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1,028 results for “modelling & simulation”
Survey on the usage of Mathematical Modelling, Simulation and Optimization software
<p>This dataset contains the result of a survey we carried out in the context of the MSO4SC project in order to know which kinds of tools for simulation were using our stakeholders. The purpose was to prioritize functionalities depending on stakeholders' preferences. It was a survey with 41 questions grouped in 10 areas (impact of simulation software on their entities, usage of pre/post-processing, usage of visualization, etc...). The pdf file includes the list of questions for clarification. Such survey was answered by academia and industry from several European countries.</p>
Assessment of mutation probabilities of KRAS G12 missense mutants and their long-time scale dynamics by atomistic molecular simulations and Markov state modeling: Datasets.
<p>Datasets related to the publication [1].<br> Including:</p> <ul> <li>KRAS G12X mutations derived from COSMIC v.79 [http://cancer.sanger.ac.uk/cosmic/] (KRAS_G12X_mut_COSMICv79..xlsx)</li> <li>RMSFs (300-2000ns) of GDP-systems (300_2000rmsf_GDP_systems_RAW_AVG_SE.xlsx)</li> <li>RMSFs (300-2000ns) of GTP-systems (300_2000RMSF_GTP_systems_RAW_AVG_SE.xlsx)</li> <li>PyInteraph analysis data for salt-bridges and hydrophobic clusters (.dat files for each system in the PyInteraph_data.zip-file)</li> <li>Backbone trajectories for each system (residues 4-164; frames for every 1ns). Last number (e.g. _1) refers to the replica of the simulated system.</li> <li>backbone_4-164.gro/.pdb/.tpr -files (resid 4-164) </li> </ul> <p><br> [1] Pantsar T et al. Assessment of mutation probabilities of KRAS G12 missense mutants and their long-time scale dynamics by atomistic molecular simulations and Markov state modeling. <em>PLoS Comput Biol Submitted</em> (2018)</p>
Data for the publication "Incorporation of inline warm rain diagnostics into the COSP2 satellite simulator for process-oriented model evaluation"
<p>Michibata et al. (2019), currently under peer-review for publication in <em>Geoscientific Model Development</em>, incorporated a diagnostic tool for warm rain microphysics into the CFMIP Observation Simulator Package (COSP; Bodas-Salcedo et al. 2011; Swales et al., 2018), designed to evaluate model representations of aerosol–cloud–precipitation interactions at a fundamental process-level. The tool automatically generates two diagnostics related to warm rain microphysics during COSP execution in a host model. One is the contoured frequency by optical depth diagram (CFODD), which visualizes a cloud-to-rain microphysical vertical structure (Suzuki et al., 2015). The other diagnostic is a global map of warm rain fraction classified as non-precipitating clouds (< –15 dBZ<sub>e</sub>), drizzling clouds (–15 < dBZ<sub>e</sub>< 0), and precipitating clouds (0 < dBZ<sub>e</sub>).</p> <p>This repository contains the MIROC6/COSP2 input data and A-Train satellite statistics used in Michibata et al. (2019). A sample of the post-processing scripts for visualization using the GrADS software is also included in this repository.</p>
Homology modelling, molecular docking and molecular dynamics simulations of wild type and mutant human CYP2J2 with three polyunsaturated fatty acids
<p>This is the "parent" repository for the Data Note : "­Molecular dynamics simulations of the interaction of wild type and mutant human CYP2J2 with polyunsaturated fatty acids" by Abelak, Bishop-Bailey and Nobeli.</p> <p>It contains a document (<strong>Abelak_etal_Methods.pdf</strong>) describing the methods used to produce the data here and the data in all repositories supplementing it.</p> <p>It also contains a shell script (<strong>create_sim4_repeats.sh</strong>) that is typical of those used to set up the molecular dynamics simulations in the repositories supplementing this one.</p> <p>Finally, it contains the results of the homology modelling and docking simulations that formed the starting points for the molecular dynamics simulations in this study.</p> <p>Description of files in this dataset:</p> <p><strong>C2J2_min3_mod_noH.pdb</strong> : Homology model of the wild type CYP2J2 built from an alignment of templates with PDB ids: 1SUO, 2P85, 3EBS and 1Z10.</p> <p><strong>docking_wild_type_C2J2.zip</strong> : Nine docked poses of arachidonic acid docked to the homology model of the wild type CYP2J2.</p> <p>Details of how this data was produced is available in the Abelak_etal_Methods.docx document.</p>
Turbulent kinetic energy over large wind farms observed and simulated by the mesoscale model WRF (3.8.1)
<p>This repository contains the WRF configuration files necessary to reproduce the simulations <br> as described in Siedersleben et al. 2019 (https://doi.org/10.5194/gmd-2019-100)</p> <p>The file windturbines_GMD.txt contains the locations of <br> all windturbines implemented in the simulations. The corresponding attributes of each <br> wind turbine type is described in the wind-turbine-xx.tbl. Be aware that all windturbines use the same power and thrust coefficients only the different hub heights and rotor diameters are taken into account as described in Siedersleben et al. (2019).</p> <p>The namelist.input_nameOfSimulation files necessary to run the simulations are provided in this repository as well. You may notice that <br> there are less namelist files than simulations. The simulations not using a TKE source use the same namelists as the ones with a TKE a source. However, the WRF model needs to be recompiled using the manipolated module_wind_fitch.F (you find this file in this repository). The sensitivity studies investigating the impact of the uncertainties in the power and thrust coefficients use the namelist of the control simulation CNTRb, but with manipulated wind-turbine-x_modMin/Max.tbl wind turbine files.</p> <p>The two python files get_era5*.py can be used to retrieve the ERA5 data, driving the WRF model. <br> Note that the dates and pathes have to be adjusted in the python files. <br> After downloading the surface and model level data some postprocessing <br> is necessary as described nicely here: "http://valcap74.blogspot.com/2017/10/how-to-run-wrf-model-driven-by-era5-on.html". For this<br> purpose the simple script called postProcessERA5 (based on the blog entry mentioned above) can be used.</p>
LMDZOR-INCA global model simulations diagnostics for mineral dust direct radiative effet calculations
<p>This dataset contains the diagnostic variable used to estimate the mineral dust aerosol direct radiative effect from LMDZOR-INCA global simulations using different refractive index data and different size modes and a multimodal size distribution.</p> <p>The NetCDF files provide the radiation fields shortwave all sky (solswad, topswad) and clear sky (solswad0, topswad0) (sol is for the surface and top for the top of the atmosphere) and the longwave all sky (sollwad, toplwad) and clear sky (sollwad0, toplwad0), as monthly means over global grids.</p> <p>Data are provided for the mean, minimum and maximum of the complex refractive index from Di Biagio et al. (2017) ( https://doi.org/10.5194/acp-17-1901-2017 ) and the refractive index by Volz et al. (1973) ( <a href="https://doi.org/10.1364/AO.12.000564">https://doi.org/10.1364/AO.12.000564</a> ) in the longwave spectral range and for the refractive index by Balkanski et al. (2007) ( https://doi.org/10.5194/acp-7-81-2007) corresponding to 1.5% hematite by volume in the shortwave range.</p> <p>Simulations are performed for four lognormal size distributions with mass median diameters (sigma) of 1 µm (1.8), 2.5 µm (2), 7 µm (1.9), 22 µm (2). The multimodal run is performed on the size distribution obtained as the sum of the four modes combined follwing the mass fractions of 0.6%, 4.3%, 31.5%, and 63.6% for the four modes, respectively.</p> <p>Variables for the dust atmospheric load and optical depth at 550 nm for each mode are in the mean run for each mode.</p> <p>Input mass extinction efficiency (Ext, m2/g), absorption exitinction efficiency (Abs, m2/g), single scattering albedo (w) and asymmetry factor (g) for the different radiative bands at at some wavelengths used in the MODIS sensor are provided in the 1MODE_xxum_dust_optical_data_1.5dielectric_mixture.</p>
Personalized in silico model for radiation-induced pulmonary fibrosis | (source code, simulation input+output data)
<p>This repository concerns the supplementary material data of the research article entitled "<em>Personalised in silico model for radiation-induced pulmonary fibrosis</em>" that is published in the Royal Society Interface journal (rsif.royalsocietypublishing.org). More specifically, the repository contains the source code of the radiation-induced pulmonary fibrosis simulator, the results produced from the medical image analysis of this study (CT scans and RT dosage maps) for each patient case, the input files necessary to run the simulator and the corresponding output produced respectively. Each patient ID corresponds to each case documented in the research article.</p>
A Modified Doyle-Fuller-Newman Model Enables the Macroscale Physical Simulation of Dual-ion Batteries - Dataset and Software
<p>This dataset contains:</p> <p>- all the raw cycling data of the three-electrode cell used to gather the experimental data for the model validation (VMP data, exported with EC-LAB);<br>- the specific, processed data used in the model validation step (0.2C discharge, 5C discharge, EIS data);<br>- the COMSOL dual-ion battery model (version 6.0). IMPORTANT: activate the "Electric potential at the positive electrode current collector (only for EIS)" boundary condition when simulating impedance spectroscopy, and deactivate it when simulating charge/discharge curves; the charge-discharge profile can be modified by changing the duration of the test, the C-rate, and the conditions set in the "Events" section.</p> <p>Update: Fixed the model to work also in the 6.2 version of COMSOL (Substituted Dleff with Dleffxx in the modified weak expression of the cathode mass conservation equation). Download the new version!</p>
Supplementary material (part 2): "Evaluation of AROME Model Valley Wind Simulations in the Inn Valley, Austria"
<p><em>Part 2</em> of supplementary material for the Master's Thesis: "Evaluation of AROME Model Valley Wind Simulations in the Inn Valley, Austria" (Wibmer 2024, available <a href="https://resolver.obvsg.at/urn:nbn:at:at-ubi:1-151801">here</a>).</p> <p>Due to memory constraints, the supplementary material consists of two parts:</p> <ul> <li><em><strong>Part 1: </strong></em>(available <a href="https://doi.org/10.5281/zenodo.10849397" target="_blank" rel="noopener">here</a>) Includes Python scripts and model setup files, along with the first part of the datasets, including ERA-reanalysis data, observational data, and the preprocessed AROME model output (NetCDF files) of the <em>0.5-km</em> simulation.</li> <li><em><strong>Part 2: </strong></em>Includes the preprocessed AROME model output (NetCDF files) of the <em>1.0-km </em>and <em>2.5-km</em> simulations (see description below).</li> </ul> <p>To reproduce part of the figures, users must download the Python scripts and the preprocessed AROME model datasets (NetCDF files). <br>The Python scripts should be placed in the same parent folder because some of them depend on each other <strong>(!! Important !!).</strong><br>Original AROME model output files (GRIB2 format) are not published due to their large size.</p> <p>The naming convention for the AROME simulations uses OP* (where * represents the grid spacing in meters) to differentiate the model runs based on their horizontal<br>grid spacing:</p> <ul> <li><strong><em>OP2500:</em></strong> for 2.5 km</li> <li><em><strong>OP1000: </strong></em>for 1.0 km</li> <li><em><strong>OP500:</strong></em> for 0.5 km</li> </ul> <h3><strong>Datasets Part 2</strong></h3> <p>Due to memory constraints, the datasets needed for the analyses are split up into two parts. The second part of the supplementary material contains:</p> <ul> <li><strong>datasets_OP1000.tar.xz</strong>: Contains the preprocessed AROME-Aut model output data (NetCDF files) of the <em>1.0-km</em> simulation. </li> <li><strong>datasets_OP2500.tar.xz</strong>: Contains the preprocessed AROME-Aut model output data (NetCDF files) of the <em>2.5-km</em> simulation. </li> </ul> <p>The datasets of the<em> 0.5-km</em> simulation (<strong>datasets_OP500.tar.xz</strong>)<strong> </strong>can be found in Part 1 of the supplementary material (available <a href="https://doi.org/10.5281/zenodo.10849397" target="_blank" rel="noopener">here</a>).<br>The NetCDF datasets of the performed AROME-Aut simulations are packaged and compressed into <code><em><strong>.tar.xz</strong></em></code> files.<br>The Python scripts, available <a href="https://doi.org/10.5281/zenodo.10849397" target="_blank" rel="noopener">here</a>, require these NetCDF datasets for plotting and analyses routines.<br>The provided NetCDF datasets are preprocessed from the <em>GRIB2</em> output of the AROME-Aut simulations. <br>For the scripts to function properly, you need to adjust the path to the datasets within <code><strong>path_handling.py</strong></code><strong>.</strong></p> <p>Each <strong><code>datasets_OP*.tar.xz</code></strong> file contains NetCDF files for different type of levels: <em>surface, hybridPressure </em>(model levels)<em>, isobaricInhPa </em>(pressure levels),<em> meanSea </em>(mean sea level)<em>, heightAboveGround </em>(constant height levels)<em>. </em> The following naming convention for the datasets is used:</p> <ul> <li><strong>ds_OP*_<em>var</em>_hybridPressure_<em>[lon1, lon2, lat1, lat2]</em>.nc</strong>: Contains data on <em>hybrid pressure model levels</em> for a specific variable (<em>var</em>; e.g., <em>u, v, z, pres, q, t</em>) for the geographical extent defined in the brackets. </li> <li><strong>ds_OP*_interp_hybridPressure_<em>(lon,lat)</em>.nc</strong>: Combined dataset on <em>hybrid pressure model levels.</em> The data is bilinearly interpolated to the specified location <em>(lon, lat)</em>.</li> <li><strong>ds_OP*_<em>var</em>_surface_<em>whole</em>.nc</strong>: Contains data on <em>model surface </em>for a specified variable (<em>var</em>; e.g., <em>z, sp, t, tcc</em>) for the <em>whole </em>available domain extent.</li> <li><strong>ds_OP*_heightAboveGround_instant_<em>whole</em>.nc</strong>: Combined dataset on <em>height levels </em>(e.g., <em>2-m and 10-m</em>) for the <em>whole </em>available domain extent.</li> <li><strong>ds_OP*_<em>var</em>_meanSea_<em>whole</em>.nc</strong>: Contains data on <em>mean sea level</em> for specified variable (<em>var;</em> e.g., <em>prmsl</em>) for the <em>whole</em> available domain extent. </li> </ul> <p>The original GRIB2 files are not provided due to their large size. For further information about the GRIB2 files or the NetCDF datasets, please feel free to contact me.</p>
Benchmarking (multi)wavelet-based dynamic and static non-uniform grid solvers for flood inundation modelling (Simulation results)
<p>Simulation result data for Environment Agency benchmark test 5, Thamesmead hypothetical flood, and Carlisle 2005 case studies, using uniform DG2, adaptive MWDG2, adaptive HWFV1, non-uniform DG2, non-uniform FV1 and non-uniform ACC solvers. </p> <p>Model results are archived in 3 zip files:</p> <ul> <li>EA5.zip contains results of Environment Agency test 5 (Néelz and Pender, 2013)</li> <li>Thamesmead.zip contains results of Thamesmead hypothetical flood (Liang et al., 2008)</li> <li>Carlisle.zip contains results of Carlisle 2005 flooding (Neal et al., 2009)</li> </ul> <p>The results are stored with the following file extensions:</p> <ul> <li>".wd" for 2D flood inundation maps in ESRI ASCII format</li> <li>".stage" for water depth or water level time-series at staging points in tabulated text format</li> <li>".velocity" for velocity time-series at staging points in tabulated text format</li> </ul> <p>Model outputs are stored under directories named for each solver.</p> <p><strong>References</strong></p> <p>Néelz, S., & Pender, G. (2013). Benchmarking the latest generation of 2D hydraulic modelling packages. <em>Environment Agency: Bristol, UK</em>.</p> <p>Liang, Q., Du, G., Hall, J. W., & Borthwick, A. G. (2008). Flood Inundation Modeling with an Adaptive Quadtree Grid Shallow Water Equation Solver. <em>Journal of Hydraulic Engineering</em>, <em>134</em>(11), 1603–1610. https://doi.org/10.1061/(ASCE)0733-9429(2008)134:11(1603)</p> <p>Neal, J. C., Bates, P. D., Fewtrell, T. J., Hunter, N. M., Wilson, M. D., & Horritt, M. S. (2009). Distributed whole city water level measurements from the Carlisle 2005 urban flood event and comparison with hydraulic model simulations. <em>Journal of Hydrology</em>, <em>368</em>(1–4), 42–55. https://doi.org/10.1016/j.jhydrol.2009.01.026</p> <p> </p>
3D geological models of dolomitized clinoforms and flow simulation results: scenario 2 in Teoh, C.P. et al (2021)
<p>3D geological models of dolomitized clinoforms (10 different realisations) and flow simulation results according to Scenario 2 in Teoh, C.P. et al (2021) doi:<a href="http://doi.org/10.1016/j.marpetgeo.2021.105344">10.1016/j.marpetgeo.2021.105344</a>.<br> Models are built using surface-based modelling approach (doi:<a href="https://doi.org/10.1007/s11004-018-9764-8">10.1007/s11004-018-9764-8</a>). Flow simulations are run with IC-FERST, using unstructured tetrahedral meshes that adapt to geological heterogeneity and flow behaviour throughout the simulation to improve simulation quality and performance.</p> <p>For each of the 10 stochastic realisations, 5 geological models are available with corresponding flow simulation results:<br> - Only clinoforms and facies boundaries<br> - 1 dolomite body per clinothem (~20% dolomite)<br> - 2 dolomite bodies per clinothem (~40% dolomite)<br> - 3 dolomite bodies per clinothem (~60% dolomite)<br> - 4 dolomite bodies per clinothem (~80% dolomite)<br> <br> Input model files for simulation are provided in Exodus (.e) and GMSH (.msh) formats.<br> Flow simulation settings are provided for IC-FERST in .mpml files (<a href="http://multifluids.github.io/">multifluids.github.io</a>)<br> Flow simulation results are provided as:</p> <ul> <li> 3D unstructured adaptive mesh in .vtu format, which can be opened with Paraview (www.paraview.org). Time interval between successive mesh outputs is 1 month.</li> <li> In- and outflow rates and volumetric proportions per phase in .csv</li> </ul> <p>Naming of files and folders:<br> <em>Sxxxxxx_yyyyz</em> where:<br> '<em>xxxxxx</em>' is the stochastic seed number used to sample the input statistics and create the geological model<br> '<em>yyyy</em>' is either 'clino' or 'dolo' to indicate if the model represents respectively only clinoforms, or contains dolomite bodies <br> '<em>z</em>' corresponds to the number of dolomite bodies per clinothem</p>
3D geological models of dolomitized clinoforms and flow simulation results: scenario 1 in Teoh, C.P. et al (2021)
<p>3D geological models of dolomitized clinoforms (10 different realisations) and flow simulation results according to Scenario 1 in Teoh, C.P. et al (2021) doi:<a href="http://doi.org/10.1016/j.marpetgeo.2021.105344">10.1016/j.marpetgeo.2021.105344</a>.<br> Models are built using surface-based modelling approach (doi:<a href="https://doi.org/10.1007/s11004-018-9764-8">10.1007/s11004-018-9764-8</a>). Flow simulations are run with IC-FERST, using unstructured tetrahedral meshes that adapt to geological heterogeneity and flow behaviour throughout the simulation to improve simulation quality and performance.</p> <p>For each of the 10 stochastic realisations, 5 geological models are available with corresponding flow simulation results:<br> - Only clinoforms and facies boundaries<br> - 1 dolomite body per clinothem (~20% dolomite)<br> - 2 dolomite bodies per clinothem (~40% dolomite)<br> - 3 dolomite bodies per clinothem (~60% dolomite)<br> - 4 dolomite bodies per clinothem (~80% dolomite)<br> <br> Input model files for simulation are provided in Exodus (.e) and GMSH (.msh) formats.<br> Flow simulation settings are provided for IC-FERST in .mpml files (<a href="http://multifluids.github.io/">multifluids.github.io</a>)<br> Flow simulation results are provided as:</p> <ul> <li> 3D unstructured adaptive mesh in .vtu format, which can be opened with Paraview (www.paraview.org). Time interval between successive mesh outputs is 1 month.</li> <li> In- and outflow rates and volumetric proportions per phase in .csv</li> </ul> <p>Naming of files and folders:<br> <em>Sxxxxxx_yyyyz</em> where:<br> '<em>xxxxxx</em>' is the stochastic seed number used to sample the input statistics and create the geological model<br> '<em>yyyy</em>' is either 'clino' or 'dolo' to indicate if the model represents respectively only clinoforms, or contains dolomite bodies <br> '<em>z</em>' corresponds to the number of dolomite bodies per clinothem</p>
Remote-sensing measurements and model simulations of peroxyacetyl nitrate (PAN)
<p>Ground-based FTIR and IASI-A and -B measurements of PAN, supplemented with GEOS-Chem simulations.</p> <p>End users of these data sets are invited to contact the authors to make sure they are using the data properly and check about the possible availability of more recent products.</p>
Dataset for simulation of a low-carbon urban energy system using the Backbone model
<p>The dataset contains the input data for cost optimization of an urban energy system. The case study has been described in the article "Impact of power-to-gas on the cost and design of the future low-carbon urban energy system" of Applied Energy.</p> <p>The dataset is in Microsoft Excel format. To make it available for GAMS, one should use e.g. the attached shell script (requires GAMS installation) to convert it to *.gdx file. The generation expansion model is available in the Git repository https://gitlab.vtt.fi/backbone/backbone (under branch projik/planet).</p>
Code and data for "KGML-ag: A Modeling Framework of Knowledge-Guided Machine Learning to Simulate Agroecosystems: A Case Study of Estimating N2O Emission using Data from Mesocosm Experiments "
<p>This is code and data for manuscript: <br> "KGML-ag: A Modeling Framework of Knowledge-Guided Machine Learning to Simulate Agroecosystems: <br> A Case Study of Estimating N<sub>2</sub>O Emission using Data from Mesocosm Experiments"<br> Licheng Liu, Shaoming Xu, Zhenong Jin*, Jinyun Tang, Kaiyu Guan, Timothy J. Griffis, <br> Matt D. Erickson, Alexander L. Frie, Xiaowei Jia, Taegon Kim, Lee T. Miller, Bin Peng, Shaowei Wu, Yufeng Yang, Wang Zhou, Vipin Kumar</p> <p>All the files belong to Prof. Zhenong Jin, University of Minnesota, UA. jinzn@umn.edu<br> "code" foler includes code for data processing, model training, and results plotting.<br> "trained_model_saved" includes all trained model so you can use to reproduce the results showed in the study;<br> "data" includes all data presented in the study. Finetuning data is refering to Miller, L.T. , Griffis, T. J., Erickson, M. D., Turner, P. A., Deventer, M. J., Chen, Z., Yu, Z., Venterea, R.T., Baker, J. M., and Frie, A. L. (2021). Response of nitrous oxide emissions to future changes in precipitation and individual rain events. Journal of Environmental Quality, In review</p>
Simulated data from abmAnimalMovement: An R package for simulating animal movement using an agent-based model
<p>Contained here are the data simulated as part of the manuscript: "abmAnimalMovement: An R package for simulating animal movement using an agent-based model" that can be found at: https://github.com/BenMMarshall/abmAnimalMovement (and archived at: https://doi.org/10.5281/zenodo.6951937).</p> <p>- BADGER_locations.csv: A csv file that contains the realised locations of the example badger simulation, where each row is equal to a timestep. Columns include: timestep, the timestep as an integer; x, the x coordinate of the animal; y, the y coordinate of the animal; sl, the step length between locations used during the simulation; sl_rescale the rescale factor required to return step lengths back to the input scale; ta, turning angle between locations in degrees; behave, the behavioural mode the animal was in at a given timestep; chosen, the location chosen out of the number of options available; destination_x and destination_y the point the animal was attracted to at that time (note exploratory behaviour is not subject attraction).</p> <p>- BADGER_options.csv: A csv file that contains the options available to the example badger simulation over the entire simulation duration, where each row is equal to an option repeated for each timestep. Columns include: timestep, the timestep as an integer; oall_x, and oall_y show the x and y coordinates of all the options available to an animal at a timestep; oall_steplengths are the step lengths from the current location compared to all the options.</p> <p>- completelist.RDS: This RDS file contains a list object of length three, where the full simulation outputs from each three examples are stored. Each species slot contains the “locations” dataframe (see description of locations.csv), the "options" dataframe (see description of options.csv), and a nested list containing all the "inputs" used to generate the simulated results (split into subsections: inputs_basic that contains inputs linked to simulation duration and intensity, inputs_destination that contains inputs linked to destination and attraction aspects, inputs_movement that contains inputs linked to movement capacity and behavioural switching, inputs_cycle that contains inputs linked to activity cycling, inputs_layerSeed that contains the environmental matrices and seed). A fourth object is returned called "others" that captures all other outputs, mainly used internally for debugging and checking.</p> <p>- KINGCOBRA_locations.csv: A csv file that contains the realised locations of the example king cobra simulation, where each row is equal to a timestep. The file structure follows the same as the BADGER_options.csv file.<br>KINGCOBRA_options.csv: A csv file that contains the options available to the example king cobra simulation over the entire simulation duration, where each row is equal to an option repeated for each timestep. The file structure follows the same as the BADGER_options.csv file.</p> <p>- VULTURE_locations.csv: A csv file that contains the realised locations of the example vulture simulation, where each row is equal to a timestep. The file structure follows the same as the BADGER_locations.csv file.<br>VULTURE_options.csv: A csv file that contains the options available to the example vulture simulation over the entire simulation duration, where each row is equal to an option repeated for each timestep. The file structure follows the same as the BADGER_locations.csv file.</p> <p>- eg_landscapedata_completelist.RDS: This RDS file contains the landscape matrices required for recreating the simulated outputs described in the manuscript named above. It is a list of three objects ("shelter", "forage", "movement"), each a numeric matrix of equal size, with values describing the quality of each landscape characteristic.</p> <p>- argument_table.csv: Descriptive table of the simulation inputs used in the walk-through manuscript.</p>
Leaf water and stem cellulose oxygen isotope ratios simulated with global dynamic vegetation model LPX-Bern
<p>Description of leaf water and stem cellulose oxygen isotope ratios simulated with LPX-Bern</p> <p>Citation of describing paper:</p> <p>Keel SG, Joos F, Spahni R, Saurer M, Weigt RB, Klesse S. 2016. Simulating oxygen isotope ratios in tree ring cellulose using a dynamic global vegetation model, Biogeosciences, 13, 3869–3886, 2016 doi:10.5194/bg-13-3869-2016</p> <p>download: www.biogeosciences.net/13/3869/2016/</p> <p>General Information: Format: NetCDF, gridded</p> <p>Model: Dynamic global vegetation model LPX-Bern Version 1.0 (Land surface Processes and eXchanges, Bern) (Spahni et al., 2013; Stocker et al., 2013)</p> <p>Resolution: 3.75° x 2.5° lat/lon global Time: Monthly from Jan 1960 to Dec 2012</p> <p>Variables:</p> <p>cellu18: monthly stem cellulose δ18O (per mil) lw18: monthly leaf water δ18O (per mil) -2 NPP: monthly net primary production (g C m ) FPC: monthly fractional plant cover</p> <p>Dimensions: i=longitude, j=latitude, l=time, k=plant functional type Codes for plant functional types (k):</p> <ol> <li> <p>1 tropical broad-leaved evergreen</p> </li> <li> <p>2 tropical broad-leaved deciduous (raingreen)</p> </li> <li> <p>3 temperate needle-leaved evergreen</p> </li> <li> <p>4 temperate broad-leaved evergreen</p> </li> <li> <p>5 temperate broad-leaved deciduous (summergreen)</p> </li> <li> <p>6 boreal needle-leaved evergreen</p> </li> <li> <p>7 boreal needle-leaved deciduous (summergreen)</p> </li> <li> <p>8 boreal broad-leaved deciduous (summergreen)</p> </li> <li> <p>9 temperate herbaceous</p> </li> <li> <p>10 tropical herbaceous</p> </li> </ol>
ITACA agent-based simulation model results
<p>This files downloaded present the raw results and derived indicators per scenario considered obtained with the agent-based model developed for policy assessment in the scope of the ITACA project (<a href="https://www.itaca-h2020.eu/">https://www.itaca-h2020.eu/</a>).</p> <p>The files are structured in three different case studies:</p> <ul> <li>CS01 - Past unsuccessful and delayed solutions</li> <li>CS02 - Past successful solutions</li> <li>CS03 - Future solutions</li> </ul> <p>In each case study there are included a set of scenarios, each of them testing one or several policy measures. Each scenario file includes the short name of the policy under study. For example, "Results_visualisation_CS01_Mandates.xlsm" includes the results when testing mandates as policy measure in the CS01 case study. The corresponding raw outputs of the simulator are included in the folder "yyyymmdd_hhmmss_CS01_Mandates".</p> <p>Each excel file contains the following tabs:</p> <ul> <li>Files - Files loaded to create the file. The loading interactive buttons have been disabled to avoid errors.</li> <li>H0 - Tab containing the visualisation of different raw outputs for the adopters agents. The user can select all agents or one specific agent to look at the results by selecting its ID in the slicers on top of each agent type (enable editing in excel to do so).</li> <li>H0_KPI_tech - Shows the number of adoptions per technology and agent type</li> <li>H0_KPI_eco - Shows the surpluses per agent type and total social welfare</li> <li>H0_KPI_eco_metrics - Same results, disaggregated in a tabular format.</li> <li>H0_KPI_op - Shows the total fuel burnt, throughput and Determined Unitary Cost (DUC) for enroute and terminal ANS.</li> <li>H1 - Configuration file used as input for the simulation.</li> <li>H2 - Raw results for airlines.</li> <li>H3 - Raw results for airports.</li> <li>H4 - Raw results for enroute ANSPs.</li> <li>H5 - Raw results for terminal ANSPs.</li> <li>H6 - Raw results per route.</li> <li>H7 - Raw results for regulators.</li> </ul> <p>The raw results folders include, apart from the data shown in the visualisation excel files, the input data per stakeholder, policy parameters, technology parameters, exogenous variables that affect the agents and the charging zones defined in the European airspace. </p> <p>The complete description of the case studies and scenarios tested is included in ITACA's deliverable <a href="http://www.nommon-files.es/itaca/ITACA-D5.1_Impact_assessment_of_policies_and_regulations_to_boost_ATM_technology_adoption_v01.00.00.pdf">D5.1 Impact Assessment of Policies and Regulations to Boost ATM Technology Adoption</a>.</p> <p>Thank you for downloading the dataset! It would be very helpful if you share your view on the data show with us. We have created the following short <a href="https://forms.gle/EgVAW57siXizsEn8A">questionnaire</a> to facilitate the task. </p>
ConFiRMa dataset_04: simulation of tests on CRM strengthened masonry buildings with the OOFEM code (intermediate, multi-layer level modelling)
<p>The Dataset collects the input files developed for the simulation of tests on one, two and three stroeys masonry buildings strengthened through Composite Reinforced Mortar with the free open-source code OOFEM (intermediate, multi-layer level modelling).</p> <p>OOFEM Version 2.5 (https://doi.org/10.5281/zenodo.4339630) was used for running the analyzes.</p> <p>ReadMe file provides a description of the different input files.</p>
Atmospheric clumped O2 isotope composition simulation data and analysis scripts from EMAC/aMC models
<p>This publication contains source code, data and analysis scripts/results of the simulations presented in the following manuscript:</p> <blockquote> <p>Laskar, A.H., G.A. Adnew, S.S. Gromov, R. Peethambaran, B. Steil, J. Lelieveld, T. Blunier and T. Röckmann (2022). "Large variations in atmospheric oxidants and temperature during the Holocene" (in review)</p> </blockquote> <p> </p> <p><strong>EMAC simulations analysis</strong></p> <p>The analysis contains integrals of species burdens and other atmospheric physicochemical parameters obtained with the clumped isotopes of oxygen (CIO)-enabled ECHAM/MESSy Atmospheric Chemistry model (EMAC, see <a href="https://www.messy-interface.org">MESSy consortium website</a> for more information) model in various climate states. Simulations were performed in 2021–2022 at the <a href="https://www.dkrz.de">German Climate Computing Centre</a> (DKRZ) with the support of the <a href="https://www.palmod.de">PalMod project</a>.</p> <p>Analysis data is stored in human/machine-readable file <code>D36-EMAC-analysis.dat</code>, please refer to its header for variables description, etc.</p> <p>Additional (to those presented in the manuscript) analysis plots from EMAC data analysis are available in <code>D36-EMAC-analysis.vsz</code> (see the hardcopy in <code>D36-EMAC-analysis.pdf</code>) prepared using the <a href="https://veusz.github.io">Veusz</a> software.</p> <p> </p> <p><strong>2BM/MC (two-box Monte-Carlo) model code, simulation data and analysis</strong></p> <p>2BM/MC code/simulation setup is implemented within the advanced Monte-Carlo framework (aMC) and is available in the <a href="https://gitlab.com/sergey.gromov/amc/-/tree/vpCIO">respective repository</a>. A copy of the source code used to perform simulations is provided here (see <code>aMC-vpCIO.tar.gz</code> archive).</p> <p>2BM/MC output is stored in the <a href="https://www.unidata.ucar.edu/software/netcdf/">netCDF format</a> (ver. 4) and can be read in by any compatible software. The output contains probe statistics (reference <em>probed</em> distributions of the variables) in <code>vpCIO-probe_stat-*.nc</code> and resulting statistics (distributions <em>matching</em> given criteria, i.e. changes to the Δ36 signature vs. PD conditions) in <code>vpCIO-delta-*.nc</code> files, respectively.</p> <p>We use <a href="https://ferret.pmel.noaa.gov">NOAA Ferret</a> software to derive additional statistics of the third parameter (viz. average STE (<em>S</em>) changes) over the obtained 2D frequency histograms of other parameters (viz. changes to equilibration rate (<em>Req)</em> and temperature (<em>Teq</em>)). The scripts exemplifying this calculation are presented in <code>D36-vpCIO-analysis__proc*</code> files, which output results/overview plots in <code>vpCIO-delta-*__proc.nc</code> and <code>vpCIO-delta-*.gif</code> files.</p> <p>The analysis of the 2BM/MC simulation is available in <code>D36-vpCIO-analysis.vsz</code> script (see the hardcopy in <code>D36-vpCIO-analysis.pdf</code>) prepared using the <a href="https://veusz.github.io">Veusz</a> software. Note that some plots require the abovementioned third-parameter statistics as input.</p> <p><strong>Performing simulations with 2BM/MC</strong></p> <p>In order to perform simulations (e.g. with altered parameters), please follow the <a href="https://gitlab.com/sergey.gromov/amc/-/tree/vpCIO#integrating-your-code-building-executing">respective guide</a> for and build the <code>aMC-vpCIO</code> model. A typical sequence of shell commands to build and run 2BM/MC (which is referred to as <code>vpCIO</code> generic model within the <code>aMC</code>) is:</p> <pre><code># clone the distribution and check-out `vpCIO` branch or particular commit referenced in the repository history [user@pc]/~> git clone https://gitlab.com/sergey.gromov/amc.git [user@pc]/~> cd amc [user@pc]/~/amc> git checkout vpCIO # or unpack the source code available in this publication: [user@pc]/~> tar -xvf `aMC-vpCIO.tar.gz` [user@pc]/~> cd amc # build the aMC/vpCIO model executable # (note that you need at least a GCC or Intel compiler suite and respective netCDF v.4 library Fortran interface available in your environment): [user@pc]/~/amc> make vpCIO # adjust model setup (see the `vpCIO/amc.nml` namelist) ... # perform simulation [user@pc]/~/amc> cd vpCIO [user@pc]/~/amc/vpCIO> ./xamc # calculate additional statistics/produce overview with NOAA Ferret: [user@pc]/~/amc/vpCIO> ferret -gif -script D36-vpCIO-analysis__proc.jnl MH [user@pc]/~/amc/vpCIO> ./D36-vpCIO-analysis__proc</code></pre> <p>Note that output files contain the build timestamp and repository commit hash for the code used in the simulation, e.g.:</p> <pre><code>[user@pc]/~/amc/vpCIO> ncdump -h ./vpCIO-delta-dMH.nc | grep 'build' :build = "vpCIO@https://gitlab.com/sergey.gromov/amc__aMC_v1.9-110-g2566229@2022-12-09T16:43:12+01:00__built@2022-12-09T16:48:03+01:00__<user>@<email.com>" ;</code></pre> <p> </p> <p>Please contact Sergey Gromov ( sergey.gromov (at) mpic.de ) for additional information and access to the original experiment data.</p> <p> </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.