Context

Oil price is a very important part of any county's economy. The Crude Oil WTI (USD/Bbl) dataset was created with the expectation to understand the impact of global oil prices on any country's economy.

Source

Using https://tradingeconomics.com/commodity/crude-oil this data set is created and will be kept updated monthly.

Acknlowgement

Thanks, tradingeconomics.com 💜

Heating Oil rose to 2.35 USD/Gal on December 2, 2025, up 0.21% from the previous day. Over the past month, Heating Oil's price has fallen 2.25%, but it is still 6.31% higher than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Heating oil - values, historical data, forecasts and news - updated on December of 2025.

Urals Oil fell to 54.22 USD/Bbl on December 1, 2025, down 0.37% from the previous day. Over the past month, Urals Oil's price has fallen 7.52%, and is down 17.95% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. This dataset includes a chart with historical data for Urals Crude.

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!

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

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 ...

This dataset provides comprehensive and up-to-date information on futures related to oil, gas, and other fuels. Futures are financial contracts obligating the buyer to purchase and the seller to sell a specified amount of a particular fuel at a predetermined price and future date.

Use Cases: 1. Trend Analysis: Scrutinize patterns and price fluctuations to anticipate future market directions in the energy sector. 2. Academic Research: Delve into the historical behavior of oil and gas prices and understand the influence of global events on these commodities. 3. Trading Strategies: Develop and test trading tactics based on the dynamics of oil, gas, and other fuel futures. 4. Risk Management: Utilize the dataset for hedging and risk management for corporations involved in the extraction, refining, or trading of fuels.

Dataset Image Source: Photo by Pixabay: https://www.pexels.com/photo/industrial-machine-during-golden-hour-162568/

Column Descriptions: 1. Date: The date when the data was documented. Format: YYYY-MM-DD. 2. Open: Market's opening price for the day. 3. High: Peak price during the trading window. 4. Low: Lowest traded price during the day. 5. Close: Price at which the market closed. 6. Volume: Number of contracts exchanged during the trading period. 7. Ticker: The unique market quotation symbol for the future. 8. Commodity: Specifies the type of fuel the future contract pertains to (e.g., crude oil, natural gas).

Natural gas rose to 4.94 USD/MMBtu on December 3, 2025, up 2.04% from the previous day. Over the past month, Natural gas's price has risen 13.71%, and is up 62.29% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Natural gas - values, historical data, forecasts and news - updated on December of 2025.

Monthly and long-term wti crude oil price data (US$/bbl): historical series and analyst forecasts curated by FocusEconomics.

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

Gasoline fell to 1.86 USD/Gal on December 2, 2025, down 0.53% from the previous day. Over the past month, Gasoline's price has fallen 2.79%, and is down 4.95% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Gasoline - values, historical data, forecasts and news - updated on December of 2025.

Scope: Brent crude and refined products narrative intelligence, events, forecasting, and fundamental indices. Sources: ~50,000+ news/articles & market events/day (deduped), structured in milliseconds. Update cadence: Real-time; weekly roll-ups; 6-month forecasts on refresh. Use cases: Signal discovery, event monitoring, price commentary, scenario analysis, model calibration, risk. Entities & grain MarketCommentary — 1 per asset/period; narrative paragraph(s). WeeklyRoundup — 1 per week per asset; week-level narrative. Event — real-time; one row per detected story/event instance. Forecast — point-in-time forecast set (current price, expected, range, path). FundamentalIndex — time series at hourly cadence (or higher) across indexed factors. Key fields & semantics Timestamps are ISO-8601 UTC. Prices in USD by default (field includes currency). Sentiment: {Positive, Negative, Neutral}. Scope: {ASSET, MACRO, SECTOR} (extensible). Importance: integer scale (1–5 recommended).

Monthly and long-term brent crude oil price data (US$/bbl): historical series and analyst forecasts curated by FocusEconomics.

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

To improve the transparency of oil prices and to strengthen the internal market, the European Commission's Oil Bulletin presents weekly consumer prices for petroleum products in EU countries.

This data set is used in the Master's thesis: "A Comparison of Price Fluctuations Between Brent Crude Oil and Retail Fuel Prices in Stavanger - An Algorithmic Model for Refueling" by Ola Nes (2021) The data set contains the fuel prices collected (Excel and CSV files), and the Python code which contains all functions used in the thesis. Abstract for thesis: "This thesis investigates and compares the volatility in the retail fuel market in Stavanger and Brent crude oil. Gasoline and diesel prices have been collected from gas stations in Stavanger in 2020 and 2021, and are used for the thesis’ main goal of developing an algorithmic mathematical model for refueling vehicles at optimal times for consumers that could be used in practice. The collected data suggests that there is higher volatility in the retail fuel market in Stavanger compared to the Brent crude oil market. Gas stations follow a characteristic Edgeworth cycle pattern that have price spikes occur when restarting their price cycles. These occur for the most part at the same time across all gas stations monitored in Stavanger. This pattern can be difficult for consumers to predict. Therefore, a practical refueling algorithm could be useful. There are many factors that go in to such a model to make it efficient such as price spike analysis from the Edgeworth cycle pattern found in retail fuel markets and estimating volatility using GARCH(1,1) method."

The US_Stock_Data.csv dataset offers a comprehensive view of the US stock market and related financial instruments, spanning from January 2, 2020, to February 2, 2024. This dataset includes 39 columns, covering a broad spectrum of financial data points such as prices and volumes of major stocks, indices, commodities, and cryptocurrencies. The data is presented in a structured CSV file format, making it easily accessible and usable for various financial analyses, market research, and predictive modeling. This dataset is ideal for anyone looking to gain insights into the trends and movements within the US financial markets during this period, including the impact of major global events.

Key Features and Data Structure

The dataset captures daily financial data across multiple assets, providing a well-rounded perspective of market dynamics. Key features include:

• Commodities: Prices and trading volumes for natural gas, crude oil, copper, platinum, silver, and gold.
• Cryptocurrencies: Prices and volumes for Bitcoin and Ethereum, including detailed 5-minute interval data for Bitcoin.
• Stock Market Indices: Data for major indices such as the S&P 500 and Nasdaq 100.
• Individual Stocks: Prices and volumes for major companies including Apple, Tesla, Microsoft, Google, Nvidia, Berkshire Hathaway, Netflix, Amazon, and Meta.

The dataset’s structure is designed for straightforward integration into various analytical tools and platforms. Each column is dedicated to a specific asset's daily price or volume, enabling users to perform a wide range of analyses, from simple trend observations to complex predictive models. The inclusion of intraday data for Bitcoin provides a detailed view of market movements.

Applications and Usability

This dataset is highly versatile and can be utilized for various financial research purposes:

• Market Analysis: Track the performance of key assets, compare volatility, and study correlations between different financial instruments.
• Risk Assessment: Analyze the impact of commodity price movements on related stock prices and evaluate market risks.
• Educational Use: Serve as a resource for teaching market trends, asset correlation, and the effects of global events on financial markets.

The dataset’s daily updates ensure that users have access to the most current data, which is crucial for real-time analysis and decision-making. Whether for academic research, market analysis, or financial modeling, the US_Stock_Data.csv dataset provides a valuable foundation for exploring the complexities of financial markets over the specified period.

Acknowledgements:

This dataset would not be possible without the contributions of Dhaval Patel, who initially curated the US stock market data spanning from 2020 to 2024. Full credit goes to Dhaval Patel for creating and maintaining the dataset. You can find the original dataset here: US Stock Market 2020 to 2024.

Russia Average World Price: Crude Oil: Urals: per 1 Barrel data was reported at 91.200 USD/Barrel in Mar 2019. This records an increase from the previous number of 80.700 USD/Barrel for Feb 2019. Russia Average World Price: Crude Oil: Urals: per 1 Barrel data is updated monthly, averaging 58.745 USD/Barrel from Jun 2000 (Median) to Mar 2019, with 226 observations. The data reached an all-time high of 129.710 USD/Barrel in Jul 2008 and a record low of 18.200 USD/Barrel in Nov 2001. Russia Average World Price: Crude Oil: Urals: per 1 Barrel data remains active status in CEIC and is reported by Ministry of Finance of the Russian Federation. The data is categorized under Global Database’s Russian Federation – Table RU.PC002: Average World Prices, Crude Oil Export Price, Crude Oil Export Duty.

Gold fell to 4,199.97 USD/t.oz on December 2, 2025, down 0.75% from the previous day. Over the past month, Gold's price has risen 4.93%, and is up 58.92% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Gold - values, historical data, forecasts and news - updated on December of 2025.

This dataset contains Saudi Arabia Gross Domestic Product by Institutional Sectors at Current Prices. Data from General Authority for Statistics .Follow datasource.kapsarc.org for timely data to advance energy economics research.2021: Preliminary Data

Crude Oil Production in France remained unchanged at 10 BBL/D/1K in July. This dataset provides - France Crude Oil Production - actual values, historical data, forecast, chart, statistics, economic calendar and news.

U.S. Federal Oil & Gas Monthly Production & Disposition (2015–2025)

🌎 Project Overview

This dataset provides a comprehensive, cleaned, and analysis-ready record of the monthly production and disposition volumes of U.S. federal oil and natural gas resources from January 2015 through September 2025. Compiled from the authoritative OGOR-B reporting forms and curated by the U.S. Department of the Interior (DOI), Office of Natural Resources Revenue (ONRR), it reflects the official federal and Native American natural resource production data.

The dataset captures not only raw production volumes but also detailed disposition categories, which indicate how these resources are sold, measured, or allocated, making it a crucial resource for energy policy analysis, market forecasting, and sustainability research.

🌐 Context & Source Information

• Publisher: U.S. Department of the Interior – Office of Natural Resources Revenue (ONRR)
• Original Dataset: Monthly Production Disposition of U.S. Natural Resources Revenue (OGOR-B)
• Coverage: Federal lands, federal waters (Offshore regions: Alaska, Gulf of Mexico, Pacific), and Native American lands. Location data is intentionally withheld for Native American production to protect privacy and sovereignty.
• Temporal Scope: January 2015 – September 2025, monthly frequency.
• License: Creative Commons CCZero (CC0) – Public Domain. Anyone can use, modify, and distribute this data.
• Official Landing Page: ONRR Monthly Production Disposition Data

This dataset is a cornerstone for those researching U.S. energy economics, resource management, climate impact studies, and policy development.

📊 Data Dictionary

🛠️ Data Cleaning & Preprocessing

To ensure the dataset is analysis-ready, the following preprocessing steps were applied:

• Missing Value Handling:

  • State / County / FIPS Code: Missing values primarily correspond to offshore records and were logically filled with the label Offshore.
  • Offshore Region: Missing values indicate onshore records and were filled with the label Onshore.

• Data Standardization:

  • Standardized date formats for seamless time-series analysis.
  • Converted numerical volumes from string with commas to clean numeric types.

• Quality Assurance:

  • Verified consistency of commodity labels and disposition codes.
  • Confirmed monthly frequency across the 10-year time span.

These enhancements enable immediate use in machine learning pipelines, econometric models, and visual analytics without additional preprocessing.

🚀 Potential Use Cases

📊 Energy Economics & Market Forecasting

• Model long-term trends in U.S. oil and natural gas production.
• Correlate production with market prices or global economic indicators.
• Forecast supply-demand dynamics using ARIMA, Prophet, or LSTM models.

🌍 Environmental & Policy Research

• Quantify the environmental impact of onshore versus offshore production.
• Assess how federal production contributes to greenhouse gas emissions when combined with emissions datasets.
• Evaluate the effectiveness of U.S. energy policies and their evolution over a decade.

📈 Financial & Business Intelligence

• Integrate production volumes into energy sector investment models.
• Build revenue projections and budget impact analyses for federal and tribal authorities.
• Use disp...