The dataset used in the paper is a real-world dataset of daily crude oil prices.

Dataset

This dataset was created by vaibhav panvalkar

Contents

📈 Daily Historical Stock Price Data for Battalion Oil Corporation (2019–2025)

A clean, ready-to-use dataset containing daily stock prices for Battalion Oil Corporation from 2019-12-24 to 2025-05-28. This dataset is ideal for use in financial analysis, algorithmic trading, machine learning, and academic research.

  🗂️ Dataset Overview

Company: Battalion Oil Corporation Ticker Symbol: BATL Date Range: 2019-12-24 to 2025-05-28 Frequency: Daily Total Records: 1363 rows (one… See the full description on the dataset page: https://huggingface.co/datasets/khaledxbenali/daily-historical-stock-price-data-for-battalion-oil-corporation-20192025.

📈 Daily Historical Stock Price Data for Fuji Oil Co., Ltd. (2001–2025)

A clean, ready-to-use dataset containing daily stock prices for Fuji Oil Co., Ltd. from 2001-01-01 to 2025-05-28. This dataset is ideal for use in financial analysis, algorithmic trading, machine learning, and academic research.

  🗂️ Dataset Overview

Company: Fuji Oil Co., Ltd. Ticker Symbol: 2607.T Date Range: 2001-01-01 to 2025-05-28 Frequency: Daily Total Records: 6085 rows (one per trading day)… See the full description on the dataset page: https://huggingface.co/datasets/khaledxbenali/daily-historical-stock-price-data-for-fuji-oil-co-ltd-20012025.

The dataset contains the post-processed results of the 2010 Deepwater Horizon oil spill incident at Macondo well in the Gulf of Mexico, as estimated from the simulations using the latest updated version of the oil application of the Connectivity Modeling System (CMS) or oil-CMS. In this version, the specified hydrocarbon pseudo-components are in the same droplet. The post-processing analysis assumed a simplified case of the uniform droplet size distribution at the release time; analysis yielded 4-D spatiotemporal data of the oil concentrations on a regular horizontal and vertical grid, as well as time evolution of the horizontally-cumulative oil mass, all of the data for the 167-day simulation period. CMS has a Lagrangian, particle-tracking framework, computing particle evolution and transport in the ocean interior. CMS simulation start date: April 20, 2010, 0000 UTC, and particles were tracked for 167 days. Oil particles release location: 28.736N, 88.365W, depth is 1222m or 300m above the oil well. 3000 particles were released every 2 hours, for 87 days, equivalent to total of 3132000 oil particles released during the simulation. Initial particle sizes were determined at random by the CMS in the range of 1-500 micron. Each particle contained three (3) pseudo-components accounting for the differential oil density as follows: 10% of light oil with the density of 800kg/m^3, 75% of the oil with 840 kg/m^3, and 15% of a heavy oil with 950 kg/m^3 density. The half-life decay rates of oil fractions were 30 days, 40 days, and 180 days, respectively. Ocean hydrodynamic forcing for the CMS model was used from the HYbrid Coordinate Ocean Model (HYCOM) for the Gulf of Mexico region on a 0.04-deg. horizontal grid and 40 vertical levels from the surface to 5500m. It provided daily average 3-D momentum, temperature and salinity forcing fields to the CMS model. The transport and evolution of the oil particles were tracked by the oil-CMS model during the 167 days of the simulation, recording each particle’s horizontal position, depth, diameter, and density into the model output every 2 hours. Model data need to be post-processed to obtain oil concentrations estimates. The post-processing algorithm took into the account the total amount of oil spilled during the 87-day incident as estimated from the reports (730000 tons), and the assumptions about the oil particle size distribution at the time of the release as estimated in the prior studies. The current dataset assumes the simplified case with the uniform droplet size distribution across the range of 1-500 micron. The data for the oil concentrations are daily average values in ppb units. Horizontal 0.01-degree grid covers the Gulf of Mexico (25N-30.75N, 84W-93W), and vertical grid extends from the surface to the depth of 2400m at 20m increments. Daily oil concentrations are also estimated for the following vertical layers: 0-1m, 1-20m, 20-50m, 50-200m, and 200-200m; separate files are for the layer of 0-1m and for the 0-20m layer. The data for the oil mass are horizontally-cumulative values in kg of crude oil, distributed in the water column on a vertical grid from the surface down to 2400m at 20m increments, and estimated bi-hourly corresponding to the oil-CMS model output interval. Post-processed NetCDF files were created using Matlab software package, v. R2014b, and used compression to keep file size small. Maximum compression, or ‘DeflateLevel’ = 9 was used in most of the files. Numerical simulations and post-processing were performed using a Pegasus supercomputer at the Center of Computational Science, University of Miami, during the period of 2016-2017.

The dataset contains the numerical results of the 2010 Deepwater Horizon oil spill incident at Macondo well in the Gulf of Mexico, as estimated from the simulations using the latest updated version of the oil application of the Connectivity Modeling System (CMS) or oil-CMS. This contains additional data that complements the dataset that is available at GRIIDC under Unique Dataset Identifier (UDI) R4.x267.000:0084 (DOI: 10.7266/N7KD1WDB). In this version of the oil-CMS model, the specified hydrocarbon pseudo-components are in the same droplet. The post-processing analysis yielded 4-D spatiotemporal data of the oil concentrations and oil mass on a regular horizontal and vertical grid. There are two sets of simulations that last 167 days and 100 days (a shorter sensitivity run). CMS has a Lagrangian, particle-tracking framework, computing particle evolution and transport in the ocean interior. CMS simulations start date: April 20, 2010, 0000 UTC, and particles were tracked for 167 days or 100 days. Oil particles release location: 28.736N, 88.365W, depth is 1222m or 300m above the oil well. 3000 particles were released every 2 hours, for 87 days, equivalent to a total of 3132000 oil particles released during the simulation. Initial particle sizes were determined at random by the CMS in the range of 1-500 micron, and are scaled during post-processing to represent the chosen droplet size distribution (DSD). Each particle contained three (3) pseudo-components accounting for the differential oil density as follows: 10% of light oil with a density of 800kg/m^3, 75% of the oil with 840 kg/m^3, and 15% of heavy oil with 950 kg/m^3 density. The half-life decay rates of oil fractions were 30 days, 40 days, and 180 days, respectively. The surface evaporation half-life was set to 250 hours; horizontal diffusion was set to 10 m^2/s in the present case. Ocean hydrodynamic forcing for the CMS model was used from the HYbrid Coordinate Ocean Model (HYCOM) for the Gulf of Mexico region on a 0.04-deg. horizontal grid and 40 vertical levels from the surface to 5500m. It provided daily average 3-D momentum, temperature and salinity forcing fields to the CMS model. The surface wind drift parameterization used surface winds and wind stressed from the 0.5-degree Navy Operational Global Atmospheric Prediction System (NOGAPS). The transport and evolution of the oil particles were tracked by the oil-CMS model during the 167 days of the main simulation (100 days for a sensitivity run), recording each particle’s horizontal position, depth, diameter, and density into the model output every 2 hours. Model data needed to be post-processed to obtain oil concentrations and oil mass estimates. The post-processing algorithm took into account the total amount of oil spilled during the 87-day incident as estimated from the reports (730000 tons), and the assumptions about the oil particle size distribution at the time of the release as estimated in the prior studies. The current dataset contains post-processed gridded and non-gridded analyses for the cases of untreated oil and cases of oil treated with the chemical dispersants at the oil release location.

📈 Daily Historical Stock Price Data for Athabasca Oil Corporation (2010–2025)

A clean, ready-to-use dataset containing daily stock prices for Athabasca Oil Corporation from 2010-04-20 to 2025-05-28. This dataset is ideal for use in financial analysis, algorithmic trading, machine learning, and academic research.

  🗂️ Dataset Overview

Company: Athabasca Oil Corporation Ticker Symbol: ATH.TO Date Range: 2010-04-20 to 2025-05-28 Frequency: Daily Total Records: 3789 rows… See the full description on the dataset page: https://huggingface.co/datasets/khaledxbenali/daily-historical-stock-price-data-for-athabasca-oil-corporation-20102025.

The dataset contains the *.gif animations of the numerical results from the far-field modeling study of the 2010 Deepwater Horizon oil spill in the Gulf of Mexico. Oil concentrations are from the experiments that used the latest updated version of the oil application of the Connectivity Modeling System (CMS) or oil-CMS. The animations show time evolution of layer-averaged oil concentrations for the several layers spanning the entire water column, as follows: 0-1m, 1-20m, 20-400m, 400-1000m, 1000-1200m, and below 1200m. Additional visualization of the 3D structure of the plume could be viewed from the time lapse sequence of the three (3) isosurfaces of oil concentrations of 10, 100, and 1000 ppb, focused over the blowout location, overlaid by the surface oil concentrations above the plume. The oil concentrations shown are daily average values in units of ppb. CMS has a Lagrangian, particle-tracking framework, computing particle evolution and transport in the ocean interior. In this version of the oil-CMS, the specified hydrocarbon pseudo-components are in the same droplet. CMS simulation start date: April 20, 2010, 0000 UTC, and particles were tracked for 167 days. Oil particles release location: 28.736N, 88.365W, depth is 1222m or 300m above the oil well. 3000 particles were released every 2 hours, for 87 days, equivalent to total of 3132000 oil particles released during the simulation. Initial particle sizes were determined at random by the CMS in the range of 1-500 micron. Each particle contained three (3) pseudo-components accounting for the differential oil density as follows: 10% of light oil with the density of 800kg/m^3, 75% of the oil with 840 kg/m^3, and 15% of a heavy oil with 950 kg/m^3 density. The half-life decay rates of oil fractions were 30 days, 40 days, and 180 days, respectively. The surface evaporation half-life was set to 250 hours; horizontal diffusion was set to 10 m^2/s in the present case. Ocean hydrodynamic forcing for the CMS model was used from the HYbrid Coordinate Ocean Model (HYCOM) for the Gulf of Mexico region on a 0.04-deg. horizontal grid and 40 vertical levels from the surface to 5500m. It provided daily average 3-D momentum, temperature and salinity forcing fields to the CMS model. The surface wind drift parameterization used surface winds and wind stressed from the 0.5-degree Navy Operational Global Atmospheric Prediction System (NOGAPS). The transport and evolution of the oil particles were tracked by the oil-CMS model during the 167 days of the simulation, recording each particle’s horizontal position, depth, diameter, and density into the model output every 2 hours. Model data need to be post-processed to obtain oil concentrations estimates. The post-processing algorithm took into the account the total amount of oil spilled during the 87-day incident as estimated from the reports (730000 tons), and the assumptions about the oil particle size distribution at the time of the release as estimated in the prior studies. The current dataset assumes the oil was not treated with the chemical dispersants, and the modal peak in initial particle distribution is between 50-70 micron. Post-processed oil concentrations were used to create *.gif animations using Matlab software package, v. R2016b and v2017a. Surface or layer-average ocean currents for corresponding days were computed from the same dataset of HYCOM hydrodynamic data used in a CMS experiment. Oil concentration and oil mass data can be found in GRIIDC dataset R4.x267.000:0084 doi:10.7266/N7KD1WDB. Numerical simulations and post-processing were performed using a Pegasus supercomputer at the Center of Computational Science, University of Miami, in 2017.

Techsalerator offers an extensive dataset of End-of-Day Pricing Data for all 30 companies listed on the Damascus Securities Exchange (XDSE) in Syria. This dataset includes the closing prices of equities (stocks), bonds, and indices at the end of each trading session. End-of-day prices are vital pieces of market data that are widely used by investors, traders, and financial institutions to monitor the performance and value of these assets over time.

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Top 5 used data fields in the End-of-Day Pricing Dataset for Syria:

1. Equity Closing Price :The closing price of individual company stocks at the end of the trading day.This field provides insights into the final price at which market participants were willing to buy or sell shares of a specific company.

2. Bond Closing Price: The closing price of various fixed-income securities, including government bonds, corporate bonds, and municipal bonds. Bond investors use this field to assess the current market value of their bond holdings.

3. Index Closing Price: The closing value of market indices, such as the Botswana stock market index, at the end of the trading day. These indices track the overall market performance and direction.

4. Equity Ticker Symbol: The unique symbol used to identify individual company stocks. Ticker symbols facilitate efficient trading and data retrieval.

5. Date of Closing Price: The specific trading day for which the closing price is provided. This date is essential for historical analysis and trend monitoring.

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Top 5 financial instruments with End-of-Day Pricing Data in Syria:

Damascus Securities Exchange (DSE): The primary stock exchange in Syria, tracking the performance of domestic companies listed on the exchange. It provides insights into the Syrian equity market.

Syrian Pound (SYP): The official currency of Syria, used for transactions and trade within the country. The Syrian Pound has faced significant challenges due to the ongoing conflict and economic instability in the country.

Central Bank of Syria (CBS): The central bank responsible for monetary policy, currency issuance, and regulation of the financial sector in Syria. It plays a crucial role in managing the country's economic challenges.

Syrian Petroleum Company (SPC): A state-owned company responsible for the exploration, production, and export of oil and natural gas. Energy resources are important for Syria's economy, and SPC is a key player in the sector.

Commercial Bank of Syria: One of the major state-owned banks in Syria, providing various financial services to individuals and businesses. Despite challenges, the banking sector remains a vital part of the Syrian economy.

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If you're interested in accessing Techsalerator's End-of-Day Pricing Data for Syria, please contact info@techsalerator.com with your specific requirements. Techsalerator will provide you with a customized quote based on the number of data fields and records you need. The dataset can be delivered within 24 hours, and ongoing access options can be discussed if needed.

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Data fields included:

Equity Ticker Symbol Equity Closing Price Bond Ticker Symbol Bond Closing Price Index Ticker Symbol Index Closing Price Date of Closing Price Equity Name Equity Volume Equity High Price Equity Low Price Equity Open Price Bond Name Bond Coupon Rate Bond Maturity Index Name Index Change Index Percent Change Exchange Currency Total Market Capitalization Dividend Yield Price-to-Earnings Ratio (P/E) ‍

Q&A:

1. How much does the End-of-Day Pricing Data cost in Syria ?

The cost of this dataset may vary depending on factors such as the number of data fields, the frequency of updates, and the total records count. For precise pricing details, it is recommended to directly consult with a Techsalerator Data specialist.

1. How complete is the End-of-Day Pricing Data coverage in Syria?

Techsalerator provides comprehensive coverage of End-of-Day Pricing Data for various financial instruments, including equities, bonds, and indices. Thedataset encompasses major companies and securities traded on Syria exchanges.

1. How does Techsalerator collect this data?

Techsalerator collects End-of-Day Pricing Data from reliable sources, including stock exchanges, financial news outlets, and other market data providers. Data is carefully curated to ensure accuracy and reliability.

1. Can I select specific financial instruments or multiple countries with Techsalerator's End-of-Day Pricing Data?

Techsalerator offers the flexibility to select specific financial instruments, such as equities, bonds, or indices, depending on your needs. While the dataset focuses on Botswana, Techsalerator also provides data for other countries and international markets.

1. How do I pay for this dataset?

Techsalerator accepts various payment methods, including credit cards, direct transfers, ACH, and wi...

*The data for this dataset is updated daily. The date(s) displayed in the details section on our Open Data Portal is based on the last date the metadata was updated and not the refresh date of the data itself.*Statewide coverage of currently registered used oil transfer facilities. A used oil transfer facility is a transportation related facility where registered used oil transporters may hold shipments of used oil, during the normal course of transportation, not longer than 35 days, without being regulated as a used oil processor. Used oil transfer facilities must meet standards set forth in Rule 62.710, F.A.C. and 40CFR 279.45.

This dataset was created from the digitized log “Journal of daily expenditure of oils, wicks, and chimneys at the South Haven Michigan light station on Black River” housed within the South Haven Michigan Lighthouse Log collection at Western Michigan University.

The lighthouse keeper log notes the daily expenditure journal for the South Pierhead Light, South Haven, Michigan, from September 1, 1888 through August 31, 1892. Entries were recorded by the keeper James S. Donahue. The log provides details on the amount of oil, wicks and chimneys used at the lighthouse and also taken for consumption within the keeper's home. The keeper noted the light's order of lens, kind of light, number of wicks in burner, and diameter of outer burner. There are also details on the weather of the day, what time the lighthouse was lit and extinguished, and the length of time it remained lit.

This dataset contains information about Daily Diesel Spot Price from 1996.Data collected from US Energy Information Administration.Notes:Ultra-Low-Sulfur No. 2 Diesel FuelNo. 2 Diesel Fuel: A gasoil type distillate for use in high speed diesel engines generally operated under uniform speed and load conditions, with distillation temperatures between 540-640 degrees Fahrenheit at the 90-percent recovery point; and the kinematic viscosities between 1.9-4.1 centistokes at 100 degrees Fahrenheit as defined in ASTM specification D975-93. Includes Type R-R diesel fuel used for railroad locomotive engines, and Type T-T for diesel-engine trucks.For pricing data: Ultra-Low Sulfur or On-Highway Diesel Fuel is No. 2 diesel fuel which has a sulfur level less than or equal to 15 ppm (parts per million); Low Sulfur Diesel Fuel is No. 2 diesel fuel which has a sulfur level greater than 15 and less than or equal to 500 ppm; and High Sulfur refers to No. 2 distillate (either diesel or fuel oil) which has a sulfur level greater than 500 ppm.Includes: New York Harbor Ultra-Low Sulfur No 2 Diesel Spot Price (Dollars per Gallon)2. U.S. Gulf Coast Ultra-Low Sulfur No Diesel Spot Price (Dollars per Gallon)Los Angeles, CA Ultra-Low Sulfur CARB Diesel Spot Price (Dollars per Gallon)

The top of the Upper Cretaceous Dakota Sandstone is present in the subsurface throughout the Uinta and Piceance basins of UT and CO and is easily recognized in the subsurface from geophysical well logs. This digital data release captures in digital form the results of two previously published contoured subsurface maps that were constructed on the top of Dakota Sandstone datum; one of the studies also included a map constructed on the top of the overlying Mancos Shale. A structure contour map of the top of the Dakota Sandstone was constructed as part of a U.S. Geological Survey Petroleum Systems and Geologic Assessment of Oil and Gas in the Uinta-Piceance Province, Utah and Colorado (Roberts, 2003). This surface, constructed using data from oil and gas wells, from digital geologic maps of Utah and Colorado, and from thicknesses of overlying stratigraphic units, depicts the overall configuration of major structural trends of the present-day Uinta and Piceance basins and was used to define the elevation of the base of a specific source-rock interval as part of the assessment. A second structure contour map of the top of the Dakota Sandstone, along with a contoured map showing the elevation of the top of the overlying Mancos Shale, was constructed from well data as part of a stratigraphic research thesis of the Douglas Creek Arch, a structural high which separates the Uinta and Piceance basins (Kuzniak, 2009). This digital dataset contains spatial datasets corresponding to the structure contour maps of the top of the Dakota Sandstone produced by the U.S. Geological Survey's petroleum assessment (Roberts, 2003) and the topical studies along the Douglas Creek Arch (Kuzniak, 2009). Both structure contour maps of the top of the Dakota Sandstone were digitized and attributed as GIS data sets so that these data could be used in digital form as part of U.S. Geological Survey and other studies of these basins. The contours depicting the elevation of the top of the Dakota Sandstone are contained in line feature classes within a geographic information system geodatabase and are also saved as individual shapefiles. Feature classes have a single attribute, elevation, that represents the contoured value. Contoured values are given in feet, to maintain consistency with the original publication, and in meters. Nonspatial tables define the data sources used, define terms used in the dataset, and describe the geologic units. A tabular data dictionary describes the entity and attribute information for all attributes of the geospatial data and the accompanying nonspatial tables.

Crude Oil Production in Syria remained unchanged at 35 BBL/D/1K in February. This dataset provides the latest reported value for - Syria Crude Oil Production - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

In this dataset, we used a multi-sensor day and night satellite approach to track the SANCHI oil tanker collision and oil spill event in January 2018 in the East China Sea. The drifted on fire oil tanker was tracked by Visible Infrared Imaging Radiometer Suite (VIIRS) Nightfire product and Day/Night Band (DNB) imagery. Such pathway and locations were also reproduced with a numerical model, with RMS error of < 15 km. MultiSpectral Instrument (MSI) optical imagery during daytime shows smokes on 13 January 2018, further confirms the drifted tanker location. MSI imagery after 4 days of the tanker’s sinking (18 January 2018) reveals oil on the ocean surface to the east and northeast of the tanker sinking location. This combination of all available remote sensing and modeling techniques can provide effective means to monitor marine accidents and oil spills to assist event response.

This layer presents detectable thermal activity from VIIRS satellites for the last 7 days. VIIRS Thermal Hotspots and Fire Activity is a product of NASA’s Land, Atmosphere Near real-time Capability for EOS (LANCE) Earth Observation Data, part of NASA's Earth Science Data.Consumption Best Practices: As a service that is subject to Viral loads (very high usage), avoid adding Filters that use a Date/Time type field. These queries are not cacheable and WILL be subject to Rate Limiting by ArcGIS Online. To accommodate filtering events by Date/Time, we encourage using the included "Age" fields that maintain the number of Days or Hours since a record was created or last modified compared to the last service update. These queries fully support the ability to cache a response, allowing common query results to be supplied to many users without adding load on the service.When ingesting this service in your applications, avoid using POST requests, these requests are not cacheable and will also be subject to Rate Limiting measures.Source: NASA LANCE - VNP14IMG_NRT active fire detection - WorldScale/Resolution: 375-meterUpdate Frequency: Hourly using the aggregated live feed methodologyArea Covered: WorldWhat can I do with this layer?This layer represents the most frequently updated and most detailed global remotely sensed wildfire information. Detection attributes include time, location, and intensity. It can be used to track the location of fires from the recent past, a few hours up to seven days behind real time. This layer also shows the location of wildfire over the past 7 days as a time-enabled service so that the progress of fires over that timeframe can be reproduced as an animation.The VIIRS thermal activity layer can be used to visualize and assess wildfires worldwide. However, it should be noted that this dataset contains many “false positives” (e.g., oil/natural gas wells or volcanoes) since the satellite will detect any large thermal signal.Fire points in this service are generally available within 3 1/4 hours after detection by a VIIRS device. LANCE estimates availability at around 3 hours after detection, and esri livefeeds updates this feature layer every 15 minutes from LANCE.Even though these data display as point features, each point in fact represents a pixel that is >= 375 m high and wide. A point feature means somewhere in this pixel at least one "hot" spot was detected which may be a fire.VIIRS is a scanning radiometer device aboard the Suomi NPP and NOAA-20 satellites that collects imagery and radiometric measurements of the land, atmosphere, cryosphere, and oceans in several visible and infrared bands. The VIIRS Thermal Hotspots and Fire Activity layer is a livefeed from a subset of the overall VIIRS imagery, in particular from NASA's VNP14IMG_NRT active fire detection product. The downloads are automatically downloaded from LANCE, NASA's near real time data and imagery site, every 15 minutes.The 375-m data complements the 1-km Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Hotspots and Fire Activity layer; they both show good agreement in hotspot detection but the improved spatial resolution of the 375 m data provides a greater response over fires of relatively small areas and provides improved mapping of large fire perimeters.Attribute informationLatitude and Longitude: The center point location of the 375 m (approximately) pixel flagged as containing one or more fires/hotspots.Satellite: Whether the detection was picked up by the Suomi NPP satellite (N) or NOAA-20 satellite (1). For best results, use the virtual field WhichSatellite, redefined by an arcade expression, that gives the complete satellite name.Confidence: The detection confidence is a quality flag of the individual hotspot/active fire pixel. This value is based on a collection of intermediate algorithm quantities used in the detection process. It is intended to help users gauge the quality of individual hotspot/fire pixels. Confidence values are set to low, nominal and high. Low confidence daytime fire pixels are typically associated with areas of sun glint and lower relative temperature anomaly (<15K) in the mid-infrared channel I4. Nominal confidence pixels are those free of potential sun glint contamination during the day and marked by strong (>15K) temperature anomaly in either day or nighttime data. High confidence fire pixels are associated with day or nighttime saturated pixels.Please note: Low confidence nighttime pixels occur only over the geographic area extending from 11 deg E to 110 deg W and 7 deg N to 55 deg S. This area describes the region of influence of the South Atlantic Magnetic Anomaly which can cause spurious brightness temperatures in the mid-infrared channel I4 leading to potential false positive alarms. These have been removed from the NRT data distributed by FIRMS.FRP: Fire Radiative Power. Depicts the pixel-integrated fire radiative power in MW (MegaWatts). FRP provides information on the measured radiant heat output of detected fires. The amount of radiant heat energy liberated per unit time (the Fire Radiative Power) is thought to be related to the rate at which fuel is being consumed (Wooster et. al. (2005)).DayNight: D = Daytime fire, N = Nighttime fireHours Old: Derived field that provides age of record in hours between Acquisition date/time and latest update date/time. 0 = less than 1 hour ago, 1 = less than 2 hours ago, 2 = less than 3 hours ago, and so on.Additional information can be found on the NASA FIRMS site FAQ.Note about near real time data:Near real time data is not checked thoroughly before it's posted on LANCE or downloaded and posted to the Living Atlas. NASA's goal is to get vital fire information to its customers within three hours of observation time. However, the data is screened by a confidence algorithm which seeks to help users gauge the quality of individual hotspot/fire points. Low confidence daytime fire pixels are typically associated with areas of sun glint and lower relative temperature anomaly (<15K) in the mid-infrared channel I4. Medium confidence pixels are those free of potential sun glint contamination during the day and marked by strong (>15K) temperature anomaly in either day or nighttime data. High confidence fire pixels are associated with day or nighttime saturated pixels.RevisionsSeptember 15, 2022: Updated to include 'Hours_Old' field. Time series has been disabled by default, but still available.July 5, 2022: Terms of Use updated to Esri Master License Agreement, no longer stating that a subscription is required!This layer is provided for informational purposes and is not monitored 24/7 for accuracy and currency.If you would like to be alerted to potential issues or simply see when this Service will update next, please visit our Live Feed Status Page!

Saudi Arabian Oil Company Aramco, Stocks

👨‍💻 Author: Azhar Saleem

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Dataset Description

Welcome to the Enhanced Saudi Arabian Oil Company (Aramco) Stock Dataset! This dataset has been meticulously prepared from Yahoo Finance and further enriched with several engineered features to elevate your data analysis, machine learning, and financial forecasting projects. It captures the daily trading figures of Aramco stocks, presented in Saudi Riyal (SAR), providing a robust foundation for comprehensive market analysis.

Columns in the Dataset

• Date: The trading day for the data recorded (ISO 8601 format).
• Open: The price at which the stock first traded upon the opening of an exchange on a given trading day.
• High: The highest price at which the stock traded during the trading day.
• Low: The lowest price at which the stock traded during the trading day.
• Close: The price at which the stock last traded upon the close of an exchange on a given trading day.
• Volume: The total number of shares traded during the trading day.
• Dividends: The dividend value paid out per share on the trading day.
• Stock Splits: The number of stock splits occurring on the trading day.
• Lag Features (Lag_Close, Lag_High, Lag_Low): Previous day's closing, highest, and lowest prices.
• Rolling Window Statistics (e.g., Rolling_Mean_7, Rolling_Std_7): 7-day and 30-day moving averages and standard deviations of the Close price.
• Technical Indicators (RSI, MACD, Bollinger Bands): Key metrics used in trading to analyze short-term price movements.
• Change Features (Change_Close, Change_Volume): Day-over-day changes in Close price and trading volume.
• Date-Time Features (Weekday, Month, Year, Quarter): Extracted components of the trading day.
• Volume_Normalized: The standardized trading volume using z-score normalization to adjust for scale differences.

Potential Uses

This dataset is tailored for a wide array of applications:

• Financial Analysis: Explore historical performance, volatility, and market trends.
• Forecasting Models: Utilize features like lagged prices and rolling statistics to predict future stock prices.
• Machine Learning: Develop regression models or classification frameworks to predict market movements.
• Deep Learning: Leverage LSTM networks for more sophisticated time-series forecasting.
• Time-Series Analysis: Dive deep into trend analysis, seasonality, and cyclical behavior of stock prices.

Whether you are a data scientist, a financial analyst, or a hobbyist interested in the stock market, this dataset provides a rich playground for analysis and model building. Its comprehensive feature set allows for the development of robust predictive models and offers unique insights into one of the world’s most significant oil companies. Unlock the potential of financial data with this carefully crafted dataset.

The dataset contains the numerical results of probabilistic forecasts for possible oil spills in the Cuban Western Continental Shelf and two animation figures (*.gif) of model results. The origin of possible oil spills scenarios is deep wells proposed to operate in and around Cabo San Antonio. Oil dispersal and concentrations were simulated using the latest updated version of the oil application of the Connectivity Modeling System (CMS) or oil-CMS. In this version, the specified hydrocarbon pseudo-components are in the same droplet. The post-processing analysis yielded 4-D spatiotemporal data of the oil concentrations and oil mass on a regular horizontal and vertical grid, as well as the time evolution of the horizontally cumulative oil mass for a period of about 6 months. In the present oil spill scenario, a deep-sea blowout is modeled at 22.08N, 85.10W, the oil droplets are released at 1222m depth, or 300m above the hypothetical oil well, in similarity to Deepwater Horizon disaster in 2010. 3000 oil droplets were released every 2 hours for 87 days, equivalent to a total of 3132000 oil droplets released during the simulation. Initial droplet sizes were determined at random by the CMS in the range of 1-500 micron. Each oil droplet contained three (3) pseudo-components accounting for the differential oil density as follows: 10% of light oil with the density of 800kg/m^3, 75% of the oil with 840 kg/m^3, and 15% of heavy oil with 950 kg/m^3 density. The half-life decay rates of oil fractions were 30 days, 40 days, and 180 days, respectively. The surface evaporation half-life was set to 250 hours; horizontal diffusion was set to 10 m^2/s. Ocean hydrodynamic forcing for the CMS model was coming from the HYbrid Coordinate Ocean Model (HYCOM) for the Gulf of Mexico region on a 0.04-deg. horizontal grid and 40 vertical levels from the surface to 5500m. HYCOM provided daily average 3-D momentum, temperature and salinity forcing fields to the CMS model. The surface wind drift parameterization used surface winds and wind stresses from the 0.5-degree Navy Operational Global Atmospheric Prediction System (NOGAPS). The transport and evolution of the oil droplets were tracked by the oil-CMS model during the 167 days of the simulation, recording each particle’s horizontal position, depth, diameter, and density into the model output file every 2 hours. Model data had to be post-processed to obtain oil concentrations estimates. The post-processing algorithm took into account the total amount of oil spilled during the 87-day incident as estimated from the reports (730000 tons). Results from the recent laboratory deep-pressure oil experiment and from the observational studies in post-Deepwater Horizon disaster were used to adopt presumed initial droplet size distribution (DSD) for the cases of untreated oil and for the oil treated with a subsea injection of chemical dispersants. The post-processing algorithm then utilized the change-of-variable technique for the probability density functions to obtain an oil mass distribution from a known DSD. A scaling factor was further determined to obtain a representative particle mass and then the volumetric concentration in water. Two postprocessing options were assumed with different initial DSD, corresponding to the untreated oil, and oil treated with subsea dispersant injection (SSDI) that shifts the modal peak in DSD to a smaller droplet. This dataset supports the publications: Paris, C. B., Vaz, A. C., Berenshtein, I., Perlin, N., Faillettaz, R., Aman, Z. M., & Murawski, S. A. (2019). Simulating Deep Oil Spills Beyond the Gulf of Mexico. Scenarios and Responses to Future Deep Oil Spills, 315–336. doi:10.1007/978-3-030-12963-7_19; and Paris, C. B., Murawski, S. A., Olascoaga, M. J., Vaz, A. C., Berenshtein, I., Miron, P., & Beron-Vera, F. J. (2019). Connectivity of the Gulf of Mexico Continental Shelf Fish Populations and Implications of Simulated Oil Spills. Scenarios and Responses to Future Deep Oil Spills, 369–389. doi:10.1007/978-3-030-12963-7_22.

Oil spills are a major source of marine pollution that affect the environment, economy, and marine ecosystems. Toxic chemicals from oil spills can remain in the ocean for years and even sink down to the seabed affecting sedimentation rates. While many oil spills are accidental, some are caused deliberately by cargo ships dumping waste oil and bilge water. It is very difficult to identify, detect and remove oil from the ocean surface and routine monitoring can help prevent illegal dumping and aid with remediation efforts.This deep learning model automates the task of detecting potential oil spills from Sentinel-1 SAR data. In addition to being inexpensive, SAR data is collected day and night in all weather conditions without getting affected by cloud cover. Use this model to identify potential oil spills that need to be reviewed or monitored, reducing time and effort required significantly.Using the modelFollow the guide to use the model. Before using this model, ensure that the supported deep learning libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS.Fine-tuning the modelThis model can be fine-tuned using the Train Deep Learning Model tool. Follow the guide to fine-tune this model.Input8-bit, 3-band Sentinel-1 C band SAR GRD VV polarization band raster.OutputFeature layer representing oil slick.Applicable geographiesThe model is expected to work globally.Model architectureThe model uses the MaskRCNN model architecture implemented in ArcGIS API for Python.Accuracy metricsThe model has an average precision score of 0.69.Training dataThis model is trained on 381 Sentinel-1 scenes downloaded from the ASF portal, and the ground truth data from NESDIS Marine Pollution Products. Sample resultsHere are a few results form the model.

The timing and location of the 2010 Deepwater Horizon (DWH) incident within the Gulf of Mexico pelagic zone likely resulted in exposure of commercially and ecologically important fish species, such as mahi-mahi (Coryphaena hippurus) during the sensitive early life stages. A 24-channel optical fluorescence oxygen sensing system for high through-put respiration measurements was used to investigate the individual and combined effects of oil exposure, temperature and developmental stage on oxygen consumption rates in embryonic and larval mahi-mahi. This dataset reports oxygen concentrations that can be used to calculate embryonic oxygen consumption (picomol inidiv-1 min-1) rates for 2 temperature treatments and 3 oil concentration treatments for day 1 (0-24 hr), day 2 (24 -48 hr) post-fertilization (hpf) mahi-mahi embryos and for recently hatched larvae (~50 hpf). Nitrogenous waste data is also reported for larvae exposed to different concentrations of oil and different temperature treatments. This data is reported as ammonia, urea and total nitrogenous waste excretion (picomol inidiv-1 min-1).