Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
1,028
datasets available to search
ShareScore release 0.7.1
Dataset results
1,028 results for “modelling & simulation”
Data for "Reduced-Order Biogeochemical Flux Model for Upper OceanBiophysical Simulations"
<p>Data, figure generation scripts, and zero-dimensional version of the 17 species Biogeochemical Flux Model (BFM17) for the paper "Reduced-Order Biogeochemical Flux Model for Upper OceanBiophysical Simulations" submitted to Geoscientific Model Development. </p>
Model input and output, performance measures and modifications in the source code for PALM simulations on Mäkelänkatu in Helsinki, Finland
<p>This dataset is for air quality simulations conducted on Mäkelänkatu in Helsinki, Finland, using the PALM model system 6.0.</p> <p>By default, simulations use modelled data as boundary conditions. This includes modelled meteorological data from MEPS (MetCoOp Ensemble Prediction System) and air pollutant background concentrations from the ADCHEM model. Alternatively, measured meteorology from the Kivenlahti mast in Espoo, Finland, and aerosol size distribution from the SMEAR III station in Kumpula, Helsinki, is applied.</p> <p>Simulations:</p> <ul> <li>9 June morning: <ul> <li>0609_morning: use modelled boundary conditions for meteorology and air pollutants</li> <li>0609_morning_allmet_smear: use measured boundary conditions for meteorology and aerosol size distribution</li> <li>0609_morning_smear: use modelled boundary conditions for meteorology and measured for aerosol size distribution</li> <li>0609_morning_wd_smear: use modelled boundary conditions for meteorology, but modify the wind direction by using the measured wind direction at Kivenlahti. For aerosol size distribution, use the measured boundary conditions.</li> <li>0609_morning_wdk_smear: use modelled boundary conditions for meteorology, but modify the wind direction by using the measured wind direction at SMEAR III. For aerosol size distribution, use the measured boundary conditions.</li> <li>precursor_0609_morning: precursor with modelled boundary conditions for meteorology</li> <li>precursor_0609_morning_allmet: precursor with measured boundary conditions for meteorology</li> <li>precursor_0609_morning_wd: precursor with modelled boundary conditions, but the wind direction is modified by using the measured wind direction at Kivenlahti.</li> <li>precursor_0609_morning_wdk: precursor with modelled boundary conditions, but the wind direction is modified by using the measured wind direction at SMEAR III.</li> </ul> </li> <li>9 June evening: <ul> <li>0609_evening: use modelled boundary conditions for meteorology and air pollutants</li> <li>0609_evening_allmet_smear: use measured boundary conditions for meteorology and aerosol size distribution</li> <li>precursor_0609_evening: precursor with modelled boundary conditions for meteorology</li> <li>precursor_0609_evening_allmet: precursor with measured boundary conditions for meteorology</li> </ul> </li> <li>12 December morning: <ul> <li>1207_morning: use modelled boundary conditions for meteorology and air pollutants</li> <li>1207_morning_allmet_smear: use measured boundary conditions for meteorology and aerosol size distribution </li> <li>precursor_1207_morning: precursor with modelled boundary conditions for meteorology</li> <li>precursor_1207_morning_allmet: precursor with measured boundary conditions for meteorology</li> </ul> </li> </ul> <p> </p> <p>Datasets are given separately for the root (no suffix), parent (suffix _N02) and child (_N03) domain. The content is following:</p> <ul> <li>input_monitoring_output_usercode <ul> <li>Input data <ul> <li><run_identifier>_chemistry: emission data for gases</li> <li><run_identifier>_dynamic: initialisation and forcing data for meteorological variables and air pollutants</li> <li><run_identifier>_p3d: parameter file for model steering</li> <li><run_identifier>_salsa: emission data for aerosol particles</li> <li><run_identifier>_static: topography information</li> </ul> </li> <li>Simulation performance information <ul> <li><run_identifier>_cpu: information on the CPU time consumed</li> <li><run_identifier>_header: information about the selected model parameters</li> <li><run_identifier>_rc: time step control output</li> </ul> </li> <li>Output data <ul> <li><run_identifier>_av_masked_N03_M01.nc: temporally averaged wind speed data close to the ground</li> <li><run_identifier>_av_masked_N03_M04.nc: temporally averaged aerosol particle concentration data close to the ground</li> <li><run_identifier>_av_masked_N03_M06.nc: temporally averaged aerosol particle concentration data in a vertical column next to the air quality monitoring station on Mäkelänkatu</li> <li><run_identifier>_av_masked_N03_M07.nc: temporally averaged aerosol particle concentration data in a vertical column on the other side of the street from the air quality monitoring station on Mäkelänkatu</li> <li><run_identifier>_pr.nc: temporally vertical profile data on meteorological variables</li> <li><run_identifier>_ts.nc: flow statistics data</li> </ul> </li> <li>Modifications made to the source code (PALM revision, https://palm.muk.uni-hannover.de/trac/browser?rev=4416, last access: 10 Sept 2019) <ul> <li>chem_gasphase_mod.f90: chemical mechanism salsa+simple</li> <li>chem_emissions_mod.f90 (modifications indicated with "MONA")</li> <li>user_module.f90 (modification listed under "Current revisions")</li> <li>Makefile (modification listed under "Current revisions")</li> </ul> </li> </ul> </li> </ul> <p>See the PALM model webpage (https://palm.muk.uni-hannover.de) for details.</p> <p> </p>
GAIA model simulate data of doubled CO2
<p>This dataset contains Temperature, wind, and density output from the GAIA model, that are related to the Figures in the paper</p>
Simulations of the air-water interface at the presence of salt, Dang ions + SPCE water model
<p>Simulations of a water–air interface. There are ~20000 water molecules with various concentrations of NaCl or CaCl_2 in a simulation box of 12*12*22 nm^3. The SPCE water and the ions by Dang et al. are used. The numbering in file names corresponds to the different concentrations, and data for a pure water–air interface "NOION" is also provided. GROMACS-compatible inputs are provided: simulation parameters (md.mdp), topologies (top), and index (ndx) files. Initial structure can be extracted from the tpr file using gmx editconf. The run input (tpr) is provided, as are the outputs: energy file (edr), trajectory (xtc), and final structure (gro). Surface tensions can be extracted by gmx energy.</p> <p>These values are reported in DOI: [ADD].</p> <p>Data for scaled ions based on the electronic continuum correction (ECC) with two different water models are provided in DOI: 10.5281/zenodo.3888383 (SPCE) and DOI: 10.5281/zenodo.3888369 (OPC).</p> <p> </p>
Simulations of the air-water interface at the presence of salt, ECC ions + SPCE water model
<p>Simulations of a water–air interface. There are ~20000 water molecules with various concentrations of NaCl or CaCl_2 in a simulation box of 12*12*22 nm^3. The SPCE water and the ions with scaled charges based on the electronic continuum correction (ECC) are used. The numbering in file names corresponds to the different concentrations, and data for a pure water–air interface "NOION" is also provided. GROMACS-compatible inputs are provided: simulation parameters (md.mdp), topologies (top), and index (ndx) files. Initial structure can be extracted from the tpr file using gmx editconf. The run input (tpr) is provided, as are the outputs: energy file (edr), trajectory (xtc), and final structure (gro). Surface tensions can be extracted by gmx energy.</p> <p>These values are reported in DOI: [ADD].</p> <p>Data for these ECC ions with OPC water is provided at DOI: 10.5281/zenodo.3888369 and for the full charge ions by Dang et al. in SPCE water at DOI: 10.5281/zenodo.3888436.</p>
Extreme model exploration of a multi-scale simulation of tumor growth
<p>The dataset comprises the output of several simulations of a model of tumor growth with different parameter values. The model is a multi-scale agent-based model of a tumor spheroid that is treated with periodic pulses of the cytokine tumor necrosis factor (TNF). The multi-scale model simulates processes including i) the diffusion, uptake, and secretion of molecular entities such as oxygen, or TNF; ii) the mechanical interaction between cells; and iii) cellular processes including cell life cycle, cell death models, signal transduction.</p> <p>The multi-scale model was implemented and simulated using the PhysiBoSS framework (Letort et al. 2019). The dataset corresponds to different simulations trajectories obtained for alternative parameter values. The parameter explored are: i) the decay rate of the TNF after it binds the cell; ii) the TNF binding rate; and iii) the TNF secretion rate by NFkB activated cells.</p> <p>The dataset includes 48 different combinations of parameters. Each simulation is stored in a folder instance_[0-9]+ which includes the PhysiBoSS standard output files (<a href="https://github.com/gletort/PhysiBoSS/wiki">https://github.com/gletort/PhysiBoSS/wiki</a>). The root folder also includes other settings, logs, and outputs as well as the binary used to run the simulation.</p>
Impact of horizontal resolution on global ocean-sea-ice model simulations based on the experimental protocols of the Ocean Model Intercomparison Project phase 2 (OMIP-2)
<p>Datasets for the Geoscientific Model Development publication: "Impact of horizontal resolution on global ocean-sea-ice model simulations based on the experimental protocols of the Ocean Model Intercomparison Project phase 2 (OMIP-2)"</p> <p>Abstract: This paper presents global comparisons of fundamental global climate variables from a suite of four pairs of matched low- and high-resolution ocean and sea-ice simulations that are obtained following the OMIP-2 protocol (Griffies et al., 2016) and integrated for one cycle (1958-2018) of the JRA55-do atmospheric state and runoff dataset (Tsujino et al., 2018). Our goal is to assess the robustness of climate-relevant improvements in ocean simulations (mean and variability) associated with moving from coarse (~1º) to eddy-resolving (~0.1º) horizontal resolutions. The models are diverse in their numerics and parameterizations, but each low-resolution and high-resolution pair of models is matched so as to isolate, to the 20 extent possible, the effects of horizontal resolution. A variety of observational datasets are used to assess the fidelity of simulated temperature and salinity, sea surface height, kinetic energy, heat and volume transports, and sea ice distribution. This paper provides a crucial benchmark for future studies comparing and improving different schemes in any of the models used in this study or similar ones. The biases in the low-resolution simulations are familiar and their gross features – position, strength, and variability of western boundary currents, equatorial currents, and Antarctic Circumpolar Current – are 25 significantly improved in the high-resolution models. However, despite the fact that the high-resolution models “resolve’’ most of these features, the improvements in temperature or salinity are inconsistent among the different model families and some regions show increased bias over their low-resolution counterparts. Greatly enhanced horizontal resolution does not deliver unambiguous bias improvement in all regions for all models.</p>
Stan code from: Simulation modeling reveals the evolutionary role of landscape shape and species dispersal on genetic variation within a metapopulation
Different shapes of landscape boundaries can affect the habitat networks within them and consequently the spatial genetic-patterns of a metapopulation. In this study, we used a mechanistic framework to evaluate the effects of landscape shape, through watershed elongation, on genetic divergence among populations at the metapopulation scale. Empirical genetic data from four, sympatric stream-macroinvertebrates having aerial adults were collected from streams in Japan to determine the roles of species-specific dispersal strategies on metapopulation genetics. Simulation results indicated that watershed elongation allows the formation of river networks with fewer branches and larger topographic constraints. This results in decreased interpopulation connectivity but a lower level of spatial isolation of distal populations (e.g., those found in headwaters) occurring in the landscapes examined. Distal populations had higher genetic divergence when their downstream-biased dispersal (relative to upstream- and/or overland-biased dispersal) was high. This underscores the importance of distal populations influencing genetic divergence at the metapopulation scale for species having downstream-biased dispersal. In turn, lower genetic divergence was observed under watershed elongation when the genetic isolation of distal populations was decreased in such species. This strong association between landscape shape and evolutionary processes highlights the importance of natural, spatial architecture in assessing the effectiveness of conservation and management strategies.
CESM1.2 simulation output for: The role of westerly wind bursts during different seasons versus ocean heat recharge in the development of extreme El Niño in a climate model
<p>This is the subset of CESM1.2 model simulation output that was used for analysis and visualization of Yu and Fedorov [2020] (DOI:10.1029/2020GL088381). Please refer to README for details.</p>
Simulation data for the model of the Sagittarius stream in the presence of the Large Magellanic Cloud
<p>This archive contains simulations of the disrupting Sagittarius galaxy in the combined potential of the Milky Way and the Large Magellanic Cloud.</p> <p><strong>Sgr_snapshot</strong><br> contains the final (present-day) snapshot from the fiducial simulation with a triaxial Milky Way halo and M_LMC=1.5e11 Msun (see the readme file in that folder for details).</p> <p><strong>Sgr_snapshot_noLMC</strong><br> contains the same data but for a model without the LMC (which does not reproduce some aspects of the observations, but is nevertheless useful for a comparison with the other one).</p> <p><strong>potentials_triax</strong><br> contains the initial and subsequently evolving potentials of both the Milky Way and LMC, represented by multipole expansions, as well as the trajectory of the LMC and the reflex motion-induced acceleration of the Milky Way -- everything that is needed to study the dynamics of the Sgr stream and other objects in a time-dependent potential of the interacting Milky Way and LMC. This model corresponds to the stream simulation in the previous folder.</p> <p><strong>potentials_axisym</strong><br> contains the same data, but for another Milky Way halo model, which is axisymmetric rather than triaxial (note that in either case, its axis ratios vary with radius). It may be more convenient in certain applications, and produces a stream that fits the observations almost as well as the triaxial model.</p> <p><strong>scripts</strong><br> contains the Python scripts illustrating how to integrate orbits in a time-dependent potential, and how to construct initial conditions for these simulations (the parameter files for various choices of Milky Way halo potentials are also included). These scripts use the Agama framework, available at http://agama.software</p>
Amundsen Sea future MAR simulations forced by the CMIP5 multi-model mean
<p><strong>Amundsen Sea future MAR simulation forced by the CMIP5 multi-model mean (RCP8.5)</strong></p> <p>This future simulation is fully described in the following article:</p> <p>Donat-Magnin, M., Jourdain, N. C., Kittel, C., Agosta, C., Amory, C., Gallée, H., Krinner, G., and Chekki, M. Future surface mass balance and surface melt in the Amundsen sector of the West Antarctic Ice Sheet. <em>The Cryosphere</em>.</p> <p>The future is derived from the CMIP5 multi-model mean under the RCP8.5 scenario and covers the 2079-2108 period. The corresponding present-day simulation is available on <a href="http://doi.org/10.5281/zenodo.4308510">http://doi.org/10.5281/zenodo.4308510</a> and was thoroughly evaluated in the following TC paper: <a href="https://doi.org/10.5194/tc-14-229-2020">https://doi.org/10.5194/tc-14-229-2020</a></p> <p>See netcdf metadata for more information. Note that what is called runoff in the outputs is not actually a runoff (into the ocean) but more the net production of liquid water at the surface (which can either form ponds or flow into the ocean).</p> <p>Monthly files provided on MAR grid (see MAR_grid10km.nc). We also provide climatological (2079-2108 average) surface mass balance (SMB), surface melt rates and net liquid water production ("runoff") on a standard 8km WGS84 stereographic grid (see files ending as mean_polar_stereo.nc). Daily snowfall and surface melt rates are provided in ICE*nc.<br> <br> The interpolation to the stereographic grid is done using interpolate_to_std_polar_stereographic.f90. The fields are extrapolated to the ocean grid points so that ice sheet models with various ice-shelf extent can use this dataset.</p> <p>To extrapolate the SMB and surface melt projections to other warming scenarios or period, see eq. (2,3) in Donat-Magnin et al.</p> <p>The following variables are provided:</p> <ul> <li>CC Cloud Cover</li> <li>LHF Latent Heat Flux</li> <li>LWD Long Wave Downward</li> <li>LWU Long Wave Upward</li> <li>QQp Specific Humidity (pressure levels)</li> <li>QQz Specific Humidity (height levels)</li> <li>RH Relative Humidity</li> <li>SHF Sensible Heat Flux</li> <li>SIC Sea ice cover</li> <li>SP Surface Pressure</li> <li>ST Surface Temperature</li> <li>SWD Short Wave Downward</li> <li>SWU Short Wave Upward</li> <li>TI1 Ice/Snow Temperature (snow-layer levels)</li> <li>TTz Temperature (height levels)</li> <li>UUp x-Wind Speed component (pressure levels)</li> <li>UUz x-Wind Speed component (height levels)</li> <li>VVp y-Wind Speed component (pressure levels)</li> <li>VVz y-Wind Speed component (height levels)</li> <li>UVp Horizontal Wind Speed (pressure levels)</li> <li>UVz Horizontal Wind Speed (height levels)</li> <li>ZZp Geopotential Height (pressure levels)</li> <li>mlt Surface melt rate</li> <li>rfz Refreezing rate</li> <li>rnf Rainfall</li> <li>rof "Runoff" (i.e. net production of surface liquid water)</li> <li>sbl Sublimation</li> <li>smb Surface Mass Balance</li> <li>snf Snowfall</li> </ul>
Observations of AOD and GEOS-Chem simulation model output dataset
<p>Archive of observations of AOD (MODIS Deep Blue, MAIAC, AERONET) used to evaluate the simulations of using high resolution offline dust emission, upon submission of the following manuscript:</p> <p>Meng, Jun, R. V. Martin, P. Ginoux, M. Hammer, M. P. Sulprizio, D. A. Ridley, and A. van Donkelaar, Grid-independent high resolution dust emissions (v1.0) for chemical transport models: application to GEOS-Chem (version 12.5.0), Geoscientific Model Development, Submitted. </p> <p>This dataset can also be accessed via this FTP ftp://stetson.phys.dal.ca/jmeng/GMD_OfflineDust_data/ </p> <p>The folder "GMD_OfflineDust_data" contains three folders, "AERONET", "MODIS" and "GC_output", representing AERONET AOD, MODIS AOD and model outputs respectively. </p>
A Conceptual Model Framework for Analyzing, Simulating, Planning and Controlling of Supply-Demand Matching of Ecosystem Services in Agricultural Landscapes
<p>The video gives an explanation of our conceptual approach for the investigation of the adaptation of societal demands for ecosystem services in agricultural landscapes.</p>
MOSTWAS models, TWAS summary statistics, and simulation results for Bhattacharya and Love, 2020
<p>MOSTWAS models, TWAS results, simulation results, and comparison to BGW-TWAS results</p>
Model Simulation Results
<p>Simulation results from the SWASH MODEX model, .mat files showing the extent of variables as they pass over the mound (spatially varying, temporally averaged)</p>
The simulation model to the prognosis of material loss in wood processing
<p>The study was conducted in a production company operating in the wood processing industry. Geometric characteristics of input material were captured and used to derive statistical distributions, which were then included in the simulation model. The conducted experiments indicated that the quality of the simulation model was significantly affected by the quality and quantity of the sample, on the basis of which the stochastic model is estimated. It was shown that small sample for wood processing data was insufficient to capture process variability. On the other hand, excessive sample size (80 or more observations) for the material with high natural geometric variability, involves taking into account outliers, which may lower the overall prognostic quality of the simulation model. Based on the conducted simulation experiments, the recommended sample size which allows development of a reliable model for estimation of material loss in the analyzed manufacturing process, ranges from 40 to 60 measurements.</p>
Simulation Data of a Model with Ionospheric Effects for Global 21-cm Experiments
<p>The files contain integrated antenna temperature of two different models in binary.</p>
Data from: Use of simulation-based statistical models to complement bioclimatic models in predicting continental scale invasion risks
Invasive species represent one of the greatest risks to global biodiversity and economic productivity of agroecosystems. The development of certain novel crops—e.g., herbaceous perennial biomass crops—may create a risk of novel invasions by these crops. Therefore, potential benefits and risks need to be weighed in making decisions about their introduction and subsequent management. Ideally, such a weighing will be based on good estimates of invasion risks in realistic scenarios pertaining to actual landscapes of concern regarding invasion. Most previous large-scale analyses of invasion risk have used species distribution models and their established methods. Unfortunately, these approaches are unable to incorporate local scale biotic and spatial factors that influence invasion risk. Here we present a case study for how such factors can be efficiently incorporated in large-scale analyses of invasion risk, by extending simulation models with statistical modeling tools. By these means, we predict invasion risk at the scale of the entire United States for a major biomass crop, Miscanthus × giganteus. We then combine invasion risk predictions for this method with those from bioclimatic methods, producing a map of aggregated invasion risk that can offer more nuanced predictions of invasion risk than either approach alone. Lastly, we evaluate potential risks for invasive crops that differ in invasiveness traits, to examine how geographic patterns of invasion risk vary among invaders as a result of their particular constellation of traits.
Data from: Are cranial biomechanical simulation data linked to known diets in extant taxa? A method for applying diet-biomechanics linkage models to infer feeding capability of extinct species
Performance of the masticatory system directly influences feeding and survival, so adaptive hypotheses often are proposed to explain craniodental evolution via functional morphology changes. However, the prevalence of "many-to-one" association of cranial forms and functions in vertebrates suggests a complex interplay of ecological and evolutionary histories, resulting in redundant morphology-diet linkages. Here we examine the link between cranial biomechanical properties for taxa with different dietary preferences in crown clade Carnivora, the most diverse clade of carnivorous mammals. We test whether hypercarnivores and generalists can be distinguished based on cranial mechanical simulation models, and how such diet-biomechanics linkages relate to morphology. Comparative finite element and geometric morphometrics analyses document that predicted bite force is positively allometric relative to skull strain energy; this is achieved in part by increased stiffness in larger skull models and shape changes that resist deformation and displacement. Size-standardized strain energy levels do not reflect feeding preferences; instead, caniform models have higher strain energy than feliform models. This caniform-feliform split is reinforced by a sensitivity analysis using published models for six additional taxa. Nevertheless, combined bite force-strain energy curves distinguish hypercarnivorous versus generalist feeders. These findings indicate that the link between cranial biomechanical properties and carnivoran feeding preference can be clearly defined and characterized, despite phylogenetic and allometric effects. Application of this diet-biomechanics linkage model to an analysis of an extinct stem carnivoramorphan and an outgroup creodont species provides biomechanical evidence for the evolution of taxa into distinct hypercarnivorous and generalist feeding styles prior to the appearance of crown carnivoran clades with similar feeding preferences.
Data from: Study on the optimization of the deposition rate of planetary GaN-MOCVD films based on CFD simulation and the corresponding surface model
Metal-organic chemical vapour deposition (MOCVD) is a key technique for fabricating GaN thin film structures for light-emitting and semiconductor laser diodes. Film uniformity is an important index to measure equipment performance and chip processes. This paper introduces a method to improve the quality of thin films by optimizing the rotation speed of different substrates of a model consisting of a planetary with seven 6-inch wafers for the planetary GaN-MOCVD. A numerical solution to the transient state at low pressure is obtained using computational fluid dynamics. To evaluate the role of the different zone speeds on the growth uniformity, single factor analysis is introduced. The results show that the growth rate and uniformity are strongly related to the rotational speed. Next, a response surface model was constructed by using the variables and the corresponding simulation results. The optimized combination of the matching of different speeds is also proposed as a useful reference for applications in industry, obtained by a response surface model and genetic algorithm with a balance between the growth rate and the growth uniformity. This method can save time, and the optimization can obtain the most uniform and highest thin film quality.
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.