The Knowledge Management Database (KMD) is a document repository that provides links to archived oil and gas documents as well as to reports stored in the DOE Office of Science and Technology (OSTI) library.

This dataset compiles heat flow and temperature gradient data from over 44,000 wells across the United States, along with more than 6,000 related geothermal exploration resources. Originally assembled prior to 2014 for the now-retired National Geothermal Data System (NGDS), the collection includes curated well data, scanned field notes, temperature-depth curves, publications, maps, and other supporting documents. SMU Geothermal Laboratory contributed two different nationwide heat flow databases to the project. One is based on equilibrium temperature measurements (over 14,000 sites) and the other is based on corrected bottom hole temperature (BHT) data from oil and gas industry wells (over 30,000 sites). In addition, scanned field notes and temperature-depth curves were associated with approximately 6,000 specific sites in the heat flow database. Records were corrected and overlapping sites in the equilibrium heat flow database were linked between the original SMU National database and the UND Global Heat Flow database. New or related sites, which were not previously published because they lacked full heat flow content, are now included as gradient only information along with their detailed temperature data to fill in data gaps. Finally, SMU submitted over 920 scanned publications, reports, and maps suitable for full text searching. The dataset is provided in two flat-structured zip archives: one containing the curated well data and another containing related resources. An Excel index file is provided for each archive, allowing filtering by well name, location, and description. Data files are labeled with state or institutional origin where available.

REVISED 1/2/2019. SEE UPDATE LINK BELOW. This database contains unit cost information for different components that may be used to integrate distributed photovotaic D-PV systems onto distribution systems. Some of these upgrades and costs may also apply to integration of other distributed energy resources DER. Which components are required and how many of each is system-specific and should be determined by analyzing the effects of distributed PV at a given penetration level on the circuit of interest in combination with engineering assessments on the efficacy of different solutions to increase the ability of the circuit to host additional PV as desired. The current state of the distribution system should always be considered in these types of analysis. The data in this database was collected from a variety of utilities PV developers technology vendors and published research reports. Where possible we have included information on the source of each data point and relevant notes. In some cases where data provided is sensitive or proprietary we were not able to specify the source but provide other information that may be useful to the user e.g. year location where equipment was installed. NREL has carefully reviewed these sources prior to inclusion in this database. Additional information about the database data sources and assumptions is included in the Unit_cost_database_guide.doc file included in this submission. This guide provides important information on what costs are included in each entry. Please refer to this guide before using the unit cost database for any purpose.

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Introduction

This dataset is a curated collection of data related to nuclear energy, covering various aspects such as power plant locations and characteristics, uranium production, electricity generation, safety, and more. The data comes from reputable sources including the U.S. Energy Information Administration (EIA), World Resources Institute, Ember Climate, U.S. Nuclear Regulatory Commission (NRC), and Our World in Data.

Nuclear energy is a critical topic as countries around the world seek to decarbonize their electricity grids and combat climate change. At the same time, concerns around safety, waste disposal, and weapons proliferation lead to ongoing debate about the role nuclear should play in the energy transition.

This dataset enables in-depth analysis to inform these important discussions. Potential use cases include:

• Examining trends in nuclear energy generation and capacity over time and across geographies
• Benchmarking the safety record of nuclear compared to other energy sources
• Analyzing uranium production and prices to understand nuclear fuel dynamics
• Identifying major nuclear plant operators and parent companies
• Assessing the current and potential future role of nuclear in the electricity mix of various countries and regions

I encourage the Kaggle community to explore and build upon this dataset. Potential future collaborations could expand the dataset to include more granular plant-level data, detailed reactor specifications, waste and decommissioning data, country-level policy information, public opinion surveys, and more. I also welcome suggestions for additional datasets to include and new analytics projects and tutorials to undertake. Please do not hesitate to create threads in the "Discussion" or "Suggestions" sections. Together we can create a rich resource to power essential research and decision-making around nuclear energy. 🤗

Starter Code

Refer to for some sample data analysis using this dataset. It also shows how to interface with the particular files.

File Descriptions

The below table contains brief descriptions of the files in the dataset. For a more in-depth description of a file titled filename, refer to README_${filename}.md. The README files do not contain any actual data. | File Name | Description | | --- | --- | | global_power_plant_database.csv | The Global Power Plant Database is a comprehensive, open-source dataset of grid-scale electricity generating facilities operating worldwide, currently containing nearly 35,000 power plants in 167 countries and representing about 72% of the world's capacity. The database provides detailed information on each power plant, including location, capacity, primary fuel type, owner, and commissioning year. It also includes both reported and estimated annual electricity generation data from 2013 to 2019. | | nuclear_energy_overview_eia.csv | The data file contains information about nuclear energy in the United States, broken down by year and month. It includes the number of operable nuclear generating units, their net summer capacity, the net generation of electricity from nuclear power, the percentage share of total electricity net generation coming from nuclear power, and the capacity factor of nuclear generating units. This dataset provides a comprehensive overview of the state of nuclear energy in the U.S. over time. | | number_of_plants_producing_uranium_in_us.csv | The file contains yearly data on the number of uranium mills and plants producing uranium concentrate in the United States. It includes columns for the year, the number of conventional milling operations, non-conventional milling operations, in-situ recovery plants, and byproduct recovery plants active each year. | | rates_death_from_energy_production_per_twh.csv | The file contains data on the mortality rates associated with different energy sources used for electricity production. It includes columns for the energy source type, the number of deaths per terawatt-hour (TWh) of electricity generated, and the year (consistently 2021 for all entries). The data provides insights into the relative safety of various energy sources in terms of deaths per ...

This document is part of the source library for NRGI's National Oil Company Database, an open database of facts and figures on more than 70 national oil companies worldwide. See the full database at https://nationaloilcompanydata.org/.

This dataset contains files used to train and test the Multi-Configuration 23 (MC23) functional and to compare the results to other methods. It includes files to carry out electronic structure calculations. These include molecular geometries in xyz format, OpenMolcas input files for CASSCF calculations, converged CASSCF natural orbitals, OpenMolcas basis set files, and Gaussian 16 formatted checkpoint files for KS-DFT calculations. It also includes data used for data processing such as stoichiometries, absolute energies, and reference energies.

Each file in this dataset is a .tar.xz archive. One can extract them by the following command:

tar -xJf name_of_archive.tar.xz

Below is a description of the content of each archive.

gaussian_16_fchk.tar.xz contains Gaussian 16 formatted checkpoint files for all KS-DFT calculations used in this work. The files in the archive are named as functional/database/system.fchk

openmolcas_basis_set.tar.xz contains OpenMolcas basis set files used for multireference calculations. To reproduce the results in this work, the basis set files should be placed in the “basis_library” directory in the OpenMolcas installation location.

openmolcas_wave_function.tar.xz contains files needed by OpenMolcas to reproduce the CASSCF wave function used in this work. The files in the archive are named database/system.*.

• The file system.xyz contains the Cartesian coordinates. Note that for Data Set 2, the coordinates are in the input files system.inp.
• The file system.inp contains the OpenMolcas input file to perform CASSCF calculations.
• The files system.RasOrb, system.rasscf.h5, and system.rasscf.molden contain the converged CASSCF natural orbitals.

gaussian_16_stoichiometry_energy.tar.xz and openmolcas_stoichiometry_energy.tar.xz contain files used for data processing.

• Files with names like database.ref contain information used to calculate the final energies and errors. They are tab-delimited files. Each row represents an energy difference (e.g. atomization energy, barrier height, etc.). The first column contains the name of the energy difference (note: spaces may be present in this column). This is followed by the file names of each electronic structure calculation and the stoichiometries used to calculate the energy difference from the absolute energies. Each name or stoichiometry occupies one column. The second from the last column contains the reference value in kcal/mol. The reference values contain spin–orbit coupling. The last column contains the factor by which the final energy should be divided. This factor usually equals 1, but it can be greater than 1 for databases calculating atomization energies per bond or per atom.
• Files with names like method.elist contain the absolute energies of each electronic structure calculation. They are tab-delimited files. Each row represents an electronic structure calculation, and each row always contains two columns. The first column is the file name of the calculation in the format database/system. The second column is the absolute energy in atomic units extracted from the output file of electronic structure programs.
• The file named SOC.dat contains the spin–orbit coupling term in kcal/mol to be added to each electronic structure calculation prior to calculating energy differences. It has the same format as files with names like method.elist.

The database names in the directory names use a slightly different convention than the ones in the article describing MC23. A prefix DS2_ or DS3_ is used to indicate the data set to which a database belongs, and the number of data points is removed from the database name. For example, the MR-MGN-BE8 database from Data Set 2 has a file name DS2_MR-MGN-BE.

Dataset Overview

This robust dataset delves into the world of biogas production from livestock farming across the United States, providing a pivotal tool for assessing renewable energy prospects. With a focus on biogas projects derived from various livestock such as cattle, dairy cows, poultry, and swine, this resource is invaluable for stakeholders in the farming industry, renewable energy sectors, and environmental policy-making. Each record encapsulates detailed information about a specific biogas project, making it a treasure trove for research, development, and strategic planning in the renewable energy domain.

Key Features:

📌 Project Name: The name of the biogas project. 📍**Project Type:** Type of the biogas project. 🌆 City: The city where the project is located. 🏞️ County: The county where the project is situated. 🗺️**State:** The state where the project is located. 🔬 Digester Type: Type of digester used in the project. 🔍 Status: Current status of the project. 📅 Year Operational: The year when the project became operational. 🐄 Animal/Farm Type(s): Types of animals or farms used in the project. 🐄 Cattle: Number of cattle involved. 🥛**Dairy:** Number of dairy cows involved. 🐔 Poultry: Number of poultry involved. 🐖 Swine: Number of swine involved. 🔄 Co-Digestion: Information on whether co-digestion is being used or not. 🌬️ Biogas Generation Estimate (cu-ft/day): Estimated daily biogas production. ⚡ Electricity Generated (kWh/yr): Estimated annual electricity generation. 💡 Biogas End Use(s): How the produced biogas is utilized. 🌿 LCFS Pathway?: Information on the Low Carbon Fuel Standard pathway. 🔌 Receiving Utility: The utility company receiving the biogas or electricity. 🌍 Total Emission Reductions (MTCO2e/yr): Estimated total emission reduction. 🏆 Awarded USDA Funding?: Information on whether the project received USDA funding or not. 📊 Operational Years:Number of years the project has been operational. 🦓 Total_Animals: Total number of animals involved in the project. 💨 Biogas_per_Animal (cu-ft/day): Estimated biogas production per animal. 🌱 Emission_Reduction_per_Year: Estimated annual emission reduction per animal. 🔋 Electricity_to_Biogas_Ratio: The ratio between electricity generation and biogas production. 🗑️ Total_Waste_kg/day: Estimated daily waste production. ⚙️ Waste_Efficiency: Efficiency of waste conversion to biogas. 🔧 Electricity_Efficiency: Efficiency of biogas conversion to electricity.

This dataset stands as a cornerstone for developing strategies that can enhance profitability for farmers, guide investment decisions for energy companies, and contribute significantly to environmental sustainability efforts.

Technical Assistance on the Energy Supply and Demand Database : To assist member countries to establish and maintain a compatible, accurate, reliable and up-to-date energy supply and demand database so as to enable effective management and planning of their national energy sectors. This is ongoing task that will provide assistance to countries to source and document energy data, however, progress has been minimum with PICs not putting priority o data collation and due to shortage of staff.

A national summary of transit Vehicles and Energy Consumption based on data reported by transit agencies to the National Transit Database (NTD) in Report Year 2024. Only Full Reporters report energy consumption, while other reporter types report vehicles.

NTD Data Tables organize and summarize data from the 2023 National Transit Database in a manner that is more useful for quick reference and summary analysis. This dataset is based on the 2023 Energy Consumption database file and Revenue Vehicles Inventory database file.

Power plants with a capacity of at least 1 MW are included in totals. Counties with no
symbol have no utility-scale renewable electric generation. Distributed generation, such as
rooftop solar, is not included. Data is classified using Jenks Natural Breaks method.
Projection is WGS 1984 California (Teale) Alberts (US Feet). Data sources are the California
Energy Commission's Quarterly Fuel and Energy Report and the Wind Generation
Reporting System databases. Data provided is for the year 2024 and is current as of July
1, 2025. For further inquiries contact John Hingtgen at john.hingtgen@energy.ca.gov.

The Electric Vehicle (EV) charging permitting processes' database is a novel, multi-jurisdictional resource designed to contain the required codes and compliances in a structured database. Within this database are three tables, each structured with 287 columns, designed to capture detailed information spanning electrical, structural, zoning, and accessibility aspects, along with data regarding fees, reviews, and process durations. The database contains 99 state-level documents pertaining to 36 U.S. states, in addition to 87 county-level and 101 city-level documents, thus offering a complete overview of guidance and practices regarding permitting. The data was gathered via an Azure-hosted GPT-4o workflow, supplemented by targeted manual Google searches. State and county materials were located and extracted using the GPT-4o model. The Large Language Models (LLM) were used in conjunction with the decision tree framework with targeted prompts to extract the key information. The structured database incorporates Tables 1-3 included below as resources, as well as Table 4 which provides the scores for each document based on the scoring criteria in the paper (to be added after publication). The database can be used to compare and identify the patterns and trends in the requirements across different authorities having jurisdictions. This resource can be used by researchers, policymakers, and project teams. Note: LLMs are known to make mistakes in the interpretation of complex procedural documents and therefore no one should rely solely on this database to inform their own real-world EV infrastructure projects.

This map outlines the total renewable electrical generation in gigawatt-hours (GWh) for all counties in California for 2019. Sources below 1 megawatt (MW) were not included in this map. Counties without a symbol had no utility-scale (commercial) renewable electric generation installed. The table depicts the amount of renewable energy production for each energy type for every county. Data obtained from Quarterly Fuel and Energy Reports (QFER) and the Wind Performance Reporting System (WPRS) databases.

Energy Climate dataset consistent with ENTSO-E Pan-European Climatic Database (PECD 2021.3) in CSV and netCDF format

TL;DR: this is a tidy and friendly version of a recreation of ENTSO-E's PECD 2021.3 data by using ERA5: hourly capacity factors for wind onshore, offshore, solar PV and hourly electricity demand are provided. All the data is provided for 28-71 climatic years (1950-2020 for wind and solar, 1982-2010 for demand).

Description
Country averages of energy-climate variables generated using the Python scripts, based on the ENTSO-E's TYNDP 2020 study. For the following scenario's data is available

• National trends 2025 (NT 2025)
• National trends 2030 (NT 2030)
• National trends 2040 (NT 2040)
• Distributed Energy 2030 (DE 2030)
• Distributed Energy 2040 (DE 2040)
• Global Ambitions (GA 2030)
• Global Ambitions (GA 2040)

The time-series are at hourly resolution and the included variables are:

• Generation wind offshore (aggregated for all years per scenario in a .zip)
• Generation wind onshore (aggregated for all years per scenario in a .zip)
• Generation solar photovoltaic (aggregated for all years per scenario in a .zip)
• Total energy demand (all zones combined in single file per scenario)

The Files are provided in CSV (.csv) & NetCDF (.nc). The data is given per ENTSO-E's bidding zone as used within the TYNDP2020.

DISCLAIMER: the content of this dataset has been created with the greatest possible care. However, we invite to use the original data for critical applications and studies.

Power plants with capacity of at least 1 MW are included in totals. Counties shaded in grey have no utility-scale renewable electric capacity. Distributed generation such as rooftop solar, is not included. Data is classified using Jenks Natural Breaks Method. Projection is WGS 1984 California *Teale Albers (US Feet). Data sources are the California Energy Commission's Quarterly Fuel and Energy Report and the Wind Generation Reporting System databases. Data provided is for the year 2023 an dis current as of June 20, 2024. For further inquiries contact John Hingtgen at john.hingtgen@energy.ca.gov.

AIMEl-DB: Atomic Properties for 44K small organic molecules

This dataset comprises atomic properties of 44K (44 470) molecules selected from the QM9 database. The file names are based on the same indexing system used for QM9.

This dataset includes four types of files:

• .com Files
  Input files for Gaussian 16. Simple-point energy calculations were carried out using the keywords
  # B3LYP/6-31G(2df,p) scf=(maxcycle=9999) nosymm output=wfx
  
  
• .log Files
  Output files from Gaussian 16 calculation with the aformentioned parameters.
  
  
• .wfx Files
  Wave function files from Gaussian 16 calculation. These files were used as inputs for QTAIM calculations.
  
  
• .sumviz Files
  Output file from AIMAll software. The keywords used for the calculations were
  aimqb -nogui -scp=false -nproc=8 -naat=4 input.wfx
  Each .sumviz file contains more than 30 properties based on the Quantum Theory of Atoms in Molecules (QTAIM).
  
  
• .csv Files
  These files contain the results of a in-house treament of .sumviz data. They cointain two calculated atomic properties:
  
  
  1. Total magnitude of the dipole moment, |mu|
  2. Total magnitude of the quadrupole moment, |Q|

and two extracted atomic properties:

3. Electronic Population, N
4. Atomic Energy, E

The aimel_merged_44k.csv presents the concatenation of the 44 470 csv Files.

Additionaly, the aimel_merged_38k.csv presents the concatenation of the 38 876 csv Files. This file corresponds to the version 1.0 of the dataset.

If you find this dataset useful, please cite the original paper:

Meza-González, B., Ramírez-Palma, D.I., Carpio-Martínez, P. et al. Quantum Topological Atomic Properties of 44K molecules. Sci Data 11, 945 (2024). https://doi.org/10.1038/s41597-024-03723-0

This dataset contains municipal solar resource data, generated using the toscana tool. Dataset description Each file corresponds to a specific village. Two types of files are available in this dataset : a solar cadastre in raster format, representing the annual solar irradiation (in kWh/m²) received by the surfaces, and a file containing the average annual solar irradiation per building (in kWh/m²) in shapefile format. Study context This dataset was created within the frame of a study analyzing the impact of topography on rooftop solar potential. In this study, 92 French villages were simulated, that are located in the Alps and in the Rhône Valley. This study has made the object of a publication in Solar Energy, 2024, doi.org/10.1016/j.solener.2024.112632 To extend the research, the dataset was supplemented with 100 additional villages to investigate the combined effect of topography and local parameters, such as local climate, urban planning, dataset, and variations in topographic features. These other villages are located in the French Pyrenees, in mountainous regions of foreign countries (Spain, Italy, Switzerland, Germany) and in French lowland areas other than the Rhône Valley. The data are categorized by village, and villages are further classified based on their geographical location. French mountain villages are stored in the "Alps" or "Pyrenees" folders, while lowland villages are found in "Rhône_Valley" and "Other_plain_villages" folders. Foreign mountain villages are in the "Foreign_mountain_villages" folder and further subdivided by country. Methodology The metholodology used to obtain municipal solar resource data is described in detail in a publication ( https://doi.org/10.1016/j.solener.2024.112632) and in the documentation of the toscana package, available on GitHub at the following address : https://github.com/locie/toscana/tree/main/doc/_build. The BDTOPO database is used to retrieve the municipal boundaries and building footprints, while the EU-DEM database is used to provide the DEM. QGIS functions are used to process geographical data, create a DSM and divide the territory into tiles to reduce the computational time. Meteorological data (Typical Meteorological Year) are retrieved from PVGIS for each tile and averaged weather files are used in the simulations to account for the difference in spatial resolution between the tiles and the meteorological database. Solar energy simulations are conducted using the SEBE algorithm for each tile. Finally, QGIS functions are applied to process and generate the final output files.

This dataset contains a comprehensive and comparable database of Canadian emissions reduction policies across federal, provincial and territorial orders of government. The dataset comprises two database files (both .csv and .xlsx formats) in English and French. Source data is primarily in text format and from a variety of government and non-government sources. These sources include federal biennial reports to the United Nations Framework Convention on Climate Change (UNFCCC); provincial, territorial, and federal climate plans; and past work by Navius Research Inc. and the Canadian Climate Institute to build a Canadian climate policy tracker. Policy information is converted into textual categories in the database according to the codebook developed by the research team and attached to this dataset. Relevant policies are those targeting climate change mitigation (e.g., emissions reductions such as emissions pricing or the Clean Fuel Regulations), and policies to change energy use and sources (e.g., subsidizing renewable energy sources). This database is a project of the Canadian Climate Policy Partnership (C2P2). The goal of C2P2 is to inform smart, coherent and new policy options to meet Canada’s net zero transition by engaging in data-driven research on and analysis of climate policies in Canada, and supporting such activities in a broader network of researchers. For more information on C2P2, please visit the project webpage: https://spp.ucalgary.ca/school-public-policy/research/energy-and-environmental-policy/canadian-climate-policy-partnership-c2p2. The project README file contains further information on data format and versioning practices. Further details about C2P2 and the database can be found in a methodology paper, available in English only, on the Social Science Research Network (SSRN). An abridged methodology document, available in both English and French, is available on PRISM, the institutional repository of the University of Calgary. Both are linked in the Related Publications section of the database metadata.