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87 results for “energy simulation”

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zenodo32/100

QG energy-aware hybrid simulations (Double-Gyre configuration)

<p>QG energy-aware hybrid simulations for different set of parameters and compensating forcing; reference QG solution and the coarse-grid runs.</p> <p>There are five different groups of data files containing quasi-geostrophic energy-aware hybrid simulations for different set of parameters and compensating forcing;&nbsp;<br>reference quasi-geostrophic solution and the coarse-grid runs:</p> <p>1) 129x129_10y_spinup50y_dt1800s_simtime2y_eta0.02_case2.tar.part*<br>2) 29x129_10y_spinup50y_dt1800s_simtime2y_eta0.02_fast_energy_growth.tar.part*<br>3) 129x129_10y_spinup50y_dt1800s_simtime2y_eta0.02_scales2.tar.part*<br>4) 129x129_2y_spinup50y_dt1800s.tar.part*<br>5) 129x129_2y_spinup50y_dt1800s_refsol.tar.part*</p> <p>All files in the repository are tar-archives split into parts. Before using them, they should be merged into single files. In order to merge them one should use the Linux cat command.&nbsp;The data in the merged tar-files can be then extracted with the Linux tar command.</p>

opencc-by-4.0Feb 2024View details →
zenodo32/100

Data for A weak coupling mechanism for the early steps of the recovery stroke of myosin VI: a free energy simulation and string method analysis

<p>Representative frames, topology for MD simulation with NAMD and GROMACS, data and notebook to reproduce analyses in the paper "A weak coupling mechanism for the early steps of the recovery stroke of myosin VI: a free energy simulation and string method analysis" (Blanc, Houdusse, Cecchini, PLOS Computational Biology 2024).</p>

opencc-by-4.0Mar 2024View details →
zenodo32/100

Surface Kinetic energy from MITgcm-GEOS5 Coupled Ocean-Atmosphere Simulation

<p>Annual mean of surface kinetic energy computed from MITgcm-GEOS5 coupled ocean-atmosphere simulation, with a spacing grid of 4 km.</p> <p>The temporal coverage for the annual mean spans from March 01, 2020, to March 01, 2021.</p>

opencc-by-4.0Nov 2024View details →
zenodo32/100

Free Energy of Membrane Pore Formation and Stability from Molecular Dynamics Simulations

Open the record for dataset details and reuse information.

opencc-by-4.0Oct 2024View details →
zenodo32/100

Large-eddy simulation of airborne wind energy farms: AWES virtual flight data

<p>Large-eddy simulation&nbsp;of airborne wind energy farms: AWES virtual flight data.</p> <p>This dataset contains virtual flight data collected from individual systems in airborne wind energy parks obtained by means of large-eddy simulations. All data are stored as Python dictionary objects in the Pickle format. Additional Python scripts are provided to visualize the data.&nbsp;&nbsp;</p> <p>&nbsp;</p>

opencc-by-4.0Nov 2021View details →
zenodo32/100

Molecular simulations to investigate the impact of N6-methylation in RNA recognition: Improving accuracy and precision of binding free energy prediction

<p>Dataset relative to Molecular dynamics simulation performed for the work "Molecular simulations to investigate the impact of N6-methylation in RNA recognition: Improving accuracy and precision of binding free energy prediction".<br><br>The dataset contains data of 42 alchemical simulations and is subdivided in 4 zip files.<br><br>Folders are named following the scheme: system_configuration_forcefield.<br>Zip file C1 contains .mdp files used for all the simulations.<br><br>Folders corresponding to simulations performed with the fit5_AC ff contains:<br>- topology files (topol.top, topol_RNA_chain_A.itp, topol_RNA_chain_B.itp)<br>- index files needed to reconstruct the demuxed trajectories (replica_index.xvg , replica_index.xvg)<br>- 16 folders, one for each replica (lam0 ... lam15), containing:<br>&nbsp;&nbsp; - final configuration (confout.gro)<br>&nbsp;&nbsp; - log file (md.log)</p> <p>&nbsp; - input file for md run (md.tpr)<br>&nbsp;&nbsp; - energies for the concatenated trajectories recomputed for the realtive replica hamiltonian (ener_trj_conc.edr)<br><br>Folders corresponding to simlations performed with fit_A parametrization only contains .edr files corresponding to energies for the concatenated trajectory computed for 14 set of DeQs drawn from&nbsp; a gaussian distribution, with the relative topologies.<br><br>Supplementary materials relative to simlations performed with fit_A parametrizationcan be found in: https://zenodo.org/records/6498021</p>

opencc-by-4.0Apr 2024View details →
zenodo32/100

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, &ldquo;Image based in silico characterisation of the effective thermal properties of a graphite foam&rdquo;, 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, &ldquo;Improving modelling of complex geometries in novel materials using 3D imaging&rdquo;, 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>

opencc-by-4.0Nov 2019View details →
dryad32/100

Data from: Computer simulations show that Neanderthal facial morphology represents adaptation to cold and high energy demands, but not heavy biting

Three adaptive hypotheses have been forwarded to explain the distinctive Neanderthal face: 1) an improved ability to accommodate high anterior bite forces, 2) more effective conditioning of cold and/or dry air, and, 3) adaptation to facilitate greater ventilatory demands. We test these hypotheses using three-dimensional models of Neanderthals, modern humans, and a close outgroup (H. heidelbergensis), applying finite element analysis (FEA) and computational fluid dynamics (CFD). This is the most comprehensive application of either approach applied to date and the first to include both. FEA reveals few differences between H. heidelbergensis, modern humans and Neanderthals in their capacities to sustain high anterior tooth loadings. CFD shows that the nasal cavities of Neanderthals and especially modern humans condition air more efficiently than does that of H. heidelbergensis, suggesting that both evolved to better withstand cold and/or dry climates than less derived Homo. We further find that Neanderthals could move considerably more air through the nasal pathway than could H. heidelbergensis or modern humans, consistent with the propositions that, relative to our outgroup Homo, Neanderthal facial morphology evolved to reflect improved capacities to better condition cold, dry air, and, to move greater air volumes in response to higher energetic requirements.

opencc-zeroDec 2017View details →
zenodo32/100

Amplification of the divergent component from balanced motions and forward kinetic energy cascade in an ocean-atmosphere simulation

<p>This dataset contains animations of:</p> <ol> <li> <p>The temporal evolution of ocean currents partitioned into balanced motions (BMs) and non-BMs (including internal gravity waves and low-frequency wind-driven currents), displayed in the physical domain for COAS (top row) and Ocean-forced (bottom row). Both simulations illustrate kinetic energy (KE) together with velocity gradients&mdash;vorticity ($\zeta$) and horizontal divergence ($\delta$)&mdash;all normalized by the Coriolis frequency.</p> </li> <li> <p>The temporal evolution of low-frequency (lf) and high-frequency (hf) non-BM currents in the physical domain for COAS (top row) and Ocean-forced (bottom row).</p> </li> </ol>

opencc-by-4.0Sep 2024View details →
zenodo32/100

Simulation dataset for "Quantifying Energy Conversion in Higher Order Phase Space Density Moments in Plasmas"

<p>Simulation dataset for the simulation figure used in the paper</p>

opencc-by-4.0Jan 2022View details →
zenodo32/100

Simulation dataset for "HORNET - A Power Density Quantifying Out of Thermodynamic Equilibrium Energy Conversion"

<p>Simulation dataset for reproducing figures used in the manuscript</p>

opencc-by-4.0Jul 2023View details →
dryad32/100

Data from: Computer simulations show that Neanderthal facial morphology represents adaptation to cold and high energy demands, but not heavy biting

Open the record for dataset details and reuse information.

publicMar 2018View details →
dryad32/100

Data from: Energy expenditure of adult green turtles (Chelonia mydas) at their foraging grounds and during simulated oceanic migration

Open the record for dataset details and reuse information.

publicApr 2016View details →
zenodo28/100

Simulation data for "Tuning Adhesion and Energy Dissipation in Polymer Films between Solid Surfaces via Grafting and Cross-Linking"

<p>LAMMPS input and data files, Jupyter notebooks used for the analysis of the MD simulations.</p>

opencc-by-4.0Jan 2024View details →
zenodo28/100

Model data for "Factors Modulating Variability of Eddy Kinetic Energy in the Southern Ocean from Idealized Simulations" "

<p>This dataset contains the all the idealized simulations with different topographic features.</p>

opencc-by-4.0Sep 2023View details →
zenodo28/100

Weather dataset (Typical Downscaled Year, Extreme Cold Year, Extreme Warm Year) for building energy simulations (.epw format) in future climate (2069-2098, RCP 8.5), Kortrijk Kennedy Park, Belgium

<p>Typical Downscaled Year (TDY), Extreme Cold Year and Extreme Warm Year&nbsp;based on the methodology of Nik (2016), is extracted for the location of Kortrijk Kenny Park&nbsp;(50&deg; 48&#39; 2&quot;N 3&deg;16&#39;13&quot; E) from the EC-Earth driven convection-permitting climate model COSMO-CLM&nbsp;for the Belgian domain extended with land-surface scheme TERRA_URB(v2.0) making use of the SURY (Semi-empirical URban canopY) parameterization ( Wouters et al. 2016).&nbsp; The integrations are&nbsp;identical to the ones which are&nbsp;described in Vanden Broucke et al. (2019).&nbsp;The climate model has a spatial resolution of 2.8 km and an hourly temporal resolution and is available for the recent past (1976-2004) and future (2070-2098, RCP 8.5 climate change scenario) as 30-year datasets. For this dataset, the TDY, ECY, and EWY are&nbsp;extracted for the future&nbsp;period.&nbsp;A bias correction is applied for the following variables:&nbsp;temperature (as described in Ramon et al. 2020), solar radiation and relative humidity as described in Ramon et al. (202X).</p>

opencc-by-4.0Dec 2021View details →
zenodo28/100

Weather dataset (Typical Downscaled Year, Extreme Cold Year, Extreme Warm Year) for building energy simulations (.epw format) in recent past climate, Sint-Katelijne-Waver, Belgium

<p>Typical Downscaled Year (TDY), Extreme Cold Year and Extreme Warm Year&nbsp;based on the methodology of Nik (2016), is extracted for the location of Sint-Katelijne-Waver (51&deg;3&#39;25&quot;N 4&deg;11&#39;24&quot; E) from the EC-Earth driven convection-permitting climate model COSMO-CLM&nbsp;for the Belgian domain extended with land-surface scheme TERRA_URB(v2.0) making use of the SURY (Semi-empirical URban canopY) parameterization ( Wouters et al. 2016).&nbsp; The integrations are&nbsp;identical to the ones which are&nbsp;described in Vanden Broucke et al. (2019).&nbsp;The climate model has a spatial resolution of 2.8 km and an hourly temporal resolution and is available for the recent past (1976-2004) and future (2070-2098) as 30-year datasets. For this dataset, the TDY, ECY, and EWY are&nbsp;extracted for the recent past period.&nbsp;A bias correction is applied for the following variables:&nbsp;temperature (as described in Ramon et al. 2020), solar radiation and relative humidity as described in Ramon et al. (202X).</p>

opencc-by-4.0Dec 2021View details →
zenodo28/100

An accurate binding free energy method from end-state MD simulations (ANI_LIE test files)

<p>Required files are added.</p>

opencc-by-4.0May 2022View details →
ClinicalTrials.gov28/100

Dual-Energy CT on Plan Quality, Dose-delivery Accuracy, and Simulated Outcomes of Patients Treated With Proton or Photon Therapy

ClinicalTrials.gov study NCT03403361. IPD Sharing: NO. Countries: 1. Publications: 0.

closedIPD-NOFeb 2026View details →
zenodo24/100

Weather dataset (Typical Downscaled Year, Extreme Cold Year, Extreme Warm Year) for building energy simulations (.epw format) in future climate (2069-2098, RCP 8.5), Antwerp Berchem, Belgium

<p>Typical Downscaled Year (TDY), Extreme Cold Year and Extreme Warm Year&nbsp;based on the methodology of Nik (2016), is extracted for the location of Antwerp Berchem&nbsp;(51&deg;12&#39;00&quot;N 4&deg;26&#39;24&quot; E) from the EC-Earth driven convection-permitting climate model COSMO-CLM&nbsp;for the Belgian domain extended with land-surface scheme TERRA_URB(v2.0) making use of the SURY (Semi-empirical URban canopY) parameterization ( Wouters et al. 2016).&nbsp; The integrations are&nbsp;identical to the ones which are&nbsp;described in Vanden Broucke et al. (2019).&nbsp;The climate model has a spatial resolution of 2.8 km and an hourly temporal resolution and is available for the recent past (1976-2004) and future (2070-2098, RCP 8.5 climate change scenario) as 30-year datasets. For this dataset, the TDY, ECY, and EWY are&nbsp;extracted for the future&nbsp;period.&nbsp;A bias correction is applied for the following variables:&nbsp;temperature (as described in Ramon et al. 2020), solar radiation and relative humidity as described in Ramon et al. (202X).</p>

opencc-by-4.0Dec 2021View details →

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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.

allen-brain-atlas
neuroscienceopenDocumentation, web resources, and API references are available online.
Last verified 2026-04-30Open record

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.

abode-home-cage
behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.
Last verified 2026-04-30Open record

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.

dandi-nwb
electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.
Last verified 2026-04-30Open record

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.

ibl
behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.
Last verified 2026-04-29Open record

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.

openneuro
neuroscienceopenPublished datasets are available on demand over the internet.
Last verified 2026-04-29Open record