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54 results for “decarbonization”
Decarbonizing primary steel production : Techno-economic assessment of green steel production in Norway
<p>Python codes for the modelling of a grid connected Hydrogen direct reduced plant combined with an electrical arc furnace for steel production. </p>
EU-27 Country Mapping of Financing Schemes to decarbonize Buildings, Heating and Cooling
<p>This dataset contains the mapping of all public and private financing instruments currently available to support the decarbonization of the building stock. The mapping is divided into two sheets: Public Schemes and Private Schemes. Each scheme is classified per country, level (European, National, Regional, Local), Name in English and in the local language, sectors (Y= directly covered, (Y)= indirectly covered, that is not explicitly mentioned, but reasonably applicable, blank= not covered), type of instrument, main and additional links, a short description and the last time the page was visited. Additional socio-economic, climate and energy indicators and a correlation matrix are provided.</p>
Planetary boundaries analysis of Fischer-Tropsch Diesel for decarbonizing heavy-duty transport
<p>Dataset associated with the publication "Planetary boundaries analysis of Fischer-Tropsch Diesel for decarbonizing heavy-duty transport" by Margarita A. Charalambous, Juan D. Medrano-Garcia, and Gonzalo Guillén-Gosálbez, available at <a href="https://doi.org/10.1016/B978-0-323-85159-6.50328-6">https://doi.org/10.1016/B978-0-323-85159-6.50328-6</a>. The dataset includes the numeric data required to plot all the figures embedded in the manuscript.</p> <p>The structure of the dataset is here elucidated sheet by sheet:</p> <ul> <li><strong>LCA-Inventories:</strong> Inventory datasets used for life cycle assessment. Includes the inventory for the production of FT-diesel from CO<sub>2</sub> and H<sub>2</sub> sources investigated in this work, carbon dioxide from direct air capture (DAC), and point source coal power plant, as well as, the production of hydrogen from biomass and polymer electrolyte water electrolysis. Moreover, required adjustments to accommodation FT-diesel fuel in the truck transport activity are summarized.</li> <li><strong>LCA-Total</strong>: numerical values associated with the total share of safe operating space for all the assessed control variables of the seven planetary boundaries quantified in the study, for all the considered scenarios. These values represent the data used to create Figure 2.</li> <li><strong>LCA-Breakdown</strong>: numerical values associated with the breakdown of the environmental impacts for the studied scenarios, for three control variables (CO<sub>2</sub> concentration, and biosphere integrity). These values represent the data used to create Figure 3. </li> </ul>
Data for Identifying Robust Decarbonization Pathways for the Western U.S. Electric Power System under Deep Climate Uncertainty
<p>Data from the paper "Identifying Robust Decarbonization Pathways for the Western U.S. Electric Power System under Deep Climate Uncertainty"</p>
GCAM input files for "Decarbonization pathways for Korea's industrial sector towards its 2050 carbon neutrality goal"
<p>GCAM input files for "Decarbonization pathways for Korea's industrial sector towards its 2050 carbon neutrality goal"</p>
Codes and Data for 'Cost-effective Planning of Decarbonized Power-Gas Infrastructure to Meet the Challenges of Heating Electrification'
<p>The codes and data used in the followng paper</p> <p>''Khorramfar, R., Santoni-Calvin, M., Mallapragada, D., Amin, S., Botterud, A.,<br>Norfork L., (2025) Cost-effective Planning of Power-Gas Infrastructure to Meet the Challenges<br>of Heating Electrification, Cell Reports Sustainability</p> <p> </p> <p>Link (open source): https://www.cell.com/cell-reports-sustainability/fulltext/S2949-7906(25)00003-5</p> <p> </p>
PSML: A Multi-scale Time-series Dataset for Machine Learning in Decarbonized Energy Grids (Dataset)
<p><strong>Abstract</strong></p> <p>The electric grid is a key enabling infrastructure for the ambitious transition towards carbon neutrality as we grapple with climate change. With deepening penetration of renewable energy resources and electrified transportation, the reliable and secure operation of the electric grid becomes increasingly challenging. In this paper, we present PSML, a first-of-its-kind open-access multi-scale time-series dataset, to aid in the development of data-driven machine learning (ML) based approaches towards reliable operation of future electric grids. The dataset is generated through a novel transmission + distribution (T+D) co-simulation designed to capture the increasingly important interactions and uncertainties of the grid dynamics, containing electric load, renewable generation, weather, voltage and current measurements at multiple spatio-temporal scales. Using PSML, we provide state-of-the-art ML baselines on three challenging use cases of critical importance to achieve: (i) early detection, accurate classification and localization of dynamic disturbance events; (ii) robust hierarchical forecasting of load and renewable energy with the presence of uncertainties and extreme events; and (iii) realistic synthetic generation of physical-law-constrained measurement time series. We envision that this dataset will enable advances for ML in dynamic systems, while simultaneously allowing ML researchers to contribute towards carbon-neutral electricity and mobility. </p> <p><strong>Data Navigation</strong></p> <p>Please download, unzip and put somewhere for later benchmark results reproduction and data loading and performance evaluation for proposed methods.</p> <pre><code>wget https://zenodo.org/record/5130612/files/PSML.zip?download=1 7z x 'PSML.zip?download=1' -o./ </code></pre> <p><strong>Minute-level Load and Renewable</strong></p> <ul> <li>File Name <ul> <li>ISO_zone_#.csv: `CAISO_zone_1.csv` contains minute-level load, renewable and weather data from 2018 to 2020 in the zone 1 of CAISO.</li> </ul> </li> <li>- Field Description <ul> <li>Field `<em>time</em>`: Time of minute resolution.</li> <li>Field `<em>load_power</em>`: Normalized load power.</li> <li>Field `<em>wind_power</em>`: Normalized wind turbine power.</li> <li>Field `<em>solar_power</em>`: Normalized solar PV power.</li> <li>Field `<em>DHI</em>`: Direct normal irradiance.</li> <li>Field `<em>DNI</em>`: Diffuse horizontal irradiance.</li> <li>Field `<em>GHI</em>`: Global horizontal irradiance.</li> <li>Field <em>`Dew Point</em>`: Dew point in degree Celsius.</li> <li>Field `<em>Solar Zeinth Angle</em>`: The angle between the sun's rays and the vertical direction in degree.</li> <li>Field `<em>Wind Speed</em>`: Wind speed (m/s).</li> <li>Field `<em>Relative Humidity</em>`: Relative humidity (%).</li> <li>Field `<em>Temperature</em>`: Temperature in degree Celsius.</li> </ul> </li> </ul> <p><strong>Minute-level PMU Measurements</strong></p> <ul> <li>File Name <ul> <li>case #: The `case 0` folder contains all data of scenario setting #0. <ul> <li>pf_input_#.txt: Selected load, renewable and solar generation for the simulation.</li> <li>pf_result_#.csv: Voltage at nodes and power on branches in the transmission system via T+D simualtion.</li> </ul> </li> </ul> </li> <li>Filed Description <ul> <li>Field <em>`time`</em>: Time of minute resolution.</li> <li>Field <em>`Vm_###`</em>: Voltage magnitude (p.u.) at the bus ### in the simulated model.</li> <li>Field <em>`Va_###`</em>: Voltage angle (rad) at the bus ### in the simulated model.</li> <li>Field <em>`P_#_#_#`</em>: `P_3_4_1` means the active power transferring in the #1 branch from the bus 3 to 4.</li> <li>Field <em>`Q_#_#_#`</em>: `Q_5_20_1` means the reactive power transferring in the #1 branch from the bus 5 to 20.</li> </ul> </li> </ul> <p><strong>Millisecond-level PMU Measurements</strong></p> <ul> <li>File Name <ul> <li>Forced Oscillation: The folder contains all forced oscillation cases. <ul> <li>row_#: The folder contains all data of the disturbance scenario #. <ul> <li>dist.csv: Three-phased voltage at nodes in the distribution system via T+D simualtion.</li> <li> info.csv: This file contains the start time, end time, location and type of the disturbance</li> <li>trans.csv: Voltage at nodes and power on branches in the transmission system via T+D simualtion.</li> </ul> </li> </ul> </li> <li>Natural Oscillation: The folder contains all natural oscillation cases. <ul> <li>row_#: The folder contains all data of the disturbance scenario #. <ul> <li>dist.csv: Three-phased voltage at nodes in the distribution system via T+D simualtion.</li> <li>info.csv: This file contains the start time, end time, location and type of the disturbance.</li> <li>trans.csv: Voltage at nodes and power on branches in the transmission system via T+D simualtion.</li> </ul> </li> </ul> </li> </ul> </li> <li>Filed Description <ul> <li>trans.csv <ul> <li> - Field <em>`Time(s)`</em>: Time of millisecond resolution.</li> <li> - Field <em>`VOLT ###`</em>: Voltage magnitude (p.u.) at the bus ### in the transmission model.</li> <li> - Field <em>`POWR ### TO ### CKT #`</em>: `POWR 151 TO 152 CKT '1 '` means the active power transferring in the #1 branch from the bus 151 to 152.</li> <li> - Field <em>`VARS ### TO ### CKT #`</em>: `VARS 151 TO 152 CKT '1 '` means the reactive power transferring in the #1 branch from the bus 151 to 152.</li> </ul> </li> <li>dist.csv <ul> <li>Field <em>`Time(s)`</em>: Time of millisecond resolution.</li> <li>Field <em>`####.###.#`</em>: `3005.633.1` means per-unit voltage magnitude of the phase A at the bus 633 of the distribution grid, the one connecting to the bus 3005 in the transmission system.</li> </ul> </li> </ul> </li> </ul>
Result data related to "Bersalli et al. (2024): Economic crises as critical junctures for policy and structural changes towards decarbonization – the cases of Spain and Germany"
<p>Result data related to "Bersalli et al (2024): Economic crises as critical junctures for policy and structural changes towards decarbonization – the cases of Spain and Germany". The following files are included:</p> <ul> <li>"Energy policy 2020-21 Germany-Spain.xlsx": Policy measures supporting clean energy during the Covid-19 pandemic in Germany and Spain. Data from <a href="http://energypolicytracker.org/">EnergyPolicyTracker.org</a>, amended by the authors.</li> <li> "factors.csv": Time series of emissions, population, GDP, energy intensity, and carbon intensity. Data derived from BP and Eurostat.</li> <li>"multiplicative-contribution-factors.csv": Time series of relative growth in the factors.</li> <li>"relative-cumulative-contribution-factors.csv": Time series of cumulative relative growth in the factors.</li> <li>"periods.csv": Relative growth in the factors during the global financial and COVID19 crises and before (pre) and after (post) the global financial crisis.</li> </ul>
Decarbonization scenario database for the Fifth National Climate Assessment (NCA5)
<p>The decarbonization scenario database for the U.S. Fifth National Climate Assessment (NCA5) forms part of the evidence base for the Mitigation chapter of the assessment. The database contains scenarios that were submitted in response to an open call for contributions for U.S. scenarios that reach net-zero emissions across the economy by midcentury. These scenarios inform the figures and text in the Mitigation chapter about decarbonization pathways for the U.S.</p><p>Visit the interactive NCA5 Scenario Explorer at <a href="https://data.ece.iiasa.ac.at/nca5">https://data.ece.iiasa.ac.at/nca5</a>.</p>
Techno-economic dataset for open modelling of decarbonization pathways in The Philippines
<p><span>This</span><span> file contains the data and data sources updated for the paper :</span></p> <p> </p> <p><span>'</span><span>The Philippines’ Energy Transition: Assessing Emerging Technology Options using OSeMOSYS (Open Source Energy Modelling System)'</span></p> <p><span>All other data in the model used for the paper is from the Philippines Starter Data kit (Allington, 2021). Renewable Energy Costs are updated from (Alexander, 2023).</span></p> <p> </p>
Model Inputs and Results - The role of coal plant retrofitting strategies in decarbonizing India's power system
<p>These files are the model inputs and results for the submission based on GenX version v0.3.6 - The role of coal plant retrofitting strategies in decarbonizing India’s power system</p>
Dataset- Decarbonization of the Indian Electricity Sector: Technology Choices and Policy Trade-Offs
<p>Input and output data for 40 scenarios for the 2040 Indian electricity sector.</p> <p>Compatible with old versions of GenX: https://github.com/GenXProject/GenX, available upon request.</p> <p> </p>
Model output data and code for Zhang et al., Cross-cutting scenarios and strategies for designing decarbonization pathways in the transport sector toward carbon neutrality
<p>Model output data and code for "Zhang et al., Cross-cutting scenarios and strategies for designing decarbonization pathways in the transport sector toward carbon neutrality" in Nature Communications.</p>
Supporting Data for "Decarbonization pathways for the residential sector in the United States"
<p>This repository contains input and processed data for the analysis presented in "Decarbonization pathways for the residential sector in the United States" by Berrill et al. (2022), in Nature Climate Change. </p> <p>Description of the files can be found in the 'Data_Descriptors.docx' file.</p> <p>Please direct any enquiries relating to this dataset to peter.berrill@aya.yale.edu</p>
Dataset and code: Decarbonizing lithium-ion battery primary raw materials supply chain: Available strategies, mitigation potential and challenges
<p>Repository to share the data and code associated with the scientific article <strong>Istrate et al. Decarbonizing lithium-ion battery primary raw materials supply chain: Available strategies, mitigation potential and challenges. Joule (2024)</strong>. The repository contains data files and code to import the life cycle inventories (LCIs), reproduce the results, and generate the figures presented in the article.</p>
Dataset for use with The Role of Hydrogen in Decarbonizing US Iron and Steel Production
<p>Sqlite file containing the database used with the Temoa model to produce the results presented in "The Role of Hydrogen in Decarbonizing US Iron and Steel Production"</p>
Distributional employment challenges and opportunities of decarbonizing the US power system
<p>This dataset presents the results in the manuscript entitled "Distributional employment challenges and opportunities of decarbonizing the US power system". The Low Carbon Transition Employment Distribution (LoCaTED) model used to generate the results can be found on GitHub (<a href="https://github.com/judyjwxie/LoCaTED">https://github.com/judyjwxie/LoCaTED</a>). The suite of data files is based on the ReEDS 2022 Standard Scenarios and our employment calculation variations. Each file shows the job creation in the number of jobs disaggregated into the year, US state, technology, and economic sector (defined by the JEDI model). </p>
TemoaProject: Databases used in Sinha et al. (2024), Diverse Decarbonization Pathways Under Near Cost-Optimal Futures
<p>Contains modeling to generate alternative databases for the U.S. energy system used as part of the Open Energy Outlook. Databases were created using the logic outlined in: https://github.com/adityasinha1992/temoa/tree/mga_parallelized</p>
Data from: Sectorial pathways to achieve net-zero and 1.5°C targets for Eu-27: Energy and emissions data to inform science-based decarbonization targets
Open the record for dataset details and reuse information.
GeoJSON files for the MCSC's Trucking Industry Decarbonization Explorer (Geo-TIDE)
<h1>Summary</h1> <p>Geojson files used to visualize geospatial layers relevant to identifying and assessing trucking fleet decarbonization opportunities with the MIT Climate & Sustainability Consortium's Geospatial Trucking Industry Decarbonization Explorer (Geo-TIDE) tool.</p> <h1>Relevant Links</h1> <p>Link to the <a href="https://climatedata.mit.edu/faf5/transportation/">online version of the tool</a> (requires creation of a free user account).</p> <p><a href="https://github.com/mcsc-impact-climate/FAF5-Analysis">Link to GitHub repo</a> with source code to produce this dataset and deploy the Geo-TIDE tool locally.</p> <h1>Funding </h1> <p>This dataset was produced with support from the MIT Climate & Sustainability Consortium.</p> <h1>Original Data Sources</h1> <p>These geojson files draw from and synthesize a number of different datasets and tools. The original data sources and tools are described below:</p> <table> <tbody> <tr> <td><strong>Filename(s)</strong></td> <td><strong>Description of Original Data Source(s)</strong></td> <td><strong>Link(s) to Download Original Data<br></strong></td> <td><strong>License and Attribution for Original Data Source(s)</strong></td> </tr> <tr> <td> <p>faf5_freight_flows/*.geojson</p> <p>trucking_energy_demand.geojson</p> <p>highway_assignment_links_*.geojson</p> <p>infrastructure_pooling_thought_experiment/*.geojson</p> </td> <td> <p>Regional and highway-level freight flow data obtained from the <a href="https://faf.ornl.gov/faf5/">Freight Analysis Framework Version 5</a>. Shapefiles for FAF5 region boundaries and highway links are obtained from the <a href="https://geodata.bts.gov/search?collection=Dataset">National Transportation Atlas Database</a>. Emissions attributes are evaluated by incorporating data from the <a href="https://rosap.ntl.bts.gov/view/dot/42632/dot_42632_DS2.zip">2002 Vehicle Inventory and Use Survey</a> and the <a href="https://greet.anl.gov/">GREET lifecycle emissions tool</a> maintained by Argonne National Lab.</p> </td> <td> <p><a href="https://geodata.bts.gov/datasets/usdot::freight-analysis-framework-faf5-regions">Shapefile for FAF5 Regions</a></p> <p><a href="https://geodata.bts.gov/datasets/usdot::freight-analysis-framework-faf5-network-links">Shapefile for FAF5 Highway Network Links</a></p> <p><a href="https://faf.ornl.gov/faf5/data/download_files/FAF5.5.1_2018-2022.zip">FAF5 2022 Origin-Destination Freight Flow database</a></p> <p><a href="https://ops.fhwa.dot.gov/freight/freight_analysis/faf/faf_highway_assignment_results/FAF5_2022_HighwayAssignmentResults_04_07_2022.zip">FAF5 2022 Highway Assignment Results</a></p> <p> </p> </td> <td> <p><strong>Attribution for Shapefiles:</strong> United States Department of Transportation Bureau of Transportation Statistics National Transportation Atlas Database (NTAD). Available at: https://geodata.bts.gov/search?collection=Dataset. </p> <p><strong>License for Shapefiles:</strong> This NTAD dataset is a work of the United States government as defined in 17 U.S.C. § 101 and as such are not protected by any U.S. copyrights. This work is available for unrestricted public use.</p> <p><strong>Attribution for Origin-Destination Freight Flow database:</strong> <a href="https://www.ornl.gov/ntrc/" target="_blank" rel="noopener">National Transportation Research Center</a> in the <a href="https://www.ornl.gov/" target="_blank" rel="noopener">Oak Ridge National Laboratory</a> with funding from the <a href="https://www.bts.gov/" target="_blank" rel="noopener">Bureau of Transportation Statistics</a> and the <a href="https://www.fhwa.dot.gov/" target="_blank" rel="noopener">Federal Highway Administration</a>. Freight Analysis Framework Version 5: Origin-Destination Data. Available from: https://faf.ornl.gov/faf5/Default.aspx. Obtained on Aug 5, 2024. In the public domain. </p> <p><strong>Attribution for the 2022 Vehicle Inventory and Use Survey Data:</strong> United States Department of Transportation Bureau of Transportation Statistics. Vehicle Inventory and Use Survey (VIUS) 2002 [supporting datasets]. 2024. https://doi.org/10.21949/1506070 </p> <p><strong>Attribution for the GREET tool (original publication):</strong> Argonne National Laboratory Energy Systems Division Center for Transportation Research. GREET Life-cycle Model. 2014. Available from <a href="https://greet.anl.gov/files/greet-model&ved=2ahUKEwiAuryGsd6HAxVMFlkFHaafHNUQFnoECBUQAQ&usg=AOvVaw29kokx-ZurrfBFsjji9UM2">this link</a>.</p> <p><strong>Attribution for the GREET tool (2022 updates):</strong> Wang, Michael, et al. Summary of Expansions and Updates in GREET® 2022. United States. https://doi.org/10.2172/1891644</p> </td> </tr> <tr> <td>grid_emission_intensity/*.geojson</td> <td> <p>Emission intensity data is obtained from the <a href="https://www.epa.gov/egrid/download-data">eGRID database</a> maintained by the United States Environmental Protection Agency.</p> <p>eGRID subregion boundaries are obtained as a shapefile from the <a href="https://www.epa.gov/egrid/egrid-mapping-files">eGRID Mapping Files</a> database.</p> </td> <td> <p><a href="https://www.epa.gov/system/files/documents/2024-01/egrid2022_data.xlsx">eGRID database</a></p> <p><a href="https://www.epa.gov/system/files/other-files/2024-05/egrid2022_subregions_shapefile.zip">Shapefile with eGRID subregion boundaries</a></p> </td> <td> <p><strong>Attribution for eGRID data: </strong>United States Environmental Protection Agency: eGRID with 2022 data. Available from https://www.epa.gov/egrid/download-data. In the public domain.</p> <p><strong>Attribution for shapefile:</strong> United States Environmental Protection Agency: eGRID Mapping Files. Available from https://www.epa.gov/egrid/egrid-mapping-files. In the public domain.</p> </td> </tr> <tr> <td> <p>US_elec.geojson</p> <p>US_hy.geojson</p> <p>US_lng.geojson</p> <p>US_cng.geojson</p> <p>US_lpg.geojson</p> </td> <td>Locations of direct current fast chargers and refueling stations for alternative fuels along U.S. highways. Obtained directly from the <a href="https://afdc.energy.gov/corridors">Station Data for Alternative Fuel Corridors</a> in the Alternative Fuels Data Center maintained by the United States Department of Energy Office of Energy Efficiency and Renewable Energy. </td> <td> <p><a href="https://developer.nrel.gov/api/alt-fuel-stations/v1.geojson?access=public&status=E&country=US&download=true&utf8_bom=true&api_key=srJv3MBMvbZaDHrSssCEnhPK4IijuOLTsvs6l35L&fuel_type=ELEC&ev_charging_level=dc_fast&ev_connector_type=J1772COMBO&beta_min_j1772combo_150plus_port_count=4&response_format=beta_dot_corridors">US_elec.geojson</a></p> <p><a href="https://developer.nrel.gov/api/alt-fuel-stations/v1.geojson?access=public&status=E&country=US&download=true&utf8_bom=true&api_key=srJv3MBMvbZaDHrSssCEnhPK4IijuOLTsvs6l35L&fuel_type=HY&hy_is_retail=true">US_hy.geojson</a></p> <p><a href="https://developer.nrel.gov/api/alt-fuel-stations/v1.geojson?access=public&status=E&country=US&download=true&utf8_bom=true&api_key=srJv3MBMvbZaDHrSssCEnhPK4IijuOLTsvs6l35L&fuel_type=LNG">US_lng.geojson</a></p> <p><a href="https://developer.nrel.gov/api/alt-fuel-stations/v1.geojson?access=public&status=E&country=US&download=true&utf8_bom=true&api_key=srJv3MBMvbZaDHrSssCEnhPK4IijuOLTsvs6l35L&fuel_type=CNG&cng_fill_type=Q&cng_psi=3600">US_cng.geojson</a></p> <p><a href="https://developer.nrel.gov/api/alt-fuel-stations/v1.geojson?access=public&status=E&country=US&download=true&utf8_bom=true&api_key=srJv3MBMvbZaDHrSssCEnhPK4IijuOLTsvs6l35L&fuel_type=LPG&lpg_include_secondary=false">US_lpg.geojson</a></p> </td> <td> <p><strong>Attribution:</strong> U.S. Department of Energy, Energy Efficiency and Renewable Energy. Alternative Fueling Station Corridors. 2024. Available from: <a href="https://afdc.energy.gov/corridors" target="_new" rel="noreferrer">https://afdc.energy.gov/corridors</a>. In the public domain. </p> <p> </p> <p>These data and software code ("Data") are provided by the National Renewable Energy Laboratory ("NREL"), which is operated by the Alliance for Sustainable Energy, LLC ("Alliance"), for the U.S. Department of Energy ("DOE"), and may be used for any purpose whatsoever.</p> </td> </tr> <tr> <td>daily_grid_emission_profiles/*.geojson</td> <td> <p>Hourly emission intensity data obtained from <a href="https://www.electricitymaps.com/data-portal/united-states-of-america">ElectricityMaps</a>.</p> </td> <td> <p>Original data can be downloaded as csv files from the <a href="https://www.electricitymaps.com/data-portal/united-states-of-america">ElectricityMaps United States of America database</a></p> <p><a href="https://raw.githubusercontent.com/electricitymaps/electricitymaps-contrib/master/web/geo/world.geojson">Shapefile with region boundaries used by ElectricityMaps</a></p> </td> <td> <p><strong>License:</strong> <a href="https://opendatacommons.org/licenses/odbl/" target="_blank" rel="noopener">Open Database License (ODbL)</a>. Details here: https://www.electricitymaps.com/data-portal</p> <p><strong>Attribution for csv files:</strong> Electricity Maps (2024). United States of America 2022-23 Hourly Carbon Intensity Data (Version January 17, 2024). Electricity Maps Data Portal. https://www.electricitymaps.com/data-portal.</p> <p><strong>Attribution for shapefile with region boundaries:</strong> ElectricityMaps contributors (2024). electricitymaps-contrib (Version v1.155.0) [Computer software]. https://github.com/electricitymaps/electricitymaps-contrib.</p> </td> </tr> <tr> <td> <p>gen_cap_2022_state_merged.geojson </p> <p>trucking_energy_demand.geojson</p> </td> <td> <p>Grid electricity generation and net summer power capacity data is obtained from the <a href="https://www.eia.gov/electricity/data/state/">state-level electricity database</a> maintained by the United States Energy Information Administration. </p> <p> </p> <p>U.S. state boundaries obtained from <a href="https://www.sciencebase.gov/catalog/item/52c78623e4b060b9ebca5be5">this United States Department of the Interior U.S. Geological Survey ScienceBase-Catalog</a>.</p> </td> <td> <p><a href="https://www.eia.gov/electricity/data/state/annual_generation_state.xls">Annual electricity generation by state</a></p> <p><a href="https://www.eia.gov/electricity/data/state/existcapacity_annual.xlsx">Net summer capacity by state</a></p> <p><a href="https://www.sciencebase.gov/catalog/file/get/52c78623e4b060b9ebca5be5?facet=tl_2012_us_state">Shapefile with U.S. state boundaries</a></p> </td> <td> <p><strong>Attribution for electricity generation and capacity data: </strong>U.S. Energy Information Administration (Aug 2024). Available from: https://www.eia.gov/electricity/data/state/. In the public domain. </p> </td> </tr> <tr> <td>electricity_rates_by_state_merged.geojson</td> <td> <p>Commercial electricity prices are obtained from the <a href="https://www.eia.gov/electricity/data.php">Electricity database</a> maintained by the United States Energy Information Administration.</p> </td> <td> <p><a href="https://www.eia.gov/electricity/data/state/sales_annual_a.xlsx">Electricity rate by state</a></p> <p> </p> </td> <td><strong>Attribution:</strong> U.S. Energy Information Administration (Aug 2024). Available from: https://www.eia.gov/electricity/data.php. In the public domain. </td> </tr> <tr> <td> <p>demand_charges_merged.geojson</p> <p>demand_charges_by_state.geojson</p> </td> <td> <p>Maximum historical demand charges for each state and zip code are derived from a dataset compiled by the National Renewable Energy Laboratory in this <a href="https://data.nrel.gov/submissions/74">this Data Catalog.</a></p> </td> <td><a href="https://data.nrel.gov/system/files/74/Demand%20charge%20rate%20data.xlsm">Historical demand charge dataset</a></td> <td> <p>The original dataset is compiled by the National Renewable Energy Laboratory (NREL), the U.S. Department of Energy (DOE), and the Alliance for Sustainable Energy, LLC ('Alliance').</p> <p><strong>Attribution:</strong> McLaren, Joyce, Pieter Gagnon, Daniel Zimny-Schmitt, Michael DeMinco, and Eric Wilson. 2017. 'Maximum demand charge rates for commercial and industrial electricity tariffs in the United States.' NREL Data Catalog. Golden, CO: National Renewable Energy Laboratory. Last updated: July 24, 2024. DOI: 10.7799/1392982.</p> </td> </tr> <tr> <td> <p>eastcoast.geojson</p> <p>midwest.geojson</p> <p>la_i710.geojson</p> <p>h2la.geojson</p> <p>bayarea.geojson</p> <p>saltlake.geojson</p> <p>northeast.geojson</p> </td> <td> <p>Highway corridors and regions targeted for heavy duty vehicle infrastructure projects are derived from a <a href="https://www.energy.gov/articles/biden-harris-administration-announces-funding-zero-emission-medium-and-heavy-duty-vehicle">public announcement</a> on February 15, 2023 by the United States Department of Energy.</p> <p>The shapefile with Bay area boundaries is obtained from <a href="https://geodata.lib.berkeley.edu/catalog/ark28722-s7hs4j">this Berkeley Library dataset</a>.</p> <p>The shapefile with Utah county boundaries is obtained from <a href="https://gis.utah.gov/products/sgid/boundaries/county/">this dataset</a> from the Utah Geospatial Resource Center. </p> </td> <td> <p><a href="https://spatial.lib.berkeley.edu/public/ark28722-s7hs4j/data.zip">Shapefile for Bay Area country boundaries</a></p> <p><a href="https://opendata.arcgis.com/datasets/90431cac2f9f49f4bcf1505419583753_0.zip">Shapefile for counties in Utah</a></p> <p> </p> </td> <td> <p><strong>Attribution for public announcement:</strong> United States Department of Energy. Biden-Harris Administration Announces Funding for Zero-Emission Medium- and Heavy-Duty Vehicle Corridors, Expansion of EV Charging in Underserved Communities (2023). Available from https://www.energy.gov/articles/biden-harris-administration-announces-funding-zero-emission-medium-and-heavy-duty-vehicle.</p> <p><strong>Attribution for Bay area boundaries:</strong> San Francisco (Calif.). Department Of Telecommunications and Information Services. Bay Area Counties. 2006. In the public domain. </p> <p><strong>Attribution for Utah boundaries:</strong> Utah Geospatial Resource Center & Lieutenant Governor's Office. Utah County Boundaries (2023). Available from https://gis.utah.gov/products/sgid/boundaries/county/. </p> <p><strong>License for Utah boundaries:</strong> <a href="https://gis.utah.gov/documentation/policy/license/#license">Creative Commons 4.0 International License</a>. </p> </td> </tr> <tr> <td>incentives_and_regulations/*.geojson</td> <td> <p>State-level incentives and regulations targeting heavy duty vehicles are collected from the <a href="https://afdc.energy.gov/laws/state">State Laws and Incentives database</a> maintained by the United States Department of Energy's Alternative Fuels Data Center. </p> </td> <td>Data was collected manually from the <a href="https://afdc.energy.gov/laws/state">State Laws and Incentives database</a>.</td> <td> <p><strong>Attribution:</strong> U.S. Department of Energy, Energy Efficiency and Renewable Energy, Alternative Fuels Data Center. State Laws and Incentives. Accessed on Aug 5, 2024 from: https://afdc.energy.gov/laws/state. In the public domain. </p> <p> </p> <p>These data and software code ("Data") are provided by the National Renewable Energy Laboratory ("NREL"), which is operated by the Alliance for Sustainable Energy, LLC ("Alliance"), for the U.S. Department of Energy ("DOE"), and may be used for any purpose whatsoever.</p> </td> </tr> <tr> <td> <p>costs_and_emissions/*.geojson</p> <p>diesel_price_by_state.geojson</p> <p>trucking_energy_demand.geojson</p> </td> <td> <p>Lifecycle costs and emissions of electric and diesel trucking are evaluated by adapting the model developed by <a href="https://chemrxiv.org/engage/chemrxiv/article-details/656e4691cf8b3c3cd7c96810">Moreno Sader et al.</a>, and calibrated to the <a href="https://runonless.com/run-on-less-electric-depot-reports/">Run on Less dataset</a> for the Tesla Semi collected from the 2023 PepsiCo Semi pilot by the North American Council for Freight Efficiency.</p> <p>In addition to the data sources outlined in <a href="https://chemrxiv.org/engage/chemrxiv/article-details/656e4691cf8b3c3cd7c96810">Moreno Sader et al.</a> et al. and the <a href="https://runonless.com/run-on-less-electric-depot-reports/">Run on Less dataset</a>, this dataset incorporates:</p> <ul> <li>Emission intensity data from the <a href="https://www.epa.gov/egrid/download-data">eGRID database</a>, described elsewhere in this metadata. </li> <li>Commercial electricity price data from the US EIA <a href="https://www.eia.gov/electricity/data.php">Electricity database</a>, described elsewhere in this metadata. </li> <li><a href="https://data.nrel.gov/submissions/74">Maximum historical demand charges</a> from the National Renewable Energy Laboratory, described elsewhere in this metadata. </li> <li>Max motor power estimate of 942,900W and frontal area of 10.7 m^s for the Tesla Semi from <a href="https://www.motormatchup.com/catalog/Tesla/Semi-Truck/2022/Empty">motormatchup.com.</a></li> <li>Drag coefficient estimate of 0.36 for the Tesla Semi from <a href="https://www.notateslaapp.com/tesla-reference/963/everything-we-know-about-the-tesla-semi">notateslaapp.com.</a></li> <li>Estimates best-in-class truck rolling resistance of 0.0044 from a <a href="https://www.lrrb.org/pdf/201539.pdf">Rolling Resistance Validation report</a> prepared by the Minnesota Department of Transportation Office of Transportation System Management.</li> <li><a href="https://www.eia.gov/petroleum/gasdiesel/">Historical diesel prices</a> by state from the United States Energy Information Administration.</li> <li>Estimate of best in class diesel powertrain engine efficiency of 44% from a <a href="https://theicct.org/sites/default/files/publications/EU-HDV-Tech-Potential_ICCT-white-paper_14072017_vF.pdf">Fuel Efficiency Technology report</a> by the International Council on Clean Transportation.</li> </ul> </td> <td> <p> </p> <p><a href="https://runonless.com/wp-content/uploads/ROL23-Web-data.zip">NACFE Run on Less dataset</a></p> <p><a href="https://www.eia.gov/petroleum/gasdiesel/xls/psw18vwall.xls">Historical diesel prices</a></p> <p> </p> </td> <td> <p><strong>Attribution for original truck model:</strong> Moreno Sader K, Biswas S, Jones R, Mennig M, Rezaei R, Green WH. Battery Electric Long-Haul Trucking in the United States: A Comprehensive Costing and Emissions Analysis. ChemRxiv. 2023; doi:10.26434/chemrxiv-2023-48zsc (link to <a href="https://colab.research.google.com/drive/124rFu_4vHx4cP6SODtdzCxnUmLY50wbW?usp=sharing">colab notebook</a> included as supplementary material).</p> <p><strong>Attribution for GitHub repository with adapted code for the truck model:</strong> Eamer, D., Moreno-Sader, K., & Biswas, S. (2024). Green_Trucking_Analysis (Version 0.1.0) [Computer software]. https://doi.org/10.5281/zenodo.13205854</p> <p><strong>Attribution for GitHub repository with analysis of the NACFE Run on Less dataset (provides inputs to Eamer, D., Moreno-Sader, K., & Biswas, S. (2024) cited above):</strong> Eamer, D. (2024). PepsiCo_NACFE_Analysis (Version 0.1.0) [Computer software]. https://doi.org/10.5281/zenodo.13173390</p> <p><strong>Attribution for <a href="https://runonless.com/run-on-less-electric-depot-reports/">Run on Less dataset</a>: </strong>North American Countil for Freight Efficiency (2023). Run on Less – Electric DEPOT data. Available from: https://runonless.com/run-on-less-electric-depot-reports/ </p> <p><strong>Attribution for data from MotorMatchup:</strong> 2022 Tesla Semi Truck Empty Specs. Available from: https://www.motormatchup.com/catalog/Tesla/Semi-Truck/2022/Empty. Copyright 2024 by MotorMatchup</p> <p><strong>Attribution for data from Not a Tesla App:</strong> Not a Tesla App. Everything We Know About the Tesla Semi. 2024. Available from: <a href="https://www.notateslaapp.com/tesla-reference/963/everything-we-know-about-the-tesla-semi" target="_new" rel="noreferrer">https://www.notateslaapp.com/tesla-reference/963/everything-we-know-about-the-tesla-semi</a></p> <p><strong>Attribution for historical diesel prices:</strong> U.S. Energy Information Administration (Aug 2024). Available from: https://www.eia.gov/petroleum/gasdiesel/. In the public domain.</p> <p><strong>Attribution for best in class diesel powertrain efficiency:</strong> Delgado O, Rodríguez F, Muncrief R. Fuel Efficiency Technology in European Heavy-Duty Vehicles: Baseline and Potential for the 2020–2030 Time Frame. 2017. Available from: <a href="https://theicct.org/sites/default/files/publications/EU-HDV-Tech-Potential_ICCT-white-paper_14072017_vF.pdf" target="_new" rel="noreferrer">https://theicct.org/sites/default/files/publications/EU-HDV-Tech-Potential_ICCT-white-paper_14072017_vF.pdf</a>.</p> </td> </tr> <tr> <td> <p>electrolyzer_operational.geojson</p> <p>electrolyzer_installed.geojson</p> <p>electrolyzer_planned_under_construction.geojson</p> <p> </p> </td> <td> <p>Data on locations and capacities of planned, under-construction, installed, operational electrolyzers was obtained from <a href="https://www.hydrogen.energy.gov/docs/hydrogenprogramlibraries/pdfs/23003-electrolyzer-installations-united-states.pdf?Status=Master">this DOE Hydrogen Program Record</a>.</p> </td> <td>Data was extracted manually from <a href="https://www.hydrogen.energy.gov/docs/hydrogenprogramlibraries/pdfs/23003-electrolyzer-installations-united-states.pdf?Status=Master">this DOE Hydrogen Program Record</a>.</td> <td><strong>Attribution:</strong> Arjona, Vanessa. DOE Hydrogen Program Record: Electrolyzer Installations in the United States. 2023. Available from https://www.hydrogen.energy.gov/docs/hydrogenprogramlibraries/pdfs/23003-electrolyzer-installations-united-states.pdf?Status=Master. </td> </tr> <tr> <td> <p>grid_emission_intensity/*.geojson</p> <p>gen_cap_2022_state_merged.geojson </p> <p>trucking_energy_demand.geojson</p> <p>electricity_rates_by_state_merged.geojson</p> <p>demand_charges_merged.geojson</p> <p>demand_charges_by_state.geojson</p> <p>trucking_energy_demand.geojson</p> <p>costs_and_emissions/*.geojson</p> <p>diesel_price_by_state.geojson</p> <p>trucking_energy_demand.geojson</p> </td> <td> <p>U.S. state boundaries obtained from <a href="https://www.sciencebase.gov/catalog/item/52c78623e4b060b9ebca5be5">this United States Department of the Interior U.S. Geological Survey ScienceBase-Catalog</a>.</p> </td> <td> </td> <td><strong>Attribution: </strong>U.S. Department of Commerce, U.S. Census Bureau, Geography Division. State boundaries (generalized for mapping). 2011. In the public domain.</td> </tr> <tr> <td> <p>refinery.geojson</p> </td> <td> <p>Locations and production rates of hydrogen from refineries are obtained from the following two complementary datasets on the <a href="https://h2tools.org">Hydrogen Tools Portal</a>:</p> <p><br>1) <a href="https://h2tools.org/hyarc/hydrogen-data/captive-purpose-refinery-hydrogen-production-capacities-individual-us">Captive, On-Purpose, Refinery Hydrogen Production Capacities at Individual U.S. Refineries</a>, and </p> <p><br>2) <a href="https://h2tools.org/hyarc/hydrogen-data/merchant-hydrogen-plant-capacities-north-america">Merchant Hydrogen Plant Capacities in North America</a></p> </td> <td> <p><a href="https://h2tools.org/file/9338/download?token=0IWTving">Dataset for Captive, On-Purpose, Refinery Hydrogen Production Capacities at Individual U.S. Refineries</a></p> <p><a href="https://h2tools.org/file/2050/download?token=Wp-XDY-h">Dataset for Merchant Hydrogen Plant Capacities in North America</a></p> </td> <td> <p><strong>Attribution: </strong>Copyright © 2024 by H2Tools; H2 Tools is intended for public use. It was built, and is maintained, by the Pacific Northwest National Laboratory with funding from the DOE Office of Energy Efficiency and Renewable Energy's Hydrogen and Fuel Cell Technologies Office. All Rights Reserved. </p> </td> </tr> <tr> <td> <p>Truck_Stop_Parking.geojson</p> <p>infrastructure_pooling_thought_experiment/*.geojson</p> </td> <td> <p>Obtained from the DOT Bureau of Transportation Statistics's <a href="https://geodata.bts.gov/datasets/usdot::truck-stop-parking">Truck Stop Parking database</a></p> </td> <td> <p>Original dataset can be downloaded using the Shapefile download link at https://geodata.bts.gov/datasets/usdot::truck-stop-parking (link for hosted download changes regularly). </p> </td> <td> <p><strong>Attribution: </strong>United States Department of Transportation Bureau of Transportation Statistics National Transportation Atlas Database (NTAD). Truck Stop Parking. Available at https://geodata.bts.gov/datasets/usdot::truck-stop-parking. </p> <p><strong>License:</strong> This NTAD dataset is a work of the United States government as defined in 17 U.S.C. § 101 and as such are not protected by any U.S. copyrights. This work is available for unrestricted public use.</p> </td> </tr> <tr> <td> <p>Principal_Port.geojson</p> </td> <td> <p>Obtained from the DOT Bureau of Transportation Statistics's <a href="https://geodata.bts.gov/datasets/usdot::principal-ports-1/about">Principal Ports database</a></p> </td> <td> <p>Original dataset can be downloaded using the Shapefile download link at https://geodata.bts.gov/datasets/usdot::principal-ports-1 (link for hosted download changes regularly). </p> </td> <td> <p><strong>Attribution: </strong>United States Department of Transportation Bureau of Transportation Statistics National Transportation Atlas Database (NTAD). Truck Stop Parking. Available at https://geodata.bts.gov/datasets/usdot::principal-ports-1. </p> <p><strong>License:</strong> This NTAD dataset is a work of the United States government as defined in 17 U.S.C. § 101 and as such are not protected by any U.S. copyrights. This work is available for unrestricted public use.</p> <p><strong> </strong></p> </td> </tr> <tr> <td> <p>ZEF_Corridor_Strategy/*.geojson</p> </td> <td> <p>Visualizes the corridors, facilities, and hubs targeted by the National Zero-Emission Freight Corridor Strategy, a framework developed by the U.S. Joint Office of Energy and Transportation to support the coordinated deployment of medium- and heavy-duty zero-emission vehicle (ZEV) infrastructure along critical freight corridors. The strategy, outlined in the publication <a href="https://driveelectric.gov/files/zef-corridor-strategy.pdf">National Zero-Emission Freight Corridor Strategy</a>, identifies priority corridors and infrastructure investment needs to accelerate the transition to zero-emission medium- and heavy-duty vehicles.</p> </td> <td> <p>Original dataset can be downloaded from https://driveelectric.gov/files/zef-gis-files.zip</p> </td> <td> <p><strong>Attribution:</strong> Chu, K.-C. (J.), Miller, K. G., Schroeder, A., Gilde, A., & Laughlin, M. (2024, September). <em>National Zero-Emission Freight Corridor Strategy: Prioritizing investments, planning, and deployment for medium- and heavy-duty vehicle fueling infrastructure to advance zero-emission freight along our nation’s corridors</em>. Joint Office of Energy and Transportation; U.S. Department of Energy. Accessed from: https://driveelectric.gov/files/zef-corridor-strategy.pdf.</p> </td> </tr> </tbody> </table> <p> </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.