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567 results for “Simulated datasets”
Datasets of the article "Femtosecond-to-nanosecond dynamics of flavin mononucleotide monitored by stimulated Raman spectroscopy and simulations"
<p>Experimental time-resolved femtosecond-stimulated Raman spectra (FSRS) and visible transient absorption (visTA) spectra of flavin mononucleotide (FMN) in water, MES pH=6, and TRIS pH=8 buffers (DATA files). The dataset includes also the amplitudes and evolution-associated difference spectra (EADS) arising from global kinetic analysis using a sequential model. </p> <p>In the DATA files, the first column contains the Stokes shift (in cm<sup>-1</sup>) or wavelength (in nm), and the first row contains the time delays (in seconds).</p> <p>In the AMPLITUDES files, the first column contains the time delays (in seconds), and the first row contains the lifetimes (in seconds).</p> <p>In the EADS files, the first column contains the wavenumbers (in cm<sup>-1</sup>) or wavelength (in nm), and the first row contains the lifetimes (in seconds). Note that the FSRS spectra have been normalized to 1.</p> <p>If you use these datasets please cite:</p> <p>Andrikopoulos, P. C.; Liu, Y.; Picchiotti, A.; Lenngren, N.; Kloz, M.; Chaudhari, A. S.; Precek, M.; Rebarz, M.; Andreasson, J.; Hajdu, J.; Schneider, B.; Fuertes, G.* Femtosecond-to-Nanosecond Dynamics of Flavin Mononucleotide Monitored by Stimulated Raman Spectroscopy and Simulations. <em>Phys. Chem. Chem. Phys. PCCP</em> <strong>2020</strong>, <em>22</em>, 6538–6552.</p>
Surfactant Proteins SP-B and SP-C in Pulmonary Surfactant Monolayers: Physical Properties Controlled by Specific Protein–Lipid Interactions - Simulation dataset
<p>Simulation data related to the article:</p> <p>Liekkinen, J., Olzynska, A., Cwiklik, L., Bernardino de la Serna, J., Vattulainen, I., & Javanainen, M. Surfactant Proteins SP-B and SP-C in Pulmonary Surfactant Monolayers: Physical Properties Controlled by Specific Protein–Lipid Interactions. <em>bioRxiv</em>, pp.2022-12. (Pre-print available at https://doi.org/10.1101/2022.12.12.520108)</p> <p> </p> <p>DPPC/POPC/POPG/CHL1 (60/20/10/10 mol-%) monolayers simulated in non-equilibrium conditions. The monolayer is compressed or expanded at a constant rate at different temperatures. Surfactant proteins SP-B and SP-C are included in the monolayers for comparison. The Charmm36 lipid model [1] is used together with the 4-point OPC water model [2]. Each system consist of two monolayers with 169 lipids each. The monolayers are separated by a water slab, and surrounded by vacuum.</p> <p>All trajectories are simulated for 5000 ns with Gromacs 5.1.x [3] using the default Charmm36 monolayer simulations parameters given in the mdp file. Topology (.top) and index (.ndx) files are also included. The topologies (.itp) for the lipids can be obtained from Charmm-GUI and for the OPC water model from https://bioinformatics.cs.vt.edu/~izadi/</p> <p>[1] DOI: 10.1021/jp101759q</p> <p>[2] DOI: 10.1021/jz501780a</p> <p>[3] DOI: 10.1016/j.softx.2015.06.001</p>
Dataset for CFD4NRS-9 conference paper "Simulation of noble metal particle growth and removal in the molten salt fast reactor"
<p>Dataset used for the results presented in the CFD4NRS-9 conference paper "Simulation of noble metal particle growth and removal in the molten salt fast reactor". Also the code used to generate results is included (it is also publicly available at https://github.com/edofrederix/flotationFoam), and contains a README with instructions. The plots can be generated with their respective Python files.</p>
Dataset of "Divergent convective outflow in ICON deep convection permitting and parameterised deep convection simulations": sample simulations
<p>Please find the full README file for the code and ICON-PER ellipse datasets by extracting the archive " PER_archive_data.tar.gz ".<br> <br> The other two archives contain example simulation output for two simulations - one with parameterised convection and one with convection-permitting set-up in ICON.</p> <p>The download_all.sh bash script allows one to select the download of the full set of simulations in this dataset. They can also be selected individually, by altering the sequence of ensemble members over which the download-loop ("wget") runs (PER-simulations!), or by removing lines from the download_all.sh-script (for removing simulations from "PAR"-dataset).</p>
A microwave link simulation dataset for rainfall observation applications
<p>A microwave link simulation dataset is developed for theoretical research of rainfall observation applications. The dataset is built based on electromagnetic propagation theory attenuation effects with measured raindrop size distribution data from a PARSIVEL disdrometer and air temperature, air pressure and humidity data from nearby weather stations. Frequencies from 6 to 180 GHz, horizontal or vertical polarization, link lengths from 0.1 km to 20 km, and quantization resolutions from 0 to 1 dB are all fully considered for various existing and future complications. The dataset can be utilised to validate the performance of the microwave link-based rainfall inversion process algorithm in various equipment configurations and to assess the potential of microwave link-based rainfall monitoring technology in future high-frequency communication infrastructure conditions on a theoretical level with a very low cost.</p>
COMSOL simulation & example datasets for: Nonuniform structural properties of wings confer sensing advantages
<p>Sensory feedback is essential to both animals and robotic systems for achieving coordinated, precise movements. Mechanosensory feedback, which provides information about body deformation, depends not only on the properties of sensors but also on the structure in which they are embedded. In insects, wing structure plays a particularly important role in flapping flight: in addition to generating aerodynamic forces, wings provide mechanosensory feedback necessary for guiding flight while undergoing dramatic deformations during each wingbeat. However, the role that wing structure plays in determining mechanosensory information is relatively unexplored. Insect wings exhibit characteristic stiffness gradients and are subject to both aerodynamic and structural damping. Here we examine how both of these properties impact sensory performance, using finite element analysis combined with sensor placement optimization approaches. We show that wings with nonuniform stiffness exhibit several advantages over uniform stiffness wings, resulting in higher accuracy of rotation detection and lower sensitivity to the placement of sensors on the wing. Moreover, we show that higher damping generally improves the accuracy with which body rotations can be detected. These results contribute to our understanding of the evolution of the nonuniform stiffness patterns in insect wings, as well as suggest design principles for robotic systems.</p>
FAFMIP forcing datasets used in MOM5 1-degree simulations
<p>FAFMIP surface flux perturbations datasets (see http://www.fafmip.org/; <a href="http://www.geosci-model-dev.net/9/3993/2016">Gregory <em>et al.</em>, 2016</a>) interpolated on the MOM5 1-degree grid. Data are climatological monthly means in the netCDF format. The data variables in these files are dimensioned (longitude,latitude,time) in Fortran order, (time,latitude,longitude) in CDL. The time dimension has size 12, for months from January to December.</p>
Data and code for: Generation and applications of simulated datasets to integrate social network and demographic analyses
<p class="MsoNormal"><span>Social networks are tied to population dynamics; interactions are driven by population density and demographic structure, while social relationships can be key determinants of survival and reproductive success. However, difficulties integrating models used in demography and network analysis have limited research at this interface. We introduce the R package genNetDem for simulating integrated network-demographic datasets. It can be used to create longitudinal social networks and/or capture-recapture datasets with known properties. It incorporates the ability to generate populations and their social networks, generate grouping events using these networks, simulate social network effects on individual survival, and flexibly sample these longitudinal datasets of social associations. By generating co-capture data with known statistical relationships it provides functionality for methodological research. We demonstrate its use with case studies testing how imputation and sampling design influence the success of adding network traits to conventional Cormack-Jolly-Seber (CJS) models. We show that incorporating social network effects in CJS models generates qualitatively accurate results, but with downward-biased parameter estimates when network position influences survival. Biases are greater when fewer interactions are sampled or fewer individuals are observed in each interaction. While our results indicate the potential of incorporating social effects within demographic models, they show that imputing missing network measures alone is insufficient to accurately estimate social effects on survival, pointing to the importance of incorporating network imputation approaches. genNetDem provides a flexible tool to aid these methodological advancements and help researchers test other sampling considerations in social network studies.</span></p>
Simulation dataset of "Wave activities throughout a low-Mach number quasi-parallel shock: 2-D hybrid simulations"
<p>Simulation dataset of "Wave activities throughout a low-Mach number quasi-parallel shock: 2-D hybrid simulations", including magnetic fields, number density and average velocities of particles.</p>
Open dataset of 10% gelatin penetration experiments and simulation code for 4.5 and 6.35 mm caliber
<p>Paper 1:</p> <p>PENETRATION EXPERIMENTS AND 1D SIMULATIONS OF AIR RIFLE BB 4.5 MM STEEL SPHERE AND GAMO HUNTER 4.5 MM IMPACTING 10% GELATINE</p> <p><strong>Keywords:</strong> Penetration, experiments, simulations, gelatine, air rifle, projectiles, high-speed camera, chronograph, BB 4.5 mm, steel sphere, Gamo Hunter 4.5 mm, penetration depth, dynamic cavity size, drag force, shear force, shear rate effect</p> <p><strong>Abstract</strong>. Gelatine of 10% concentration is often used as projectile target to simulate soft tissues of animals and humans. The 10% gelatine used in the tests fulfills the FBI standard protocol for penetration depth of BB 4.5 mm steel sphere impacting with 180 m/s. Here, experiments were conducted with BB 4.5 mm steel sphere and diabolo Gamo Hunter 4.5 mm pellets with varying impact velocity. The impact velocity was measured by chronograph. The deceleration and dynamic cavity size was studied by using high-speed camera with 10000 frames per second. Final penetration depth was manually measured. A 1D simulation model, considering the retardation force generated by drag and shear, was used to predict penetration depth in both 10% and 20% gelatine for projectiles with spherical nose shape. From the simulations it becomes evident that the drag force and static shear force alone can’t explain the total retardation force to achieve the expected penetration depth. In the literature for experiments conducted on both 10% and 20% gelatine, there is confirmation that projectiles impacting with velocity above 100 m/s, the shear strength in the gelatine is increased by a factor 40 and 70 times for 10% and 20% gelatine, respectively. This concept was successfully used in the simulations by introducing a shear rate dependent shear force model which increases the static shear force of 10% and 20% gelatine. All experimental results, including high speed films and python simulation code of gelatine penetration, are publicly available, see [1], [2]. </p> <p>Paper 2:</p> <p>Penetration experiments of air rifle projectiles with caliber 6.35 mm and different nose shapes impacting 10% gelatinE</p> <p><strong>Keywords:</strong> Penetration, experiments, gelatine, air rifle, projectiles, high speed camera, chronograph, caliber 6.35 mm, H&N pellets, penetration depth, dynamic cavity size</p> <p><strong>Abstract</strong><em>. Air rifles in caliber 6.35 mm and more than 10 J in kinetic energy at impact can have a significant effect on soft tissues of animals and humans. To study the effect, experimental penetration studies have been conducted by using test specimens made of 10% gelatine substitute. The 10% gelatine used in the tests fulfilled the FBI standard protocol for penetration depth of 4.5 mm BB steel sphere impacting with 180 m/s. The deceleration, penetration depth, and dynamic penetration cavity size for the projectiles with different nose shapes impacting gelatine 10% have been studied. The impact velocity was measured by using a chronograph and the initial kinetic energy before impact of the projectiles was up to 84 J (330 m/s). The deceleration and dynamic cavity size was studied by using high-speed camera with 10000 frames per second. Final penetration depth was manually measured. The nose and projectile shape influenced the dynamic cavity size and final penetration depth in the gelatine to a large extent. All experimental results, including high speed films and python simulation code of gelatine penetration, are publicly available, see [1]. </em></p> <p>Any results used from this open access data shall be cited to paper 1 and paper 2, the papers are found in the proceedings of the 14<sup>th </sup>International Conference on Shock & Impact Loads on Structures, 30-31 March 2023, Singapore.</p> <p> </p>
LiftWEC deliverable 3.6 - Part I: Dataset from 3D validation simulations of LiftWEC device using a high-fidelity RANS model
<p>This dataset contains numerical simulation results obtained from 3D-validation studies of the high-fidelity RANS model employed in the LiftWEC project. The case identifiers (ID) correspond to the case numbering employed in the experimental reference cases defined by École Centrale de Nantes. It is highly recommended to read the corresponding project reports on numerical modelling (D3.6) and on experimental modelling (D4.5, D4.6, D4.7, D4.8) which are also available in the LiftWEC community on zenodo (https://zenodo.org/communities/liftwec/).</p> <p>The cases comprise simulations of a rotor at constant velocity in calm water and regular waves in full 3D simulations. It further includes 2D simulation results of a rotor at constant rotational velocity in irregular waves and at variable velocity in monochromatic waves.</p> <p>All loads in the data set are given in force per unit span length (N/m), torque and power output is given as values per unit span as well. Wave elevation data up and down-wave of the rotor is given in (m).</p> <p> </p> <p> </p> <p> </p>
LiftWEC deliverable 3.6 - Part II: Dataset from numerical simulations of full-scale LiftWEC device using a high-fidelity RANS model
<p>This dataset contains the numerical simulation results for a full-scale LiftWEC device in regular and irregular wave conditions. The regular wave cases occur at static pitch and fixed rotational velocity. The parameters used for these cases are described in the corresponding *CaseParameters.csv file. These simulations were used to derive a first estimate of the maximum conversion efficiency of the rotor from wave power to shaft power. A detailed description of the employed numerical model and the case setup can be found in LiftWEC deliverable 3.6 Hydrodynamic Validation of Final Design, which was also uploaded to the LiftWEC community on zenodo. In accordance with the coordinate system definition used in the validation case, a relative phase angle of 270° degree corresponds to foil1 at 3 o'clock position while the wave crest passes over the rotor axis position. Rotation is clockwise, Rotor rotates in the direction of orbital wave particle velocities.</p> <p>The control reference case describes the first test case of a cyclorotor in irregular waves under active control of angular velocity and foil pitch. This case is also documented in deliverable D3.6. The case files can be used to recreate wave conditions and motion signal. The load-file can be used to validate the obtained tangential and radial forces on foil 1 as well as the total power output over time. More information on the control model can be found in the corresponding project deliverables D5.X.</p> <p>All loads given as values per unit span length (e.g. for forces [N/m]).</p>
LiftWEC deliverable 3.3 - Dataset from 2D validation simulations of LiftWEC device using a high-fidelity RANS model
<p>This dataset contains results obtained from numerical simulations of the LiftWEC model scale device in a two-dimensional setting. The simulations were done based on the experimental validation campaign conducted in the scope of the LiftWEC project and documented in deliverables D4.2, D4.3 and D.4. The corresponding experimental datasets are also available within the LiftWEC community on zenodo.</p> <p>The numerical setup as well as a presentation and discussion of obtained results is available in LiftWEC deliverable D3.3 Tool Validation and Extension report, which also contains information on the potential flow model. All forces presented in this document are given as forces per unit span length. As the 2D RANS model was found to be rather sensitive to high fluctuations at this preliminary investigation stage, results are presented as mean forces and force fluctuations at rotation period, analysed by means of an FFT post-processing routine. The case identifiers correspond to the case numbering employed in the experimental model tests.</p>
Ptatin2D : dataset for run simulations presented in the article Slab pull driven South China Sea opening implies a Mesozoic Proto South China Sea
<p>Overview of pTatin2D</p> <p>pTatin2D is a software package designed for studying long time-scale processes relevant to geodynamics. The original motivation for this development was to provide the community with an open-source toolkit capable of studying high-resolution, two-dimensional models of lithospheric deformation.</p> <p>Functionality</p> <ul> <li>mixed finite elements (Q2-P1_disc) for the Stokes problem</li> <li>material points for tracking Lagrangian state and history variables</li> <li>energy equation solved with Q1 elements + SUPG</li> <li>ALE formulations (with a variety of remeshing solutions)</li> <li>extensible rheology components (currently supports: iso-viscous, Frank-Kamenetskii, Arrhenius, power-law, von Mises, Drucker Prager)</li> <li>full support for spatio-temporal Dirichlet and non-zero Neumann boundary conditions</li> <li>coupling with landscape evolution models</li> <li>full support for Newton and Picard non-linear solvers</li> <li>massively parallel implementation</li> </ul> <p>pTatin2D leverages functionality from <a href="http://www.mcs.anl.gov/petsc">PETSc</a></p> <p>Requirements</p> <ul> <li>PETSc (only support for version 3.2)</li> </ul>
Dataset and supporting materials of "Numerical simulation of orographic gravity waves observed over Syowa Station. Part 1: Wave propagation and breaking in the troposphere and lower stratosphere" by Kohma et al.
<p>Dataset and supporting materials of "Numerical simulation of orographic gravity waves observed over Syowa Station. Part 1: Wave propagation and breaking in the troposphere and lower stratosphere" by Kohma, M., K. Sato, D. C. Fritts, & T. S. Lund.</p>
GWAS dataset with simulated binary phenotypes for 1000 Genome Project
<p>Phenotypes in this dataset are generated by simulating quantitative blood lipid level phenotype using the method introduced in by (https://app.terra.bio/#workspaces/amp-t2d-op/2019_ASHG_Reproducible_GWAS-V2), and binarized to be suitable as a test case for GWAS methods.</p> <p>ALL.shapeit2_integrated_v1a.GRCh38.20181129.phased.rsid files and allpopid.txt are the 1000 Genome Project reference data used for generating MDS plot in https://github.com/csbio/BridGE-Python</p> <p> </p>
3D simulations of the Archean Earth including photochemical haze profiles [dataset]
<p>Dataset of 3D simulations of the Archean Earth including photochemical haze profiles</p> <p>We present results from 3D simulations of the Archean Earth including a prescribed spherical haze generated through a 1D photochemical model. Our simulations suggest that a thin haze layer, formed when CH4/CO2 = 0.1, leads to global warming of ~10.6K due to the change of water vapour and cloud feedback, compared to the simulation without any haze. However, a thicker haze layer, formed when CH4/CO2 > 0.1, leads to global cooling of up to ~65K as the scattering and absorption of shortwave radiation from the haze reduces the radiation from reaching the planetary surface. A thermal inversion is formed with a lower tropopause as the CH4/CO2 ratio increases. The haze reaches an optical threshold thickness when CH4/CO2 ~ 0.175 beyond which the atmospheric structure and the global surface temperature do not vary much.</p> <p> </p> <p>The IRIS python package developed by the Met Office is needed to analyse the data.</p>
Global Datasets of Hourly Carbon and Water Fluxes Simulated Using a Satellite-based Process Model with Dynamic Parameterizations
<p>This new global hourly dataset serves as a 'handshake' among process-based models, remote sensing, and the eddy covariance flux network, providing a reliable long-term estimate of global gross primary productivity (GPP) and evapotranspiration (ET) with diurnal patterns and facilitating studies related to ecosystem functional properties, global carbon, and water cycles.</p> <p>The dataset include the GPP and ET of sunlit and shaded leaf components at an hourly timescale and a spatial resolution of 0.25-degree from 2001 to 2020.</p>
Datasets that simulated ion channel currents
<p>This repository contains pseudo-ion-channel current datasets described in the 2023 publication “Model-Free Idealization: Adaptive Integrated Approach for Idealization of Ion Channel Currents (AI2)”. The datasets simulate ion channel currents based on the two-state model (Gating Kinetics.tif) and contain two kinds of noise (experimental or white Gaussian noise) at different signal-to-noise ratios (SNRs=10.2, 5.14, and 1.82). Four values (1, 10, 100, and 1000 s<sup>-1</sup> ) were examined for k<sub>1</sub> and k<sub>2</sub> of the gating kinetics, yielding 16 combinations of k<sub>1</sub> and k<sub>2</sub> . The repository contains five time-series data for each combination of k<sub>1</sub> , k<sub>2</sub> , and SNR. Each dataset contains 5×10<sup>5</sup> points which correspond to a 20 s recording with a sampling frequency of 25 kHz.</p> <p><br> Time-series 0-1 (closed-open) sequences were first simulated using the QuB software. White Gaussian noise was added to the 0-1 sequence by Python 3.7. Experimental noise was added to the 0-1 sequence through electrophysiological recordings using a patch-clamp amplifier and a model cell (Molecular Devices). Voltage sequences consisting of V<sub>0</sub> and V<sub>1</sub> , which corresponded to the closed and open states, respectively, were input to the model cell (10 MΩ resister), and the resulting currents were recorded with the amplifier. Each CSV file has time, ground truth (0 or 1), and current.</p>
Dataset and Python Scripts used in the manuscript "Global-MHD Simulations using MagPIE : Impact of Flux Transfer Events on the Ionosphere"
<p>Dataset and Python Scripts used in the manuscript "Global-MHD Simulations using MagPIE : Impact of Flux Transfer Events on the Ionosphere".</p> <p>Authors: Arghyadeep Paul, Antoine Strugarek and Bhargav Vaidya<br> Date: 21 May, 2023</p> <p> </p> <ul> <li>Figure 1 has been plotted from two data files named Bx_By_Bz_prs_t_4783_C0.vtk and Bx_By_Bz_prs_t_4783_C1.vtk using the visualisation toolkit VisIt. Visit can be freely downloaded from https://wci.llnl.gov/simulation/computer-codes/visit</li> <li>Figure 2 has been plotted using the ipython notebook named "figure_2.ipynb" and the associated data files have been uploaded alongwith.</li> <li>Figure 3 has been plotted using the data file named "visit_prs_Bx_By_Bz_t_4964s.vtk" and the visualisation toolkit VisIt.</li> <li>Figure 4 has been plotted using the associated data files and the visualisation toolkit VisIt.</li> <li>Figures 5 and 6 has been plotted using the ipython notebook named "new_fig_5_and_6.ipynb" and the associated data files have been uploaded alongwith.</li> <li>Figure 7 has been plotted using the ipython notebook named "figure_7.ipynb" and the associated data files have been uploaded alongwith.</li> <li>Figure 8 has been plotted using the ipython notebook named "figure_8.ipynb" and the associated data files have been uploaded alongwith.</li> <li>Figure 9 has been plotted using the ipython notebook named "figure_9.ipynb" and the associated data files have been uploaded alongwith. An example swarm CSV data file is added for the python script. The original SWARM data can be downloaded from <a href="https://swarm-diss.eo.esa.int/">https://swarm-diss.eo.esa.int/</a> and the FAC data from two spacecrafts, Swarm A and C, named SW_OPER_FAC_TMS_2F has been used. This data was first published in Dong et.al. 2023 [https://doi.org/10.1029/2022GL102460].</li> </ul>
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.