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326 results for “electricity data”
Experimental Electrical Impedance Tomography Data Set Using the Spectra Bioimpedance and EIT Complete Kit
<p><strong>Abstract</strong></p> <p>The amount of open-source EIT measurement data is low. This data set concerns the acquisition and processing of different measurement data using the SpectraEIT-Kit as well as information about the deposited files.</p> <p><strong>Aknoelegenment</strong><br> Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – SFB 1270/2 - 299150580.</p>
U.S. building energy efficiency and flexibility as an electric grid resource (Data and Code)
<p><strong>* New in Version 2.1 *</strong></p> <ul> <li> <p>All residential measure savings shapes data (<strong>Latest_Res_Shapes.zip</strong> and residential measures in <strong>Latest_BM_Shapes.zip</strong>) were updated to correct post-processing errors present in version 2.</p> </li> <li> <p>The raw baseline-case data that are used in Scout to estimate sector-level baseline hourly loads (file <a href="https://github.com/trynthink/scout/blob/master/supporting_data/tsv_data/tsv_load.gz">tsv_load</a>) are now included in this data resource (see files <strong>Latest_Res_Baselines.zip</strong> and <strong>Latest_Com_Baselines.zip</strong>).</p> </li> <li> <p>Additional residential measure run documentation is available (<a href="https://github.com/NREL/resstock/blob/e2a98b7345d5c453ba35341b70af2f8859dd22fe/GEB_Potential.yml">here</a> for all except water heating efficiency plus flexibility (EE+DF) measure and <a href="https://github.com/NREL/resstock/blob/9611d92388e1e23466c9dc451e115c21321b4012/GEB_Potential_v2.5.0_appl_ee_dr.yml">here</a> for the water heating EE+DF measure).</p> </li> <li>A guide to reading and/or preparing savings shapes CSVs is available <a href="https://scout-bto.readthedocs.io/_/downloads/en/latest/pdf/">in the Scout documentation</a>, p. 36. The documentation also summarizes the net system load conditions that measures with flexibility (DF) characteristics respond to (Table 1, p. 37).</li> </ul> <p><strong>* New in Version 2 *</strong></p> <p>All hourly savings shapes CSV files that support the original <a href="https://doi.org/10.1016/j.joule.2021.06.002">analysis</a> have been updated to reflect the following improvements:</p> <ul> <li> <p>Generate residential data using ResStock v2.5.0 and commercial data using DOE Commercial Prototypes generated with OpenStudio v3.3.0.</p> </li> <li> <p>Residential and commercial measures with flexibility (DF) features respond to updated grid conditions (net peak/low load periods) that are consistent with projections from the EIA 2022 Annual Energy Outlook (AEO) “Low renewables cost” <a href="https://www.eia.gov/outlooks/aeo/tables_side_xls.php">side case</a>.</p> </li> <li> <p>Residential baseline loads and load savings are now distinguished by three building types (single family, multi family, and mobile homes).</p> </li> </ul> <p>Updated savings shape CSVs are organized into three ZIP files that may be separately downloaded depending on user interests:</p> <p><strong>Latest_BM_Shapes.zip</strong> includes only the subset of savings shape CSVs needed to execute the <a href="https://doi.org/10.5281/zenodo.3158929">Scout Benchmark Scenarios</a>.</p> <p><strong>Latest_Res_Shapes.zip</strong> includes all residential savings shape CSVs.</p> <p><strong>Latest_Com_Shapes.zip</strong> includes all commercial savings shape CSVs.</p> <p>Baseline load shapes in Scout have also been updated based on the same versions of ResStock and the DOE Commercial Prototypes, and peak/take period impact calculations have been updated to reflect the 2022 AEO system conditions. These updated data are contained in <a href="https://github.com/trynthink/scout/releases/tag/v0.8">Scout v0.8</a> (see ./supporting_data/tsv_data).</p> <p><br> <strong>Summary of Original Data Files</strong></p> <p>These data underpin an analysis of the near- and long-term technical potential bulk power grid resource offered by best available U.S. building efficiency and flexibility measures. Using multiple openly-available modeling frameworks supported by the U.S. Department of Energy, including <a href="https://scout.energy.gov/">Scout</a>, <a href="https://resstock.nrel.gov/">ResStock</a>, and the <a href="https://www.energycodes.gov/development/commercial/prototype_models">Commercial Building Prototype Models</a>, we pair bottom-up simulations of measures' building-level impacts with regional representations of the building stock and its projected electricity use to estimate the impacts of multiple building efficiency and flexibility scenarios on hourly regional system loads across the contiguous U.S. in 2030 and 2050. We find that demand-side management via building efficiency and flexibility could avoid up to nearly ⅓ of annual fossil-fired generation and ½ of fossil-fired capacity additions after 2020.<strong> </strong>Results are reported at both the national and regional scales and are disaggregated by building type and end use, facilitating a quantitative understanding of the role that buildings as a whole and specific building technologies or operational approaches can play in the future evolution of the U.S. electricity system.</p> <p>The four ZIP files that make up this data record are interpreted as follows:</p> <p><strong>Measure_Data.zip: </strong>Includes the Scout energy conservation measure (ECM) JSON definitions that were used to generate the main baseline and efficient/flexible scenario results ("Baseline_Measures" and "Efficiency_Flexibility_Measures", respectively), as well as side cases that assess the sensitivity of results to higher levels of variable renewable penetration ("High_RE_Sensitivity_Analysis") and a high degree of building load electrification ("High_Electrification_Measures"). Each measure set includes supporting 8760 load savings shapes in the sub-folder "Savings_Shapes". Additional details about defining and interpreting Scout measures with time-sensitive analysis features are available <a href="https://scout-bto.readthedocs.io/en/latest/tutorials.html#time-sensitive-valuation">here</a>.</p> <p><strong>Results_Data.zip: </strong>Includes the main and side case results data. Baseline-case outcomes, which are consistent with the <a href="https://www.eia.gov/outlooks/archive/aeo19/">EIA 2019 Annual Energy Outlook</a>, are stored in "Baseline_Loads". Efficient/flexible scenario results are stored in "Efficiency_Flexibility_Measure_Impacts_Individual" and "Efficiency_Flexibility_Measure_Impacts_Portfolio," respectively, where the former includes results for individual measures in our analysis without considering any interactions across measures, and the latter includes results for aggregations of energy efficiency (EE), demand flexibility (DF), and efficiency and flexibility (EE+DF) portfolios that do consider interactions across measures in each portfolio. Results for the high electrification side case are stored in the "High_Electrification" sub-folder in the EE+DF case only. Results for the high renewable sensitivity analysis are stored in "High_RE_Sensitivity_Analysis", and residential and commercial 8760 savings shape outcomes for each of the EE, DF, and EE+DF measure portfolios and five of the 2019 EIA Electricity Market Module (EMM) <a href="https://www.eia.gov/outlooks/aeo/nems/documentation/archive/pdf/m068(2018).pdf">regions</a> (p.6) of focus are stored in "Sector_Level_8760s".</p> <p><strong>Source_Code.zip: </strong>Includes the source code needed to translate the measure inputs provided in "Measures_Data.zip" into the outputs provided in "Results_Data.zip". The core set of files required to execute the main analysis results is stored in "Base_Code_Package", while variants to certain files in the core package needed to execute the high renewable sensitivity and high electrification side cases are stored in "Code_Variants". In general, the process of running an analysis is as described in the Scout <a href="https://scout-bto.readthedocs.io/en/latest/quick_start_guide.html">Quick Start Guide</a>; however, the file "ecm_prep_batch.py" should be substituted for "ecm_prep.py" and the file "run_batch.py" should be substituted for "run.py". These batch files execute multiple versions of "ecm_prep.py" and "run.py" that are tailored to generate individual measure and whole portfolio results for annual, net peak summer and winter, and net off-peak summer and winter metrics (individual measures: "ecm_prep.json," "ecm_prep_spa," "ecm_prep_wpa," "ecm_prep_sta," "ecm_prep_wta"; whole portfolio: "ecm_results.json," "ecm_results_spa.json," "ecm_results_wpa.json," and "ecm_results_sta.json," and "ecm_results_wta.json"). Results for the side cases are generated by replacing the versions of the "ecm_prep" and "run" files included in the "Base_Code_Package" folder with those in the "Code_Variants" folder. Sector-level 8760 shapes are generated using the "--sect_shapes" command line option as described <a href="https://scout-bto.readthedocs.io/en/latest/tutorials.html#sector-level-hourly-energy-loads">here</a>. See Scout's <a href="https://scout-bto.readthedocs.io/en/latest/tutorials.html#local-execution-tutorials">Local Execution Tutorials</a> for more details on how to develop Scout inputs and outputs.</p> <p><strong>Supporting_Data.zip: </strong>Includes supplemental data files provided by EIA that describe key inputs and outputs to the <a href="https://www.eia.gov/outlooks/aeo/nems/documentation/archive/pdf/m068(2018).pdf">Electricity Market Module</a> in the AEO 2019 run of the National Energy Modeling System ("EIA EMM Data (AEO 2019)"), as well as raw EnergyPlus outputs that were used to develop the baseline Scout hourly load shape file found in "./Source_Code/Base_Code_Package/supporting_data/tsv_data/tsv_load.json". </p>
2022 electricity and heat demand data for a city district (Belgium)
<p>Dataset of electricity and heat demand in 2022 in a city district in Belgium (similar data for 2021 is available on Zenodo as well, https://doi.org/10.5281/zenodo.5155659). For the time period of the data, the district was still under construction and no full inhabitation of the buildings was present. Electricity data include electricity demand for:</p> <ul> <li>Individual households</li> <li>EV charging stations</li> <li>Decentralised waste water treatment</li> <li>Heat pump</li> <li>District heating pumps</li> <li>Vacuum network pumps</li> <li>Miscellaneous</li> </ul> <p>'Total' in the electricity dataset (ElectricPower) refers to the sum of the separate time series. 'Total measured' is a measurement of the total electricity use (in W). Data is averaged out over 15 minutes and expressed in Watt. </p> <p>The electricity demand for the individual households (ElectricPowerPrivateUnits) is expressed in Watt for the complete period. Column names have the form x.y in which x is a random number assigned to an apartment and y refers to the electricity consumption during the day (1) or at night (2).</p> <p>The heat demand data (HeatDemand) describes the heat demand of the complete district, i.e. all private living units as well as common areas, office buildings, sports hall... Like electricity demand data, heat demand data is averaged out over 15 minutes and expressed in Watt.</p> <div> </div>
SECURES-Met - A European wide meteorological data set suitable for electricity modelling (supply and demand) for historical climate and climate change projections
<p>For the modelling of electricity production and demand, meteorological conditions are becoming more relevant due to the increasing contribution from renewable electricity production. But the requirements on meteorological data sets for electricity modelling are quite high. One challenge is the high temporal resolution, since a typical time step for modelling electricity production and demand is one hour. On the other side the European electricity market is highly connected, so that a pure country based modelling does not make sense and at least the whole European Union area has to be considered. Additionally, the spatial resolution of the data set must be able to represent the thermal conditions, which requires high spatial resolution at least in mountainous regions. All these requirements lead to huge data amounts for historic observations and even more for climate change projections for the whole 21st century. Thus, we have developed an aggregated European wide data set that has a temporal resolution of one hour, covers the whole EU area, has a reasonable size but is considering the high spatial variability. This meteorological data set for Europe for the historical period and climate change projections fulfills all relevant criteria for energy modelling. It has a hourly temporal resolution, considers local effects up to a spatial resolution of 1 km and has a suitable size, as all variables are aggregated to NUTS regions. Additionally meteorological information from wind speed and river run-off is directly converted into power productions, using state of the art methods and the current information on the location of power plants. Within the research project SECURES (https://www.secures.at/) this data set has been widely used for energy modelling.</p> <p> </p> <p>The SECURES-Met dataset provides variables visible in the table.</p> <table> <tbody><tr> <th>Variable</th> <th>Short name</th> <th>Unit</th> <th>Aggregation methods</th> <th>Temporal resolution</th> </tr> </tbody><tbody> <tr> <th>Temperature (2m)</th> <td>T2M</td> <td> <p>°C</p> <p>°C</p> </td> <td> <p>spatial mean</p> <p>population weighted mean (recommended)</p> </td> <td>hourly</td> </tr> <tr> <th>Radiation</th> <td> <p>GLO (mean global radiation)</p> <p>BNI (direct normal irradiation)</p> </td> <td> <p>Wm-2</p> <p>Wm-2</p> </td> <td> <p>spatial mean</p> <p>population weighted mean (recommended)</p> </td> <td>hourly</td> </tr> <tr> <th><strong>Potential Wind Power </strong></th> <td>WP</td> <td>1</td> <td>normalized with potentially available area</td> <td>hourly</td> </tr> <tr> <th><strong>Hydro Power Potential</strong></th> <td> <p>HYD-RES (reservoir)</p> <p>HYD-ROR (run-of-river)</p> </td> <td> <p>MW</p> <p>1</p> </td> <td> <p>summed power production</p> <p>summed power production normalized with average daily production</p> </td> <td>daily</td> </tr> </tbody> </table> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p>SECURES-Met is available in a tabular csv format for the historical period (1981-2020, Hydro only until 2010) created from ERA5 and ERA5-Land and two future emission scenarios (<strong>RCP 4.5 </strong>and <strong>RCP 8.5</strong>, both 1951-2100, wind power starting from 1981, hydro power from 1971) created from one CMIP5 EUROCORDEX model (GCM: ICHEC-EC-EARTH, RCM: KNMI-RACMO22E, ensemble run: r12i1p1) on the <strong>spatial aggregation level</strong></p> <ul> <li>NUTS0 (country-wide),</li> <li>NUTS2 (province-wide),</li> <li>NUTS3 (Austria only),</li> <li>and EEZ (Exclusive Economic Zones, offshore only).</li> </ul> <p>The data is divided into the historical (Historical.zip) and the two emission scenarios (Future_RCP45.zip and Future_RCP85.zip), a README file, which describes, how the files are organized, and a folder (Meta.zip), which has information and shape files of the different NUTS levels. As <strong>population weighted</strong> temperature and radiation represent values in geographical areas more relevant for solar power, it is highly relevant to use population weighted files. Spatial mean should be used for reference only.</p> <p>The project SECURES, in which this dataset was produced, was funded by the Climate and Energy Fund (Klima- und Energiefonds) under project number KR19AC0K17532.</p>
A rich dataset of hourly residential electricity consumption data and survey answers from the iFlex dynamic pricing experiment
<p>An experiment was conducted to understand if and how households change their power consumption in response to variable hourly electricity prices. The data were collected from several Norwegian regions, and various price signals were tested over two winter periods from early 2020 to spring 2021. The dataset includes hourly consumption data of all participating households and answers to three surveys about household characteristics such as electric appliances, living conditions, socio-demographic variables, and willingness to be flexible. Temperature data are added to the dataset from public sources. This comprehensive dataset can be used for in-depth analysis of household flexibility potential. Furthermore, subgroups, such as low-income households or highly electrified households with electricity as a primary heating source, can be investigated to enhance the understanding of how these are affected by variable power prices.</p> <p>The dataset is described in detail in an accompanying data article in Data in Brief: <a href="https://www.sciencedirect.com/science/article/pii/S2352340923006716">A rich dataset of hourly residential electricity consumption data and survey answers from the iFlex dynamic pricing experiment - ScienceDirect</a></p> <p>Supplementary figures containing the survey results are available here: <a href="../records/11580541">Supplementary result diagrams from household surveys on implicit demand response (zenodo.org)</a></p> <p>Survey answers in Norwegian are available here: <a href="https://zenodo.org/records/15063303">iFleks-prosjekt: Spørreundersøkelser med husholdninger og næringsliv om forbruksrespons på elektrisitetspriser</a></p>
Hourly electric power load and transmission data at the provincial level in China
<p><strong>Description:</strong></p> <p>This dataset provides hourly electric power load data for all 31 provinces in mainland China. Additionally, it includes comprehensive information about the primary transmission grids that interconnect these provinces, encompassing both High-Voltage Direct Current (HVDC) and High-Voltage Alternating Current (HVAC) transmission lines.</p> <p><strong>Dataset Overview:</strong></p> <ul> <li> <p><strong>Electric Power Load Data:</strong> The dataset comprises hourly records of electric power consumption for each of the 31 provinces in mainland China. These records span a significant time period, allowing for detailed analysis and insights into electricity demand patterns.</p> </li> <li> <p><strong>Transmission Grid Information:</strong> In addition to power load data, this dataset contains detailed information on the primary transmission grids responsible for distributing electricity across provinces. This includes data on both HVDC and HVAC transmission lines, enabling researchers to study the infrastructure that supports the electric power network in mainland China.</p> </li> </ul> <p><strong>Potential Uses:</strong></p> <p>Researchers and analysts can leverage this dataset for a wide range of applications relevant with power system operation and expansion.</p> <p><strong>Citation:</strong></p> <p>If you utilize this dataset in your research or analysis, we kindly request that you cite it using the following Zenodo reference:</p> <p>[1] H. Wu and X. Kan, “Hourly electric power load and transmission data at the provincial level in China.” Zenodo, Sep. 06, 2023. doi: 10.5281/zenodo.8322210.</p> <p>Your citation helps acknowledge the effort and resources invested in collecting and curating this valuable dataset.</p>
Data from: Sensation of electric fields in Drosophila melanogaster
Open the record for dataset details and reuse information.
Data from: Efficiency of using electric toothbrush as an alternative to tuning fork for artificial buzz pollination is independent of instrument buzzing frequency
Open the record for dataset details and reuse information.
Underlying data for "The role of electrostatics in enzymes: do biomolecular force fields reflect protein electric fields?"
<p>This dataset contains code, data, trajectories, and figures used in the article "The role of electrostatics in enzymes: do biomolecular force fields reflect protein electric fields?".</p> <p> </p> <p>Contents:</p> <p>code/* - Code used to calculate electric fields from simulation trajectories with either polarizable or additive force fields</p> <p>data/* - Electric fields calculated for the CypA WT cis, WT trans, R55A cis, and R55A trans systems, with AMOEBA, Amber, or Charmm force fields. Each subfolder also includes a set of structural coordinates extracted at 2.5 ns intervals from the first simulation trajectory and used to calculate ONETEP DFT electric fields.</p> <p>figures/* - Underlying data and scripts used to create all figures and movies used in the article.</p> <p>trajectories/* - Simulation trajectories of the CypA WT cis, WT trans, R55A cis, and R55A trans systems</p> <p> </p> <p>Where appropriate, README files include instructions for regenerating data used in the article, and details of the Python packages and other software used to generate data are available in Dependencies.txt</p>
Fine-Grained Activities of Daily Living Data with Structural Vibration and Electrical Load Sensing
<p>Fine-grained non-intrusive monitoring of activities of daily living (ADL) enables various smart building applications, including ADL pattern assessments for older adults at risk for loss of safety or independence. We utilize structural vibration sensing and electrical load sensing to acquire multiple fine-grained kitchen activities under a lab structure setting.</p> <p>Each file contains the following values:<br> -RawData: time series of data for each channel (vibration on the table, vibration on the floor, load)<br> -Label: manually fine-grained labels of events<br> -Table: detected events start/stop index for vibration sensor on the table<br> -Floor: detected events start/stop index for vibration sensor on the floor<br> -Load: detected events start/stop index for load sensor<br> <br> Label notation:<br> 1 -- operating the kettle<br> 2 -- kettle on<br> 3 -- operating the microwave<br> 4 -- microwave on<br> 5 -- put things on the stove<br> 6 -- operating with stove<br> 7 -- stove on<br> 8 -- operating vacuum<br> 9 -- sweep floor<br> 10 -- walking/step<br> 11 -- miscellaneous<br> 12 -- synchronization signal (knock on the floor)<br> 13 -- vacant<br> 14 -- microwave door open</p>
Data associated with "Modulation of intercolumnar synchronization by endogenous electric fields in cerebral cortex"
<p>These is data associated with the article "Modulation of intercolumnar synchronization by endogenous electric fields in cerebral cortex" in Science Advances 2021, by the same authors.</p>
Data Sets and Code for Simple formulas for pseudoposition for electrical resistivity and IP in vertical boreholes based on mean positions of the sensistivity
<p>Data and Matlab code in figures.</p>
GR-PLW6-Y07Y11 Electricity Power Loads and Weather data, Greece, Jan.2007-Dec.2011
<p> Dataset: GR-PLW6-Y07Y11</p> <p> Electricity Power Loads and Weather data<br /> Greece, Jan.2007 - Dec.2011<br /> <br /> Release Notes</p> <p> Copyright (c) 2016 by Harris V. Georgiou</p> <p>========================================================<br /> Release: Jul 30, 2016</p> <p> - Version: 1.1a<br /> - Format: .mat<br /> ========================================================</p> <p>This file contains important information about the current version of the dataset package. Downloading and using this material hints that you accept the EULA/Terms-of-Use (please read carefully).</p> <p>We welcome your comments and suggestions.</p> <p>_______________________________________________<br /> WHAT'S IN THIS PACKAGE?</p> <p>- Overview<br /> - Available file formats<br /> - Files and Datasets<br /> - License Agreement</p> <p>_______________________________________________<br /> OVERVIEW</p> <p>Power load forecasting is one of the major technical challenges today, especially for urban areas in developed and developing countries. Short-term forecasting is based on the power load data series, as well as localized weather data (e.g. temperature, cloud cover, etc).</p> <p>The concept of "smart grid" is such an effort for addressing, among other things, the tasks of early detection, emergency response and self-healing of the energy grids in "smart cities".</p> <p>This package contains two sets of data including five-year power load logs for the entire Greece, as registered by the Independent Power Transmission Operator (IPTO or ADMIE), and eight-year weather data from six measuring stations in areas of major trends in the mainland, as registered by the NOAA Satellite and Information Service (USA).</p> <p>_______________________________________________</p> <p>AVAILABLE FILE FORMATS</p> <p>The datasets are available in the following formats (included):</p> <p>*.mat : Matlab/Octave native data file (workspace)</p> <p> </p>
Supplementary Data: The Benefits of Cooperation in a Highly Renewable European Electricity Network
<p>Supplementary Data</p> <p><em>The Benefits of Cooperation in a Highly Renewable European Electricity Network</em><br> <em>doi:10.1016/j.energy.2017.06.004</em><br> <em>arXiv:1704.05492</em></p> <p>The files in this record contain the model-specific code, input data, and output data considered in the Benefits of Cooperation paper.</p> <p>You are welcome to use the provided data under the given open-source licence, and if you do please cite the paper <em>doi:10.1016/j.energy.2017.06.004</em>.<br> Please note that the derivation of the data in data/renewables/ is not open, because it uses the REatlas software [7] which has a closed source server part. (There is an free software implementation of the REatlas at https://github.com/FRESNA/atlite but it wasn't ready in time to be used for this dataset.)</p> <p>The code that is required to generate the output data consists of</p> <ul> <li>the python code opt_ws_network.py that builds and runs the PyPSA [0] model</li> <li>a SLURM script parameter_batch.py to run the model with different parameters</li> <li>a YAML file options.yml with the default parameter settings</li> </ul> <p>The code heavily relies on the python package <em>vresutils</em> which is available at https://github.com/FRESNA/vresutils</p> <p>The record also contains the input data in the data/ directory. They are described in detail in the paper, but a short summary is provided here:</p> <ul> <li><strong>costs</strong>: cost and other input parameter assumptions, see <em>Table 1</em> in the paper.</li> <li><strong>graph</strong>: the network topology is given by a list of nodes (country names) and a list of edges connecting two nodes. Based on [1,2].</li> <li><strong>hydro</strong>: hydro generation data provided by the Restore2050 project [3] <ul> <li>inflow/: contains a csv files with daily inflow data for each country</li> <li>emil_hydro_capas.csv: country-scale power and energy capacity</li> <li>ror_ENTSOe_Restore2050.csv: the share of run-of-river of the total hydro generation, from ENTSO-E [4] or if unavailable from [3]</li> </ul> </li> <li><strong>load</strong>: hourly country-scale consumption for 2011 from ENTSO-E [5]</li> <li><strong>renewables</strong>: generation potentials for the renewable technologies onshore wind, offshore wind, and solar per country based on historic weather data [6]. The jupyter-notebook europe_renewables_potentials.ipynb describes the data generation and uses the REatlas software [7] which has open-source client but closed-source server software. The used cutout can therefore not be made available here, but is solely based on data from [8]. The processed data are in: <ul> <li>store_p_nom_max/: installation potential per technology per region</li> <li>store_o_max_pu_betas/: hourly maximum generation per unit of capacity per technology per region</li> </ul> </li> </ul> <p>The output data generated by the model is in sub-folders of the results/ directory following the naming scheme [costsource]-CO[CO2costs]-T[timerange]-[technologies]-LV[linevolume]_c[crossover]_base_[costsource]_solar1_7_[formulation]-[startdate]/, where</p> <ul> <li>costsource = diw2030</li> <li>CO2costs = 0</li> <li>timerange = 1_8761</li> <li>technologies = wWsgrpHb</li> <li>linevolume = [float], None (line volume constraint of float * 5e8 TWkm, or optimised line volume)</li> <li>crossover = 0 (deactivated the cross-over phase of the Gurobi optimiser)</li> <li>formulation = angles, [blank] (power flow formulations: 'angles', or 'cycles')</li> <li>startdate = time the optimisation was started</li> </ul> <p>Footnotes</p> <p>[0] https://pypsa.org/ , https://doi.org/10.5281/zenodo.582307</p> <p>[1] S Becker, Transmission grid extensions in renewable electricity systems, PhD thesis (2015)</p> <p>[2] ENTSO-E, Indicative values for Net Transfer Capacities (NTC) in Continental Europe. European Transmission System Operators, 2011, https://www.entsoe.eu/publications/market-reports/ntc-values/ntc-matrix/Pages/default.aspx, accessed Jul 2014.</p> <p>[3] A Kies, K Chattopadhyay, L von Bremen, E Lorenz, D Heinemann, Simulation of renewable feed-in for power system studies, RESTORE 2050 project report, https://doi.org/10.5281/zenodo.804244</p> <p>[4] European Transmission System Operators, Installed Capacity per Production Type in 2015, ENTSO-E (2016), https://transparency.entsoe.eu/generation/r2/installedGenerationCapacityAggregation/show</p> <p>[5] https://www.entsoe.eu/db-query/country-packages/production-consumption-exchange-package</p> <p>[6] D. Heide, M. Greiner, L. Von Bremen, C. Hoffmann, Reduced storage and balancing needs in a fully renewable European power system with excess wind and solar power generation, Renewable Energy 36 (9) (2011) 2515–2523. https://doi.org/10.1016/j.renene.2011.02.009</p> <p>[7] G. B. Andresen, A. A. Søndergaard, M. Greiner, Validation of Danish wind time series from a new global renewable energy atlas for energy system analysis, Energy 93, Part 1 (2015) 1074 – 1088. https://doi.org/10.1016/j.energy.2015.09.071</p> <p>[8] S Saha et al., 2014: The NCEP Climate Forecast System Version 2. J. Climate, 27, 2185–2208, https://doi.org/10.1175/JCLI-D-12-00823.1</p>
Dataset of Concurrent EEG, ECG, and Behavior with Multiple Doses of transcranial Electrical Stimulation-Exp2-Data Downsampled
<p><strong>GX Dataset downsampled - Experiment 2</strong></p> <p>The GX Dataset is a dataset of combined tES, EEG, physiological, and behavioral signals from human subjects.<br>Here the GX Dataset for <strong>Experiment 2</strong> is downsampled to 1 kHz and saved in .MAT format which can be used in both MATLAB and Python.</p> <p><strong>Publication</strong></p> <p>A full data descriptor is published in <a href="https://doi.org/10.1038/s41597-021-01046-y">Nature Scientific Data</a>. <strong>Please cite this work as</strong>:</p> <blockquote> <p>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</p> </blockquote> <p><strong>Descriptions</strong></p> <p>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.</p> <p> </p> <p><strong>Acknowledgments</strong></p> <p>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.</p> <p><strong>Extras</strong></p> <p>Back to <a href="https://doi.org/10.5281/zenodo.4456079">Full GX Dataset</a> : https://doi.org/10.5281/zenodo.4456079</p> <p> </p> <p>For downsampled data (1 kHz ) please see (in .mat format):</p> <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> <p> </p> <p>Code used to import, process, and plot this dataset can be found here:</p> <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> <p> </p> <p>Additional figures for this project have been shared on Figshare. Trial-wise figures can be found here:</p> <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> <p> </p> <p>The full dataset is also provided in <a href="https://bids.neuroimaging.io/">BIDS</a> format here:</p> <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> <p><strong>Data License </strong><br><a href="https://creativecommons.org/licenses/by/4.0/">Creative Common 4.0 with attribution (CC BY 4.0)</a></p> <p> </p> <p><strong>NOTE</strong></p> <p><strong>Please email ngebodh01@citymail.cuny.edu with any questions.</strong></p> <p> </p> <p> </p> <p><br><strong>Updates</strong></p> <ul> <li><strong>Version 2.1.0</strong> <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> </ul>
Data for paper titled "Penetrating electric field during the Nov 4 2021 Geomagnetic Storm"
<p>The data is part of publication for a paper titled "Penetrating electric field during the Nov 4 2021 Geomagnetic Storm" to be submitted to the JGR Space Physics. The data contains simulation from the NCAR MAGE model of the Nov 4, 2021 Geomagnetic storm. We used the simulation to study the penetrating electric field, which affects the low latitude region ionosphere.</p>
A data set on_Electrical and colloidal properties of hydrogenated nanodiamonds: effects of structure, composition and size
<p>The data set to paper: </p> <p>Electrical and colloidal properties of hydrogenated nanodiamonds: effects of structure, composition and size</p> <p>Stepan Stehlik1,2*, Ondrej Szabo2, Ekaterina Shagieva2, Daria Miliaieva2, Alexander Kromka2, Zuzana Nemeckova3, Jiri Henych3, 4, Jan Kozempel5, Evgeny Ekimov6, Bohuslav Rezek7</p> <p>1 New Technologies – Research Centre, University of West Bohemia, Univerzitní 8, 306 14, Pilsen, Czechia<br>2 Institute of Physics of the Czech Academy of Sciences, Cukrovarnická 10, 162 00 Prague 6, Czechia<br>3 Institute of Inorganic Chemistry of the Czech Academy of Sciences, 250 68 Husinec-Řež, Czechia<br>4 Faculty of Environment, Jan Evangelista Purkyně University in Ústí nad Labem, Pasteurova 3632/15, 400 96 Ústí nad Labem, Czechia<br>5 Department of Nuclear Chemistry, Faculty of Nuclear Sciences and Physical Engineering, Czech Technical University in Prague, Břehová 7, Prague 1, 115 19, Czechia<br>6 Vereshchagin Institute for High Pressure Physics, Russian Academy of Sciences, Moscow 108840, Russia<br>7 Faculty of Electrical Engineering, Czech Technical University in Prague, Technická 2, 166 27 Prague, Czechia</p> <p>*principal investigator: stehlik@fzu.cz</p> <p>Data manager: Jan Jaroš: Jan.Jaros2@vut.cz</p> <p>Date of data collection: 1. 6. 2020 - 18. 1. 2024</p> <p>All the data showed in the pictures are provided in X-Y format with described sample. Always, the respective Figure to which the data belong is provided in high resolution. <br>The data are in the following formats: <br>Figure 1: tiff, csv<br>Figure 2: tiff, csv<br>Figure 3: tiff, csv<br>Figure 4: tiff, csv<br>Figure 5: tiff, csv<br>Figure 6: tiff</p> <p>Data acquistion methods and conditions and data processing is provided in the Experimental part in the publication: DOI:10.1016/j.cartre.2024.100327</p> <p> </p> <p><span> </span></p>
Data for "Electric polarization near vortices in the extended Kitaev model"
<p>We formulate a Majorana mean-field theory for the extended <span><span><span><span>J</span><span>K</span><span>Γ</span></span></span></span> Kitaev model in a magnetic Zeeman field of arbitrary direction, and apply it for studying spatially inhomogeneous states harboring vortices. This mean-field theory is exact in the pure Kitaev limit and captures the essential physics throughout the Kitaev spin liquid phase. We determine the charge profile around vortices and the corresponding quadrupole tensor. The quadrupole-quadrupole interaction between distant vortices is shown to be either repulsive or attractive, depending on the parameters. We predict that electrically biased scanning probe tips enable the creation of vortices at preselected positions. Our results open new perspectives for the electric manipulation of Ising anyons in Kitaev spin liquids.</p> <p> </p>
Electric Guitars Basic Data
<p>This dataset contains the basic information of the electric guitars that can be found on the alfasoni web portal, such as brand, name, price and the instrument description. This dataset is only for academic purpose. </p>
The Global and National Energy Systems Techno-Economic (GNESTE) Database: Economic and performance data for nuclear power in current and future electricity systems
<p><span><span>Here, we </span><span>present a</span><span> database </span><span>which collates </span><span>historical, </span><span>current</span><span>,</span><span> and future </span><span>cost and performance</span><span> data</span> <span>and </span><span>assumption</span><span>s</span> <span>for </span></span><span><span>nuclear power generation</span></span><span> <span>from</span><span> the open literature</span><span>. </span></span><span><span>Nuclear energy is the second-largest source of low-carbon generation, supplying 9% of global electricity</span><span>.</span></span><span> <span>The data are </span><span>global in scope but with </span><span>regional and national</span> <span>specificity</span><span>, </span><span>cover</span><span>s</span><span> the years 2015 t</span><span>hrough </span><span>to 2050, </span><span>and </span><span>span</span> </span><span><span>604</span></span><span><span> datapoints from </span></span><span><span>19</span></span><span><span> sources</span><span>.</span> </span></p> <p><span><span>The database </span><span>enables modellers to select and justify</span> <span>model input data and </span><span>provides </span><span>a </span><span>benchmark for comparing assumptions and projections to </span><span>other source</span><span>s</span><span> across the literature</span> <span>to </span><span>validate</span><span> model inputs and outputs</span><span>.</span> <span>It is </span><span>designed to be easily updated with </span><span>new sources of</span><span> data, ensuring its utility</span><span>, comprehensiveness,</span><span> and broad applicability over time.</span></span> Technoeconomic data on nuclear power was collected from websites, reports, academic articles and databases of national and international organisations.</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.