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858 results for “Energy Data”
Data and code repository for Science Advances submission: Uncovering the biological basis of control energy: structural and metabolic correlates of energy inefficiency in temporal lobe epilepsy
<p>Data and codes related to the findings reported in the manuscript, "Uncovering the biological basis of control energy: structural and metabolic correlates of energy inefficiency in temporal lobe epilepsy", are deposited. Please refer to the notes located within each folder for further descriptions.</p>
Data, scripts, and figures of the article: Processing weights of chickens determined by Dual-Energy X-Ray Absorptiometry. 3. Validation of prediction models
<p>This data set contains the data, JMP scripts, and figures of the article titled "Processing weights of chickens determined by Dual-Energy X-Ray Absorptiometry. 3. Validation of prediction models" to be published in the journal Animal - Open Space.</p>
Excitation energy transfer and vibronic coherence in intact phycobilisomes — multidimensional electronic spectroscopy data set and MATLAB and Julia analysis code
<p>Data sets used in the article "Excitation energy transfer and vibronic coherence in intact phycobilisomes" by Sil et al. The phycobilisomes were isolated from the short-filament mutant (SF33) of <em>Fremyella diplosiphon</em> UTEX 481 (also known as <em>Tolypothrix</em> sp. PCC 7601). Multidimensional electronic spectroscopy was performed with 6.7 fs mid-visible pulses (520–700 nm) using a pump–probe optical configuration using adaptive pulse shaping techniques. In addition to the full set of two-dimensional spectra and analysis files generated using global and target modeling and analysis of coherences (3DES oscillation maps), we provide here a linear absorption spectrum with phycobiliprotein component analysis as well as a set of 2D excitation–emission fluorescence spectra of intact and broken phycobilisome preparations. </p> <p>Sil, S.; Tilluck, R. W.; Mohan TM, N.; Leslie, C. H.; Rose, J. B.; Domínguez-Martín, M. A.; Lou, W.; Kerfeld, C. A.; Beck, W. F. Excitation energy transfer and vibronic coherence in intact phycobilisomes. Nat. Chem. (2022), DOI: 10.1038/s41557-022-01026-8.</p> <p><a href="https://urldefense.com/v3/__https://www.nature.com/articles/s41557-022-01026-8__;!!HXCxUKc!yaVwTZFk8T-j3ROhygpOGW5Xy_E2wQvf-QgNGr9FZZbp4oNpfp_ZmhkdWYLdg2mKSDP8yYrNAZs$">https://www.nature.com/articles/s41557-022-01026-8</a></p> <p> </p> <p> </p>
Data for FEgrow: An Open-Source Molecular Builder and Free Energy Preparation Workflow
<p>Data illustrating the use of de novo design in building and scoring protein-ligand complexes.</p> <p>This is relationship to the FEgrow publication with the intiial preprint here: <br> https://chemrxiv.org/engage/chemrxiv/article-details/6287bb98a42e9c78d34769f6<br> </p> <p>The FEgrow software snapshot used can be found here: https://zenodo.org/record/7105647#.YzFwINLMIUE</p>
Research data for "Exploring the configurational space of amorphous graphene with machine-learned atomic energies"
<p>This dataset supports the paper: "Exploring the configurational space of amorphous graphene with machine-learned atomic energies" (<a href="https://doi.org/10.1039/D2SC04326B">https://doi.org/10.1039/D2SC04326B</a>).</p> <p>Trajectory data for the 200-atom structures (Fig. 3) and the final configurations for the 612-atom structures as well as the GAP-17-optimised 610-atom structure from Toh et al are provided (Fig. 4). Additionally, the structures used for data analysis in Fig. 5 are given.</p> <p>The files are in extended xyz (.xyz) format and contain the raw data for coordinates, forces, and atomic energies (labelled 'c_1'). The files also contain the atomic energies relative to pristine graphene, labelled "Energy_per_atom", and the locally averaged energy relative to pristine graphene, labelled "NN_Energy_per_atom". Topological information is included at the end of the .xyz file for the 612-atom structures ('fig_4'/) and for the structures in 'fig_5/'.</p> <p>All raw atomic energies were computed using LAMMPS default settings and were output with six significant figures, with the exception of the Toh et al. structure (for which ASE was used, outputting a higher number of significant figures). </p> <p>The data can be read using, for example, the Atomic Simulation Environment (ASE), or visualised using Ovito.</p> <p> </p>
Data and R script for 'Early-life begging effort reduces adult body mass but strengthens behavioural defence of the rate of energy intake in European starlings (Sturnus vulgaris)'
<p>Data files and R script for Dunn et al. "Early-life begging effort reduces adult body mass but strengthens behavioural defence of the rate of energy intake in European starlings (<em>Sturnus vulgaris</em>)"</p> <p>Includes a single R script that produces all the analyses in the paper. The script makes use of three different .csv data files.</p>
Supplementary Data: Full Results: Synergies of sector coupling and transmission extension in a cost-optimised, highly renewable European energy system
<p>Supplementary Data</p> <p><a href="https://arxiv.org/abs/1801.05290"><strong>Synergies of sector coupling and transmission extension in a cost-optimised, highly renewable European energy system</strong></a></p> <p>Authors: T. Brown, D. Schlachtberger, A. Kies, S. Schramm, M. Greiner</p> <p><a href="https://arxiv.org/abs/1801.05290">arXiv:1801.05290</a></p> <p>The files in this record contain the full output data from each of the scenarios considered in the above publication. They also include the post-processed input data, which might be useful if you want to rerun the scenarios with only small changes to the input data.</p> <p>The scripts to build the model, input data and result summaries can be found in a <a href="https://zenodo.org/record/1146665">companion Zenodo repository</a>. (The supplementary data was split because of the size of the full results.)</p> <p>For each scenario, there is a <a href="https://github.com/PyPSA/PyPSA">PyPSA</a> network file in <a href="https://en.wikipedia.org/wiki/Hierarchical_Data_Format">HDF5 format</a> and a CSV of shadow prices.</p> <p>To read in a network file do:</p> <pre><code class="language-python">import pypsa network = pypsa.Network("network_file_name.h5")</code></pre> <p>All data is released under the <a href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International Licence</a> (CC BY 4.0).</p>
Data for "Sintering activation energies of anisotropic layered and particle alumina/zirconia-based composites and their mechanical response"
<p>The dataset contains several folders that provide open data files for the manuscript "Sintering activation energies of anisotropic layered and particle alumina/zirconia-based composites and their mechanical response".</p>
Data files for "Quantifying the global climate feedback from energy-based adaptation"
<p>Data files for "Quantifying the global climate feedback from energy-based adaptation".</p> <p>Findings of the paper can be replicated using these data files, along with code at https://github.com/xabajian/ACDM_Climate_Adaptation_Feedback.</p> <p>Please contact Alexander Abajian <xander.abajian@gmail.com> with any questions regarding the enclosed files.</p> <p> </p> <p><strong>Attribution:</strong></p> <p><br>Some processed data contain excerpts of Non-Creative Commons Material as defined by the International Energy Agency (IEA -- see their terms of use at `https://www.iea.org/terms/terms-of-use-for-non-cc-material'). The emissions factors we use in our analysis are generated using IEA datasets. These data are aggregates of the underlying country-by-fuel level emissions factors and as presented contain only insubstantial amounts of the Non-CC Material. We attest they cannot be used to reconstruct individual data points in the original dataset. The factors we produce are attributable to the following two sources: </p> <p>IEA. Emissions factors. Tech. Rep., International Energy Agency (IEA 2021). URL https://www.iea.org/data-and-statistics/data-product/910emissions-factors-2021. All Rights Reserved.</p> <p>IEA. World energy balances 2021. Tech. Rep., International Energy Agency (IEA) (2022). URL https://www.iea.org/data-and-statistics/data-product/world-energy-balances. All Rights Reserved.</p> <p> </p>
Data from: Energy efficient homes for rodent control across cityscapes
<p>Cities spend millions of dollars on rodent mitigation to reduce public health risks. Despite these efforts, infestations often remain high. Rodents thrive in the built environment in part due to reduced natural predators and the exploitation of garbage. Though sanitation and greenspace are important factors in rodent mitigation, more complex governance and action are needed. Urban rodents are dynamic and commensal in nature, so understanding the influence of prolific urban features, like building attributes, warrants scrutiny and additionally intersects mitigation strategies with stakeholders at a localized level. Here, we model how residential structures' efficiency influences urban rodent populations. To do so, we created an agent-based model using characteristics of urban brown rats and their natural predator, red foxes, based on three distinct neighborhoods in Philadelphia, Pennsylvania. We varied whether retrofitting occurred and its duration as well as the percent of initial energy-efficient homes in each neighborhood. We found that initial housing conditions, retrofitting, and the duration of retrofitting all significantly reduced final rodent populations. However, retrofitting was most effective in reducing rodent populations in neighborhoods with extensive park access and low commercial activity. Additionally, across neighborhoods, single large efficiency initiatives showed greater potential for rodent reduction. Lastly, we show that the costs of large-scale retrofitting schemes are comparable to ten-year public health spending, demonstrating that retrofitting may have the potential to offset near-term costs. Our results showcase how system-view investments in integrated pest management can lead to sustained rodent pest mitigation and advance sustainable development goals, infrastructure innovation (Goal #9), reduced inequalities (Goal #10), and sustainable cities and communities (Goal #11). </p>
"Demand-side policies for power generation in response to the energy crisis: A model analysis for Italy", scripts and data
<p>This repository contains the data, scripts and results for the paper "Demand-side policies for power generation in response to the energy crisis: A model analysis for Italy", https://doi.org/10.1016/j.esr.2024.101329.</p> <p>Results in the paper are divided into three sections, corresponding to the numbers of the folders inside this dataset. They are described as follows:</p> <p>1 - EU policy impact: What is the impact on the Italian electricity of the european proposal of cutting power demand and shifting it during peak hours on gas consumption, system costs and emissions?</p> <p>2 - Gas cost sensitivity: Which would be Italy’s most convenient power system considering different gas prices?</p> <p>3 - DSM in mitigation: What could be the role of demand side measures in power systems with a high penetration of RES?</p>
Data for: Environment-dependent relationships between corticosterone and energy expenditure during reproduction: insights from seabirds in the context of climate change
<p>We studied the relationship between baseline levels of the steroid hormone corticosterone and daily energy expenditure (DEE) in the little auk (<em>Alle alle</em>), an Arctic sea bird that is experiencing mounting energetic challenges due to climate change. We specifically investigated the hypothesis that there might be environment-dependent relationships between baseline corticosterone, DEE, time activity budgets, diving behavior and fitness-related traits (chick provisioning rate, adult body condition). Furthermore, we also examined whether mercury (Hg) contamination might interfere with corticosterone production, and hence potentially the capacity to upregulate DEE. In addition, we performed a phylogenetically controlled analysis across breeding seabird species to assess the relationship between baseline corticosterone and DEE, which we estimated via <span>a model derived from a phylogenetically controlled meta-analysis, </span><span>available within a <span>web-based app (‘Seabird FMR Calculator’, </span></span><span><a href="https://ruthedunn.shinyapps.io/seabird_fmr_calculator/"><span>https://ruthedunn.shinyapps.io/seabird_fmr_calculator/</span></a></span><span>) (Dunn et al. 2018). These datasets contain information on corticosterone levels, DEE, TABs and Hg in little auks, and the data used in our phylogenetically controlled analysis. Please see the READ me file for details.</span></p>
Data used in "The Backscatter Gating method for time, energy, and position resolution characterization of long form factor organic scintillators" by Hunter N. Ratliff et al.
<p>This repository contains the raw experimental and PHITS-simulated data used in the JINST article “The Backscatter Gating method for time, energy, and position resolution characterization of long form factor organic scintillators” by Hunter N. Ratliff et al., available at <a href="https://doi.org/10.1088/1748-0221/19/07/P07002">https://doi.org/10.1088/1748-0221/19/07/P07002</a> (the accepted manuscript can also be found at <a href="https://hdl.handle.net/11250/3145287">https://hdl.handle.net/11250/3145287</a> and <a href="https://hratliff.com/publications/">https://hratliff.com/publications/</a>).</p> <p> </p> <p>The data consists of three top-level directories, each with various subdirectories. Within the “PHITS-simulated-data” directory are the PHITS simulations (including input and output files) used in producing Figures 4 and 5 in the manuscript, showing energy spectra in the bar for BSG events from a Cs-137 emission for placement of the source on the bar and on the BSG detector and with spacings between the bar and BSG detector of 20, 80, and 150 mm, along with the energy-dependent spatial distribution of these recoil electrons in the bar.</p> <p> </p> <p>The other two top-level directories contain raw data produced by the CAEN CoMPASS software when acquiring data experimentally. In the analysis for each measurement, the contents of the “RAW” folders were used. The “Energy-calibrations-of-CeBr3” directory contains the energy spectra of the CeBr3 detector (used as the BSG detector) when exposed to a variety of radioactive sources, used together to energy-calibrate the CeBr3 detector. The “Backscatter-gating-measurements” directory contains all of the list-mode data acquired with CoMPASS used for all of the other experimental measurements presented in the manuscript. It contains subdirectories for varied sources, source positions along the bar, distances between the bar and BSG detector, measurements with the source affixed to the BSG detector instead, and a few miscellaneous measurements. The “images” directories within each measurement’s directory (at the same level as the “RAW” folders) contain images produced by the analysis script written for this work used for diagnostics and presenting analyzed data.</p> <p> </p>
EnerGAware monitored energy consumption data
<p>Energy consumption data acquired during the monitoring period, from the houses that were part of the EnerGAware pilot.</p> <p>The data discriminated by dwelling, but it is anonymized.</p>
Data set used in the Article "Evaluation of Monte Carlo tools for high-energy atmospheric physics II: relativistic runaway electron avalanches"
<p>Data used for the Relativistic Runaway Electron Avalanches (RREA) simulations of the Article : "Evaluation of Monte Carlo tools for high energy atmospheric physics II: relativistic runaway electron avalanches" by D. Sarria et al.</p> <p>Includes two set of results : The Probability of Generating RREAs, and the Characterizations of RREAs.</p> <p>Link to the article : <a href="https://www.geosci-model-dev.net/11/4515/2018/gmd-11-4515-2018.html">https://www.geosci-model-dev.net/11/4515/2018/gmd-11-4515-2018.html</a></p> <p>DOI of the article: 10.5194/gmd-2018-119</p>
Participant observation data from local, storytelling, multi-stakeholder energy policy workshops
<p>The SHAPE ENERGY (EU Horizon 2020) undertook 17 multi-stakeholder energy policy workshops over November 2017 – May 2018. These workshops were mostly in partnership with municipalities who posed local energy problems that needed to be ‘solved’. The workshops brought local stakeholders together through storytelling facilitation techniques. The included data herein are anonymised components of the completed participant observation templates that partners used at these workshops. In particular, the data predominately relates to the conversational exchanges in one very closely monitored session across all the workshops.</p>
Data for: Contrasting response of precipitation to aerosol perturbation in the tropics and extra-tropics explained by energy budget considerations. Dagan et al., 2019 GRL
<p>Data available form the simulation conducted with ICON.</p> <p>The data include the different terms of the energy budget from all the different simulations presented in the paper.</p> <p>ARC is the atmospheric radiative cooling, l is the precipitation, Qsh is the sensiblel heat flux and R is the residual (or the divergent term).</p> <p>ref is for the reference simulation while the rest of the files denote the location of the plume, its size (in degree) and the SSA. for example:</p> <p>p_40N_25_9_5.nc</p> <p>continue the precipitation for the simulation with a plume located at 40N, the size of the plume is 25 degrees and the SSA=9.5.</p> <p> </p> <p>The 3D varblees of temperature and winds are given is the files :</p> <p>monmean_atm_trop_2_4aod_10deg_8ssa_3D_atm_3d_ml.nc</p> <p>and:</p> <p>monmean_atm_40N_2_4aod_10deg_8ssa_3D_atm_3d_ml.nc</p> <p> </p> <p>for two different simulations with the same plume size and SSA but different plume locations (this data is used in Figs. 3 and 4 in the paper).</p> <p> </p> <p> </p>
SparBOFWEC Spar Buoy for Offshore Floating Wind Energy Conversion - Data Storage Report
<p>The present work describes the experiences gained from the design methodology and operation of a 3D physical model experiment aimed to investigate the dynamic behaviour of a spar buoy (SB) off-shore floating wind turbine (WT) under different wind and wave conditions. The physical model tests have been performed at Danish Hydraulic Institute (DHI) off-shore wave basin within the European Union-Hydralab+ Initiative, in April 2019. The floating WT model has been subjected to a combination of regular and irregular wave attacks and wind loads.</p>
H2020 OPERA Project: Mooring System Experimental data from MARMOK-A-5 Wave Energy Converter at BiMEP
<p>Funded under European Union's Horizon 2020 Programme, <a href="http://opera-h2020.eu/">OPERA</a> project’s main objective is to reduce the time to market of wave energy, by further advancing in 4 key innovations aiming to reduce up to 50% the Levelized Cost of Energy (LCOE) projections of a floating Oscillating Water Column (OWC) technology.</p> <p>As part of project activities, a condition monitoring system was deployed during the open-sea testing campaign of IDOM's MARMOK-A-5 wave energy converter, while this was deployed in the Biscay Marine Energy Platform (BiMEP) from October 2016 to June 2019.</p> <p>The dataset herein contains a collection of experimental results obtained during this extensive testing campaign, The campaign covers two deployment periods, where the first testing period includes polyesther tethers and the second testing campaing includes innovative elastomeric tethers as described in more detail in the project documentation. This experimental dataset aims to provide quantitative comparison data of the dynamic behavior of the system under these two different configurations.</p>
Data set for the PLOS ONE paper Expecto transitio: Exploring non-experts' techno-economic expectations of the energy future
<p>Raw data file (SPSS and .cv versions) consisting of all data used for the Plos One publication.</p> <p> </p>
ScienceDex guides
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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.