Base data • Name/Brand • Adress • Geocoordinates • Opening Hours • Phone • ...

25+ Fuel Types like • Regular • Mid-Grade • Premium • Diesel • DEF • CNG •...

30+ Services and characteristics like • Carwash • Shop • Restaurant • Toilet • ATM • Pay at Pump •...

20+ Payment options • Cash • Visa • MasterCard • Fueling Cards • Google Pay • ...

Xavvy fuel is the leading source for Gas Station Location Data and Gasoline Price data worldwide and specialized in data quality and enrichment. Xavvy provides POI Data of gas stations at a high quality level for the United States. Next to base information like name/brand, address, geo-coordinates or opening hours, there are also detailed information about available fuel types, accessibility, special services, or payment options for each station. The level of information to be provided is highly customizable. One-time or regular data delivery, push or pull services, and any data format – we adjust to our customer’s needs.

Total number of stations per country or region, distribution of market shares among competitors or the perfect location for new gas stations, charging stations or hydrogen dispensers - our gas station data and gasoline price data provides answers to various questions and offers the perfect foundation for in-depth analyses and statistics. In this way, our data helps customers from various industries to gain more valuable insights into the fuel market and its development. Thereby providing an unparalleled basis for strategic decisions such as business development, competitive approach or expansion.

In addition, our data can contribute to the consistency and quality of an existing dataset. Simply map data to check for accuracy and correct erroneous data.

Especially if you want to display information about gas stations on a map or in an application, high data quality is crucial for an excellent customer experience. Therefore, our processing procedures are continuously improved to increase data quality:

• regular quality controls • Geocoding systems correct and specify geocoordinates • Data sets are cleaned and standardized • Current developments and mergers are taken into account • The number of data sources is constantly expanded to map different data sources against each other

Integrate the largest database of Retail Gas Station Location Data, Amenities and accurate Diesel and Gasoline Price Data in Europe and North America into your business. Check out our other Data Offerings available, and gain more valuable market insights on gas stations directly from the experts!

🚗⛽ Explore around 38,000 Shell Gas Stations Worldwide 🌍

This dataset includes detailed info on Shell stations across various countries, cities, and locations. Find out:

📍 Latitude & Longitude for precise mapping

🌆 Country & City names

⚡ EV Charging availability

🛠️ Services offered at each station

Perfect for location-based projects or analysis of EV infrastructure!

I strongly recommend analyzing this data with World Cities dataset, displaying stations dispersion against city population dispersion.

Consider upvoting if you find the dataset interesting.

Base data • Name/Brand • Adress • Geocoordinates • Opening Hours • Phone •...

25+ Fuel Types like • Super E5 • Super 98 • Diesel • AdBlue • LPG • CNG •...

60+ Services and characteristics like • Carwash • Shop • Restaurant • Toilet • ATM • Toll •...

300+ Payment options • Cash • Visa • MasterCard • Fueling Cards •...

We are the leading source for Gas Station Location Data and Petrol Price Data worldwide and specialized in data quality and enrichment. We provide high quality POI Data of gas stations for all European countries.

The gas station location data is delivered country by country and the level of information to be provided is highly customizable One-time or regular data delivery, push or pull services, and any data format – we adjust to our customer’s needs.

Total number of stations per country or region, distribution of market shares among competitors or the perfect location for new gas stations, charging stations or hydrogen dispensers - our data provides answers to various questions and offers the perfect foundation for in-depth analyses and statistics. In this way, our gas station location data and petrol price data helps customers from various industries to gain more valuable insights into the fuel market and its development. Thereby providing an unparalleled basis for strategic decisions such as business development, competitive approach or expansion.

In addition, our data can contribute to the consistency and quality of an existing dataset. Simply map data to check for accuracy and correct erroneous data.

200+ sources including governments, petroleum companies, fuel card providers and crowd sourcing enable xavvy to provide various information. Next to base information like name/brand, address, geo-coordinates or opening hours, there are also detailed information about available fuel types, accessibility, special services, or payment options for each station:

Especially if you want to display information about gas stations on a map or in an application, high data quality is crucial for an excellent customer experience. Therefore, processing procedures are continuously improved to increase data quality:

• regular quality controls (e.g. via monitoring dashboards) • Geocoding systems correct and specify geocoordinates • Data sets are cleaned and standardized • Current developments and mergers are taken into account • The number of data sources is constantly expanded to map different data sources against each other

Check out our other Data Offerings available and gain more valuable market insights on gas stations directly from the experts!

Gas pricing going through large turmoils amongest thd Ukraine and Russia confilict. This data set continas 50000+ rows of data from California gasBuddy stations. Dataset covers different types of gas products such as Regular Midgrade Premium Diesel ... Fields include: services_included,price_time_stamp,currency,postal_code,loc_name,city,review_count,state,zip_code_searched,latitude,product_name,payment_type, source_url,phone,loc_number,price_current,country,longitude,address_1,address_2,overall_rating

This dataset is for educational purpose only. Contact info@barkingdata.com if you are interested in building similar dataset for other countries or regions. We specialize in web mining and web data harvesting from the world wide web (including mobile apps), we have built 5000+ datasets for researchers, analysts, scholars , retailers, ... Learn more from https://www.barkingdata.com

📝 Dataset Overview: This dataset represents real-world, enhanced transactional data from Timac Global Concept, one of Nigeria’s prominent players in fuel and petroleum distribution. It includes comprehensive sales records across multiple stations and product categories (AGO, PMS, Diesel, Lubricants, LPG), along with revenue and shift-based operational tracking.

The dataset is ideal for analysts, BI professionals, and data science students aiming to explore fuel economy trends, pricing dynamics, and operational analytics.

🔍 Dataset Features: Column Name Description Date Transaction date Station_Name Name of the fuel station AGO_Sales (L) Automotive Gas Oil sold in liters PMS_Sales (L) Premium Motor Spirit sold in liters Lubricant_Sales (L) Lubricant sales in liters Diesel_Sales (L) Diesel sold in liters LPG_Sales (kg) Liquefied Petroleum Gas sold in kilograms Total_Revenue (₦) Total revenue generated in Nigerian Naira AGO_Price Price per liter of AGO PMS_Price Price per liter of PMS Lubricant_Price Unit price of lubricants Diesel_Price Price per liter of diesel LPG_Price Price per kg of LPG Product_Category Fuel product type Shift Work shift (e.g., Morning, Night) Supervisor Supervisor in charge during shift Weekday Day of the week for each transaction

🎯 Use Cases: Build Power BI dashboards to track fuel sales trends and shifts

Perform revenue forecasting using time series models

Analyze price dynamics vs sales volume

Visualize station-wise performance and weekday sales patterns

Conduct operational audits per supervisor or shift

🧰 Best Tools for Analysis: Power BI, Tableau

Python (Pandas, Matplotlib, Plotly)

Excel for pivot tables and summaries

SQL for fuel category insights

👤 Created By: Fatolu Peter (Emperor Analytics) Data analyst focused on real-life data transformation in Nigeria’s petroleum, healthcare, and retail sectors. This is Project 11 in my growing portfolio of end-to-end analytics challenges.

✅ LinkedIn Post: ⛽ New Dataset Alert – Fuel Economy & Sales Data Now on Kaggle! 📊 Timac Fuel Distribution & Revenue Dataset (Nigeria – 500 Records) 🔗 Explore the data here

Looking to practice business analytics, revenue forecasting, or operational dashboards?

This dataset contains:

Daily sales of AGO, PMS, Diesel, LPG & Lubricants

Revenue breakdowns by station

Shift & supervisor tracking

Fuel prices across product categories

You can use this to: ✅ Build Power BI sales dashboards ✅ Create fuel trend visualizations ✅ Analyze shift-level profitability ✅ Forecast revenue using Python or Excel

Let’s put real Nigerian data to real analytical work. Tag me when you build with it—I’d love to celebrate your work!

FuelAnalytics #KaggleDatasets #PowerBI #PetroleumIndustry #NigeriaData #RevenueForecasting #EmperorAnalytics #FatoluPeter #Project11 #TimacGlobal #RealWorldData

There is no description for this dataset.

ABSTRACT Purpose: to evaluate the understanding of noise and the perception about quality of life of gas station workers. Methods: this is an exploratory study with a sample of 32 employees, of both sexes from three gas stations of a country town in the state of Rio Grande do Sul. Data were collected during the activities allusive to the International Noise Awareness Day, in April 2015, by a questionnaire on noise and hearing health and by the World Health Organization Quality of Life (WHOQOL-Bref). Results: most workers reported not having a hearing loss, discomfort or pain when subjected to noise. However, they believe that exposure to noise can lead to hearing loss as well as tinnitus and dizziness. The working environment was indicated as noisy, but the workers do not perceive themselves as noise producers and do not adopt preventive measures. Concerning the quality of life, the lowest score was for the environmental domain, in both sexes and age equal and less than 30 years. Conclusion: the study allowed to understand that the majority of workers does not have significant knowledge about the need for protective measures against noise; also showed that the environmental domain was the most compromised in the perception of employees on quality of life.

Indonesia Number of Customers: City Gas: Fuel Gas Filling Stations data was reported at 16.000 Person in 2017. This records an increase from the previous number of 14.000 Person for 2015. Indonesia Number of Customers: City Gas: Fuel Gas Filling Stations data is updated yearly, averaging 18.000 Person from Dec 2010 (Median) to 2017, with 7 observations. The data reached an all-time high of 43.000 Person in 2014 and a record low of 14.000 Person in 2015. Indonesia Number of Customers: City Gas: Fuel Gas Filling Stations data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Global Database’s Indonesia – Table ID.RBE008: City Gas: Sales.

The Oil and Gas Infrastructure Mapping (OGIM) database is a global, spatially explicit, and granular database of oil and gas infrastructure, developed at Environmental Defense Fund (EDF) (www.edf.org). The OGIM database is developed to support the quantification and source characterization of oil and gas methane emissions. The database development is based on the acquisition, analysis, curation, integration, and quality-assurance, performed at EDF, of public-domain datasets reported by official government sources, industry, academic, and other non-government entities.

The OGIM database includes locations and facility attributes of oil and gas infrastructure types that are important sources of methane emissions, including oil and gas production wells, offshore production platforms, natural gas compressor stations, processing facilities, liquefied natural gas facilities, crude oil refineries, pipelines, etc.

The OGIM_v1 database includes approximately six million features, including 2.6 million point locations of oil and gas facility types and over 2.6 million kilometers of oil and gas pipelines. This work and the OGIM database, which we anticipate updating on a regular cadence, helps fill a crucial oil and gas geospatial data need, in support of the quantification and attribution of global oil and gas methane emissions at high resolution.

Full details for database development and related analytics can be found in the following journal paper, which is under review at Earth System Science Data journal.

Please cite the paper when using the database:

Omara, M., Gautam, R., O'Brien, M., Himmelberger, A., Franco, A., Meisenhelder, K., Hauser, G., Lyon, D., Chulakadaba, A., Miller, C., Franklin, J., Wofsy, S., and Hamburg, S.: Developing a spatially explicit global oil and gas infrastructure database for characterizing methane emission sources at high resolution, Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2022-452, 2023.

Important note: While we describe these datasets in detail in the manuscript above, and include maps for all acquired datasets, this open-access version of the OGIM database does not include the locations of about 300 natural gas compressor stations in Russia. Future updates may include these datasets when appropriate permissions to make them publicly accessible are obtained.

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OGIM_v1.1.gpkg. May-10-2023 update:

• This update includes the addition of natural gas flaring detections for the year 2021 based on the VIIRS dataset, available from the Earth Observation Group at Colorado School of Mines, as described in the manuscript text.
  
  
• The current version of the OGIM (OGIM_v1.1) is based on public-domain datasets reported on or prior to January 2023. Each record in OGIM includes a source date when the data was last updated. Some records may have out-of-date information, for example, if facility status has changed since we last acquired the data. We are continuing to update the OGIM database as new public-domain datasets become available.

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Point of Contact at Environmental Defense Fund: Ritesh Gautam (rgautam@edf.org).

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."

Number of Service Station: by Operation: WP Energy data was reported at 514.000 Unit in Sep 2018. This stayed constant from the previous number of 514.000 Unit for Jun 2018. Number of Service Station: by Operation: WP Energy data is updated quarterly, averaging 440.000 Unit from Mar 2011 (Median) to Sep 2018, with 31 observations. The data reached an all-time high of 514.000 Unit in Sep 2018 and a record low of 158.000 Unit in Mar 2011. Number of Service Station: by Operation: WP Energy data remains active status in CEIC and is reported by Department of Energy Business. The data is categorized under Global Database’s Thailand – Table TH.RB012: Number of Fuel Distribution Service Station.

The following dataset was collected from a set of cybersecurity experiments conducted in an Electricity and Natural Gas environment. The architecture was instantiated within the powerNET testbed at Pacific Northwest National Laboratory, and is comprised of both simulated components and hardware-in-the-loop devices. The test environment consisted of a substation and control center network representative of electrical systems. In addition, it also contained a compressor station, and an odorizer and pressure regulation station representative of oil and natural gas systems. The various devices on the electrical and gas systems were organized into multiple networks to mimic real-world deployments. There were 14 testing scenarios overall that covered a wide variety of cybersecurity and infrastructure events.

Operating-Income Time Series for Global Partners LP. Global Partners LP engages in the purchasing, selling, gathering, blending, storing, and logistics of transporting gasoline and gasoline blendstocks, distillates, residual oil, renewable fuels, crude oil, and propane to wholesalers, retailers, and commercial customers. The company operates through three segments: Wholesale, Gasoline Distribution and Station Operations (GDSO), and Commercial. The Wholesale segment sells home heating oil, branded and unbranded gasoline and gasoline blendstocks, diesel, kerosene, and residual oil to retailers and wholesale distributors. This segment transports the products by railcars, barges, trucks and/or pipelines. The GDSO segment sells branded and unbranded gasoline to gasoline station operators and sub-jobbers; operates convenience stores and prepared food sales; and provides car wash, lottery, and ATM services, as well as leases gasoline stations. The Commercial segment sells and delivers unbranded gasoline, home heating oil, diesel, kerosene, residual oil, and bunker fuel to customers in the public sector, as well as to commercial and industrial end-users; and sells custom blended fuels. The company is involved in the transportation of petroleum products and renewable fuels through rail from the mid-continent region of the United States and Canada. Global Partners LP was founded in 2005 and is based in Waltham, Massachusetts.

In the ALE/GAGE/AGAGE global network program, continuous high frequency gas chromatographic measurements of four biogenic/anthropogenic gases (methane, CH4; nitrous oxide, N2O; hydrogen, H2; and carbon monoxide, CO) and several anthropogenic gases that contribute to stratospheric ozone destruction and/or to the greenhouse effect have been carried out at five globally distributed sites for several years. The program, which began in 1978, is divided into three parts associated with three changes in instrumentation: the Atmospheric Lifetime Experiment (ALE), which used Hewlett Packard HP5840 gas chromatographs; the Global Atmospheric Gases Experiment (GAGE), which used HP5880 gas chromatographs; and the present Advanced GAGE (AGAGE). AGAGE uses two types of instruments: a gas chromatograph with multiple detectors (GC-MD), and a gas chromatograph with mass spectrometric analysis (GC-MS). Beginning in January 2004, an improved cryogenic preconcentration system (Medusa) replaced the absorption-desorption module in the GC-MS systems at Mace Head and Cape Grim; this provided improved capability to measure a broader range of volatile perfluorocarbons with high global warming potentials. The Medusa GC-MS systems were subsequently used at other AGAGE stations (Trinidad Head, Barbados, American Samoa, Zeppelin, Jungfraujoch, and Goan) after the initial setup at Mace Head and Cape Grim. More information may be found on the AGAGE home page: https://agage.mit.edu/instruments.Data from the AGAGE and affiliated stations (total of 10 sites) between August 1993 and June 2023 are provided in “Agage_gcmd_gcms_data_2024_07_11.tar.gz” (compressed tar file). The metadata file has information on each station and currently released species. The standard scales used in archived species are listed in "AGAGE_scale_2024_v1.pdf". Additional information can be found on the AGAGE website (https://agage.mit.edu).Update: Dec. 4, 20241. The Cape Grim H2_PDD data are updated in this release ((H2_PDD values in previous release are wrong due to coding error). All other data in this release are the same as previous version ("Agage_gcmd_gcms.data.2024_07_11.tar.gz")2. The latest data are saved in "Agage_gcmd_gcms.data.2024_12_04.tar.gz"

This submission includes publicly available data extracted in its original form. Please reference the Related Publication listed here for source and citation information If you have questions about the underlying data stored here, please contact the Office of Energy Efficiency and Renewable Energy at eere.webmaster@ee.doe.gov. If you have questions or recommendations related to this metadata entry and extracted data, please contact the CAFE Data Management team at: climatecafe@bu.edu. "Data Collection Methods The data in the Alternative Fueling Station Locator are gathered and verified through a variety of methods. The National Renewable Energy Laboratory (NREL) obtains information about new stations from trade media, Clean Cities and Communities coalitions, the Submit New Station form on the Station Locator website, and through collaborating with infrastructure equipment and fuel providers, original equipment manufacturers (OEMs), and industry groups. Users submitting updates through the "Submit New Station" or "Report a Change" forms will receive an email confirmation of their submittal. NREL will verify station details before the station is added or updated in the Station Locator. The turnaround time for updates will depend on the completeness of the information provided, as well as the responsiveness of the station or point of contact. NREL regularly compares its station data with those of other relevant trade organizations and websites. Differences in methodologies, data confirmation, and inclusion criteria may result in slight variations between NREL's database and those maintained by other organizations. NREL also collaborates with alternative fuel industry groups to identify discrepancies in data and develop data sharing processes and best practices. NREL and its data collection subcontractor are currently collaborating with natural gas, electric drive, biodiesel, ethanol, hydrogen, and propane industry groups to ensure best practices are being followed for identifying new stations and confirming station changes in the most-timely manner possible. Station Update Schedule Most existing stations in the database are contacted at least once a year on an established schedule to verify they are still operational and providing the fuel specified. Based on an established data collection schedule, the database is updated on an ongoing basis. Stations that are no longer operational or no longer provide alternative fuel are removed from the database as they are identified. Public and private non-networked electric vehicle (EV) charging stations are proactively verified every other year, with half of the EV charging stations verified each year. Additionally, all private EV charging stations at multi-family housing are verified every other year. This difference in the update schedule for non-networked EV charging stations accommodates the growing number of EV charging stations in the Station Locator. NREL will continue to make updates to any station record if changes are reported. Mapping and Counting Methods Each point on the map is counted as one station in the station count. A station appears as one point on the map, regardless of the number of fuel dispensers or electric vehicle supply equipment (EVSE) ports at that location. Station addresses are geocoded and mapped using an automatic geocoding application. The geocoding application returns the most accurate location based on the provided address. Station locations may also be provided by external sources (e.g., station operators) and/or verified in a geographic information system (GIS) tool. This information is considered highly accurate, and these coordinates override any information generated using the geocoding application. Notes about Specific Station Types Private Stations The Station Locator defaults to searching only for public stations. To include private stations in the search, use the Station button on the "Advanced Filters" tab. Stations with an access listing of "Private - Fleet customers only" may allow other entities to fuel through a business-to-business arrangement. For more information, fleet customers should refer to the information listed in the details section for that station and contact the station directly. The Station Locator includes information on private fleet fueling stations (e.g., transit bus fueling facilities, other medium- and heavy-duty fueling and charging infrastructure), workplace charging stations, and multi-family housing charging stations. Note that information on these stations is not always published online or in the data download but may be tracked only in the backend Station Locator database. Information tracked only in the backend database may be provided by request to the webmaster listed in the "More Information" section below. Public Restricted Access Stations Stations that are reserved for patrons of a business, such as guests of a hotel, visitors of a museum, or customers of a retail store, are...

Hamme et al. (2019) Global noble gas and N2/Ar database, version 1.0. These data are a compilation of dissolved noble gas and N2/Ar ratio measurements collected from 1998-2016 in locations spanning the globe.

This database contains the data on dissolved gas measurements published in:
Hamme, R. C., Nicholson, D. P., Jenkins, W. J., & Emerson, S. R. (2019). Using Noble Gases to Assess the Ocean’s Carbon Pumps. Annual Review of Marine Science, 11(1), 75–103. doi:10.1146/annurev-marine-121916-063604

Data Originators: Roberta Hamme, William Jenkins, Steven Emerson, David Nicholson, Rachel Stanley

Date contributed to BCO-DMO: 17 January 2022

Version 2.0 corrects an incorrect sign in the longitude for cruise 33KI20040814:HOT162 in version 1.0. The error in the database does not affect any figures in the publication.

This data is provided free for educational and non-profit research purposes. We ask that you appropriately cite Hamme et al. (2019) Annual Review of Marine Science in any work that uses this database. Please also send an e-mail to rhamme@uvic.ca, letting her know that you have downloaded the data, so that she can keep you apprised of any further corrections or changes. If you discover what you believe to be an error in the database, it is your responsibility to send an e-mail to me at rhamme@uvic.ca before using the data in a publication.

Both MATLAB .mat databases and comma-delimited .csv text files were provided to BCO-DMO. For the flat, ASCII version (csv) use the "Get Data" button on the BCO-DMO metadata landing page. For convenience, the MATLAB file is also provided as a Supplemental File: Global_Hammeetal2019.mat (400 kb)

These two formats contain identical information. Different cruises can be identified by the sequence number, cruisename, or date.

Secondary data - On some cruises, Ar concentration and N2/Ar ratio measurements were performed at two different labs on separate samples, for inter-calibration purposes. In these cases, data from both labs is given separately with data from the second lab labeled "secondary".

Non-Gasoline Alternative Fueling Stations

Locations of non-gasoline alternative fueling stations in the United States

By Homeland Infrastructure Foundation [source]

About this dataset

With over 250 million vehicles consuming millions of barrels of petroleum daily in the country, finding sustainable and clean alternatives to traditional gasoline is crucial for reducing dependence on fossil fuels and mitigating environmental impact. This dataset serves as a valuable resource for vehicle fleet managers, corporate decision-makers, public transportation planners, and other stakeholders involved in promoting energy conservation strategies.

The data is collected by Clean Cities—a nationwide network of local coalitions—and the U.S. Department of Energy's Vehicle Technologies Office. Clean Cities plays a vital role in facilitating project assistance to help both public and private sectors adopt alternative and renewable fuels along with various technologies aimed at reducing idling time and improving fuel economy.

Each entry in the dataset includes essential details about individual fueling stations such as their geographical coordinates (longitude and latitude), address including street name and intersection location, city information with associated ZIP codes (including additional 4-digit codes when available), contact phone numbers for each station's operations (station phone number), status codes indicating operational or non-operational status along with expected dates of becoming operational if applicable.

Furthermore, the dataset includes comprehensive information about the types of alternative fuels provided by each station—specifically specifying biodiesel blends available at biodiesel-capable stations; fill type options for CNG stations; available charging levels for electric vehicle charging stations; DC fast charging availability; information regarding other electric vehicle charging networks/providers; primary liquefied petroleum gas (LPG) types offered at LPG-capable stations; ethanol blends available at specific ethanol-capable locations; pressure rating for natural gas filling points where applicable.

Additionally listed are details regarding federal agency affiliations/associations, ownership or operator types, accessibility days of the week, accepted payment cards, associated groups or organizations with each fueling station offering alternative fuels.

The dataset provides a comprehensive and up-to-date resource for those looking to promote clean energy and sustainable transportation solutions. It can be accessed through the Department of Energy's Alternative Fuels Data Center Web Feature Service

How to use the dataset

Here is a guide on how to use this dataset effectively:

Step 1: Understanding the Columns - X: The longitude coordinate of the fueling station. - Y: The latitude coordinate of the fueling station. - Fuel_Type: The type of alternative fuel provided at the station (biodiesel, CNG, electric, ethanol, hydrogen, LNG or propane). - Station_Na: The name of the fueling station. - Street_Add: The street address of the fueling station. - Intersecti: The intersection where the fueling station is located. - City: The city where the fueling station is located. - State: The state where the fueli ngstationislocatedZ: IP code ofthefuelingstaion Plus4:The additional4-digit codefortheZIPcodeStation_Ph:aThephonenumbeofthefuelngstations

Step 2. Location Visualization Since this dataset provides coordinates (longitude and latitude) for each fueling station location in addition to their names and addresses. Mapping tools like ArcGIS or Tableau can be used to plot these locations on a map for visual analysis.

Using these mapping visualization techniques will allow users to identify clusters or patterns in certain areas with higher densities of alternative fuelling stations. It can also help identify gaps in certain regions that lack access to such fuelling facilities.

Step 3. Filtering by Specific Fuel Types This dataset contains information about various alternative fuels including biodiesel, CNG, electric, ethanol, hydrogen, LNG and propane. Users can filter the dataset based on their specific fuel type of interest to obtain the relevant fueling stations.

By using filters or queries on columns such as Fuel_Type or EV_Level2 (electric vehicle charging level), users can focus on a specific category of alternative fuels that they are interested in exploring further.

Step 4: Analyzing Station Status and Availability The dataset includes information about the status code of each fueling station, indicating whether it is operational or not. Us...

Property-Plant-and-Equipment-Net Time Series for Global Partners LP. Global Partners LP engages in the purchasing, selling, gathering, blending, storing, and logistics of transporting gasoline and gasoline blendstocks, distillates, residual oil, renewable fuels, crude oil, and propane to wholesalers, retailers, and commercial customers. The company operates through three segments: Wholesale, Gasoline Distribution and Station Operations (GDSO), and Commercial. The Wholesale segment sells home heating oil, branded and unbranded gasoline and gasoline blendstocks, diesel, kerosene, and residual oil to retailers and wholesale distributors. This segment transports the products by railcars, barges, trucks and/or pipelines. The GDSO segment sells branded and unbranded gasoline to gasoline station operators and sub-jobbers; operates convenience stores and prepared food sales; and provides car wash, lottery, and ATM services, as well as leases gasoline stations. The Commercial segment sells and delivers unbranded gasoline, home heating oil, diesel, kerosene, residual oil, and bunker fuel to customers in the public sector, as well as to commercial and industrial end-users; and sells custom blended fuels. The company is involved in the transportation of petroleum products and renewable fuels through rail from the mid-continent region of the United States and Canada. Global Partners LP was founded in 2005 and is based in Waltham, Massachusetts.

This study is part of a joint effort by the University of Wyoming (UW) School of Energy Resources (SER), the UW Engineering Department, Idaho National Laboratories (INL), and the United States Geological Survey (USGS) to describe rare earth element concentrations in oil and gas produced waters and in coal-fired power station ash ponds. In this work we present rare earth element (REE) and trace metal behavior in produced water from four Wyoming oil and gas fields and surface ash pond water from two coal-fired power stations. Using the methods of the INL team members, we measured REEs in high salinity oil and gas produced waters. Our results show that REEs exist as a dissolved species in all waters measured for this project, typically within the parts per trillion range.

UNICON, a large-scale open dataset on UNIversity CONsumption of utilities, electricity, gas and water. This dataset is publicly released as part of La Trobe University’s commitment to Net Zero Carbon Emissions by 2029, for which we are building the La Trobe Energy AI/Analytics Platform (LEAP) that leverages Artificial Intelligence (AI) and Data Analytics to analyse, predict and optimize the consumption, generation and utilization of electricity, renewables, gas and water resources. UNICON contains consumption data for La Trobe’s five campuses in geographically distributed regions, across four years, 2018-2021 inclusive. This includes the COVID-19 global pandemic timeline of university shutdown and work from home measures that led to a significant decrease in the consumption of utilities. The consumption data consists of smart electricity meter readings at 15-minute granularity, gas meter readings at hourly intervals and water meter readings at 15-minute intervals. UNICON also contains weather data from the closest weather station to each campus, collected at two-speed latency of 1 minute and 10 minutes. The dataset is annotated with internal events of significance, such as energy conservation measures (ECMs) and other measurement and validation (M&V) activities conducted as part of LEAP optimization. To the best of our knowledge, this is the first large-scale, comprehensive, open dataset for the three main utilities, electricity, gas, and water consumption in a multi-campus university setting.

Dataset file descriptions

• campus_meta.csv – This file contains information about each campus in the university network.
• nmi_meta.csv – Information about NMIs such as campus location and peak demand is listed in this file.
• building_meta.csv – This file contains meta information about buildings in each campus which include campus location, floor area and etc.
• calender.csv – University calendar for the data collection period is included in this file.
• events.csv – There are series of events happened at each building which include energy efficiency projects such as LED installation and HVAC system updates. This file contains the dates related to each event at building level.
• nmi_consumption.csv – Consumption data of NMIs are recorded in this file.
• building_consumption.csv – Consumption data of buildings are recorded in this file.
• building_submeter_consumption.csv – Consumption data of building sub-meters are recorded in this file.
• gas_consumption.csv – Gas consumption data of available campuses are recorded in this file.
• water_consumption.csv – Water consumption data of available campuses are recorded in this file.
• weather_data.csv – Weather data collected from respective weather stations.

Acknowledgements

Please cite the following paper if you use this dataset:

• H. Moraliyage, N. Mills, P. Rathnayake, D. De Silva and A. Jennings, "UNICON: An Open Dataset of Electricity, Gas and Water Consumption in a Large Multi-Campus University Setting," 2022 15th International Conference on Human System Interaction (HSI), 2022, pp. 1-8, https://doi.org/10.1109/HSI55341.2022.9869498

Usage Policy and Legal Disclaimer

This dataset is being distributed only for Research purposes, under Creative Commons Attribution-Noncommercial-ShareAlike license (CC BY-NC-SA 4.0). By clicking on download button(s) below, you are agreeing to use this data only for non-commercial, research, or academic applications. You may need to cite the above papers if you use this dataset.

Github: https://github.com/CDAC-lab/UNICON