Context

More international students are flocking to China than ever before. According to a report, over 540,000 foreigners studied in China in 2018 – marking a 40 percent increase from 2012. China attracts more international students than any other Asian power and ranks third globally, behind the United States and the United Kingdom.

Content

In 2018 there were a total of 492,185 international students from 196 countries/areas pursuing their studies in 1,004 higher education institutions in China’s 31 provinces/autonomous regions/provincial-level municipalities, marking an increase of 3,013 students or 0.62% compared to 2017. International students in Hong Kong, Macau and Taiwan are not included in the datasets. The datasets contain three CSV files (Continent, Country, Province) with different data about international students in China.

Columns Description

@Continent (Number/percent of international students by continent) Continent- The name of continent Number - The number of total international students Deaths- The percentage of total international students

@Country (Number of international students by country of origin) Rank- The rank of the country based on total students in China Country- The name of the country Number- The number of total international students

@Province (The top provinces/cities with the largest number of international students) Province- The name of the city/province Number- The number of total international students

Acknowledgements

This data collected from moe.gov.cn.

Inspiration

Currently, I'm studying at a Chinese university. Every year many international students come to China for their higher study, and the ratio of international students is growing steadily. This data will help us to understand the ratio of international students in China.

Using a multilevel modelling approach to analyse a novel dataset of academic publications at all business schools in 11 European countries, this paper finds that the influence of organisational- and country-level contextual factors on researchers varies considerably based on the type of institution and the development level of the country they are located in. At the organisational-level, we find that greater spatial connectivity–operationalised through proximity to nearby business schools, rail stations, and airports–is positively related to scientific research volume and public dissemination (news mentions). While this result is significant only for high-income countries (above EU-average 2018 GDP per capita), this is likely because the low-income countries (below EU-average 2018 GDP per capita) examined here lack a ‘critical mass’ of well-connected universities to generate observable agglomeration effects. At the country-level, the results indicate that in high-income countries, less prestigious schools benefit from higher rates of recent international immigration from any foreign country, providing a direct policy pathway for increasing research output for universities that aren’t already well-known enough to attract the most talented researchers. In low-income countries, recent immigration rates are even stronger predictors of research performance across all levels of institutional prestige; more open immigration policies would likely benefit research performance in these countries to an even greater extent. Finally, the paper’s results show that, in low-income countries, a composite measure of a country’s quality of life (including self-rated life satisfaction, health, working hours, and housing overcrowding) is positively related to research outcomes through its interaction with school prestige. This suggests that the lower a country’s quality of life, the more researchers are incentivised to produce higher levels of research output. While this may in part reflect the greater disparities inherent in these countries’ economic systems, it is noteworthy–and perhaps concerning–that we have observed a negative correlation between country-level quality of life and research performance in low-income countries, which is particularly felt by researchers at less prestigious institutions.

The study, first of all, tested the hypothesis of “there is a relationship between democracy and FDI” to answer the research question raised at the beginning. The research sample was selected as BRICS-TM (Brazil, Russia, India, China, South Africa, Türkiye, Mexico) countries that have come to the fore in the world economy in recent years and whose strategic importance and power are expected to increase in the upcoming years. These countries were preferred because of their potential to attract FDI. FDI (LNFDI) was modeled as the dependent variable in this study. The democracy variable (DEMOC) was fictionalized as the independent variable. In addition, inflation (INF) and per capita income (PGDP) variables affecting FDI were added to the model as control variables based on the literature. First of all, the data on the indices of "political rights" and "civil liberties", which are accepted as indicators of "democracy" in the literature, were collected from the Freedom House database, and then the means of these indices were included in the analysis as values ​​for the variable of democracy. The index takes a value between 1 and 7; 1 is the best state of the level of democracy and 7 is the worst state of the level of democracy. Index values were attached to the model by scaling so that the minimum was 0 and the maximum was 100 in case of problems in analyses, calculation, and interpretation. In this study, inflation and income per capita variables were preferred in terms of both being the most preferred variables in the literature (details are given in Literature Review) and being the variables that affect foreign direct capital as the most inclusive in terms of macroeconomics.

Context

The World Bank is an international financial institution that provides loans to countries of the world for capital projects. The World Bank's stated goal is the reduction of poverty. Source: https://en.wikipedia.org/wiki/World_Bank

Content

This dataset combines key education statistics from a variety of sources to provide a look at global literacy, spending, and access.

For more information, see the World Bank website.

Fork this kernel to get started with this dataset.

Acknowledgements

https://bigquery.cloud.google.com/dataset/bigquery-public-data:world_bank_health_population

http://data.worldbank.org/data-catalog/ed-stats

https://cloud.google.com/bigquery/public-data/world-bank-education

Citation: The World Bank: Education Statistics

Dataset Source: World Bank. 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.

Banner Photo by @till_indeman from Unplash.

Inspiration

Of total government spending, what percentage is spent on education?

The industry standard for studying multinational corporations (MNCs) has been to evaluate patterns in aggregate country-level measures of foreign direct investment (FDI). Though certainly related, these data are at best a proxy for the actual commercial and productive activities of multinationals that most political scientists purport to be interested in. Simply put, this is a very indirect way of testing theories about the sociopolitical and economic factors that motivate MNCs’ choice of host countries. This article introduces a new firm-level data set designed to get around this problem by permitting more direct analysis of multinationals’ foreign operations. It then revisits the relationship between regime type and direct investment, finding evidence that MNCs are more likely to establish new subsidiaries in democracies than in nondemocracies. However, further analysis reveals that the strength of this relationship varies by context. Specifically, MNCs rely on regime type as an indicator of political risk when they lack an existing relationship with the host state. In addition, those operating in extractive industries are generally less responsive to political institutions than those operating in manufacturing or services. These results suggest that firm- and sector-specific factors deserve greater consideration than they have been given in the existing literature.

Context

The World Bank is an international financial institution that provides loans to countries of the world for capital projects. The World Bank's stated goal is the reduction of poverty. Source: https://en.wikipedia.org/wiki/World_Bank

Content

This dataset combines key health statistics from a variety of sources to provide a look at global health and population trends. It includes information on nutrition, reproductive health, education, immunization, and diseases from over 200 countries.

Update Frequency: Biannual

For more information, see the World Bank website.

Fork this kernel to get started with this dataset.

Acknowledgements

https://datacatalog.worldbank.org/dataset/health-nutrition-and-population-statistics

https://cloud.google.com/bigquery/public-data/world-bank-hnp

Dataset Source: World Bank. 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.

Citation: The World Bank: Health Nutrition and Population Statistics

Banner Photo by @till_indeman from Unplash.

Inspiration

What’s the average age of first marriages for females around the world?

This dataset measures food availability and access for 76 low- and middle-income countries. The dataset includes annual country-level data on area, yield, production, nonfood use, trade, and consumption for grains and root and tuber crops (combined as R&T in the documentation tables), food aid, total value of imports and exports, gross domestic product, and population compiled from a variety of sources. This dataset is the basis for the International Food Security Assessment 2015-2025 released in June 2015. This annual ERS report projects food availability and access for 76 low- and middle-income countries over a 10-year period. Countries (Spatial Description, continued): Democratic Republic of the Congo, Ecuador, Egypt, El Salvador, Eritrea, Ethiopia, Gambia, Georgia, Ghana, Guatemala, Guinea, Guinea-Bissau, Haiti, Honduras, India, Indonesia, Jamaica, Kenya, Kyrgyzstan, Laos, Lesotho, Liberia, Madagascar, Malawi, Mali, Mauritania, Moldova, Mongolia, Morocco, Mozambique, Namibia, Nepal, Nicaragua, Niger, Nigeria, North Korea, Pakistan, Peru, Philippines, Rwanda, Senegal, Sierra Leone, Somalia, Sri Lanka, Sudan, Swaziland, Tajikistan, Tanzania, Togo, Tunisia, Turkmenistan, Uganda, Uzbekistan, Vietnam, Yemen, Zambia, and Zimbabwe. Resources in this dataset:Resource Title: CSV File for all years and all countries. File Name: gfa25.csvResource Title: International Food Security country data. File Name: GrainDemandProduction.xlsxResource Description: Excel files of individual country data. Please note that these files provide the data in a different layout from the CSV file. This version of the data files was updated 9-2-2021

More up-to-date files may be found at: https://www.ers.usda.gov/data-products/international-food-security.aspx

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Context

The World Bank is an international financial institution that provides loans to countries of the world for capital projects. The World Bank's stated goal is the reduction of poverty. Source: https://en.wikipedia.org/wiki/World_Bank

Content

This dataset contains both national and regional debt statistics captured by over 200 economic indicators. Time series data is available for those indicators from 1970 to 2015 for reporting countries.

For more information, see the World Bank website.

Fork this kernel to get started with this dataset.

Acknowledgements

https://bigquery.cloud.google.com/dataset/bigquery-public-data:world_bank_intl_debt

https://cloud.google.com/bigquery/public-data/world-bank-international-debt

Citation: The World Bank: International Debt Statistics

Dataset Source: World Bank. 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.

Banner Photo by @till_indeman from Unplash.

Inspiration

What countries have the largest outstanding debt?

https://cloud.google.com/bigquery/images/outstanding-debt.png" alt="enter image description here"> https://cloud.google.com/bigquery/images/outstanding-debt.png

The PEN network was launched in September 2004 by the Center for International Forestry Research (CIFOR) with the aim of collecting uniform socio-economic and environmental data at household and village levels in rural areas of developing countries. The data presented here were collected by 33 PEN partners (mainly PhD students) and comprise 8,301 households in 334 villages located in 24 countries in Asia, Africa and Latin America. Three types of quantitative surveys were conducted: 1. Village surveys (V1, V2) 2. Annual household surveys (A1, A2) 3. Quarterly household surveys (Q1, Q2, Q3, Q4) The village surveys (V1-V2) collected data that were common to all or showed little variation among households. The first village survey, V1, was conducted at the beginning of the fieldwork to get background information on the villages while the second survey, V2 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 (Q1, Q2, Q3, Q4) to collect income information, and, household surveys (A1, A2) to collect all other household information. A critical feature of the PEN research project was to collect detailed, high-quality data on forest use. This was done through quarterly income household surveys, for two reasons: first, short recall periods increase accuracy and reliability and, second, quarterly data would allow us to document seasonal variation in (forest) income and thus, inter alia, help us understand to what extent forests act as seasonal gap fillers. There are three partners (10101, 10203, and 10301 ) who, because of various particular circumstances, only conducted three of the four income surveys. In addition, 598 of the households missed out on one of the quarterly surveys, e.g., due to temporal absence or sickness, or insecurity in the area. These are still included in the database, while households missing more than one quarter were excluded. Two other household surveys were conducted. The first annual household survey (A1) collected basic household information (demographics, assets, forest-related information) and was done at the beginning of the survey period while the second (A2) 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. Note, however, that we did not collect any systematic data on the time allocation of households: while highly relevant for many analyses, we believed that it would be too time-consuming a component to add to our standard survey questions. The project is further described and discussed in two edited volumes by Angelsen et al. (2011) (describes particular the methods used) and Wunder et al. (2014) (includes six articles based on the PEN project).

Please get the new version of this dataset at https://doi.org/10.17882/100023 IBTS surveys (International Bottom Trawl Survey) are carried out within an international framework. Main countries bordering the North Sea participate to it according to the European Community regulations (EC N°1543/2000 and N° 1639/2001) which specify that countries from E.U. have to carry out surveys at sea in order to evaluate abundance and stocks distribution, independently of commercial fisheries data. The first target of the IBTS survey is to have a diagnosis on the main commercial fish stock and to calculate abundances index by age for these species. This survey started in the years 70’s and gradually standardised. Since the years 80’s, a common protocol is implemented and used by all participants. The same fishing gear and the same working methods are used. In addition, to calculate an index for herring and sprat larvae (0 groups), each participating vessel operates with a MIK net during the night (Methot Isaac Kidd). For 20 years, the southern part of the North Sea has been allocated to the French vessel and since 2007, the Eastern Channel has been integrated to the whole sampled area. As interactions and circulation of stock between these two areas are important, Eastern Channel is often associated the North Sea for stock assessment. Herring for example which is exploited all the year in the North Sea comes into the Channel during November and December for reproduction. More precise information on larvae indices will be obtained when this area is sampled. In order to study the whole marine ecosystem of the North Sea and English Channel, some additional studies are carried out during the Survey on the R/V Thalassa. For example, the Continuous Underwater Fish Eggs Sampler device (CUFES) is used to study fish spawning areas. Abundance and distribution of the winter planktonic community (phyto and zoo plankton) and a monitoring study on the structure and distribution of the benthic macroinvertebrates community are also carried out. At last, more samples are done for the Marine Strategy framework since 2015.

@youtube

Our National Footprint Accounts (NFAs) measure the ecological resource use and resource capacity of nations from 1961 to 2014. The calculations in the National Footprint Accounts are primarily based on United Nations data sets, including those published by the Food and Agriculture Organization, United Nations Commodity Trade Statistics Database, and the UN Statistics Division, as well as the International Energy Agency. The 2018 edition of the NFA features some exciting updates from last year’s 2017 edition, including data for more countries and improved data sources and methodology. Methodology changes:

1. Our conversion of carbon to CO2 increased in precision, which increased the world’s carbon footprint by approximately 1%.
2. We implemented a new data quality scoring system. This allowed us to publish data for more countries by omitting unreliable data for some years rather than the entire country’s Ecological Footprint timeline.
3. We used more precise data from the Global Carbon Project to calculate ocean carbon sequestration rates for 2014.

National Footprint Accounts 2018 Edition

To visualize our data in our data explorer click here. Dataset provides Ecological Footprint per capita data for years 1961-2014 in global hectares (gha). Ecological Footprint is a measure of how much area of biologically productive land and water an individual, population, or activity requires to produce all the resources it consumes and to absorb the waste it generates, using prevailing technology and resource management practices. The Ecological Footprint is measured in global hectares. Since trade is global, an individual or country's Footprint tracks area from all over the world. Without further specification, Ecological Footprint generally refers to the Ecological Footprint of consumption (rather than only production or export). Ecological Footprint is often referred to in short form as Footprint.

About this Dataset

This data includes total and per capita national biocapacity, ecological footprint of consumption, ecological footprint of production and total area in hectares. This dataset, however, does not include any of our yield factors (national or world) nor any equivalence factors. To view these click here.

Objectives

Revealing links between human consumption and other human behaviors, geographic characteristics, political landscapes,

Get involved

How can others contribute? - [ ] Join this table on other data.world datasets (prefereably country-level data) - [ ] Write queries - [ ] Create graphics - [ ] Post and share discoveries

External resources

• Data Explorer
• Footprint Website
• Calculate your own Ecological Footprint

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

Tourist Arrivals in Turkey increased to 5772328 in June from 5037447 in May of 2025. This dataset provides the latest reported value for - Turkey Tourist Arrivals - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Wikipedia Movie Data

Exploring Production and Distribution Trends Across Four Decades

By Michael Tauberg [source]

About this dataset

This comprehensive dataset spans a substantial sampling of movies from the last five decades, giving insight into the financial and creative successes of Hollywood film productions. Containing various production details such as director, actors, editing team, budget, and overall gross revenue, it can be used to understand how different elements come together to make a movie successful. With information covering all aspects of movie-making – from country of origin to soundtrack composer – this collection offers an unparalleled opportunity for a data-driven dive into the world of cinematic storytelling

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

The columns are important factors to analyze the data in depth – they range from general information such as year, name and language of movie to more specific info such as directors and editors of movie production teams. A good first step is to get an understanding of what kind of data exists and getting familiar with different columns.

Good luck exploring!

Research Ideas

• Analyzing the correlations between budget, gross revenue, and number of awards or nominations won by a movie. Movie-makers and studios can use this data to understand what factors have an impact on the success of a movie and make better creative decisions accordingly.
• Studying the trend of movies from different countries over time to understand how popular genres are changing over time across regions and countries; this data could be used by international film producers to identify potential opportunities for co-productions with other countries or regions.
• Identifying unique topics for films (based on writers, directors, music etc) that hadn’t been explored in previous decades - studios can use this data to find unique stories or ideas for new films that often succeed commercially due to its novelty factor with audiences

Acknowledgements

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

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: movies_1970_2018.csv | Column name | Description | |:-------------------|:----------------------------------------------------------| | year | Year the movie was released. (Integer) | | wiki_ref | Reference to the Wikipedia page for the movie. (String) | | wiki_query | Query used to search for the movie on Wikipedia. (String) | | producer | Name of the producer of the movie. (String) | | distributor | Name of the distributor of the movie. (String) | | name | Name of the movie. (String) | | country | Country of origin of the movie. (String) | | director | Name of the director of the movie. (String) | | cinematography | Name of the cinematographer of the movie. (String) | | editing | Name of the editor of the movie. (String) | | studio | Name of the studio that produced the movie. (String) | | budget | Budget of the movie. (Integer) | | gross | Gross box office receipts of the movie. (Integer) | | runtime | Length of the movie in minutes. (Integer) | | music | Name of the composer of the movie's soundtrack. (String) | | writer | Name of the writer of the movie. (String) | | starring | Names of the actors in the movie. (String) | | language | Language of the movie. (String) |

Acknowledgements

If you use this dataset in your research, p...

Analyzing and simulating trade policy scenarios in a complex and intertwined global economy requires a database with a complete bilateral trade matrix at the level of highly disaggregated industries over several decades. Such a database has not been created until now. This paper introduces the International Trade and Production Database for Simulation (ITPD-S). In combination with the International Trade and Production Database for Estimation (ITPD-E), we use it to quantify the impact of globalization on bilateral trade, real income, and inequality in the world at the detailed industry level in 1990-2019. To perform the analysis, we rely on a new quantitative trade model that enables us to estimate the magnitude of globalization and then perform a counterfactual analysis of the impact of globalization on real output within the same framework. Our estimates reveal that, on average, bilateral globalization forces have led to a remarkable increase in international trade of about 570%, between 1990 and 2019, with very wide but intuitive variation across industries. Our counterfactual analysis reveals that globalization has benefited most countries but relatively more so smaller and more open economies, which are typically developing countries. As a result, this ‘catch-up’ implies less cross-country income inequality.

This table contains 45 series, with data for years 2014 - 2014 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (1 item: Canada) Country of origin (15 items: United States; United Kingdom; France; China; ...) Traveller characteristics (3 items: Trips; Nights; Spending in Canada).

Study of perceptions, attitudes and knowledge of Dutch high school students regarding Germany and some other countries. Respondent is interested in Dutch politics and international politics / scale of sympathy for the 12 member states of the European Community ( EC ) / free associations with the term "Germany" and the positive-negative orientation of these associations / interested in Great Britain, France, Germany, Belgium, number of visits paid to these countries / preferred countries for study tour / positive-negative orientation of several character traits and the occurrence of these traits in Dutch, English, French, German and Belgium people / positive-negative orientation of country characteristics and the occurrence of these in the Netherlands, England, France, Germany and Belgium/ score on democracy scale for these countries / choice of 11 EC countries to go to in case respondent was forced to emigrate / choice of people from 11 EC countries as neighbours / subjects at school / knowledge of Germany / German acquaintances or family / watch German television / sources of information about Germany / general opinion about Germany is positive-negative / respondent expects to be on good terms with Germans / like to get in touch with German youth / two most important problems in the Netherlands / troubled about some important and threatening international events ( environment, Bosnia-Hercegovina, ultra-right-wing boom in Belgium, rows around centres for people who seek asylum in Germany ). Background variables: basic characteristics/ residence/ occupation/employment/ education/ politics/ religion/ readership, mass media, and 'cultural' exposure

This table provides the number of temporary foreign workers in Canada and in provinces by their country of citizenship.

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