Heating Oil rose to 2.27 USD/Gal on August 8, 2025, up 0.09% from the previous day. Over the past month, Heating Oil's price has fallen 5.82%, and is down 3.06% compared to the same time last year, 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 August of 2025.

This dataset contains information about world's crude oil prices for 1861-2020. Data from BP. Follow datasource.kapsarc.org for timely data to advance energy economics research.Notes: 1861-1944 US Average 1945-1983 Arabian Light posted at Ras Tanura 1984-2016 Brent dated. $2020 (deflated using the Consumer Price Index for the US

Analysis of ‘Heating Oil Consumption And Cost (2010 - 2020)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/fd65b87a-c274-4f1f-965f-95bf0b5fb780 on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Monthly consumption and cost data by borough and development. Data set includes utility vendor and meter information.

--- Original source retains full ownership of the source dataset ---

The table Heating Oil is part of the dataset NYC Consumption and Cost 2010-2022, available at https://redivis.com/datasets/dnb0-5qcp7zzcb. It contains 14628 rows across 21 variables.

Crude oil price datasets provide valuable information about the pricing trends of crude oil over a given period. Learn about the structure of these datasets and how they are used for financial analysis, economic analysis, trading, and forecasting. Find out where to obtain crude oil price datasets and how they can be used to assess market risks and make informed decisions.

Urals Oil fell to 63.17 USD/Bbl on August 7, 2025, down 2.76% from the previous day. Over the past month, Urals Oil's price has fallen 4.94%, and is down 15.37% 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.

Crude Oil Production in Vietnam increased to 161 BBL/D/1K in March from 160 BBL/D/1K in February of 2025. This dataset provides the latest reported value for - Vietnam Crude Oil Production - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

This dataset contains India Oil Database for 2002-2021. Data from Joint Organisations Data Initiative. Follow datasource.kapsarc.org for timely data to advance energy economics research.

Crude Oil Production in Russia remained unchanged at 9795 BBL/D/1K in March. This dataset provides the latest reported value for - Russia Crude Oil Production - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

This dataset contains world oil database from 2002-2021. Data from Joint Organisations Data Initiative. Follow datasource.kapsarc.org for timely data to advance energy economics research.

The United States Documented Unplugged Orphaned Oil and Gas Well (DOW) dataset contains 117,672 wells in 27 states. The definition of an orphaned oil or gas well varies across data sources; the dataset includes oil or gas wells where the state indicates that the well is an unplugged orphan, or the following criteria are met: 1) no production for an average of 12 months (6 to 24 months depending on the state), 2) the well is unplugged, 3) there is no responsible party to manage the well for future re-use or for plugging and abandonment, and 4) the location of the well is documented. The dataset includes location coordinates, American Petroleum Institute (API) number, or other identification number, well type, well status, and additional information for each unplugged orphaned well. All data were collected by direct requests to the respective state agency overseeing oil and gas wells or data downloads from their online databases. Location format conversion was performed on wells without coordinate locations using tools provided by the Bureau of Land Management and some state agencies. No other data manipulations were performed to the source data aside from reformatting or the addition of explanatory notes.

Crude Oil Production in Azerbaijan decreased to 577 BBL/D/1K in March from 581 BBL/D/1K in February of 2025. This dataset provides the latest reported value for - Azerbaijan Crude Oil Production - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Crude Oil Production in Iraq increased to 3627 BBL/D/1K in June from 3605 BBL/D/1K in May of 2025. This dataset provides the latest reported value for - Iraq Crude Oil Production - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Crude Oil Production in Iran decreased to 3241 BBL/D/1K in June from 3303 BBL/D/1K in May of 2025. This dataset provides the latest reported value for - Iran Crude Oil Production - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

What this collection is: A curated, binary-classified image dataset of grayscale (1 band) 400 x 400-pixel size, or image chips, in a JPEG format extracted from processed Sentinel-1 Synthetic Aperture Radar (SAR) satellite scenes acquired over various regions of the world, and featuring clear open ocean chips, look-alikes (wind or biogenic features) and oil slick chips.

This binary dataset contains chips labelled as: - "0" for chips not containing any oil features (look-alikes or clean seas)
- "1" for those containing oil features.

This binary dataset is imbalanced, and biased towards "0" labelled chips (i.e., no oil features), which correspond to 66% of the dataset. Chips containing oil features, labelled "1", correspond to 34% of the dataset.

Why: This dataset can be used for training, validation and/or testing of machine learning, including deep learning, algorithms for the detection of oil features in SAR imagery. Directly applicable for algorithm development for the European Space Agency Sentinel-1 SAR mission (https://sentinel.esa.int/web/sentinel/missions/sentinel-1 ), it may be suitable for the development of detection algorithms for other SAR satellite sensors.

Overview of this dataset: Total number of chips (both classes) is N=5,630 Class 0 1 Total 3,725 1,905

Further information and description is found in the ReadMe file provided (ReadMe_Sentinel1_SAR_OilNoOil_20221215.txt)

Crude Oil Price: Indonesia data was reported at 61.320 USD/Barrel in Jul 2019. This records an increase from the previous number of 60.990 USD/Barrel for Jun 2019. Crude Oil Price: Indonesia data is updated monthly, averaging 42.500 USD/Barrel from Dec 1991 (Median) to Jul 2019, with 332 observations. The data reached an all-time high of 134.960 USD/Barrel in Jul 2008 and a record low of 9.990 USD/Barrel in Dec 1998. Crude Oil Price: Indonesia data remains active status in CEIC and is reported by Directorate General of Oil and Gas. The data is categorized under Global Database’s Indonesia – Table ID.PC001: Retail Price: By Major Commodities.

Existing models estimating oil spill costs at sea are based on data from the past, and they usually lack a systematic approach. This make them passive, and limits their ability to forecast the effect of the changes in the oil combating fleet or location of a spill on the oil spill costs. In this paper we make an attempt towards the development of a probabilistic and systematic model estimating the costs of clean-up operations for the Gulf of Finland. For this purpose we utilize expert knowledge along with the available data and information from literature. Then, the obtained information is combined into a framework with the use of a Bayesian Belief Networks. Due to lack of data, we validate the model by comparing its results with existing models, with which we found good agreement. We anticipate that the presented model can contribute to the cost-effective oil-combating fleet optimization for the Gulf of Finland. It can also facilitate the accident consequences estimation in the framework of formal safety assessment (FSA).

Crude Oil Production in Trinidad And Tobago remained unchanged at 51 BBL/D/1K in March. This dataset provides - Trinidad And Tobago Crude Oil Production- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Natural gas account for 1/4 of the global demand and roughly 1/3 of the US energy demand. After oil, Natural gas is the most dominate sort of energy. So, being about to improve natural gas demand prediction is extremely valuable.

Therefore, this project aims to predict the demand of Natural Gas in the US by combining a wide range of datasets including the time series of major Natural Gas Prices including US Henry Hub. Data comes from U.S. Energy Information Administration. Need to forecast the price of natural gas based on the historical data.

Data

Dataset contains Daily prices of Natural gas, starting from January 1997 to current year. Prices are in nominal dollars.

Paintings Collection

A diverse collection of paintings from artists throughout history

By Gove Allen [source]

About this dataset

The Paintings Dataset is a rich and diverse collection of various paintings from different artists spanning across multiple time periods. It includes a wide range of art styles, techniques, and subjects, providing an extensive resource for art enthusiasts, historians, researchers, and anyone interested in exploring the world of visual arts.

This dataset aims to capture the essence of artistic expression through its vast array of paintings. From classical masterpieces to contemporary works, it offers a comprehensive perspective on the evolution of artistic creativity throughout history.

Each record in this dataset represents an individual painting with detailed information such as artist's name, artwork title (if applicable), genre/style classification (e.g., landscape, portrait), medium (e.g., oil on canvas), dimensions (height and width), and provenance details if available. Additionally, some records may include additional metadata like the year or era in which the artwork was created.

By providing such comprehensive data about each painting included within this dataset, it enables users to study various aspects of art history. Researchers can analyze trends across different time periods or explore specific artistic movements by filtering the dataset based on genre or style categories. Art enthusiasts can also use this dataset to discover new artists or artworks that align with their interests.

This valuable collection appeals not only to those seeking knowledge or inspiration from renowned artworks but also encourages exploration into lesser-known pieces that may have been overlooked in mainstream discourse. It fosters engagement with cultural heritage while promoting diversity and inclusivity within the realm of visual arts.

Whether you are interested in studying classical works by universally acclaimed painters like Leonardo da Vinci or exploring modern expressions by emerging contemporary artists—this Paintings Dataset has something for everyone who appreciates aesthetics and enjoys unraveling stories through brushstrokes on canvas

How to use the dataset

How to Use the Paintings Dataset

Welcome to the Paintings Dataset! This dataset is a comprehensive collection of various paintings from different artists and time periods. It contains information about the artist, title, genre, style, and medium of each painting. Whether you are an art enthusiast, researcher, or just curious about paintings, this guide will help you navigate through this dataset easily.

1. Understanding the Columns

This dataset consists of several columns that provide detailed information about each painting. Here is a brief description of each column:

• Artist: The name of the artist who created the painting.
• Title: The title or name given to the artwork by the artist.
• Genre: The artistic category or subject matter depicted in the painting.
• Style: The specific artistic style or movement associated with the painting.
• Medium: The materials and techniques used by the artist to create the artwork.

2. Exploring Artists and Their Paintings

One interesting way to use this dataset is to explore individual artists and their artworks. You can filter by a specific artist's name in order to retrieve all their paintings included in this collection.

For example: If you are interested in exploring all paintings by Leonardo da Vinci, simply filter using Leonardo da Vinci in Artist column using your preferred data analysis tool.

3. Analyzing Painting Genres

The genre column allows you to analyze different categories within this collection of paintings. You can examine popular genres or compare them across different eras.

To analyze genres: - Get unique values for Genre column. - Count frequency for each genre value. - Visualize results using bar charts or other graphical representations.

You might discover which genres were more predominant during certain periods or which artists were known for specific subjects!

4. Investigating Artistic Styles

Similar to genres, artistic styles also play an essential role in the world of painting. This dataset includes various styles like Impressionism, Cubism, Realism, etc. By analyzing the artistic styles column, you can explore trends and shifts in artistic movements.

To investigate styles: - Get unique values for Style column. - Count frequency for each style value. - Visualize results using bar charts or other graphical...