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4,230 results for “Energie”
Life cycle Energy and Ghg of Hydroelectricity of Mekin (HydroMekin) Cameroon
<p>The documents show the calculation of Ghg emission ans Energy used on the life cycle of HydroMekin. Thé software used is eiolca.</p>
Energy Modelling for DRCONGO: Sand files
<p>Dataset used as part of the training organized in April 2023 in Windhoek (Namibia) by CCG (Climate Compatible Growth) in OSeMOSYS (open source energy modeling system) to run 3 scenarios for Democratic Republic of Congo.</p>
Structure Databases: Analysis and Augmentation of Guest-Host Interaction Energy Models as CHA and AEI Zeolite Crystallization Phase Predictors
<p>Structures and energies associated with the paper: Analysis and Augmentation of Guest-Host Interaction Energy Models as CHA and AEI Zeolite Crystallization Phase Predictors</p>
Data for: Hunting behavior of a solitary sailfish Istiophorus platypterus and estimated energy gain after prey capture
<p>Foraging behavior and interaction with prey is an integral component of the niche of predators but is inherently difficult to observe for highly mobile animals in the marine environment. Billfish have been described as 'energy speculators', expending a large amount of energy foraging, expecting to offset high costs with periodic high energetic gain. Surface-based group feeding of sailfish, <em>Istiophorus</em> <em>platypterus</em>, is commonly observed, yet sailfish are believed to be solitary roaming predators with high metabolic requirements, suggesting that individual foraging also represents a major component of predator-prey interactions. Here, we use biologging data and video to examine daily activity levels and foraging behavior, estimate metabolic costs, and document a solitary predation event. We estimate a median active metabolic rate of 218.9 ± 70.5 mgO<sub>2</sub> kg<sup>-1</sup> h<sup>-1</sup> which increased to 518.8 ± 586.3 mgO<sub>2</sub> kg<sup>-1</sup> h<sup>-1</sup> during prey pursuit. Assuming a successful predation, we estimate a daily net energy gain of 2.4 MJ (5.1 MJ acquired, 2.7 MJ expended), supporting the energy speculator model. While group hunting may be a common activity used by sailfish to acquire energy, our calculations indicate that opportunistic individual foraging events offer a net energy return that contributes to the fitness of these highly mobile predators. </p>
Energy storage [section 1.2]
<p>Battery performance and durability sizing tool</p>
A prototyping software engineering approach for designing and implementing model-based cloud mobile application for rationalized energy consumption
<p>The dataset used in this study comprises four files containing household consumers' energy consumption records. These records are collected at hourly intervals, providing detailed information on the energy usage patterns of the households. The dataset serves as a valuable resource for analyzing energy consumption trends, developing energy management strategies, and exploring the potential for energy efficiency improvements in residential settings.</p> <p> </p> <p> </p> <p> </p> <p> </p>
Dataset for Energy Resource Management Considering Participation in the Wholesale Day-Ahead Market
<p>This release is associated with a paper entitled "A Novel Framework for the Day-Ahead Market Clearing Process Featuring the Participation of Distribution System Operators and a Hybrid Pricing Mechanism".</p> <p>In this document, we provide the mathematical manipulation employed to linearize an MPEC problem that models the market-clearing process considering the participation of generation companies and distribution operators in the day-ahead market. Additionally, we provide a numerical example of the pricing mechanism designed for solving the optimization problem. </p> <p>Finally, we include information regarding the transmission and distribution power systems adopted to present the results shown in the paper. These systems are modified versions of the IEEE <a href="https://icseg.iti.illinois.edu/ieee-14-bus-system/">14-bus</a> and the IEEE <a href="https://cmte.ieee.org/pes-testfeeders/resources/">34-bus</a>, respectively. Three renewable non-dispatchable generators were added to the original 14-bus system, while 5 dispatchable generators and 3 non-dispatchable generators were added to the distribution system. 13 load shapes were considered as well as 5 scenarios for each uncertain variable (wind velocity, solar irradiance, distribution load oscillation, and transmission load oscillation). The data regarding the costs and physical parameters of each of the power system's elements are provided in this document.</p>
Supplementary Documents for Manuscript 'Investigating the use of two-dimensional OSL laser scanning instruments and energy-dispersive x-ray spectroscopy for OSL exposure dating'
<p>Appendix Data for Manuscript 'Investigating the use of two-dimensional OSL laser scanning instruments and energy-dispersive x-ray spectroscopy for OSL exposure dating' - with added "Readme's" for relevant databases.</p>
Parametrising non-linear dark energy perturbations
<p>This directory contains all the necessary data, codes, and notebooks to reproduce the results of the paper titled "Parametrising non-linear dark energy perturbations" (<a href="https://arxiv.org/abs/1910.01105">https://arxiv.org/abs/1910.01105</a>).</p> <p><br> This paper has also been published JCAP and can be found in Volume 2020, April 2020, which can be accessed at this link: <a href="https://iopscience.iop.org/article/10.1088/1475-7516/2020/04/039">https://iopscience.iop.org/article/10.1088/1475-7516/2020/04/039</a>.</p> <p>Directories</p> <ul> <li><strong>codes</strong>: This directory contains k-evolution code used to generate and post-process the simulation data.</li> <li><strong>notebooks_data</strong>: This directory includes the data and jupyter notebooks to reproduce the figures presented in the paper.</li> <li><strong>supplementary_materials</strong>: This directory contains the supplementary materials associated with the project.</li> </ul> <p>How to Use</p> <ol> <li>Download the files to your local machine.</li> <li>Navigate to the directory where the files are saved.</li> <li>Install the necessary packages</li> <li>Navigate to the "<strong>notebooks_data</strong>" directory and open the Jupyter notebooks in your preferred environment.</li> <li>Run the cells in the notebooks to reproduce the figures.</li> <li>Navigate to the "<strong>codes</strong>" directory and use the appropriate code to generate and post-process the simulation data.</li> <li>Navigate to the "<strong>notebooks_data</strong>" directory to access the simulation data.</li> </ol> <p><br> If you have any feedback or request feel free to email farbod.hassani@gmail.com</p>
Clustering dark energy imprints on cosmological observables of the gravitational field
<p>This file contains all the necessary data, codes, and notebooks to reproduce the results of the paper titled "Clustering dark energy imprints on cosmological observables of the gravitational field" (<a href="https://arxiv.org/abs/2007.04968">https://arxiv.org/abs/2007.04968</a>).</p> <p><br> This paper has also been published in MNRAS which can be accessed at this link: <a href="https://doi.org/10.1093/mnras/staa3589">https://doi.org/10.1093/mnras/staa3589</a>.</p> <p>Directories</p> <ul> <li><strong>codes</strong>: This directory includes the codes to generate and post-process the simulation data.</li> <li><strong>data</strong>: This directory contains the data generated as part of the project.</li> <li><strong>jupyter_notebooks</strong>: This directory includes Jupyter notebooks to reproduce the figures presented in the paper.</li> <li><strong>supplementary_materials</strong>: This directory contains the supplementary materials associated with the project.</li> </ul> <p>How to Use</p> <ol> <li>Download the files to your local machine.</li> <li>Navigate to the directory where the files are saved.</li> <li>Install the necessary packages</li> <li>Navigate to the "<strong>jupyter_notebooks</strong>" directory and open the Jupyter notebooks in your preferred environment.</li> <li>Run the cells in the notebooks to reproduce the figures.</li> <li>Navigate to the "<strong>codes</strong>" directory and use the appropriate code to generate and post-process the simulation data.</li> <li>Navigate to the "<strong>data</strong>" directory to access the simulation data.</li> </ol> <p><br> If you have any feedback or request feel free to email farbod.hassani@gmail.com</p>
Wyniki zawarte w Energy-Efficient OFDM Radio Resource Allocation Optimization With Computational Awareness: A Survey
<p>This resource contains the results included in Energy-Efficient OFDM Radio Resource Allocation Optimization With Computational Awareness: A Survey</p>
Input data for executing SELARU 1.0 application for Indonesia energy system optimization case study
<p>Input data for SELARU modelling framework application in a case study to demonstrate the impact of spatially explicit representation for energy system optimization, particularly in depicting incremental grid expansion and its contribution to accumulation of economies of scale. The case study focuses on the electricity sector of Indonesia to represent a highly complex and geographically diverse energy system. SELARU modelling framework is applied using three spatial representations: single-node model, low resolution multi-node model and high resolution multi-node model. Detailed information about the spatial representations can be found in <a href="https://doi.org/10.31219/osf.io/aw4bd">https://doi.org/10.31219/osf.io/aw4bd</a>.</p>
Can industrial transfer lead to carbon transfer in China's high energy consumption industry?
<p>The above is the data for all images in the manuscript, mainly including: Carbon emissions and carbon transfer of energy-intensive industries in eight regions、Path of energy-intensive industrial transfer and carbon transfer in 2002, 2007, 2012 and 2017、MGWR regression results in 2002, 2007, 2012 and 2017</p>
Dataset for the paper "Deblending and Purification of Hydrogen from Natural Gas Mixtures using the Electrochemical Hydrogen Pump", International Journal of Hydrogen Energy, doi.org/10.1016/j.ijhydene.2023.05.065
<p>The data in this spreadsheet was used to produce the figures in the paper</p> <p>Authors:C Jackson, GT Smith, ARJ Kucernak</p> <p>Title:Deblending and Purification of Hydrogen from Natural Gas Mixtures using the Electrochemical Hydrogen Pump</p> <p>Journal:International Journal of Hydrogen Energy</p> <p>DOI:https://doi.org/10.1016/j.ijhydene.2023.05.065</p> <p>Please cite the above reference if you wish to use this data</p> <p>DOI of data:10.5281/zenodo.7963348</p>
Rysunki dla Communication and Computing Task Allocation for Energy-Efficient Fog Networks
<p>This resource contains the figures for Communication and Computing Task Allocation for Energy-Efficient Fog Networks</p>
Wyniki symulacji dla Communication and Computing Task Allocation for Energy-Efficient Fog Networks
<p>This resource contains simulation results for Communication and Computing Task Allocation for Energy-Efficient Fog Networks</p>
The spectral energy distributions of classical Cepheids in the Magellanic Clouds
<p>The spectral energy distributions (SEDs) of a sample of 142 LMC and 77 SMC fundamental mode classical Cepheids (CCs) were constructed using photometric data in the literature.<br> The sample was build from stars that have a metallicity determination from high-resolution spectroscopy,<br> have been used in Baade-Wesselink type of analysis, have a radial velocity curve published in {\it Gaia} DR3, have Walraven photometry, or have their light- and radial-velocity curves modelled by pulsation codes.<br> <br> The SEDs were fitted with stellar photosphere models to derive the best-fitting luminosity and effective temperature.<br> Distance and reddening were taken from the literature.<br> <br> Only one star with a significant infrared (IR) excess was found in the LMC and none in the SMC, contrary to earlier work on the Milky Way (MW) suggesting that IR excess may be more prominent in MW cepheids than in the Magellanic Clouds.</p> <p>The stars were plotted in a Hertzsprung-Russell diagram (HRD) and compared<br> to evolutionary tracks for CCs and to theoretical instability strips.<br> For the large majority of stars, the position in the HRD is consistent with the instability strip.<br> <br> Period-luminosity ($PL$) and period-radius relations are derived and compared to these relations in the MW.</p>
SESMG Model Definitions: "Potential-Risk and No-Regret Options for Urban Energy System Design - A Sensitivity Analysis"
<p>Each of the files is one SESMG model definition used for the study "Potential-Risk and No-Regret Options for Urban Energy System Design - A Sensitivity Analysis". Further information can be found in this publication. The file names indicate to which sensitivity analysis of the study the individual model definition belongs to. Used acronyms: "ng" = natural gas.</p>
SESMG Model Results: "Potential-Risk and No-Regret Options for Urban Energy System Design - A Sensitivity Analysis"
<p>Each of the folders contains SESMG results for a sensitivity analysis of the study "Potential-Risk and No-Regret Options for Urban Energy System Design - A Sensitivity Analysis". More information can be found in this publication. Each folder contains two subfolders. The "cost-minimum" subfolder contains the results for financially optimized systems, and the "emission-minimum" subfolder contains the results for GHG emission-optimized systems. Within these subfolders, the results for different gradations of the respective sensitivity parameters are stored in separate sub-subfolders. The 01_reference_total_ghg_emissions folder has a slightly different structure. Since the results are not separated into financially and emissions-optimized scenarios, the results of different gradations are stored directly in the main folder of this sensitivity analysis.</p>
Supporting Information for "Broadening the scope of binding free energy calculations using a Separated Topologies approach"
<p>Supporting Information for the publication "Broadening the scope of binding free energy calculations using a Separated Topologies approach" including input files for the datasets used in that study.</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.