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691 results for “Molecular dynamics”
Research data supporting: "Detecting dynamic domains and local fluctuations in complex molecular systems via timelapse neighbors shuffling"
<p>This repository contains the set of data shown in the paper "Detecting dynamic domains and local fluctuations in complex molecular systems via timelapse neighbors shuffling" published on PNAS (DOI: 10.1073/pnas.2300565120).</p>
Interaction of biomolecules with anatase, rutile and amorphous TiO2 surfaces: A molecular dynamics study / supplement
<ul> <li>S1 File - Short videos representing the interaction of the 6 different biomolecules (KGD, KRSR, LGD, LRSR, RGD and RSR) with the 3 different TiO<sub>2</sub> surface (anatase, amorphous and rutile).</li> <li>S2 File - A representative trajectory file of the LRSR peptide and amorphous TiO<sub>2</sub> surface MD simulation without the ions and water molecules.</li> <li>S3 File - Force-distance curve data for the binding energy calculations.</li> </ul>
Molecular dynamics simulation data 3: Structure of the connexin-43 gap junction channel in a putative closed state
<p>Molecular dynamics data for the manuscript Qi C.*, Acosta-Gutierrez S.*, Lavriha P., Othman A., Lopez-Pigozzi D., Bayraktar E., Schuster D., Picotti P., Zamboni N., Bortolozzi M., Gervasio F.L., Korkhov V.M. Structure of the connexin-43 gap junction channel in a putative closed state. eLife (2023) <a href="https://doi.org/10.7554/eLife.87616.2">https://doi.org/10.7554/eLife.87616.2</a></p> <p>The dataset includes: Production run gromacs trajectories for the Cx43 gap junction channel (500 mV)</p>
Molecular dynamics simulation data 2: Structure of the connexin-43 gap junction channel in a putative closed state
<p>Molecular dynamics data for the manuscript Qi C.*, Acosta-Gutierrez S.*, Lavriha P., Othman A., Lopez-Pigozzi D., Bayraktar E., Schuster D., Picotti P., Zamboni N., Bortolozzi M., Gervasio F.L., Korkhov V.M. Structure of the connexin-43 gap junction channel in a putative closed state. eLife (2023) <a href="https://doi.org/10.7554/eLife.87616.2">https://doi.org/10.7554/eLife.87616.2</a></p> <p>The dataset includes:</p> <p>1. The starting coordinates, topology, MD inputs</p> <p>2. Production run gromacs trajectories for the Cx43 hemichannel</p>
Input files for the MD simulations and free energy calculations for the article "Water Dissolved in a Variety of Polymers Studied by Molecular Dynamics Simulation and a Theory of Solutions"
<p>Article:<em> </em><a href="https://pubs.acs.org/doi/10.1021/acs.jpcb.1c04818">J. Phys. Chem. B. 125, 9357–9371 (2021) [DOI: 10.1021/acs.jpcb.1c04818]</a></p> <p>The structures of the homopolymers and copolymers simulated are shown in Figures 1 and S1 and Tables 2 and 3. All-atom MD simulation was carried out using GROMACS, and this repository provides the input files with the GAFF/RESP force and initial coordinate files. The free energy of water dissolution was obtained with <a href="https://sourceforge.net/projects/ermod/">ERmod</a>, and the input files for the free-energy calculations are also contained. See the README files for details.</p>
Extending a generic and fast coarse-grained molecular dynamics model to examine the mechanical behavior of grafted polymer nanocomposites: data set
<p>Abstract:<br> from [1]</p> <blockquote> <p>Polymer nanocomposites are an important class of materials for engineering applications due to their high versatility and good mechanical properties combined with low density. By directly attaching the polymer chains to the nanofillers, the so-called grafting, a better load transfer between matrix and filler is achieved, and, in addition, a better dispersion of the fillers is obtained. Both result in enhanced mechanical properties. Since experimental investigations on the nanoscale are extremely challenging, complementary numerical studies are needed to unravel the mechanical behavior of polymer nanocomposites. To this end, molecular dynamics is ideally suited since it captures the microstructure, but is also numerically expensive. Therefore, this contribution presents a fast coarse-grained molecular dynamics model for the investigation of the mechanical behavior of grafted polymer nanocomposites. For this purpose, we extend an existing model by grafting bonds, which allows us to compare the effect of untreated and grafted fillers directly. In particular, we investigate the influence of filler content, grafting degree, and filler size on the stiffness and strength of the polymer (grafted) nanocomposites. We conclude that the grafting bonds have little effect on the stiffness, while the strength is significantly improved compared to the untreated fillers, which is in agreement with the literature. The presented molecular dynamics model for polymer grafted nanocomposites provides the basis for further investigations, particularly of the crucial matrix-filler interphase. In addition, this contribution translates molecular dynamics insights into mechanical properties, which bridges the gap to the engineering scale and thus represents a step towards exploiting the full potential of polymer (grafted) nanocomposites.</p> </blockquote> <p> </p> <p><strong>Contact:</strong></p> <p>Maximilian Ries<br> Institute of Applied Mechanics<br> Friedrich-Alexander-Universität Erlangen-Nürnberg<br> Egerlandstr. 5<br> 91058 Erlangen</p> <p><strong>Software:</strong></p> <p>All MD simulations were performed with LAMMPS [2,3], version: 29 Oct 2020 / 20201029</p> <p>Compiled with<br> Compiler: GNU C++ 4.8.5 20150623 (Red Hat 4.8.5-39) with OpenMP not enabled<br> C++ standard: C++11</p> <p>Active compile time flags:<br> -DLAMMPS_GZIP<br> -DLAMMPS_SMALLBIG</p> <p>Installed packages:<br> CLASS2, KSPACE, MANYBODY, MC, MOLECULE, MPIIO, OPT, VORONOI, USER-INTEL, USER-MISC, USER-MOLFILE, USER-NETCD</p> <p>Polymer and polymer composite samples generated with self-avoiding random-walk algorithm [4]</p> <p>Post-processing Matlab R2019b</p> <p><strong>License:</strong></p> <p>Creative Commons Attribution 4.0 International</p> <p><strong>Context:</strong></p> <p>Data set supplementing journal paper:</p> <p>[1] M. Ries, S. Reber, P. Steinmann, & S. Pfaller, “Extending a generic and fast coarse-grained molecular dynamics model to examine the mechanical behavior of grafted polymer nanocomposites,” <em>Forces in Mechanics</em>, vol. 12, p. 100 207, <strong>2023</strong>.</p> <p><strong>Content:</strong></p> <p>structure of data set:</p> <ul> <li>04_Equilibration<br> folders containing the sample equilibration used in the presented parameter study <ul> <li>01_filler_content<br> variation of filler content</li> <li>02_grafting_density<br> variation of grafting density</li> <li>03_grafting_potential<br> variation of grafting potential</li> <li>04_filler_size<br> variation of filler size</li> <li>05_reference<br> reference samples without grafting</li> </ul> </li> <li>05_UT<br> folders containing the uniaxial tension simulations used in the presented parameter study <ul> <li>01_filler_content<br> variation of filler content</li> <li>02_grafting_density<br> variation of grafting density</li> <li>03_grafting_potential<br> variation of grafting potential</li> <li>04_filler_size<br> variation of filler size</li> <li>05_reference<br> reference samples without grafting</li> </ul> </li> </ul> <p>Each simulation directory contains:</p> <ul> <li> <p>lammps input file (*.in) of the specific simulation</p> </li> <li> <p>data file (*.data) containing the initial sample configuration</p> </li> <li> <p>input.prm: input parameters of the specific simulation (read by the input file)</p> </li> <li> <p>meta.info: meta data of the specific simulation run</p> </li> <li> <p>LAMMPS_out:<br> simulation results (lammps thermo_out) in tabulated form, an overview of columns is given below</p> <ul> <li> <p>thermo_out.Dat: raw output </p> </li> <li> <p>thermo_out_SG.Dat: smoothed output (Savitzky-Golay filter)</p> </li> <li> <p>thermo_out_STD.Dat: standard deviation of raw output</p> </li> </ul> </li> </ul> <p>Output quantities (columns of *.Dat files):<br> Please note that the normalized Lennard-Jones unit set is used, so all quantities are normalized to fundamental mass, length, energy, time and the Boltzmann constant. Thus all entries are unitless [1].</p> <ul> <li> <p>Step: time step </p> </li> <li> <p>Time: time </p> </li> <li> <p>TotEng: total energy </p> </li> <li> <p>PotEng: potential energy</p> </li> <li> <p>KinEng: kinetic energy </p> </li> <li> <p>E_pair: pair energy </p> </li> <li> <p>E_bond: bond energy </p> </li> <li> <p>E_angle: angle energy </p> </li> <li> <p>E_dihed: dihedral energy </p> </li> <li> <p>Temp: temperature</p> </li> <li> <p>Press: hydrostatic pressure</p> </li> <li> <p>Pxx: xx component of pressure tensor </p> </li> <li> <p>Pyy: yy component of pressure tensor </p> </li> <li> <p>Pzz: zz component of pressure tensor </p> </li> <li> <p>Pxy: xy component of pressure tensor</p> </li> <li> <p>Pxz: xz component of pressure tensor</p> </li> <li> <p>Pyz: yz component of pressure tensor</p> </li> <li> <p>Volume: volume of simulation box </p> </li> <li> <p>Lx: box length in x direction </p> </li> <li> <p>Ly: box length in y direction </p> </li> <li> <p>Lz: box length in z direction </p> </li> <li> <p>Density: density </p> </li> <li> <p>c_RG: radius of gyration scalar </p> </li> <li> <p>c_RG[1]: squared radius of gyration tensor (xx component) </p> </li> <li> <p>c_RG[2]: squared radius of gyration tensor (yy component) </p> </li> <li> <p>c_RG[3]: squared radius of gyration tensor (zz component) </p> </li> <li> <p>c_RG[4]: squared radius of gyration tensor (xy component) </p> </li> <li> <p>c_RG[5]: squared radius of gyration tensor (xz component) </p> </li> <li> <p>c_RG[6]: squared radius of gyration tensor (yz component) </p> </li> <li> <p>c_bondave[1]: bond energy averaged over all atoms </p> </li> <li> <p>c_bondave[2]: bond distance averaged over all atoms </p> </li> <li> <p>c_bondave[3]: squared bond distance averaged over all atoms </p> </li> <li> <p>c_angleave[1]: angle energy averaged over all atoms </p> </li> <li> <p>c_angleave[2]: angle averaged over all atoms degree</p> </li> <li> <p>c_angleave[3]: cosine of angle </p> </li> <li> <p>c_angleave[4]: squared cosine of angle </p> </li> <li> <p>c_MSD[1]: mean squared displacement x-direction </p> </li> <li> <p>c_MSD[2]: mean squared displacement y-direction </p> </li> <li> <p>c_MSD[3]: mean squared displacement z-direction </p> </li> <li> <p>c_MSD[4]: total mean squared displacement </p> </li> <li> <p>c_COM[1]: x coordinate of center of mass </p> </li> <li> <p>c_COM[2]: y coordinate of center of mass </p> </li> <li> <p>c_COM[3]: z coordinate of center of mass </p> </li> <li> <p>v_strain_xx: xx component of engineering strain tensor </p> </li> <li> <p>v_strain_yy: yy component of engineering strain tensor </p> </li> <li> <p>v_strain_zz: zz component of engineering strain tensor </p> </li> <li> <p>v_vMisesequivstress: von Mises equivalent stress </p> </li> <li> <p>v_Cauchy_xx: xx component of stress tensor </p> </li> <li> <p>v_Cauchy_yy: yy component of stress tensor</p> </li> <li> <p>v_Cauchy_zz: zz component of stress tensor</p> </li> <li> <p>v_Cauchy_xy: xy component of stress tensor </p> </li> <li> <p>v_Cauchy_xz: xz component of stress tensor </p> </li> <li> <p>v_Cauchy_yz: yz component of stress tensor </p> </li> <li> <p>v_strain_xy: xy component of engineering strain tensor </p> </li> <li> <p>v_strain_xz: xz component of engineering strain tensor </p> </li> <li> <p>v_strain_yz: yz component of engineering strain tensor </p> </li> </ul> <p><strong>References</strong>:</p> <p>[1] M. Ries et al., “Extending a generic and fast coarse-grained molecular dynamics model to examine the mechanical behavior of grafted polymer nanocomposites,” <em>Forces in Mechanics</em>, vol. 12, p. 100 207, <strong>2023</strong>.</p> <p>[2] S. Plimpton, “Fast parallel algorithms for short-range molecular dynamics,” <em>Journal of computational physics</em>, <strong>1995</strong>, 117, 1-19.</p> <p>[3] A. P. Thompson et al., “LAMMPS - a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales,” <em>Computer Physics Communications</em>, vol. 271, p. 108171, <strong>2022</strong>.</p> <p>[4] M. Ries, V. Dötschel, J. Seibert, S. Pfaller. “A self-avoiding random walk algorithm (SARW) for generic thermoplastic polymers and nanocomposites”, <em>Zenodo</em>, 2022. <a href="https://doi.org/10.5281/zenodo.6245699">https://doi.org/10.5281/zenodo.6245699</a></p>
Eliminating Finite-size Effects on the Calculation of X-ray Scattering from Molecular Dynamics Simulations
<p>Data repository for the work with the same name. <a href="https://gitlab.com/asod/grsq_examples/-/tree/final_resubmission?ref_type=tags">Gitlab version</a></p> <p>All plots for the figures in the work can be generated via <code>plots_resubmission.ipynb</code></p> <p><code>This version is accompanying the final resubmission to JCP.</code></p> <p> </p> <p><code>Uses the pypi package <a href="https://pypi.org/project/grsq/">grsq</a></code></p>
The GET insertase exhibits conformational plasticity and induces membrane thinning - The Molecular Dynamics Dataset
<p>The molecular dynamics simulation systems.</p> <p>List of files: </p> <ol> <li><strong>SimulationSystems.pdf</strong>: List of all simulation systems reported and their compositions</li> <li><strong>ProteinComplex.pdb</strong>: The initial model for the hsGet2ΔN-Get1/Get3 complex used in simulations was constructed based on the cryo-EM structure (PDB accession 6SO5). Missing residues (except the terminal ones) were modeled using Modeller.</li> <li><strong>prod.mdp</strong>: The GROMACS molecular dynamics parameters (mdp) file used for all simulations</li> <li><strong>toppar.zip</strong>: The Charmm36(m) force field parameter and topology set used for all simulations generated by CHARMM-GUI.</li> <li>Compressed (zip) files containing simulations inputs and trajectories</li> </ol> <p><strong>1-PC.zip<br> 2-1:4_PI:PC.zip<br> 3-1:4_PE:PC.zip<br> 4-1:4_PS:PC.zip<br> 5-1:4_CL:PC.zip<br> 6-1:4_chol:PC.zip<br> 7-1:1:1:1_PC:PI:PS:PE.zip<br> 8-1:1:1:1:1:1_PC:PDPC:PS:PI:PE:chol.zip</strong></p> <p>Each zip file contains the following files:</p> <ol> <li><strong>System_0ns.pdb</strong>: The initial configuration used for the simulations generated using CHARMM-GUI and equilibrated following the CHARMM-GUI equilibration protocol</li> <li><strong>index.ndx</strong>: GROMACS index file</li> <li><strong>topol.top</strong>: GROMACS topology file</li> <li><strong>prod0.tpr, prod1.tpr, prod2.tpr</strong>: GROMACS run topology files (tpr) for each repeat</li> <li><strong>prod0.gro, prod1.gro, prod2.gro</strong>: The final configuration after 3 μs production runs for each repeat</li> <li><strong>noW_0ns.pdb</strong>: The initial configuration without the water molecules.</li> <li><strong>noW_0.xtc, noW_1.xtc, noW_2.xtc</strong>: The 3 μs processed production trajectories. The water molecules were removed, and the trajectories were subsampled at 1 ns intervals.</li> </ol> <p> </p> <p> </p>
Zika virus prM protein contains cholesterol binding motifs required for virus entry and assembly - Molecular Dynamics Simulation Dataset
<p>The molecular dynamics (MD) simulation dataset. The contents:</p> <ul> <li><strong>5ire_BIOMT_expanded.pdb</strong>: The complete biological assembly of the cryo-EM structure of Zika Virus (PDB ID:5IRE) </li> <li><strong>5ire_Mprotein_BIOMT_expanded.pdb</strong>: The M proteins extracted from the complete biological assembly of the cryo-EM structure of Zika Virus (PDB ID:5IRE). The biological assembly shows the dimeric organization of M proteins.</li> <li><strong>0chol.zip, 10chol.zip, 20chol.zip, and 30chol.zip</strong> contain simulation input and output files for the simulated membrane compositions: 0:100, 10:90, 20:80, 30:70 (mol%:mol%) Cholesterol:POPC, respectively. <ul> <li>In each zip file, there are 5 directories: <strong>wt, R253L+F257A, R253L+F257S, K275L+Y278A, K275L+Y278S</strong> corresponding to each simulated M protein dimer variant: wild type, CARC 2-A, CARC 2-S, CARC 3-A, and CARC 3-S. In each directory, there are the following files: <ul> <li><strong>toppar</strong>: This directory contains all force field topologies and parameters</li> <li><strong>topol.top</strong>: GROMACS topology (top) file</li> <li><strong>index.ndx</strong>: GROMACS index (ndx) file</li> <li><strong>prod.mdp</strong>: GROMACS MD parameters (mdp) file</li> <li><strong>0, 1, 2, 3, 4, 5, 6, 7, 8, 9</strong>: These directories contain the simulation inputs and outputs for each simulation repeat. In each of these directories, there are the following files: <ul> <li><strong>t0.pdb</strong>: The pdb file of the starting coordinates</li> <li><strong>prod0.tpr</strong>: GROMACS binary run input (tpr) file </li> <li><strong>prod0.edr</strong>: GROMACS energy (edr) file</li> <li><strong>prod0.gro</strong>: GROMACS output coordinates and velocities after 1 microsecond of simulation</li> <li><strong>prod0.cpt</strong>: GROMACS checkpoint file after 1 microsecond of simulation</li> <li><strong>noW.pdb</strong>: The pdb file of the starting coordinates with all water molecules removed</li> <li><strong>noW.xtc</strong>: GROMACS compressed trajectory (xtc) file with all water molecules removed</li> </ul> </li> </ul> </li> </ul> </li> </ul>
Scrutinizing the protein hydration shell from molecular dynamics simulations against consensus small-angle scattering data (Simulation input files)
<p>Simulation input files for gromacs to reproduce the data from the manuscript "Scrutinizing the protein hydration shell from molecular dynamics simulations against consensus small-angle scattering data" (submitted to Comm. Chem.)</p>
Data related to the publication "Efficient molecular dynamics simulations of deep eutectic solvents with first-principles accuracy using machine learning interatomic potentials"
<p>The training data sets, the trained machine learning models, and input scripts for the training and molecular dynamics simulations.</p>
Updated MD trajectories for "Hidden GPCR structural transitions addressed by multiple walker supervised molecular dynamics (mwSuMD)"
<p>MD trajectories relative to the preprint "Hidden GPCR structural transitions addressed by multiple walker supervised molecular dynamics (mwSuMD)"</p> <p>For a summary of all the simulations performed and the settings employed see Table S1 of the preprint:</p> <p>https://www.biorxiv.org/content/10.1101/2022.10.26.513870v1</p> <p> </p>
Molecular dynamics simulations of intrinsically disordered proteins p53TAD and Pup
Open the record for dataset details and reuse information.
Molecular dynamics simulation data of designed stapled α-helix
<p>Raw simulations data (protein only) and simulation set-up files of designed stapled α-helix peptides. More details can be found in this paper: </p> <p>Arusha Acharyya*, Yunhui Ge*, Haifan Wu*, William DeGrado, Vincent Voelz, Feng Gai. <a href="https://pubs.acs.org/doi/abs/10.1021/acs.jpcb.8b12220"><em>Exposing the Nucleation Site in α-Helix Folding: A Joint Experimental and Simulation Study.</em></a><em> </em>J. Phys. Chem. B., 2019, 123 (8), pp 1797-1807 (* shared first author)</p>
Molecular dynamics simulation data of designed cyclic peptide - ligand 4 (receptor-ligand bound)
<p>Trajectories of receptor-ligand bound simulation and simulation set-up files of designed cyclic peptide as MDM2 binders. This dataset contains simulations of ligand 4. Due to the file size limitation, ligand 1-3 data and simulation set-up files can be found here: http://doi.org/10.5281/zenodo.3780463<br> The original paper of these designed cyclic peptide: Danelius, E., Pettersson, M., Bred, M., Min, J., Waddell, M. B., Guy, R. K., et al. (2016). Flexibility is important for inhibition of the MDM2/p53 protein–protein interaction by cyclic β-hairpins. <em>Org. Biomol. Chem.</em>, <em>14</em>(44), 10386–10393. http://doi.org/10.1039/C6OB01510G</p>
Molecular dynamics simulation data of designed cyclic peptide - ligand 1-3 (receptor-ligand bound)
<p>Trajectories of receptor-ligand bound simulation and simulation set-up files of designed cyclic peptide as MDM2 binders. This dataset contains simulations of ligand 1-3. Ligand 4 data can be found here: http://doi.org/10.5281/zenodo.3782629<br> The original paper of these designed cyclic peptide: Danelius, E., Pettersson, M., Bred, M., Min, J., Waddell, M. B., Guy, R. K., et al. (2016). Flexibility is important for inhibition of the MDM2/p53 protein–protein interaction by cyclic β-hairpins. <em>Org. Biomol. Chem.</em>, <em>14</em>(44), 10386–10393. http://doi.org/10.1039/C6OB01510G</p>
Molecular dynamics simulation data of regulatory ACT domain dimer of human phenylalanine hydroxylase (PAH) (dimer only)
<p>Raw data of molecular dynamics simulations of regulatory ACT domain dimer. Simulation starts from the crystal pose (PDB: 5FII) and is motivated by this paper:</p> <p>Yunhui Ge, Elias Borne, Shannon Stewart, Michael R. Hansen, Emilia C. Arturo, Eileen K. Jaffe and Vincent A. Voelz. <a href="http://www.jbc.org/content/293/51/19532"><em>Simulation of the regulatory ACT domain of human PAH unveil the mechanism of phenylalanine binding.</em></a> J. Biol. Chem., 2018, 293(51), pp 19532-19543</p>
Molecular recognition and dynamics of linear poly-ubiquitins: integrating coarse-grain simulations and experiments
<p>Poly-ubiquitin chains are flexible multidomain proteins, whose conformational dynamics enable their molecular recognition by a large number of partners in multiple biological pathways. By using alternative linkage, it is possible to obtain poly-ubiquitin molecules with different dynamical properties. This flexibility is further increased by the possibility to tune the length of poly-ubiquitin chains. Characterizing the dynamics of poly-ubiquitins as a function of their length is thus relevant to understand their biology. Structural characterization of poly-ubiquitin conformational dynamics is challenging both experimentally and computationally due to increasing system size and conformational variability. Here, by developing highly efficient and accurate small-angle X-ray scattering driven Martini coarse-grain simulations, we characterize the dynamics of linear M1-linked di-, tri- and tetra-ubiquitin chains. Our data show that the behavior of the di-ubiquitin subunits is independent of the presence of additional ubiquitin modules. We propose that the conformational space sampled by linear poly-ubiquitins, in general, may follow a simple self-avoiding polymer model. These results, combined with experimental data from small angle X-ray scattering, biophysical techniques and additional simulations show that binding of NEMO, a central regulator in the NF-κB pathway, to linear poly-ubiquitin obeys a 2:1 (NEMO:poly-ubiquitin) stoichiometry in solution, even in the context of four ubiquitin units. Eventually, we show how the conformational properties of long poly-ubiquitins may modulate the binding with their partners in a length-dependent manner.</p>
Supplementary Information for Heterogeneous Parallelization and Acceleration of Molecular Dynamics Simulations in GROMACS
<p>Supplementary information for<br> Páll, S., Zhmurov, A., Bauer, P., Abraham, M., Lundborg, M., Gray, A., Hess, B, & Lindahl, E.. (2020). Heterogeneous Parallelization and Acceleration of Molecular Dynamics Simulations in GROMACS. The Journal of Chemical Physics, 2020</p> <p>Contains benchmark methodology description as well as all inputs used in the application performance benchmarks included the paper.</p>
Supplementary data for "Molecular dynamics study of confined water in the periclase-brucite system under conditions of reaction-induced fracturing"
<p>In this dataset you can find sample data from periclase and brucite simulations and python scripts that can be used to confirm the plots in the paper.</p> <ul> <li>"bruciteSimualtions" and "periclaseSimualtions" contains the simulations where the mineral in contact with water is either brucite or periclase. Within each of these two folders, there are two subfolders, "waterProperties" and "waterThickness". <ul> <li>The data in "waterProperties" is used to calculate water properties. Each folder inside "simulations" represent one simulation.</li> <li>The data in "waterThickness" is used to calculate the change in water film thickness with time under different conditions. The simulations are run for either 3 ns or 10 ns. Each folder inside "simulations_Xns" represent one simulation.</li> </ul> </li> <li>For each folder containing one simulations, we provide: <ul> <li>NAME.run: The input script</li> <li>NAME.data: The input data</li> <li>job.sh: Script to run the simulation</li> <li>log.lammps: Thermodynamic output from the simulation</li> <li>Note that the pressures given in the folder names and in the simulations are in atm, not MPa.</li> </ul> </li> </ul> <p> </p> <ul> <li>"bruciteSimulations" and "periclaseSimualtions" are in zip containers. In order to use them, please unzip them and leave the resulting folders in the same directory as this README file. </li> </ul> <p> </p> <ul> <li>The python scripts shows how to extract the relevant data from the lammps log files, which enables reproduction of the figures in the paper. See instructions below to use the scripts.</li> </ul> <p><br> Installation instructions to make the python plot scripts working, assuming you already have numpy and matplotlib:</p> <p>> pip3 install git+https://github.com/henriasv/regex-file-collector.git</p> <p>> pip3 install git+https://github.com/henriasv/lammps-logfile.git<br> </p> <p>If this does not work, please contact Marthe Grønlie Guren, m.g.guren@geo.uio.no</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.