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4,230 results for “Energie”
Data used in the publication "High Energy Emissions induced by air density fluctuations of discharges"
<p>This data was used to generate the Figures in the publication "High Energy Emissions induced by air density fluctuations of discharges".</p>
Additional material for the Wind Energy manuscript "An engineering approach for the estimation of slewing bearing stiffness in Wind Turbine Generators"
<p>This documment compiles the additional material for the manuscript “An engineering approach for the estimation of slewing bearing stiffness in Wind Turbine Generators”. The document shows the results for all the considered design points, comparing the the proposed approach with the Finite Element model.</p>
Data file for paper: Rubio Garcia, Javier; Kucernak, Anthony; Zhao, Dong; Li, Danlei; Fahy, Kieran; Yufit, Vladimir; Brandon, Nigel; Gomez-Gonzalez, Miguel, "Hydrogen/manganese hybrid redox flow battery", Journal of Physics: Energy, 2018
<p>The data in this spreadsheet was used to produce the figures in the paper</p> <p>Rubio Garcia, Javier; Kucernak, Anthony; Zhao, Dong; Li, Danlei; Fahy, Kieran; Yufit, Vladimir; Brandon, Nigel; Gomez-Gonzalez, Miguel, "Hydrogen/manganese hybrid redox flow battery", Journal of Physics: Energy, 2018</p> <p>DOI: 10.1088/2515-7655/aaee17 </p> <p>Please cite the above reference if you wish to use this data</p>
Probing the effect of cadence on the estimates of photospheric energy and helicity injections in eruptive active region NOAA AR 11158 – movies of the data series
<p>This dataset contains movies of all photospheric data series used in the article Lumme et al. (2019) “Probing the effect of cadence on the estimates of photospheric energy and helicity injections in eruptive active region NOAA AR 11158”, submitted. Movies track the evolution of several photospheric quantities in the NOAA active region 11158 from the emergence of the active region well beyond the time of the strongest eruptive activity in the region.</p> <p>The tracked quantities include: magnetic, plasma velocity and electric fields, as well as the vertical component of the Poynting vector and the relative helicity flux density. All quantities are given in several spatial and temporal resolutions: the cadences range from 2.25 minutes to 24 hours, and the spatial resolution is either the maximum resolution of the SDO/HMI instrument (0.03 deg in heliographic coordinates, 364 km on the Sun) or 15 times lower (0.45 deg, 5470 km). Since Lumme et al. (2019) employs three electric field inversion methods, PDFI, raw DAVE4VM and inductive DAVE4VM methods, there are three versions of the electric field maps as well as the derivative quantities Poynting and helicity fluxes for each cadence and spatial resolution.</p> <p>All series, except for the magnetic field and LOS plasma velocity series, are plotted only at the central strong field parts of the active region. The temporal extent of the series used in the paper is Feb 10 14:00 – Feb 17 00:00, 2011. However, some of the movies span only the the most interesting part of the evolution Feb 13 00:00 onward, after which the active region started to exhibit strong flux emergence and energy and helicity fluxes.</p> <p>All movie files in the dataset are given using the following naming convention: “{quantity}_{method}_{cadence}_{res_info}.avi”, where “quantity” specifies the plotted quantity (e.g. horizontal electric field “Eh”), “method” specifies the method used to derive the quantity (e.g., “PDFI” for electric field; if empty, no method is specified), “cadence” specifies the cadence of the data either in minutes or hours (e.g. “2.25_min” or “12_h”), and “res_info” specifies the spatial resolution (if empty then, the data is given in full resolution, otherwise “res_info” is “rebin_15x” corresponding to the 15 lower spatial resolution).</p> <p> Movies of the following data series are included for all cadences and spatial resolutions.:<br> - All three components of the photospheric magnetic field “(Bx,By,Bz)” in a Cartesian basis where the solar surface is approximated flat via Mercator projection, as well as the vertical component of the magnetic field “Bz” and the LOS component of the plasma velocity “Vlos” (i.e. Dopplergram velocity) in the same system (note that the unlike in the usual convention, the LOS plasma velocity is negative for motions away from the observer). These data series are given only for two cadences 2.25- and 12-minutes in full spatial resolution, since rest of the cadences are created by sampling the 2.25-minute data. Data in the 15 times lower spatial resolution are given only at a cadence of 2.025 hours, as it is the highest cadence for which the 15 times lower resolution was used (see Lumme et al., 2019 for details).<br> - Horizontal photospheric plasma velocity "Vh = (Vx,Vy)" estimates derived using two optical flow methods FLCT (Fourier Local Correlation Tracking) and DAVE4VM (Differential Affine Velocity Estimator For Vector Magnetograms) plotted as arrows above the Bz component of the magnetic field.<br> - Horizontal photospheric electric field “Eh = (Ex,Ey)” estimates derived using three methods, PDFI, raw DAVE4VM and the inductive DAVE4VM method, plotted as arrows above the Bz component of the magnetic field.<br> - Vertical component of the Poynting vector “Sz” derived for each electric field estimate.<br> - Photospheric relative helicity flux density (denoted by “dHR/dt”) derived for each electric field estimate.</p> <p>January 16, 2019<br> Erkka Lumme<br> Doctoral student, MSc<br> Department of Physics<br> erkka.lumme@helsinki.fi<br> P.O. Box 68<br> FI-00014 University of Helsinki</p>
Input files and data for paper "Modules for Experiments in Stellar Astrophysics (MESA): Pulsating Variable Stars, Rotation, Convective Boundaries, and Energy Conservation"
<p>This entry contains input files to reproduce the results of the paper:</p> <p>Modules for Experiments in Stellar Astrophysics (MESA): Pulsating Variable Stars, Rotation, Convective Boundaries, and Energy Conservation.</p> <p>Each zip archive corresponds to a section of the paper, and includes README files in ASCII format with a description. Raw output data and plotting tools are also provided for some of the results.</p>
Fundamentals of cathodoluminescence in a STEM: The impact of sample geometry and electron beam energy on light emission of semiconductors_experimental dataset
<p>This dataset contains the raw underlying data for the paper "Fundamentals of cathodoluminescence in a STEM: The impact of sample geometry and electron beam energy on light emission of semiconductors" available in open acess under DOI 10.5281/zenodo.2668222. The archive contains experimental files titled with references to the figures as they appear in the paper. </p>
Data for Energy Landscapes for Digital Alchemy
<p>Data relating to the paper 'Energy Landscapes for Digital Alchemy'. See the README for details.</p>
Dataset for: "On the role of electric vehicles towards low-carbon energy systems: Italy and Germany in comparison"
<p>Input files for EnergyPlan models (Italy and Germany energy systems in 2016).</p> <p>See EnergyPlan website (<a href="https://www.energyplan.eu/">https://www.energyplan.eu/</a>) for instructions.</p>
Results from the Open Call: How Citizens can participate in solar energy research?
<p>Results from the public consultation that GRECO Project (H2020-787289) launched in November 2018. These responses were collected from around 70 research teams along the world. The document is structured in three sections to think beyond researchers' needs. Any kind of successful collaboration implies that both parts must have a benefit and the consultation was also focused on such perspective.</p>
IEEE-30 energy system data of multi-period market with intertemporal constraints
<p>This is the dataset that is used for the original article: "Locational marginal pricing in multi-period AC OPF environment"</p> <p>The dataset consists of the following files</p> <ul> <li>Case1.zip</li> <li>Case2.zip</li> <li>Case3.zip</li> <li>case_modifications.py</li> <li>data_spec.py</li> <li>OPF_formulation.pdf</li> </ul> <p>Multiperiod AC OPF is given in OPF_formulation.pdf. Modifications of traditional IEEE 30-node case are given in case_modifications.py</p> <p>The case files incorporate input and output multiperiod AC OPF and LMP decomposition data in csv and pickle formats. Data structure of case files is given in data_spec.py.</p> <p>For python users pickle files are given. Nevertheless, python environment is not required. Specification can be read as a text file. All necessary data are repeated in csv format.</p> <p>Step 1 are to define LMPs of limited energy resources or storage resources that are formed by actual marginal resources from all time periods (first LMP definition in fig. 6 in the paper).</p> <p>Step 2 are to define all other LMPs at price-taking nodes (second LMP definition in fig. 6 in the paper).</p> <p>The following interrelation between Lagrange multipliers, LMP components, and price-bonding factors holds true:</p> <pre>assert np.max(np.abs(output_ramp.sensitivities.dot(output_ramp.offer_gen_data.price).tolist() - output_ramp.ramping_gen_data.price)) < 1e-2 if output_pt_step1.components.shape[0]: step1_pf_filter = (~output_pf.is_limited_energy) & (~output_pf.is_storage) assert np.max(np.abs(output_pt_step1.components.node_price - (output_pt_step1.components.f + output_pt_step1.components.tc_sum + output_pt_step1.components.vc_sum))) < 1e-2 assert (output_pt_step1.components.f - output_pt_step1.w_f.dot(output_pf.node_price[step1_pf_filter])).abs().max() < 1e-2 assert (output_pt_step1.components.tc_sum - pd.concat( (w.dot(output_pf.offer_price[step1_pf_filter]) for w in output_pt_step1.w_tc_list), axis=1 ).sum(axis=1)).abs().max() < 1e-2 assert np.max(np.abs(output_pt_step2.components.node_price - (output_pt_step2.components.f + output_pt_step2.components.tc_sum + output_pt_step2.components.vc_sum))) < 1e-2 assert (output_pt_step2.components.f - output_pt_step2.w_f.dot(output_pf.node_price)).abs().max() < 1e-2 assert (output_pt_step2.components.tc_sum - pd.concat( (w.dot(output_pf.offer_price) for w in output_pt_step2.w_tc_list), axis=1 ).sum(axis=1) ).abs().max() < 1e-2</pre> <p> </p>
Dataset for solar energy intake correlated with weather forecast data
<p>This is the first release.</p>
Finite element analysis results from simulation of fusion energy heat exchange component: hybrid CAD/IBSim model including a graphite foam interlayer
<p>Temperature profile data from a finite element analysis of a conceptual design for a fusion energy heat exchange component (monoblock). The mesh is a hybrid from a computer aided design (CAD) drawing for the pipe and armour and IBSim for the interlayer. The IBSim interlayer is generated directly from a 3D volumetric image of a graphite foam block (KFoam). The 3D image was generated with an X-ray tomography scan performed by Dr Llion Evans with Manchester X-ray Imaging Facility equipment, which was funded in part by the EPSRC (grants EP/F007906/1, EP/F001452/1 and EP/I02249X/1). Conversion of the data to FE mesh was achieved using ScanIP, part of the Simpleware suite of programmes, version 7 (Synopsys Inc., Mountain View, CA, USA).</p> <p>The mesh used for the analysis is available as a separate dataset:</p> <p><a href="https://doi.org/10.5281/zenodo.3522319">https://doi.org/10.5281/zenodo.3522319</a></p> <p>This data was used originally for the following publications (please cite if re-using the data):</p> <p>Ll.M. Evans, L. Margetts, P.D. Lee, C.A.M. Butler, E. Surrey, “Image based in silico characterisation of the effective thermal properties of a graphite foam”, Carbon, Vol. 143, pp. 542-558, 2018. <a href="https://doi.org/10.1016/j.carbon.2018.10.031">https://doi.org/10.1016/j.carbon.2018.10.031</a></p> <p>Ll.M. Evans, L. Margetts, P.D. Lee, C.A.M. Butler, E. Surrey, “Improving modelling of complex geometries in novel materials using 3D imaging”, Proceedings of NEA International Workshop on Structural Materials for Innovative Nuclear Systems, Manchester, UK, July 2016. <a href="https://www.oecd-nea.org/science/smins4/documents/P1-18_LlME_SMINS4_paper_reviewed.pdf">https://www.oecd-nea.org/science/smins4/documents/P1-18_LlME_SMINS4_paper_reviewed.pdf</a></p>
Agroecosystem energy fluxes in Austria 1830-2010
<p>Agroecosystem energy fluxes in Austria 1830-2010 - Online database<br> <br> Projects<br> "Hidden Emissions of Forest Transitions", European Research Council ERC-2017-STG 757995<br> "Sustainable Farm Systems", Social Sciences and Humanities Research Council Partnership Grant 895-2011-1020<br> <br> Contact<br> Simone Gingrich: simone.gingrich@boku.ac.at<br> Institute of Social Ecology, Department of Economics and Social Sciences (WiSo), University of Natural Resources & Life Sciences, Vienna (BOKU)<br> Schottenfeldgasse 29, 1070 Vienna, Austria<br> <br> URL<br> http://www.wiso.boku.ac.at/sec/data-download/<br> <br> Quote as<br> Gingrich, S., Krausmann, F., 2018. At the core of the socio-ecological transition: Agroecosystem energy fluxes in Austria 1830–2010. Science of The Total Environment 645, 119–129. https://doi.org/10.1016/j.scitotenv.2018.07.074<br> <br> For definitions and accounting procedures, see publication</p>
Drivers of renewable energy promotion in the EU-27, 2013-2021
<p><span>27 EU member states data on economic, geographical, environmental and structural indicators for the period 2013-2021</span></p>
data for "Observations of significant ion energy outflows associated with cusp ion outflows and the role of Poynting flux as an energy source"
Open the record for dataset details and reuse information.
Energy-dependence of the response of X-ray multimeter for mammography- radiation qualities - Supplemental Data
<p>Supplementary data to the manuscript “Energy-dependence of the response of X-ray multimeter for mammography- radiation qualities” by Elisabeth Salomon and Paula Toroi</p>
Dataset for "Temperature dependence of Young's modulus, damping and dilatation during repeated thermal cycling of silica refractories for high-temperature thermal energy storage (TES) "
Open the record for dataset details and reuse information.
Protein structure data for "AI-predicted protein deformation encodes energy landscape perturbation"
<p>AF2-predicted protein structures of WT and mutant proteins that have corresponding ddG measurements in the ThermoMutDB database of protein mutant stability measurements. PDB structures are compressed using <a href="https://github.com/steineggerlab/foldcomp/">FoldComp</a>, and saved in "structures.zip".</p> <p>Summary of the final dataset and results can be found in "results_summary.pkl".</p> <p>Code used to plot figures can be found in "code4figs.zip".</p>
Water, energy and carbon fluxes and ancillary meteorological and remote sensing measurements at Encanto Golf Course, Phoenix, Arizona during 2019-2020
<p>Water, energy and carbon fluxes as wells as ancillary meteorological and NDVI measurements from the Encanto Park Golf Course in Phoenix, AZ. Measurements were done from March 2019 to March 2020.</p> <p>The database includes three files:</p> <ul> <li>Encanto_AZMET.xlsx, that include hourly values and daily average meteorological variables (T<sub>air</sub>, RH, VPD, R<sub>s</sub>, T<sub>soil</sub>, etc.) obtained from the Phoenix Encanto meteorological station of The Arizona Meteorological Network (AZMET). Please refer to https://cals.arizona.edu/AZMET/15.htm and the spreadsheet ReadMe within the excel file for additional information.</li> <li>Encanto_EddyCovariance.xlsx includes the half-hour water, energy and carbon fluxes obtained using an eddy covariance (EC) system. The file also includes half-hour ancillary meteorological and soil measurements obtained from the EC tower. Water, energy and carbon fluxes were processed using the software EddyPro 7.0. For more information, please refer to Vivoni <em>et al</em> (2020). Note: This new version includes Eddy Covariance and Met data up to July, 2020.</li> <li>Encanto_NDVI.xlsx includes the average NDVI for the net radiometer footprint installed in the EC tower. NDVI was obtained from 4-band PlanetScope scene images obtained from Planet website (www.planet.com). For more information, please refer to Vivoni <em>et al</em> (2020).</li> </ul> <p>The use of the datasets requires the citation of the next paper:</p> <ul> <li>Vivoni, E. R., Kindler, M., Wang, Z., and Perez-Ruiz, E. R. 2020.Abiotic Mechanisms Drive Enhanced Evaporative Losses under Urban Oasis Conditions . <em>Geophysical Research Letters</em>. 47: e2020GL090123. <a href="https://doi.org/10.1029/2020GL090123">https://doi.org/10.1029/2020GL090123</a></li> </ul>
Data and code for "Design Principles for Energy Transfer in the Photosystem II Supercomplex from Kinetic Transition Networks"
<p>Data and code for "Design Principles for Energy Transfer in the Photosystem II Supercomplex from Kinetic Transition Networks".</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.