In the academic year 2023/24, there were 331,602 international students from India studying in the United States. International students The majority of international students studying in the United States are originally from India and China, totaling 331,602 students and 277,398 students respectively in the 2023/24 school year. In 2022/23, there were 467,027 international graduate students , which accounted for over one third of the international students in the country. Typically, engineering and math & computer science programs were among the most common fields of study for these students. The United States is home to many world-renowned schools, most notably, the Ivy League Colleges which provide education that is sought after by both foreign and local students. International students and college Foreign students in the United States pay some of the highest fees in the United States, with an average of 24,914 U.S. dollars. American students attending a college in New England paid an average of 14,900 U.S. dollars for tuition alone and there were about 79,751 international students in Massachusetts . Among high-income families, U.S. students paid an average of 34,700 U.S. dollars for college, whereas the average for all U.S. families reached only 28,026 U.S. dollars. Typically, 40 percent of families paid for college tuition through parent income and savings, while 29 percent relied on grants and scholarships.

In 2018, students from *** different countries and regions were studying in China. The highest number of students came from South Korea amounting to ******, while only ****** students came from the United States. International students in ChinaThe total number of foreign students in China increased steadily over recent years and reached more than ******* in 2018. That was roughly double as much as ten years ago and made China one of the leading host destinations for international students. Looking at their origins in terms of global regions reveals that by far the largest share of students come from Asia, while the Americas and Europe together accounted for only slightly more than ** percent of all students in 2018. While the share of students from Western countries has been shrinking steadily in recent years, more and more students from Asia and Africa were attracted to study in China. Regarding the United States, the figures interestingly not only decreased in relation to other regions, but also in total numbers. In contrast, students particularly from Africa are increasingly able and willing to study in China, and numbers from countries participating in China's Belt and Road Initiative displayed the highest growth rates over recent years. Student situationRegarding the financial situation of international students in China, most of them were either self-funded or receiving a scholarship from foreign institutions. However, the number of students supported by the Chinese government increased considerably over the last ten years, with a growing number of scholarships granted to students from developing countries. Preferred universities for study were either located in the two most developed cities Beijing and Shanghai, or in the eastern and southern coastal regions of China.

Dataset for content analysis published in "Hornikx, J., Meurs, F. van, Janssen, A., & Heuvel, J. van den (2020). How brands highlight country of origin in magazine advertising: A content analysis. Journal of Global Marketing, 33 (1), 34-45."*Abstract (taken from publication)Aichner (2014) proposes a classification of ways in which brands communicate their country of origin (COO). The current, exploratory study is the first to empirically investigate the frequency with which brands employ such COO markers in magazine advertisements. An analysis of about 750 ads from the British, Dutch, and Spanish editions of Cosmopolitan showed that the prototypical ‘made in’ marker was rarely used, and that ‘COO embedded in company name’ and ‘use of COO language’ were most frequently employed. In all, 36% of the total number of ads contained at least one COO marker, underlining the importance of the COO construct.*Methodology (taken from publication)SampleThe use of COO markers in advertising was examined in print advertisements from three different countries to increase the robustness of the findings. Given the exploratory nature of this study, two practical selection criteria guided our country choice: the three countries included both smaller and larger countries in Europe, and they represented languages that the team was familiar with in order to reliably code the advertisements on the relevant variables. The three European countries selected were the Netherlands, Spain, and the United Kingdom. The dataset for the UK was discarded for testing H1 about the use of English as a foreign language, as will be explained in more detail in the coding procedure.The magazine Cosmopolitan was chosen as the source of advertisements. The choice for one specific magazine title reduces the generalizability of the findings (i.e., limited to the corresponding products and target consumers), but this magazine was chosen intentionally because an informal analysis suggested that it carried advertising for a large number of product categories that are considered ethnic products, such as cosmetics, watches, and shoes (Usunier & Cestre, 2007). This suggestion was corroborated in the main analysis: the majority of the ads in the corpus referred to a product that Usunier and Cestre (2007) classify as ethnic products. Table 2 provides a description of the product categories and brands referred to in the advertisements. Ethnic products have a prototypical COO in the minds of consumers (e.g., cosmetics – France), which makes it likely that the COOs are highlighted through the use of COO markers.Cosmopolitan is an international magazine that has different local editions in the three countries. The magazine, which is targeted at younger women (18–35 years old), reaches more than three million young women per month through its online, social and print platforms in the Netherlands (Hearst Netherlands, 2016), has about 517,000 readers per month in Spain (PrNoticias, 2016) and about 1.18 million readers per month in the UK (Hearst Magazine U.K., 2016).The sample consisted of all advertisements from all monthly issues that appeared in 2016 in the three countries. This whole-year cluster was selected so as to prevent potential seasonal influences (Neuendorf, 2002). In total, the corpus consisted of 745 advertisements, of which 111 were from the Dutch, 367 from the British and 267 from the Spanish Cosmopolitan. Two categories of ads were excluded in the selection process: (1) advertisements for subscription to Cosmopolitan itself, and (2) advertisements that were identical to ads that had appeared in another issue in one of the three countries. As a result, each advertisement was unique.Coding procedureFor all advertisements, four variables were coded: product type, presence of types of COO markers, COO referred to, and the use of English as a COO marker. In the first place, product type was assessed by the two coders. Coders classified each product to one of the 32 product types. In order to assess the reliability of the codings, ten per cent of the ads were independently coded by a second coder. The interrater reliability of the variable product category was good (κ = .97, p < .000, 97.33% agreement between both coders). Table 2 lists the most frequent product types; the label ‘other’ covers 17 types of product, including charity, education, and furniture.In the second place, it was recorded whether one or more of the COO markers occurred in a given ad. In the third place, if a marker was identified, it was assessed to which COO the markers referred. Table 1 lists the nine possible COO markers defined by Aichner (2014) and the COOs referred to, with examples taken from the current content analysis. The interrater reliability for the type of COO marker was very good (κ = .80, p < .000, 96.30% agreement between the coders), and the interrater reliability for COO referred to was excellent (κ = 1.00, p < .000).After the independent assessments of the two...

In 2021/22, the highest number of international students arriving in Sweden came from Germany. Of the total ****** international students who began to study in Sweden in the academic year of 2020/21, nearly ***** came from Germany. French students made up the second largest group, followed by students from India.

Market Inside is a global leader in import-export data and analytics for the major industries and markets. We accelerate business progress by delivering essential intelligence that unlocks opportunities and fosters growth.

Our database contains: • 220+ Countries’ Global Trade Data • 2+ Billion Importer-Exporter Shipment Records • 60+ Million Import-Export Companies • 40+ Million Decision Maker Direct Phone Numbers • 50+ Million Decision Maker Direct Email Addresses

By using our dashboard, customers can access: • Bill of lading data by HS Code or product description. • Product specifications like brand, model, type, etc. • Company information like name, size, location and so on. • Companies’ business information such as market share, industry, etc. • Contacts data including name & profile of employees and phone numbers & email addresses of key decision makers. • Location data like origin country, destination and port of loading & unloading. • Our import export data can be used by investment firms and banks, private equity firms, hedge funding corporations, government agencies, insurance firms, corporations, exporters and importers, environmental studies agencies, academics, logistics, pharma, FMCG, Consulting, Law Firms and other private players to track, analyze, research and gain better insights of global trade of more than 50M+ Commodities.

For data authenticity, we collect raw data from the most genuine sources like Federal & State Government, Embassies, Customs & Taxes Departments, Ministries, Ports Authorities and filter information to remove duplicates and make every detail clean and error-free. Our database is updated frequently and customers can directly view and download shipment records on keywords of their choice in seconds anywhere and anytime

This data set is a subset of the "Records of foreign capital" (Registros de capitais estrangeiros", RCE) published by the Central Bank of Brazil (CBB) on their website.The data set consists of three data files and three corresponding metadata files. All files are in openly accessible .csv or .txt formats. See detailed outline below for data contained in each. Data files contain transaction-specific data such as unique identifier, currency, cancelled status and amount. Metadata files outline variables in the corresponding data file.RCE_Unclean_full_dataset.csv - all transactions published to the Central Bank website from the four main categories outlined belowMetadata_Unclean_full_dataset.csvRCE_Unclean_cancelled_dataset.csv - data extracted from the RCE_Unclean_full_dataset.csv where transactions were registered then cancelledMetadata_Unclean_cancelled_dataset.csvRCE_Clean_selection_dataset.csv - transaction data extracted from RCE_Unclean_full_dataset.csv and RCE_Unclean_cancelled_dataset.csv for the nine companies and criteria identified belowMetadata_Clean_selection_dataset.csvThe data include the period between October 2000 and July 2011. This is the only time span for the data provided by the Central Bank of Brazil at this stage. The records were published monthly by the Central Bank of Brazil as required by Art. 66 in Decree nº 55.762 of 17 February 1965, modified by Decree nº 4.842 of 17 September 2003. The records were published on the bank’s website starting October 2000, as per communique nº 011489 of 7 October 2003. This remained the case until August 2011, after which the amount of each transaction was no longer disclosed (and publication of these stopped altogether after October 2011). The disclosure of the records was suspended in order to review their legal and technical aspects, and ensure their suitability to the requirements of the rules governing the confidentiality of the information (Law nº 12.527 of 18 November 2011 and Decree nº 7724 of May 2012) (pers. comm. Central Bank of Brazil, 2016. Name of contact available upon request to Authors).The records track transfers of foreign capital made from abroad to companies domiciled in Brazil, with information on the foreign company (name and country) transferring the money, and on the company receiving the capital (name and federative unit). For the purpose of this study, we consider the four categories of foreign capital transactions which are published with their amount and currency in the Central Bank’s data, and which are all part of the “Register of financial transactions” (abbreviated RDE-ROF): loans, leasing, financed import and cash in advance (see below for a detailed description). Additional categories exist, such as foreign direct investment (RDE-IED) and External Investment in Portfolio (RDE-Portfólio), for which no amount is published and which are therefore not included.We used the data posted online as PDFs on the bank’s website, and created a script to extract the data automatically from these four categories into the RCE_Unclean_full_dataset.csv file. This data set has not been double-checked manually and may contain errors. We used a similar script to extract rows from the "cancelled transactions" sections of the PDFs into the RCE_Unclean_cancelled_dataset.csv file. This is useful to identify transactions that have been registered to the Central Bank but later cancelled. This data set has not been double-checked manually and may contain errors.From these raw data sets, we conducted the following selections and calculations in order to create the RCE_Clean_selection_dataset.csv file. This data set has been double-checked manually to secure that no errors have been made in the extraction process.We selected all transactions whose recipient company name corresponds to one of these nine companies, or to one of their known subsidiaries in Brazil, according to the list of subsidiaries recorded in the Orbis database, maintained by Bureau Van Dijk. Transactions are included if the recipient company name matches one of the following:- the current or former name of one of the nine companies in our sample (former names are identified using Orbis, Bloomberg’s company profiles or the company website);- the name of a known subsidiary of one of the nine companies, if and only if we find evidence (in Orbis, Bloomberg’s company profiles or on the company website) that this subsidiary was owned at some point during the period 2000-2011, and that it operated in a sector related to the soy or beef industry (including fertilizers and trading activities).For each transaction, we extracted the name of the company sending capital and when possible, attributed the transaction to the known ultimate owner.The name of the countries of origin sometimes comes with typos or different denominations: we harmonized them.A manual check of all the selected data unveiled that a few transactions (n=14), appear twice in the database while bearing the same unique identification number. According to the Central Bank of Brazil (pers. comm., November 2016), this is due to errors in their routine of data extraction. We therefore deleted duplicates in our database, keeping only the latest occurrence of each unique transaction. Six (6) transactions recorded with an amount of zero were also deleted. Two (2) transactions registered in August 2003 with incoherent currencies (Deutsche Mark and Dutch guilder, which were demonetised in early 2002) were also deleted.To secure that the import of data from PDF to the database did not contain any systematic errors, for instance due to mistakes in coding, data were checked in two ways. First, because the script identifies the end of the row in the PDF using the amount of the transaction, which can sometimes fail if the amount is not entered correctly, we went through the extracted raw data (2798 rows) and cleaned all rows whose end had not been correctly identified by the script. Next, we manually double-checked the 486 largest transactions representing 90% of the total amount of capital inflows, as well as 140 randomly selected additional rows representing 5% of the total rows, compared the extracted data to the original PDFs, and found no mistakes.Transfers recorded in the database have been made in different currencies, including US dollars, Euros, Japanese Yens, Brazilian Reais, and more. The conversion to US dollars of all amounts denominated in other currencies was done using the average monthly exchange rate as published by the International Monetary Fund (International Financial Statistics: Exchange rates, national currency per US dollar, period average). Due to the limited time period, we have not corrected for inflation but aggregated nominal amounts in USD over the period 2000-2011.The categories loans, cash in advance (anticipated payment for exports), financed import, and leasing/rental, are those used by the Central Bank of Brazil in their published data. They are denominated respectively: “Loans” (“emprestimos” in original source) - : includes all loans, either contracted directly with creditors or indirectly through the issuance of securities, brokered by foreign agents. “Anticipated payment for exports” (“pagamento/renovacao pagamento antecipado de exportacao” in original source): defined as a type of loan (used in trade finance)“Financed import” (“importacao financiada” in original source): comprises all import financing transactions either direct (contracted by the importer with a foreign bank or with a foreign supplier), or indirect (contracted by Brazilian banks with foreign banks on behalf of Brazilian importers). They must be declared to the Central Bank if their term of payment is superior to 360 days.“Leasing/rental” (“arrendamento mercantil, leasing e aluguel” in original source) : concerns all types of external leasing operations consented by a Brazilian entity to a foreign one. They must be declared if the term of payment is superior to 360 days.More information about the different categories can be found through the Central Bank online.(Research Data Support provided by Springer Nature)

Market Inside is a global leader in providing import-export data and analytics for the major industries and markets. We accelerate business progress by delivering essential intelligence that unlocks opportunities and fosters growth.

Our database contains: • 220+ Countries’ Global Trade Data • 2+ Billion Importer-Exporter Shipment Records • 100+ Million Import-Export Companies • 40+ Million Decision Maker Direct Phone Numbers • 50+ Million Decision Maker Direct Email Addresses

By using our dashboard, customers can access: • Bill of lading data by HS Code or product description. • Product specifications like brand, model, type, etc. • Company information like name, size, location and so on. • Companies’ business information such as market share, industry, etc. • Contacts data including name & profile of employees and phone numbers & email addresses of key decision makers. • Location data like origin country, destination and port of loading & unloading. • Our import export data can be used by investment firms and banks, private equity firms, hedge funding corporations, government agencies, insurance firms, corporations, exporters and importers, environmental studies agencies, academics, logistics, pharma, FMCG, Consulting, Law Firms and other private players to track, analyze, research and gain better insights of global trade of more than 50M+ Commodities.

For data authenticity, we collect raw data from the most genuine sources like Federal & State Government, Embassies, Customs & Taxes Departments, Ministries, Ports Authorities and filter information to remove duplicates and make every detail clean and error-free. Our database is updated frequently and customers can directly view and download shipment records on keywords of their choice in seconds anywhere and anytime

In 2023, there were more than one million international students in Canada. Of these, approximately 427,000 students were from India, followed by China with 102,150 students.

This dataset lists all the country to country ties derived from the Research in Svalbard (RIS) database using the country of origin of the organisations with joint research projects in Svalbard and the projects year. This edgelist is broken down into two time periods: 1972-2004 ; 2005-2022. Per each pair of countries, it gives the number of joint research projects registered in the RIS database per period of time. It can be used for network analysis purposes. It has been created and analysed using a core-periphery approach within the publication: Strouk, M. & Maisonobe, M. (2024). "Field science and scientific collaboration in the Svalbard Archipelago: beyond science diplomacy". Science and Public Policy. DOI: https://doi.org/10.1093/scipol/scae012/

Learn how imported rice prices are influenced by factors such as country of origin, transportation costs, trade policies, currency exchange rates, and global supply and demand dynamics. Understand why prices of imported rice can be higher compared to locally produced rice and how fluctuations in currency exchange rates can impact pricing. Explore the role of source country and geopolitical factors in determining rice prices, as well as the importance of considering market conditions and import regulations w

In 2022, university students from Malaysia made up the majority of international students in Indonesia. Malaysia had ***** tertiary-level students in Indonesia, followed by Timor-Leste and Thailand, which had ***** and *** students, respectively. In comparison to other Southeast Asian countries such as Malaysia and Thailand, Indonesia has a much lower number of international students.

[Deprecated version, used in the support article, please download the last version]

This record is a global open-source passenger air traffic dataset primarily dedicated to the research community.
It gives a seating capacity available on each origin-destination route for a given year, 2019, and the associated aircraft and airline when this information is available.

Context on the original work is given in the related article (https://journals.open.tudelft.nl/joas/article/download/7201/5683) and on the associated GitHub page (https://github.com/AeroMAPS/AeroSCOPE/).
A simple data exploration interface will be available at www.aeromaps.eu/aeroscope.
The dataset was created by aggregating various available open-source databases with limited geographical coverage. It was then completed using a route database created by parsing Wikipedia and Wikidata, on which the traffic volume was estimated using a machine learning algorithm (XGBoost) trained using traffic and socio-economical data.

1- DISCLAIMER

The dataset was gathered to allow highly aggregated analyses of the air traffic, at the continental or country levels. At the route level, the accuracy is limited as mentioned in the associated article and improper usage could lead to erroneous analyses.

2- DESCRIPTION

Each data entry represents an (Origin-Destination-Operator-Aircraft type) tuple.

Please refer to the support article for more details (see above).

The dataset contains the following columns:

• "First column" : index
• airline_iata : IATA code of the operator in nominal cases. An ICAO -> IATA code conversion was performed for some sources, and the ICAO code was kept if no match was found.
• acft_icao : ICAO code of the aircraft type
• acft_class : Aircraft class identifier, own classification.
  • WB: Wide Body
  • NB: Narrow Body
  • RJ: Regional Jet
  • PJ: Private Jet
  • TP: Turbo Propeller
  • PP: Piston Propeller
  • HE: Helicopter
  • OTHER
• seymour_proxy: Aircraft code for Seymour Surrogate (https://doi.org/10.1016/j.trd.2020.102528), own classification to derive proxy aircraft when nominal aircraft type unavailable in the aircraft performance model.
• source: Original data source for the record, before compilation and enrichment.
  • ANAC: Brasilian Civil Aviation Authorities
  • AUS Stats: Australian Civil Aviation Authorities
  • BTS: US Bureau of Transportation Statistics T100
  • Estimation: Own model, estimation on Wikipedia-parsed route database
  • Eurocontrol: Aggregation and enrichment of R&D database
  • OpenSky
  • World Bank
• seats: Number of seats available for the data entry, AFTER airport residual scaling
• n_flights: Number of flights of the data entry, when available
• iata_departure, iata_arrival : IATA code of the origin and destination airports. Some BTS inhouse identifiers could remain but it is marginal.
• departure_lon, departure_lat, arrival_lon, arrival_lat : Origin and destination coordinates, could be NaN if the IATA identifier is erroneous
• departure_country, arrival_country: Origin and destination country ISO2 code. WARNING: disable NA (Namibia) as default NaN at import
• departure_continent, arrival_continent: Origin and destination continent code. WARNING: disable NA (North America) as default NaN at import
• seats_no_est_scaling: Number of seats available for the data entry, BEFORE airport residual scaling
• distance_km: Flight distance (km)
• ask: Available Seat Kilometres
• rpk: Revenue Passenger Kilometres (simple calculation from ASK using IATA average load factor)
• fuel_burn_seymour: Fuel burn per flight (kg) when seymour proxy available
• fuel_burn: Total fuel burn of the data entry (kg)
• co2: Total CO2 emissions of the data entry (kg)
• domestic: Domestic/international boolean (Domestic=1, International=0)

3- Citation

Please cite the support paper instead of the dataset itself.

Salgas, A., Sun, J., Delbecq, S., Planès, T., & Lafforgue, G. (2023). Compilation of an open-source traffic and CO2 emissions dataset for commercial aviation. Journal of Open Aviation Science. https://doi.org/10.59490/joas.2023.7201

A medical tourist is defined by the World Health Organization (WHO) as a person who travels across international borders to receive some form of medical treatment, most commonly dental care, cosmetic surgery, and fertility treatments. Japanese made up the largest share of medical tourists to South Korea in 2023, at around 31 percent of inbound tourists, followed by mainland Chinese tourists. The number of patients from all countries recorded the highest ever since, after recovering from the coronavirus (COVID-19) pandemic. Korea first permitted foreign patients in 2009, and since then, around 3.8 million international medical tourists have come to Korea, with about 605,800 in 2023 alone. Why is Korea so popular? South Korea is a popular destination for medical tourism, being one of the leading countries for inbound medical tourism worldwide. One reason is that Korea has modern and advanced medical facilities and staff. Korea also has a reputation for superior cosmetic surgeries and high-quality beauty products. Many foreigners visit Korea for cosmetic surgeries, and even more foreigners purchase Korean beauty products. In addition, some treatments are covered by the public health insurance system. This means even foreigners can receive treatment at low cost. Together with the increasing popularity of K-pop and K-drama, the number of Japanese patients grew by 762 percent compared to the previous year and Japanese made up the largest share of medical tourists to Korea in 2023. Korean public health insurance in the red A problem with medical tourists is they put strain onto the Korean public health insurance system. For years, there were claims that insurance payments were too low compared to the medical expenses. Public health insurance has been in the black since 2011, but this was because the average employer-insured employee paid twice as much as they actually benefited from. The average monthly payment was around 113,000 South Korean won (95 U.S. dollars), and the total revenue of the national health insurance in South Korea amounted to around 81.7 trillion South Korean won. Hundreds of thousands of foreigners are exacerbating the situation and the government is taking action. For example, foreigners living in Korea for more than six months are now required to register for public health insurance. Prior to 2019, registration was optional for foreigners and as a result, foreigners took advantage of insurance coverage without paying for it.