Context

The United States Census Bureau’s international dataset provides estimates of country populations since 1950 and projections through 2050. Specifically, the dataset includes midyear population figures broken down by age and gender assignment at birth. Additionally, time-series data is provided for attributes including fertility rates, birth rates, death rates, and migration rates.

Querying BigQuery tables

You can use the BigQuery Python client library to query tables in this dataset in Kernels. Note that methods available in Kernels are limited to querying data. Tables are at bigquery-public-data.census_bureau_international.

Sample Query 1

What countries have the longest life expectancy? In this query, 2016 census information is retrieved by joining the mortality_life_expectancy and country_names_area tables for countries larger than 25,000 km2. Without the size constraint, Monaco is the top result with an average life expectancy of over 89 years!

standardSQL

SELECT age.country_name, age.life_expectancy, size.country_area FROM ( SELECT country_name, life_expectancy FROM bigquery-public-data.census_bureau_international.mortality_life_expectancy WHERE year = 2016) age INNER JOIN ( SELECT country_name, country_area FROM bigquery-public-data.census_bureau_international.country_names_area where country_area > 25000) size ON age.country_name = size.country_name ORDER BY 2 DESC /* Limit removed for Data Studio Visualization */ LIMIT 10

Sample Query 2

Which countries have the largest proportion of their population under 25? Over 40% of the world’s population is under 25 and greater than 50% of the world’s population is under 30! This query retrieves the countries with the largest proportion of young people by joining the age-specific population table with the midyear (total) population table.

standardSQL

SELECT age.country_name, SUM(age.population) AS under_25, pop.midyear_population AS total, ROUND((SUM(age.population) / pop.midyear_population) * 100,2) AS pct_under_25 FROM ( SELECT country_name, population, country_code FROM bigquery-public-data.census_bureau_international.midyear_population_agespecific WHERE year =2017 AND age < 25) age INNER JOIN ( SELECT midyear_population, country_code FROM bigquery-public-data.census_bureau_international.midyear_population WHERE year = 2017) pop ON age.country_code = pop.country_code GROUP BY 1, 3 ORDER BY 4 DESC /* Remove limit for visualization*/ LIMIT 10

Sample Query 3

The International Census dataset contains growth information in the form of birth rates, death rates, and migration rates. Net migration is the net number of migrants per 1,000 population, an important component of total population and one that often drives the work of the United Nations Refugee Agency. This query joins the growth rate table with the area table to retrieve 2017 data for countries greater than 500 km2.

SELECT growth.country_name, growth.net_migration, CAST(area.country_area AS INT64) AS country_area FROM ( SELECT country_name, net_migration, country_code FROM bigquery-public-data.census_bureau_international.birth_death_growth_rates WHERE year = 2017) growth INNER JOIN ( SELECT country_area, country_code FROM bigquery-public-data.census_bureau_international.country_names_area

Update frequency

Historic (none)

Dataset source

United States Census Bureau

Terms of use: This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source - http://www.data.gov/privacy-policy#data_policy - and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

See the GCP Marketplace listing for more details and sample queries: https://console.cloud.google.com/marketplace/details/united-states-census-bureau/international-census-data

Which county has the most Facebook users?

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

              Facebook – the most used social media

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

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

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

Exports in Germany increased to 130.50 EUR Billion in June from 129.50 EUR Billion in May of 2025. This dataset provides - Germany Exports - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The Comparative National Elections Project (CNEP) is a multi-year, multi-country examination of citizen voting behavior in democracies around the, world conducted by the Mershon Center for International Security Studies, a unit of the Office of International Affairs at The Ohio State University. In addition to including the conventional factors in explaining vote decisions, it has pioneered a focus on how voters receive information about policies, parties, and candidates during election campaigns. CNEP began in 1990 with surveys in the first national elections of the 1990s in Germany, Britain, the United States and Japan. It expanded in 1993 to include eight more countries and additional questions. CNEP recently expanded again so that it now includes 35 national election surveys in 21 countries. It is now the third-largest international project of its kind. This dataset is a South African subset of the international dataset from the 2004 wave of the CNEP survey, CNEP III (South Africa was added as a survey country in this wave).

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

I have been a fan of Paradox Interactive's Victoria 2 for a while now. This dataset is based off my most recent campaign playing as the small nation of Biafra in Western Africa. Using a software I found on the web, I was able to extract much of the data however, I really wish I were able to get more data. That game has loads of interesting data trapped in it. Hopefully, in the nearest future, a software can be built to help me get that done.

The data, I think, is fairly comprehensive. It maps out a 38 year period between 1993 and 2030, tracking each countries gdp, GDP per Capita, unemployment rate e.t.c.

Note: Keen observers will notice that 4 of the largest economies in the world seem to nose dive around the year 2023-2024. This is because, within the game, India nukes The United States, France, and Great Britain in a great war. All three countries retaliate with their own nukes, thereby reducing all 4 countries to economic obscurity within a matter of 5 years. It was indeed a scary thing to watch. Nearly 700 million people lost their lives due to the fallout.

Edit: You will find a lot of zero's in the gdp data. This is not because those countries gdp were actually 0. For the vast majority of countries with 0 as their GDP, they simply did not exist officially that year. For instance Ambazonia has many years of 0 GDP data. This is because Ambazonia did not exist as a country all those years. Also, within the game there was never any country with a population of 0. Therefore, any country with a population of 0 in our dataset did not exist.

For over five decades since attachment theory was first introduced by John Bowlby (1969), it has garnered substantial academic interest, including its application to student-teacher relationships. Various efforts have been made to assess student-teacher relationships, one of which is the development of the Student-Teacher Relationship Scale (STRS). This scale was originally developed by Pianta & Nimetz, (2001) to assess teachers' closeness with students from preschool through third grade. In this scale, the student-teacher relationship is linked to three dimensions of attachment: closeness, conflict, and dependency. Over time, several academics have examined and revalidated Pianta & Nimetz's STRS across different relationships and cultural settings. For example, Koomen et al., (2012) assessed teachers' perspectives on their relationships with students aged 3-12, specifically measuring the dimensions of closeness, conflict, and dependency among teachers and students in the Netherlands by adding additional items to the dependency indicator. However, previous studies have predominantly developed instruments limited to measurements based on teachers’ perspectives within elementary education settings. This highlights a substantial need for further research to re-examine the factor structure and validity of this measurement tool from another perspective—that of middle school students. As the study of student-teacher relationships progresses, there remains a lack of suitable instruments for use in Indonesia, and few studies specifically address student-teacher relationships, such as Mint Husen Raya et al., (2023). Additionally, it is essential to test the validity of STRS in higher age groups, particularly within the context of Indonesian middle school culture, considering that Indonesia is an archipelagic country with the world’s fourth-largest population (>275 million) and diverse ethnicities and cultures across its regions. Therefore, the primary aim of this study is to adopt and validate the STRS by Koomen et al., (2012) through Rasch Model analysis on a sample of Indonesian middle school students. The study will test the scale based on measurements of age, gender, and ethnicity, and adapt it from a teacher's perspective to a student's perspective. Key analyses will include checking for misfit items, internal consistency reliability, and separation indices, as well as unidimensionality and local dependency, item and person measures, item bias through DIF analysis, and rating scale diagnostics. Finally, we will present a comprehensive correlation between our scale (STRS-Student) and similar scales developed by other academics. An additional objective is to popularize and disseminate this scale throughout Indonesia.

Poverty and Environment Network (PEN) is an international research project and network. Launched in 2004, PEN is the largest and most comprehensive global analysis of tropical forests and poverty. Its database contains survey data on 8000+ households in 40+ study sites in 25 developing countries. At the core of PEN is comparative, detailed socio-economic data that was collected quarterly at the household and village level by 50+ research partners using standardised definitions, questionnaires and methods. The study sites were chosen to obtain widely representative coverage of different geographical regions, forest types, forest tenure regimes, levels of poverty, infrastructure and market access, and population density. The dataset is available from CIFOR Dataverse via the link in Related ResourcesForests are crucial to the livelihoods of hundreds of millions of poor people worldwide, but just how important, and for what functions? Can they help lift people out of poverty, or are they mainly useful as gap-fillers and safety nets in response to shocks? Are certain types of forest-tenure and management regimes more favourable than others? And under what conditions can increased integration into forest-product markets help? These are the questions to be answered by this tropics-wide, multi-partner research project. In the Poverty and Environment Network (PEN) consortium, led by the Centre for International Forestry Research (CIFOR), around 30 partners (mostly PhD students) gather quantitative and qualitative socioeconomic data using the same questionnaire in all three developing-country continents to illuminate the role of forests and environmental income in preventing and reducing rural poverty. A centrally coordinated pan-tropical data bank with high-quality primary household and village data is being created for the global-comparative analysis. DFID-ESRC kindly finances those PEN research components related to data-bank establishment, global analysis, publication of scientific outputs, and the dissemination of policy recommendations for tangible forest-poverty interventions. Three types of quantitative surveys were conducted: 1. Village surveys; 2. Annual household surveys; 3. Quarterly household surveys. The village surveys collected data that were common to all or showed little variation among households. The first village survey was conducted at the beginning of the fieldwork to get background information on the villages while the second survey was conducted the end of the fieldwork period to get information for the 12 months period covered by the surveys. The household surveys were grouped into two categories: quarterly surveys to collect income information, and, household surveys to collect all other household information. Two other household surveys were conducted. The first annual household survey collected basic household information (demographics, assets, forest-related information) and was done at the beginning of the survey period while the second collected information for the 12-month period covered by the surveys (e.g., on risk management) and was done at the end of the survey period.

This data collection consists of transcripts from 12 focus group discussions on themes related to social equality in Russia. The focus group discussions were conducted by the Institute of Applied Politics in Moscow, directed by Dr Kryshtanovskaya; using a discussion guide written by the Investigators. They were held in 12 cities chosen to represent different regions of the country, with an emphasis on provincial cities: Ufa, Kaliningrad, Ekaterinburg, Tiumen, Saratov, Ulyanovsk, Volgograd, Ivanovo, Irkutsk, Obolensk, Vladivostok and Protvino. The respondents included a mix of ages, genders, blue and white collar workers. The focus groups in Protvino and Ulyanovsk were held only for respondents age 18-29. The focus group discussions dealt with household and national economic change, perceptions of social fairness, and welfare values. Specifically, respondents were asked about the state of the national and local economies, their household economy, how they define rich and poor people and how they position themselves in relation to these categories. They were asked about whether they perceived differences in wealth between individuals, regions and between urban and rural areas as fair, and whether such differences are increasing or decreasing. Finally they were asked about whether the rich should take more responsibility for the welfare of the poor, about their own personal responsibility and that of the state and businesses, as well as about progressive income taxes and the degree to which the state should control the economy. The discussion guide is provided in Russian and English. Basic information about the respondents, including gender, age, and occupation are provided at the top of each focus group transcript. Each participant is identified by their given name only. The transcripts are provided in Russian. The Russian text was transcribed by the Institute of Applied Politics from audio files. A parallel set of focus groups was conducted in China and are available as the collection Social equality forum China: Focus group transcripts (see Related Resources). Taken together, Russia and China account for 41 per cent of the total territory of the BRICs and 63 per cent of their GDP/PPP. On Goldman Sachs projections China will be the world’s largest economy by 2050, and Russia its sixth largest. The project will seek to examine the following propositions: (1) that these two BRIC countries are becoming increasingly unequal; (2) that within them, political power and economic advantage are increasingly closely associated; (3) that their political systems have increasingly been employed to ensure that no effective challenge can be mounted to that combination of government position and economic advantage; (4) that set against a broader comparative perspective, an increasingly unequal society in which government is effectively immune from conventional challenge is likely to become increasingly regressive, or unstable, or both. Evidence will be drawn from official statistics, interviews with policy specialists and government officials, two dozen focus groups, and an analysis of the composition of the management boards of the largest companies in both countries. A final part of the analysis will employ crossnational evidence to test a series of hypotheses relating to the association between inequality and political instability on a more broadly comparative basis. Focus group discussions held in 12 Russian cities with 6 participants each drawn from a range of ages, both genders and different professions. Two focus groups were held for respondents age 18-29 only.

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

The Fiscal Monitor surveys and analyzes the latest public finance developments, it updates fiscal implications of the crisis and medium-term fiscal projections, and assesses policies to put public finances on a sustainable footing.

Country-specific data and projections for key fiscal variables are based on the April 2020 World Economic Outlook database, unless indicated otherwise, and compiled by the IMF staff. Historical data and projections are based on information gathered by IMF country desk officers in the context of their missions and through their ongoing analysis of the evolving situation in each country; they are updated on a continual basis as more information becomes available. Structural breaks in data may be adjusted to produce smooth series through splicing and other techniques. IMF staff estimates serve as proxies when complete information is unavailable. As a result, Fiscal Monitor data can differ from official data in other sources, including the IMF's International Financial Statistics.

The country classification in the Fiscal Monitor divides the world into three major groups: 35 advanced economies, 40 emerging market and middle-income economies, and 40 low-income developing countries. The seven largest advanced economies as measured by GDP (Canada, France, Germany, Italy, Japan, United Kingdom, United States) constitute the subgroup of major advanced economies, often referred to as the Group of Seven (G7). The members of the euro area are also distinguished as a subgroup. Composite data shown in the tables for the euro area cover the current members for all years, even though the membership has increased over time. Data for most European Union member countries have been revised following the adoption of the new European System of National and Regional Accounts (ESA 2010). The low-income developing countries (LIDCs) are countries that have per capita income levels below a certain threshold (currently set at $2,700 in 2016 as measured by the World Bank's Atlas method), structural features consistent with limited development and structural transformation, and external financial linkages insufficiently close to be widely seen as emerging market economies. Zimbabwe is included in the group. Emerging market and middle-income economies include those not classified as advanced economies or low-income developing countries. See Table A, "Economy Groupings," for more details.

Most fiscal data refer to the general government for advanced economies, while for emerging markets and developing economies, data often refer to the central government or budgetary central government only (for specific details, see Tables B-D). All fiscal data refer to the calendar years, except in the cases of Bangladesh, Egypt, Ethiopia, Haiti, Hong Kong Special Administrative Region, India, the Islamic Republic of Iran, Myanmar, Nepal, Pakistan, Singapore, and Thailand, for which they refer to the fiscal year.

Composite data for country groups are weighted averages of individual-country data, unless otherwise specified. Data are weighted by annual nominal GDP converted to U.S. dollars at average market exchange rates as a share of the group GDP.

In many countries, fiscal data follow the IMF's Government Finance Statistics Manual 2014. The overall fiscal balance refers to net lending (+) and borrowing ("") of the general government. In some cases, however, the overall balance refers to total revenue and grants minus total expenditure and net lending.

The fiscal gross and net debt data reported in the Fiscal Monitor are drawn from official data sources and IMF staff estimates. While attempts are made to align gross and net debt data with the definitions in the IMF's Government Finance Statistics Manual, as a result of data limitations or specific country circumstances, these data can sometimes deviate from the formal definitions.

We present the GLOBAL ROADKILL DATA, the largest worldwide compilation of roadkill data on terrestrial vertebrates. We outline the workflow (Fig. 1) to illustrate the sequential steps of the study, in which we merged local-scale survey datasets and opportunistic records into a unified roadkill large dataset comprising 208,570 roadkill records. These records include 2283 species and subspecies from 54 countries across six continents, ranging from 1971 to 2024.Large roadkill datasets offer the advantage ofpreventing the collection of redundant data and are valuable resources for both local and macro-scale analyses regarding roadkill rates, road and landscape features associated with roadkill risk, species more vulnerable to road traffic, and populations at risk due to additional mortality. The standardization of data - such as scientific names, projection coordinates, and units - in a user-friendly format, makes themreadily accessible to a broader scientific and non-scientific community, including NGOs, consultants, public administration officials, and road managers. The open-access approach promotes collaboration among researchers and road practitioners, facilitating the replication of studies, validation of findings, and expansion of previous work. Moreover, researchers can utilize suchdatasets to develop new hypotheses, conduct meta-analyses, address pressing challenges more efficiently and strengthen the robustness of road ecology research. Ensuring widespreadaccess to roadkill data fosters a more diverse and inclusive research community. This not only grants researchers in emerging economies with more data for analysis, but also cultivates a diverse array of perspectives and insightspromoting the advance of infrastructure ecology.MethodsInformation sources: A core team from different continents performed a systematic literature search in Web of Science and Google Scholar for published peer-reviewed papers and dissertations. It was searched for the following terms: “roadkill* OR “road-kill” OR “road mortality” AND (country) in English, Portuguese, Spanish, French and/or Mandarin. This initiative was also disseminated to the mailing lists associated with transport infrastructure: The CCSG Transport Working Group (WTG), Infrastructure & Ecology Network Europe (IENE) and Latin American & Caribbean Transport Working Group (LACTWG) (Fig. 1). The core team identified 750 scientific papers and dissertations with information on roadkill and contacted the first authors of the publications to request georeferenced locations of roadkill andofferco-authorship to this data paper. Of the 824 authors contacted, 145agreed to sharegeoreferenced roadkill locations, often involving additional colleagues who contributed to data collection. Since our main goal was to provide open access to data that had never been shared in this format before, data from citizen science projects (e.g., globalroakill.net) that are already available were not included.Data compilation: A total of 423 co-authors compiled the following information: continent, country, latitude and longitude in WGS 84 decimal degrees of the roadkill, coordinates uncertainty, class, order, family, scientific name of the roadkill, vernacular name, IUCN status, number of roadkill, year, month, and day of the record, identification of the road, type of road, survey type, references, and observers that recorded the roadkill (Supplementary Information Table S1 - description of the fields and Table S2 - reference list). When roadkill data were derived from systematic surveys, the dataset included additional information on road length that was surveyed, latitude and longitude of the road (initial and final part of the road segment), survey period, start year of the survey, final year of the survey, 1st month of the year surveyed, last month of the year surveyed, and frequency of the survey. We consolidated 142 valid datasets into a single dataset. We complemented this data with OccurenceID (a UUID generated using Java code), basisOfRecord, countryCode, locality using OpenStreetMap’s API (https://www.openstreetmap.org), geodeticDatum, verbatimScientificName, Kingdom, phylum, genus, specificEpithet, infraspecificEpithet, acceptedNameUsage, scientific name authorship, matchType, taxonRank using Darwin Core Reference Guide (https://dwc.tdwg.org/terms/#dwc:coordinateUncertaintyInMeters) and link of the associatedReference (URL).Data standardization - We conducted a clustering analysis on all text fields to identify similar entries with minor variations, such as typos, and corrected them using OpenRefine (http://openrefine.org). Wealsostandardized all date values using OpenRefine. Coordinate uncertainties listed as 0 m were adjusted to either 30m or 100m, depending on whether they were recorded after or before 2000, respectively, following the recommendation in the Darwin Core Reference Guide (https://dwc.tdwg.org/terms/#dwc:coordinateUncertaintyInMeters).Taxonomy - We cross-referenced all species names with the Global Biodiversity Information Facility (GBIF) Backbone Taxonomy using Java and GBIF’s API (https://doi.org/10.15468/39omei). This process aimed to rectify classification errors, include additional fields such as Kingdom, Phylum, and scientific authorship, and gather comprehensive taxonomic information to address any gap withinthe datasets. For species not automatically matched (matchType - Table S1), we manually searched for correct synonyms when available.Species conservation status - Using the species names, we retrieved their conservation status and also vernacular names by cross-referencing with the database downloaded from the IUCNRed List of Threatened Species (https://www.iucnredlist.org). Species without a match were categorized as "Not Evaluated".Data RecordsGLOBAL ROADKILL DATA is available at Figshare27 https://doi.org/10.6084/m9.figshare.25714233. The dataset incorporates opportunistic (collected incidentally without data collection efforts) and systematic data (collected through planned, structured, and controlled methods designed to ensure consistency and reliability). In total, it comprises 208,570 roadkill records across 177,428 different locations(Fig. 2). Data were collected from the road network of 54 countries from 6 continents: Europe (n = 19), Asia (n = 16), South America (n=7), North America (n = 4), Africa (n = 6) and Oceania (n = 2).(Figure 2 goes here)All data are georeferenced in WGS84 decimals with maximum uncertainty of 5000 m. Approximately 92% of records have a location uncertainty of 30 m or less, with only 1138 records having location uncertainties ranging from 1000 to 5000 m. Mammals have the highest number of roadkill records (61%), followed by amphibians (21%), reptiles (10%) and birds (8%). The species with the highest number of records were roe deer (Capreolus capreolus, n = 44,268), pool frog (Pelophylax lessonae, n = 11,999) and European fallow deer (Dama dama, n = 7,426).We collected information on 126 threatened species with a total of 4570 records. Among the threatened species, the giant anteater (Myrmecophaga tridactyla, VULNERABLE) has the highest number of records n = 1199), followed by the common fire salamander (Salamandra salamandra, VULNERABLE, n=1043), and European rabbit (Oryctolagus cuniculus, ENDANGERED, n = 440). Records ranged from 1971 and 2024, comprising 72% of the roadkill recorded since 2013. Over 46% of the records were obtained from systematic surveys, with road length and survey period averaging, respectively, 66 km (min-max: 0.09-855 km) and 780 days (1-25,720 days).Technical ValidationWe employed the OpenStreetMap API through Java todetect location inaccuracies, andvalidate whether the geographic coordinates aligned with the specified country. We calculated the distance of each occurrence to the nearest road using the GRIP global roads database28, ensuring that all records were within the defined coordinate uncertainty. We verified if the survey duration matched the provided initial and final survey dates. We calculated the distance between the provided initial and final road coordinates and cross-checked it with the given road length. We identified and merged duplicate entries within the same dataset (same location, species, and date), aggregating the number of roadkills for each occurrence.Usage NotesThe GLOBAL ROADKILL DATA is a compilation of roadkill records and was designed to serve as a valuable resource for a wide range of analyses. Nevertheless, to prevent the generation of meaningless results, users should be aware of the followinglimitations:- Geographic representation – There is an evident bias in the distribution of records. Data originatedpredominantly from Europe (60% of records), South America (22%), and North America (12%). Conversely, there is a notable lack of records from Asia (5%), Oceania (1%) and Africa (0.3%). This dataset represents 36% of the initial contacts that provided geo-referenced records, which may not necessarily correspond to locations where high-impact roads are present.- Location accuracy - Insufficient location accuracy was observed for 1% of the data (ranging from 1000 to 5000 m), that was associated with various factors, such as survey methods, recording practices, or timing of the survey.- Sampling effort - This dataset comprised both opportunistic data and records from systematic surveys, with a high variability in survey duration and frequency. As a result, the use of both opportunistic and systematic surveys may affect the relative abundance of roadkill making it hard to make sound comparisons among species or areas.- Detectability and carcass removal bias - Although several studies had a high frequency of road surveys,the duration of carcass persistence on roads may vary with species size and environmental conditions, affecting detectability. Accordingly, several approaches account for survey frequency and target speciesto estimate more

Techsalerator’s Import/Export Trade Data for North America

Techsalerator’s Import/Export Trade Data for North America delivers an exhaustive and nuanced analysis of trade activities across the North American continent. This extensive dataset provides detailed insights into import and export transactions involving companies across various sectors within North America.

Coverage Across All North American Countries

The dataset encompasses all key countries within North America, including:

1. United States

The dataset provides detailed trade information for the United States, the largest economy in the region. It includes extensive data on trade volumes, product categories, and the key trading partners of the U.S. 2. Canada

Data for Canada covers a wide range of trade activities, including import and export transactions, product classifications, and trade relationships with major global and regional partners. 3. Mexico

Comprehensive data for Mexico includes detailed records on its trade activities, including exports and imports, key sectors, and trade agreements affecting its trade dynamics. 4. Central American Countries:

Belize Costa Rica El Salvador Guatemala Honduras Nicaragua Panama The dataset covers these countries with information on their trade flows, key products, and trade relations with North American and international partners. 5. Caribbean Countries:

Bahamas Barbados Cuba Dominica Dominican Republic Grenada Haiti Jamaica Saint Kitts and Nevis Saint Lucia Saint Vincent and the Grenadines Trinidad and Tobago Trade data for these Caribbean nations includes detailed transaction records, sector-specific trade information, and their interactions with North American trade partners. Comprehensive Data Features

Transaction Details: The dataset includes precise details on each trade transaction, such as product descriptions, quantities, values, and dates. This allows for an accurate understanding of trade flows and patterns across North America.

Company Information: It provides data on companies involved in trade, including names, locations, and industry sectors, enabling targeted business analysis and competitive intelligence.

Categorization: Transactions are categorized by industry sectors, product types, and trade partners, offering insights into market dynamics and sector-specific trends within North America.

Trade Trends: Historical data helps users analyze trends over time, identify emerging markets, and assess the impact of economic or political events on trade flows in the region.

Geographical Insights: The data offers insights into regional trade flows and cross-border dynamics between North American countries and their global trade partners, including significant international trade relationships.

Regulatory and Compliance Data: Information on trade regulations, tariffs, and compliance requirements is included, helping businesses navigate the complex regulatory environments within North America.

Applications and Benefits

Market Research: Companies can leverage the data to discover new market opportunities, analyze competitive landscapes, and understand demand for specific products across North American countries.

Strategic Planning: Insights from the data enable companies to refine trade strategies, optimize supply chains, and manage risks associated with international trade in North America.

Economic Analysis: Analysts and policymakers can monitor economic performance, evaluate trade balances, and make informed decisions on trade policies and economic development strategies.

Investment Decisions: Investors can assess trade trends and market potentials to make informed decisions about investments in North America's diverse economies.

Techsalerator’s Import/Export Trade Data for North America offers a vital resource for organizations involved in international trade, providing a thorough, reliable, and detailed view of trade activities across the continent.

This dataset contains information on the 300 largest cooperatives worldwide, as reported by the World Cooperative Monitor (WCM). The data is sourced from the WCM annual reports, published by the International Cooperative Alliance (ICA) with the support of the European Research Institute on Cooperative and Social Enterprises (Euricse). These reports, published annually since 2012, provide global data on the cooperative movement with a two-year time lag, capturing key metrics on the economic impact and sectoral distribution of cooperatives across the globe. The dataset covers the period from 2010 to 2020, focusing on three main variables: turnover, country of origin, and activity sector for the top 300 cooperatives. These cooperatives are predominantly concentrated in three sectors: Insurance, Agriculture and Food Industries, and Wholesale and Retail Trade. The countries with the highest concentration of large cooperatives include the United States, France, and Germany, with significant contributions from smaller European countries such as Finland and Denmark. This dataset enables the analysis of cooperative concentration, measured as the share of turnover of the largest cooperatives relative to the total market. Additionally, geographic and sectoral trends are examined. The dataset also provides insights into the relative importance of cooperatives in different countries, highlighting their share of turnover in relation to GDP and population.

Associated with manuscript titled: Fifty Muslim-majority countries have fewer COVID-19 cases and deaths than the 50 richest non-Muslim countriesThe objective of this research was to determine the difference in the total number of COVID-19 cases and deaths between Muslim-majority and non-Muslim countries, and investigate reasons for the disparities. Methods: The 50 Muslim-majority countries had more than 50.0% Muslims with an average of 87.5%. The non-Muslim country sample consisted of 50 countries with the highest GDP while omitting any Muslim-majority countries listed. The non-Muslim countries’ average percentage of Muslims was 4.7%. Data pulled on September 18, 2020 included the percentage of Muslim population per country by World Population Review15 and GDP per country, population count, and total number of COVID-19 cases and deaths by Worldometers.16 The data set was transferred via an Excel spreadsheet on September 23, 2020 and analyzed. To measure COVID-19’s incidence in the countries, three different Average Treatment Methods (ATE) were used to validate the results. Results published as a preprint at https://doi.org/10.31235/osf.io/84zq5(15) Muslim Majority Countries 2020 [Internet]. Walnut (CA): World Population Review. 2020- [Cited 2020 Sept 28]. Available from: http://worldpopulationreview.com/country-rankings/muslim-majority-countries (16) Worldometers.info. Worldometer. Dover (DE): Worldometer; 2020 [cited 2020 Sept 28]. Available from: http://worldometers.info

Dataset Card for Russia Ukraine Conflict

  Dataset Summary

Context

On 24 February 2022, Russia invaded Ukraine in a major escalation of the Russo-Ukrainian War that began in 2014. The invasion caused Europe's largest refugee crisis since World War II, with more than 6.3 million Ukrainians fleeing the country and a third of the population displaced (Source: Wikipedia).

Content

This dataset is a collection of 407 news articles from NYT and Guardians related to ongoing… See the full description on the dataset page: https://huggingface.co/datasets/hugginglearners/russia-ukraine-conflict-articles.

This is an updated version of Gütschow et al. (2017, http://doi.org/10.5880/pik.2017.001). Please use this version which incorporates updates to input data as well as correction of errors in the original dataset and its first update. For a detailed description of the changes please consult the CHANGELOG included in the data description document. This dataset combines several published datasets to create a comprehensive set of greenhouse gas emission pathways for every country and Kyoto gas covering the years 1850 to 2015 and all UNFCCC (United Nations Framework Convention on Climate Change) member states as well as most non-UNFCCC territories. The data resolves the main IPCC (Intergovernmental Panel on Climate Change) 1996 categories. For CO2‚‚ from energy and industry time series for subsectors are available. List of datasets included in this data publication:(1) PRIMAP-hist_v1.2_14-Dec-2017.csv: With numerical extrapolation of all time series to 2014. (only in .zip folder)(2) PRIMAP-hist_no_extrapolation_v1.2_14-Dec-2017.csv: Without numerical extrapolation of missing values. (only in .zip folder)(3) PRIMAP-hist_v1.2_data-format-description: including CHANGELOG(4) PRIMAP-hist_v1.2_updated_figures: updated figures of those published in Gütschow et al. (2016)(all files are also included in the .zip folder) When using this dataset or one of its updates, please also cite the data description article (Gütschow et al., 2016, http://doi.org/10.5194/essd-8-571-2016) to which this data are supplement to. Please consider also citing the relevant original sources. SOURCES:- UNFCCC National Communications and National Inventory Reports for developing countries: UNFCCC (2017B)- UNFCCC Biennal Update Reports: UNFCCC (2016)- UNFCCC Common Reporting Format (CRF): UNFCCC (2016), UNFCCC (2017)- BP Statistical Review of World Energy: BP (2017)- CDIAC: Boden et al. (2017)- EDGAR versions 4.2 and 4.2 FT2010: JRC and PBL (2011), Olivier and Janssens-Maenhout (2012)- FAOSTAT database: Food and Agriculture Organization of the United Nations (2016)- Houghton land use CO2: Houghton (2008)- RCP historical data: Meinshausen et al. (2011)- EDGAR-HYDE 1.4: Van Aardenne et al. (2001), Olivier and Berdowski (2001)- HYDE land cover data: Klein Goldewijk et al. (2010), Klein Goldewijk et al. (2011)- SAGE Global Potential Vegetation Dataset: Ramankutty and Foley (1999)- FAO Country Boundaries: Food and Agriculture Organization of the United Nations (2015)

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

London Fire Brigade is the busiest fire and rescue service in the country and one of the largest firefighting and rescue organisations in the world. This dataset contains the details of every fire engine (pumping appliance) sent to an incident since January 2009. Information is provided for the appliance mobilised, where it was deployed from and the times recorded for arriving at the incident. The main dataset on this page (zip file) contains the details of every fire engine mobilisation to an incident since 1 January 2009. This is a large data file, more suitable for data analysts, and those who have data manipulation tools and experience. There is a smaller dataset which has the fire engine mobilisations in the last three years, which will be easier for those who have more limited data analysis skills and tools. Both datasets are updated every month. Also available are all LFB incident records since January 2009. This data about the incidents attended by the London Fire Brigade includes the fire station area where the incident happened; the fire station area is currently used for mobilising and other administrative purposes. On 9 January 2014, ten London fire stations were closed as part of the Authority’s Fifth London Safety Plan (LSP5), and fire station areas were changed to reflect these closures with the station areas of closed stations being distributed to adjacent fire station areas. To provide consistent incident data, stations grounds have been changed for all incidents in this data set and reflect the station areas in use since 9 January 2014.