The total amount of data created, captured, copied, and consumed globally is forecast to increase rapidly. While it was estimated at ***** zettabytes in 2025, the forecast for 2029 stands at ***** zettabytes. Thus, global data generation will triple between 2025 and 2029. Data creation has been expanding continuously over the past decade. In 2020, the growth was higher than previously expected, caused by the increased demand due to the coronavirus (COVID-19) pandemic, as more people worked and learned from home and used home entertainment options more often.

How many people use social media? Social media usage is one of the most popular online activities. In 2025, over *** billion people were estimated to be using social media worldwide, a number projected to increase to over *** billion in 2030. Who uses social media? Social networking is one of the most popular digital activities worldwide, and it is no surprise that social networking penetration across all regions is constantly increasing. As of January 2023, the global social media usage rate stood at ** percent. This figure is anticipated to grow as less developed digital markets catch up with other regions when it comes to infrastructure development and the availability of cheap mobile devices. In fact, most of social media’s global growth is driven by the increasing usage of mobile devices. The mobile-first market of Eastern Asia topped the global ranking of mobile social networking penetration, followed by established digital powerhouses such as the Americas and Northern Europe. How much time do people spend on social media? Social media is an integral part of daily internet usage. On average, internet users spend *** minutes per day on social media and messaging apps, an increase of ** minutes since 2015. On average, internet users in Latin America had the highest average time spent per day on social media. What are the most popular social media platforms? Market leader Facebook was the first social network to surpass *** billion registered accounts and currently boasts approximately *** billion monthly active users, making it the most popular social network worldwide. In June 2023, the top social media apps in the Apple App Store included mobile messaging apps WhatsApp and Telegram Messenger, as well as the ever-popular app version of Facebook.

The global number of Facebook users was forecast to continuously increase between 2023 and 2027 by in total 391 million users (+14.36 percent). After the fourth consecutive increasing year, the Facebook user base is estimated to reach 3.1 billion users and therefore a new peak in 2027. Notably, the number of Facebook users was continuously increasing over the past years. User figures, shown here regarding the platform Facebook, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period and count multiple accounts by persons only once.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).

Japan JP: Number of Listed Domestic Companies: Total data was reported at 3,598.000 Unit in 2017. This records an increase from the previous number of 3,535.000 Unit for 2016. Japan JP: Number of Listed Domestic Companies: Total data is updated yearly, averaging 1,766.000 Unit from Dec 1975 (Median) to 2017, with 43 observations. The data reached an all-time high of 3,598.000 Unit in 2017 and a record low of 1,389.000 Unit in 1978. Japan JP: Number of Listed Domestic Companies: Total data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Japan – Table JP.World Bank.WDI: Financial Sector. Listed domestic companies, including foreign companies which are exclusively listed, are those which have shares listed on an exchange at the end of the year. Investment funds, unit trusts, and companies whose only business goal is to hold shares of other listed companies, such as holding companies and investment companies, regardless of their legal status, are excluded. A company with several classes of shares is counted once. Only companies admitted to listing on the exchange are included.; ; World Federation of Exchanges database.; Sum; Stock market data were previously sourced from Standard & Poor's until they discontinued their 'Global Stock Markets Factbook' and database in April 2013. Time series have been replaced in December 2015 with data from the World Federation of Exchanges and may differ from the previous S&P definitions and methodology.

United States US: Stocks Traded: Total Value data was reported at 39,785.881 USD bn in 2017. This records a decrease from the previous number of 42,071.330 USD bn for 2016. United States US: Stocks Traded: Total Value data is updated yearly, averaging 17,934.293 USD bn from Dec 1984 (Median) to 2017, with 34 observations. The data reached an all-time high of 47,245.496 USD bn in 2008 and a record low of 1,108.421 USD bn in 1984. United States US: Stocks Traded: Total Value data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Financial Sector. The value of shares traded is the total number of shares traded, both domestic and foreign, multiplied by their respective matching prices. Figures are single counted (only one side of the transaction is considered). Companies admitted to listing and admitted to trading are included in the data. Data are end of year values converted to U.S. dollars using corresponding year-end foreign exchange rates.; ; World Federation of Exchanges database.; Sum; Stock market data were previously sourced from Standard & Poor's until they discontinued their 'Global Stock Markets Factbook' and database in April 2013. Time series have been replaced in December 2015 with data from the World Federation of Exchanges and may differ from the previous S&P definitions and methodology.

Egypt EG: Stocks Traded: Total Value data was reported at 14.429 USD bn in 2017. This records an increase from the previous number of 10.080 USD bn for 2016. Egypt EG: Stocks Traded: Total Value data is updated yearly, averaging 21.767 USD bn from Dec 2006 (Median) to 2017, with 12 observations. The data reached an all-time high of 95.827 USD bn in 2008 and a record low of 10.080 USD bn in 2016. Egypt EG: Stocks Traded: Total Value data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Egypt – Table EG.World Bank.WDI: Financial Sector. The value of shares traded is the total number of shares traded, both domestic and foreign, multiplied by their respective matching prices. Figures are single counted (only one side of the transaction is considered). Companies admitted to listing and admitted to trading are included in the data. Data are end of year values converted to U.S. dollars using corresponding year-end foreign exchange rates.; ; World Federation of Exchanges database.; Sum; Stock market data were previously sourced from Standard & Poor's until they discontinued their 'Global Stock Markets Factbook' and database in April 2013. Time series have been replaced in December 2015 with data from the World Federation of Exchanges and may differ from the previous S&P definitions and methodology.

In 2024, spending on digital transformation (DX) is projected to reach 2.5 trillion U.S. dollars. By 2027, global digital transformation spending is forecast to reach 3.9 trillion U.S. dollars. What is digital transformation? Digital transformation refers to the adoption of digital technology to transform business processes and services from non-digital to digital. This encompasses, among others, moving data to the cloud, using technological devices and tools for communication and collaboration, as well as automating processes. What is driving digital transformation? Digital transformation growth is due to several contributing factors. Among these was COVID-19 pandemic, which has increased the digital transformation tempo in organizations around the globe in 2020 considerably. Although the pandemic is over, working from home among organizations globally has not only remained, but also increased, increasing the drive for digital transformation. Other contributing causes include customer demand and the need to be on par with competitors. Overall, utilizing technologies for digital transformation render organizations more agile in responding to changing markets and enhance innovation, thereby making them more resilient.

US Mail Statistics

US Mail History: Mail Volume, Post Offices, Income, Expenses (1790-2017)

By Throwback Thursday [source]

About this dataset

The dataset contains multiple columns that provide specific information for each year recorded. The column labeled Year indicates the specific year in which the data was recorded. The Pieces of Mail Handled column shows the total number of mail items that were processed or handled in a given year.

Another important metric is represented in the Number of Post Offices column, revealing the total count of post offices that were operational during a specific year. This information helps understand how postal services and infrastructure have evolved over time.

Examining financial aspects, there are two columns: Income and Expenses. The former represents the total revenue generated by the US Mail service in a particular year, while the latter showcases the expenses incurred by this service during that same period.

The dataset titled Week 22 - US Mail - 1790 to 2017.csv serves as an invaluable resource for researchers, historians, and analysts interested in studying trends and patterns within the US Mail system throughout its extensive history. By utilizing this dataset's wide range of valuable metrics, users can gain insights into how mail volume has changed over time alongside fluctuations in post office numbers and financial performance

How to use the dataset

• Familiarize yourself with the columns:

  • Year: This column represents the specific year in which data was recorded. It is represented by numeric values.
  • Pieces of Mail Handled: This column indicates the number of mail items processed or handled in a given year. It is also represented by numeric values.
  • Number of Post Offices: Here, you will find information on the total count of post offices in operation during a specific year. Like other columns, it consists of numeric values.
  • Income: The Income column displays the total revenue generated by the US Mail service in a particular year. Numeric values are used to represent this data.
  • Expenses: This column shows the total expenses incurred by the US Mail service for a particular year. Similar to other columns, it uses numeric values.

• Understand data relationships: By exploring and analyzing different combinations of columns, you can uncover interesting patterns and relationships within mail statistics over time. For example:

  • Relationship between Year and Pieces of Mail Handled/Number of Post Offices/Income/Expenses: Analyzing these variables over years will allow you to observe trends such as increasing mail volume alongside changes in post office numbers or income and expenses patterns.

  • Relationship between Pieces of Mail Handled and Number Postal Office: By comparing these two variables across different years, you can assess if there is any correlation between mail volume growth and changes in post office counts.

• Visualization:

  To gain better insights into this vast amount of data visually, consider making use graphs or plots beyond just numerical analysis. You can use tools like Matplotlib, Seaborn, or Plotly to create various types of visualizations:

  • Time-series line plots: Visualize the change in Pieces of Mail Handled, Number of Post Offices, Income, and Expenses over time.
  • Scatter plots: Identify potential correlations between different variables such as Year and Pieces of Mail Handled/Number of Post Offices/Income/Expenses.

• Drawing conclusions:

  This dataset presents an extraordinary opportunity to learn about the history and evolution of the US Mail service. By examining various factors together or individually throughout time, you can draw conclusions about

Research Ideas

• Trend Analysis: The dataset can be used to analyze the trends and patterns in mail volume, post office numbers, income, and expenses over time. This can help identify any significant changes or fluctuations in these variables and understand the factors that may have influenced them.
• Benchmarking: By comparing the performance of different years or periods, this dataset can be used for benchmarking purposes. For example, it can help assess how efficiently post offices have been handling mail items by comparing the number of pieces of mail handled with the corresponding expenses incurred.
• Forecasting: Based on historical data on mail volume and revenue generation, this dataset can be used for forecasting future trends. This could be valuable for planning purposes, such as determining resource allocation or projecting financial o...

The total amount of data created, captured, copied, and consumed globally is forecast to increase rapidly, reaching 149 zettabytes in 2024. Over the next five years up to 2028, global data creation is projected to grow to more than 394 zettabytes. In 2020, the amount of data created and replicated reached a new high. The growth was higher than previously expected, caused by the increased demand due to the COVID-19 pandemic, as more people worked and learned from home and used home entertainment options more often. Storage capacity also growing Only a small percentage of this newly created data is kept though, as just 2 percent of the data produced and consumed in 2020 was saved and retained into 2021. In line with the strong growth of the data volume, the installed base of storage capacity is forecast to increase, growing at a compound annual growth rate of 19.2 percent over the forecast period from 2020 to 2025. In 2020, the installed base of storage capacity reached 6.7 zettabytes.

Dominican Republic DO: External Debt: Total Amount Rescheduled data was reported at 0.000 USD mn in 2017. This stayed constant from the previous number of 0.000 USD mn for 2016. Dominican Republic DO: External Debt: Total Amount Rescheduled data is updated yearly, averaging 0.000 USD mn from Dec 1970 (Median) to 2017, with 48 observations. The data reached an all-time high of 1.209 USD bn in 2005 and a record low of 0.000 USD mn in 2017. Dominican Republic DO: External Debt: Total Amount Rescheduled data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Dominican Republic – Table DO.World Bank.WDI: External Debt: Arrears and Reschedulings. Total amount of debt rescheduled includes the debt stock, principal, interest, charges and penalties rescheduled. Data are in current U.S. dollars.; ; World Bank, International Debt Statistics.; Sum;

US Postal Service Historical Data

US Postal Service Data: Mail Volume, Revenue, and Metrics (1790-2017)

By Throwback Thursday [source]

About this dataset

This dataset provides comprehensive historical data on the United States Postal Service (USPS) from its establishment in 1790 until 2017. The dataset includes various key metrics and statistics related to mail volume, revenue, and other important aspects of USPS operations over the years.

The dataset offers a valuable resource for researchers, analysts, and historians interested in studying the evolution of USPS and its impact on American society. It provides insights into the changing patterns of mail usage and consumption throughout different periods in history.

Additionally, this dataset can be used to examine trends in USPS revenue generation over time, offering a deeper understanding of the financial performance of one of America's most important institutions. By analyzing fluctuations in mail volume and revenue figures across decades or even centuries, researchers may gain valuable knowledge about how technological advancements, economic conditions, or societal changes have influenced postal services.

Other key metrics included in this dataset could shed light on various operational aspects such as delivery timescales or customer satisfaction levels throughout different eras. Researchers might explore how innovations like automatic sorting machines or online tracking systems have impacted efficiency and service quality at USPS.

By utilizing this extensive historical data spanning more than two centuries, researchers can uncover fascinating insights into not only the functions but also the significance that postal services hold within American life

How to use the dataset

How to Use this Dataset: US Mail - 1790 to 2017

Welcome to the dataset US Mail - 1790 to 2017: Historical Data on Postal. This dataset provides valuable information regarding the mail volume, revenue, and other key metrics of the US Postal Service from 1790 to 2017. To make the most out of this dataset, follow this guide:

Overview:

The dataset contains several columns that provide different types of data. Here's what each column represents:

• Year: The year for which the data is recorded.
• Total_Volume: The total volume of mail delivered in a particular year.
• First_Class_Volume: The volume of first-class mail delivered in a particular year.
• Second_Class_Volume: The volume of second-class mail (periodicals) delivered in a particular year.
• Standard_Postal_Service_Volume: The volume of standard postal service mail (bulk business mail) delivered in a particular year.
• Package_Service_Volume: The volume of package services (Parcels and Express Mail) delivered in a particular year.
• Standard_Postage_Revenue: The total revenue generated from standard postage services in a particular year.
• Package_Revenue: The total revenue generated from package services (Parcels and Express Mail) in a particular year.

Insights you can gain:

Using this dataset, you can explore various aspects related to US mail service over time, such as:

• Analyzing trends and changes: Track how different types of mail volumes have changed through history; observe patterns or spikes during specific years or events that influenced their distribution.

• Revenue analysis: Observe how revenues from standard postal services and package services have evolved over time; identify any relationship between revenue growth and changes in overall mailing habits.

• Proportional Analysis: Assess the proportion of different mail volumes within the total volume, and how these proportions have changed.

• Comparative analysis: Compare revenue generation from standard postal services and package services to identify any trends or significant differences in growth rates.

Tips for Exploration:

To make the most of this dataset, consider using the following techniques:

• Visualization: Utilize various charts like line plots, bar graphs, or area plots to visualize the trends in mail volume and revenue changes over time effectively.

• Aggregation: Group the data by relevant time periods (e.g., decades) to get a clearer view of long-term patterns rather than solely focusing

Research Ideas

• Analyzing Mail Volume Trends: This dataset can be used to analyze the overall trend in mail volume over the years. It can help identify periods of growth or decline in mail usage and understand factors influencing these trends. This information could be useful for businesses planning marketing strategies, policymaker...

Hong Kong HK: Stocks Traded: Total Value: % of GDP data was reported at 572.012 % in 2017. This records an increase from the previous number of 421.058 % for 2016. Hong Kong HK: Stocks Traded: Total Value: % of GDP data is updated yearly, averaging 129.643 % from Dec 1975 (Median) to 2017, with 38 observations. The data reached an all-time high of 952.667 % in 2007 and a record low of 2.086 % in 1977. Hong Kong HK: Stocks Traded: Total Value: % of GDP data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Hong Kong SAR – Table HK.World Bank.WDI: Financial Sector. The value of shares traded is the total number of shares traded, both domestic and foreign, multiplied by their respective matching prices. Figures are single counted (only one side of the transaction is considered). Companies admitted to listing and admitted to trading are included in the data. Data are end of year values.; ; World Federation of Exchanges database.; Weighted average; Stock market data were previously sourced from Standard & Poor's until they discontinued their 'Global Stock Markets Factbook' and database in April 2013. Time series have been replaced in December 2015 with data from the World Federation of Exchanges and may differ from the previous S&P definitions and methodology.

Syria SY: External Debt: Total Amount Rescheduled data was reported at 0.000 USD mn in 2017. This stayed constant from the previous number of 0.000 USD mn for 2016. Syria SY: External Debt: Total Amount Rescheduled data is updated yearly, averaging 0.000 USD mn from Dec 1970 (Median) to 2017, with 48 observations. The data reached an all-time high of 566.445 USD mn in 2000 and a record low of 0.000 USD mn in 2017. Syria SY: External Debt: Total Amount Rescheduled data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Syrian Arab Republic – Table SY.World Bank.WDI: External Debt: Arrears and Reschedulings. Total amount of debt rescheduled includes the debt stock, principal, interest, charges and penalties rescheduled. Data are in current U.S. dollars.; ; World Bank, International Debt Statistics.; Sum;

Abstract

The documented dataset covers Enterprise Survey (ES) panel data collected in Niger in 2005, 2009 and 2016, as part of Africa Enterprise Surveys rollout, an initiative of the World Bank. The objective of the survey is to obtain feedback from enterprises on the state of the private sector as well as to help in building a panel of enterprise data that will make it possible to track changes in the business environment over time, thus allowing, for example, impact assessments of reforms.

Through interviews with firms in the manufacturing and services sectors, the survey assesses the constraints to private sector growth and creates statistically significant business environment indicators that are comparable across countries. Only registered businesses are surveyed in the Enterprise Survey.

Data from 151 establishments was analyzed. Stratified random sampling was used to select the surveyed businesses. The data was collected using face-to-face interviews.

The standard Enterprise Survey topics include firm characteristics, gender participation, access to finance, annual sales, costs of inputs and labor, workforce composition, bribery, licensing, infrastructure, trade, crime, competition, capacity utilization, land and permits, taxation, informality, business-government relations, innovation and technology, and performance measures. Over 90 percent of the questions objectively ascertain characteristics of a country’s business environment. The remaining questions assess the survey respondents’ opinions on what are the obstacles to firm growth and performance.

Geographic coverage

National

Analysis unit

The primary sampling unit of the study is an establishment. An establishment is a physical location where business is carried out and where industrial operations take place or services are provided. A firm may be composed of one or more establishments. For example, a brewery may have several bottling plants and several establishments for distribution. For the purposes of this survey an establishment must make its own financial decisions and have its own financial statements separate from those of the firm. An establishment must also have its own management and control over its payroll.

Universe

The whole population, or the universe, covered in the Enterprise Surveys is the non-agricultural private economy. It comprises: all manufacturing sectors according to the ISIC Revision 3.1 group classification (group D), construction sector (group F), services sector (groups G and H), and transport, storage, and communications sector (group I). Note that this population definition excludes the following sectors: financial intermediation (group J), real estate and renting activities (group K, except sub-sector 72, IT, which was added to the population under study), and all public or utilities sectors. Companies with 100% government ownership are not eligible to participate in the Enterprise Surveys.

Kind of data

Sample survey data [ssd]

Sampling procedure

Three levels of stratification were used in this country: industry, establishment size, and region.

Industry stratification was designed as follows: the universe was stratified as into manufacturing and services industries- Manufacturing (ISIC Rev. 3.1 codes 15 - 37), and Services (ISIC codes 45, 50-52, 55, 60-64, and 72).

For the 2009 sample stratification purposes, the number of employees was defined on the basis of reported permanent full-time workers. Size stratification was defined following the standardized definition used for the Enterprise Surveys: small (5 to 19 employees), medium (20 to 99 employees), and large (more than 99 employees). For stratification purposes, the number of employees was defined on the basis of reported permanent full-time workers. Regional stratification was defined in terms of the geographic regions with the largest commercial presence in the country: Maradi and Niamey were the two areas selected in Niger.

Two frames were used for Niger. The first one included official lists from the Chamber of commerce, craft and industries of Niger 2008 and the Repertoire of Companies (2008) operating in Niger. The second frame (the panel sample) consisted of enterprises interviewed for the Enterprise Survey in 2005, which were to be re-interviewed where they were in the selected geographical regions and met eligibility criteria. Both database contained the following information: -Name of the firm -Contact details -ISIC code -Number of employees.

Given the impact that non-eligible units included in the sample universe may have on the results, adjustments may be needed when computing the appropriate weights for individual observations. The percentage of confirmed non-eligible units as a proportion of the total number of sampled establishments contacted for the survey was 39.9% (134 out of 344 establishments). Breaking down by industry, the following numbers of establishments were surveyed: Manufacturing - 52, Services - 98.

For 2017: Regional stratification for the Niger ES was done across two regions: Niamey and Rest of the Country.

The sample frame consisted of listings of firms from three sources: - the list of 150 firms from the Niger 2009 ES for panel firms - firm data from La Caisse Nationale de Sécurité Sociale (CNSS) and a list of exporting firms by the Institut National des Statistiques (INS) for fresh firms (firms not covered in 2009).

Given the impact that non-eligible units included in the sample universe may have on the results, adjustments may be needed when computing the appropriate weights for individual observations. The percentage of confirmed non-eligible units as a proportion of the total number of sampled establishments contacted for the survey was 18.6% (76 out of 409 establishments).

Mode of data collection

Face-to-face [f2f]

Cleaning operations

Data entry and quality controls are implemented by the contractor and data is delivered to the World Bank in batches (typically 10%, 50% and 100%). These data deliveries are checked for logical consistency, out of range values, skip patterns, and duplicate entries. Problems are flagged by the World Bank and corrected by the implementing contractor through data checks, callbacks, and revisiting establishments.

Response rate

Survey non-response must be differentiated from item non-response. The former refers to refusals to participate in the survey altogether whereas the latter refers to the refusals to answer some specific questions. Enterprise Surveys suffer from both problems and different strategies were used to address these issues.

Item non-response was addressed by two strategies: a- For sensitive questions that may generate negative reactions from the respondent, such as corruption or tax evasion, enumerators were instructed to collect "Refusal to respond" (-8) as a different option from "Don't know" (-9). b- Establishments with incomplete information were re-contacted in order to complete this information, whenever necessary.

Survey non-response was addressed by maximizing efforts to contact establishments that were initially selected for interview. Attempts were made to contact the establishment for interview at different times/days of the week before a replacement establishment (with similar strata characteristics) was suggested for interview. Survey non-response did occur but substitutions were made in order to potentially achieve strata-specific goals.

United States US: Stocks Traded: Total Value: % of GDP data was reported at 205.181 % in 2017. This records a decrease from the previous number of 225.893 % for 2016. United States US: Stocks Traded: Total Value: % of GDP data is updated yearly, averaging 155.485 % from Dec 1984 (Median) to 2017, with 34 observations. The data reached an all-time high of 320.992 % in 2008 and a record low of 27.431 % in 1984. United States US: Stocks Traded: Total Value: % of GDP data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Financial Sector. The value of shares traded is the total number of shares traded, both domestic and foreign, multiplied by their respective matching prices. Figures are single counted (only one side of the transaction is considered). Companies admitted to listing and admitted to trading are included in the data. Data are end of year values.; ; World Federation of Exchanges database.; Weighted average; Stock market data were previously sourced from Standard & Poor's until they discontinued their 'Global Stock Markets Factbook' and database in April 2013. Time series have been replaced in December 2015 with data from the World Federation of Exchanges and may differ from the previous S&P definitions and methodology.

The statistic shows the number of data centers worldwide in 2015, 2017, and 2021. In 2017, it was estimated that the number of data centers globally had fallen to *** million.

Ukraine UA: External Debt: Total Amount Rescheduled data was reported at 0.000 USD mn in 2017. This stayed constant from the previous number of 0.000 USD mn for 2016. Ukraine UA: External Debt: Total Amount Rescheduled data is updated yearly, averaging 0.000 USD mn from Dec 1970 (Median) to 2017, with 48 observations. The data reached an all-time high of 3.074 USD bn in 1999 and a record low of 0.000 USD mn in 2017. Ukraine UA: External Debt: Total Amount Rescheduled data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Ukraine – Table UA.World Bank.WDI: External Debt: Arrears and Reschedulings. Total amount of debt rescheduled includes the debt stock, principal, interest, charges and penalties rescheduled. Data are in current U.S. dollars.; ; World Bank, International Debt Statistics.; Sum;

Norway NO: Stocks Traded: Total Value: % of GDP data was reported at 29.547 % in 2017. This records an increase from the previous number of 28.386 % for 2016. Norway NO: Stocks Traded: Total Value: % of GDP data is updated yearly, averaging 20.933 % from Dec 1980 (Median) to 2017, with 38 observations. The data reached an all-time high of 101.684 % in 2007 and a record low of 0.130 % in 1980. Norway NO: Stocks Traded: Total Value: % of GDP data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Norway – Table NO.World Bank.WDI: Financial Sector. The value of shares traded is the total number of shares traded, both domestic and foreign, multiplied by their respective matching prices. Figures are single counted (only one side of the transaction is considered). Companies admitted to listing and admitted to trading are included in the data. Data are end of year values.; ; World Federation of Exchanges database.; Weighted average; Stock market data were previously sourced from Standard & Poor's until they discontinued their 'Global Stock Markets Factbook' and database in April 2013. Time series have been replaced in December 2015 with data from the World Federation of Exchanges and may differ from the previous S&P definitions and methodology.

Vietnam VN: Stocks Traded: Total Value: % of GDP data was reported at 17.001 % in 2017. This records an increase from the previous number of 10.850 % for 2016. Vietnam VN: Stocks Traded: Total Value: % of GDP data is updated yearly, averaging 10.850 % from Dec 2008 (Median) to 2017, with 9 observations. The data reached an all-time high of 21.384 % in 2009 and a record low of 5.527 % in 2011. Vietnam VN: Stocks Traded: Total Value: % of GDP data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Vietnam – Table VN.World Bank.WDI: Financial Sector. The value of shares traded is the total number of shares traded, both domestic and foreign, multiplied by their respective matching prices. Figures are single counted (only one side of the transaction is considered). Companies admitted to listing and admitted to trading are included in the data. Data are end of year values.; ; World Federation of Exchanges database.; Weighted average; Stock market data were previously sourced from Standard & Poor's until they discontinued their 'Global Stock Markets Factbook' and database in April 2013. Time series have been replaced in December 2015 with data from the World Federation of Exchanges and may differ from the previous S&P definitions and methodology.

NOAA-20 (hereafter, N20; also known as JPSS-1 or J1 prior to launch) is the second satellite in the US National Oceanic and Atmospheric Administration (NOAA) latest generation Joint Polar Satellite System (JPSS). N20 was launched on November 18, 2017. In conjunction with the first US satellite in JPSS series, Suomi National Polar-orbiting Partnership (S-NPP) satellite launched on October 28, 2011, N20 form the new NOAA polar constellation. The ACSPO N20/VIIRS L3U (Level 3 Uncollated) product is a gridded version of the ACSPO N20/VIIRS L2P product. The L3U output files are 10-minute granules in netCDF4 format, compliant with the GHRSST Data Specification version 2 (GDS2). There are 144 granules per 24hr interval, with a total data volume of 500MB/day. Fill values are reported at all invalid pixels, including pixels with >5 km inland. For each valid water pixel (defined as ocean, sea, lake or river, and up to 5 km inland), the following layers are reported: SSTs, ACSPO clear-sky mask (ACSM; provided in each grid as part of l2p_flags, which also includes day/night, land, ice, twilight, and glint flags), NCEP wind speed, and ACSPO SST minus reference (Canadian Met Centre 0.1deg L4 SST). Only L2P SSTs with QL=5 were gridded, so all valid SSTs are recommended for the users. Per GDS2 specifications, two additional Sensor-Specific Error Statistics layers (SSES bias and standard deviation) are reported in each pixel with valid SST. The ACSPO VIIRS L3U product is monitored and validated against iQuam in situ data (Xu and Ignatov, 2014) in SQUAM (Dash et al, 2010).