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326 results for “electricity data”
Soil moisture, temperature, and electrical conductivity data from the black sand extended growing season length experiment, 2018 - 2024, hourly.
As a result of climate change, the Rocky Mountain Front Range is experiencing warmer summers and earlier snowmelt. Due to the importance of snow for regulating soil temperature, growing season length, and available moisture in alpine ecosystems, even small shifts in the snow-free period could have large impacts. The focus of the Growing Season Length Experiment is to examine how terrain-related differences in climate exposure influence the way alpine habitats respond to climate change via earlier snowmelt. To simulate how changes in growing season length may affect biotic and abiotic components, NWT LTER researchers established 5 experimental sites each containing a pair 10 x 40m rectangular plots. These blocks include north and south facing aspects, subalpine and alpine tundra meadows in a range of hydrological conditions (e.g. dry meadows, moist meadows, wet meadows). We accelerated snowmelt in one plot of each block by adding chemically inert black sand, while keeping the second plot as an unmanipulated control (black sand was added to these plots after snow had naturally melted). This dataset includes measurements of soil temperature, moisture, and electrical conductivity.
Auxiliary Euro-Calliope datasets: Spatio-temporal data representing national cooking demand and electric vehicle characteristic profiles in Europe
<p>Output generated by the <a href="https://github.com/RAMP-project/">RAMP engine</a> for use in the <a href="https://github.com/calliope-project/sector-coupled-euro-calliope">Sector-Coupled Euro-Calliope model</a>. The three datasets in this repository are described briefly here and in more detail in the accompanying README files. Each dataset has an hourly temporal resolution spanning the years 2000 - 2018 (inclusive) and a national spatial resolution spanning 26* - 28** countries in Europe. All datasets are dimensionless; only the profile shapes are used in Euro-Calliope.</p> <ul> <li>Cooking energy demand profiles (<em>ramp-cooking-profiles</em>): Profiles of heat energy demand for cooking in buildings in Europe, stochastically generated using the <a href="https://github.com/RAMP-project/RAMP">RAMP model</a> [1]. These profiles are used to distribute annual cooking energy demand in the Euro-Calliope workflow. This dataset covers 28 European countries**.</li> <li>Electric vehicle plug-in profiles (<em>ramp-ev-plugin-profiles</em>): Profiles of the percentage of parked electric vehicles, stochastically generated using the <a href="https://github.com/RAMP-project/RAMP-mobility">RAMP-Mobility model</a> [2]. These profiles are used in Euro-Calliope to define the maximum number of electric vehicles that could be plugged in and therefore available to be charged at any given time, assuming controlled (or "smart") charging. This dataset covers 26 European countries*.</li> <li>Electric vehicle energy consumption profiles (<em>ramp-ev-consumption-profiles</em>): Profiles of the electricity consumption of electric vehicles, stochastically generated using the <a href="https://github.com/RAMP-project/RAMP-mobility">RAMP-Mobility model</a> [2]. These profiles are aggregated in Euro-Calliope to provide a required percentage of total vehicle electricity demand that must be met in each month. This dataset covers 26 European countries*.</li> </ul> <p>* AUT, BEL, CHE, CZE, DEU, DNK, ESP, EST, FIN, FRA, GBR, HRV, HUN, IRL, ITA, LTU, LUX, LVA, NLD, NOR, POL, PRT, ROU, SVK, SVN, SWE</p> <p>** (*) + BGR, SRB</p> <p>*** ALB, MKD, GRC, CYP, BIH, MNE, ISL</p> <p>[1] Lombardi, Francesco, Sergio Balderrama, Sylvain Quoilin, and Emanuela Colombo. 2019. ‘Generating High-Resolution Multi-Energy Load Profiles for Remote Areas with an Open-Source Stochastic Model’. <em>Energy</em> 177 (June): 433–44. https://doi.org/10.1016/j.energy.2019.04.097.</p> <p>[2] Mangipinto, Andrea, Francesco Lombardi, Francesco Davide Sanvito, Matija Pavičević, Sylvain Quoilin, and Emanuela Colombo. 2022. ‘Impact of Mass-Scale Deployment of Electric Vehicles and Benefits of Smart Charging across All European Countries’. <em>Applied Energy</em> 312 (April): 118676. https://doi.org/10.1016/j.apenergy.2022.118676.</p>
Survey data on households' use of smart home technology and their time of use of electric appliances (eCAPE)
<p>This survey data includes the responsed from a survey questionnaire which was used to collect information on smart home technologies and time of use of electric appliances in Danish households. The survey covers themes like adoption and use ofhousehold appliances, households’ division of everyday chores, timing of everyday activities, and everyday flexibility.</p> <p>The purpose of this survey is to gather information about Danish households and their everyday practices and flexibility related to electricity use. The intention is to combine questions from the survey with real time data of electricity consumption at household level with a time resolution of few minutes, and to do so for a large representative population. However, the electricity consumption is not allowed to share publicly, and therefore not included in this data upload. </p> <p>The survey includes questions of socio-economic factors.</p> <p>The questionnaire was distributed in Danish but was developed in and translated from English because ofinternational cooperation.</p> <p>The survey was developed under the project eCAPE - New Energy Consumer Roles and Smart Technologies– Actors, Practices and Equality. The eCAPE project is financed by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program under the grant agreement number 786643 (https://www.ecape.aau.dk/). The project is led by Professor Kirsten Gram-Hanssen from Department of the Built Environment, Aalborg University. <br><em>See also </em> <a href="https://www.researchgate.net/publication/373453718_Survey_questionnaire_on_households'_use_of_smart_home_technology_and_their_time_of_use_of_electric_appliances">https://www.researchgate.net/publication/373453718_Survey_questionnaire_on_households'_use_of_smart_home_technology_and_their_time_of_use_of_electric_appliances</a> </p>
Hydrochemical Data from a Tropical Andean Glacierized Catchment: δ18O, electrical conductivity, maximum fluorescence intensity, and dissolved organic carbon concentrations from short-term sampling campaigns, Ecuador (2022 and 2024)
Fluorescent dissolved organic matter (FDOM) quality, dissolved organic carbon (DOC) concentration, electrical conductivity (EC), and stable water isotopes (δ¹⁸O and δ2H) were determined in water, snow, and ice samples from a tropical glacierized catchment in the Ecuadorian Andes. The sampling locations were selected to capture the major hydrologic inputs to the main stream channel (glacial melt, tributaries, wetlands, and groundwater springs) and constrain the in-stream spatiotemporal variation in DOM quality and other hydrochemical characteristics. Two sets of high-resolution time series were collected on Oct 13, 2022 and Jun 14, 2024. Time series samples were collected at various upper catchment locations and the outlet simultaneously. DOM quality was characterized via fluorescence spectroscopy and processed using parallel factor analysis (PARAFAC). The DOM quality data are expressed as %FMax values obtained through a 4-component PARAFAC model, where %Fmax 1– 4 are interpreted as terrestrial humic-like, tyrosine-like, tryptophan-like, and microbial humic-like fluorescent components, respectively. DOC concentrations were quantified using high-temperature catalytic combustion, stable water isotopes were analyzed using laser-based spectroscopy, and EC was measured in situ with handheld multiparameter water quality probes.
Voltage and current data for IEC 62600-30 power quality monitoring from the Mutriku Wave Power Plant and Lir National Ocean Test Facility electrical laboratory
<p>This Technical Note describes the electrical data collected from the Mutriku Wave Power Plant (MWPP) and the Lir National Ocean Test Facility (NOTF) electrical laboratory at the MaREI Centre in the Environmental Research Institute, at University College Cork.</p> <p>In summary, the electrical data collect is for the purpose of analysing the power quality output of a Wave Energy Converter (WEC). The data includes voltage and current signals from the output of a WEC sampled at 15 kHz from the MWPP and a WEC emulator sampled at 20 kHz from the Lir NOTF electrical laboratory. There are 24 datasets from the MWPP taken at various sea state conditions, and there are 56 datasets from the Lir NOTF which are taken with at various sea state conditions, with different control laws, and grid connections.</p> <p>This data is published for purpose of power quality analysis and comparison for future tests. For OPERA, power quality analysis was performed as part of WP5 T5.2 and T5.5, and presented in depth in Deliverables D5.2 and D5.4.</p> <p>See accompanying technical note for more Information.</p>
Accompanying data for paper "Electrical and Thermal Conductivity of Complex-Shaped Contact Spots"
<div> </div> <div>This repository contains the numerical data of the conductivity of complex-shaped contact spots on isotropic and linear conducting half-space obtained by Boundary and Finite Element methods. These data were used to construct some figures from the manuscript "Electrical and Thermal Conductivity of Complex-Shaped Contact Spots". The data is organized in folders corresponding to different types of contact spots: annular, flower-, star- and gear-shaped, Koch's snowflake, and self-affine spots. Each folder contains the results of numerical simulations in the form of `.npz` files, which can be loaded using `numpy` library in Python. The data is used to construct figures in the manuscript and can be used to reproduce the results or to perform additional analysis.</div> <div> </div>
Data files: Electric vehicle charging dataset with 35,000 charging sessions from 12 residential locations in Norway
<p>Please refer to the data article where the data is described (Data-in-brief, <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.dib.2024.110883" target="_blank" rel="noreferrer noopener"><span><span>https://doi.org/10.1016/j.dib.2024.110883</span></span></a>).</p> <p>The data article refers to the paper "A method for generating complete EV charging datasets and analysis of residential charging behaviour in a large Norwegian case study". The Electric Vehicle (EV) charging dataset includes detailed information on plug-in times, plug-out times, and energy charged for over 35,000 residential charging sessions, covering 267 user IDs across 12 locations within a mature EV market in Norway. Utilising methodologies outlined in the paper, realistic predictions have been integrated into the datasets, encompassing EV battery capacities, charging power, and plug-in State-of-Charge (SoC) for each EV-user and charging session. In addition, hourly data is provided, such as energy charged and connected energy capacity for each charging session.</p> <p>The comprehensive dataset provides the basis for assessing current and future EV charging behaviour, analysing and modelling EV charging loads and energy flexibility, and studying the integration of EVs into power grids.</p>
SignAture_Electricity_generation_data_compare_Latvia_2020_2022
<p>This dataset, related to the article 'Power System Modelling in the Baltic Countries: Data Accessibility and Consistency Aspects' (2023), compares electricity generation data for 2020 and 2022 from various sources in Latvia, providing both input and output values and associated metadata.</p>
Data on the Swiss energy system and electric vehicles
<p>This repository gathers the data used in the paper:</p> <p>Loris Di Natale, Luca Funk, Martin Rüdisüli, Bratislav Svetozarevic, Giacomo Pareschi, Philipp Heer and Giovanni Sansavini. <strong>The Potential of Vehicle-to-Grid to Support the Energy Transition: A Case Study on Switzerland. </strong><em>Energies.</em> 2021; 14(16):4812. <a href="https://doi.org/10.3390/en14164812">https://doi.org/10.3390/en14164812</a>.</p> <p>The linked code can be found <a href="https://gitlab.nccr-automation.ch/loris.dinatale/v2g-in-switzerland">here</a>.</p> <p>Small description of the different files:</p> <ul> <li><em>Car_trips.csv:</em> List of trips from different cars in Switzerland.<br> Data provided by Giacomo Pareschi and based on the result of the 2015 edition of MZMV (Bundesamt für Statistik / Bundesamt für Raumentwicklung, Verkehrsverhalten der Bevölkerung, Ergebnisse des Mikrozensus Mobilität und Verkehr 2015, Neuchâtel und Bern (2017), <a href="https://www.are.admin.ch/are/de/home/mobilitaet/grundlagen-und-daten/mzmv.html">https://www.are.admin.ch/are/de/home/mobilitaet/grundlagen-und-daten/mzmv.html</a> ). Each weekly profile is not representative and any result obtained with less than 50 profiles should be interpreted with extreme caution.</li> <li><em>ch.bfe.ladestellen-elektromobilitaet.json:</em> Data on the charging stations in Switzerland.<br> Online data from the Swiss Federal Office of Energy.</li> <li><em>cs_power_Home.csv</em> and<em> cs_power_Work.csv: </em>Own data on the charging powers of charging stations located at home or at work.</li> <li><em>energy_system_model_empa_results_sc_1.csv: </em>Swiss Energy System model used in our work to generate the fixed hydropower output profile.<br> Data provided by Dr. Martin Rüdisüli.</li> <li><em>EVs_cap.csv:</em> Data on different EV brands, from own research.</li> <li><em>gCO2_eq_kWh_techs.csv: </em>CO2-equivalent greenhouse gas emission factors for different technologies, from own research.</li> <li><em>Heat_BEV_demand_2018.csv:</em> Electricity demand for heating and EVs in Switzerland.<br> Data provided by Dr. Martin Rüdisüli.</li> <li><em>inflows_Beer.csv: </em>Data on the water inflows in the Swiss dams over the year.<br> Data provided by Michael Beer, from Beer, M. Abschätzung des Potenzials der Schweizer Speicherseen zur Lastdeckung bei Importrestriktionen. Z. Energiewirtschaft <strong>2018</strong>, 42, 1–12.</li> <li><em>MeteoSchweiz_pop_weight_2018.csv:</em> Temperature data in Switzerland taken from MeteoSwiss and population-weighted.<br> Data provided by Dr. Martin Rüdisüli.</li> <li><em>Scenarios.csv </em>and <em>Scenarios+.csv: </em>Different scenarios for electricity production and consumption, generated in-house based on <ul> <li>the Energy Strategy 2050 (Kirchner, A.; Bredow, D.; Ess, F.; Grebel, T.; Hofer, P.; Kemmler, A.; Ley, A.; Piégsa, A.; Schütz, N.; Strassburg, S.; et al. Energy Perspectives, Die Energieperspektiven für die Schweiz bis 2050; Prognos AG: Basel, Switzerland, 2012), respectively</li> <li>the Energy Strategy 2050+ (Prognos AG and INFRAS AG and TEP Energy GmbH and Ecoplan AG. ENERGIEPERSPEKTIVEN 2050+ Kurzbericht. 2020).</li> </ul> </li> <li><em>transfer_15min_2018.csv: </em>Swiss power system model of electricity production and consumption in 2018.<br> Data provided by Dr. Martin Rüdisüli.</li> </ul> <p> </p>
EPA Integrated Planning Model (IPM) National Electric Energy Data System (NEEDS) database
EPA is making the latest power sector modeling platform available, including the associated input data and modeling assumptions, outputs, and documentation.
Result data related to "Tröndle et al (2020) -- Trade-offs between geographic scale, cost, and infrastructure requirements for fully renewable electricity in Europe"
<p>The dataset contains aggregated result data of our study. See `README.md` for more information.</p> <p>If you use this data in an academic publication, please cite the following article:</p> <blockquote> <p>Tröndle, T., Lilliestam, J., Marelli, S., Pfenninger, S., 2020. Trade-offs between geographic scale, cost, and infrastructure requirements for fully renewable electricity in Europe. Joule.</p> </blockquote> <p>CHANGELOG:</p> <p>Version 1.2 (2020-09-28)</p> <p>* Add location name to scenario results.<br> * Add scenario results in CSV format, next to already existing NetCDF format.<br> * Remove capacity factors from scenario results.</p> <p>Version 1.1 (2020-07-17)</p> <p>* Remove macOS resource forks cluttering the zip file.</p> <p> </p>
A new repository of electrical resistivity tomography and ground penetrating radar data from summer 2022 near Ny-Ålesund, Svalbard.
<p>We present the geophysical data set acquired in summer 2022 close to Ny-Ålesund (Western Svalbard, Brøggerhalvøya peninsula, Norway) as part of the project ICEtoFLUX (MUR/PRA2021 project-0027). The data set is composed of Electrical Resistivity Tomography (ERT) and GroundPenetrating Radar (GPR) surveys, which are well-known geophysical techniques for the characterization of glacial and hydrological processes and features. 18 ERT profiles and 10 GPR lines were acquired, for a total surveyed length of 9.3 km. The data have been organized in a consistent repository that includes both raw and processed (filtered) data. Some representative examples of 2D models of the subsurface are provided, that is, 2D sections of electrical resistivity (from ERT) and 2D radargrams (from GPR). These examples can support the identification of the active layer and the occurrence of spatial variation of soil conditions at depth. The aim of the investigation is to characterize the role of groundwater flow in correspondence of the active layer as well as through and/or below the permafrost. The data set is of major relevance because scant attention has been paid to the publication of geophysical data from the Ny-Ålesund area so far. Moreover, these geophysical data can foster multidisciplinary scientific collaborations in the fields of hydrology, glaciology, climate, geology, geomorphology, etc. To a large extent, the data set can provide new insight into the hydrological dynamics and polar and climate changes studies on the Ny-Ålesund area. </p>
Prebuilt Electricity Network for PyPSA-Eur based on OpenStreetMap Data
<p>This dataset contains a<strong> topologically connected representation of the European high-voltage grid (220 kV to 750 kV)</strong> <strong>constructed using OpenStreetMap data</strong>. Input data was retrieved using the Overpass turbo API (<a title="Overpass turbo" href="https://overpass-turbo.eu" target="_blank" rel="noopener">https://overpass-turbo.eu</a>). A heurisitic cleaning process was used to for lines and links where electrical parameters are incomplete, missing, or ambiguous. Close substations within a radius of <strong>500 m</strong> are aggregated to single buses, exact locations of underlying substations is preserved. Unique identifiers for lines and links are preserved, e.g. an AC line/cable with the ID <em>way/83742802-1</em> can be viewed on OpenStreetMap using the query <a title="OpenStreetMap example (AC)" href="https://www.openstreetmap.org/way/83742802" target="_blank" rel="noopener">https://www.openstreetmap.org/way/83742802</a>. A DC line/cable with the ID <em>relation/15781671</em> can be accessed using the query <a title="OpenStreetMap example (DC)" href="https://www.openstreetmap.org/relation/15781671" target="_blank" rel="noopener">https://www.openstreetmap.org/relation/15781671</a></p> <p>A detailed explanation on the <strong>background, methodology, and validation </strong>can be found in the article published in <a href="https://www.nature.com/articles/s41597-025-04550-7"><strong>Nature Scientific Data</strong></a>:</p> <blockquote> <p><em>Xiong, B., Fioriti, D., Neumann, F., Riepin, I., Brown, T.</em> Modelling the high-voltage grid using open data for Europe and beyond. <em>Sci Data</em> <strong>12</strong>, 277 (2025). <a href="https://doi.org/10.1038/s41597-025-04550-7" target="_blank" rel="noopener">https://doi.org/10.1038/s41597-025-04550-7</a></p> </blockquote> <p><strong>Countries</strong> included in the dataset:</p> <blockquote> <p>Albania (AL), Austria (AT), Belgium (BE), Bosnia and Herzegovina (BA), Bulgaria (BG), Croatia (HR), Czech Republic (CZ), Denmark (DK), Estonia (EE), Finland (FI), France (FR), Germany (DE), Greece (GR), Hungary (HU), Ireland (IE), Italy (IT), Kosovo (XK), Latvia (LV), Lithuania (LT), Luxembourg (LU), Moldova (MD), Montenegro (ME), Netherlands (NL), North Macedonia (MK), Norway (NO), Poland (PL), Portugal (PT), Romania (RO), Serbia (RS), Slovakia (SK), Slovenia (SI), Spain (ES), Sweden (SE), Switzerland (CH), Ukraine (UA), United Kingdom (GB)</p> </blockquote> <p>The dataset was constructed as part of the workflow within the open-source, sector-coupling model PyPSA-Eur and will be updated continuously as data and/or the cleaning process improves. </p> <p><strong>PyPSA-Eur</strong> is an open model dataset of the European power system at the transmission network level that covers the full ENTSO-E area. It can be built using the code provided at <a href="https://github.com/PyPSA/PyPSA-eur">https://github.com/PyPSA/PyPSA-eur</a>.</p> <p><strong>Not all data dependencies</strong> are shipped with the <a href="https://github.com/PyPSA/PyPSA-eur">code repository</a>, since git is not suited for handling large changing files. Instead we provide separate <strong>data bundles</strong> to be downloaded and extracted as noted in the <a href="https://pypsa-eur.readthedocs.io/en/latest/installation.html">documentation</a>.</p> <p>While the <a href="https://github.com/PyPSA/PyPSA-eur">code</a> and provided dataset in PyPSA-Eur is released as free software under the MIT, <strong>different licenses and terms of use</strong> apply to the underlying input data.</p> <p><strong>Extract from OpenStreetMap Terms of Use</strong></p> <blockquote> <p>OpenStreetMap<sup><a href="https://www.openstreetmap.org/copyright#trademarks">®</a></sup> is <em>open data</em>, licensed under the <a href="https://opendatacommons.org/licenses/odbl/">Open Data Commons Open Database License</a> (ODbL) by the <a href="https://osmfoundation.org/">OpenStreetMap Foundation</a> (OSMF).</p> <p>You are free to copy, distribute, transmit and adapt our data, as long as you credit OpenStreetMap and its contributors. If you alter or build upon our data, you may distribute the result only under the same licence. The full <a href="https://opendatacommons.org/licenses/odbl/1.0/">legal code</a> explains your rights and responsibilities.</p> <p>Our documentation is licensed under the <a href="https://creativecommons.org/licenses/by-sa/2.0/">Creative Commons Attribution-ShareAlike 2.0</a> license (CC BY-SA 2.0).</p> </blockquote> <p>This processed dataset is provided under the Open Data Commons Open Database License (ODbL 1.0) license.</p> <p><strong>Changelog from version 0.5 to 0.6:<br></strong></p> <ul> <li>Added electric parameters to lines (e.g. nominal current, resistance r, reactance x, susceptance b). This allows the dataset to be used outside of PyPSA/PyPSA-Eur.</li> <li>Interactive map.html now bundled with the dataset.</li> <li>Tags columns include what the element contains (e.g. merged lines contain lines that were aggregated together).</li> </ul> <p><strong>Changelog from version 0.4 to 0.5:<br></strong></p> <ul> <li>Exact locations of original substations and converter stations (interior point/Pole of Inaccessibility) are preserved.</li> <li>Clustering resolution improved from 5000 to 500 meters.</li> <li>Lines of same electric parameters are merged, if they cross a virtual bus (that is not a real substation).</li> <li>Information from OSM relations are used, wherever applicable. To avoid doubling, members (ways) of the relation are dropped in the set of lines, accordingly.</li> <li>There are now unique transformers for each voltage level in each station. Transformers now have a nominal capacity, representing the maximum of line capacities connected to either side/bus of the transformer (n-0, nominal capacity).</li> <li>Wherever applicable, OSM IDs are preserved and used in the index of the network components.</li> </ul>
Supporting data to the paper "Modelling charge profiles of electric vehicles based on charges data"
<p>This dataset contains the<em> underling data</em> and the <em>extended data</em> for the paper Modelling charge profiles of electric vehicles based on charges data”, submitted for consideration and open review in Open Research Europe.</p> <p>In the follow the description of the files is reported:</p> <p>HISTORIC DATA 2019 ELECTROLINERES AMB.csv: contains information on the charge events at the public charging points managed by the municipality in the metropolitan area of Barcelona in 2019. Fields are: charging point name; connector typology and number; charge start time; charge stop time; charge duration in minutes, energy delivered in kWh; vehicle manufacturer (optional); vehicle model (optional).</p> <p>STATIC INFORMATION CHARGING POINTS AMB 29042020.csv: contains the information about the public charging points of the metropolitan area of Barcelona. Fields are: charger typology (Quick/Normal); Charging point name and address; OCCP version; charger location; longitude; latitude; 7 flag fields for the connector type; observations; charging point maker.</p> <p>Lataustapahtumat, julkiset latauslaitteet 2019.csv: contains the information about the Turku Energia charge events for the city of Turku in 2019. Fields are: date of record creation, Station ID, Station name, charge start time, charge stop time, charge duration in minutes, energy delivered in Wh, Plug type (AC 22 kW/DC 50 kW), Cumulative energy delivered in the year (Wh), Average charge power (W)</p> <p>EV.csv: containes data on battery size retrived from vehicle datasheet or manufacturer website. Fields are: record ID, vehicle manufacturer ; vehicle model; battery size in kWh.</p> <p>Charge2019_EV_AMB.csv: contains the data on charge requests ( HISTORIC DATA 2019 ELECTROLINERES AMB.csv ) combined with the information on vehicle battery (EV.csv).</p> <p> </p>
Data file for: Three-Dimensional Electrical Imaging Across the Cona Woka Rift and Yalaxiangbo Dome in Southern Tibetan Plateau
<p>The magnetotellurics data were used to study the lithospheric electrical structure across the Cona Woka rift and Yalaxiangbo dome in the southern Tibetan plateau, conducted by Institute of Geophysical and Geochemical Exploration, Chinese Academy of Geological Sciences. The data file of CN6.dat was generated by the Matlab code EM3DVP.</p> <p>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.</p> <p> </p>
Raw Data - 3D Printing Temperature Tailors Electrical and Electrochemical Properties through Changing Inner Distribution of Graphite/Polymer
<p>This Data set contains the raw data of the article:</p> <p>3D Printing Temperature Tailors Electrical and Electrochemical Properties through Changing Inner Distribution of Graphite/Polymer, Small, 2021, 17, 2101233.</p> <p>C. Iffelsberger, C. W. Jellett, and M. Pumera*,</p> <p>https://doi.org/10.1002/smll.202101233</p> <p>Related to the MSCA Project: 888797 LoCatSpot</p>
Reference Data Set: Electricity, Heat, and Gas Sector Data for Modeling the German System
<p>This reference data set representing the status quo of the German electricity, heat, and natural gas sectors was compiled within the research project ‘LKD-EU’ (Long-term planning and short-term optimization of the German electricity system within the European framework: Further development of methods and models to analyze the electricity system including the heat and gas sector).</p> <p>While the focus is on the electricity sector, the heat and natural gas sectors are covered as well. With this reference data set, we aim to increase the transparency of energy infrastructure data in Germany. Where not otherwise stated, the data included in this report is given with reference to the year 2015 for Germany. The data set is documented in DIW Data Documentation 92 (see references).</p> <p>The project is a joined effort by the German Institute for Economic Research (DIW Berlin), the Workgroup for Infrastructure Policy (WIP) at Technische Universität Berlin (TUB), the Chair of Energy Economics (EE2) at Technische Universität Dresden (TUD), and the House of Energy Markets & Finance at University of Duisburg-Essen. The project was funded by the German Federal Ministry for Economic Affairs and Energy through the grant ‘LKD-EU’, FKZ 03ET4028A-D.</p>
Data for Identifying Robust Decarbonization Pathways for the Western U.S. Electric Power System under Deep Climate Uncertainty
<p>Data from the paper "Identifying Robust Decarbonization Pathways for the Western U.S. Electric Power System under Deep Climate Uncertainty"</p>
Electricity consumption and real vaue-added data for the Swiss and Genevan secondary and tertiary sectors.
<p>This file contains datasets of electricity consumption and real value added (base year 2000) in the secondary and tertiary sector for Switzerland and Geneva for the years between 2000 and 2015. The structure of the Swiss and Genevan datasets has been matched to ensure comparability of results from index decomposition analyses conducted on each region. It also contains heating and cooling degrees for both Switzerland and Geneva.</p>
Result data related to "Tröndle et al (2019) -- Home-made or imported: on the possibility for renewable electricity autarky on all scales in Europe"
<p>The files include results to out study investigating the possibility for renewable electricity autarky in Europe. For each administrative unit on the continental, national, regional, and municipal levels these files include:</p> <ol> <li>Name, country, population, current electricity demand, land cover statistics, shared coast with exclusive economic zone</li> <li>Potential in terms of area [km2], installable capacity [MW], annual electricity yield [TWh]</li> </ol> <p>If you use this data in an academic publication, please cite the following article:</p> <blockquote> <p>Tröndle, T., Pfenninger, S., & Lilliestam, J. (2019). Home-made or imported: on the possibility for renewable electricity autarky on all scales in Europe. <em>Energy Strategy Reviews</em>, <em>26</em>.</p> </blockquote> <p>CHANGELOG:</p> <p>Version 3 (2021-07-19)</p> <p>* Fix ID of EEZ in shared-coast.csv files.</p> <p>Version 2 (2019-11-08)</p> <p>* Add land cover statistics for each unit.</p>
ScienceDex guides
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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.