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39 results for “electricity generation”
VERTEX simulator snapshot with pre-generated data and analysis scripts for electric field stimulation simulations
<p>This dataset contains a snapshot of the VERTEX Matlab toolbox for simulating spiking neural networks, along with Matlab files for pre-generated results from simulations run using VERTEX. The scripts and results files here are to accompany the manuscript 'Predicting the impact of electric field stimulation in a detailed computational model of cortical tissue' and pertain to simulations of electric field stimulation on a cortical tissue model. Analysis scripts for the simulation results along with scripts for setting up and running simulations are included.</p>
Ultrafast electric control of cavity mediated single-photon and photon-pair generation with semiconductor quantum dots
<p>Dataset of the publication "Ultrafast electric control of cavity mediated single-photon and photon-pair generation with semiconductor quantum dots" (10.1103/PhysRevB.104.085308)</p>
The influence of electric circuit parameters on NOx generation by transient spark discharge _ data
<p>dataset for</p> <p>The influence of electric circuit parameters on NOx generationby transient spark discharge</p> <p> </p> <p>Abstract</p> <p>Nitrogen fixation, production of NO and NO<sub>2</sub> from N<sub>2</sub> and O<sub>2</sub> in air, has been investigated with<br> transient spark self-pulsing DC discharges. NO production is boosted by the addition of capacitors<br> and an inductor to the electrical circuit which drives the discharge. The quantity of NO produced<br> per joule of electrical input energy is doubled, though the quantity of NO<sub>2</sub> produced drops. The<br> yield of NO is also increased because the modified circuit enables higher discharge currents to be<br> used. NO concentrations as high as 2000 ppm were obtained with input energy densities of around<br> 300 J per litre of input gas, whilst NO<sub>2</sub> concentrations were around 150 ppm. This simple<br> modification of the driving circuit may have potential for optimizing the plasma chemistry with<br> other input gas mixtures and for scaling up nitrogen fixation from air.</p>
A complete energy community dataset with photovoltaic generation, battery energy storage systems and electric vehicles (v1.5)
<p>This dataset represents a complete European energy community based on actual data. In this scenario, a community of 250 households was built using real energy consumption and solar generation data obtained in homes throughout Europe. In total, 200 community members were assigned solar generation, while 150 were assigned a battery storage system. From the acquired sample, new profiles were created and randomly assigned to each end-user while also receiving two electric cars with information on their capacity, state-of-charge, and usage. Furthermore, it is provided the electric vehicle chargers’ information on their location, type, and cost of operation.</p> <p> </p> <p>Version 1.5 update: <span>on the Sheet EVs, lines 29 (Capacity kW), 30 (Charge kW), and 31 (Discharge kW) were updated to the correct values.</span></p> <p> </p> <p>This work has been published in Elsevier's Data in Brief journal:<br><em> Ricardo Faia, Calvin Goncalves, Luis Gomes, Zita Vale<br> Dataset of an energy community with prosumer consumption, photovoltaic generation, battery storage, and electric vehicles<br> Data in Brief, 2023, 109218, ISSN 2352-3409<br> <a href="https://doi.org/10.1016/j.dib.2023.109218.">https://doi.org/10.1016/j.dib.2023.109218</a><br> (<a href="https://www.sciencedirect.com/science/article/pii/S2352340923003372)">https://www.sciencedirect.com/science/article/pii/S2352340923003372)</a></em></p> <p> </p> <p>We would be grateful if you could acknowledge the use of this dataset in your publications. Please use the Data in Brief publication to cite this work.</p> <p> </p> <p>Reference data used to create this dataset:</p> <ul> <li>Filtered energy profiles and renewable energy production profiles: <a href="../record/6778401">https://zenodo.org/record/6778401</a></li> </ul> <ul> <li>Battery storage systems and electric vehicles: <a href="../record/4737293">https://zenodo.org/record/4737293</a></li> </ul>
STEG data of manuscript "Electrical Generation of a Ground Level Solar Thermoelectric Generator: Experimental Tests and One-year Cycle Simulation" submitted to Energies
<p>Figure_7_data: laboratory data of TEG output power working at low temperature differences. Data used in Figure 7 of manuscript "Electrical Generation of a Ground Level Solar Thermoelectric Generator: Experimental Tests and One-year Cycle Simulation" submitted to Energies.</p> <p>Figure_9_data: experimental data of TEG temperature differences from July 24 to July 31, 2017. Data used in Figure 9 of manuscript "Electrical Generation of a Ground Level Solar Thermoelectric Generator: Experimental Tests and One-year Cycle Simulation" submitted to Energies.</p> <p>Figures_11_14_data: input and output data of the STEG model. One-year cycle data. Used to obtain figures 11 to 14 of manuscript "Electrical Generation of a Ground Level Solar Thermoelectric Generator: Experimental Tests and One-year Cycle Simulation" submitted to Energies</p>
Input data for Open-data based carbon emission intensity signals for electricity generation in European countries -- top down vs. bottom up approach
<p>This dataset contains all necessary input data to reproduce the results of the paper "Open-data based carbon emission intensity signals for electricity generation in European countries -- top down vs. bottom up approach".</p>
Output data for Open-data based carbon emission intensity signals for electricity generation in European countries -- top down vs. bottom up approach
<p>This dataset contains all output data (results) of the paper "Open-data based carbon emission intensity signals for electricity generation in European countries -- top down vs. bottom up approach".</p>
Data for "Tracking physical delivery of electricity from generators to loads with power flow tracing"
<p>Data and analysis code for "Tracking physical delivery of electricity from generators to loads with power flow tracing".</p> <p>Data includes the following folders:</p> <ul> <li>validation: Ground truth generation data for 2020</li> <li>tracing: Power flow tracing results for one day</li> <li>secondary_analysis: Node-level power flow statistics</li> <li>networks: Solved networks, including Eastern U.S., Europe, Texas at 4 levels of clustering, and Europe with altered dispatch</li> <li>meta: between-node straight line distance for Eastern U.S. and Europe</li> <li>boundaries: shapefiles and per-node mappings for existing boundaries (Eastern U.S. and Europe) and example boundaries (Texas)</li> <li>analysis: physical delivery and deliverability metrics for all networks over all modeled days</li> </ul> <p>Code (`flow-delivery-analysis-main`) includes notebooks and helper functions for all visualizations and statistics in "Tracking physical delivery of electricity from generators to loads with power flow tracing". See the README for more information.</p>
Data from: Generating electricity while walking with a medial-lateral oscillating load carriage device
Biomechanical energy harvesters generate electricity, from human movement, to power portable electronics. We developed an energy harvesting module to be used in conjunction with a load carriage device that allows carried mass in a backpack to oscillate in the medial-lateral direction. The energy harvesting module was designed to tune medial-lateral oscillations of the carried mass to create favourable device-user interaction. We tested seven energy harvesting conditions and compared them to walking with the device when the weight was rigidly fixed to the backpack frame. For each energy harvesting condition, we changed the external load resistance: altering how much electricity was being generated and how much the carried mass would oscillate. We then correlated device behaviour to the biomechanical response of the user. The energy harvesting load carriage system generated electricity at no additional metabolic cost to the user, when compared to walking with the carried weight rigidly fixed. The device was able to generate up to 0.22±0.03 W of electricity, while walking with 9 kg of carried weight. The device also reduced the interaction forces experienced by the user, in the medial-lateral direction, compared to walking with the device when the mass was rigidly fixed.
Experimental data for "Experimental Demonstration of Electric Power Generation from Earth's Rotation Through Its Own Magnetic Field"
Open the record for dataset details and reuse information.
Daily profiles (2050) of load and generation of a semi-urban electricity distribution network from Portugal – ATTEST project
<p>This dataset was prepared under the framework of the ATTEST project, financed by the European Commission with grant number 864298. The dataset contains information about a real Portuguese Distribution test network, totally anonymized, located in a semi-urban area as in 2050. A network file (including grid topology, nodes, generators, consumption, all of which connected by power lines or transformers) and auxiliary files are provided. The network file (MatPower format) includes a “snapshot” or “steady-state” at a given time with a converged power flow solution. The grid, which is operated at 15 kV, 30 kV and 60 kV, has 229 nodes (118 with consumption) and 229 branches. The auxiliary load data comprises 12 typical days representing the combination of each season and the type of day (business day, Saturday, Sunday) gathering the active and reactive consumption at each node of the network in intervals of 15 minutes. In addition, a “read me” file called “Manual” includes technical detailed information about how to read the data properly. For the sake of coherence, each generation file should be used together with the respective load data file.</p>
Daily profiles (2020) of load and generation of a semi-urban electricity distribution network from Portugal – ATTEST project
<p>This dataset was prepared under the framework of the ATTEST project, financed by the European Commission with grant number 864298. The dataset contains information about a real Portuguese Distribution test network, totally anonymized, located in a semi-urban area as in 2020. A network file (including grid topology, nodes, generators, consumption, all of which connected by power lines or transformers) and auxiliary files are provided. The network file (MatPower format) includes a “snapshot” or “steady-state” at a given time with a converged power flow solution. The grid, which is operated at 15 kV, 30 kV and 60 kV, has 229 nodes (118 with consumption) and 229 branches. The auxiliary load data comprises 12 typical days representing the combination of each season and the type of day (business day, Saturday, Sunday) gathering the active and reactive consumption at each node of the network in intervals of 15 minutes. The auxiliary generation files comprises 12 typical days representing the combination of each season and the type of day (business day, Saturday, Sunday) gathering the active and reactive generation at each node with production in intervals of 15 minutes. In addition, a “read me” file called “Manual” includes technical detailed information about how to read the data properly. For the sake of coherence, each generation file should be used together with the respective load data file.</p>
Daily profiles (2040) of load and generation of a semi-urban electricity distribution network from Portugal – ATTEST project
<p>This dataset was prepared under the framework of the ATTEST project, financed by the European Commission with grant number 864298. The dataset contains information about a real Portuguese Distribution test network, totally anonymized, located in a semi-urban area as in 2040. A network file (including grid topology, nodes, generators, consumption, all of which connected by power lines or transformers) and auxiliary files are provided. The network file (MatPower format) includes a “snapshot” or “steady-state” at a given time with a converged power flow solution. The grid, which is operated at 15 kV, 30 kV and 60 kV, has 229 nodes (118 with consumption) and 229 branches. The auxiliary load data comprises 12 typical days representing the combination of each season and the type of day (business day, Saturday, Sunday) gathering the active and reactive consumption at each node of the network in intervals of 15 minutes. In addition, a “read me” file called “Manual” includes technical detailed information about how to read the data properly. For the sake of coherence, each generation file should be used together with the respective load data file.</p>
Taiwan electricity generation and demand data, 2017 January - 2022 July
<p>This is Taiwan's electricity generation/demand data from 2017 January to 2022 July, in 10-min resolution.</p> <p>Two files are included so far:</p> <ul> <li>loadarea: the electricity demand in four areas (i.e. north, central, south, east) in Taiwan. The information of the area can be found on <a href="https://www.taipower.com.tw/en/page.aspx?mid=4487&cid=2876&cchk=2c550ab4-907c-45df-822d-f35b4d2c63d0">Taipower's web page</a>.</li> <li>powerRatio: the power of each type of electricity generation. The data include both the generation from Taipower company and IPP (independent power plant); 'lng' is refer to gas power plant.</li> </ul> <p><strong>Source</strong>:</p> <ul> <li>the original data can be obtained from the Taiwanese government's open-data platform data.gov.tw.</li> <li>The link to the corresponding dataset is <a href="https://data.gov.tw/dataset/37331">https://data.gov.tw/dataset/37331</a>. (please note this link can only download 3 months of data that be collected a half year ago)</li> <li>live data can be obtained here (update very 10 min) <a href="https://data.gov.tw/dataset/8931">https://data.gov.tw/dataset/8931</a> with Chinese characters</li> <li>More information can be found on the Taipower webpage of <a href="https://www.taipower.com.tw/en/pageList.aspx">Information Disclosure</a>.</li> </ul> <p><strong>License:</strong></p> <ul> <li><a href="https://data.gov.tw/license">Open Government Data License, version 1.0</a> (Taiwan)</li> </ul>
Data from: Generating electricity while walking with a medial-lateral oscillating load carriage device
Open the record for dataset details and reuse information.
Next generation RNA sequencing of embryonic dorsal root ganglions to study the effects of electrical stimulation.
GEO Series GSE190745. Mus musculus. 4 samples. Type: Expression profiling by high throughput sequencing.
Supplementary material: "Impact analysis of the expansion of new variable renewables on the cost of electricity generation: a case study of Brazil"
<p>Supplementary material of " Impact analysis of the expansion of new variable renewables on the cost of electricity generation: a case study of Brazil"</p>
Generation and regulation of the electrical membrane potential (Δψ) in Campylobacter jejuni, the major constituent of the proton motive force
GEO Series GSE92644. Campylobacter jejuni. 3 samples. Type: Expression profiling by high throughput sequencing.
Typical daily generation and consumption electrical power profiles per month in three public buildings
<p>Electrical power values in kW representing the daily average profile of PV production and building consumption per month in three Mediterannean countries: Cyprus, Greece, and Italy. The time step of the data sets is 15min. The PV production is provided per kWp excluding the inverter losses, i.e., at the dc side of the inverter, based on data from PVGIS platfrom for the case of Cyprus and Italy, and on real measurements for the case of Greece.</p>
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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)
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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.