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55 results for “Model Configuration”
Assessment of the sea surface temperature diurnal cycle in CNRM-CM6-1 based on its 1D coupled configuration - model outputs
<p>These tar file are associated with an article submitted to Geoscientific Model Development under identification number gmd-2021-413 (https://www.geoscientific-model-development.net): Assessment of the sea surface temperature diurnal cycle in CNRM-CM6-1 based on its 1D coupled configuration<br> By A. Voldoire, R. Roehrig, H. Giordani, R. Waldman, Y. Zhang, S. Xie, MN Bouin</p> <p>3 files correspond to code components that can be distributed freely</p> <p>- surfex.tgz for the surfex v8.0 distributed under a Cecill-C License</p> <p>- oasis-mct-3.0.tgz for oasis-mct3.0 distributed under a GNU General Public License</p> <p>- nemo_v3.6.tgz for the nemo, distibuted under a Cecill-C License</p> <p>These three components are mainly fortran codes.</p> <p>The last file "<a href="https://zenodo.org/api/files/e94922e7-22eb-445b-acc3-03e11ca1af6b/CNRM-CM6-1D_published_experiments.tgz?versionId=2460ec63-89f5-415f-9613-1954f048b238">CNRM-CM6-1D_published_experiments.tgz </a>" contains all model outputs that have been used in this article. These model outputs are in netcdf format and organized by experiment.</p>
Systematic DFT Modeling van der Waals Heterostructures from a Complete Configurational Basis Applied to γ-PC/WS2
<p>See the paper:</p> <p> </p> <p>Celis, J.; Cao, W. <em>J. Chem. Theory Comput.</em> <strong>2024</strong>, 20, 6, 2377-2389</p>
Trajectories of backtracked passive particles for: "CARIB12: A Regional Community Earth System Model / Modular Ocean Model 6 Configuration of the Caribbean Sea"
<p>This collection hosts the trajectories of bactracked passive particles using Ocean Parcels v2.0 (The Parcels v2.0 Lagrangian framework: new field interpolation schemes. Delandmeter, P and E van Sebille (2019), <em>Geoscientific Model Development</em>, <em>12</em>, 3571–3584) and ocean surface velocity fields from the output of: "CARIB12: A Regional Community Earth System Model / Modular Ocean Model 6 Configuration of the Caribbean Sea". </p> <p>trajectories_2009.tar: trajectories for particles released between 2009-01-01 and 2009-12-31</p> <p>trajectories_2010.tar: trajectories for particles released between 2010-01-01 and 2010-12-31 (as shown in "CARIB12: A Regional Community Earth System Model / Modular Ocean Model 6 Configuration of the Caribbean Sea")</p> <p>trajectories_2011.tar: trajectories for particles released between 2011-01-01 and 2011-12-31</p> <p>release_sites.csv: release sites for each release date.</p> <p>ocean_parcels_backtrack_VIB_carib12.py : python script to run ocean parcels to generate the trajectories published here.</p>
2D Ising model Monte Carlo configurations for the paper Entropy from Machine Learning
<p>Monte Carlo configurations for the 2D Ising model on a periodic 20x20 lattice used in the paper <em>Entropy from Machine Learning.</em></p> <p>Contains:</p> <ul> <li>20000 configurations for each temperature in the range T=1.0 to T=4.0 in 0.1 intervals</li> <li>40000 configurations at T=Tc</li> <li>README.md file with information about reading into Python</li> </ul> <p>See repository <a href="https://github.com/rmldj/ml-entropy">github.com/rmldj/ml-entropy.</a></p>
Displacement time series from Foamquake and Gelquake in single- and double asperity configurations: Supplementary material to "Scaled seismotectonic models of megathrust seismic cycles through the lens of dynamical system theory"
<p><span>This dataset includes displacement data from 4 experiments performed with Foamquake and Gelquake (Mastella et al. 2022, Corbi et al., 2013), two scaled seismotectonic models reproducing the megathrust seismic cycle running at the Laboratory of Experimental Tectonics LET (Univ. Roma Tre). These models enable the generation of hundreds of quasi-periodic cycles of stress accumulation and sudden release through the spontaneous nucleation of frictional instabilities within one or many analog seismic asperities. Models are monitored by the means of a high-resolution top-view monitoring camera acquiring images at 7.5 and 50 frames per second for Gelquake and Foamquake, respectively. This dataset has been created with particle image velocimetry (PIV, using MatPIV (Sveen 2004)) through the cross-correlation between consecutive images. The PIV provides us with velocity field time series. These are integrated to obtain displacement time series. From the whole model surface, in each experiment we selected data from a cross-section striking parallel to the trench and located at the downdip center of the asperities. Cross sections are discretized in 28 and 29 target points in Gelquake and Foamquake, respectively. </span></p> <p><span>Displacement time series have been normalized to zero mean and unit variance to ensure the same level of magnitude for comparison between different experiments. Linear and second order polynomial trends have been removed to make the stick-slip confined in a given range and avoid non-stationary behavior. Time series data are not passed through filters (e.g., smoothing or moving average).</span></p> <p><span>Filename informs about the nature of the analog upper plate (i.e., foam and gel) and geometrical configuration of asperities (i.e., mono and twin). Together with individual files for each experiment, this dataset includes a Matlab script (i.e., all_timeseries.m) that allows visualization of displacement time series from individual target points. </span></p> <p><span>This dataset is supplementary to the paper in SEISMICA "Scaled seismotectonic models of megathrust seismic cycles through the lens of dynamical system theory” by Corbi et al. (2024), where detailed descriptions of models and experimental results can be found.</span></p>
The configurations, inputs and outputs of the EFDC model for all simulated episodes
<p>TAIHU EFDC.zip includes two file folders named 2015 and 2018. The 2015 file folder is the configurations, inputs and outputs of the EFDC model for the numerical experiment named EFDC of 2015. The 2018 file folder is the configurations, inputs and outputs of the EFDC model for the numerical experiment named EFDC of 2018.</p>
Molecular models of the FtsQ-FtsL-FtsB-FtsW-FtsI complex (FtsQLBWI) in mono- and diprotomeric configurations
<p>The data consists of five Protein Data Bank (PDB) structure files of the complex formed by FtsQ, FtsL, FtsB, FtsW, and FtsI of the divisome of Escherichia coli (FtsQLBWI). The five PDB files consist of an original AlphaFold2 model, partially validated through mutagenesis in vivo, and a series of derivatives remodeled seeking insight into the potential structural transitions that lead to activation of the FtsWI complex, which produced peptidoglycan during cell division. In the original model (file <strong>FtsQLBWI_protomer_original.pdb</strong>), FtsLB serves as a support for FtsI, placing its periplasmic domain in an extended and possibly active conformation. We remodeled the periplasmic domain of FtsI to assess it the model is compatible with a compact and possibly inactive conformation (file <strong>FtsQLBWI_protomer_compact.pdb</strong>). Additionally, the complex was remodeled to assume an Fts[QLBWI]<sub>2</sub> diprotomeric configuration, using FtsLB as a central hub (file <strong>FtsQLBWI_diprotomer_clashing.pdb</strong>). This was performed by applying the C2 symmetry operation (180° rotation) to the Fts[QLBWI]<sub>1</sub> complex that reconstructs the Fts[LB]<sub>2</sub> complex in the Y-model configuration from the Fts[LB]<sub>1</sub> AlphaFold2 prediction. In this model, a severe steric overlap occurs between FtsQ and FtsI, which occupy the same region of space adjacent to FtsLB. To address whether this clash could be solved by providing flexibility to a hinge in the CCD region of FtsLB, we used a procedure, based on docking of FtsQ with HADDOCK followed by loop reconstruction with Rosetta (file <strong>FtsQLBWI_diprotomer_extended.pdb</strong>). Finally, we reconfigured the initial diprotomeric model in a compact state (file <strong>FtsQLBWI_diprotomer_compact.pdb</strong>).</p>
WRF model configuration and data used for the NHESS manuscript "Heat wave characteristics: evaluation of regional climate model performances for Germany"
<p>The file contains:</p> <ul> <li>the namelist.input document with the description of the WRF model configuration used in Warscher et al. (2019)</li> <li>WRF simulation outputs from the reanalysis run: daily values of maximum temperature for the time period 1980-2009 from the innermost (5 km grid resolution) and second innermost (15 km) domain; from both domains the same section, relevant for the study, was taken; the data was bilineraily interpolated to 12.5 km horizontal grid resolution to match the EUR-11 CORDEX format</li> </ul>
Molecular models of the FtsQ-FtsL-FtsB-FtsW-FtsI complex (FtsQLBWI) in mono- and diprotomeric configurations
Open the record for dataset details and reuse information.
Cluster configurations of the Hegselmann-Krause model on network ensembles
<p>This is the raw data underlying the results of the preprint [arxiv:2102.10910](https://arxiv.org/abs/2102.10910).</p> <p> </p> <p>## Data</p> <p>For each measured combination of the confidence and system size, there is one gzipped<br> file. For different ensembles, we collected data in different ranges and quality.<br> The paramters are:</p> <p>* Number of samples `m` per parameter combination<br> * Range `r` of confidences epsilon<br> * Distances `d` between values of epsilon (basically the resolution of the data)<br> * Largest size `N_max`</p> <p>The single files follow a naming scheme of `n{N}_e{epsilon}.cluster.dat.gz`, where<br> `{N}` signals the system size of the simulation and `{epsilon}` is the confidence<br> value of the simulation (without a decimal point, i.e., `0050` corresponds to `epsilon = 0.050`).<br> The sizes `N` are usually powers of two (or for the lattices, perfect squares close to powers of two).</p> <p>We present the data for each ensemble in one archive.</p> <p><br> * Fully connected `full.tar`<br> * `m = 1000`, `r = [0.0, 0.6]`, `d = 0.001`, `N_max = 262144`<br> * Barabasi Albert with a mean degree of 4 `BA4.tar`<br> * `m = 1000`, `r = [0.0, 0.6]`, `d = 0.002`, `N_max = 32768`<br> * Barabasi Albert with a mean degree of 10 `BA10.tar`<br> * `m = 1000`, `r = [0.0, 0.6]`, `d = 0.001`, `N_max = 65536`<br> * Square lattice with first nearest neighbors `lat1.tar`<br> * `m = 1000`, `r = [0.0, 0.6]`, `d = 0.001`, `N_max = 16384`<br> * Square lattice with second nearest neighbors `lat2.tar`<br> * `m = 1000`, `r = [0.0, 0.6]`, `d = 0.001`, `N_max = 16384`<br> * Square lattice with third nearest neighbors `lat3.tar`<br> * `m = 1000`, `r = [0.0, 0.6]`, `d = 0.001`, `N_max = 65536`<br> * Square lattice with fourth nearest neighbors `lat4.tar`<br> * `m = 1000`, `r = [0.0, 0.6]`, `d = 0.001`, `N_max = 65536`<br> * Square lattice with third nearest neighbors and 1% rewired edges `lat3_ws.tar`<br> * `m = 1000`, `r = [0.0, 0.3]`, `d = 0.001`, `N_max = 16384`<br> * connected Erdos Renyi with mean degree of 10 `ER10.tar`<br> * `m = 1000`, `r = [0.0, 0.3]`, `d = 0.002`, `N_max = 32768`</p> <p> </p> <p>## Data format</p> <p>Each final state is encoded as three lines:</p> <p>* The convergence time is a single integer with a line prefix '# sweeps: '<br> * The positions of all clusters in opinion space with a line prefix '# ' (unsorted)<br> * The number of agents in each of the clusters without a line prefix</p> <p> </p> <p>## Python example for reading the format</p> <p>An example script, which visualizes the S vs eps graph for the largest size of the fully connected<br> case, with a function to read this format is given in `example.py`.</p>
How Configurable is the Linux Kernel? Analyzing Two Decades of Feature-Model History
<p>Reproduction package for the TOSEM'25 paper "How Configurable is the Linux Kernel? Analyzing Two Decades of Feature-Model History"</p>
WRF model configuration and data used for the NHESS manuscript "Droughts in Germany: Performance of Regional Climate Models in reproducing observed characteristics"
<p>The file contains:</p> <ul> <li>the namelist.input document with the description of the WRF model configuration used in Warscher et al. (2019)</li> <li>WRF simulation outputs from the reanalysis run: monthly values for the time period 1980-2009 of precipitation, maximum and minimum temperature (needed for the SPEI calculation) from the innermost (5 km grid resolution) and second innermost (15 km) domain; from both domains the same section, relevant for the study, was taken; the data was bilineraily interpolated to 12.5 km horizontal grid resolution to match the EUR-11 CORDEX format</li> </ul> <p> </p>
Comparison between MAST-U conventional and Super-X configurations through SOLPS-ITER modelling
<p>Input and output files for the simulations presented in the Nuclear Fusion paper "Comparison between MAST-U conventional and Super-X configurations through SOLPS-ITER modelling"</p>
Confronting the convective gray zone in the global configuration of the Met Office Unified Model
<p>Supporting data for the manuscript "Confronting the convective gray zone in the global configuration of the Met Office Unified Model" by L. Tomassini et al.</p>
Model configuration and input files for: "CARIB12: A Regional Community Earth System Model / Modular Ocean Model 6 Configuration of the Caribbean Sea"
<p>This collection hosts the configuration and input files to reproduce the regional CESM/MOM6 simulation in: "CARIB12: A Regional Community Earth System Model / Modular Ocean Model 6 Configuration of the Caribbean Sea". <br><br>The initial, open boundary conditions and monthly means for the sponge layers were generated from the GLORYS12V1 reanalysis: <a href="https://doi.org/10.3389/feart.2021.698876">https://doi.org/10.3389/feart.2021.698876</a></p> <p>The tidal amplitudes and phases were generated from the TPXO model: <a href="https://doi.org/10.1175/1520-0426(2002)019<0183:EIMOBO>2.0.CO;2">https://doi.org/10.1175/1520-0426(2002)019<0183:EIMOBO>2.0.CO;2 </a></p> <p>The monthly Chlorophyll-a climatology was generated from the SeaWifs mission dataset: <a href="10.5067/ORBVIEW-2/SEAWIFS/L3M/CHL/2018">10.5067/ORBVIEW-2/SEAWIFS/L3M/CHL/2018</a></p> <p>The river runoff to ocean was generated from the GloFAS dataset: <a href="https://doi.org/10.24381/cds.a4fdd6b9">https://doi.org/10.24381/cds.a4fdd6b9 </a></p> <p>The topography was generated from the Shuttle Radar Topography Mission: <a href="https://doi.org/10.1029/2019EA000658">https://doi.org/10.1029/2019EA000658</a> and smoothed with a Cressman weighted interpolation scheme.</p> <p>This collection does not include the JRA55-do atmospheric forcing dataset: <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.ocemod.2018.07.002" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.ocemod.2018.07.002</a> </p> <p>Steps to setup a regional configuration of CESM/MOM6 can be found here: <a href="https://github.com/NCAR/regional_cesm_mom6.git">https://github.com/NCAR/regional_cesm_mom6.git</a> </p> <p> </p>
Model output for: "CARIB12: A Regional Community Earth System Model / Modular Ocean Model 6 Configuration of the Caribbean Sea"
<p>This collection hosts the model output fields used in : "CARIB12: A Regional Community Earth System Model / Modular Ocean Model 6 Configuration of the Caribbean Sea". <br><br>daily_surface_fields.tar: contains daily output of sea surface salinity, surface u and v velocity components, sea-surface height and mixed layer depths. Files are split in two: 2000-01-01:2009-12-31 and 2010-01-01:2019-12-31 as specified by each filename.</p> <p>monthly_3D_tracers.tar: contains monthly mean output of temperature and salinity for the full 3D field (lat,lon,depth). Files are split in two: 2000-01-01:2009-12-31 and 2010-01-01:2019-12-31 as specified by each filename.</p> <p> </p> <p> </p>
SUMMA/mizuRoute model configurations and parameters for global ensemble modeling
<p>Meteorological forcing is a major source of uncertainty in hydrological modeling. The recent development of probabilistic large-domain meteorological datasets enables convenient uncertainty characterization, which however is rarely explored in large-domain research. </p> <p>We analyze how uncertainties in meteorological forcing data affect hydrological modeling on the global scale by forcing the Structure for Unifying Multiple Modeling Alternatives (SUMMA) and mizuRoute models with precipitation and air temperature ensembles from the Ensemble Meteorological Dataset for Planet Earth (EM-Earth). EM-Earth probabilistic estimates are used in ensemble simulation for uncertainty analysis. The global land area is divided into ~3 million sub-basins using the MERIT-Basins dataset. </p> <p>This dataset contains the SUMMA and mizuRoute configuration files (e.g., diverse land attributes, model parameters, and model physics decisions) to reproduce the global simulation results. The global land is divided into different continents, including Africa, Arctic, Europe, North America, North Asia, Oceania, SouthAmerica, and SouthAsia. Greenland is not included because of its complexity and low quality of available data. Antarctic is not included in MERIT-Basins. </p>
one dimensional model configurations for MITgcm+Darwin3
<p>Contents:</p> <ul> <li>code_2+1+0_1D</li> <li>code_31+16+3_RT_1D</li> <li>code_6+4+0_1D</li> <li>code_6+4+0_RT_1D</li> <li>code_6+4+0_RT_QUOTA_1D</li> <li>diagnostics</li> <li>input_2+1+0_1D</li> <li>input_31+16+3_RT_1D</li> <li>input_6+4+0_1D</li> <li>input_6+4+0_RT_1D</li> <li>input_6+4+0_RT_QUOTA_1D</li> </ul>
Model simulation output for New Configuration for Impact of microphysics and convection schemes on the mean-state and variability of clouds and precipitation in the E3SM Atmosphere Model
<p>Simulation output from the new configuration model used in the manuscript Impact of microphysics and convection schemes on the mean-state and variability of clouds and precipitation in the E3SM Atmosphere Model</p>
Topologies, Checkpoints, and Configurations for the paper "GVI-RL: Graph-Invariant RL for Attack Paths Discovery using Vulnerabilities Embedded with Large Language Models"
<p>This repository consists of the <strong>files</strong> related to the <strong>paper</strong> "GVI-RL: Graph-Invariant RL for Attack Paths Discovery using Vulnerabilities Embedded with Large Language Models". In particular, this repository contains tensorboard logs, topologies, checkpoints, seeds, and results to ensure reproducibility of the results of the paper.</p> <p>The results included are related to the training/validation and hyper-parameters optimization of the outcome multi-label classifier, the GVI-RL agent, and the world model.<br>The data folder contains also the topologies used in the study, the vulnerabilities data used to generate them and the dataset for multi-label classification.</p> <p>The README.md file describes the folders' structure.</p>
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