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4,230 results for “Energie”
Screaming Channels on Bluetooth Low Energy
<p>Publication of 2 datasets from the <a href="https://github.com/pierreay/screaming_channels_ble">Screaming Channels on Bluetooth Low Energy</a> project for the <a href="https://github.com/pierreay/screaming_channels_ble/blob/main/docs/demo_20240828_acsac/README.org">ACSAC24 Artifact Evaluation.</a></p>
Data bundle for powerd-data: A transparent and reproducible data processing pipeline for energy system modeling based on egon-data
<div> <p><strong>powerd-data</strong> provides a transparent and reproducible open data based data processing pipeline for generating data models suitable for energy system modeling. Is is a fork from the open-source tool <strong>egon-data</strong>. </p> <p>powerd-data and egon-data retrieve and process data from several different external input sources. As not all data dependencies can be downloaded automatically from external sources, we provide a data bundle to be downloaded by egon-data.</p> <p>The following data sets are part of the available data bundle:</p> <ol> <li>district_heating_shares: <ul> <li>Assumed district heating share for all European countries in 2050</li> <li>Source: Own representation</li> <li>License: Attribution 4.0 International (CC BY 4.0)</li> </ul> </li> <li>egon_demandregio_cts_ind:<br> <ul> <li>Industrial and CTS demands per branch and NUTS3 region in Germany for the year 2050</li> <li>Source: egon-data, based on data from DemandRegio disaggregator tool</li> <li>License: Data license Germany – © FfE 2019, © Statistisches Bundesamt (Destatis), 2008-2017 – version 2.0</li> </ul> </li> <li>industrial_gas_demand: <ul> <li>This folder contains 5 files. The files CH4_for_industry_eGon100RE.json, CH4_for_industry_eGon2035.json, H2_for_industry_eGon100RE.json and H2_for_industry_eGon2035.json contain the industrial hourly demands for hydrogen and methane in NUTS3 resolution for the scenarios eGon100RE and eGon2035. The file region_corr.json provides information that make it possible to correlate each load to a geographical position.</li> <li>License: Attribution 4.0 International (CC BY 4.0) © FfE, eXtremOS Project</li> </ul> </li> </ol> <p> </p> </div>
The effect of uncertainty in humidity and model parameters on the prediction of contrail energy forcing
<p>Previous work has shown that while the net effect of aircraft condensation trails (contrails) on the<br>climate is warming, the exact magnitude of the energy forcing per meter of contrail remains uncertain.<br>In this paper, we explore the skill of a Lagrangian contrail model (CoCiP) in identifying flight<br>segments with high contrail energy forcing. We find that skill is greater than climatological<br>predictions alone, even accounting for uncertainty in weather fields and model parameters.</p> <p>We estimate the uncertainty in weather by using the ensemble ERA5 weather reanalysis from the European<br>Centre for Medium-Range Weather Forecasts (ECMWF) as Monte Carlo inputs to CoCiP. We unbias and correct<br>under-dispersion on the ERA5 humidity data by forcing a match to the distribution of in situ humidity<br>measurements taken at cruising altitude. We set aside CoCiP energy forcing estimates calculated using<br>one of the ensemble members as a proxy for ground truth, and report the skill of CoCiP in identifying<br>segments with large positive proxy energy forcing. We further estimate the uncertainty in the model<br>parameters in CoCiP by performing Monte Carlo simulations with CoCiP model parameters drawn from<br>uncertainty distributions consistent with the literature.</p> <p>When CoCiP outputs are averaged over seasons to form climatological predictions, the skill in<br>predicting the proxy is 44%, while the skill of per-flight CoCiP outputs is 84%. If these results carry<br>over to the true (unknown) contrail EF, they indicate that per-flight energy forcing predictions can<br>reduce the number of potential contrail avoidance route adjustments by 2x, hence reducing both the cost<br>and fuel impact of contrail avoidance.</p>
GODEEEP future energy drought data
<p>This dataset has 3 components, (1) historical and future energy drought data for balancing authorities in the Western US (2) physically consistent wind, solar and load data aggregated to the balacing authority level in the Western US (3) plant level wind and solar generation data for both historical and future scenarios.</p> <p>For more information please refer to Bracken et al. 2024, Climate change impacts on compound renewable energy droughts under evolving infrastructure in the Western United States, in prep, or refer to the Github repository https://github.com/GODEEEP/future-energy-droughts</p> <h2>Usage</h2> <ol> <li>Clone the repo: https://github.com/GODEEEP/future-energy-droughts</li> <li>Unzip <code>ba-aggregated.zip</code> into <code>data/</code>, this is required to run <code>future-energy-droughts.R</code></li> <li>Unzip <code>future-wind-solar.zip</code> into your preferred directory and change the path in the <code>process-data.R</code> script</li> </ol> <h2>Energy Drought data</h2> <p>The file <code>future-energy-droughts.zip</code> contains energy drought data for historical and future scenarios. The files have the naming convention <code><drought type>_droughts_ba_<data period>_<infrastructure year>_<infrastructure scenario>_<time scale>.csv</code>where <code><data period></code> is either <code>historical</code> or <code>future</code>, <code><infrastructure year></code> is any 5 year increment between 2020 and 2050, <code><infrastructure scenario></code> is either <code>bau</code> (business as usual) or <code>nz</code> (net zero), and <code><time scale></code> is <code>daily</code>. Several kinds of energy droughts are available:</p> <ul> <li>solar - Solar only droughts defined using a 10th percentile threshold</li> <li>wind - Wind only droughts defined using a 10th percentile threshold</li> <li>ws - Wind and solar droughts defined using a 10th percentile threshold</li> </ul> <p>Each drought file has the following columns:</p> <ul> <li>ba - Abbreviated name for the BA</li> <li>run_id - unique id for each drought event</li> <li>datetime_utc - Date stamp for the start of the drought, in UTC</li> <li>timezone - The predominant time zone for the BA</li> <li>run_length - The length of a drought in time steps</li> <li>run_length_days - The length of the drought in days</li> <li>severity_ws - Drought severity for wind and solar droughts, computed using the compound drought magnitude metric</li> <li>severity_mwh - Drought severity expressed as MWh</li> <li>zero_prob - For solar, this value indicates if the timestep has zero probability of solar production, i.e. night time</li> <li>year - The year of the timestep</li> <li>month - The month of the timestep</li> <li>hour - The hour of the timestep</li> <li>wind_cf - Wind capacity factor for the drought</li> <li>solar_cf - Solar capacity factor for the drought</li> <li>srepi_solar - Standardized renewable energy production index for solar</li> <li>srepi_wind - Standardized renewable energy production index for wind</li> </ul> <h2>BA level generation data</h2> <p>The file <code>ba-aggregated.zip</code> contains ba level generation data that has been aggregated from plant level data. The files have the naming convention <code>ba_<data period>_<infrastructure year>_<infrastructure scenario>_<time scale>.csv</code> where <code><data period></code> is either <code>historical</code> or <code>future</code>, <code><infrastructure year></code> is any 5 year increment between 2020 and 2050, <code><infrastructure scenario></code> is either <code>bau</code> (business as usual) or <code>nz</code> (net zero), and <code><time scale></code> is either <code>hourly</code> or <code>daily</code>.</p> <ul> <li>ba - Abbreviated name for the BA</li> <li>year - The current year as an integer</li> <li>period - A unique integer for the current time step</li> <li>solar_gen_mwh - Aggregated solar generation in units of MWh</li> <li>solar_capacity_mwh - Aggregated solar plant capacity expresed as MWh</li> <li>wind_gen_mwh - Aggregated wind generation in units of MWh</li> <li>wind_capacity_mwh - Aggregated wind plant capacity expresed as MWh</li> <li>load_mwh - BA load in MWh, not used in this study</li> <li>load_max_mwh - The maximum BA load over the entire historical period, not used in this study</li> <li>datetime_utc - Time stamp for the current time step, in UTC, All time stamps are beginning of period.</li> <li>timezone - The predominant time zone for the BA</li> <li>wind_cf - Wind capacity factor, wind_gen_mwh/wind_capacity_mwh</li> <li>solar_cf - Solar capacity factor, wind_gen_mwh/wind_capacity_mwh</li> <li>load_cf - Load "capacity factor", expresed as a fraction of the maximum BA load, load_mwh/load_max_mwh, not used in this study</li> </ul> <h2>Plant level wind and solar generation data</h2> <p>The file <code>future-wind-solar.zip</code> contains hourly plant level generation data used to derive the energy drought data. Each folder contains one csv file per simulated year (eg. <code>solar_gen_cf_2020.csv</code>). The files <code>eia_solar_configs.csv</code> and <code>eia_wind_configs.csv</code> contain metadata for each EIA plant.</p> <ul> <li>baseline-future - 2020 infrastructure under future weather years, 2020-2059</li> <li>baseline-historical - 2020 infrastructure under historical weather years</li> <li>baseline-historical-bc - 2020 infrastructure under historical weather years, bias corrected</li> <li>baseline2020 - 2020 infrastructure under future weather years, 2020-2099</li> <li>cerf-config - cerf sitings</li> <li>cerf-future-2040 - cerf sitings run through future climate 2020-2050</li> <li>cerf-future-2045 - cerf sitings run through future climate 2020-2050</li> <li>cerf-future-2050 - cerf sitings run through future climate 2020-2050</li> <li>cerf-historical-2040 - cerf sitings run through historical climate 2020-2050</li> <li>cerf-historical-2045 - cerf sitings run through historical climate 2020-2050</li> <li>cerf-historical-2050 - cerf sitings run through historical climate 2020-2050</li> </ul> <h2>Funding statement</h2> <p>This research was supported by the Grid Operations, Decarbonization, Environmental and Energy Equity Platform (GODEEEP) Investment, under the Laboratory Directed Research and Development (LDRD) Program at Pacific Northwest National Laboratory (PNNL).</p> <p>PNNL is a multi-program national laboratory operated for the U.S. Department of Energy (DOE) by Battelle Memorial Institute under Contract No. DE-AC05-76RL01830.</p>
Dataset related to "Material recycling in energy system modeling: a review and showcase"
<p>This dataset has been generated and used for the publication:</p> <blockquote> <p>Zwickl-Bernhard, S., 2024. Material recycling in energy system modeling: a review and showcase. ... DOI: ...</p> </blockquote> <p>The corresponding code is published on *Github. #add link after publication</p> <p>The dataset includes:</p> <ol> <li>Compiled data for manufacturing costs of Solar Modules and Wind Turbines in the EU (manufacturing costs in the EU.xlsx)</li> <li>Compiled data for modified prices (modified prices.xlsx)</li> <li>Additional Data (scalars.xlsx)</li> <li>Sources (sources.txt)</li> <li>Compiled data for yearly capacity of Solar Modules and Wind Turbines (vectors_capacity.xlsx)</li> <li>Compiled data for yearly costs of Solar Modules and Wind Turbines (vectors_costs.xlsx)</li> <li>Compiled data for yearly manufacturing costs of Solar Modules and Wind Turbines (vectors_manufacturing.xlsx)</li> </ol>
Global gridded scenarios of residential cooling energy demand to 2050
<p># ggACene (global gridded Air Conditioning energy) projections</p> <p>### Output AC and AC electricity gridded data</p> <p>This repository hosts output data for SSPs126, 245, 370 and 585 on the estimated and future projected ownership of residential air conditioning (% of households), the related energy consumption (TWh/yr.), and the underlying population counts (useful to quantify the per-capita average consumption or the headcount of people affected by the cooling gap). These data are contained in the multi-layer .nc (NCDF) files, which can be opened and processed in any GIS software/library, or visualised in softwares such as Panoply.</p> <p>### Input data and analysis replication</p> <p>The repository also hosts input data to replicate the entire data generating process. A twin Github repository hosts code (<a href="https://github.com/giacfalk/ggACene">https://github.com/giacfalk/ggACene</a>) to run the model generating the ggACene (global gridded Air Conditioning energy) projections dataset.</p> <p>## Instructions<br>To reproduce the model and generate the dataset from scratch, please refer to the following steps:<br>- Download input data "replication_package_input_data.7z" by cloning the repository<br>- Decompress the folder using 7-Zip (https://www.7-zip.org/download.html)<br>- Open RStudio and adjust the path folder in the sourcer.R script<br>- Run the sourcer.R script to train the ML model, make projections, and represent result files<br><br></p> <p>### Figures replication package</p> <p>Finally, the source_code_data_replication_figures.zip archive contains an R script and processed input data to replicate all the figures contained in the manuscript.<br><br></p> <p>### Reference<br><br>Falchetta, G., De Cian, E., Pavanello, F., & Wing, I. S. Inequalities in global residential cooling energy use to 2050. Nature Communications. https://www.nature.com/articles/s41467-024-52028-8</p>
ERL-118550 Data: Rebates and Grid Decarbonization from the Inflation Reduction Act Promote Equitable Adoption of Energy Efficiency Retrofits
<p>The authors have self-reported an issue in how they used RSMeans 2019 City Cost Index (CCI) data to adjust for regional cost differences. The publicly available webpage stated these data could be used to “adjust for cost differences when compared to the national average, show cost differences between cities, compare cost differences between quarters of the same year, or adjust costs to Canadian cities.” However, the RSMeans Data and Engineering Department later clarified that these values “were intended to show how much CCI values changed for each city at the start of 2019 compared to the values in our 2019 book.” Nevertheless, our capital cost estimates closely align with several peer-reviewed studies and publicly available data sources. Based on our review, we do not believe our method significantly affected the study’s overall findings or conclusions. Further discussion is provided in the manuscript’s Limitations section and Appendix S5 of the Supplementary Materials.</p> <p>Peer-reviewed article available here: https://iopscience.iop.org/article/10.1088/1748-9326/adb765</p> <p>Article DOI: 10.1088/1748-9326/adb765</p>
Data from: Built structures influence patterns of energy demand and CO2 emissions across countries
Open the record for dataset details and reuse information.
SPARCS_WP3_Espoo_City_Energy consumption in Espoo, Finland
<p>Energy consumption in Espoo, Finland, divided by sector. Provided by the Helsinki Region Environmental Authority. 2000-2022</p>
Research data for "Exploring the energy landscape of aluminas through machine learning interatomic potential"
<p>This dataset supports the paper "Exploring the energy landscape of aluminas through machine learning interatomic potential". The paper is online here:</p> <p>The following folders are provided:</p> <ul> <li><em>classical_potential_files</em>: Contains all the empirical potentials used in this study.</li> </ul> <p> </p> <ul> <li><strong><em>crystal_structure_file</em></strong>: Contains structural files of aluminas with various crystal structures, which can be distinguished by their respective filenames. Configurations of alumina with partially occupied cation sites can be obtained from the references provided in the supplementary materials of our article.</li> </ul> <p> </p> <ul> <li><strong><em>lowest_E-config</em></strong>: The files named <code>poscar_{0..19}</code> represent the 20 structure files identified through our developed structural search workflow in conjunction with the final NEP of Aluminas. These structures exhibit different distributions of Al cation occupancy sites. The suffix numbers in the file names indicate that these 20 structures are arranged in ascending order based on their corresponding energy values after structural relaxation using the NEP. In other words, <code>poscar_0</code>, after structural optimization, has the lowest energy among these 20 configurations. Additionally, we have included the CIF files for the crystal structures with partial occupancies provided by the Smrcok model in this folder.</li> </ul> <p> </p> <ul> <li><strong><em>the_final-dataset_alumina</em></strong>: This folder contains all the relevant files for training the final NEP of Aluminas, including the final training dataset named <code>train.xyz</code>, the training parameter file <code>nep.in</code>, and log files. The file <code>nep.txt</code> refers to the final NEP of Aluminas. </li> </ul> <p> </p> <ul> <li><strong><em>various_test-datasets</em></strong>: This folder provides all the test datasets used for testing the final NEP of Aluminas. We have categorized them into four types based on composition: clusters, amorphous structures, crystals, and datasets with physically unallowed configurations that exhibit nearest-neighbor cation occupancy according to the Smrcok model.</li> </ul> <p>Additionally, for ease of retrieval, we have placed the file for the final NEP of aluminas in the main directory and named it <code>nep_3335.txt</code>, where the suffix indicates that the final training dataset <code>train.xyz</code> contains 3,335 structures. This file is identical to the file named <code>nep.txt</code> located in the folder <code>the_final-dataset_alumina</code>.</p>
Resources for "BMF CP 99: Parents as motivations for children's energy conservation behaviors"
<p>The current study is conducted to examine the following research questions:</p> <ul> <li>How are the father’s interactions with children regarding energy saving associated with the children’s energy conservation behaviors?</li> <li>How are the mother’s interactions with children regarding energy saving associated with the children’s energy conservation behaviors?</li> </ul>
AURORA Energy Tracker App
<p>The Excel document contains the algorithm of the AURORA Energy Tracker app. <span>Each of the sheets of the document corresponds to each of the 27 EU member states. This document serves as a calculator of CO2 emissions and energy consumption by entering the data in the corresponding cells. </span>The last version corresponds to April 2024.</p>
Assessing many-body methods on the potential energy surface of the H2_2 hydrogen dimer
<p>Density functional theory, RPA, and quantum Monte Carlo datasets produced for the "Assessing many-body methods on the potential energy surface of the H2_2 hydrogen dimer" paper submitted to the Journal of Chemical Physics and to the arXiv (https://arxiv.org).</p>
Advancing Large Language Models through Story Energy, Universal Harmony Energy, and SA-UUH-UPP
<p><span>In this groundbreaking exploration of AI, we unveil how Story Energy, Universal Harmony Energy, and the SA-UUH-UPP framework could revolutionize large language models (LLMs). Discover how these advanced concepts push AI beyond current boundaries, enabling deeper contextual understanding, energy-efficient models, and steps toward self-awareness. Whether you’re an AI researcher, developer, or enthusiast, this video provides insights that could redefine the future of AI. Watch now to dive into the next frontier of artificial intelligence!</span></p>
Dataset for publication Harnessing Ti3C2-WS2 Nanostructures as Efficient Energy Scaffoldings for Photocatalytic Hydrogen Generation
<p>The dataset contains all relevant data and figures regarding the manuscript "Harnessing Ti3C2-WS2 Nanostructures as Efficient Energy Scaffoldings for Photocatalytic Hydrogen Generation".</p> <p>All Figures are in tiff format and all relevant data are in csv formats. </p> <p>The data in csv format are labelled as specified in the corresping images (e.g. Figure 1a csv file corresponds to data used to plot graphs from Figure 1a etc.). </p> <p>Axis labeling and units are always specified at the beginning of individual columns. If more than one curve was plotted from the csv file, the conditions can also be found at the beginning of corresponding columns.</p>
Data for Paper: A rigorous optimization method for long-term multi-stage investment planning: Integration of hydrogen into a decentralized multi-energy system
<p>Data containing the results and figures presented in the paper "A rigorous optimization method for long-term multi-stage investment planning: Integration of hydrogen into a decentralized multi-energy system" by Luka Bornemann and Jelto Lange and Martin Kaltschmitt, submitted to the Journal Energy Reports.</p>
DATASET for Biomethanol production via electrolysis, oxy-fuel combustion, water-gas shift reaction, and LNG cold energy recovery
<p>DATASET for the paper entitled: Biomethanol production via electrolysis, oxy-fuel combustion, water-gas shift reaction, and LNG cold energy recovery</p>
The Economic Integration of Wind Energy: An Analysis of the ECOWAS Subregion
<p>This study evaluates the economic integration of wind energy in the <br>Economic Community of West African States (ECOWAS) between 2010 <br>and 2020. Wind energy is the energy source that can cost-effectively <br>meet the energy needs of the sub-regions due to the theoretical and <br>economic potential of the sub-regions. For this reason, the study uses data <br>from the World Bank Development Indicators using Panel Vector Auto <br>Regression to analyze the determinants underpinning the economic <br>integration of wind energy. The Panel VAR estimate shows a significant <br>direct link between fossil fuel consumption and private sector investment <br>in renewable energy. This implies that the sub-region consumes a <br>significant amount of fossil fuels, hence the need to increase clean energy <br>investments to move the sub-region towards a low-carbon future. <br>Another significant lag variable is the power consumption per capita in <br>the subregion. Per capita electricity consumption in the sub-region is <br>woefully insufficient. Therefore, wind energy can ensure access via the <br>development of small community wind farms where the national power <br>grid cannot be extended to. When assessing the economic justification of <br>wind integration, the LCOE for wind power is 2.98 cents per kilowatt for <br>the lowest cost scenario compared to nuclear power’s 2.26 per kilowatt <br>hour. The FEVD shows that 13.4% of renewable energy investments are <br>self-explanatory within the first and last periods. The FEVD for wind <br>energy illustrates the short-term variance of 16.4 percent and increases to <br>51.1 percent in the following years after system shocks. This implies that <br>the expansion of wind capacity in the sub-region is expected to increase <br>in the long-term. This serves as a blueprint for integrating wind energy <br>into the sub-region.</p>
Development of a High-Energy-Density Lithiated Silicon-Sulfur Full Cell with Enhanced Stability and Longevity
<p>The raw materials for the draft: "Development of a High-Energy-Density Lithiated Silicon-Sulfur Full Cell with Enhanced Stability and Longevity"</p>
Solar and Wind Energy Drought Data for 15 BAs in the CONUS
<p><strong>Solar and wind energy drought data for 15 BAs in the CONUS</strong></p> <p>This dataset has 2 components, (1) physically consistent wind, solar and load data for 15 Balancing Authorities (BAs) in the CONUS and (2) pre-computed BA-level energy droughts for a variety of time scales from 1 hour to 5 days. The generation and load data is aggregated from plant level data based on EIA-860 2020 infrastructure. </p> <p>For more information please refer to Bracken et al. 2023, Standardized Benchmark of Historical Compound Wind and Solar Energy Droughts Across the Continental United States, in prep, or refer to the Github repository https://github.com/GODEEEP/energy-droughts</p> <p><strong>Wind, solar and load data</strong></p> <p>The data is broken up with one csv file per time scale, the available time scales are 1-hour, 4-hour, 12-hour, 1-day, 2-day, 3-day, and 5-day. Each file has the following columns</p> <ul> <li>ba - Abbreviated name for the BA </li> <li>year - The current year as an integer</li> <li>period - A unique integer for the current time step</li> <li>solar_gen_mwh - Aggregated solar generation in units of MWh</li> <li>solar_capacity_mwh - Aggregated solar plant capacity expresed as MWh </li> <li>wind_gen_mwh - Aggregated wind generation in units of MWh</li> <li>wind_capacity_mwh - Aggregated wind plant capacity expresed as MWh </li> <li>load_mwh - BA load in MWh</li> <li>load_max_mwh - The maximum BA load over the entire historical period</li> <li>datetime_utc - Time stamp for the current time step, in UTC, All time stamps are beginning of period. </li> <li>timezone - The predominant time zone for the BA</li> <li>wind_cf - Wind capacity factor, wind_gen_mwh/wind_capacity_mwh</li> <li>solar_cf - Solar capacity factor, wind_gen_mwh/wind_capacity_mwh </li> <li>load_cf - Load "capacity factor", expresed as a fraction of the maximum BA load, load_mwh/load_max_mwh </li> </ul> <p><strong>Energy drought data</strong></p> <p>Several kinds of energy droughts are available</p> <ul> <li><strong>lws</strong> - Load, wind, and solar compound droughts defined using a moving 10th percentile threshold based on the week of the year</li> <li><strong>rl</strong> - Residual load (load minus wind and solar gen) droughts defined using a moving 10th percentile threshold based on the week of the year</li> <li><strong>solar</strong> - Solar only droughts defined using a moving 10th percentile threshold based on the week of the year</li> <li><strong>solar_fixed</strong> - Solar only droughts defined using a single 10th percentile threshold </li> <li><strong>wind</strong> - Wind only droughts defined using a moving 10th percentile threshold based on the week of the year</li> <li><strong>wind_fixed</strong> - Wind only droughts defined using a single 10th percentile threshold </li> <li><strong>ws</strong> - Wind only droughts defined using a moving 10th percentile threshold based on the week of the year</li> <li><strong>ws_fixed</strong> - Wind and solar droughts defined using a single 10th percentile threshold </li> </ul> <p>Each drought type and time scale is in a csv file with the following columns (not all columns are available for every drought type)</p> <ul> <li>ba - Abbreviated name for the BA </li> <li>run_id - unique id for each drought event</li> <li>datetime_utc - Date stamp for the start of the drought, in UTC</li> <li>timezone - The predominant time zone for the BA</li> <li>run_length - The length of a drought in time steps</li> <li>run_length_days - The length of the drought in days</li> <li>severity_ws - Drought severity for wind and solar droughts, computed using the compound drought magnitude metric</li> <li>severity_lws - Drought severity load, wind, and solar droughts, computed using the compound drought magnitude metric</li> <li>severity_mwh - Drought severity expressed as MWh</li> <li>zero_prob - For solar, this value indicates if the timestep has zero probability of solar production, i.e. night time</li> <li>year - The year of the timestep</li> <li>month - The month of the timestep </li> <li>hour - The hour of the timestep </li> <li>wind_cf - Wind capacity factor for the drought</li> <li>solar_cf - Solar capacity factor for the drought </li> <li>srepi_solar - Standardized renewable energy production index for solar</li> <li>srepi_wind - Standardized renewable energy production index for wind</li> </ul> <p>This research was supported by the Grid Operations, Decarbonization, Environmental and Energy Equity Platform (GODEEEP) Investment, under the Laboratory Directed Research and Development (LDRD) Program at Pacific Northwest National Laboratory (PNNL).</p> <p>PNNL is a multi-program national laboratory operated for the U.S. Department of Energy (DOE) by Battelle Memorial Institute under Contract No. DE-AC05-76RL01830.</p> <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.