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4,230 results for “Energie”
ECMWF ERA interim derived atmospheric mass, moisture and energy budget products
<p>As observations and atmospheric reanalyses have improved, the diagnostics that can be computed with confidence also increase. Accordingly, a new formulation of the energetics of the atmosphere is laid out, with a view to advancing diagnostic studies of Earth's energy budget and flows. It is utilized to produce assessments of the vertically integrated divergences in both the atmosphere and ocean. Careful conservation of mass is required, with special attention given to the hydrological cycle and redistribution of mass associated with precipitation and evaporation, and a new method for ensuring this is developed. It guarantees that the atmospheric divergence is associated with moisture and precipitation, unlike previous methods. A new term, identified as associated with the enthalpy of precipitation, is included in a preliminary way. It is sensitive to the formulation, and the use of temperature in degrees Celsius instead of Kelvin greatly reduces errors and produces the extra term with values up to about 65 W/m2. New results for 2000 to 2017 are presented for the vertical-mean and annual-mean diabatic atmospheric heating, atmospheric moistening, and total atmospheric energy divergence. Results for the atmospheric divergence are combined with top-of-atmosphere radiation observations to deduce total surface energy fluxes.</p> <p>These data files are monthly and span from 1979 to 2017, smoothed at T-106 resolution. The data format is NetCDF. A full dataset description is available at https://journals.ametsoc.org/view/journals/clim/31/16/jcli-d-17-0838.1.xml</p>
Energy and specific heat for J=7/2 magnetic crystals
<p>Complementary data for the manuscript "Specific heat of Gd3+ and Eu2+ -based magnetic compounds" by D.J. Garcia, J. Sereni and A.A. Aligia[1].</p> <p>We include the energy and specific heat for many magnetic lattices. The magnetic moment is S=7/2. Interactions are taken as Heisenberg-like</p> <p>H = Sum_{i,\delta} J_\delta S_i S_{i+\delta}</p> <p>and \delta is taken to consider first, second, and possibly more nearest neighbors.</p> <p>Interactions are either all FM or AF to keep the system non-frustrated. We take all |J_\delta|=1K unless otherwise stated.</p> <p>Computations are performed using quantum Monte Carlo (QMC) simulations from the ALPS libraries (“dirloop_sse” package) [2, 3]. Most calculations are done using 20^3 conventional unit cells.</p> <p><strong>Directory name</strong> convention is as follows:</p> <p>fig*data/{Lattice Name}( {effective z}) , J_2 = ... , J_3 =... , K=... , L={size of the linear length} )</p> <p>fig*data refers to data of the corresponding figure in reference [1].</p> <p><em>Lattice names</em> are BCC, Diamond, FCC, HCP, and SC with their classic meaning of Body-Centered Cubic, Face-centered cubic, Hexagonal close-packed, and simple cubic.</p> <p>LHc corresponds to a layered honeycomb lattice with three NN within the plane of the honeycomb layer and one above or below the layer in alternating sites.</p> <p><em>magnetic order</em> is either ferromagnetic (FM) or antiferromagnetic (AF).</p> <p><em>effective z </em>agree<em>s </em>with the<em> number of neighbors</em> when all J_\delta are equal. It is computed by increasing the considered neighbor radius. See [1].</p> <p>The file<strong> SpecificHeatAndEnergy.tgz</strong> contains the raw data for all lattices in txt format. This version updates results with larger statistics. An ALPS example directory is also included.</p> <p>Finite size effects are almost negligible in these cases except for the antiferromagnetic simple cubic lattice ( SC_AF(6) ).</p> <p> </p> <p>[1] D. J. Garcia, J. G. Sereni, A. A. Aligia, "Specific heat of Gd3+ and Eu2+-based magnetic compounds", arXiv:2410.23519</p> <p>[2] A. Albuquerque, F. Alet, P. Corboz, P. Dayal, A. Feiguin, S. Fuchs, L. Gamper, E. Gull, S. Gürtler, A. Honecker, R. Igarashi, M. Körner et al., The ALPS project release 1.3: Open- source software for strongly correlated systems, Journal of Magnetism and Magnetic Materials 310(2, Part 2), 1187 (2007), doi:https://doi.org/10.1016/j.jmmm.2006.10.304, Proceedings of the 17th International Conference on Magnetism. </p> <p>[3] B. Bauer, L. D. Carr, H. G. Evertz, A. Feiguin, J. Freire, S. Fuchs, L. Gamper, J. Gukelberger, E. Gull, S. Guertler, A. Hehn, R. Igarashi et al., The ALPS project release 2.0: open source software for strongly correlated systems, Journal of Statistical Mechanics: Theory and Experiment 2011(05), P05001 (2011), doi:10.1088/1742-5468/2011/05/P05001.</p>
Koasidis et al_2023_Energies_DATASET
<p>This dataset contains the underlying data for the journal article Koasidis et al., 2023 published in Energies in November 2023.</p><p> </p>
Energy characteristics and the source of a ball lightning obtained by investigating its spectra
<p>This work, based on quantitative spectrometric analysis, is an exploratory research on the radiated power density and its evolution feature of a ball lightning. The radiated power density has shown a periodic pulse feature, like the spectral characteristics and temperature evolution of this BL. We proposed that this BL may be the discharge from the residual charge at the bottom of the previous cloud-to-ground (CG) lightning channel initiating it. Meanwhile, the electromagnetic field produced by the power line may be a potential outside energy source that supports the life of the BL. The optical radiation from soil constituent dominates the bright light of this BL.</p><p> </p>
Global sensitivity analysis to enhance the transparency and rigour of energy system optimisation modelling - Supplementary Material
<p>Supplementary material for the manuscript "Global sensitivity analysis to enhance the transparency and rigour of energy system optimisation modelling".</p> <p>This deposit contains all data and visualization scripts needed to replicate results in the manuscript.This includes user created figures, model input files, model output files, configuration files for running the workflow, and all scripts needed to process results.</p> <p>In addition to the European Commission, we acknowledge that Trevor Barnes' contribution to this paper was funded via a Mitacs Globalink Research Award, grant number IT2569</p>
Optimal planning of autonomous electric vehicles charging stations with photovoltaic generations and energy storage systems
<p>This database contains technical information on the 69-bus electrical distribution system. This system was tested in a mixed integer linear programming model for allocating autonomous electric vehicle charging stations equipped with photovoltaic generation and energy storage systems. Additionally, this document contains data related to charging stations, energy storage systems, and operational scenarios applied to the case studies.</p>
Intelligent Energy Systems Ontology: Local flexibility market and power system co-simulation demonstration
<p>The Intelligent Energy Systems Ontology (IESO) provides semantic interoperability within a society of multi-agent systems (MAS) developed in the scope of power and energy systems (PES). It leverages the knowledge from existing and publicly available semantic models developed for specific PES subdomains to accomplish a shared vocabulary among the agents of the MAS community, overcoming heterogeneity among the reused ontologies. IESO provides agents with semantic reasoning, constraints validation, and data uniformization. The use of IESO is demonstrated through the simulation of the management of a rural distribution network, considering the validation of the grid’s technical constraints. This dataset publishes files demonstrating: i) a snapshot of the initial semantic knowledge base (KB); ii) queries to the KB to get services inputs; iii) conversions between syntactic and semantic models; <br> iv) constraints validations; v) automatic conversion of units of measure.</p>
Silica dissolution under a flow of pure water at pressure and temperature conditions relevant to geothermal energy extraction
<p>This dataset is a published product of the 'REFLECT' Project - a Horizon Europe project which aims to inform the processes of geothermal energy extraction by determining the effect of relevant fluid properties and reactions in order to enhance predictive geochemical modelling and thus the energy exploitation and life-time of geothermal power plants.</p><p>The dataset records the concentration of silica measured in water that had been passed through a packed column of quartz grains at temperatures from 200 to 450°C and pressures from 150 to 450 bar. Concentrations are reported as g/ml SiO2, measured photometrically. The reader is referred to the full report for deliverable 1.4 of the REFLECT project for details of the experimental set up and interpretation of the data.</p><p>Silica concentrations marked with (a) are believed to be artificially reduced compared to the rest of the dataset due to a reduction in the surface density of active sites during some of the highest dissolution experiments. The final column lists the chronological order in which the measurements were taken to assist with interpretation of this factor.</p><p>The density marked with (b) represents the density of water at 150 bar and 342°C, rather than the measured condition of 148 bar and 344°C. This is to reflect the fact that the solubility measurement suggests the presence of a liquid phase - the conditions chosen represent the closest point on the phase boundary to the measured conditions. The discrepancy may reflect a small shift in the phase envelope due to silica dissolution in addition to any uncertainty in the <i>pT</i> measurements.</p>
Topological fine structure of an energy band
<p>This folder contains both the code and the data to generate the results in the paper "Topological fine structure of an energy band".</p>
An energy harvester based on UV-polymerized short-alkyl-chain-modified [DBU][TFSI] ionic liquid electrets
<p>This dataset contains the measurement data for figures published in the journal article: </p> <p>An energy harvester based on UV-polymerized short-alkyl-chain-modified [DBU][TFSI] ionic liquid electrets (https://doi.org/10.1039/D3TA05448A)</p> <p>by Topias Järvinen, Nemanja Vucetic, Petra Palvölgyi, Olli Pitkänen, Tuomo Siponkoski, Helene Cabaud, Robert Vajtai, Jyri-Pekka Mikkola and Krisztian Kordas</p>
Tool for Renewable Energy Potentials - Database
<p>Database for scenarios of "Potentials of Renewable Energy Sources in Germany and the Influence of Land Use Datasets"</p> <p>The used datasets and applied methodology can be found in the paper <a href="https://doi.org/10.3390/en15155536">Potentials of Renewable Energy Sources in Germany and the Influence of Land Use Datasets</a><br> . Please cite the paper if you utilize the dataset. Applied datasets among others:</p> <ul> <li>Geobasisdaten: © GeoBasis-DE / BKG (2021), ‘Digitales Basis-Landschaftsmodell (Ebenen) (Basis-DLM)’. 2021. (Conditions of use: <a href="https://sg.geodatenzentrum.de/web_public/nutzungsbedingungen.pdf">https://sg.geodatenzentrum.de/web_public/nutzungsbedingungen.pdf</a>)</li> <li>Geobasisdaten: © GeoBasis-DE / BKG (2021), ‘Amtliche Hausumringe Deutschland (HU-DE)’. 2021. (Conditions of use: <a href="https://sg.geodatenzentrum.de/web_public/nutzungsbedingungen.pdf">https://sg.geodatenzentrum.de/web_public/nutzungsbedingungen.pdf</a>)</li> <li>Geobasisdaten: ©GeoBasis-DE / BKG (2021), 3D-Gebäudemodelle LoD2 Deutschland (LoD2-DE) (2021). (Conditions of use: <a href="https://sg.geodatenzentrum.de/web_public/nutzungsbedingungen.pdf">https://sg.geodatenzentrum.de/web_public/nutzungsbedingungen.pdf</a>)</li> <li>UNEP-WCMC, IUCN, ‘The world database on protected areas’. 2016. Accessed: Oct. 22, 2021. [Online]. Available: https://www.protectedplanet.net/</li> </ul> <p>Please be aware of the conditions of use for parts of the applied datasets (<a href="https://sg.geodatenzentrum.de/web_public/nutzungsbedingungen.pdf">https://sg.geodatenzentrum.de/web_public/nutzungsbedingungen.pdf</a>) if you utilize the data.</p>
Costless renewable energy distribution model based on cooperative game theory for energy communities considering its members' active contributions
<p>This dataset was used in the case study of the following publication:</p> <p> - Luis Gomes, Zita Vale, "Costless renewable energy distribution model based on cooperative game theory for energy communities considering its members’ active contributions," Sustainable Cities and Society, Volume 101, 2024, 105060, ISSN 2210-6707, <a href="https://doi.org/10.1016/j.scs.2023.105060">https://doi.org/10.1016/j.scs.2023.105060</a> </p> <p><em>(if you used this dataset in your publications, please send us your information so we can add your publication to the list above)</em></p> <p> </p> <p>The dataset is composed by energy generation, consumption, and forecast (for generation, and for consumption) expressed in Wh. The data considers an energy community of 10 prosumers in 30 days.</p> <p>The dataset also has energy prices that have been collected from MIBEL (Iberian Electricity Market).</p> <p> </p> <p>We would be grateful if you could acknowledge the use of this dataset in your publications. Please use the Zenodo publication to cite this work.</p>
EnergyPROSPECTS Energy Citizenship Factsheet Series, Part 1: Introduction and Methodology
<p>This document is Part 1 of the EnergyPROSPECTS Factsheet Series summarising the methodology used for collecting and presenting the data as well as introducing the database.</p> <p>We have created the Series to publish the results of a mapping of energy citizenship in Europe, along with the first stage of our analysis of the respective data. The EnergyPROSPECTS consortium mapped 596 cases of energy citizenship between November 2020 and May 2021 using desk research, collecting data on many aspects of the cases. Although the analysis is a work in progress, we believe it is important to share our data and, through doing this, contribute to the understanding of energy citizenship in Europe.</p> <p>EnergyPROSPECTS (PROactive Strategies and Policies for Energy Citizenship Transformation), a H2020 project between 2021-2024, works with a critical understanding of energy citizenship that is grounded in state-of-the-art social sciences and humanities (SSH) insights.</p>
Energy input, habitat heterogeneity, and host specificity on avian haemosporidian diversity at continental scales
<p>The correct identification of biotic and abiotic drivers affecting parasite diversity and assemblage composition at different spatial scales is crucial for understanding how pathogen distribution responds to anthropogenic disturbance and climate change. Here, we used a database of avian haemosporidian parasites to identify such drivers and their effect on the taxonomic and phylogenetic diversity of genera Plasmodium, Haemoproteus, and Leucocytozoon from three zoogeographic regions. We explored how parasite diversity is related to energy input (i.e., temperature, precipitation, and potential evapotranspiration [PET]), to habitat heterogeneity (i.e., climatic seasonality, vegetation density, ecosystem heterogeneity, human disturbance, and host richness), and to a novel assemblage-level metric related to parasite niche overlap (degree of generalism). We found that the relative importance of the predictors differed between the three studied parasite genera and across diversity metrics. Among the most consistent predictors, host richness was positively related to the taxonomic diversity of the three genera. Energy input and human footprint explained the phylogenetic diversity of Haemoproteus. Finally, the degree of generalism explained the diversity of Plasmodium and Leucocytozoon. Our results suggest that different dimensions of haemosporidian diversity are shaped by energy input, host heterogeneity, and assembly processes related to parasite resource use within local parasite assemblages.</p>
Scout Benchmark Scenarios for U.S. Building Energy and CO2 Emissions to 2050
<p><strong>Overview and Intended Use Cases</strong></p> <p>These scenarios establish a range of futures for U.S. buildings sector energy use and CO<sub>2</sub> emissions to 2050 using <a href="https://scout-bto.readthedocs.io/en/latest/">Scout</a>, a reproducible and granular model of U.S. building energy use, emissions, and consumer costs developed by the U.S. national labs for the U.S. Department of Energy's Building Technologies Office (BTO).</p> <p>Scout benchmark scenario data are suitable for the following example use cases:</p> <ul> <li>Setting high-level policy goals for U.S. buildings sector energy use, electricity demand, and CO<sub>2</sub> emissions over both the near- and long-term (e.g., X% building CO<sub>2</sub> emissions reductions vs. 2005 levels by 2030, Y% reductions vs. 2005 levels by 2050);</li> <li>Exploring the effects of key deployment dynamics driving U.S. buildings sector energy and CO<sub>2</sub> emissions to 2050 that could be affected by policy levers (e.g., raising minimum technology performance levels; improving market penetration of commercially available technologies; accelerating electrification and/or retrofit rates; introducing breakthrough technologies to the market);</li> <li>Determining priority segments (regions, building types, and end use/technology types) and sequencing of U.S. buildings sector energy and CO<sub>2</sub> emissions reductions and/or changes in total consumption by fuel type to 2050 under a given set of assumptions;</li> <li>Identifying the energy and CO<sub>2</sub> impacts or cost effectiveness of specific technologies or operational approaches of interest—in isolation or after considering competition with other measures in a scenario portfolio; and/or</li> <li>Exploring the total cost of deploying different portfolios of building energy efficiency and end-use electrification measures, as well as the total consumer energy cost savings potential of those portfolios. </li> </ul> <p><strong>Scenario Summary</strong></p> <p>A total of 5 scenarios explore total building energy use, CO<sub>2</sub> emissions, and technology and energy costs from 2024–2050 under varying levels of demand-side deployment of building efficiency and electrification measures and parallel decarbonization of buildings’ electricity supply. Narrative descriptions of these scenarios are as follows:</p> <ul> <li><strong>Stated Policies: </strong>Existing policies and regulations (mainly IRA for buildings) lead to modestly accelerated deployment of HPs/HPWHs but not other efficiency measures in the buildings sector. The power sector decarbonizes consistent with a “<a href="https://www.nrel.gov/docs/fy23osti/84916.pdf">Mid-case (with tax credit phaseout)</a>” scenario.</li> <li><strong>Mid: </strong>Policy makers rely mostly on market-based instruments to moderately increase deployment of efficient technology and fuel switching to heat pumps. The power sector decarbonizes consistent with a “Mid-case with 95% Decarbonization by 2050 (without tax credit phaseout)” scenario.</li> <li><strong>High: </strong>Policy makers use both regulations and market-based instruments to dramatically accelerate deployment of high efficiency technologies and fuel switching to heat pumps, though building technologies with breakthrough increases in performance at low cost do not materialize on the market. The power sector decarbonizes consistent with a “<a href="https://www.nrel.gov/docs/fy23osti/84916.pdf">Mid-case with 100% Decarbonization by 2035 (without tax credit phaseout)</a>” scenario.</li> <li><strong>Breakthrough: </strong>Research and innovation breakthroughs lead to market availability of cost-effective, high-performance building technologies by 2030; these, coupled with accelerated deployment of high efficiency technologies and fuel switching to heat pumps, lead to aggressive buildings sector transformation. The power sector decarbonizes consistent with a “<a href="https://www.nrel.gov/docs/fy23osti/84916.pdf">Mid-case with 100% Decarbonization by 2035 (without tax credit phaseout)</a>” scenario.</li> <li><strong>Inefficient Electrification Sensitivity: </strong>Policy makers use regulations and market-based instruments to encourage fuel switching but do not include provisions that require switching to efficient heat pumps, resulting in a substantial amount of switching to inefficient electric resistance heating and water heating technologies. The power sector decarbonizes consistent with a “<a href="https://www.nrel.gov/docs/fy23osti/84916.pdf">Mid-case (with tax credit phaseout)</a>” scenario.</li> </ul> <p>The key input dimensions that are varied to produce the above range of scenarios are as follows:</p> <ul> <li><u>Market-available technology performance range:</u> the energy performance levels of building technologies available for purchase by end use consumers, bounded by a minimum performance “floor” and maximum performance “ceiling”;</li> <li><u>Load electrification rate and efficiency:</u> the rate at which fossil-fired equipment is converted to electric service, and the efficiency level of the electric equipment; </li> <li><u>Early retrofits:</u> the fraction of consumers that choose to replace existing building equipment and/or envelope components before the end of their useful lifetimes; and</li> <li><u>Power grid decarbonization:</u> the annual average CO<sub>2</sub> emissions intensity of the electricity supplied to the buildings sector across the modeled time horizon (2024–2050), resolved by grid region. </li> </ul> <p>Refer to the attached “Scenario_Guide" PDF for further scenario details and results; instructions for reproducing scenario results are available in “Scenario_Execution” XLSX.</p> <p>Results data are reported as an annual time series (2024–2050) at both a national and regional (<a href="https://www.eia.gov/outlooks/aeo/pdf/nerc_map.pdf">EMM grid region</a>) spatial resolution. While not reflected in this dataset, annual time series data may be further translated to a sub-annual, hourly resolution for integration with grid modeling—please contact the authors for more information.</p> <p><strong>What's New in This Version</strong></p> <p><strong><em>Note: v6.1 updates the file ./Results/Results_Summary.xlsx to reflect the latest scenario runs. Please disregard the outdated version of this file that was posted in v6.</em></strong></p> <p>This set of benchmark scenarios provides an update to <a href="../records/8087519">Version 5</a> of the Scout Benchmark Scenarios (June 2023) using the same scenario definitions but an updated set of baseline and measure input data alongside several minor methodological changes. </p> <p>The following scenario features are new in this dataset:</p> <ul> <li>Reference case data and energy use projections updated to <a href="https://www.eia.gov/outlooks/aeo/">AEO 2023</a>, including updates to energy and stock and technology cost, performance, and lifetime data; updated site-source energy conversions, CO2 emissions intensities, and energy prices; and revised peak and take period definitions that are consistent with 2023 EMM projections.</li> <li>Integration of federal and state cost incentives from AEO 2023 (see <a href="https://www.eia.gov/outlooks/aeo/IIF_IRA/pdf/IRA_IIF.pdf">AEO2023 Issues in Focus: Inflation Reduction Act Cases</a> in the AEO2023 for details); these incentives reduce the initial cost of upgrades for applicable measures.</li> <li>Revised method for allocating end use electricity baselines in AEO from census divisions to EMM regions and states by using <a href="https://www.nrel.gov/buildings/end-use-load-profiles.html">End Use Load Profiles</a> (EULP) data. EULP data now also underpin updated, EMM-resolved hourly load baseline shapes.</li> <li>Retail price projections for grid scenarios are updated to match those produced by NREL under the Department of Energy’s DECARB Initiative (these are similar to but differ in slight ways from NREL’s <a href="https://www.nrel.gov/analysis/standard-scenarios.html">Standard Scenarios</a>). Three scenarios are included: <ul> <li><em>Stated Policies</em>: includes moderate estimates for inputs such as technology costs, fuel prices, and demand growth with no nascent technologies and electric sector policies that match current federal laws and regulations (including IRA & BIL); achieves an 88% reduction in building site electricity emissions <em>intensity</em> (Mt CO2/quad site) from 2005 levels by 2050.</li> <li><em>Mid</em>: consistent with<em> Stated Policies</em> except achieves 97% reduction in building site electricity emissions intensity from 2005 levels by 2050.</li> <li><em>High:</em> includes low demand growth projections with advanced inputs for technology costs and allowance of transmission expansion between regions (without limitations based on historical build rates); federal policies are consistent with implemented laws (including IRA & BIL); building electricity is fully decarbonized after 2035.</li> <li>The previous version of the benchmark datasets used retail price data from EIA’s <a href="https://www.eia.gov/outlooks/aeo/">Annual Energy Outlook</a> scenarios.</li> </ul> </li> <li>In contrast to <a href="https://doi.org/10.5281/zenodo.8087519">Version 5</a>, measures in the “best available” measure tier are not deployed with load flexibility features. </li> </ul>
Solar PV and wind power Model Supply Region (MSR) dataset as energy model input for countries in Central and South America
<p>This dataset provides model-ready data to include geospatial differentiation in solar and wind power investment options in energy models (primarily capacity expansion models and dispatch models) at the level of every Central and South American country. </p> <p>The methodology used to create the dataset takes into account resource quality, land use restrictions, distance from infrastructure, and other factors. It was previously applied to create an all-Africa dataset explained in Sterl et al. (2022) and published by Sterl, Hussain & Elabbas (2023). </p> <p>Folder (1) provides shapefiles of each country's overall feasible area for developing solar and wind power projects, under the restrictions/criteria mentioned above and described in Sterl et al. (2022).</p> <p>Folder (2) provides the best 5% ("best" measured by expected LCOE, from lowest to highest, including grid and road extension costs; 5% measured in terms of coverage of a country's area) of each country's solar and wind development potential, including hourly time series for model input.</p> <p>Folder (3) provides the corresponding shapefiles.</p> <p>Folder (4) provides simplified/aggregated results in terms of MSR clusters (see Sterl et al. 2022 for details), alongside hourly time series based on the meteorological year 2018. The amount of clusters was chosen to be 3, 5 or 10 depending on country size.</p> <p>Folder (5) provides PDF-file maps at the country level, showing resource strength and clustering outcomes by MSR (post-screening).</p> <p>Explanations of the headers in any spreadsheet files are provided in the Supplementary Information of Sterl et al. (2022).</p> <p>Countries/territories included in the dataset: </p> <p>Argentina<br>Belize<br>Bolivia<br>Brazil<br>Chile<br>Colombia<br>Costa Rica<br>Cuba<br>Dominican Republic<br>Ecuador<br>El Salvador<br>French Guiana<br>Guatemala<br>Guyana<br>Haiti<br>Honduras<br>Jamaica<br>Nicaragua<br>Panama<br>Paraguay<br>Peru<br>Suriname<br>Uruguay<br>Venezuela</p> <p> </p> <p><strong>References</strong></p> <p>Sterl, S., Hussain, B., Miketa, A. <em>et al.</em> An all-Africa dataset of energy model “supply regions” for solar photovoltaic and wind power. <em>Sci Data</em> <strong>9</strong>, 664 (2022). <a href="https://doi.org/10.1038/s41597-022-01786-5">https://doi.org/10.1038/s41597-022-01786-5</a></p> <p>Sterl, S., Hussain, B., & Elabbas, M. (2023). Data for the paper « An all-Africa dataset of energy model "supply regions" for solar PV and wind power » (1.2.0) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.14870967">https://doi.org/10.5281/zenodo.14870967</a></p>
Data from: Energy harvesting in a flow-induced vibrating flapper with biomimetic gaits
<p>Energy harvesting from flow induced vibrations (FIV) in flexible bodies offer opportunities for power generation in biomimicking robotic devices and is an active area of research. The focus of this study is on investigating the underlying physics and qualitatively analysing the energy extraction scenarios in similar structural systems, comprising of a flexible piezoelectric flapper in a low Reynolds number flow regime. A high-fidelity three-way fully coupled fluid-structure-electric energy solver is developed in-house to study the energy harvesting capabilities of such a flapper, its hydrodynamic characteristics and the associated unsteady flow-field. The results indicate that the flapper deformation profiles at the most efficient harvesting regimes, resemble the propulsion gaits of natural swimmers. Investigations on the effects of a sinusoidal heaving actuation reveal no significant impact on the harvested power at the high yield (high power output) regime, identified under the passive condition showing biomimetic gait. This study provides mechanics based insights that is expected to be useful for bio-inspired designs of FIV based harvesters.</p>
Benchmark for energy efficient obstacle detection on head mounted wearable for the vision impaired
<p>Here we present a novel benchmark dataset with the associated challenge, that is to detect obstacles based on head-mounted sensors and lightweight wearable devices to assist Blind and Visually Impaired individuals (BVIs) navigate in indoor environments. The challenge encompasses three objectives: (1) as accurately as possible to detect the obstacles on the pathway that likely lead to a collision; (2) as durably as possible on a given amount of battery power for the detection algorithm or model to run; (3) as reliably as possible to compensate natural head turns so nearby objects would not trigger false alarms. The data provided in the benchmark are collected from the following head mounted sensors: (i) nine low-cost ultrasonic sensors; (ii) one high-end ultrasonic sensor with a larger detection range but higher power consumption; (iii) a 9-Degrees of Freedom (DOF) Inertial Measurement Unit (IMU). The resulting dataset consists of more than 188,000 unique sequences obtained from multiple subjects walking in three different indoor scenarios. This benchmark is to facilitate and encourage accurate yet fast obstacle detection solutions that can really benefit BVIs. </p>
Supporting Data for Figures in "Localized, tidal energy extraction in Puget Sound can adjust estuary resonance and friction, modifying barotropic tides system-wide"
<p>Supporting data for figures in "Localized, tidal energy extraction in Puget Sound can adjust estuary resonance and friction, modifying barotropic tides system-wide" by Preston S. Spicer, Parker MacCready, and Zhaoqing Yang. The manuscript is being considered for publication in Journal of Geophysical Research: Oceans (2024). The article analyzes the effect of a tidal turbine farm on incident and reflected tidal energy fluxes in the Salish Sea. Files are in MATLAB data and .m format with some .txt and shape files. Files named figX.m create the corresponding Figure X using provided .mat and other files. Variable names and units correspond to graphed data of each figure in the journal article.</p>
Energy Sufficiency Policy Database
<p>This folder contains the policy database belonging to the publication <strong>"Building a Database for Energy Sufficiency Policies"</strong> (<a href="https://doi.org/10.12688/f1000research.108822.2">https://doi.org/10.12688/f1000research.108822.2</a>).</p> <p><span>Version 1.1 of the database is replacing version 1.0 from January 2023 of the sufficiency policy database. We made the following changes and revisions:</span></p> <ul> <li><span>> 70 new policies added</span></li> <li><span>New literature added</span></li> <li><span>One Double ID deleted, two IDs merged</span></li> <li><span>New policy strategies added, others renamed</span></li> </ul> <p><strong>Acknowledgements:</strong></p> <p>The database is funded by the Federal Ministry of Education and Research (BMBF) as part of the project “EnSu - Energy Sufficiency in Energy Transition and Society” (https://energysufficiency.de/) within the framework of the Strategy „Research for Sustainability" (FONA) (http://www.fona.de/en) as part of its Social-Ecological Research funding priority, funding nos. [01UU2004A, 01UU2004B, 01UU2004C]. Responsibility for the content of this publication lies with the authors.</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)
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.