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858 results for “Energy Data”
Bayesian analysis of (3+1)D relativistic nuclear dynamics with the RHIC beam energy scan data
<p>This dataset contains the MCMC chain (LHD+HPP) without any constraints on the parameters for the (3+1)D Bayesian inference study for the RHIC beam energy scan program.<br>We also provide the nine trained emulator objects, which were generated with the code available at <a title="GPBayesTools-HIC: v1.1.0" href="https://doi.org/10.5281/zenodo.12807892" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12807892</a>.<br>The training data is given in pickle format as dictionaries for the training points. The first 1000 points in the files correspond to the Latin Hypercube design points and the last 100 points are points sampled from the posterior distribution.</p>
Raw Data for Energy Consumption Comparison of DMA-Based and FatFs Storage Systems on Wearable Devices
<p>This dataset contains raw oscilloscope measurements comparing the energy consumption of a Direct Memory Access (DMA)-based storage system versus the FatFs file system for wearable devices. The data was collected as part of the study "Direct Memory Access-Based Data Storage for Long-Term Acquisition Using Wearables in an Energy-Efficient Manner".</p> <p>The dataset includes voltage drop measurements across a 2-ohm shunt resistor, captured using an Analog Discovery 2 digital oscilloscope at a 500 kHz sampling rate. Measurements were taken under various conditions:</p> <ul> <li>Storage systems: DMA-based (proposed) and FatFs</li> <li>SD card capacities: 4 GB and 8 GB</li> <li>Write frequencies: 2 Hz and 5 Hz (referring to the frequency of writing a specific data block of 15,872 bytes)</li> <li>With and without a smoothing capacitor</li> </ul> <p>Each CSV file contains 20 million samples, equivalent to 40 seconds of data acquisition. File names encode the experimental conditions, including the storage system, write frequency, number of samples, acquisition rate, acquisition time, data format, SD card size, and absence of the smoothing capacitor.</p> <p>The data is organized into two main folders:</p> <ol> <li>"cap": Contains measurements with the smoothing capacitor</li> <li>"no_cap": Contains measurements without the smoothing capacitor</li> </ol> <p>This raw data can be used to reproduce the energy consumption and write speed analyses presented in the article, as well as for further investigation into the performance of embedded storage systems for wearable devices.</p>
Raw data on Renewable Energies
<p>Raw data corresponding to the paper "Worldwide trends in research, funding and international collaboration on renewable energies"</p>
Input Data for paper "Energy Storage Profit Risk under Stochastic Fuel Prices"
<p>This is a supplementary information accompanying "Energy Storage Profit Risk under Stochastic Fuel Prices" paper submitted to <a href="https://www.journals.elsevier.com/energy-economics/">Energy Economics</a>.</p>
SET-NAV: WP5: Invert modelling output for the building sector final energy demand and cost data
<p>This data set contains the Invert modelling results for final energy demand for space heating, cooling and hot water in buildings; hourly data for district heating and electricity (for different technologies) for 3-4 building types; annual data for the other energy carriers.</p> <p>It also contains all annual cost (Annuity of investments, O&M, fuel cost ) of electricity generation and / or heat generation and considered efficiency measures.</p> <p>This data is also available and visualised in our dedicated SET-NAV open data platform: The SET-NAV Scenario Explorer: https://data.ene.iiasa.ac.at/set-nav/#/workspaces</p>
Data release for the "First measurement of muon neutrino charged-current interactions on hydrocarbon without pions in the final state using multiple detectors with correlated energy spectra at T2K"
<p>### On-/Off-Axis Data Release<br>#### (Version 1.0.1, dated 2024/08/12)</p> <p>This tar archive contains the data release for ‘First measurement of muon neutrino charged-current interactions on hydrocarbon without pions in the final state using multiple detectors with correlated energy spectra at T2K’. It contains the cross-section data points and supporting information in ROOT and text format, which are detailed below:</p> <p>+ `onoffaxis_xsec_data.root`<br>This ROOT file contains the extracted cross section and the nominal MC prediction as TH1D histograms for both the flattened 1D array of bins and in the angle binning for the analysis. The ROOT file also contains both the covariance and inverted covariance matrix for the result stored as TH2D histograms. The angle bin numbering and the corresponding bin edges are detailed at the end of the README.</p> <p>+ `flux_analysis.root`<br>This ROOT file contains the nominal and post-fit flux histograms for ND280 and INGRID. Two different binnings are included: a fine binned histogram (220 bins) and a coarse binned histogram (20 bins). The coarse binned histogram corresponds to the flux parameters detailed in the paper (and bin edges listed in the appendix).</p> <p>+ `xsec_data_mc.csv`<br>The extracted cross-section data points and the nominal MC prediction for each bin is stored as a comma-separated value (CSV) file with header row.</p> <p>+ `cov_matrix.csv` and `inv_matrix.csv`<br>The covariance matrix and the inverted covariance matrix are both stored as CSV files with each row stored as a single line and columns separated by commas (there is no header row). Matrix element (0,0) corresponds to the first number in the file.</p> <p>+ `nd280_analysis_binning.csv` and `ingrid_analysis_binning.csv`<br>The analysis bin edges are included as CSV files. The columns are labeled with a header row and denote the linear bin index and the lower and upper bin edge for the angle and momentum bins. The units are in cos(angle) for the angle bins and in MeV/c for the momentum bins.</p> <p>+ `calc_chisq.cxx`<br>This is an example ROOT script to calculate the chi-square between the data and the nominal MC prediction using the ROOT file in the data release. To run, open ROOT and load the script (`.L calc_chisq.cxx`) and execute the function `calc_chisq("/path/to/file.root")`.</p> <p>+ `calc_chisq.py`<br>This is an example Python script to calculate the chi-square between the data and the nominal MC prediction using the text/CSV files in the data release. The code requires NumPy as an external dependency, but otherwise uses built-in modules. To run, execute using a Python3 interpreter and give the file paths to the data/MC text file and the inverse covariance text file as the first and second arguments respectively -- e.g. `python3 calc_chisq.py /path/to/xsec_data_mc.csv /path/to/inv_matrix.csv`</p> <p>+ ND280 angle bin numbering<br> - 0: `-1.0 < cos(#theta) < 0.20`<br> - 1: `0.20 < cos(#theta) < 0.60`<br> - 2: `0.60 < cos(#theta) < 0.70`<br> - 3: `0.70 < cos(#theta) < 0.80`<br> - 4: `0.80 < cos(#theta) < 0.85`<br> - 5: `0.85 < cos(#theta) < 0.90`<br> - 6: `0.90 < cos(#theta) < 0.94`<br> - 7: `0.94 < cos(#theta) < 0.98`<br> - 8: `0.98 < cos(#theta) < 1.00`</p> <p>+ INGRID angle bin numbering<br> - 0: `0.50 < cos(#theta) < 0.82`<br> - 1: `0.82 < cos(#theta) < 0.94`<br> - 2: `0.94 < cos(#theta) < 1.00`<br> <br>### Changelog</p> <p>#### v1.0.1<br>Fix transcription error in INGRID momentum binning. The lowest momentum bin edge is at 350 MeV/c, not 300 MeV/c.</p>
Data Set "Protein-Ligand Interaction Energies from Quantum-Chemical Fragmentation Methods: Upgrading the MFCC-Scheme with Many-Body Contributions"
<p>This data set accompanies the publication "Protein-Ligand Interaction Energies from Quantum-Chemical Fragmentation Methods: Upgrading the MFCC-Scheme with Many-Body Contributions" by Johannes Vornweg and Christoph R. Jacob (TU Braunschweig, Germany) </p> <p>It contains the following files:</p> <p><br>Directory 1_structures:</p> <p> PDB files of all structures used for the test calculations. <br> The PDB files correspond to the protonated structures obtained <br> as described in the main text.</p> <p><br>Directory 02_figure_scripts:</p> <p> Jupyter Notebook for generating all plots included in the manuscript, <br> including raw numerical data.</p> <p><br>Directory 03_input_scripts:</p> <p> - min_congrad.mdp: input file for partial optimization of protonated <br> protein--ligand complexes with Gromacs</p> <p> PyADF input scripts:</p> <p> - sp_single.pyadf: single-point calculations of separate protein and ligand<br> - sp_complex.pyadf: single-point calculation of protein-ligand complex<br> - mfccmbe3.pyadf: MFCC and MFCC-MBE(2) calculations of P-L interaction energy</p> <p> These scripts can be used with PyADF v1.5 (DOI: 10.5281/zenodo.13236550)</p>
Network files and Python code used in "Designing a sector-coupled European energy system robust to 60 years of historical weather data"
<p><strong>Description</strong></p> <p>This repository contains data presented in the paper <a href="https://www.nature.com/articles/s41467-024-54853-3" target="_blank" rel="noopener">Designing a sector-coupled European energy system robust to 60 years of historical weather data</a>. It contains the derived metrics (.csv) files from a:</p> <ol> <li>joint capacity and dispatch optimization with weather years (design years) from 1960 to 2021 as input</li> <li>dispatch optimization of the 62 capacity layouts using weather years (operational years) different from the design year.</li> </ol> <p>All results from (1) are found in "Capacity_optimization.zip" and results from (2) are found in "Dispatch_optimization.zip".</p> <p>The resulting network files (both from the capacity and dispatch optimization) are located <a href="https://anon.erda.au.dk/cgi-sid/ls.py?share_id=DuGvDWlkeI">here</a>.</p> <p>We also provide the Python code used to derive the metrics and to create the visualizations included in the paper. This is located in "Jupyter_notebooks". The Jupyter notebooks refer to Python scripts located <a href="https://github.com/ebbekyhl/multi-weather-year-assessment">here</a>.</p> <p><strong>Revisions:</strong></p> <p>This version includes the following additions compared to the previous versions: </p> <ul> <li>Timeseries of nodal loads for all years</li> <li>Timeseries of nodal heat pump Coefficient of Performance (COP) </li> <li>Nodal capacity and hourly capacity factors </li> </ul>
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>
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>
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.
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>
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>
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>
Experimental and simulated data for the article "Reassessing the role and lifetime of Qx in the energy transfer dynamics of Chlorophyll a"
<p>The .zip file contains:</p> <ul> <li>Linear absorption spectra of Chl a in EtOH, acetone, and benzonitrile (BN)</li> <li>Low-temperature emission and excitation anisotropy datasets of Chl a in isopropanol</li> <li>Transient absorption (TA) datasets of Chl a in EtOH, acetone, and BN after B- and Q-band excitation (including pump spectra)</li> <li>Transient absorption anisotropy (TAA) datasets of Chl a in acetone after B-band excitation (including a pump spectrum)</li> <li>Optimized geometries for the Q-band ESA calculations</li> <li><span>Geometries for normal modes used for PES construction</span></li> </ul>
Data to estimate energy efficiency and environmental friendliness of Ukrainian GDP from indicators of structural, economic, and social development in Ukraine
<p><span>Data to estimate regression dependences of energy efficiency and environmental friendliness of GDP from indicators of structural, economic, and social development in Ukraine </span></p>
Data to calculate metal demand from IAM energy projections and add flexibility to the technological mix
<p>In the "Data" folder, you’ll find all necessary data to run the <strong>"IAM_metal_optimisation.py"</strong> script, available on GitHub (<a href="https://github.com/Pelotte/iam-metal-demand-optimizer/tree/master" target="_new" rel="noopener">https://github.com/Pelotte/iam-metal-demand-optimizer/tree/master</a>).</p> <p>The <strong>"IAM_metal_optimisation.py"</strong> script is a tool designed to:</p> <ol> <li>Quantify metal supply and demand by sector through 2050, using energy projections from various IAMs and SSP-RCP scenarios.</li> <li>Optimize the IAM technological mix, minimizing adjustments needed to prevent metal demand from exceeding supply constraints.</li> </ol> <p>This tool supports all IAMs with power projections for SSP-RCP scenarios (except SSP3) provided in the IPCC’s Sixth Assessment Report (AR6), Working Group III, and available in the IIASA database.</p> <p>Data for RCP 2.6, SSPs 1, 2, 4, 5, of the SSP marker IAM models of the IPCC, are organized in the "Capacity Factor IAM," "GDP IAM," and "Power Capacity IAM" folders. See the GitHub README for more details.</p>
Data from " Allostery can convert binding free energies into concerted domain motions in enzymes"
<p>Data from " Allostery can convert binding free energies into concerted domain motions in enzymes"</p> <p> </p> <p>Electrophysiology data corresponding to the main text figures and supporting information figures. One representative set was chosen for each triplicate and included in this data set. For details are found in the ‘read me explanation.txt’</p> <p>PDB used for this paper can be found at; 4ake [http://doi.org/10.2210/pdb4AKE/pdb] and 1ake [http://doi.org/10.2210/pdb1AKE/pdb]</p> <p>Full uncropped scans of any cropped gel/blot images are provided.</p> <p>The zip folder contains the MATLAB code package HMM inference, specifically tailored to nanopore ionic current flow data, as analyzed in the publication and can also be found at: https://github.com/yulanvanoppen/nanopore-HMM</p> <pre><br> </pre>
Code and data for publication "pyGRETA, pyCLARA, pyPRIMA: A pre-processing suite to generate flexible model regions for energy system models"
<p>This dataset contains the code of the three pre-processing tools <a href="https://github.com/tum-ens/pyGRETA">pyGRETA</a>, <a href="https://github.com/tum-ens/pyPRIMA">pyPRIMA</a> and <a href="https://github.com/tum-ens/pyCLARA">pyCLARA</a> and an examplary database for the scope of Austria.</p> <p>To run the code with full functionality additional data is needed. Check the documentation of the tools for further information.</p> <p> </p> <p>Sources for data can be found here: </p> <p>pyGRETA: https://pygreta.readthedocs.io/en/stable/user_manual.html#recommended-input-sources</p> <p>pyPRIMA: https://pyprima.readthedocs.io/en/stable/user_manual.html#recommended-input-sources</p> <p>pyCLARA: https://pyclara.readthedocs.io/en/stable/user_manual.html#recommended-input-sources</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.