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

OECD Global Crude Oil Imports Prices

Regional and Temporal Trends

By OECD [source]

About this dataset

This dataset contains global crude oil import prices from the OECD. It provides important insight into international trading of oil and its related products, enabling users to analyse market trends and compare prices across different countries. This data is essential for understanding the development of different economies, as well as their dependence on crude oil imports. Through analysis of this dataset, users can understand the role that regional and global factors play in impacting global crude oil import prices over time. The dataset includes columns tracking country/region of origin (LOCATION), indicator measured (INDICATOR), subject tracked (SUBJECT), measure taken (MEASURE), frequency interval (FREQUENCY), time period covered (TIME) as well as numerical value and flag codes associated with the data captured in each row. This invaluable source is perfect for researchers looking to take a deep dive into international markets over time or academics studying the complexities surrounding trade in the energy sector!

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How to use the dataset

This dataset is a great resource for anyone looking to analyze the current and historical prices of crude oil imports from the OECD. The data contains prices from member countries of the OECD and is updated regularly. This dataset can be used to study long term trends in price as well as explore differences between countries with different levels of crude oil import demand.

• In order to make use of this dataset, it’s important to familiarize yourself with the column names and descriptions. The first column is LOCATION which indicates which country or region the data applies to. INDICATOR indicates what information is being displayed (e.g., import market share, import value, etc.). SUBJECT describes what category that metric falls into (e.g., fuel energy). MEASURE tells you whether an amount is expressed in a unit or currency while FREQUENCY says how often data has been collected: monthly, quarterly or annually (average monthly/quarterly/annual etc..). TIME displays measure period start date in year-month format and Value denotes numerical value for each row's measurement respectively while flag codes indicate if any values are estimates or outlier measurements that should be examined further before using them

• Using this understanding, one could filter their search by creating filters on these columns accordingly depending on their research topic such as – pulling all records for China for Q4 2019 - then apply sorting on “VALUE” column based on imported measurements have become cheaper during given time frame etc.. Additionally formulas like SUMIFS() can also be used across multiple columns available within this agreement document at same time such as – total Imports Value from India & Japan combined during May 2019 till October 2020 – based upon bringing together Matching condition criteria met across few columns where needed at same time . As such this dataset provides flexible solutions which potentially allow us to explore patterns related either just single country's current trends -or- cross references since global side-by-side evaluation possible here featuring more than just one nation alone too ...........

Research Ideas

• Analyzing the impact of changes in crude oil prices on global economic growth.
• Examining the evolving dynamics of crude oil trade flows between different countries and regions.
• Tracking trends in crude oil import prices across different industries to identify potential opportunities for cost savings and efficiency gains

Acknowledgements

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

License

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

Columns

File: crude_oil_import_prices.csv | Column name | Description ...

Online Retail Sales and Customer Data

Transactional Data with Product and Customer Details in Online Retail

By Marc Szafraniec [source]

About this dataset

The InvoiceNo column holds unique identifiers for each transaction conducted. This numerical code serves a twofold purpose: it facilitates effortless identification of individual sales or purchases while simultaneously enabling treasury management by offering a repository for record keeping.

In concordance with the invoice number is the InvoiceDate column. It provides a date-time stamp associated with every transaction, which can reveal patterns in purchasing behaviour over time and assists with record-keeping requirements.

The StockCode acts as an integral part of this dataset; it encompasses alphanumeric sequences allocated distinctively to every item in stock. Such a system aids unequivocally identifying individual products making inventory records seamless.

The Description field offers brief elucidations about each listed product, adding layers beyond just stock codes to aid potential customers' understanding of products better and make more informed choices.

Detailed logs concerning sold quantities come under the Quantity banner - it lists the units involved per transaction alongside aiding calculations regarding total costs incurred during each sale/purchase offering significant help tracking inventory levels based on products' outflow dynamics within given periods.

Retail isn't merely about what you sell but also at what price you sell- A point acknowledged via our inclusion of unit prices exerted on items sold within transactions inside our dataset's UnitPrice column which puts forth pertinent pricing details serving as pivotal factors driving metrics such as gross revenue calculation etc

Finally yet importantly is our dive into foreign waters - literally! With impressive international outreach we're looking into segmentation bases like geographical locations via documenting countries (under the name Country) where transactions are conducted & consumers reside extending opportunities for businesses to map their customer bases, track regional performance metrics, extend localization efforts and overall contributing to the formulation of efficient segmentation strategies.

All this invaluable information can be found in a sortable CSV file titled online_retail.csv. This dataset will prove incredibly advantageous for anyone interested in or researching online sales trends, developing customer profiles, or gaining insights into effective inventory management practices

How to use the dataset

Identifying Products: StockCode is the unique identifier for each product. You can use it to identify individual products, track their sales, or discover patterns related to specific items.

Assessing Sales Volume: Quantity column tells you about the number of units of a product involved in each transaction. Along with InvoiceNo, you can analyze overall sales volume or specific purchases throughout your selected period.

Observing Price Fluctuations: By using the UnitPrice, not only can the total cost per transaction be calculated (by multiplying with Quantity), but also insightful observations like price fluctuations over time or determining most profitable items could be derived.

Analyzing Description Patterns/Trends: The Description field sheds light upon what kind of products are being traded. This could provide some inspiration for text analysis like term frequency-inverse document frequency (TF-IDF), sentiment analysis on descriptions, etc., to figure out popular trends at given times.

Analysing Geographical Trends: With the help of Country column, geographical trends in sales volumes across different nations can easily be analyzed i.e., which location has more customers or which country orders more quantity or expensive units based on unit price and quantity columns respectively.

Keep in mind that proper extraction and transformation methodology should be applied while handling data from different columns as per their datatypes (textual/alphanumeric/numeric) requirements.

This dataset not only allows retailers to gain an immediate understanding into their operations but could also serve as a base dataset for those interested in machine learning regarding predicting future transactions

Research Ideas

• Inventory Management: By tracking the 'Quantity' and 'StockCode' over time, a business could use this data to notice if certain products are frequently purchased together or in specific seasons, allowing them to better stock their inventory.
• Pricing Strategy:...

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

Abstract

Food price inflation is an important metric to inform economic policy but traditional sources of consumer prices are often produced with delay during crises and only at an aggregate level. This may poorly reflect the actual price trends in rural or poverty-stricken areas, where large populations reside in fragile situations. This data set includes food price estimates and is intended to help gain insight in price developments beyond what can be formally measured by traditional methods. The estimates are generated using a machine-learning approach that imputes ongoing subnational price surveys, often with accuracy similar to direct measurement of prices. The data set provides new opportunities to investigate local price dynamics in areas where populations are sensitive to localized price shocks and where traditional data are not available.

Geographic coverage notes

The data cover the following areas: Afghanistan, Armenia, Bangladesh, Burkina Faso, Burundi, Cameroon, Central African Republic, Chad, Congo, Dem. Rep., Congo, Rep., Gambia, The, Guinea, Guinea-Bissau, Haiti, Indonesia, Iraq, Kenya, Lao PDR, Lebanon, Liberia, Libya, Malawi, Mali, Mauritania, Mozambique, Myanmar, Niger, Nigeria, Philippines, Senegal, Somalia, South Sudan, Sri Lanka, Sudan, Syrian Arab Republic, Yemen, Rep.

European Rye and Meslin Price Index by Country, 2022 Discover more data with ReportLinker!

The TIC Form SLT collects monthly data on the market value of long-term cross-border securities holdings by country, type of foreign holder (official or private), and type of security. We estimate transactions as well as valuation change that is, the monthly change in the market value of the securities arising from price or exchange rate changes. Since the valuation change estimates are based on the country of issuer, the price indexes used for U.S. securities are the same for all holder countries. Over the ten years that TIC SLT data have been collected, this method has yielded estimated transactions more consistent with positions reported in the TIC SLT, with the findings of the annual security-level survey data, and with our expectations based on other information, such as market commentary or patterns observed across time.This dataset includes position, estimated transaction, and estimated valuation change data for counterparty countries that (1) have published TIC SLT position data and (2) have significant reported positions. This set of countries accounts for 95 to 99 percent of all long-term cross-border securities holdings.

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

This dataset tracks annual reduced-price lunch eligibility from 2019 to 2023 for Nord Country vs. California and Nord Country School District

The average for 2021 based on 165 countries was 79.81 index points. The highest value was in Bermuda: 212.7 index points and the lowest value was in Syria: 33.25 index points. The indicator is available from 2017 to 2021. Below is a chart for all countries where data are available.

As of the second quarter of 2019, the price for *** Gigabyte amounted to approximately *** U.S. dollars, a decrease of approximately *** U.S. dollars compared to 2017. Compared to its neighboring countries like Singapore and Malaysia, the data price in Indonesia was the lowest. Affordable price versus broadband infrastructure As smartphone users tend to communicate through mobile apps such as Whatsapp or Messenger more than via text message or phone call, the affordability of mobile internet is crucial. Good broadband infrastructure and economic growth in the country determine whether the internet providers can fulfill the demand while maintaining affordable prices. In late 2019 Indonesia’s government completed the Palapa Ring Project, an infrastructure project that aimed to provide access to ** internet services across the country. With this, Indonesia’s digital economy is expected to grow faster. PT Telkomsel, the largest mobile internet provider
Other than communication related apps, shopping and social media apps had the highest reach levels among Indonesian smartphone users. On average, a smartphone user in Indonesia spent about **** minutes per day for communication. In 2018, PT Telkom Indonesia Group had a share of **** percent of the fixed broadband market in Indonesia. Besides being the largest telecommunications and network provider in Indonesia, Telkomsel is also the most popular mobile internet provider to browse the internet, followed by Indosat and XL.

The "World Literacy Rate by Country" dataset provides a detailed snapshot of literacy rates across different countries in the world. This dataset is organized into several key columns:

S.No: This column lists the serial number for each country, helping to keep the data organized and easy to reference. Country: This column names the countries included in the dataset, allowing for a clear understanding of which nation each literacy rate pertains to. Literacy rate in percentage: This column shows the literacy rate of each country, expressed as a percentage. This figure represents the proportion of the population that can read and write. Year: This column indicates the year in which the literacy rate was recorded, providing a temporal context for the data. By examining this dataset, you can gain insights into the educational progress and challenges faced by different countries in the world.

Hungary Import Price Index: Extra EU Countries data was reported at 112.500 Same Mth PY=100 in Aug 2018. This records an increase from the previous number of 109.100 Same Mth PY=100 for Jul 2018. Hungary Import Price Index: Extra EU Countries data is updated monthly, averaging 99.500 Same Mth PY=100 from Jan 2004 (Median) to Aug 2018, with 176 observations. The data reached an all-time high of 117.200 Same Mth PY=100 in Dec 2011 and a record low of 82.000 Same Mth PY=100 in Nov 2009. Hungary Import Price Index: Extra EU Countries data remains active status in CEIC and is reported by Hungarian Central Statistical Office. The data is categorized under Global Database’s Hungary – Table HU.I023: Import Price Index: Same Month Previous Year=100.

Dataset Overview 📝

The dataset includes the following key indicators, collected for over 200 countries:

• Government Expenditure on Education (% of GDP): Shows the percentage of a country’s GDP allocated to education.
• Literacy Rate (Adult Total): Represents the percentage of the population aged 15 and above who can read and write.
• Primary Completion Rate: The percentage of children who complete their primary education within the official age group.
• Pupil-Teacher Ratio (Primary and Secondary Education): Indicates the average number of students per teacher at the primary and secondary levels.
• School Enrollment Rates (Primary, Secondary, Tertiary): Reflects the percentage of the relevant age group enrolled in schools across different education levels.

Data Source 🌐

World Bank: This dataset is compiled from the World Bank's educational database, providing reliable, updated statistics on educational progress worldwide.

Potential Use Cases 🔍 This dataset is ideal for anyone interested in:

Educational Research: Understanding how education spending and policies impact literacy, enrollment, and overall educational outcomes. Predictive Modeling: Building models to predict educational success factors, such as completion rates and literacy. Global Education Analysis: Analyzing trends in global education systems and how different countries allocate resources to education. Policy Development: Helping governments and organizations make data-driven decisions regarding educational reforms and funding.

Key Questions You Can Explore 🤔

How does government expenditure on education correlate with literacy rates and school enrollment across different regions? What are the trends in pupil-teacher ratios over time, and how do they affect educational outcomes? How do education indicators differ between low-income and high-income countries? Can we predict which countries will achieve universal primary education based on current trends?

Important Notes ⚠️ - Missing Data: Some values may be missing for certain years or countries. Consider using techniques like forward filling or interpolation when working with time series models. - Data Limitations: This dataset provides global averages and may not capture regional disparities within countries.

This dataset contains Food Prices data for Benin, sourced from the World Food Programme Price Database. The World Food Programme Price Database covers foods such as maize, rice, beans, fish, and sugar for 98 countries and some 3000 markets. It is updated weekly but contains to a large extent monthly data. The data goes back as far as 1992 for a few countries, although many countries started reporting from 2003 or thereafter.

China Retail Price: MoM: Rural Market Fair: Beef data was reported at 1.300 % in Nov 2013. This records an increase from the previous number of 1.200 % for Oct 2013. China Retail Price: MoM: Rural Market Fair: Beef data is updated monthly, averaging 1.100 % from Jan 2009 (Median) to Nov 2013, with 57 observations. The data reached an all-time high of 5.700 % in Jan 2013 and a record low of -1.900 % in Mar 2010. China Retail Price: MoM: Rural Market Fair: Beef data remains active status in CEIC and is reported by Ministry of Agriculture and Rural Affairs. The data is categorized under China Premium Database’s Price – Table CN.PA: Ministry of Agriculture and Rural Affairs: Retail Price: Agricultural Product.

A computerized data set of demographic, economic and social data for 227 countries of the world. Information presented includes population, health, nutrition, mortality, fertility, family planning and contraceptive use, literacy, housing, and economic activity data. Tabular data are broken down by such variables as age, sex, and urban/rural residence. Data are organized as a series of statistical tables identified by country and table number. Each record consists of the data values associated with a single row of a given table. There are 105 tables with data for 208 countries. The second file is a note file, containing text of notes associated with various tables. These notes provide information such as definitions of categories (i.e. urban/rural) and how various values were calculated. The IDB was created in the U.S. Census Bureau''s International Programs Center (IPC) to help IPC staff meet the needs of organizations that sponsor IPC research. The IDB provides quick access to specialized information, with emphasis on demographic measures, for individual countries or groups of countries. The IDB combines data from country sources (typically censuses and surveys) with IPC estimates and projections to provide information dating back as far as 1950 and as far ahead as 2050. Because the IDB is maintained as a research tool for IPC sponsor requirements, the amount of information available may vary by country. As funding and research activity permit, the IPC updates and expands the data base content. Types of data include: * Population by age and sex * Vital rates, infant mortality, and life tables * Fertility and child survivorship * Migration * Marital status * Family planning Data characteristics: * Temporal: Selected years, 1950present, projected demographic data to 2050. * Spatial: 227 countries and areas. * Resolution: National population, selected data by urban/rural * residence, selected data by age and sex. Sources of data include: * U.S. Census Bureau * International projects (e.g., the Demographic and Health Survey) * United Nations agencies Links: * ICPSR: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/08490

Dataset shows the Average import price of energy sources by country of origin

Description:

This dataset provides key economic indicators from various countries between 2010 and 2023. The dataset includes monthly data on inflation rates, GDP growth rates, unemployment rates, interest rates, and stock market index values. The data has been sourced from reputable global financial institutions and is suitable for economic analysis, machine learning models, and forecasting economic trends.

Data Sources:

The data has been generated to simulate real-world economic conditions, mimicking information from trusted sources like: - World Bank for GDP growth and inflation data - International Monetary Fund (IMF) for macroeconomic data - OECD for labor market statistics - National Stock Exchanges for stock market index values

Columns:

1. Date: The specific date (in Year/Month/Day format) representing when the data was collected.
2. Country: The country the data pertains to (e.g., USA, Germany, Japan).
3. Inflation Rate (%): The rate of inflation for that country, showing how fast prices for goods and services are increasing.
4. GDP Growth Rate (%): The percentage growth of the country’s Gross Domestic Product (GDP), indicating economic expansion or contraction.
5. Unemployment Rate (%): The percentage of the working-age population that is unemployed.
6. Interest Rate (%): The central bank's interest rate, used to control inflation and influence the economy.
7. Stock Index Value: The value of the country’s main stock market index, reflecting the performance of the stock market.

Potential Uses: - Economic Analysis: Researchers and analysts can use this dataset to study trends in inflation, GDP growth, unemployment, and other economic factors. - Machine Learning: This dataset can be used to train models for predicting economic trends or market performance. Financial Forecasting: Investors and economists can leverage this data for forecasting market movements based on economic conditions. - Comparative Studies: The dataset allows comparisons across countries and regions, offering insights into global economic performance.

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