AndreaBozzo/ceres-open-data-index huggingface dataset:
DEPRECATED - Sharing the Sun Community Solar Project Data (May 2020)

This data set is no longer current – The most current data and all historical data sets can be found at https://data.nlr.gov/submissions/244&nbsp;This database represents a list of community solar projects identified through various sources as of May 2020. The list has been reviewed but errors may exist and the list may not be comprehensive. Errors in the sources e.g. press releases may be duplicated in the list. Blank spaces represent missing information. NREL invites input to improve the database including to - correct erroneous information - add missing projects - fill in missing information - remove inactive projects.&nbsp;Updated information can be submitted to the contact(s) located on the current data set page linked at the top.

Tags: community-solar, solar, utility-service-territory
Organization: Department of Energy
Mayor's Management Report - Agency Performance Indicators

This dataset reflects measures of agency performance, organized by goal. Components of this full dataset appear in the Dynamic Mayor's Management Report (DMMR, dmmr.nyc.gov), the Mayor's Management Report (MMR) and Preliminary Mayor's Management Report (PMMR).

Tags: actual, critical, desired-direction, goals, indicators, key-performance-indicators, kpi, kpis, management, mayors-management-report, metrics, mmr, mmr-goals, mmr-services, mmr-targets, operations, ops, performance, pmmr, report, resources, services, target
Organization: City of New York
DEPRECATED: Levelized Cost of Charging Electric Vehicles

DEPRECATED: SEE https://data.nlr.gov/submissions/150 FOR CORRECTED DATA.&nbsp;This data set includes the levelized cost of charging (LCOC) and lifetime fuel cost savings (LFCS) values as reported in "Levelized Cost of Charging of Electric Vehicles in the United States." Values are reported at the state and national levels for battery electric vehicles (BEVs) and plug-in hybrid electric vehicles (PHEVs).&nbsp;The data set also includes the four annual direct current fast charging (DCFC) station load profiles used to approximate the levelized cost of DCFC charging. Each profile provides 15-min resolved power requirements for one full year.&nbsp;Borlaug, B., Salisbury, S., Gerdes, M., and Muratori, M., Levelized Cost of Charging Electric Vehicles in the United States, Joule (2020), https://doi.org/10.1016/j.joule.2020.05.013.&nbsp;DEPRECATED: SEE https://data.nlr.gov/submissions/150 FOR CORRECTED DATA.

Tags: charging, cost-of-electricity, demand-charge, electric-vehicle, ev, fuel-cost, time-of-use, utility-rate-analysis
Organization: Department of Energy
DEPRECATED: dGen (Distributed Generation Market Demand) Model Data: Alpha Release

DEPRECATED. DO NOT USE. See current version at https://dx.doi.org/10.7799/1812548. See active link below in the resources section.&nbsp;Open sourced data needed to run the basic alpha release version of the dGen model. Includes a pre-generated agent file of 100,000 agents in pickle file format along with the base schema and table data in parquet format that are needed to create a postgreSQL database for the model to interact with.

Tags: adoption, agent-based-model, agent-based-modeling, dgen, dgen-alpha-release, distributed-generation-market-demand-model, photovoltaic, pv-adoption, resilient-planning-for-distributed-energy-resources, rider, solar, solar-adoption, tea, techno-economic-analysis, techno-economic-modeling
Organization: Department of Energy
Electrification Futures Study Flexible Load Profiles

This data set includes hourly profiles for flexible load developed for the Electrification Futures Study (EFS). The load profiles represent projected end-use electricity demand that is assumed to be flexible (i.e., can be shifted throughout a day) for various scenarios of flexibility (Base, Enhanced), electrification (Reference, Medium, High), and technology advancement (Slow, Moderate, Rapid), and were developed as inputs into the ReEDS model. The quantity of flexible load is estimated using assumptions on the level of flexibility and customer participation within each subsector modeled in the EFS. Detailed assumptions and modeling implementation will be documented in ongoing EFS analyses. Flexible load profiles are provided for a subset of years (2018, 2020, 2024, 2030, 2040, 2050) and are aggregated to the state and sector level. Total electricity load profiles can be found in a related EFS data set (https://dx.doi.org/10.7799/1593122).

NOTE: Due to the file size, Mac users may experience issues decompressing the zip files using the Mac Archive Utility. In those cases, decompressing using the command line is recommended.

- Mai, Trieu, Paige Jadun, Jeffrey Logan, Colin McMillan, Matteo Muratori, Daniel Steinberg, Laura Vimmerstedt, Ryan Jones, Benjamin Haley, and Brent Nelson. 2018. Electrification Futures Study: Scenarios of Electric Technology Adoption and Power Consumption for the United States. National Renewable Energy Laboratory. NREL/TP-6A20-71500. https://doi.org/10.2172/1459351.
- Murphy, Caitlin, Trieu Mai, Yinong Sun, Paige Jadun, Matteo Muratori, Brent Nelson, Ryan Jones. Forthcoming. Electrification Futures Study: Scenarios of Power System Evolution and Infrastructure Development for the United States. National Renewable Energy Laboratory. 
- Sun, Yinong, Paige Jadun, Brent Nelson, Matteo Muratori, Caitlin Murphy, Jeffrey Logan, and Trieu Mai. Forthcoming. Electrification Futures Study: Methodological Approaches for Assessing Long-Term Power System Impacts of End-Use Electrification. National Renewable Energy Laboratory.

Tags: buildings, demand-side-flexibility, efs, electrification, electrification-futures-study, end-use-technologies, industry, load-profile, transportation, united-states
Organization: Department of Energy
DEPRECATED - State Policies and Programs for Community Solar (2022)

This data set is no longer current – The most current data and all historical data sets can be found at https://data.nlr.gov/submissions/249&nbsp;The purpose of this dataset is to summarize current community solar policies and low-income stipulations by the state in the United States as of December 2022. The "State_Program" sheet summarizes the key policy details for each state. This dataset is associated with the National Renewable Energy Laboratory (NREL) publication "Expanding Solar Access: State Community Solar Landscape (2022)." https://www.nlr.gov/docs/fy23osti/84247.pdf&nbsp;This list has been reviewed, but errors may exist, and the list may not be comprehensive. NREL invites input to update or add to the database. Please submit updates, additions, or corrections please find contact information on the current data set page linked above.

Tags: community-solar, market, policy, solar
Organization: Department of Energy
Electrification Futures Study Load Profiles

This data set includes hourly load profiles developed for the Electrification Futures Study (EFS). The load profiles represent projected end-use electricity demand for various scenarios of electrification (Reference, Medium, High) and technology advancement (Slow, Moderate, Rapid), developed using the EnergyPATHWAYS (EP) model, as described in Mai et al. (2018). The EP load profiles were further calibrated for use in the ReEDS capacity expansion model (detailed in forthcoming EFS analyses), thus numerical values in this data set differ slightly from those reported in Mai et al. (2018). Load profiles are provided for a subset of years (2018, 2020, 2024, 2030, 2040, 2050) and are aggregated to the state, sector, and select subsector level. 

NOTE: Due to the file size, Mac users may experience issues decompressing the zip files using the Mac Archive Utility. In those cases, decompressing using the command line is recommended.
 
Mai, Trieu, Paige Jadun, Jeffrey Logan, Colin McMillan, Matteo Muratori, Daniel Steinberg, Laura Vimmerstedt, Ryan Jones, Benjamin Haley, and Brent Nelson. 2018. Electrification Futures Study: Scenarios of Electric Technology Adoption and Power Consumption for the United States. National Renewable Energy Laboratory. NREL/TP-6A20-71500. https://doi.org/10.2172/1459351.

Tags: 2050, buildings, efs, electrification, electrification-futures-study, electrotechnologies, end-use-technologies, energy-transitions, industry, load-profile, transportation, united-states
Organization: Department of Energy
Election Districts

New York City Board of Elections election district boundaries for New York City clipped to the shoreline.

All previously released versions of this data are available on the <a href="https://www.nyc.gov/content/planning/pages/resources/datasets/election-districts">DCP Website: BYTES of the BIG APPLE</a>. Current version: 26a

Tags: boundaries, boundary, cscl, dcp, department-of-city-planning, districts, election-districts, lion, new-york-city, nyc, political
Organization: City of New York
2018 Industrial Energy Data Book

The Industrial Energy Data Book (IEDB) aggregates and synthesizes information on the trends in industrial energy use, energy prices, economic activity, and water use. The IEDB also estimates county-level industrial energy use and combustion energy use of large energy-using facilities (i.e., facilities required to report greenhouse gas emissions under the EPA's Greenhouse Gas Reporting Program). These estimates are derived from publicly available sources from EPA, Energy Information Administration, Census Bureau, USDA, and USGS. The estimation methodology is meant to be improved over time with input from the energy analysis and developer communities. Please refer to https://github.com/NREL/Industry-energy-data-book.

Tags: agriculture, construction, county, industrial-energy-data-book, industry, manufacturing, mining
Organization: Department of Energy
Rooftop Photovoltaic Technical Potential in the United States

This data submission contains a workbook with data, and three sets of shapefiles that correspond to that data.&nbsp;Contact: Meghan Mooney, Meghan.Mooney@nrel.gov&nbsp;The workbook contains data that underlies the 2016 technical report Rooftop Solar Photovoltaic Technical Potential in the United States: A Detailed Assessment.&nbsp;This data is separated into small, medium, and large buildings. A small building is less than 5,000 square feet, a medium building is between 5,000 and 25,000 square feet, and a large building is greater than 25,000 square feet. The suitability of roof area for hosting PV was determined based on shading (energy production not less than 70% of an identical unshaded system in the same location), tilt (no greater than 60 degree tilt), azimuth (not facing north), and contiguous roof area (at least 10 square meters available) criteria. For further documentation of methods and assumptions, see the 2016 report.&nbsp;The tab Rooftop_PV_with_Lidar_Coverage contains estimates for zip codes where at least some lidar data was available. The zip_percentage column indicates the percentage of area of each zip code was covered by lidar data -- only buildings within the covered area are reported here, meaning that the values for incompletely covered zip codes are under-reported.&nbsp;The tab Small_Building_Suitability contains estimates of the number of small buildings and the percentage of those buildings that have at least some area that is suitable for rooftop PV, for all zip codes in the contiguous US. Where lidar data was not available, a model was used to estimate the data. Estimates for medium and large buildings were not produced at the zip code level, because of insufficient input data.

Tags: photovoltaic, pv, rooftop, technical-potential
Organization: Department of Energy
Leveraging invasive mussel contaminant survey data for stepwise prioritization of chemicals of potential concern in the Great Lakes basin

Supplementary data for "Leveraging invasive mussel contaminant survey data for stepwise prioritization of chemicals of potential concern in the Great Lakes basin". 

This dataset is associated with the following publication:
Fuller, N., K. Kimbrough, M. Edwards, E. Maloney, S. Corsi, M. Pronschinske, L. DeCicco, J. Frisch, A. Baldwin, S. Hummel, N. Garcia Reyero, and D. Villeneuve. Leveraging invasive mussel contaminant survey data for stepwise prioritization of chemicals of potential concern in the Great Lakes basin.   ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY. Society of Environmental Toxicology and Chemistry, Pensacola, FL, USA, 44(7): 2070-2087, (2025).

Tags: chemical-prioritization, contaminants-of-emerging-concern, ecotoxicology, risk-assessment
Organization: U.S. Environmental Protection Agency
U.S. Voluntary Green Power Market Data 2018

This data book provides certain data behind figures and tables found in the NREL presentation Status and Trends in the U.S. Voluntary Green Power Market 2018 Data. These data reflect estimates based on the best available data.

Tags: community-solar, corporate-procurement, energy-attribute-certificate, green-power-sales, rec, renewable-energy-certificate, voluntary-market
Organization: Department of Energy
Utah FORGE: Laboratory Data for Emergent Pore Pressure Heterogeneity Controlling Slip Timing and Microseismicity

Included are experimental data recorded from shear experiments that explore the effects of pore pressure heterogeneity on microseismic character and fault slip timing resulting from shear reactivation of laboratory faults. Raw mechanical and acoustic data from 15 experiments are included alongside two MATLAB scripts (uniform and non-uniform pore pressure profiles) that import and plot the data, as well as use it to calculate shear and normal stress.

Experiments are performed on 2.5-3 inch long granitoid cores from the Utah FORGE EGS demonstration site, containing a single inclined fracture with small-scale roughness added to the fracture surface. The raw data included here were recorded from an aluminum triaxial pressure vessel (TEMCO) configured with three independent servo-controlled pumps, with DI water used as the working fluid. The pumps control confining pressure, upstream pore pressure, and axial pressure, with each pump connected to a LabView interface to record applied pressures, cumulative injected water volumes, and pump flow rates. The downstream outlet from the fracture is closed to allow pressurization, which is measured by an external pressure transducer. A linear variable differential transformer (LVDT) attached to the axial piston measures axial displacement, from which we calculate shear displacement along the fracture. Additionally, P-wave transducers are used to record acoustic signatures, where acoustic emission events and maximum amplitudes are compared against seismic moment and shear slip velocity.

Fluid injection rates range between 0.05 mL/min, 0.25 mL/min, and 0.75 mL/min for each experiment. Triggered shear displacement is used as a proxy for seismic moment and is indexed against cumulative injection volume and rate. Each experiment is performed under constant shear stress conditions, and the sample is fully saturated with DI water. Axial and confining stresses are applied to 3 MPa through pressure-stepping in 500 kPa increments. The pore pressure is held constant at 200 kPa prior to initiating the experiment, and initial axial displacement is recorded. The axial stress is then increased to initiate shear mobilization during the loading phase (run-in) until a peak steady state is achieved. The initial shear stress is reduced to approximately 60, 80, or 90% of the peak shear strength by decreasing the axial stress, then held constant for the duration of each experiment.

Tags: acoustic-emissions, egs, energy, fault-reactivation, fault-slip-timing, fluid-injection, geomechanics, geothermal, granitoid-cores, induced-seismicity, injection-rate, laboratory-data, microseismicity, pore-pressure-heterogeneity, raw-data, shear-experiments, triaxial-deformation, utah-forge
Organization: Department of Energy
Newberry Volcano Joint Active-Source and Teleseismic P-Wave Tomography Slowness Model and Processing Code

This dataset contains a three-dimensional seismic slowness model and MATLAB processing code derived from P-wave seismic data collected at Newberry Volcano in central Oregon. The associated study combines active-source first-arrival travel times with teleseismic P-wave delay times to jointly image upper-crustal velocity structure beneath the volcano (targeting the shallow crust to ~10 km depth).

The included workflow uses a one-dimensional velocity model and a three-dimensional slowness model to compute a fractional velocity perturbation (dv/v) volume and to plot horizontal slices and vertical sections. The script reads the 1D model, interpolates it onto the depth grid of the 3D model, converts the 1D velocity profile to slowness, and then forms dv/v by comparing the 1D slowness volume to the 3D slowness model before plotting results. A python version of the workflow is also included, which was used to create the provided 3D model grid CSV files.

These data have been used by and uploaded to the Geothermal Data Repository (GDR) as part of the DE-risking Exploration of geothermal Plays in magmatic ENvironments (DEEPEN) project. The data were originally produced through the 2008 Newberry Seismic Experiment, funded by a USGS Venture Capital grant and NSF grants (EAR-207670 and EAR-207671). Seismic data from the 2008 experiment and from earlier 1983 and 1984 USGS experiments are archived at the IRIS DMC. Please refer to the associated manuscript (linked below) for additional details on the original data collection and research acknowledgements.

Tags: active-source-seismology, deepen, energy, geophysics, geothermal, joint-inversion, matlab, newberry-volcano, p-wave, processed-data, seismic-tomography, slowness, slowness-model, superhot, teleseismic-delay-times, upper-crust, velocity-model
Organization: Department of Energy
Data for "Gold-Induced Chemical Perturbations in CdTe-Based Photovoltaic Cells"

Back contacting p-type CdTe has been identified as one of the major areas of loss in CdTe photovoltaic (PV) power conversion efficiency (PCE).&nbsp; In research settings, Au is a common contact material due to its ease of use and decent performance.&nbsp; This work provides a detailed investigation into using gold for back contacting As-doped, CdCl2 treated, polycrystalline CdTe that has been exposed to air after absorber processing, another routine practice.&nbsp;First, X-ray photoemission spectroscopy (XPS) is used to determine the native oxide to be 1.6 nm of CdTeO3 using a combination of angle-resolved XPS and the cadmium modified Auger parameter.&nbsp; During gold metallization of CdTe, oxygen and oxidized tellurium are eliminated from the thin CdTeO3 native oxide.&nbsp; The fate of the released oxygen and possibly cadmium and tellurium are not known, but these reaction byproducts can enter the absorber bulk or grain boundaries, stay at the interface, or dissolve in the Au.&nbsp; Interfacial hole barriers between CdTe and Au are measured for samples with and without the native oxide present prior to metallization.&nbsp;Results show that the thin CdTeO3 alleviates the downward band bending by 40 meV from 470 meV to 430 meV even though it is consumed during interface formation.&nbsp; The implications of these chemical reactions on the device are assessed through photoluminescence (PL) spectroscopy which shows losses in internal open circuit voltage (iVoc) from 820 meV to 795 meV, carrier lifetime from 123 ns to 45 ns, and PL quantum yield from 2.9x10-5 to 1.2x10-5.&nbsp; Modeling time-resolved PL lifetimes demonstrates the back surface recombination velocity due to metallization reduces minority carrier lifetimes.&nbsp; These results identify the native oxide and show that it plays an important role in mediating downward band bending along with how the back interface reaction can negatively impact device-scale parameters and reduce PV PCE.&nbsp;

Tags: auger, cdte, metallization, native-oxide, photoemission, photoluminesence, photovoltaic
Organization: Department of Energy
Low-Income Communities Bonus Credit Program Layers (Update 2026)

These geospatial data resources and the linked mapping tool below reflect data available as of January 2026 on three categories of potentially qualifying Low-Income communities:&nbsp;Census tracts that meet the CDFI's New Market Tax Credit Program's threshold for Low Income, thereby are able to apply to Category 1.Census tracts that meet the CEJST threshold for disadvantage in the 'Energy' category, thereby are able to apply for Additional Selection Criteria Geography.Counties that meet the USDA's threshold for Persistent Poverty, thereby are able to apply for Additional Selection Criteria Geography.&nbsp;Note that Category 2 - Indian Lands are not shown on this map. Note that Persistent Poverty is not calculated for US Territories. Note that CEJST Energy disadvantage is not calculated for US Territories besides Puerto Rico.&nbsp;The excel tool provides the land area percentage of each 2025 census tract meeting each of the above categories. To examine geographic eligibility for a specific address or latitude and longitude, visit the program's mapping tool.&nbsp;Additional information on this tax credit program can be found at https://www.irs.gov/credits-deductions/low-income-communities-bonus-credit.&nbsp;Maps last updated: January 2026Next map update expected: September 2027Disclaimer: The spatial data and mapping tool is intended for geolocation purposes. It should not be relied upon by taxpayers to determine eligibility for the Low-Income Communities Bonus Credit Amount Program.&nbsp;Source Acknowledgements:&nbsp;The New Market Tax Credit (NMTC) Tract layer is from the CDFI Information Mapping System (CIMS) and is created by the U.S. Department of Treasury Community Development Financial Institutions Fund. To learn more, visit CDFI Information Mapping System (CIMS) | Community Development Financial Institutions Fund (cdfifund.gov). https://www.cdfifund.gov/mapping-system. Tracts are displayed that meet the threshold for the New Market Tax Credit Program.CEJST-Energy Tracts are displayed that meet the threshold for the 'Energy' Category of burden. I.e., census tracts that are at or above the 90th percentile for (energy burden OR PM2.5 in the air) AND are at or above the 65th percentile for low income.The Persistent Poverty County layer is created by joining the U.S. Department of Agriculture, Economic Research Service's Poverty Area Official Measures dataset, with relevant county TIGER/Line Shapefiles from the US Census Bureau. To learn more, visit https://www.ers.usda.gov/data-products/poverty-area-measures/. Counties are displayed that meet the thresholds for Persistent Poverty according to 'Official' USDA updates. i.e. areas with a poverty rate of 20.0 percent or more for 4 consecutive time periods, about 10 years apart, spanning approximately 30 years (baseline time period plus 3 evaluation time periods).

Tags: low-income-communities, solar-energy, wind-energy
Organization: Department of Energy