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11,506 results for “Dynamics”
Data and code for "Quantifying Dynamic Tilting in Halide Perovskites: Chemical Trends and Local Correlations"
<p>This record contains data, scripts, and models associated with the publication "Quantifying Dynamic Tilting in Halide Perovskites: Chemical Trends and Local Correlations".</p> <h3>Databases</h3> <p>The <code>*.db</code> files are databases with the results from density functional theory (DFT) calculations. These are sqlite databases in ase format, see <a href="https://wiki.fysik.dtu.dk/ase/tutorials/tut06_database/database.html">here</a> for more information. The <code>demo-database-access.py</code> script illustrates the most basic access.</p> <h3>Models</h3> <p>The neuroevolution potential (NEP) models described in the publication can be found in the <code>nep-*.txt</code> files. They can be used in conjunction with the <a href="https://gpumd.org">GPUMD package</a>. The <a href="https://calorine.materialsmodeling.org">calorine package</a> provides a Python interface to GPUMD.</p> <h3>Primitive structures</h3> <p>Several primitive structures in extended xyz format can be found in the <code>*.xyz</code> files. These structures have been relaxed using the NEP models included here. The <code>demo-for-using-structures-and-models.py</code> script illustrates how to access the structures and models.</p> <h3>Tools for analyzing tilt angles</h3> <p><strong><code>standardize-cell-orientation.py</code></strong><br>Converts each frame in `movie.xyz` to the standardized cell setting and writes the resulting trajectory to `movie-standardized.xyz`.<br>This is useful when working with triclinic cells.</p> <p><code><strong>analyze-tilt-angles-for-orthorhombic-cells.py</strong></code><br>Analyzes the distribution of tilt angles for each frame in a trajectory.<br>The analysis assumes that the B-B bonds are oriented along the Cartesian coordinate system either without further rotation or after a "simple" rotation of the entire cell.</p> <p><code><strong>angle-analysis.ovito</strong></code><br>Ovito script that approximately implements the procedure used in <code>analyze-tilt-angles-for-orthorhombic-cells.py</code>.</p>
The 30 m annual land cover datasets and its dynamics in China from 1985 to 2024
<p>Using 335,709 Landsat images on the Google Earth Engine, we built the first Landsat-derived annual land cover product of China (CLCD) from 1985 to 2019. We collected the training samples by combining stable samples extracted from China's Land-Use/Cover Datasets (CLUD), and visually-interpreted samples from satellite time-series data, Google Earth and Google Map. Several temporal metrics were constructed via all available Landsat data and fed to the random forest classifier to obtain classification results. A post-processing method incorporating spatial-temporal filtering and logical reasoning was further proposed to improve the spatial-temporal consistency of CLCD. </p> <p>"*_albert.tif" are projected files via a proj4 string "+proj=aea +lat_1=25 +lat_2=47 +lat_0=0 +lon_0=105 +x_0=0 +y_0=0 +datum=WGS84 +units=m +no_defs".</p> <p>CLCD in 2024 is now available.</p> <p>1. Given that the USGS no longer maintains the Landsat Collection 1 data, we are now using the <a href="https://www.usgs.gov/landsat-missions/landsat-collection-2">Collection 2</a> SR data to update the CLCD.</p> <p>2. All files in this version have been exported as Cloud Optimized GeoTIFF for more efficient processing on the cloud. Please check <a href="https://www.cogeo.org/">here</a> for more details.</p> <p>3. Internal overviews and color tables are built into each file to speed up software loading and rendering.</p>
Evaluation of dynamically downscaled CMIP6-CCAM models over Australia
<p>Downscaled CCAM-CMIP6 model data used in the evaluation of CCAM-CMIP6 models against AGCD observations:</p><ol><li>Data required for daily evaluation of precipitation and temperature variables, and calculation of Perkins skill score</li><li>Data required for evaluation of bias for precipitation and temperature variables</li><li>Data required for KGE skill score</li></ol>
Dynamics of activation in the voltage-sensing domain of Ci-VSP
<p>This dataset contains unbiased molecular dynamics trajectories, stripped of water and lipid coordinates due to size constraints. It also contains full initial and final structures for all trajectories.</p><h3>Description of the data and file structure</h3><p>The data are deposited as a .tar.gz file: it can be unpacked by running tar -xzvf traj-data.tar.gz.</p><p>The trajectories are organized into two groups (group1 and group2), each containing a folder with stripped_trajs as .xtc files, as well as the full starting and ending coordinates (with waters and lipids) as intial_structs and final_structs. The models for the down--, down, up, and up+ states are in models.</p><p>The parameter file is civsd-amber-top.parm7 and the topology files with and without<br>the waters/lipids are civsd.psf and civsd-pro.psf</p><h3>Sharing/Access information</h3><p>Derived data (collective variables computed from the raw trajectories) are available as supplementary information to the accompanying publication.</p><h3>Code/Software</h3><p>Analysis of the trajectories can be performed using the MDAnalysis, MDTraj, PyEMMA, scikit-learn, and VMD, softwares along with custom codes written in Python/Jupyter notebooks and tcl. Custom codes are available at <a href="https://github.com/dinner-group/ci-vsd">https://github.com/dinner-group/ci-vsd</a>.</p>
Dataset from paper "Quantifying landscape fragmentation and forest carbon dynamics over 35 years in the Brazilian Atlantic Forest"
<h3><strong><span>Dataset from the paper “Quantifying landscape fragmentation and forest carbon dynamics over 35 years in the Brazilian Atlantic Forest”</span></strong></h3> <p><span> </span><span>This repository contains:</span></p> <ul> <li><span>Dataset Description: “raster_labels.xlsx” (an Excel spreadsheet detailing raster pixel values and their respective fragmentation classes).</span></li> <li><span>Fragmentation Raster Files: “forest_fragmentation_mspa_2020.tif” and “forest_fragmentation_mspa_2020.tif” (GeoTIFF files of landscape forest fragmentation for 1985 and 2020).</span></li> </ul> <p><span> </span><span>If you need anything else, please contact the corresponding author, Igor Broggio (<a href="mailto:isbbroggio@gmail.com">isbbroggio@gmail.com</a>).</span></p> <p><span> </span></p> <p><span>If you use these data, please cite the paper: </span><span>[Citation]</span></p>
Supporting Data: ontophylo: Reconstructing the evolutionary dynamics of phenomes using new ontology-informed phylogenetic methods
<p>This dataset contains all scripts and data for reproducing the analyses of the paper. The README files contain additional information.</p>
Supplementary files for: A dynamic 2000–540 Ma Earth history: From cratonic amalgamation to the age of supercontinent cycle
<p>Supplementary materials for the Earth-science Reviews paper 'A dynamic 2000–540 Ma Earth history: From cratonic amalgamation to the age of supercontinent cycle'. </p> <p>Supplementary Material 1 – Palaeomagnetic pole list for the ca. 2000–540 Ma interval.<br> Supplementary Material 2 – IGCP 440 pre-700 Ma geotectonic database (with minor corrections made) in shapefiles format<br> Supplementary Material 3 – Neoproterozoic sedimentary facies point data of Li et al. (2013) in shapefile format<br> Supplementary Material 4 – Generalised global large igneous province (LIP) database for 2010–0 Ma (after Ernst et al., 2021) in both shapefile and Excel formats<br> Supplementary Material 5 – Global passive margin database of (Bradley, 2008) in shapefile format<br> Supplementary Material 6 – Global orogen database of Condie et al. (2021) with minor modifications and in shapefile format<br> Supplementary Material 7 – Global 2000–540 Ma full-plate animation following the extended orthoversion principle, Scenario Ia (0-90W-0)<br> Supplementary Material 8 – Global 2000–540 Ma full-plate animation following the extended orthoversion principle, Scenario Ib (0-90E-0)<br> Supplementary Material 9 – 2000–540 Ma global animation highlighting the occurrence of LIP events in time and space, including possible plume centres.<br> Supplementary Material 10 – GPlates project files for the two alternative global 2000–540 Ma full-plate animations with associated geotectonic databases</p>
Data for the publication "Sodium Triflate Water-in-Salt Electrolyte in Advanced Battery Applications: A First-principles Based Molecular Dynamics Study"
<p>The datasets 'CONTCAR_aiMLMD' and 'CONTCAR_AIMD' represent the final structures obtained from the aiMLMD and AIMD simulations, respectively. These simulations were conducted using VASP at T=333K and c=9.25 m.</p> <p>The datasets 'NP.rdf' and 'MSD_NP.xlsx' represent the radial pair distribution functions at different time steps and the time-dependent variations of mean squared displacement for sodium in 10 segments of the classical MD trajectory. The associated MD simulation was performed using a nonpolarizable force field in the LAMMPS package at T=333K and c=9.25 m. The file 'dataNP.lmp' includes the initial configuration for this simulation. The GROMOS parameters were employed for LJ interactions of sodium and all other force field parameters were set according to Table 1 in the manuscript.</p> <p>The datasets 'P.rdf' and 'MSD_P.xlsx,' respectively, represent the radial pair distribution functions at different time steps and the time-dependent variations of mean squared displacement for sodium in 10 segments of the classical MD trajectory. These data were obtained employing the Drude oscillator model in the LAMMPS package at T=333K and c=10 m. The file 'dataP.lmp' includes the initial configuration for this simulation. The simulation was conducted using the optimal force field parameters 'Sys. 1,' as described in table 3 of the manuscript.</p> <p>The second column in the files 'NP.rdf' and 'NP.rdf' represents the distance from sodium. The subsequent odd columns display the radial distribution functions for the Na-C, Na-F, Na-S, Na-O, Na-Na, Na-Hw, and Na-Ow pairs, while the even columns present the coordination numbers for the same atom pairs.</p>
Data for: Bound impurities in a one-dimensional Bose lattice gas: low-energy properties and quench-induced dynamics
<p>Dataset for <em>Bound impurities in a one-dimensional Bose lattice gas: </em><em>low-energy properties and quench-induced dynamics</em> [<a href="https://scipost.org/SciPostPhysCore.7.3.049">SciPost Phys. Core 7, 049 (2024)</a>].</p>
Two 100 ns NVT molecular dynamics simulations of dsDNA and dsRNA "GGGG" 18-mers (GCGGGGGGGGGGGGGGGC)
<p>Supporting information for "Molecular origin of distinct hydration dynamics in double helical DNA and RNA sequences" by E. Frezza, D. Laage and E. Duboué-Dijon, <span><em>J. Phys. Chem. Lett.</em></span> <span>2024</span><span>, 15</span><span>, </span><span>4351–4358</span><br>Two 100 ns-long NVT molecular dynamics simulation: one of dsDNA "GGGG" 18-mer (GCGGGGGGGGGGGGGGGC) and one of the analogous dsRNA. The nucleic acid is explicitly solvated in water and neutralized with 0.15M KCl. Simulations were performed using the Gromacs 5 software. DNA is described with the Amber 99SB-ILDN force field with the BSC0 modifications, RNA is described with the Amber 99SB-ILDN force field with the BSC0 and χOL3 modifications, the SPC/E force field is used for water, and the Joung Cheatham paraeters for ions. The shared coordinates are saved every 500fs, twice less frequently than the original trajectories used for the publication, to reduce the size of the shared dataset below the allowed size limit.</p>
Full Results from Simulations for Static and Dynamic Workloads Using BSC Slurm Simulator
<p>This dataset contains two CSV files. One is for the static runs, done by emulating LUMI's supercomputer environment, and dynamic, using CEA-Curie workload taken from Feitelson's repository.</p>
Data supporting for: A High-Throughput Framework for Lattice Dynamics
<p><span>We develop an automated high-throughput workflow for calculating lattice dynamical properties </span><span>from first principles including those dictated by anharmonicity. The pipeline automatically calculates </span><span>interatomic force constants (IFC) up to 4th order from perturbed training supercells, and uses the </span><span>IFC to calculate lattice thermal conductivity, coefficient of thermal expansion, and vibrational free </span><span>energy and entropy. It performs phonon renormalization for dynamically unstable compounds to</span><br><span>obtain real effective phonon spectra at finite temperatures and calculates the associated free energy </span><span>corrections.</span> <span>The choice of methods and parameter selection process are done in a manner that </span><span>strikes a balance of computational efficiency and accuracy of results (as assessed through convergence </span><span>testing and comparison to experimental measurements). </span></p> <p><span>The following data includes:</span></p> <ul> <li><span>materials we calculated lattice thermal conductivities (kappa folder)</span></li> <li><span>materials we applied renormalization to get corrected free energies (renormalization folder)</span></li> </ul> <p><span>The target of this dataset is giving users a reference for our inputs and outputs when they want to run our workflow by their own.</span></p>
Supporting data for "Unveiling Vertebrate Development Dynamics in Frog Xenopus laevis using Micro-CT Imaging"
<p>The dataset contains X-ray Micro Computed Tomography data of Xenopus laevis frog. There are twenty datasets of ten individual animals. Each animal was CT scanned twice – once as a native scan to visualize the hard tissues, and once contrast-stained to visualize the soft tissues. The datasets include nine developmental stages (NF44-45, NF52, NF53, NF54, NF57, NF59, NF62, NF66 and adult). There are two adults, one male and one female. The CT data (in 8bit .tiff format compressed as .tar.gz files) are supported by .stl files created from each dataset. The database also includes .stl files of selected structures of interest (body, skeleton, skull, brain and guts of individual animals).</p>
Data related to the article "Impedance of nanocapacitors from molecular simulations to understand the dynamics of confined electrolytes"
<p>Contains input files and data used to generate the figures of the article:</p> <p>Impedance of nanocapacitors from molecular simulations to understand the dynamics of confined electrolytes<br>(Giovanni Pireddu, Connie J. Fairchild, Samuel P. Niblett, Stephen J. Cox and Benjamin Rotenberg)</p> <p>ChemRxiv: https://doi.org/10.26434/chemrxiv-2023-2ccrw</p> <p>Published version: to be inserted upon publication</p> <p>The folder EXAMPLE_INPUT_FILES contains typical [MetalWalls](https://doi.org/10.21105/joss.02373) ([repository](https://gitlab.com/ampere2/metalwalls)) and [LAMMPS]([repository](https://github.com/lammps/lammps)) input files used to perform the molecular simulations.</p> <p>The folder DATA_FIGURES contains the processed data used to plot all the figures of the paper (see below).</p> <p><br>Notes: <br>1) In the file names, the notation 'M01', 'M05', 'M10' and 'M15' refers to the salt concentration in each system (0.1, 0.5, 1.0 and 1.5, respectively). 'W' refers to pure water (0 M) systems.<br>2) In the file names, the notation 'd1', 'd2', 'd3', 'd4', refers to different interelectrode distances (d1= 2.56 nm; d2= 5.07 nm; d3= 9.80 nm; d4= 19.84 nm) <br>3) The files containing the polarization cross-correlation are marked with 'AxB' indicating the cross-correlation between the contributions A and B. Specifically A and B can be: <br> - T = total<br> - I = ion<br> - W = water</p> <p><br>Figure 1:<br>- Panel B<br> - 'Fig1_CapConcentration': Differential capacitance scaled by electrode area as a function of NaCl concentration<br>- Panel C<br> - 'Fig1_QACF_*': Electrode charge autocorrelation function<br>- Panel D<br> - 'Fig1_Norm_QACF_*': Normalized electrode charge autocorrelation function<br> - 'Fig1_NormChar_*': Normalized non-equilibrium charge response</p> <p>Figure 2:<br>- Panel A: <br> - 'Fig2_ReZ_*': Real part of impedance<br>- Panel B:<br> - 'Fig2_nImZ_*': Negative imaginary part of impedance<br>- Panel C:<br> - 'Fig2_ReZint_*': Real part of interfacial impedance<br> - 'Fig2_Resistivities.dat': Resistivity as a function of NaCl concentration (bulk, confined, Nernst-Einstein)<br>- Panel D:<br> - 'Fig2_nImZint_*': Negative imaginary part of interfacial impedance<br> - 'Fig2_ECM*': Capacitor contributions to the imaginary part of interfacial impedance (finite concentrations)<br> - 'Fig2_ECW1.dat': Capacitor contributions to the imaginary part of interfacial impedance (pure water). Full cell capacitance taken into account<br> - 'Fig2_ECW2.dat': Capacitor contributions to the imaginary part of interfacial impedance (pure water). Interfacial capacitance taken into account </p> <p>Figure 3:<br>- Panel A:<br> - 'Fig3_ReCond_Peyman_M10.dat': Real part of conductivity (data from: A Peyman, C Gabriel, E Grant, Complex permittivity of sodium chloride solutions at microwave frequencies. Bioelectromagnetics 28, 264–274 (2007))<br> - 'Fig3_ReCond_Querry_M10.dat': Real part of conductivity (data from: MR Querry, RC Waring, WE Holland, GM Hale, W Nijm, Optical Constants in the Infrared for Aqueous Solutions of NaClt. J. Opt. Soc. Am. 62 (1972)) <br> - 'Fig3_ReCond_Vinh_M10.dat': Real part of conductivity (data from: NQ Vinh, et al., High-precision gigahertz-to-terahertz spectroscopy of aqueous salt solutions as a probe of the femtosecond-to-picosecond dynamics of liquid water. The J.<br>Chem. Phys. 142, 164502 (2015).)<br> - 'Fig3_ReCond_M10.dat': Real part of conductivity from MD simulations<br>- Panel B:<br> - 'Fig3_ReCond_M*/W.dat': Real part of conductivity from MD simulations<br> - 'Fig3_ReCond_Peyman_M*': Real part of conductivity (data from: A Peyman, C Gabriel, E Grant, Complex permittivity of sodium chloride solutions at microwave frequencies. Bioelectromagnetics 28, 264–274 (2007))<br>- Panel C:<br> - 'Fig3_Cond0.dat': Static conductivity as a function of concentration (MD data)<br> - 'Fig3_Cond0_Buchner.dat': Static conductivity as a function of concentration (data from: R Buchner, GT Hefter, PM May, Dielectric relaxation of aqueous nacl solutions. The J. Phys. Chem. A 103, 1–9 (1999))<br> - 'Fig3_Cond0_Peyman.dat': Static conductivity as a function of concentration (data from: A Peyman, C Gabriel, E Grant, Complex permittivity of sodium chloride solutions at microwave frequencies. Bioelectromagnetics 28, 264–274 (2007))</p> <p>Figure 4:<br>- Panel A: <br> - 'Fig4_ReZ_d*': Real part of impedance (MD simulations)<br> - 'Fig4_ReZEC_d*': Real part of impedance (equivalent circuit model)<br>- Panel B:<br> - 'Fig4_nImZ_d*': Negative imaginary part of impedance (MD simulations)<br> - 'Fig4_nImZEC_d*': Negative imaginary part of impedance (equivalent circuit model)</p> <p>Figure 5:<br>- 'Fig5_TauQ.dat': timescales from the total charge autocorrelation functions<br>- 'Fig5_iontot.dat': timescales from the TxI autocorrelation function<br>- 'Fig5_RC.dat': timescales from the RC estimates<br>- 'Fig5_RbulkC.dat': timescales from the RbulkC estimates<br>- 'Fig5_Taudiff.dat': timescales from the difference between electrolyte and pure water QACFs<br>- 'Fig5_taud.dat': tau_d analytical timescales<br>- 'Fig5_tauDebye.dat': tau_Debye analytical timescales<br>- 'Fig5_taumix.dat': tau_mix analytical timescales</p> <p>Figure 6:<br>- Panel A:<br> - 'Fig6_Static_*: Static correlation between polarization contributions as a function of salt concentration<br>- Panel B:<br> - 'Fig6_Dynamic_EQ_*_M01' Dynamical correlations between polarization contributions (equilibrium MD results)<br> - 'Fig6_Dynamic_NEQ_*_M01' Dynamical correlations between polarization contributions (non-equilibrium MD results)<br>- Panel C:<br> - 'Fig6_Dynamic_EQ_*_M10' Dynamical correlations between polarization contributions (equilibrium MD results)<br> - 'Fig6_Dynamic_NEQ_*_M10' Dynamical correlations between polarization contributions (non-equilibrium MD results)</p> <p> </p> <p> </p>
Figures datasets for "Wave momentum shaping for moving objects in heterogeneous and dynamic media"
<p>Source data for Figures used in the manuscript "Wave momentum shaping for moving objects in heterogeneous and dynamic media".</p>
Data for "Dynamic of binary molecular systems – advantages and limitations of NMR relaxometry"
<p>Raw data for "Dynamic of binary molecular systems – advantages and limitations of NMR relaxometry". DOI of article: https://doi.org/10.1063/5.0188257</p>
Strong coupling electron-photon dynamics: a real-time investigation of energy redistribution in molecular polaritons - Dataset
<p>Dataset complement to "Strong coupling electron-photon dynamics: a real-time investigation of energy redistribution in molecular polaritons" - includes output and video files obtained using the <a href="https://etprogram.org/">eT program</a>, an open-source electronic (and molecular-polaritonic) structure program.</p> <p>See the paper at <a href="https://doi.org/10.1103/PhysRevResearch.6.033283">https://doi.org/10.1103/PhysRevResearch.6.033283</a></p>
Data-driven physics-based modeling of pedestrian dynamics - dataset: Pedestrian trajectories at Eindhoven train station
<p>Pedestrian trajectories measured at train station Eindhoven Centraal (the Netherlands) on platform 2 with acces to tracks 3 and 4.</p> <p>The dataset is partitioned in files containing 10 consecutive days each, recording 4 data fields:</p> <ul> <li><strong>time_ms:</strong> Passed time since start of the measurements. Unit: milliseconds.</li> <li><strong>object_identifier:</strong> unique id identifying an object.</li> <li><strong>x_position_mm: </strong>coordinates of the object along the x-axis at the given time. Unit: millimeters.</li> <li><strong>y_position_mm:</strong> coordinates of the object along the y-axis at the given time. Unit: millimeters.</li> </ul> <p>Each object resembles a pedestrian on the train platform recorded with 10 frames per second. We deliberately removed exact date and time information for privacy reasons (see additional note). The data set consists of 60 consecutive days starting at an unkown time between 00:00 AM and 01:00 AM of a random date between April 1st and May 1st 2022. An overhead image of the platform is included showing train track 3 in the bottom and train track 4 in the top of the image.</p> <p>The data set is supplemented to the paper <a title="Data-driven physics-based modeling of pedestrian dynamics" href="https://doi.org/10.48550/arXiv.2407.20794" target="_blank" rel="noopener">Data-driven physics-based modeling of pedestrian dynamics</a> and can be processed by the associated <a title="Software: Data-driven physics-based modeling of pedestrian dynamics" href="https://github.com/c-pouw/physics-based-pedestrian-modeling" target="_blank" rel="noopener">Python implementation</a> to create pedestrian models. </p>
Dataset for article - Dislocation dynamics in Ni: Parameterising dislocation trajectories from atomistic simulations
<p>Data supporting findings in "Dislocation dynamics in Ni: Parameterising dislocation trajectories from atomistic simulations". In this work, 8 identical Molecular Dynamics (MD) simulations of edge dislocations in pure Nickel were run and analysed, where dislocation positions were extracted and each dislocation is tracked for further analysis. Each simulation only differs in the random seed used for setting the initial atom velocities. Files needed to reproduce a specific simulation can be found in the directories provided in <em>data.zip</em>:</p> <ul> <li>fr_18430</li> <li>fr_27743</li> <li>fr_44345</li> <li>fr_46165</li> <li>fr_54382</li> <li>fr_54992</li> <li>fr_97181</li> <li>fr_98232</li> </ul> <p>The random seed needed to set the initial velocities of the atoms are set in the LAMMPS script. The log files generated by LAMMPS are provided for the different stages of a given simulation in the <em>LAMMPS_logs</em> sub-directories. The corresponding output files from the OVITO DXA are also included in the sub-directory <em>Ni_disloc_const</em>. The files <em>disloc_data_*.ca</em> contain the raw output from the OVITO DXA and can be opened directly in OVITO. The <em>disloc_data_*.txt </em>files contain information extracted from the OVITO DXA in a format that can be read in and processed by the dislocation tracking code (<a href="https://github.com/geraldineanis/DislocCode/tree/v1.0.0">https://github.com/geraldineanis/DislocCode/tree/v1.0.0</a>). The wildcard character "*" is replaced with the simulation timestep. In each directory, the dislocation position vs. time data is included as a text file<em> </em>named<em> </em><em>perfect_pos_<seed>.txt</em>. The Mishin 2004 EAM interatomic potential file <em>NiAl_Mishin_2004.eam.alloy</em> used to generate the data is also provided.</p> <p>Additional simulations were carried out at a range of applied shear stresses (20 MPa - 50 MPa). Files needed to reproduce theses simulations are provided in <em>data_stress.zip </em>and follow the same format described above.</p> <p>The data provided here was generated with the <code>LAMMPS/29Sep2021-kokkos</code><em> </em>module on the SULIS Tier 2 HPC platform, which has been built with the OpenMP backend of the <code>kokkos</code> package and uses the <code>foss-2021b</code> toolchain (<a href="https://docs.easybuild.io/common-toolchains/">https://docs.easybuild.io/common-toolchains/</a>). For further information, please refer to <a href="https://sulis-hpc.github.io/appnotes/lammps.html">https://sulis-hpc.github.io/appnotes/lammps.html</a>.</p> <p>For more details on the calculations, please refer to the publication (in preparation). Please refer to the GitHub repository at <a href="https://github.com/geraldineanis/DislocCode/tree/v1.0.0">https://github.com/geraldineanis/DislocCode/tree/v1.0.0</a> for the analysis tools developed and for detailed instructuctions for their use.</p>
Molecular Dynamic Simulation on the Role of CL5D in Accelerate the Product Dissociation of SIRT6
<p>The source data used to generate figures in the main text is stored in the ‘Source Data.xlsx’ file, and 'Source Data Description.docx' is a brief description of the source data table.<br>'SIRT6.prmtop' and 'SIRT6.inpcrd' are initial structure of SIRT6 system,'SIRT6-CL5D.prmtop' and 'SIRT6-CL5D.inpcrd' are initial structure of SIRT6-CL5D system.<br>'SIRT6_equ.pdb', 'SIRT6-CL5D'_equ.pdb are snapshots of the equilibrium structure of the SIRT6 system and the SIRT6-CL5D system, respectively.<br>'ramd.conf' is an example configuration file that uses RAMD simulations to obtain the AR6 dissociation path in the SIRT6 system, with the acceleration of 0.0625 kcal/Å/g and a cutoff distance of 0.005 Å.<br>'win1.conf' and 'win1.in' are example configuration files for the first window of the umbrella sampling, which calculates the dissociation energy barrier of AR6 in the SIRT6 system,'win1.in' is the parameter file for umbrella sampling, with A 2.5 kcal/mol/Ų spring constant, and window center is 9 Å.</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.