the United Nations Security Council, which side the United States should take in the Israeli-Palestinian conflict, what measures should be taken to deal with Iran's nuclear program, the military effort in Afghanistan, opinions on efforts to combat terrorism and the use of torture to extract information from prisoners, whether the respondent favors or opposes the government selling military equipment to other nations and using nuclear weapons in various circumstances, the economic development of China, and the conflict between North and South Korea. Domestic issues included economic prospects for American children when they become adults, funding for government programs, the fairness of the current distribution of income in the United States, the role of government, whether the government can be trusted to do what is right, climate change, greenhouse gas emissions, United States' dependence on foreign energy sources, drilling for oil and natural gas off the coast of the United States, and relations with Mexico including such issues as the ongoing drug war, as well as immigration and immigration reform. Demographic and other background information included age, gender, race/ethnicity, marital status, left-right political self-placement, political affiliation, employment status, highest level of education, and religious preference. Also included are household size and composition, whether the respondent is head of household, household income, housing type, ownership status of living quarters, household Internet access, Metropolitan Statistical Area (MSA) status, and region and state of residence.

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.

This Ipsos survey indicates that ** percent of respondents believed the most concerning environmental issue facing the world is climate change. Air pollution was the second most important environmental issue around the world, closely followed by the amount waste that is generated.

This dataset offers insight into the ways that public opinion shapes the world news cycle. Gathering posts from various topics such as politics, current affairs, socio-economic issues, sports, entertainment and more from the subredditworldnews subreddit, this dataset provides engagement data from each post in order to analyze public sentiment. With columns including title, score, url, comms_num, created timestamp and body text for each post in the collection it is easy to assess discussion thread topics or dig deep into individual posts to ascertain what perspectives on world news have the most traction. From questions of foreign policy to environmental action and social movements it is possible with this tool analyse how these stories shape our global outlook

More Datasets For more datasets, click here.

Featured Notebooks 🚨 Your notebook can be here! 🚨! Research Ideas Looking at correlations between post engagement and the topics of the posts to better understand the most popular topics on world news. Analyzing the differences in post engagement according to geographic regions to better understand what is trending in certain areas of the world. Tracking changes in engagement over time as a way to assess public opinion about specific news cycles or events

License

CC0

Original Data Source: Reddit: /r/worldnews (Submissions & Comments)

Journal of Political Economy Impact Factor 2024-2025 - ResearchHelpDesk - The Journal of Political Economy is a monthly peer-reviewed academic journal published by the University of Chicago Press. Established by James Laurence Laughlin in 1892, it covers both theoretical and empirical economics. In the past, the journal published quarterly from its introduction through 1905, ten issues per volume from 1906 through 1921, and bimonthly from 1922 through 2019. The editor-in-chief is Magne Mogstad (University of Chicago). Abstract & Indexing Articles that appear in the Journal of Political Economy are indexed in the following abstracting and indexing services: Ulrich's Periodicals Directory (Print) Ulrichsweb (Online) J-Gate HINARI Association for Asian Studies Bibliography of Asian Studies (Online) Business Index CABI Abstracts on Hygiene and Communicable Diseases (Online) Agricultural Economics Database CAB Abstracts (Commonwealth Agricultural Bureaux) Dairy Science Abstracts (Online) Environmental Impact Global Health Leisure Tourism Database Nutrition and Food Sciences Database Rural Development Abstracts (Online) Soil Science Database Soils and Fertilizers (Online) Tropical Diseases Bulletin (Online) World Agricultural Economics and Rural Sociology Abstracts (Online) Clarivate Analytics Current Contents Social Sciences Citation Index Web of Science De Gruyter Saur Dietrich's Index Philosophicus IBZ - Internationale Bibliographie der Geistes- und Sozialwissenschaftlichen Zeitschriftenliteratur Internationale Bibliographie der Rezensionen Geistes- und Sozialwissenschaftlicher Literatur EBSCOhost America: History and Life ATLA Religion Database (American Theological Library Association) Biography Index: Past and Present (H.W. Wilson) Book Review Digest Plus (H.W. Wilson) Business Source Alumni Edition (Full Text) Business Source Complete (Full Text) Business Source Corporate (Full Text) Business Source Corporate Plus (Full Text) Business Source Elite (Full Text) Business Source Premier (Full Text) Business Source Ultimate (Full Text) Current Abstracts EBSCO MegaFILE (Full Text) EBSCO Periodicals Collection (Full Text) EconLit with Full Text (Full Text) ERIC (Education Resources Information Center) GeoRef Historical Abstracts (Online) Humanities & Social Sciences Index Retrospective: 1907-1984 (H.W. Wilson) Humanities Index Retrospective: 1907-1984 (H.W. Wilson) Humanities Source Humanities Source Ultimate Index to Legal Periodicals Retrospective: 1908-1981 (H.W. Wilson) Legal Source Library & Information Science Source MLA International Bibliography (Modern Language Association) OmniFile Full Text Mega (H.W. Wilson) Poetry & Short Story Reference Center Political Science Complete Public Affairs Index Readers' Guide Retrospective: 1890-1982 (H.W. Wilson) Russian Academy of Sciences Bibliographies Social Sciences Abstracts Social Sciences Full Text (H.W. Wilson) Social Sciences Index Retrospective: 1907-1983 (H.W. Wilson) SocINDEX SocINDEX with Full Text TOC Premier Women's Studies International Elsevier BV GEOBASE Scopus ERIC (Education Resources Information Center) ERIC (Education Resources Information Center) Gale Academic ASAP Academic OneFile Advanced Placement Government and Social Studies Book Review Index Plus Business & Company ProFile ASAP Business ASAP Business ASAP International Business Collection Business Insights: Essentials Business Insights: Global Business, Economics and Theory Collection Expanded Academic ASAP General Business File ASAP General OneFile General Reference Center Gold General Reference Centre International InfoTrac Custom InfoTrac Student Edition MLA International Bibliography (Modern Language Association) Popular Magazines US History Collection H.W. Wilson Social Sciences Index National Library of Medicine PubMed OCLC ArticleFirst Periodical Abstracts Sociological Abstracts (Online), Selective Ovid EconLit ERIC (Education Resources Information Center) GeoRef ProQuest ABI/INFORM Collection ABI/INFORM Global (American Business Information) ABI/INFORM Research (American Business Information) Business Premium Collection EconLit ERIC (Education Resources Information Center) GeoRef Health Management Database Health Research Premium Collection Hospital Premium Collection International Bibliography of the Social Sciences, Core MLA International Bibliography (Modern Language Association) PAIS Archive Professional ABI/INFORM Complete Professional ProQuest Central ProQuest 5000 ProQuest 5000 International ProQuest Central ProQuest Pharma Collection Research Library Social Science Database Social Science Premium Collection Sociological Abstracts (Online), Selective Worldwide Political Science Abstracts, Selective SCIMP (Selective Cooperative Index of Management Periodicals) Taylor & Francis Educational Research Abstracts Online Wiley-Blackwell Publishing Asia Asian - Pacific Economic Literature (Online)

This Voice of the People poll seeks the opinions of Canadians, on predominantly economic, political, and social issues. The questions ask opinions of the state of the economy, and predictions for 2008. There are also questions on other topics of interest such as job safety, opinion of political and business leaders, trust in people, opinion of international social organisations including The International Committee of the Red Cross (ICRC), The United Nations Children's Fund (UNICEF), The United Nations High Commission for Refugees (UNHCR), and more. The respondents were also asked questions so that they could be grouped according to geographic and social variables. Topics of interest include: economy; 2008; job safety; trust; opinion of international social organisations; and unemployment. Basic demographic variables are also included.

Overview The Office of the Geographer and Global Issues at the U.S. Department of State produces the Large Scale International Boundaries (LSIB) dataset. The current edition is version 11.4 (published 24 February 2025). The 11.4 release contains updated boundary lines and data refinements designed to extend the functionality of the dataset. These data and generalized derivatives are the only international boundary lines approved for U.S. Government use. The contents of this dataset reflect U.S. Government policy on international boundary alignment, political recognition, and dispute status. They do not necessarily reflect de facto limits of control. National Geospatial Data Asset This dataset is a National Geospatial Data Asset (NGDAID 194) managed by the Department of State. It is a part of the International Boundaries Theme created by the Federal Geographic Data Committee. Dataset Source Details Sources for these data include treaties, relevant maps, and data from boundary commissions, as well as national mapping agencies. Where available and applicable, the dataset incorporates information from courts, tribunals, and international arbitrations. The research and recovery process includes analysis of satellite imagery and elevation data. Due to the limitations of source materials and processing techniques, most lines are within 100 meters of their true position on the ground. Cartographic Visualization The LSIB is a geospatial dataset that, when used for cartographic purposes, requires additional styling. The LSIB download package contains example style files for commonly used software applications. The attribute table also contains embedded information to guide the cartographic representation. Additional discussion of these considerations can be found in the Use of Core Attributes in Cartographic Visualization section below. Additional cartographic information pertaining to the depiction and description of international boundaries or areas of special sovereignty can be found in Guidance Bulletins published by the Office of the Geographer and Global Issues: https://data.geodata.state.gov/guidance/index.html Contact Direct inquiries to internationalboundaries@state.gov. Direct download: https://data.geodata.state.gov/LSIB.zip Attribute Structure The dataset uses the following attributes divided into two categories: ATTRIBUTE NAME | ATTRIBUTE STATUS CC1 | Core CC1_GENC3 | Extension CC1_WPID | Extension COUNTRY1 | Core CC2 | Core CC2_GENC3 | Extension CC2_WPID | Extension COUNTRY2 | Core RANK | Core LABEL | Core STATUS | Core NOTES | Core LSIB_ID | Extension ANTECIDS | Extension PREVIDS | Extension PARENTID | Extension PARENTSEG | Extension These attributes have external data sources that update separately from the LSIB: ATTRIBUTE NAME | ATTRIBUTE STATUS CC1 | GENC CC1_GENC3 | GENC CC1_WPID | World Polygons COUNTRY1 | DoS Lists CC2 | GENC CC2_GENC3 | GENC CC2_WPID | World Polygons COUNTRY2 | DoS Lists LSIB_ID | BASE ANTECIDS | BASE PREVIDS | BASE PARENTID | BASE PARENTSEG | BASE The core attributes listed above describe the boundary lines contained within the LSIB dataset. Removal of core attributes from the dataset will change the meaning of the lines. An attribute status of “Extension” represents a field containing data interoperability information. Other attributes not listed above include “FID”, “Shape_length” and “Shape.” These are components of the shapefile format and do not form an intrinsic part of the LSIB. Core Attributes The eight core attributes listed above contain unique information which, when combined with the line geometry, comprise the LSIB dataset. These Core Attributes are further divided into Country Code and Name Fields and Descriptive Fields. County Code and Country Name Fields “CC1” and “CC2” fields are machine readable fields that contain political entity codes. These are two-character codes derived from the Geopolitical Entities, Names, and Codes Standard (GENC), Edition 3 Update 18. “CC1_GENC3” and “CC2_GENC3” fields contain the corresponding three-character GENC codes and are extension attributes discussed below. The codes “Q2” or “QX2” denote a line in the LSIB representing a boundary associated with areas not contained within the GENC standard. The “COUNTRY1” and “COUNTRY2” fields contain the names of corresponding political entities. These fields contain names approved by the U.S. Board on Geographic Names (BGN) as incorporated in the ‘"Independent States in the World" and "Dependencies and Areas of Special Sovereignty" lists maintained by the Department of State. To ensure maximum compatibility, names are presented without diacritics and certain names are rendered using common cartographic abbreviations. Names for lines associated with the code "Q2" are descriptive and not necessarily BGN-approved. Names rendered in all CAPITAL LETTERS denote independent states. Names rendered in normal text represent dependencies, areas of special sovereignty, or are otherwise presented for the convenience of the user. Descriptive Fields The following text fields are a part of the core attributes of the LSIB dataset and do not update from external sources. They provide additional information about each of the lines and are as follows: ATTRIBUTE NAME | CONTAINS NULLS RANK | No STATUS | No LABEL | Yes NOTES | Yes Neither the "RANK" nor "STATUS" fields contain null values; the "LABEL" and "NOTES" fields do. The "RANK" field is a numeric expression of the "STATUS" field. Combined with the line geometry, these fields encode the views of the United States Government on the political status of the boundary line. ATTRIBUTE NAME | | VALUE | RANK | 1 | 2 | 3 STATUS | International Boundary | Other Line of International Separation | Special Line A value of “1” in the “RANK” field corresponds to an "International Boundary" value in the “STATUS” field. Values of ”2” and “3” correspond to “Other Line of International Separation” and “Special Line,” respectively. The “LABEL” field contains required text to describe the line segment on all finished cartographic products, including but not limited to print and interactive maps. The “NOTES” field contains an explanation of special circumstances modifying the lines. This information can pertain to the origins of the boundary lines, limitations regarding the purpose of the lines, or the original source of the line. Use of Core Attributes in Cartographic Visualization Several of the Core Attributes provide information required for the proper cartographic representation of the LSIB dataset. The cartographic usage of the LSIB requires a visual differentiation between the three categories of boundary lines. Specifically, this differentiation must be between: International Boundaries (Rank 1); Other Lines of International Separation (Rank 2); and Special Lines (Rank 3). Rank 1 lines must be the most visually prominent. Rank 2 lines must be less visually prominent than Rank 1 lines. Rank 3 lines must be shown in a manner visually subordinate to Ranks 1 and 2. Where scale permits, Rank 2 and 3 lines must be labeled in accordance with the “Label” field. Data marked with a Rank 2 or 3 designation does not necessarily correspond to a disputed boundary. Please consult the style files in the download package for examples of this depiction. The requirement to incorporate the contents of the "LABEL" field on cartographic products is scale dependent. If a label is legible at the scale of a given static product, a proper use of this dataset would encourage the application of that label. Using the contents of the "COUNTRY1" and "COUNTRY2" fields in the generation of a line segment label is not required. The "STATUS" field contains the preferred description for the three LSIB line types when they are incorporated into a map legend but is otherwise not to be used for labeling. Use of the “CC1,” “CC1_GENC3,” “CC2,” “CC2_GENC3,” “RANK,” or “NOTES” fields for cartographic labeling purposes is prohibited. Extension Attributes Certain elements of the attributes within the LSIB dataset extend data functionality to make the data more interoperable or to provide clearer linkages to other datasets. The fields “CC1_GENC3” and “CC2_GENC” contain the corresponding three-character GENC code to the “CC1” and “CC2” attributes. The code “QX2” is the three-character counterpart of the code “Q2,” which denotes a line in the LSIB representing a boundary associated with a geographic area not contained within the GENC standard. To allow for linkage between individual lines in the LSIB and World Polygons dataset, the “CC1_WPID” and “CC2_WPID” fields contain a Universally Unique Identifier (UUID), version 4, which provides a stable description of each geographic entity in a boundary pair relationship. Each UUID corresponds to a geographic entity listed in the World Polygons dataset. These fields allow for linkage between individual lines in the LSIB and the overall World Polygons dataset. Five additional fields in the LSIB expand on the UUID concept and either describe features that have changed across space and time or indicate relationships between previous versions of the feature. The “LSIB_ID” attribute is a UUID value that defines a specific instance of a feature. Any change to the feature in a lineset requires a new “LSIB_ID.” The “ANTECIDS,” or antecedent ID, is a UUID that references line geometries from which a given line is descended in time. It is used when there is a feature that is entirely new, not when there is a new version of a previous feature. This is generally used to reference countries that have dissolved. The “PREVIDS,” or Previous ID, is a UUID field that contains old versions of a line. This is an additive field, that houses all Previous IDs. A new version of a feature is defined by any change to the

The Global Welfare Dataset (GLOW) is a cross-national panel dataset that aims at facilitating comparative social policy research on the Global North and Global South. The database includes 381 variables on 61 countries from years between 1989 and 2015. The database has four main categories of data: welfare, development, economy and politics.The data is the result of an original data compilation assembled by using information from several international and domestic sources. Missing data was supplemented by domestic sources where available. We sourced data primarily from these international databases:Atlas of Social Protection Indicators of Resilience and Equity – ASPIRE (World Bank)Government Finance Statistics (International Monetary Fund)Social Expenditure Database – SOCX (Organisation for Economic Co-operation and Development)Social Protection Statistics – ESPROSS (Eurostat)Social Security Inquiry (International Labour Organization)Social Security Programs Throughout the World (Social Security Administration)Statistics on Income and Living Conditions – EU-SILC (European Union)World Development Indicators (World Bank)However, much of the welfare data from these sources are not compatible between all country cases. We conducted an extensive review of the compatibility of the data and computed compatible figures where possible. Since the heart of this database is the provision of social assistance across a global sample, we applied the ASPIRE methodology in order to build comparable indicators across European and Emerging Market economies. Specifically, we constructed indicators of average per capita transfers and coverage rates for social assistance programs for all the country cases not included in the World Bank’s ASPIRE dataset (Austria, Belgium, Bulgaria, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Latvia, Luxembourg, Netherlands, Norway, Poland, Portugal, Romania, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, and United Kingdom.)For details, please see:https://glow.ku.edu.tr/about

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

This dataset is about book subjects. It has 2 rows and is filtered where the books is The practices of global ethics : historical backgrounds, current issues and future prospects. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

Governance of climate change has become a major global environmental issue in the 21st century, and in the absence of wider citizen engagement poses risks of global proportions. Much of the current climate governance debate, unfortunately, is limited to scientists, politicians, and interest groups. With few exceptions, everyday citizens are spectators at best, their views, if not absent, are dismally represented in policy processes. To close the widening gap between citizens and policymakers, thereby increasing the sense of ownership of environmental policies by ordinary people, several methods of citizen engagement for global environmental governance have emerged. The effectiveness of these methods, however, relies upon the ability of citizens to deliberate meaningfully, especially in issues such as climate change. We conducted a study in conjunction with World Wide Views on Climate and Energy, a global citizen consultation that aims to solicit carefully considered public views on pressing issues, to determine whether American citizens are receptive to deliberation, and to ascertain what effect it had on their opinions, if any, could be observed. Along with the descriptive analysis, we performed a non-parametric Wilcoxon signed-rank test of selected pre-and post-event opinions of the participants from the US. Our study revealed that providing US citizens with the opportunity to engage in deliberation resulted in increased awareness regarding climate change and greater trust in science, technology, and international agreements. The change in opinion was more pronounced among people whose political orientation titled to the right or who considered themselves as neutral. Citizen’s opinions, especially after the event, resulted in less polarized views towards the global consensus on climate change. This finding suggests that US citizens are receptive to scientific information if it is communicated in an appropriate manner – a characteristic necessary for the creation of deliberative democratic governance on socially contested issues.

Abstract

The World Bank is interested in gauging the views of clients and partners who are either involved in development in China or who observe activities related to social and economic development. The World Bank Country Assessment Survey is meant to give the Bank's team that works in China, more in-depth insight into how the Bank's work is perceived. This is one tool the Bank uses to assess the views of its critical stakeholders. With this understanding, the World Bank hopes to develop more effective strategies, outreach and programs that support development in China. The World Bank commissioned an independent firm to oversee the logistics of this effort in China.

The survey was designed to achieve the following objectives: - Assist the World Bank in gaining a better understanding of how stakeholders in China perceive the Bank; - Obtain systematic feedback from stakeholders in China regarding: · Their views regarding the general environment in China; · Their perceived overall value of the World Bank in China; · Overall impressions of the World Bank as related to programs, poverty reduction, personal relationships, effectiveness, knowledge base, collaboration, and its day-to-day operation; and · Perceptions of the World Bank's communication and outreach in China. - Use data to help inform the China country team's strategy.

Geographic coverage

National

Analysis unit

Stakeholder

Universe

Stakeholders of the World Bank in China

Kind of data

Sample survey data [ssd]

Sampling procedure

December 2011 thru March 2012, 518 stakeholders of the World Bank in China were invited to provide their opinions on the Bank's assistance to the country by participating in a country survey. Participants in the survey were drawn from among employees of a ministry or ministerial department of central government; local government officials or staff; project management offices at the central and local level; the central bank; financial sector/banks; NGOs; regulatory agencies; state-owned enterprises; bilateral or multilateral agencies; private sector organizations; consultants/contractors working on World Bank supported projects/programs; the media; and academia, research institutes or think tanks.

Mode of data collection

Face-to-face [f2f]

Research instrument

The Questionnaire consists of 8 Sections: 1. Background Information: The first section asked respondents for their current position; specialization; familiarity, exposure to, and involvement with the Bank; and geographic location.

1. General Issues facing China: Respondents were asked to indicate what they thought were the most important development priorities, which areas would contribute most to poverty reduction and economic growth in China, as well as rating their perspective on the future of the next generation in China.

2. Overall Attitudes toward the World Bank: Respondents were asked to rate the Bank's overall effectiveness in China, the extent to which the Bank's financial instruments meet China's needs, the extent to which the Bank meets China's need for knowledge services, and their agreement with various statements regarding the Bank's programs, poverty mission, relationships, and collaborations in China. Respondents were also asked to indicate the areas on which it would be most productive for the Bank to focus its resources and research, what the Bank's level of involvement should be, and what they felt were the Bank's greatest values and greatest weaknesses in its work.

3. The Work of the World Bank: Respondents were asked to rate their level of importance and the Bank's level of effectiveness across fifteen areas in which the Bank was involved, such as helping to reduce poverty and encouraging greater transparency in governance.

4. The Way the World Bank does Business: Respondents were asked to rate the Bank's level of effectiveness in the way it does business, including the Bank's knowledge, personal relationships, collaborations, and poverty mission.

5. Project/Program Related Issues: Respondents were asked to rate their level of agreement with a series of statements regarding the Bank's programs, day-to-day operations, and collaborations in China.

6. The Future of the World Bank in China: Respondents were asked to rate how significant a role the Bank should play in China's development and to indicate what the Bank could do to make itself of greater value and what the greatest obstacle was to the Bank playing a significant role in China.

7. Communication and Outreach: Respondents were asked to indicate where they get information about development issues and the Bank's development activities in China, as well as how they prefer to receive information from the Bank. Respondents were also asked to indicate their usage of the Bank's website and PICs, and to evaluate these communication and outreach efforts.

Response rate

A total of 207 stakeholders participated in the country survey (40%).

Trends in Covid total cases per million people. The latest data for over 100 countries around the world.

This dataset is about books. It has 2 rows and is filtered where the book is Global environmental issues : a climatological approach. It features 7 columns including author, publication date, language, and book publisher.

This dataset provides values for CORONAVIRUS CASES reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

This dataset contains an overview of the Top 50 Topics per number of (funded) publications, for the World, and the Top 50 Topics for EU-27 plus the 5 selected knowledge economies. These Top 50 Topics are compared to the EC funded Topics to show the differences in productivity (i.e. World vs EC, and EU-27 plus 5 vs EC). This is a simplified overview of earlier data shared, which shows similar data but then for all 23755 Topics. These can be found in dataset; https://doi.org/10.34894/KQRWEN and dataset: https://doi.org/10.34894/DNWIXV. Note: the World is defined by 180 countries and territories that have produced output in the 23755 topics.

Overview

The Associated Press is sharing data from the COVID Impact Survey, which provides statistics about physical health, mental health, economic security and social dynamics related to the coronavirus pandemic in the United States.

Conducted by NORC at the University of Chicago for the Data Foundation, the probability-based survey provides estimates for the United States as a whole, as well as in 10 states (California, Colorado, Florida, Louisiana, Minnesota, Missouri, Montana, New York, Oregon and Texas) and eight metropolitan areas (Atlanta, Baltimore, Birmingham, Chicago, Cleveland, Columbus, Phoenix and Pittsburgh).

The survey is designed to allow for an ongoing gauge of public perception, health and economic status to see what is shifting during the pandemic. When multiple sets of data are available, it will allow for the tracking of how issues ranging from COVID-19 symptoms to economic status change over time.

The survey is focused on three core areas of research:

• Physical Health: Symptoms related to COVID-19, relevant existing conditions and health insurance coverage.
• Economic and Financial Health: Employment, food security, and government cash assistance.
• Social and Mental Health: Communication with friends and family, anxiety and volunteerism. (Questions based on those used on the U.S. Census Bureau’s Current Population Survey.) ## Using this Data - IMPORTANT This is survey data and must be properly weighted during analysis: DO NOT REPORT THIS DATA AS RAW OR AGGREGATE NUMBERS!!

Instead, use our queries linked below or statistical software such as R or SPSS to weight the data.

Queries

If you'd like to create a table to see how people nationally or in your state or city feel about a topic in the survey, use the survey questionnaire and codebook to match a question (the variable label) to a variable name. For instance, "How often have you felt lonely in the past 7 days?" is variable "soc5c".

Nationally: Go to this query and enter soc5c as the variable. Hit the blue Run Query button in the upper right hand corner.

Local or State: To find figures for that response in a specific state, go to this query and type in a state name and soc5c as the variable, and then hit the blue Run Query button in the upper right hand corner.

The resulting sentence you could write out of these queries is: "People in some states are less likely to report loneliness than others. For example, 66% of Louisianans report feeling lonely on none of the last seven days, compared with 52% of Californians. Nationally, 60% of people said they hadn't felt lonely."

Margin of Error

The margin of error for the national and regional surveys is found in the attached methods statement. You will need the margin of error to determine if the comparisons are statistically significant. If the difference is:

• At least twice the margin of error, you can report there is a clear difference.
• At least as large as the margin of error, you can report there is a slight or apparent difference.
• Less than or equal to the margin of error, you can report that the respondents are divided or there is no difference. ## A Note on Timing Survey results will generally be posted under embargo on Tuesday evenings. The data is available for release at 1 p.m. ET Thursdays.

About the Data

The survey data will be provided under embargo in both comma-delimited and statistical formats.

Each set of survey data will be numbered and have the date the embargo lifts in front of it in the format of: 01_April_30_covid_impact_survey. The survey has been organized by the Data Foundation, a non-profit non-partisan think tank, and is sponsored by the Federal Reserve Bank of Minneapolis and the Packard Foundation. It is conducted by NORC at the University of Chicago, a non-partisan research organization. (NORC is not an abbreviation, it part of the organization's formal name.)

Data for the national estimates are collected using the AmeriSpeak Panel, NORC’s probability-based panel designed to be representative of the U.S. household population. Interviews are conducted with adults age 18 and over representing the 50 states and the District of Columbia. Panel members are randomly drawn from AmeriSpeak with a target of achieving 2,000 interviews in each survey. Invited panel members may complete the survey online or by telephone with an NORC telephone interviewer.

Once all the study data have been made final, an iterative raking process is used to adjust for any survey nonresponse as well as any noncoverage or under and oversampling resulting from the study specific sample design. Raking variables include age, gender, census division, race/ethnicity, education, and county groupings based on county level counts of the number of COVID-19 deaths. Demographic weighting variables were obtained from the 2020 Current Population Survey. The count of COVID-19 deaths by county was obtained from USA Facts. The weighted data reflect the U.S. population of adults age 18 and over.

Data for the regional estimates are collected using a multi-mode address-based (ABS) approach that allows residents of each area to complete the interview via web or with an NORC telephone interviewer. All sampled households are mailed a postcard inviting them to complete the survey either online using a unique PIN or via telephone by calling a toll-free number. Interviews are conducted with adults age 18 and over with a target of achieving 400 interviews in each region in each survey.Additional details on the survey methodology and the survey questionnaire are attached below or can be found at https://www.covid-impact.org.

Attribution

Results should be credited to the COVID Impact Survey, conducted by NORC at the University of Chicago for the Data Foundation.

AP Data Distributions

​To learn more about AP's data journalism capabilities for publishers, corporations and financial institutions, go here or email kromano@ap.org.