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4,230 results for “Energie”
Data from: Effect of parasite infection and invasion history on feeding, growth and energy allocation of cane toads
<p>The energy allocation decisions that organisms make can differ between sexes and populations and be influenced by factors such as age and parasite infection. We conducted experimental parasite infections on common-garden reared cane toads originating from sites across the species' invasive range in Australia to assess how sex, parasite infection and invasion history affected the toad's food intake, growth rate and organ weights. Female toads had larger fat stores, larger livers and larger gonads than did males, reflecting increased investment into gametes. Growth rate did not differ between the sexes. Lungworm infection increased feeding by male but not female toads and increased fat storage in all toads. Fat body, liver, gonad sizes and feeding rates all differed among toads from different locations within the toad's invasion transect across Australia, even though our measurements were made under standardized conditions on captive animals. Toads from populations close to the invasion front ate more and had heavier fatbodies, and livers than did toads from long-colonised areas, but they had smaller gonads. This pattern reflects the evolution of a more dispersive phenotype among invasive populations, whereby the rate of dispersal is enhanced by increased energy intake and storage, and delayed reproduction.</p>
Dataset from: "Uncertainty-Aware Interpretable Prognosis for Wave Energy Converters with Recurrent Expansion"
<p>This dataset comprises run-to-failure sensor data derived from a mathematical model simulating wave energy converter behavior, particularly for Oscillating Water Column Turbines (OWCTs). The dataset includes vibration, temperature, pressure, acceleration, strain, flow, torque, rotation, and remaining useful life (RUL) readings for one life cycle. Through normalization, the data is scaled uniformly for robust analysis and interpretation. Researchers can leverage this dataset to develop and validate their prognostic models for OWCTs.</p> <p>To cite this dataset, please refer to:</p> <p>Berghout Tarek and Benbouzid Mohamed. (2024). Uncertainty-Aware Interpretable Prognosis for Wave Energy Converters with Recurrent Expansion. SSRN, 1–22. <span><a href="https://dx.doi.org/10.2139/ssrn.4825408" target="_blank" rel="noopener"><span>http://dx.doi.org/10.2139/ssrn.4825408</span></a> </span></p>
A neural network-based four-body potential energy surface for parahydrogen
<p>We created an isotropic <em>ab initio</em> four-body potential energy surface (PES) for parahydrogen.<br>The energies were calculated using the CCSD(T) method, with an AVDZ atom-centred basis set.</p> <p>This repository contains the input and output files for the ab initio calculations.</p> <p>A detailed description of the data is provided in the README.md file.</p>
Data archive for paper "Energy and environmental impacts of air-to-air heat pumps in a mid-latitude city"
<p><strong>Overview</strong></p> <p>This is the data archive for paper "<a href="https://www.nature.com/articles/s41467-024-49836-3" target="_blank" rel="noopener">Energy and environmental impacts of air-to-air heat pumps in a mid-latitude city</a>". It contains the paper's data archive with model outputs (see <code>notebooks</code> folder) and the Singularity image for (optionally) re-running experiments.</p> <p>For the standalone models to model air conditioners and heat pumps please refer to <a href="https://github.com/dmey/minimal-dx">MinimalDX</a>.</p> <p><strong>Prerequisites</strong></p> <ul> <li>Linux with Bash shell.</li> <li><a>Git</a> version >= 2.</li> <li><a href="https://sylabs.io/">Singularity</a> version >= 3.</li> <li><a href="https://en.wikipedia.org/wiki/Portable_Batch_System">Portable Batch System</a>*.</li> <li><a href="https://en.wikipedia.org/wiki/Intel_Fortran_Compiler">Intel Fortran Compiler</a> [<em>Required for MesoNH simulations</em>].</li> <li>A compatible version of the MPI library implementation version 3 [<em>Required for MesoNH simulations</em>].</li> </ul> <p>Please note that most steps require <a href="https://sylabs.io/">Singularity</a>. If you are looking for information on how to install or use Singularity, please refer to the <a href="https://sylabs.io/docs">Singularity documentation</a>. Please note that depending on your specific system settings and resource availability, you may need to modify PBS parameters at the top of submit scripts stored in the hpc directory.</p> <p><strong>Simulations</strong></p> <p><em><strong>Offline</strong></em></p> <ol> <li>Build Surfex with <code>qsub hpc/surfex_build.pbs</code>.</li> <li>Run scenarios with the following commands: <pre><code> qsub -v case_name=fincap hpc/surfex_run.pbs qsub -v case_name=fincap_extended_autosize hpc/surfex_run.pbs qsub -v case_name=minidx_cop=2.5 hpc/surfex_run.pbs qsub -v case_name=minidx_cop=3.0 hpc/surfex_run.pbs qsub -v case_name=minidx_cop=3.5 hpc/surfex_run.pbs qsub -v case_name=minidx_cop=4.0 hpc/surfex_run.pbs<br> qsub -v case_name=minidx_cop=4.5 hpc/surfex_run.pbs </code></pre> </li> </ol> <p><em><strong>Online</strong></em></p> <p>To run MesoNH simulations, follow the three steps outlined below in the same order. Note: Depending on your system and configuration, submit scripts may require change.</p> <ol> <li>Build MesoNH with <code>qsub hpc/build_mnh_intel.pbs</code>.</li> <li>Run the preprocessing step with <code>qsub hpc/submit_mnh_prep.pbs</code>.</li> <li>Finally run MesoNH cases with the following commands for <code>fincap</code> and <code>minidx</code> simulations respectively: <pre><code> hpc/submit_mnh.sh fincap 20050120 12 1 toulouse hpc/submit_mnh.sh minidx 20050120 12 1 toulouse </code></pre> </li> <li>Post-process the results with <code>qsub hpc/submit_mnh_post.pbs</code></li> </ol> <p><strong>Analyses</strong></p> <pre><code>qsub hpc/postprocess_results.pbs # Plots in notebooks/ </code></pre>
Dataset for publication "Composite MAX phase/MXene/Ni electrodes with a porous 3D structure for hydrogen evolution and energy storage application
<p>Dataset for publication "Composite MAX phase/MXene/Ni electrodes with a porous 3D structure for hydrogen evolution and energy storage application". The dataset contains relevant data and figures from the publication. Description on how to work with the dataset is given in the readme file. DOI for the original paper: DOI <a title="Link to landing page via DOI" href="https://doi.org/10.1039/D3RA07335A">https://doi.org/10.1039/D3RA07335A</a>.</p>
Challenges to Profitability for Energy Storage in High Renewable Energy Systems
<p>This figure illustrates the impact of renewable energy source (RES) penetration on electricity price spreads and the profitability of energy storage systems.</p> <p>In scenarios with a low share of RES, the price spread is significant. During low-demand periods, electricity is predominantly supplied by cost-effective RES, leading to lower prices. Conversely, during high-demand hours, traditional power plants with higher operational costs are required, resulting in elevated prices. Energy storage systems can capitalize on this large price spread by charging during low-price periods and discharging during high-price periods, thereby maximizing their profits.</p> <p>In contrast, with a high share of RES, both low and high-demand periods are largely covered by renewable sources. This extensive reliance on RES minimizes the price differential between these periods, resulting in a much smaller price spread. Consequently, the potential for storage systems to profit from price arbitrage is reduced, as the opportunities to buy low and sell high diminish.</p>
State-resolved mutual neutralization of O$^+$ with $^1$H$^-$ and $^2$H$^-$ at collision energies below 100 meV
<p>The data files found here contain the data as obtained and displayed in : "State-resolved mutual neutralization of O$^+$ with $^1$H$^-$ and $^2$H$^-$ at collision energies below 100 meV" published in Physical Review A (2024). Each file contains an explanatory header. Header lines start with #.</p>
Datasets Energy Citizenship Scale Development and Validation (Task 2.4, D2.2, D2.3)
<p>The datasets are part of the Reports on the development (D2.2) and validation (D2.3) of the energy citizenship scale. They are also part of the publication "Energy citizenship as people's perceived (collective) rights and responsibilities in a just and sustainable energy transition - scale development and validation" (https://doi.org/10.1016/j.jenvp.2024.102310). </p>
Binding energies of ethanol and ethylamine on interstellar water ices
<p>This Supporting Material contains the fractional coordinates of HF-3c optimized adsorption complexes for crystalline and amorphous periodic ice models in <a href="https://www.moldraw.unito.it/_sgg/m1m1s43_1.htm">.mol</a> format, editable with <a href="http://www.moldraw.unito.it/">MOLDRAW</a>, using <a href="http://www.crystal.unito.it/">CRYSTAL17</a> computer code of:</p> <ul> <li>Ethylamine and ethanol adsorption complexes on crystalline ice (CI);</li> <li>Ethylamine and ethanol adsorption complexes on amorphous ice (ASW);</li> <li>A-layer and B-layer (100) surfaces of EtNH2, plus three adsorption complexes of ethylamine on these surfaces;</li> <li>A-layer and B-layer (010) surfaces of EtOH, plus three adsorption complexes of ethanol on these surfaces.</li> </ul>
Experimental datasets on "Real-time Observation of sub-100-fs Charge and Energy Transfer Processes in DNA Dinucleotides"
<p>Experimental datasets referring to the manuscript entitled "Real-time Observation of sub-100-fs Charge and Energy Transfer Processes in DNA Dinucleotides"</p>
Fig. 2 in Angiostrongylus cantonensis induces energy imbalance and dyskinesia in mice by reducing the expression of melanin-concentrating hormone
Fig. 2 (See legend on previous page.)
Datasets on experimental lab studies on visions and energy citizenship (Task 4.3)
<p>We conducted two further experiments about visions and the transition to a sustainable and just energy system (one in January 2024, one in April 2024). In addition to replication, we tested possible moderating variables and new explanatory approaches for the effect of such visions. The experiments belong to Task 4.3.<br><br>Not all measures are included in these datasets to ensure anonymity. More extensive versions of these datasets (e.g. including open answers and socio-demographics) are available upon request.</p>
Data for the Article "Maturity Assessment of Grid-Scale Flexibility and Energy Storage Services Towards a Decarbonized Europe"
<p>This dataset includes the data collected form various stakeholders regarding grid-scale flexibility and energy services as part of the SINNOGENES Project.</p>
The Global and National Energy Systems Techno-Economic (GNESTE) Database: Cost and performance data for electricity generation and storage technologies
<p>Here, we present a database which collates historical, current, and future cost and performance data and assumptions for the six most prominent electricity generation technologies; coal, gas, hydroelectric, nuclear, solar photovoltaic (PV) and wind power, which together accounted for over 92% of installed generation capacity in 2022. In addition, we provide the same data for utility-scale battery energy storage systems (BESS), regarded as critical to the integration of variable renewables such as wind and solar PV.</p> <p>The data are global in scope but with regional and national specificity, covers the years 2015 through to 2050, and span 5510 datapoints from 56 sources. The database enables modellers to select and justify model input data and provides a benchmark for comparing assumptions and projections to other sources across the literature to validate model inputs and outputs. It is designed to be easily updated with new sources of data, ensuring its utility, comprehensiveness, and broad applicability in future.</p>
Hypocenters, Focal Mechanisms, and Radiated Energy of Small Earthquakes in Northern Ibaraki Prefecture, Japan
<p>Estimated hypocenters, focal mechanisms, and radiated energy of small earthquakes in northern Ibaraki Prefecture, Japan, are available here.</p> <ul> <li>ASCII file "hypo.dat" contains the relocated hypocenters.</li> <li>ASCII files "mec1.dat" and "mec2.dat" contain the focal mechanisms estimated for the MJMA2.0-4.0 and MJMA1.0-2.0 earthquakes, respectively.</li> <li>ASCII file "er. dat" contains the estimated radiated energy.</li> </ul> <p>The contents of each file are listed on the first line of the file.</p>
Stochastic Ocean Energy Backscatter via Pressure and Momentum Perturbation
<p>Our research aims to enhance the representation of mesoscale eddies in ocean models, particularly for eddy-permitting resolutions, by incorporating a dynamic backscatter parameterization and additional stochastic perturbations. This study addresses several key modeling issues, including enhancing the representation of missing variability through stochastic forcing, the need for incorporating stochastic terms alongside dynamic backscatter, the propagation of energy across scales and regimes, and distinguishing between different stochastic approaches.</p> <div> <div> <div> <div> <div> </div> </div> </div> </div> </div> <div> <div> <div> <div> <div> <div> <p>The output data from the FESOM2 model (<a href="https://fesom.de/" target="_new" rel="noreferrer">https://fesom.de/</a>) is available here. The file names include information about the corresponding plot in the paper, the simulation name, and the relevant diagnostic variable. Additionally, the uploaded data includes the high-resolution array used to produce stochastic perturbation.</p> </div> </div> </div> </div> </div> </div>
Material Intensity data for Renewable Energy Technologies
<p>The file contains detailed data on material intensities (measured in tons per gigawatt) collected from various literature sources. This data encompasses the material intensity of 30 metals through a range of technologies, including offshore and onshore wind power, solar energy, nuclear power, heat pumps, hydroelectric power, hydrogen production via electrolyzers, biomass energy, and biomass with carbon capture and storage (CCS).</p> <p>The variables are:</p> <ul> <li><strong>tech:</strong> Technology (<em>offshore_wind, onshore_wind, Solar, heat_pumps, hydrogen, biomass, biomassccs, hydroelectric, nuclear</em>).</li> <li><strong>material: </strong>Metal the material intensity refers to (<em>Aluminium, Arsenic, Bismuth, Boron, Cadmium, Chromium, Copper, Gallium, Germanium, Indium, Iron, Lead, Molybdenum, Nickel, Selenium, Silicon, Silver, Tellurium, Tin, Vanadium, Zinc, Dysprosium, Manganese, Neodymium, Niobium, Praseodymium, Terbium, Titanium, Iridium, Platinum</em>).</li> <li><strong>type: </strong>for Solar defines whether it is <em>roof_mounted</em> or <em>open_field.</em></li> <li><strong>m_int:</strong> The value of material intensity for the corresponding metal in the corresponding technology.</li> <li><strong>classification: </strong>specific classification for Solar, Wind and Heat pumps' technologies: e.g. for solar, defines the type of photovoltaic cell.</li> <li><strong>year: </strong>year in which it is assumed the data was collected, usually defined as the year of publication of the source paper.</li> </ul> <p>This dataset is part of the Master Thesis <em><span>Mining Industry and Energy Transition </span><span>Scenarios for Europe: Promoting a </span><span>Pluriversal Approach, </span></em><span>developed in the TISE (Transition, Innovation, and Sustainability Environments) program with collaboration with the Complexity Science Hub.</span></p>
A Novel Surface Energy Balance Method for Thermal Inertia Studies of Terrestrial Analogs
<p>Thermophysical data collected from Woodhouse Mesa, AZ, USA in May 2021 and Sept 2022</p>
Coastal Upwelling Modulates Winds and Air-Sea Fluxes, Impacting Offshore Wind Energy
<p>Model Output supporting the paper "Coastal Upwelling Modulates Winds and Air-Sea Fluxes, Impacting Offshore Wind Energy"</p> <p>The dataset includes four WRF runs, with upwelling (labeled 'operational') and with upwelling removed (labeled 'experimental'). Two of the runs have parameterized wind turbines, labeled "Fitch". </p> <p>This work was supported by NJ Board of Public Utilities. </p> <p> </p>
2023 Portuguese Energy Tariffs: Retail and Feed-In Tariff Data
<p><strong>Introduction</strong></p> <p>This dataset contains historic data for 20 electric grid consumption and energy injection tariffs in Portugal, during 2023. The data includes both retail tariffs for grid consumption energy and feed-in income tariffs for energy injected back into the grid.</p> <p> </p> <p><strong>Data Overview</strong></p> <p>The dataset comprises the following key elements:</p> <p> 1. Grid Consumption (Retail Tariffs):</p> <ul> <li>Retail tariffs refer to the cost per unit of electricity consumed from the grid.</li> <li>Tariff values are given in euro (€) per kWh and vary across different retailers and hour schemes.</li> </ul> <p> 2. Energy Injection (Feed-in Tariffs):</p> <ul> <li>Feed-in tariffs represent the compensation per unit of electricity generated by solar systems and fed back into the grid.</li> <li>Tariff values are presented in € per kWh and vary across different retailers and conditions.</li> </ul> <p><br><strong>Assumptions</strong></p> <p><br>The retail tariffs in this dataset refer to the consumed energy only and do not include taxes, contracted power costs, and other fees.</p> <p>The dataset starts on Jan 1st, 2023, and ends on Dec 31st, 2023.</p> <p>The dataset uses a 15min time step for each line. The price per hour is applied to each of the four 15 minutes steps.</p> <p><br><strong>Reference data used to create this dataset:</strong></p> <ul> <li>ERSE Regulatory data simulator - https://simulador.precos.erse.pt/eletricidade/</li> <li>2023 Energy Cost Description - https://www.erse.pt/en/activities/market-regulation/tariffs-and-prices-electricity</li> <li>GALP - https://casa.galp.pt/planos-eletricidade-e-gas</li> <li>Eni Plenitude - https://eniplenitude.pt/eletricidade</li> <li>Repsol - https://www.repsol.pt/particulares/casa/eletricidade-gas/</li> <li>EDP - https://www.edp.pt/empresas/energia/tarifarios/</li> </ul> <p> </p> <p>We would be grateful if you could acknowledge the use of this dataset in your publications.</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.