Find research datasets worth reusing

opencc-by-4.0Sep 2021View details →

Electricity demand data and solar generation data from Plymouth. UK

This dataset was used in the Western Power Distribution Presumed Open Data competition in 2021. The data is provided under the Western Power Distribution Open Data Licence. There are five files: pv_train_set4.csv - contains solar panel data - an irradiance, power output and solar panel temperature for each half-hour from 3rd November 2017 through 2nd July 2020. weather_train_set4.csv - contains hourly reanalysis temperature and solar radiation at 6 weather stations near the solar panels (near Plymouth, UK). demand_train_set4.csv - contains half-hourly electricity demand data from a substation near Plymouth, UK, running from 3rd November 2017 through 2nd July 2020. pv_test_set4.csv - contains an extra week of data to pv_train_set4.csv, running from 3rd July 2020 through 9th July 2020. demand_test_set4.csv - contains an extra week of data to demand_train_set4.csv, running from 3rd July 2020 through 9th July 2020.

opencc-by-4.0Mar 2021View details →

Case study result data set for Energy Economics (submitted) article "On Wholesale Electricity Prices and Market Values in a Carbon-Neutral Energy System"

The data set contains wholesale power price time series data for Germany and France focussing on price setting effects in a long term low carbon European energy system context (scenario year 2050) generated with the model SCOPE SD of Fraunhofer Institute for Energy Economics and Energy System Technology IEE. The single time series are focussing on the price setting effects of different flexible technologies including both traditional and new market participants due to cross-sectoral integration. Unit: Euro/Megawatthour Abbreviations: <ul> <li>BEV - Battery Electric Vehicles</li> <li>GER - Germany</li> <li>FRA - France</li> <li>OCGT - Open Cycle Gas Turbine</li> <li>PHEV - Plug-In Hybrid Vehicles</li> <li>RES - Renewable energy sources (here: wind and solar power)</li> <li>th. - thermal</li> </ul>

opencc-by-4.0Feb 2023View details →

Open synthetic data on travel and charging demand of battery electric cars: An agent-based simulation on three charging behavior archetypes

Background Battery electric vehicles (BEVs) are crucial for a sustainable transportation system. As more people adopt BEVs, it becomes increasingly important to accurately assess the demand for charging infrastructure. However, much of the current research on charging infrastructure relies on outdated assumptions, such as the assumption that all BEV owners have access to home chargers and the "Liquid-fuel" mental model. To address this issue, we simulate the travel and charging demand on three charging behavior archetypes. We use a large synthetic population of Sweden, including detailed individual characteristics, such as dwelling types (detached house vs. apartment) and activity plans (for an average weekday). This data repository aims to provide the BEV simulation's input, assumptions, and output so that other studies can use them to study sizing and location design of charging infrastructure, grid impact, etc. A journal paper published in Transportation Research Part D: Transport and Environment details the method to create the data (particularly Section 2.2 BEV simulation). <a href="https://doi.org/10.1016/j.trd.2023.103645">https://doi.org/10.1016/j.trd.2023.103645</a> Methodology This data product is centered on the 1.7 million inhabitants of the Västra Götaland (VG) region, which includes the second largest city in Sweden, Gothenburg. We specifically simulated 284,000 car agents who live in VG, representing 35% of all car users and 18% of the total population in the region. They spend their simulation day (representing an average weekday) in a variety of locations throughout Sweden. This open data repository contains the core model inputs and outputs. The numbers in parentheses correspond to the data sets. We use individual agents' activity plans (1) and travel trajectories from MATSim simulation for the BEV simulation (2), in which we consider overnight charger access (3), car fleet composition referencing the current private car fleet in Sweden (4), and Swedish road network with slope information (5) with realistic BEV charging & discharging dynamics. For the BEV simulation, we tested ten scenarios of charging behavior archetypes and fast charging powers (6). The output includes the time history of travel trajectories and charging of the simulated BEVs across the different scenarios (7). Data description The current data product covers seven data files. (1) Agents' experienced activity plans File name: 1_activity_plans.csv <table> <tbody> <tr> <td> Column </td> <td> Description </td> <td> Data type </td> <td> Unit </td> </tr> <tr> <td> person </td> <td> Agent ID </td> <td> Integer </td> <td> - </td> </tr> <tr> <td> act_id </td> <td> Activity index of each agent </td> <td> Integer </td> <td> - </td> </tr> <tr> <td> deso </td> <td> Zone code of Demographic statistical areas (DeSO)1 </td> <td> String </td> <td> - </td> </tr> <tr> <td> POINT_X </td> <td> Coordinate X of activity location (SWEREF99TM) </td> <td> Float </td> <td> meter </td> </tr> <tr> <td> POINT_Y </td> <td> Coordinate Y of activity location (SWEREF99TM) </td> <td> Float </td> <td> meter </td> </tr> <tr> <td> act_purpose </td> <td> Activity purpose (work, home, other) </td> <td> String </td> <td> - </td> </tr> <tr> <td> mode </td> <td> Transport mode to reach the activity location (car) </td> <td> String </td> <td> - </td> </tr> <tr> <td> dep_time </td> <td> Departure time in decimal hour (0-23.99) </td> <td> Float </td> <td> hour </td> </tr> <tr> <td> trav_time </td> <td> Travel time to reach the activity location </td> <td> String </td> <td> hour:minute:second </td> </tr> <tr> <td> trav_time_min </td> <td> Travel time in decimal minute </td> <td> Float </td> <td> minute </td> </tr> <tr> <td> speed </td> <td> Travel speed to reach the activity location </td> <td> Float </td> <td> km/h </td> </tr> <tr> <td> distance </td> <td> Travel distance between the origin and the destination </td> <td> Float </td> <td> km </td> </tr> <tr> <td> act_start </td> <td> Start time of activity in minute (0-1439) </td> <td> Integer </td> <td> minute </td> </tr> <tr> <td> act_time </td> <td> Activity duration in decimal minute </td> <td> Float </td> <td> minute </td> </tr> <tr> <td> act_end </td> <td> End time of activity in decimal hour (0-23.99) </td> <td> Float </td> <td> hour </td> </tr> <tr> <td> score </td> <td> Utility score of the simulation day given by MATSim </td> <td> Float </td> <td> - </td> </tr> </tbody> </table> 1 <a href="https://www.scb.se/vara-tjanster/oppna-data/oppna-geodata/deso--demografiska-statistikomraden/">https://www.scb.se/vara-tjanster/oppna-data/oppna-geodata/deso--demografiska-statistikomraden/</a>   (2) Travel trajectories File name: 2_input_zip Produced by MATSim simulation, the zip folder contains ten files (events_batch_X.csv.gz, X=1, 2, …, 10) of input events for the BEV simulation. They are the moving trajectories of the car agents in their simulation days. <table> <tbody> <tr> <td> Column </td> <td> Description </td> <td> Data type </td> <td> Unit </td> </tr> <tr> <td> time </td> <td> Time in second in a simulation day (0-86399) </td> <td> Integer </td> <td> Second </td> </tr> <tr> <td> type </td> <td> Event type defined by MATSim simulation2 </td> <td> String </td> <td> - </td> </tr> <tr> <td> person </td> <td> Agent ID </td> <td> Integer </td> <td> - </td> </tr> <tr> <td> link </td> <td> Nearest road link consistent with (5) </td> <td> String </td> <td> - </td> </tr> <tr> <td> vehicle </td> <td> Vehicle ID identical to person </td> <td> Integer </td> <td> - </td> </tr> </tbody> </table> 2 One typical episode of MATSim simulation events: Activity ends (actend) -> Agent’s vehicle enters traffic (vehicle enters traffic) -> Agent’s vehicle moves from previous road segment to its next connected one (left link) -> Agent’s vehicle leaves traffic for activity (vehicle leaves traffic) -> Activity starts (actstart)   (3) Overnight charger access File name: 3_home_charger_access.csv <table> <tbody> <tr> <td> Column </td> <td> Description </td> <td> Data type </td> <td> Unit </td> </tr> <tr> <td> person </td> <td> Agent ID </td> <td> Integer </td> <td> - </td> </tr> <tr> <td> home_charger </td> <td> Whether an agent has access to a home garage charger/living in a detached house (0=no, 1=yes) </td> <td> Integer </td> <td> - </td> </tr> </tbody> </table>   (4) Car fleet composition File name: 4_car_fleet.csv <table> <tbody> <tr> <td> Column </td> <td> Description </td> <td> Data type </td> <td> Unit </td> </tr> <tr> <td> person </td> <td> Agent ID </td> <td> Integer </td> <td> - </td> </tr> <tr> <td> income_class </td> <td> Income group (0=None, 1=below 180K, 2=180K-300K, 3=300K-420K, 4=above 420K) </td> <td> Integer </td> <td> - </td> </tr> <tr> <td> car </td> <td> Car model class (B=40 kWh, C=60 kWh, D=100 kWh) </td> <td> String </td> <td> - </td> </tr> </tbody> </table>   (5) Road network with slope information File name: 5_road_network_with_slope.shp (5 files in total) <table> <tbody> <tr> <td> Column </td> <td> Description </td> <td> Data type </td> <td> Unit </td> </tr> <tr> <td> length </td> <td> The length of road link </td> <td> Float </td> <td> meter </td> </tr> <tr> <td> freespeed </td> <td> Free speed </td> <td> Float </td> <td> km/h </td> </tr> <tr> <td> capacity </td> <td> Number of vehicles </td> <td> Integer </td> <td> - </td> </tr> <tr> <td> permlanes </td> <td> Number of lanes </td> <td> Integer </td> <td> - </td> </tr> <tr> <td> oneway </td> <td> Whether the segment is one-way (0=no, 1=yes) </td> <td> Integer </td> <td> - </td> </tr> <tr> <td> modes </td> <td> Transport mode (car) </td> <td> String </td> <td> - </td> </tr> <tr> <td> link_id </td> <td> Link ID </td> <td> String </td> <td> - </td> </tr> <tr> <td> from_node </td> <td> Start node of the link </td> <td> String </td> <td> - </td> </tr> <tr> <td> to_node </td> <td> End node of the link </td> <td> String </td> <td> - </td> </tr> <tr> <td> count </td> <td> Aggregated traffic (number of cars travelled per day) </td> <td> Integer </td> <td> - </td> </tr> <tr> <td> slope </td> <td> Slope in percent from -6% to 6% </td> <td> Float </td> <td> - </td> </tr> <tr> <td> geometry </td> <td> LINESTRING (SWEREF99TM) </td> <td> geometry </td> <td> meter </td> </tr> </tbody> </table>   (6) Simulation scenarios specifying the parameter sets File name: 6_scenarios.txt <table> <tbody> <tr> <td> Parameter set (paraset) </td> <td> Strategy 1 </td> <td> Strategy 2 </td> <td> Strategy 3 </td> <td> Fast charging power (kW) </td> <td> Minimum parking time for charging (min) </td> <td> Intermediate charging power (kW) </td> </tr> <tr> <td> 0 </td> <td> 0.2 </td> <td> 0.2 </td> <td> 0.9 </td> <td> 150 </td> <td> 5 </td> <td> 22 </td> </tr> <tr> <td> 1 </td> <td> 0.2 </td> <td> 0.2 </td> <td> 0.9 </td> <td> 50 </td> <td> 5 </td> <td> 22 </td> </tr> <tr> <td> 2 </td> <td> 0.3 </td> <td> 0.3 </td> <td> 0.9 </td> <td> 150 </td> <td> 5 </td> <td> 22 </td> </tr> <tr> <td> 3 </td> <td> 0.3 </td> <td> 0.3 </td> <td> 0.9 </td> <td> 50 </td> <td> 5 </td> <td> 22 </td> </tr> </tbody> </table>   (7) Time history of travel trajectories and charging of the simulated BEVs File name: 7_output.zip Produced by the BEV simulation, the zip folder contains four files (parasetX.csv.gz, X=1, 2, 3, 4) corresponding to the four parameter sets specified in (6). They are the moving trajectories of the car agents with simulated energy and charging time history in their simulation days. <table> <tbody> <tr> <td> Column </td> <td> Description </td> <td> Data type </td> <td> Unit </td> </tr> <tr> <td> person </td> <td> Agent ID </td> <td> Integer </td> <td> - </td> </tr> <tr> <td> home_charger </td> <td> Whether an agent has access to a home garage charger/living in a detached house (0=no, 1=yes) </td> <td> Integer </td> <td> - </td> </tr> <tr> <td> car </td> <td> Car model class (B=40 kWh, C=60 kWh, D=100 kWh) </td> <td> String </td> <td> - </td> </tr> <tr> <td> seq </td> <td> Sequence ID of time history by agent </td> <td> Integer </td> <td> - </td> </tr> <tr> <td> time </td> <td> Time (0-86399) </td> <td> Integer </td> <td> Second </td> </tr> <tr> <td> purpose </td> <td> Valid for activities (home, work, school, other) </td> <td> String </td> <td> - </td> </tr> <tr> <td> type </td> <td> Event type defined by MATSim simulation </td> <td> String </td> <td> - </td> </tr> <tr> <td> link </td> <td> Link ID (link_id in File 5) </td> <td> String </td> <td> - </td> </tr> <tr> <td> distance_driven </td> <td> Cumulative driven distance in the simulation day </td> <td> Float </td> <td> km </td> </tr> <tr> <td> energy_1 </td> <td> Energy consumed while driving (-) or charging (+) (Strategy 1) </td> <td> Float </td> <td> kWh </td> </tr> <tr> <td> energy_2 </td> <td> Energy consumed while driving (-) or charging (+) (Strategy 2) </td> <td> Float </td> <td> kWh </td> </tr> <tr> <td> energy_3 </td> <td> Energy consumed while driving (-) or charging (+) (Strategy 3) </td> <td> Float </td> <td> kWh </td> </tr> <tr> <td> charger_1 </td> <td> Power rating of the charger (Strategy 1) </td> <td> Float </td> <td> kW </td> </tr> <tr> <td> charger_2 </td> <td> Power rating of the charger (Strategy 2) </td> <td> Float </td> <td> kW </td> </tr> <tr> <td> charger_3 </td> <td> Power rating of the charger (Strategy 3) </td> <td> Float </td> <td> kW </td> </tr> <tr> <td> soc_1 </td> <td> State of charge (0-1, Strategy 1) </td> <td> Float </td> <td> - </td> </tr> <tr> <td> soc_2 </td> <td> State of charge (0-1, Strategy 2) </td> <td> Float </td> <td> - </td> </tr> <tr> <td> soc_3 </td> <td> State of charge (0-1, Strategy 3) </td> <td> Float </td> <td> - </td> </tr> </tbody> </table>

opencc-by-4.0Aug 2023View details →

Data file for: Electrical imaging for the subduction channel north of Mount Everest

The magnetotellurics data were used to study the the subduction channel north of Mount Everest, conducted by Institute of Geophysical and Geochemical Exploration, Chinese Academy of Geological Sciences.  The data file of zf.dat was generated by the Matlab code EM3DVP. You are recommended to refer to the Kelbert et al., 2014 paper: https://doi.org/10.1016/j.cageo.2014.01.010 for a brief understanding of the data file formats.

opencc-by-4.0Sep 2023View details →

Phantom measurement data for 'Configuration-based electrical properties tomography', Iyyakkunnel et al. (2021)

This dataset contains the phantom bSSFP measurement data used in the published article Iyyakkunnel et al., 'Configuration-based electrical properties tomography', Magn Reson Med. 2021;85:1855–1864 (doi: 10.1002/mrm.28542). The acquisitions were made with a 3 T MRI system (Magnetom Prisma; Siemens Healthcare, Erlangen, Germany) using a dual-tuned 1H/23Na quadrature head coil for transmission and reception (Rapid Biomedical, Rimpar, Germany). The data includes the magnitude and phase measurements for eight phase-cycled scans (in dicom (.dcm) format). The RF phase increment for the phase cycled scans corresponds to 0°, 45°, 90°, 135°, 180°, 225°, 270° and 315°. For further measurement details, please refer to the mentioned original article.

opencc-by-4.0Sep 2023View details →

Phantom measurement data for 'Complex B1+ mapping with Carr-Purcell spin echoes and its application to electrical properties tomography', Iyyakkunnel et al. (2022)

This dataset contains the phantom measurement data used in the published article Iyyakkunnel et al., 'Complex B1+ mapping with Carr-Purcell spin echoes and its  application to electrical properties tomography', Magn Reson Med. 2022;87:1250–1260 (doi: 10.1002/mrm.29020). The acquisitions were made with a 3 T MRI system (Magnetom Prisma; Siemens Healthcare, Erlangen, Germany) using the body coil for transmission and the a 20-channel head and neck coil for reception. Phase images from multichannel coil data were reconstructed using the manufacturer’s “adaptive coil combine” method. Magnitude measurements for B1 reconstruction using actucal flip angle imaging (AFI) is also included. Phantom scans were also performed with a 1H/23Na transmit/receive birdcage coil (RAPID Biomedical, Rimpar, Germany) for the Supporting Information Figure S1. The data includes the magnitude and phase measurements for the suggested Carr Purcell spin echo sequence with 10 echoes (in dicom (.dcm) format). For further measurement details, please refer to the mentioned original article.

opencc-by-4.0Jan 2017View details →

Data and results for manuscript "Multi-frequency electrical impedance tomography as a non-invasive tool to characterize and monitor crop root systems "

Root systems are essential in nutrient uptake and translocation, but are difficult to characterize non-invasively with existing methods. We propose electrical impedance tomography (EIT) as a new tool for the imaging and monitoring of crop root systems. In a laboratory experiment we demonstrate the capability of the method to capture physiological responses of root systems with high spatial and temporal resolution. We conclude that EIT is a promising functional imaging technique for crop roots. This package contains measured raw EIT data, electrical imaging results, spectral results from the Debye decomposition, and the Python scripts used to generate the plots in the manuscript.

opencc-by-4.0May 2020View details →

Dataset of Concurrent EEG, ECG, and Behavior with Multiple Doses of transcranial Electrical Stimulation-Exp1-Data Downsampled

GX Dataset downsampled - Experiment 1 The GX Dataset is a dataset of combined tES, EEG, physiological, and behavioral signals from human subjects. Here the GX Dataset for Experiment 1 is downsampled to 1 kHz and saved in .MAT format which can be used in both MATLAB and Python. Publication A full data descriptor is published in <a href="https://doi.org/10.1038/s41597-021-01046-y">Nature Scientific Data</a>. Please cite this work as: <blockquote> Gebodh, N., Esmaeilpour, Z., Datta, A. et al. Dataset of concurrent EEG, ECG, and behavior with multiple doses of transcranial electrical stimulation. Sci Data 8, 274 (2021). https://doi.org/10.1038/s41597-021-01046-y </blockquote> Descriptions A dataset combining high-density electroencephalography (EEG) with physiological and continuous behavioral metrics during transcranial electrical stimulation (tES). Data includes within subject application of nine High-Definition tES (HD-tES) types targeted three brain regions (frontal, motor, parietal) with three waveforms (DC, 5Hz, 30Hz), with more than 783 total stimulation trials over 62 sessions with EEG, physiological (ECG, EOG), and continuous behavioral vigilance/alertness metrics. Acknowledgments Portions of this study were funded by X (formerly Google X), the Moonshot Factory. The funding source had no influence on study conduction or result evaluation. MB is further supported by grants from the National Institutes of Health: R01NS101362, R01NS095123, R01NS112996, R01MH111896, R01MH109289, and (to NG) NIH-G-RISE T32GM136499. Extras Back to <a href="https://doi.org/10.5281/zenodo.4456079">Full GX Dataset</a> : https://doi.org/10.5281/zenodo.4456079   For downsampled data (1 kHz ) please see (in .mat format): <ul> <li><a href="https://doi.org/10.5281/zenodo.3840614">Experiment 1</a> : https://doi.org/10.5281/zenodo.3840614</li> <li><a href="https://doi.org/10.5281/zenodo.3840616">Experiment 2</a> : https://doi.org/10.5281/zenodo.3840616</li> </ul>   Code used to import, process, and plot this dataset can be found here: <ul> <li><a href="https://github.com/ngebodh/GX_tES_EEG_Physio_Behavior">GitHub</a> : <a href="https://github.com/ngebodh/GX_tES_EEG_Physio_Behavior">https://github.com/ngebodh/GX_tES_EEG_Physio_Behavior</a></li> </ul>   Additional figures for this project have been shared on Figshare. Trial-wise figures can be found here: <ul> <li><a href="https://figshare.com/articles/figure/Dataset_of_Concurrent_EEG_ECG_and_Behavior_with_Multiple_Doses_of_transcranial_Electrical_Stimulation-_Stimulation_Trials_PSD/14810517">PSD</a>: <a href="https://doi.org/10.6084/m9.figshare.14810517.v1">https://doi.org/10.6084/m9.figshare.14810517.v1</a></li> <li><a href="https://figshare.com/articles/figure/Dataset_of_Concurrent_EEG_ECG_and_Behavior_with_Multiple_Doses_of_transcranial_Electrical_Stimulation-_Stimulation_Trials_Topoplots/14810478">Topoplots During Stimulation:</a> <a href="https://doi.org/10.6084/m9.figshare.14810478">https://doi.org/10.6084/m9.figshare.14810478</a></li> <li><a href="https://doi.org/10.6084/m9.figshare.14810442.v1">Voltage timeseries, spectrogram and behavior:</a> <a href="https://doi.org/10.6084/m9.figshare.14810442">https://doi.org/10.6084/m9.figshare.14810442.v1</a></li> </ul>   The full dataset is also provided in <a href="https://bids.neuroimaging.io/">BIDS</a> format here: <ul> <li><a href="https://doi.org/10.18112/openneuro.ds003670.v1.1.0">Data in BIDS format:</a> https://doi.org/10.18112/openneuro.ds003670.v1.1.0</li> </ul> Data License  <a href="https://creativecommons.org/licenses/by/4.0/">Creative Common 4.0 with attribution (CC BY 4.0)</a>   NOTE Please email ngebodh01@citymail.cuny.edu with any questions.   Updates <ul> <li>Version 2.1.0 <ul> <li>Behavioral data (ptracker) adjusted for all files. Previous version had one participant's data overwriting all behavioral data when downsampled. Mat format now explicitly >v7.3  </li> </ul> </li> <li>Version 2  <ul> <li>Stimulation trigger labels now adjusted. Previous labels were missmatched for Experiment 1's data. </li> </ul> </li> </ul>

opencc-by-4.0Dec 2021View details →

Supporting data for Wu et al. "Penetrating Electric Field Simulated by the MAGE and Observed by ICON" paper

This dataset contains the necessary data supporting the paper titled "Penetrating Electric Field Simulated by the MAGE and Observed by ICON", to be submitted by Wu et al., 2022. The dataset includes the Multiscale Atmosphere-Geospace Environment (MAGE) model simulation for two intervals to support 2020 Sep 26 9-10 UT and 2020 Sep 24 5-6 UT. The data contain neutral wind, ion drift, and electron density output.

Impedance data for various electrochemical and electrical systems

Impedance dataset for different electrochemical and electrical systems: <table> <tbody> <tr> <td> </td> <td> Lithium-Ion battery LFP (A123 26650) </td> <td> Lithium-Ion battery NCR (Panasonic 18650) </td> <td>Vanadium redox-flow Battery</td> <td>Polymer electrolyte membrane fuel cell</td> <td> High-temperature PEM (Custom made) </td> <td> Double-layer capacitor 3.4 kF (Maxwell) </td> <td> Analogue RLC circuit (Custom made) </td> </tr> <tr> <td>Details</td> <td>A123 Systems,  26650 lithium iron phosphate | graphite, 2,5 Ah</td> <td>Panasonic NCR-18650B, 18650 nickel-manganese-cobalt-oxid | graphite, 2.5 Ah</td> <td>Micro Flow Cell  (Electrocell A/S, Tarm, DK), surface 10 cm2 </td> <td>ElringKlinger (EK) single PEM fuel cell, active surface area 50 cm2 </td> <td>Custom made fuel cell assembly, metallic bipolar plates with single serpentine flow-fields, electrode surface area of 4 cm², polybenzimidazole (PBI) membranes (Dapazol1, Danish Power Systems-DPS1) </td> <td>Maxwell, capacitance 3400 F, 2.85 V</td> <td>Custom made circuit with a parallel connection of a 2.2 μF foil capacitor and a lossy inductor with 9.7 mΩ  and 5 mH, factory values may vary ± 10 %</td> </tr> <tr> <td>Operating point</td> <td>50% state of charge, 3.27 V, 20 °C</td> <td>50 % state of charge, 3.27 V, 20 °C</td> <td>50% state of charge, 1:4 V </td> <td>1 A, RH of 83.4%, stoichiometry of 4.0/2.0</td> <td>H2 / air stoichiometry 1.8 / 2.5, 160 °C, 800 mA/cm²</td> <td>voltage 603.5 mV, temperature 30 °C</td> <td>-</td> </tr> <tr> <td>Device</td> <td>Zahner Zennium Pro workstation</td> <td>Zahner Zennium Pro workstation</td> <td>Scribner 857, Gamry Reference 3000</td> <td>Scribner Associates fuel cell test station with 885 Fuel Cell Potentiostat</td> <td>Zahner Zennium Pro workstation</td> <td>Zahner Zennium Pro workstation</td> <td>Biologic VMP300</td> </tr> <tr> <td>EIS mode</td> <td>galvanostatic</td> <td>galvanostatic</td> <td>potentiostatic</td> <td>galvanostatic</td> <td>galvanostatic</td> <td>potentiostatic</td> <td>galvanostatic</td> </tr> <tr> <td>Amplitude</td> <td>30 mA</td> <td>250 mA</td> <td>10 mV</td> <td>100 mA</td> <td>100 mA</td> <td>10 mV</td> <td>30 mA</td> </tr> <tr> <td>Frequency</td> <td>52 mHz - 17 kHz</td> <td>10 mHz - 10 kHz</td> <td>100 mHz - 10 kHz</td> <td>10 mHz - 3.98 kHz</td> <td>100 mHz - 100 kHz</td> <td>100 mHz - 1 kHz</td> <td>100 Hz - 10 kHz</td> </tr> <tr> <td>Reference</td> <td>Danzer, M.A. (2019). Generalized Distribution of Relaxation Times Analysis for the Characterization of Impedance Spectra.</td> <td>Rüther, T., Schamel, M., Plank, C., Schomburg, F., Röder, F., & Danzer, M. A. (2023). Cell-to-Cell-Variations of a Panasonic NCR18650B. Zenodo. https://doi.org/10.5281/zenodo.8369275</td> <td> Danzer, M.A. (2019). Generalized Distribution of Relaxation Times Analysis for the Characterization of Impedance Spectra. Schneider, J., Tichter, T., Khadke, P., Zeis, R., and Roth, C. (2020). Deconvolution of electrochemical impedance data for the monitoring of electrode degradation in VRFB. Electrochimica Acta 336, 135510.   </td> <td>Ivan Pivac, Ivar J. Halvorsen, Dario Bezmalinovic, Frano Barbir, & Federico Zenith. (2020). Low-frequency EIS intercept as a diagnostic tool for PEM fuel cells degradation. European Fuel Cell Technology & Applications Conference - Piero Lunghi Conference (EFC17), Naples, Italy. Zenodo. https://doi.org/10.5281/zenodo.3631156</td> <td>Weiß, A., Schindler, S., Galbiati, S., Danzer, M.A., and Zeis, R. (2017). Distribution of Relaxation Times Analysis of High-Temperature PEM Fuel Cell Impedance Spectra. Electrochimica Acta 230, 391–398.</td> <td>Danzer, M.A. (2019). Generalized Distribution of Relaxation Times Analysis for the Characterization of Impedance Spectra.</td> <td>-</td> </tr> </tbody> </table>

The Global and National Energy Systems Techno-Economic (GNESTE) Database: Economic and performance data for hydro power in current and future electricity systems

Here, we present a database which collates historical, current, and future cost and performance data and assumptions for hydroelectric generation from the open literature. Hydroelectric power is the largest source of renewable energy, supplying 15% of global electricity. The data are global in scope but with regional and national specificity, covers the years 2015 through to 2050, and span 1011 datapoints from 11 sources. The database enables modellers to select and justify model input data and provides a benchmark for comparing assumptions and projections to other sources across the literature to validate model inputs and outputs. It is designed to be easily updated with new sources of data, ensuring its utility, comprehensiveness, and broad applicability over time.  Technoeconomic data on utility-scale hydroelectric power was collected from websites, reports, academic articles and databases of national and international organisations.

The Global and National Energy Systems Techno-Economic (GNESTE) Database: Economic and performance data for battery storage in current and future electricity systems

Here, we present a database which collates historical, current, and future cost and performance data and assumptions for battery energy storage systems from the open literature. Battery energy storage is the fastest growing form of power system flexibility, and will be critical to integrating large shares of variable renewable energy. The data are global in scope but with regional and national specificity, covers the years 2015 through to 2050, and span 671 datapoints from 18 sources. The database enables modellers to select and justify model input data and provides a benchmark for comparing assumptions and projections to other sources across the literature to validate model inputs and outputs. It is designed to be easily updated with new sources of data, ensuring its utility, comprehensiveness, and broad applicability over time. Technoeconomic data on utility-scale batteries was collected from websites, reports, academic articles and databases of national and international organisations.

The Global and National Energy Systems Techno-Economic (GNESTE) Database: Economic and performance data for wind power in current and future electricity systems

Here, we present a database which collates historical, current, and future cost and performance data and assumptions for wind power generation from the open literature. Wind energy supplies 7% of global electricity, and production has grown three-fold in the decade to 2022. The data are global in scope but with regional and national specificity, covers the years 2015 through to 2050, and span 1506 datapoints from 28 sources. The database enables modellers to select and justify model input data and provides a benchmark for comparing assumptions and projections to other sources across the literature to validate model inputs and outputs. It is designed to be easily updated with new sources of data, ensuring its utility, comprehensiveness, and broad applicability over time. Technoeconomic data on utility-scale wind energy was collected from websites, reports, academic articles and databases of national and international organisations.

The Global and National Energy Systems Techno-Economic (GNESTE) Database: Economic and performance data for gas power in current and future electricity systems

Here, we present a database which collates historical, current, and future cost and performance data and assumptions for gas-fired power generation from the open literature. Natural gas supplies 23% of global electricity, but must be rapidly phased down to meet global decarbonisation objectives. The data are global in scope but with regional and national specificity, covers the years 2015 through to 2050, and span 620 datapoints from 14 sources.   The database enables modellers to select and justify model input data and provides a benchmark for comparing assumptions and projections to other sources across the literature to validate model inputs and outputs. It is designed to be easily updated with new sources of data, ensuring its utility, comprehensiveness, and broad applicability over time.Technoeconomic data on new-build gas-fired power generation was collected from websites, reports, academic articles and databases of national and international organisations.

The Global and National Energy Systems Techno-Economic (GNESTE) Database: Economic and performance data for coal power in current and future electricity systems

Here, we present a database which collates historical, current, and future cost and performance data and assumptions for coal-fired power generation from the open literature. Coal supplies 35% of global electricity, but must be rapidly phased down to meet global decarbonisation objectives. The data are global in scope but with regional and national specificity, covers the years 2015 through to 2050, and span 345 datapoints from 12 sources. The database enables modellers to select and justify model input data and provides a benchmark for comparing assumptions and projections to other sources across the literature to validate model inputs and outputs. It is designed to be easily updated with new sources of data, ensuring its utility, comprehensiveness, and broad applicability over time. Technoeconomic data on new-build coal-fired power generation was collected from websites, reports, academic articles and databases of national and international organisations.

The Global and National Energy Systems Techno-Economic (GNESTE) Database: Economic and performance data for solar power in current and future electricity systems

Here, we present a database which collates historical, current, and future cost and performance data and assumptions for solar power generation from the open literature. Solar energy supplies 5% of global electricity, and production has grown ten-fold in the decade to 2022. The data are global in scope but with regional and national specificity, covers the years 2015 through to 2050, and span 753 datapoints from 31 sources. The database enables modellers to select and justify model input data and provides a benchmark for comparing assumptions and projections to other sources across the literature to validate model inputs and outputs. It is designed to be easily updated with new sources of data, ensuring its utility, comprehensiveness, and broad applicability over time. Technoeconomic data on utility-scale solar PV was collected from websites, reports, academic articles and databases of national and international organisations.

opencc-by-4.0Nov 2024View details →

Replication Data for: Beyond the Dailey-Townes model: chemical information from the electric field gradient

This dataset collects the unprocessed (= outputs from calculations) results discussed in the paper entitled  "Beyond the Dailey-Townes model: chemical information from the electric field gradient" by G. Fabbro, J. Pototschnig, and T. Saue.

opencc-by-4.0Oct 2019View details →

EIA_Cleaned_Hourly_Electricity_Demand_Data: 2015 - 2024

<div> Cleaned hourly electricity demand data for electric balancing authorities within the contiguous US.  Raw data is based on the U.S. Energy Information Administration's collected data <a href="http://www.eia.gov/opendata/qb.php?category=2122628">here</a>. Cleaned hourly data spans July 2, 2015 - Dec 31, 2024 (9.5 continuous year).   Please consider citing: Ruggles, T.H., Farnham, D.J., Tong, D. et al. Developing reliable hourly electricity demand data through screening and imputation. Sci Data 7, 155 (2020). <a href="https://doi.org/10.1038/s41597-020-0483-x">https://doi.org/10.1038/s41597-020-0483-x</a> </div>   Since the prior releases of this data, the EIA has limited the range of historical data available via their API. This release focuses on cleaning calendar years 2020 through 2024 and including the many balancing area subregions. Users can combine prior data releases with this latest release to have data extending from from 2 July 2015 through 31 Dec 2024.

opencc-by-4.0Nov 2021View details →

Experimental data for "Electric field drives Chern transition in Hofstadter bands of twisted double bilayer graphene"

This experimental dataset was used in our study of "Electric field drives Chern transition in Hofstadter bands of twisted double bilayer graphene".