External Debt in Mexico increased to 633750.90 USD Million in the second quarter of 2025 from 618661.90 USD Million in the first quarter of 2025. This dataset provides - Mexico External Debt - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Financial Access and Usage

Global Comparative Ratios Across 189 Jurisdictions

By International Monetary Fund [source]

About this dataset

This dataset provides an unprecedented opportunity to explore global financial access and usage trends from 2004-2016 from 189 of the world's reporting jurisdictions—which cover over 99 percent of the total adult population. With 152 time series and 47 indicator ratios, this Financial Access Survey gives insight into ways that access to and usage of financial services differ by households vs small/medium enterprises, life vs non-life insurance, deposits & microfinance institutions as well as credit unions & financial cooperatives. Utilizing this data, we can gain a better understanding of how policies or shifts in the global economy may influence or relate to access or utilization of services in certain regions while having comparable cross-economy comparisons. The IMF Monetary and Financial Statistics Manual Compilation Guide is utilized for all methodologies used in accumulating these datasets, while all data is available “as-is” with no guarantee provided either express or implied. Are you looking for ways to implement insightful macroeconomic analyses? Download FAS 2004–2016 now!

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How to use the dataset

The Financial Access Survey provides global supply-side data on access to and usage of financial services by households and firms for 189 reporting jurisdictions, covering 99 percent of the world’s adult population. With a robust selection of time series in this dataset, users can make meaningful insights into trends over time or across countries concerning various indicators related to access and usage of financial services. To help users navigate this large dataset, the following guide explains how to use the data most effectively.

Understanding The Dataset Columns

The columns in the dataset provide information about each indicator such as country name, indicator name, code for that indicator, its attribute (i.e., rate/ratio), when data is available for that particular indicator. Once you have identified an interesting measure/indicator whether it be credit union density or life insurance penetration rate measure in a given country during a certain year period then you can look up those numbers from the rows provided in this dataset .

Understanding The Different Years Available & Comparing Numbers Over Time

It is useful for users to compare different indicators over time by looking at specific years within this dataset which will allow us to see if rates are increasing or decreasing worldwide patterns across these trends among different countries based on these various measures listed provided in this survey such as mortgage lending rate or ratio GDP per capita etc that have been collected . We can therefore make use of our knowledge off economic changes that have occurred over time within certain parts of world , no matter if they are longer term economic effects due increases certain capabilities within a geographical area or shorter term changes due taxation laws by governments etc driving some people either towards using or away from using certain kinds financial products .

#### Comparing Between Countries

This datasets allows us direct comparisons between different countries with regards how many people are currently making use particular types off finances services , we certainly be able analyse current international relationships between services providers as well customers where ever concerned about particular attributes mentioned above whether being deposit interest rates small business credits terms tenders so forth . Knowing more about related dynamics helps build better user experiences with providers who understand needs risks impacts generating larger customer bases globally which often beneficial both parties involved exchange relationship so not forget always keep cross border motif whenever eye process from afar !

Research Ideas

• Comparing the access to and usage of financial services in different countries to better inform research policy decisions.
• Analyzing trends in financial access and usage by jurisdiction over time, to identify areas needing improvement in order to promote financial inclusion and stability.
• Cross-referencing FAS data with macroeconomic indicators such as GDP information to measure the potential impact of changes in level of access on economic growth or other metrics specific to a country or region of interest

Acknowledgements

If you use this dataset in yo...

Time series data for the data Current Account and Its Components - Current USD, TTM for the country United States. The Current Account and Its Components The current account is a component of a country's balance of payments that records the transactions of goods, services, income, and current transfers between residents of the country and the rest of the world. It consists of four main components:

a. Trade in Goods Balance

b. Trade in Services Balance

c. Primary Income Balance

d. Secondary Income Balance

1. Trade in Goods Balance Definition: This includes the export and import of physical items such as machinery, food, clothing, etc.

Credit Example: A German car manufacturer exports cars to the United States (value of exported cars).

Debit Example: A German electronics retailer imports smartphones from South Korea (value of imported smartphones).

1. Trade in Services Balance Definition: This includes the export and import of services such as tourism, financial services, consulting, transportation, etc.

Credit Example: A German IT company provides software development services to a client in Japan (value of exported services).

Debit Example: A German tourist books a hotel room in France (value of imported tourism services).

1. Primary Income Balance Definition: This includes earnings from the provision of factors of production such as labor, financial assets, land, and natural resources. It covers income from interest, profits, and dividends.

Credit Example: A German investor receives dividends from shares held in a U.S. company (value of received dividends).

Debit Example: Foreign investors receive interest payments on bonds issued by a German company (value of interest payments).

1. Secondary Income Balance Definition: This includes current transfers such as foreign aid, remittances, and other one-way payments that do not involve an exchange of goods or services.

Credit Example: Remittances sent by German residents working abroad to their families in Germany (value of received remittances).

Debit Example: Germany sends humanitarian aid to a developing country (value of sent aid). Current Account Balance (USD)The indicator "Current Account Balance (USD)" stands at -1.37 Trillion United States Dollars as of 3/31/2025, the lowest value at least since 6/30/2001, the period currently displayed. Regarding the One-Year-Change of the series, the current value constitutes an decrease of -0.4138 Trillion United States Dollars compared to the value the year prior.The 1 year change is -0.4138 Trillion United States Dollars.The 3 year change is -0.4223 Trillion United States Dollars.The 5 year change is -0.9494 Trillion United States Dollars.The 10 year change is -0.9961 Trillion United States Dollars.The Serie's long term average value is -0.579 Trillion United States Dollars. It's latest available value, on 3/31/2025, is -0.795 Trillion United States Dollars lower, compared to it's long term average value.The Serie's change in United States Dollars from it's minimum value, on 3/31/2025, to it's latest available value, on 3/31/2025, is +0.0 Trillion.The Serie's change in United States Dollars from it's maximum value, on 6/30/2014, to it's latest available value, on 3/31/2025, is -1.04 Trillion.Trade in Services Balance (USD)The indicator "Trade in Services Balance (USD)" stands at 0.3089 Trillion United States Dollars as of 3/31/2025. Regarding the One-Year-Change of the series, the current value constitutes an increase of 0.0158 Trillion United States Dollars compared to the value the year prior.The 1 year change is 0.0158 Trillion United States Dollars.The 3 year change is 0.0674 Trillion United States Dollars.The 5 year change is 0.012 Trillion United States Dollars.The 10 year change is 0.0373 Trillion United States Dollars.The Serie's long term average value is 0.187 Trillion United States Dollars. It's latest available value, on 3/31/2025, is 0.122 Trillion United States Dollars higher, compared to it's long term average value.The Serie's change in United States Dollars from it's minimum value, on 12/31/2003, to it's latest available value, on 3/31/2025, is +0.2635 Trillion.The Serie's change in United States Dollars from it's maximum value, on 12/31/2024, to it's latest available value, on 3/31/2025, is -0.003 Trillion.Trade in Goods Balance (USD)The indicator "Trade in Goods Balance (USD)" stands at -1.40 Trillion United States Dollars as of 3/31/2025, the lowest value at least since 6/30/2001, the period currently displayed. Regarding the One-Year-Change of the series, the current value constitutes an decrease of -0.3327 Trillion United States Dollars compared to the value the year prior.The 1 year change is -0.3327 Trillion United States Dollars.The 3 year change is -0.2509 Trillion United States Dollars.The 5 year change is -0.5657 Trillion United States Dollars.The 10 year change is -0.6467 Trillion United States Dollars.The ...

"ForeignAssistance.gov is the U.S. government’s flagship website for making U.S. foreign assistance data available to the public. It serves as the central resource for budgetary and financial data produced by U.S. government agencies that manage foreign assistance portfolios. In keeping with the U.S. government’s commitment to transparency, ForeignAssistance.gov presents a picture of U.S. foreign assistance in accurate and understandable terms. The website also includes links to associated strategies and evaluations for U.S. foreign assistance programs. This site will be continually updated as data are available. Look for new features and enhancements as they come online.The primary objective of the site is to fulfill the requirements set forth in the Foreign Aid Transparency and Accountability Act of 2016 (FATAA) through the collection, tracking, and publication of the full lifecycle of all USG foreign assistance data." Retrieved 2/20/25 from https://foreignassistance.gov/aboutContents US International Development Finance Corporation - usdfc_ActiveProjects.xlsx The Active Projects database reflects active DFC commitments as of the most recent quarter. The database is updated approximately 45 days after the end of each quarter. Last updated 9/30/24https://www.dfc.gov/what-we-do/active-projects Data from ForeignAssistance.gov Last updated on: 12/19/2024https://foreignassistance.gov/data#tab-data-download The complete ForeignAssistance.gov dataset: us_foreign_aid_complete.csv Budget Dataset - The complete foreign aid budget dataset: President's Budget Request, initial allocations, and final allocations. us_foreign_budget_complete.csv Country Summary - These tables offer a summary of obligations and disbursements in current and constant dollars by country from 1946 to the most recent year. us_foreign_aid_country.csv OECD/DAC Sector Summary These tables offer a summary of obligations and disbursements by OECD/DAC sector and sector category from 2001 to the most recent year. us_foreign_aid_dac_sector.csv USG Sector Summary These tables offer a summary of obligations and disbursements in current and constant dollars by U.S. Government (USG) sector and country from 2001 to the most recent year. us_foreign_aid_usg_sector.csv Managing Agency Summary These tables offer a summary of obligations and disbursements in current and constant dollars by managing agency and country from 2001 to the most recent year. us_foreign_aid_implementing.csv Funding Agency Summary These tables offer a summary of obligations and disbursements in current and constant dollars by funding agency, funding account, and country from 2001 to the most recent year. us_foreign_aid_usg_funding.csv Data Dictionary A table with information describing the contents and structure of the U.S. ForeignAssistance.gov data fields. DataDictionary_ForeignAssistancegov.pdf

Countries by Gross National Income (GNI)

Economic health by nation

About this dataset

Gross National Income (GNI) is a marker of the economic health of a nation - it encompasses a nation's GDP while also taking into account money flowing in and out of the country from foreign trade. This dataset provides GNI rankings for countries around the world, allowing for comparisons of economic health and growth. Explore how different nations fare in terms of GNI, and what this says about their overall economic stability!

How to use the dataset

The Gross National Income (GNI) of countries around the world is a measure of the economic health of a nation. It is a summation of a nation's GDP (Gross Domestic Product) plus the money flowing into and out of the country from foreign countries.

This dataset provides Rankings of countries by their GNI. The data is divided into two files: df_1.csv and df_2.csv. Both files contain the following columns:

No.: The number of the country. (Numeric)

Country: The name of the country. (String)

Research Ideas

• Measuring the economic health of a nation
• Comparing the GDP of different countries
• Determining the money flow into and out of a country

Acknowledgements

GNI data is sourced from wikipedia

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: df_1.csv

File: df_4.csv | Column name | Description | |:----------------------------|:----------------------------------------------------------------------| | No. | The rank of the country based on GNI. (Numeric) | | Country | The name of the country. (String) | | GNI (Atlas method)[8] | The GNI of the country, in US dollars. (Numeric) | | GNI (Atlas method)[8].1 | The GNI of the country, as a percentage of the world total. (Numeric) | | GNI[9] | The GNI of the country, in US dollars. (Numeric) | | GNI[9].1 | The GNI of the country, as a percentage of the world total. (Numeric) | | GDP[10] | The GDP of the country, in US dollars. (Numeric) |

File: df_9.csv | Column name | Description | |:--------------|:----------------------| | 0 | Country Name (String) | | 1 | GNI (Integer) |

File: df_3.csv | Column name | Description | |:--------------|:----------------------| | 0 | Country Name (String) |

File: df_2.csv

File: df_6.csv | Column name | Description | |:--------------|:------------------------------------------------------------------| | Rank | The rank of the country based on GNI. (Numeric) | | 2021 | The GNI of the country in 2021. (Numeric) | | 2021.1 | The GNI of the country in 2021, adjusted for inflation. (Numeric) | | 2016 | The GNI of the country in 2016. (Numeric) | | 2016.1 | The GNI of the country in 2016, adjusted for inflation. (Numeric) | | 2014 | The GNI of the country in 2014. (Numeric) | | 2014.1 | The GNI of the country in 2014, adjusted for inflation. (Numeric) | | 2013 | The GNI of the country in 2013. (Numeric) | | 2013.1 | The GNI of the country in 2013, adjusted for inflation. (Numeric) | | 2012 | The GNI of the country in 2012. (Numeric) | | 2012.1 | The GNI of the country in 2012, adjusted for inflation. (Numeric) | | 2011 | The GNI of the country in 2011. (Numeric) | | 2011.1 | The GNI of the country in 2011, adjusted for inflation. (Numeric) | | 2010 | The GNI of the country in 2010. (Numeric) | | 2010.1 | The GNI of the country in 2010, adjusted for inflation. (Numeric) | | 2009 | The GNI of the country in 2009. (Numeric) | | 2009.1 | The GNI of the country in 2009, adjusted for inflation. (Numeric) | | 2008 | The GNI of the country in 2008. (Numeric) | | 2008.1 | The GNI of the country in 200...

DCA Loan Dataset

Investment Opportunities in Underserved Markets

By United States Agency for International Development [source]

About this dataset

USAID's Development Credit Authority (DCA) provides innovative and comprehensive investments designed to unlock financing for U.S. Government development priorities in economically underserved areas of the world. For more than 20 years, USAID has supported private lenders, financial institutions, and development organizations by offering loan guarantees that encourage investments in sectors and regions previously untapped, across a variety of currencies and locales.

This dataset contains the complete list of all private loans guaranteed under USAID's DCA since 1999. Included data points include: loan guarantee numbers; transaction report IDs; countries where the loan was issued; amount value in US Dollars; currency name the loan is issued in; end date of the loan agreement; business sector related to the loan contract; city or town location information (including latitude & longitude); state/province/region name & code where it is located as well as country associated to region; whether business is woman-owned or first-time borrowers' status, plus size of said businesses too!

This public dataset helps demonstrate a commitment from USAID towards not only transparency but also providing investors with reliable data that helps inform better decisions when looking at investment opportunities abroad. All strategic and personal identifiable information were removed from this dataset in order to protect borrowers' privacy

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How to use the dataset

This dataset contains all the private loan transactions made through USAID's DCA since its founding in 1999. With this information, individuals, businesses, and organizations can gain a better understanding of how the loan system works to help unlock financing for U.S. Government priorities.

Using this dataset, individuals and businesses can easily gain insights into their financial needs by looking at important information such as the country in which the loan was issued, currency name, amount of loan taken in US Dollars (USD), and business size. Furthermore, users can also check if a business is owned by a woman or is it a first-time borrower or not that could give an insight into other potential areas for investment. Moreover, location related information like latitude and longitude coordinates are also shown that helps determine exact location to better understand market trends related to these loans from different regions around the world.

This dataset is ideal for those interested in comparing loan transactions between different countries or comparing transactions within one country over time or between different regions within one country to gain advanced insights about investing opportunities around the world through USAID’s DCA program!

Research Ideas

• Analyzing the efficacy of USAID’s DCA in different countries, sectors, and regions by measuring the repayment rates of loans issued.
• Identifying new opportunities for investments by analyzing trends of the loan transaction data over different periods of time.
• Providing Insights into financial inclusion and access to finance based on borrower demographics such as location, gender, and small business size

Acknowledgements

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

License

Unknown License - Please check the dataset description for more information.

Columns

File: userssharedsdfusaiddevelopmentcagloantransactions.csv | Column name | Description | |:---------------------------------------|:---------------------------------------------------------------------------------| | Guarantee Number | Unique identifier for the loan guarantee. (String) | | Guarantee Country Name | Name of the country in which the loan was issued. (String) | | Amount (USD) | Amount of money loaned in US Dollars. (Integer) | | Currency Name | Name of the currency in which the loan was issued. (String) | | End Date | Date when the loan term ends. (Date) | | Business Sector | Business se...

Context:

The provided dataset is extracted from yahoo finance using pandas and yahoo finance library in python. This deals with stock market index of the world best economies. The code generated data from Jan 01, 2003 to Jun 30, 2023 that’s more than 20 years. There are 18 CSV files, dataset is generated for 16 different stock market indices comprising of 7 different countries. Below is the list of countries along with number of indices extracted through yahoo finance library, while two CSV files deals with annualized return and compound annual growth rate (CAGR) has been computed from the extracted data.

Number of Countries & Index:

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F15657145%2F90ce8a986761636e3edbb49464b304d8%2FNumber%20of%20Index.JPG?generation=1688490342207096&alt=media" alt="">

Content:

Unit of analysis: Stock Market Index Analysis

This dataset is useful for research purposes, particularly for conducting comparative analyses involving capital market performance and could be used along with other economic indicators.

There are 18 distinct CSV files associated with this dataset. First 16 CSV files deals with number of indices and last two CSV file deals with annualized return of each year and CAGR of each index. If data in any column is blank, it portrays that index was launch in later years, for instance: Bse500 (India), this index launch in 2007, so earlier values are blank, similarly China_Top300 index launch in year 2021 so early fields are blank too.

The extraction process involves applying different criteria, like in 16 CSV files all columns are included, Adj Close is used to calculate annualized return. The algorithm extracts data based on index name (code given by the yahoo finance) according start and end date.

Annualized return and CAGR has been calculated and illustrated in below image along with machine readable file (CSV) attached to that.

To extract the data provided in the attachment, various criteria were applied:

1. Content Filtering: The data was filtered based on several attributes, including the index name, start and end date. This filtering process ensured that only relevant data meeting the specified criteria.

2. Collaborative Filtering: Another filtering technique used was collaborative filtering using yahoo finance, which relies on index similarity. This approach involves finding indices that are similar to other index or extended dataset scope to other countries or economies. By leveraging this method, the algorithm identifies and extracts data based on similarities between indices.

In the last two CSV files, one belongs to annualized return, that was calculated based on the Adj close column and new DataFrame created to store its outcome. Below is the image of annualized returns of all index (if unreadable, machine-readable or CSV format is attached with the dataset).

Annualized Return:

As far as annualised rate of return is concerned, most of the time India stock market indices leading, followed by USA, Canada and Japan stock market indices.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F15657145%2F37645bd90623ea79f3708a958013c098%2FAnnualized%20Return.JPG?generation=1688525901452892&alt=media" alt="">

Compound Annual Growth Rate (CAGR):

The best performing index based on compound growth is Sensex (India) that comprises of top 30 companies is 15.60%, followed by Nifty500 (India) that is 11.34% and Nasdaq (USA) all is 10.60%.

The worst performing index is China top300, however this is launch in 2021 (post pandemic), so would not possible to examine at that stage (due to less data availability). Furthermore, UK and Russia indices are also top 5 in the worst order.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F15657145%2F58ae33f60a8800749f802b46ec1e07e7%2FCAGR.JPG?generation=1688490409606631&alt=media" alt="">

Geography: Stock Market Index of the World Top Economies

Time period: Jan 01, 2003 – June 30, 2023

Variables: Stock Market Index Title, Open, High, Low, Close, Adj Close, Volume, Year, Month, Day, Yearly_Return and CAGR

File Type: CSV file

Inspiration:

• Time series prediction model
• Investment opportunities in world best economies
• Comparative Analysis of past data with other stock market indices or other indices

Disclaimer:

This is not a financial advice; due diligence is required in each investment decision.

Cleaned dataset from the Billionaires Statistic Dataset (2023) that can be found here.

The code I used to clean and re-structure the data is also here.

First things first: a big shout-out to Nidula Elgiriyewithana for providing the original data.

As with it, this dataset contains various information about the world's wealthiest persons in different columns that can be grouped into three different types:

• Business-related information. These columns contain data about the industry in which the billionaires' operate, their source of wealth, total wealth and position they occupy in the ranking.
• Personal information. Such as name, age, nationality, country and city of residence.
• Economic activity information. These columns are related to the country in which the billionaire resides and provide different economic indicators like GDP, education enrollment or Consumer Price Index (CPI).

Column names

• position. Ranking of the billionaire measured by their wealth.
• wealth. The wealth of the billionaire measured in $.
• industry. Industry in which the billionaire's operates their businesses.
• full_name. Complete name of the billionaire.
• age. The age of the billionaire.
• country_of_residence. Country in which the billionaire resides.
• city_of_residence. City in which the billionaire resides.
• source. The source of the billionaire's wealth.
• citizenship. The country of citizenship of the billionaire.
• gender. The gender of the billionaire.
• birth_date. The birth date of the billionaire.
• last_name. The last name of the billionaire.
• first_name. The first name of the billionaire.
• residence_state. State in which the billionaire resides (only for billionaires who reside in the U.S.).
• residence_region. Region in which the billionaire resides (only for billionaires who reside in the U.S.).
• birth_year. The birth year of the billionaire.
• birth_month. The birth month of the billionaire.
• birth_day. The birth data of the billionaire.
• cpi_country. Consumer Price Index (CPI) for the billionaire's country.
• cpi_change_country. CPI change for the billionaire's country.
• gdp_country. Gross Domestic Product (GDP) in $ for the billionaire's country.
• g_tertiary_ed_enroll. Enrollment in tertiary education in the billionaire's country.
• g_primary_ed_enroll. Enrollment in primary education in the billionaire's country.
• life_expectancy. Life expectancy in the billionaire's country.
• tax_revenue. Tax revenue in the billionaire's country.
• tax_rate. Total tax rate in the billionaire's country.
• country_pop. Population of the billionaire's country.
• country_lat. Latitude coordinate of the billionaire's country.
• country_long. Longitude coordinate of the billionaire's country.
• continent. Continent in which the country of the billionaire's residence is located.

Potential analyses

• Analyze which industries contain the biggest groups of billionaires overall and in different countries.
• Explore number of billionaires and total wealth across countries and continents and display the result in a map.
• Focus on personal information columns such as age or gender to explore the distribution of billionaires from this perspective.
• Discover if countries' economic indicators have any impact in the presence of billionaires.
• The U.S. is the country with most billionaires presented in the dataset and also the only one with attributes in the residence_state and residence_region columns. This makes the American billionaires a good focus for a specific analysis.

Bonus

If you want a challenge, you can create a dashboard using tools such as Plotly to dynamically visualize the data using one or different attributes (such as industry, age or country). I did it, leave the link below in case you want to investigate:

Dashboard notebook here

If you find this dataset informative or inspirational, a vote is appreciated for others to easily discover value in it 💎💰

Time series data for the data Current Account and Its Components - Current USD, TTM for the country Switzerland. The Current Account and Its Components The current account is a component of a country's balance of payments that records the transactions of goods, services, income, and current transfers between residents of the country and the rest of the world. It consists of four main components:

a. Trade in Goods Balance

b. Trade in Services Balance

c. Primary Income Balance

d. Secondary Income Balance

1. Trade in Goods Balance Definition: This includes the export and import of physical items such as machinery, food, clothing, etc.

Credit Example: A German car manufacturer exports cars to the United States (value of exported cars).

Debit Example: A German electronics retailer imports smartphones from South Korea (value of imported smartphones).

1. Trade in Services Balance Definition: This includes the export and import of services such as tourism, financial services, consulting, transportation, etc.

Credit Example: A German IT company provides software development services to a client in Japan (value of exported services).

Debit Example: A German tourist books a hotel room in France (value of imported tourism services).

1. Primary Income Balance Definition: This includes earnings from the provision of factors of production such as labor, financial assets, land, and natural resources. It covers income from interest, profits, and dividends.

Credit Example: A German investor receives dividends from shares held in a U.S. company (value of received dividends).

Debit Example: Foreign investors receive interest payments on bonds issued by a German company (value of interest payments).

1. Secondary Income Balance Definition: This includes current transfers such as foreign aid, remittances, and other one-way payments that do not involve an exchange of goods or services.

Credit Example: Remittances sent by German residents working abroad to their families in Germany (value of received remittances).

Debit Example: Germany sends humanitarian aid to a developing country (value of sent aid). Trade in Goods Balance (USD)The indicator "Trade in Goods Balance (USD)" stands at 138.92 Billion United States Dollars as of 3/31/2025, the highest value at least since 6/30/2001, the period currently displayed. Regarding the One-Year-Change of the series, the current value constitutes an increase of 14.88 Billion United States Dollars compared to the value the year prior.The 1 year change is 14.88 Billion United States Dollars.The 3 year change is 9.65 Billion United States Dollars.The 5 year change is 66.90 Billion United States Dollars.The 10 year change is 74.98 Billion United States Dollars.The Serie's long term average value is 52.34 Billion United States Dollars. It's latest available value, on 3/31/2025, is 86.59 Billion United States Dollars higher, compared to it's long term average value.The Serie's change in United States Dollars from it's minimum value, on 9/30/2001, to it's latest available value, on 3/31/2025, is +139.21 Billion.The Serie's change in United States Dollars from it's maximum value, on 3/31/2025, to it's latest available value, on 3/31/2025, is 0.0 Billion.Secondary Income Balance (USD)The indicator "Secondary Income Balance (USD)" stands at -15.35 Billion United States Dollars as of 3/31/2025, the lowest value since 12/31/2021. Regarding the One-Year-Change of the series, the current value constitutes an decrease of -2.90 Billion United States Dollars compared to the value the year prior.The 1 year change is -2.90 Billion United States Dollars.The 3 year change is -0.5885 Billion United States Dollars.The 5 year change is -1.90 Billion United States Dollars.The 10 year change is 2.40 Billion United States Dollars.The Serie's long term average value is -9.48 Billion United States Dollars. It's latest available value, on 3/31/2025, is -5.87 Billion United States Dollars lower, compared to it's long term average value.The Serie's change in United States Dollars from it's minimum value, on 12/31/2014, to it's latest available value, on 3/31/2025, is +2.75 Billion.The Serie's change in United States Dollars from it's maximum value, on 3/31/2001, to it's latest available value, on 3/31/2025, is -12.72 Billion.Primary Income Balance (USD)The indicator "Primary Income Balance (USD)" stands at -30.25 Billion United States Dollars as of 3/31/2025, the lowest value since 3/31/2024. Regarding the One-Year-Change of the series, the current value constitutes an decrease of -7.49 Billion United States Dollars compared to the value the year prior.The 1 year change is -7.49 Billion United States Dollars.The 3 year change is 5.02 Billion United States Dollars.The 5 year change is -18.51 Billion United States Dollars.The 10 year change is -32.49 Billion United States Dollars.The Serie's long term average value is -0.618 Billion United ...

Overview This dataset contains information about the largest companies in the United States by revenue. It includes key attributes such as company name, industry, annual revenue, profit, number of employees, and the state where the company is headquartered. The dataset provides valuable insights into the financial and operational aspects of these major corporations.

Columns Rank: Ranking of the company based on its annual revenue. Name: Name of the company. Industry: Industry in which the company operates. Revenue: Annual revenue of the company in millions of dollars. Profit: Annual profit of the company in millions of dollars. Employees: Number of employees working for the company. State: State where the company’s headquarters are located. Key Insights Revenue Distribution: Significant variation in revenue among the top companies, with some generating much higher revenues. Profit Margins: Wide variation in profit margins, indicating different levels of profitability across industries. Employee Numbers: Disparity in the number of employees, reflecting differences in business models and operational scales. Geographic Spread: Companies are headquartered in various states, with certain states having a higher concentration of large companies. Potential Uses Industry Analysis: Understand trends and performance in different industries. Economic Research: Analyze the economic impact of these large companies. Business Strategy: Inform business strategies and market analysis. Educational Purposes: Use as a case study for business and economic courses. Future Work In-Depth Industry Analysis: Explore specific industries to identify trends and outliers. Time-Series Analysis: Analyze trends over time if historical data becomes available. Comparative Analysis: Compare with similar datasets from other countries. Advanced Visualization: Create interactive dashboards for better data presentation. This dataset is a valuable resource for anyone interested in the financial and operational characteristics of the largest companies in the United States.

Time series data for the data Current Account and Its Components - Current USD, TTM for the country Ireland. The Current Account and Its Components The current account is a component of a country's balance of payments that records the transactions of goods, services, income, and current transfers between residents of the country and the rest of the world. It consists of four main components:

a. Trade in Goods Balance

b. Trade in Services Balance

c. Primary Income Balance

d. Secondary Income Balance

1. Trade in Goods Balance Definition: This includes the export and import of physical items such as machinery, food, clothing, etc.

Credit Example: A German car manufacturer exports cars to the United States (value of exported cars).

Debit Example: A German electronics retailer imports smartphones from South Korea (value of imported smartphones).

1. Trade in Services Balance Definition: This includes the export and import of services such as tourism, financial services, consulting, transportation, etc.

Credit Example: A German IT company provides software development services to a client in Japan (value of exported services).

Debit Example: A German tourist books a hotel room in France (value of imported tourism services).

1. Primary Income Balance Definition: This includes earnings from the provision of factors of production such as labor, financial assets, land, and natural resources. It covers income from interest, profits, and dividends.

Credit Example: A German investor receives dividends from shares held in a U.S. company (value of received dividends).

Debit Example: Foreign investors receive interest payments on bonds issued by a German company (value of interest payments).

1. Secondary Income Balance Definition: This includes current transfers such as foreign aid, remittances, and other one-way payments that do not involve an exchange of goods or services.

Credit Example: Remittances sent by German residents working abroad to their families in Germany (value of received remittances).

Debit Example: Germany sends humanitarian aid to a developing country (value of sent aid). Trade in Goods Balance (USD)The indicator "Trade in Goods Balance (USD)" stands at 249.76 Billion United States Dollars as of 6/30/2025, the highest value at least since 6/30/2003, the period currently displayed. Regarding the One-Year-Change of the series, the current value constitutes an increase of 95.03 Billion United States Dollars compared to the value the year prior.The 1 year change is 95.03 Billion United States Dollars.The 3 year change is 46.36 Billion United States Dollars.The 5 year change is 104.84 Billion United States Dollars.The 10 year change is 163.02 Billion United States Dollars.The Serie's long term average value is 96.39 Billion United States Dollars. It's latest available value, on 6/30/2025, is 153.37 Billion United States Dollars higher, compared to it's long term average value.The Serie's change in United States Dollars from it's minimum value, on 12/31/2006, to it's latest available value, on 6/30/2025, is +216.07 Billion.The Serie's change in United States Dollars from it's maximum value, on 6/30/2025, to it's latest available value, on 6/30/2025, is 0.0 Billion.Secondary Income Balance (USD)The indicator "Secondary Income Balance (USD)" stands at -5.04 Billion United States Dollars as of 6/30/2025. Regarding the One-Year-Change of the series, the current value constitutes an increase of 0.2158 Billion United States Dollars compared to the value the year prior.The 1 year change is 0.2158 Billion United States Dollars.The 3 year change is 0.1139 Billion United States Dollars.The 5 year change is -1.02 Billion United States Dollars.The 10 year change is -1.43 Billion United States Dollars.The Serie's long term average value is -3.68 Billion United States Dollars. It's latest available value, on 6/30/2025, is -1.36 Billion United States Dollars lower, compared to it's long term average value.The Serie's change in United States Dollars from it's minimum value, on 9/30/2022, to it's latest available value, on 6/30/2025, is +0.3534 Billion.The Serie's change in United States Dollars from it's maximum value, on 3/31/2003, to it's latest available value, on 6/30/2025, is -3.83 Billion.Primary Income Balance (USD)The indicator "Primary Income Balance (USD)" stands at -208.60 Billion United States Dollars as of 6/30/2025, the lowest value at least since 6/30/2003, the period currently displayed. Regarding the One-Year-Change of the series, the current value constitutes an decrease of -90.74 Billion United States Dollars compared to the value the year prior.The 1 year change is -90.74 Billion United States Dollars.The 3 year change is -53.01 Billion United States Dollars.The 5 year change is -106.49 Billion United States Dollars.The 10 year change is -157.34 Billion United States Dollars.The Serie's long term average value is -70.36 ...

American Labor Who's Who dataset, version 2.2.0

A dataset derived from the digitized text of Solon de Leon, et al., The American Labor Who's Who (New York: Hanford Press, 1925). This release includes separate files the U.S. and "Other Countries" sections of the directory.

The American Labor Who's Who (ALWW), published in 1925, is a directory of activists in the fields of trade unionism, immigrant rights, civil liberties, progressive and radical politics. The directory includes roughly 1,300 entries for U.S. activists and 300 additional non-US activists. Each entry is a telegraphic biography. Some provide only name, professional title and address at the time of publication, but many sketch rich life histories. Nearly all provide details on birth date and place, family background, education, migration, and work histories, as well as key organizations, events publications, home and work addresses.

The ALWW dataset is derived from the text hosted on the HathiTrust digital library: https://catalog.hathitrust.org/Record/000591300. Faculty, staff, and students at UCLA corrected the plain text from the scanned document and parsed the text into comma-separated fields. This release includes separate files for US entries and "Other Countries" entries. About 30 individuals are listed in the US section with the notation "see other countries," mainly Canadians and Mexicans. This subset is also in a separate file in this release.

For more information about this and related projects see: http://socialjusticehistory.org/projects/networkedlabor/.

Contributors Tobias Higbie, Principal Investigator, UCLA History Department Craig Messner, UCLA Center for Digital Humanities Nick de Carlo, UCLA Center for Digital Humanities Zoe Borovsky, UCLA Library

Contents of Release The US and Other Country datasets were developed separately as reflected in their different version numbers. The US entries are more developed and clean. Consider the Other Country files as beta releases. The files listed below are the most up-to-date available. Previous versions are also available via GitHub.

alww-us-2-2-0.csv (all US entries)

alww-othercountries-o.3.2.csv (all other country entries)

alww-othercountries-0.3.2-subset-crossrefd.csv (other country entries cross-referenced in the US entry section)

Field Layouts

The field layouts for the US and Other Country files are slightly different in this release.

US Entries The fields for the US file (alww-us-2-2-0.csv) include: NAME [first and last], NAME-ALWW [name as it appears in the original text], TITLES [named offices or occupations in 1925], ORGS [compiled list of organizations belonged to at any time], BIRTHDATES [m/d/y where present], BIRTHCOUNTRY [derived from Birthplace], BIRTHPLACE [as listed], FATHER [father's occupation, in a few cases includes mother], CAREER (UNABBREVIATED) [education and experience, usually chronological, most common abbreviations expanded to full words], CAREER (ABBREVIATED) [same as previous with original abbreviations], HOME ADDRESS [where present], WORK ADDRESS [where present], PUBLICATIONS [incomplete], INDEX CATEGORY 1 [categories derived from the ALWW index, many have more than one category], INDEX CATEGORY 2, INDEX CATEGORY 3, INDEX CATEGORY 4, INDEX CATEGORY 5, INDEX CATEGORY 6, INDEX CATEGORY 7, INDEX CATEGORY 8, ORIGINAL [unparsed entry text carried over from earlier versions].

Other Countries The other country files (alww-othercountries-o.3.2.csv and alww-othercountries-0.3.2-subset-crossrefd.csv) include these fields: Name [last, first], Titles [named offices or occupations in 1925], Organizations [compiled list of organizations belonged to at any time], Birthdate [as listed], Birthplace [as listed], Father [father's occupation], Other [same as Career above], HomeAddress [as listed], WorkAddress [as listed], Publications [derived from entries].

Related datasets American Labor Press Directory (1925); American Labor Press Directory (1940); Who's Who in Labor (1947).

These tables provide additional detail on the investment holdings of U.S. money market funds, based on a monthly dataset of security-level holdings for all U.S. money market funds. Table 1 reports the aggregate dollar amount of investments of U.S. money market funds since 2010, by the world region and country of the security issuer. Table 2 provides a finer level of detail by month, showing, for each country of issuer, the aggregate dollar amount of investments of U.S. money market funds by type of money fund (i.e., prime, government, and municipal bond funds), type of security (i.e., direct debt and deposits, repurchase agreement, asset-backed commercial paper, and other), and by maturity of the security. Table 3 depicts the asset allocation of U.S. money market fund portfolios over time. Tables 4, 5, and 6 show the asset allocation of prime, government, and tax-exempt money market funds, respectively, over time. The sum of the values in these three tables equals the total value of Table 3. Tables 7 and 8 report additional detail on the repurchase agreement holdings and the commercial paper holdings, respectively, for U.S. money market funds.

TThe ERS International Macroeconomic Data Set provides historical and projected data for 181 countries that account for more than 99 percent of the world economy. These data and projections are assembled explicitly to serve as underlying assumptions for the annual USDA agricultural supply and demand projections, which provide a 10-year outlook on U.S. and global agriculture. The macroeconomic projections describe the long-term, 10-year scenario that is used as a benchmark for analyzing the impacts of alternative scenarios and macroeconomic shocks.

Explore the International Macroeconomic Data Set 2015 for annual growth rates, consumer price indices, real GDP per capita, exchange rates, and more. Get detailed projections and forecasts for countries worldwide.

Annual growth rates, Consumer price indices (CPI), Real GDP per capita, Real exchange rates, Population, GDP deflator, Real gross domestic product (GDP), Real GDP shares, GDP, projections, Forecast, Real Estate, Per capita, Deflator, share, Exchange Rates, CPI

Afghanistan, Albania, Algeria, Angola, Antigua and Barbuda, Argentina, Armenia, Australia, Austria, Azerbaijan, Bahamas, Bahrain, Bangladesh, Barbados, Belarus, Belgium, Belize, Benin, Bhutan, Bolivia, Bosnia and Herzegovina, Botswana, Brazil, Brunei, Bulgaria, Burkina Faso, Burundi, Côte d'Ivoire, Cabo Verde, Cambodia, Cameroon, Canada, Central African Republic, Chad, Chile, China, Colombia, Congo, Costa Rica, Croatia, Cuba, Cyprus, Denmark, Djibouti, Dominica, Dominican Republic, Ecuador, Egypt, El Salvador, Equatorial Guinea, Eritrea, Estonia, Eswatini, Ethiopia, Fiji, Finland, France, Gabon, Gambia, Georgia, Germany, Ghana, Greece, Grenada, Guatemala, Guinea, Guinea-Bissau, Guyana, Haiti, Honduras, Hungary, Iceland, India, Indonesia, Iran, Iraq, Ireland, Israel, Italy, Jamaica, Japan, Jordan, Kazakhstan, Kenya, Kuwait, Kyrgyzstan, Laos, Latvia, Lebanon, Lesotho, Liberia, Libya, Lithuania, Luxembourg, Madagascar, Malawi, Malaysia, Maldives, Mali, Malta, Mauritania, Mauritius, Mexico, Moldova, Mongolia, Morocco, Mozambique, Myanmar, Namibia, Nepal, Netherlands, New Zealand, Nicaragua, Niger, Nigeria, Norway, Oman, Pakistan, Panama, Papua New Guinea, Paraguay, Peru, Philippines, Poland, Portugal, Qatar, Romania, Russia, Rwanda, Samoa, Saudi Arabia, Senegal, Serbia, Seychelles, Sierra Leone, Singapore, Slovakia, Slovenia, Solomon Islands, South Africa, Spain, Sri Lanka, Sudan, Suriname, Sweden, Switzerland, Syria, Tajikistan, Tanzania, Thailand, Togo, Tonga, Trinidad and Tobago, Tunisia, Turkey, Turkmenistan, Uganda, Ukraine, United Arab Emirates, United Kingdom, Uruguay, Uzbekistan, Vanuatu, Venezuela, Vietnam, Yemen, Zambia, Zimbabwe, WORLD Follow data.kapsarc.org for timely data to advance energy economics research. Notes:

Developed countries/1 Australia, New Zealand, Japan, Other Western Europe, European Union 27, North America

Developed countries less USA/2 Australia, New Zealand, Japan, Other Western Europe, European Union 27, Canada

Developing countries/3 Africa, Middle East, Other Oceania, Asia less Japan, Latin America;

Low-income developing countries/4 Haiti, Afghanistan, Nepal, Benin, Burkina Faso, Burundi, Central African Republic, Chad, Democratic Republic of Congo, Eritrea, Ethiopia, Gambia, Guinea, Guinea-Bissau, Liberia, Madagascar, Malawi, Mali, Mozambique, Niger, Rwanda, Senegal, Sierra Leone, Somalia, Tanzania, Togo, Uganda, Zimbabwe;

Emerging markets/5 Mexico, Brazil, Chile, Czech Republic, Hungary, Poland, Slovakia, Russia, China, India, Korea, Taiwan, Indonesia, Malaysia, Philippines, Thailand, Vietnam, Singapore

BRIICs/5 Brazil, Russia, India, Indonesia, China; Former Centrally Planned Economies

Former centrally planned economies/7 Cyprus, Malta, Recently acceded countries, Other Central Europe, Former Soviet Union

USMCA/8 Canada, Mexico, United States

Europe and Central Asia/9 Europe, Former Soviet Union

Middle East and North Africa/10 Middle East and North Africa

Other Southeast Asia outlook/11 Malaysia, Philippines, Thailand, Vietnam

Other South America outlook/12 Chile, Colombia, Peru, Bolivia, Paraguay, Uruguay

Indicator Source

Real gross domestic product (GDP) World Bank World Development Indicators, IHS Global Insight, Oxford Economics Forecasting, as well as estimated and projected values developed by the Economic Research Service all converted to a 2015 base year.

Real GDP per capita U.S. Department of Agriculture, Economic Research Service, Macroeconomic Data Set, GDP table and Population table.

GDP deflator World Bank World Development Indicators, IHS Global Insight, Oxford Economics Forecasting, as well as estimated and projected values developed by the Economic Research Service, all converted to a 2015 base year.

Real GDP shares U.S. Department of Agriculture, Economic Research Service, Macroeconomic Data Set, GDP table.

Real exchange rates U.S. Department of Agriculture, Economic Research Service, Macroeconomic Data Set, CPI table, and Nominal XR and Trade Weights tables developed by the Economic Research Service.

Consumer price indices (CPI) International Financial Statistics International Monetary Fund, IHS Global Insight, Oxford Economics Forecasting, as well as estimated and projected values developed by the Economic Research Service, all converted to a 2015 base year.

Population Department of Commerce, Bureau of the Census, U.S. Department of Agriculture, Economic Research Service, International Data Base.

About this dataset

Context

The dataset presents median household incomes for various household sizes in Country Club, MO, as reported by the U.S. Census Bureau. The dataset highlights the variation in median household income with the size of the family unit, offering valuable insights into economic trends and disparities within different household sizes, aiding in data analysis and decision-making.

Key observations

• Of the 7 household sizes (1 person to 7-or-more person households) reported by the census bureau, Country Club did not include 7-person households. Across the different household sizes in Country Club the mean income is $77,320, and the standard deviation is $32,313. The coefficient of variation (CV) is 41.79%. This high CV indicates high relative variability, suggesting that the incomes vary significantly across different sizes of households.
• In the most recent year, 2023, The smallest household size for which the bureau reported a median household income was 1-person households, with an income of $33,125. It then further increased to $41,750 for 6-person households, the largest household size for which the bureau reported a median household income.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Household Sizes:

• 1-person households
• 2-person households
• 3-person households
• 4-person households
• 5-person households
• 6-person households
• 7-or-more-person households

Variables / Data Columns

• Household Size: This column showcases 7 household sizes ranging from 1-person households to 7-or-more-person households (As mentioned above).
• Median Household Income: Median household income, in 2023 inflation-adjusted dollars for the specific household size.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Country Club median household income. You can refer the same here

About this dataset

Context

The dataset presents median household incomes for various household sizes in Hill Country Village, TX, as reported by the U.S. Census Bureau. The dataset highlights the variation in median household income with the size of the family unit, offering valuable insights into economic trends and disparities within different household sizes, aiding in data analysis and decision-making.

Key observations

• Of the 7 household sizes (1 person to 7-or-more person households) reported by the census bureau, Hill Country Village did not include 7-person households. Across the different household sizes in Hill Country Village the mean income is $242,905, and the standard deviation is $22,775. The coefficient of variation (CV) is 9.38%. This high CV indicates high relative variability, suggesting that the incomes vary significantly across different sizes of households. Please note that the U.S. Census Bureau uses $250,001 as a JAM value to report incomes of $250,000 or more. In the case of Hill Country Village, there were 2 household sizes where the JAM values were used. Thus, the numbers for the mean and standard deviation may not be entirely accurate and have a higher possibility of errors. However, to obtain an approximate estimate, we have used a value of $250,001 as the income for calculations, as reported in the datasets by the U.S. Census Bureau.
• In the most recent year, 2021, The smallest household size for which the bureau reported a median household income was 1-person households, with an income of $233,747. It then further increased to $270,229 for 6-person households, the largest household size for which the bureau reported a median household income.

https://i.neilsberg.com/ch/hill-country-village-tx-median-household-income-by-household-size.jpeg" alt="Hill Country Village, TX median household income, by household size (in 2022 inflation-adjusted dollars)">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Household Sizes:

• 1-person households
• 2-person households
• 3-person households
• 4-person households
• 5-person households
• 6-person households
• 7-or-more-person households

Variables / Data Columns

• Household Size: This column showcases 7 household sizes ranging from 1-person households to 7-or-more-person households (As mentioned above).
• Median Household Income: Median household income, in 2022 inflation-adjusted dollars for the specific household size.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Hill Country Village median household income. You can refer the same here

The Sea Around Us is a research initiative at The University of British Columbia (located at the Institute for the Oceans and Fisheries, formerly Fisheries Centre) that assesses the impact of fisheries on the marine ecosystems of the world, and offers mitigating solutions to a range of stakeholders.

The Sea Around Us was initiated in collaboration with The Pew Charitable Trusts in 1999, and in 2014, the Sea Around Us also began a collaboration with The Paul G. Allen Family Foundation to provide African and Asian countries with more accurate and comprehensive fisheries data.

The Sea Around Us provides data and analyses through View Data, articles in peer-reviewed journals, and other media (News). The Sea Around Us regularly update products at the scale of countries’ Exclusive Economic Zones, Large Marine Ecosystems, the High Seas and other spatial scales, and as global maps and summaries.

The Sea Around Us emphasizes catch time series starting in 1950, and related series (e.g., landed value and catch by flag state, fishing sector and catch type), and fisheries-related information on every maritime country (e.g., government subsidies, marine biodiversity). Information is also offered on sub-projects, e.g., the historic expansion of fisheries, the performance of Regional Fisheries Management Organizations, or the likely impact of climate change on fisheries.

The information and data presented on their website is freely available to any user, granted that its source is acknowledged. The Sea Around Us is aware that this information may be incomplete. Please let them know about this via the feedback options available on this website.

If you cite or display any content from the Site, or reference the Sea Around Us, the Sea Around Us – Indian Ocean, the University of British Columbia or the University of Western Australia, in any format, written or otherwise, including print or web publications, presentations, grant applications, websites, other online applications such as blogs, or other works, you must provide appropriate acknowledgement using a citation consistent with the following standard:

When referring to various datasets downloaded from the website, and/or its concept or design, or to several datasets extracted from its underlying databases, cite its architects. Example: Pauly D., Zeller D., Palomares M.L.D. (Editors), 2020. Sea Around Us Concepts, Design and Data (seaaroundus.org).

When referring to a set of values extracted for a given country, EEZ or territory, cite the most recent catch reconstruction report or paper (available on the website) for that country, EEZ or territory. Example: For the Mexican Pacific EEZ, the citation should be “Cisneros-Montemayor AM, Cisneros-Mata MA, Harper S and Pauly D (2015) Unreported marine fisheries catch in Mexico, 1950-2010. Fisheries Centre Working Paper #2015-22, University of British Columbia, Vancouver. 9 p.”, which is accessible on the EEZ page for Mexico (Pacific) on seaaroundus.org.

To help us track the use of Sea Around Us data, we would appreciate you also citing Pauly, Zeller, and Palomares (2020) as the source of the information in an appropriate part of your text;

When using data from our website that are not part of a typical catch reconstruction (e.g., catches by LME or other spatial entity, subsidies given to fisheries, the estuaries in a given country, or the surface area of a given EEZ), cite both the website and the study that generated the underlying database. Many of these can be derived from the ’methods’ texts associated with data pages on seaaroundus.org. Example: Sumaila et al. (2010) for subsides, Alder (2003) for estuaries and Claus et al. (2014) for EEZ delineations, respectively.

The Sea Around Us data are (where not otherwise regulated) under a Creative Commons Attribution Non-Commercial 4.0 International License (https://creativecommons.org/licenses/by-nc/4.0/). Notices regarding copyrights (© The University of British Columbia), license and disclaimer can be found under http://www.seaaroundus.org/terms-and-conditions/. References:

Alder J (2003) Putting the coast in the Sea Around Us Project. The Sea Around Us Newsletter (15): 1-2.

Cisneros-Montemayor AM, Cisneros-Mata MA, Harper S and Pauly D (2015) Unreported marine fisheries catch in Mexico, 1950-2010. Fisheries Centre Working Paper #2015-22, University of British Columbia, Vancouver. 9 p.

Pauly D, Zeller D, and Palomares M.L.D. (Editors) (2020) Sea Around Us Concepts, Design and Data (www.seaaroundus.org)

Claus S, De Hauwere N, Vanhoorne B, Deckers P, Souza Dias F, Hernandez F and Mees J (2014) Marine Regions: Towards a global standard for georeferenced marine names and boundaries. Marine Geodesy 37(2): 99-125.

Sumaila UR, Khan A, Dyck A, Watson R, Munro R, Tydemers P and Pauly D (2010) A bottom-up re-estimation of global fisheries subsidies. Journal of Bioeconomics 12: 201-225.

World Countries Generalized provides a generalized basemap layer for the countries of the world. It has fields for official names and country codes. The generalized boundaries improve draw performance and effectiveness at global and continental levels.This layer is best viewed out beyond a maximum scale (zoomed in) of 1:5,000,000.The sources of this dataset are Esri, Garmin, and U.S. Central Intelligence Agency (The World Factbook). It is updated every 12-18 months as country names or significant borders change.

Project Tycho datasets contain case counts for reported disease conditions for countries around the world. The Project Tycho data curation team extracts these case counts from various reputable sources, typically from national or international health authorities, such as the US Centers for Disease Control or the World Health Organization. These original data sources include both open- and restricted-access sources. For restricted-access sources, the Project Tycho team has obtained permission for redistribution from data contributors. All datasets contain case count data that are identical to counts published in the original source and no counts have been modified in any way by the Project Tycho team. The Project Tycho team has pre-processed datasets by adding new variables, such as standard disease and location identifiers, that improve data interpretabilty. We also formatted the data into a standard data format.

Each Project Tycho dataset contains case counts for a specific condition (e.g. measles) and for a specific country (e.g. The United States). Case counts are reported per time interval. In addition to case counts, datsets include information about these counts (attributes), such as the location, age group, subpopulation, diagnostic certainty, place of aquisition, and the source from which we extracted case counts. One dataset can include many series of case count time intervals, such as "US measles cases as reported by CDC", or "US measles cases reported by WHO", or "US measles cases that originated abroad", etc.

Depending on the intended use of a dataset, we recommend a few data processing steps before analysis:

• Analyze missing data: Project Tycho datasets do not inlcude time intervals for which no case count was reported (for many datasets, time series of case counts are incomplete, due to incompleteness of source documents) and users will need to add time intervals for which no count value is available. Project Tycho datasets do include time intervals for which a case count value of zero was reported.
• Separate cumulative from non-cumulative time interval series. Case count time series in Project Tycho datasets can be "cumulative" or "fixed-intervals". Cumulative case count time series consist of overlapping case count intervals starting on the same date, but ending on different dates. For example, each interval in a cumulative count time series can start on January 1st, but end on January 7th, 14th, 21st, etc. It is common practice among public health agencies to report cases for cumulative time intervals. Case count series with fixed time intervals consist of mutually exxclusive time intervals that all start and end on different dates and all have identical length (day, week, month, year). Given the different nature of these two types of case count data, we indicated this with an attribute for each count value, named "PartOfCumulativeCountSeries".

Project Tycho datasets contain case counts for reported disease conditions for countries around the world. The Project Tycho data curation team extracts these case counts from various reputable sources, typically from national or international health authorities, such as the US Centers for Disease Control or the World Health Organization. These original data sources include both open- and restricted-access sources. For restricted-access sources, the Project Tycho team has obtained permission for redistribution from data contributors. All datasets contain case count data that are identical to counts published in the original source and no counts have been modified in any way by the Project Tycho team. The Project Tycho team has pre-processed datasets by adding new variables, such as standard disease and location identifiers, that improve data interpretability. We also formatted the data into a standard data format.

Each Project Tycho dataset contains case counts for a specific condition (e.g. measles) and for a specific country (e.g. The United States). Case counts are reported per time interval. In addition to case counts, datasets include information about these counts (attributes), such as the location, age group, subpopulation, diagnostic certainty, place of acquisition, and the source from which we extracted case counts. One dataset can include many series of case count time intervals, such as "US measles cases as reported by CDC", or "US measles cases reported by WHO", or "US measles cases that originated abroad", etc.

Depending on the intended use of a dataset, we recommend a few data processing steps before analysis: - Analyze missing data: Project Tycho datasets do not include time intervals for which no case count was reported (for many datasets, time series of case counts are incomplete, due to incompleteness of source documents) and users will need to add time intervals for which no count value is available. Project Tycho datasets do include time intervals for which a case count value of zero was reported. - Separate cumulative from non-cumulative time interval series. Case count time series in Project Tycho datasets can be "cumulative" or "fixed-intervals". Cumulative case count time series consist of overlapping case count intervals starting on the same date, but ending on different dates. For example, each interval in a cumulative count time series can start on January 1st, but end on January 7th, 14th, 21st, etc. It is common practice among public health agencies to report cases for cumulative time intervals. Case count series with fixed time intervals consist of mutually exclusive time intervals that all start and end on different dates and all have identical length (day, week, month, year). Given the different nature of these two types of case count data, we indicated this with an attribute for each count value, named "PartOfCumulativeCountSeries".