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10 results for “atomistic modeling”
Unveiling the atomistic and electronic structure of NiII–NO adduct in a MOF-based catalyst by EPR spectroscopy and quantum chemical modelling
<p><strong>Description of the dataset: </strong></p> <ul> <li><strong>Data type</strong>: Experimental spectroscopic measurements, computer simulation and analysis</li> <li>Files are with filename extensions: <strong>DSC</strong>, <strong>DAT</strong>, <strong>m</strong>, <strong>txt</strong></li> <li>Information on <strong>origin of the data</strong>:</li> </ul> <ul> <li>EPR spectroscopic measurements with filename extensions <strong>DSC</strong>, <strong>DTA.</strong></li> <li>EPR spectroscopic simulation and analyses with filename extension <strong>m</strong>.</li> <li>EPR spectra are exported as <strong>txt</strong> files in ASCII format.</li> </ul> <ul> <li>X-band CW-EPR spectroscopic measurements were generated by EMX spectrometer equipped with SHQ cavity produced by Bruker.</li> <li><strong>If the dataset includes multiple files that relate to each other:</strong> <ul> <li>Files in <strong>PARACAT_WP4_20230706_01_CW_Xband </strong>folder includes X-band CW-EPR spectroscopic measurements; original data are in DTA/DSC and txt formats.</li> <li>Files in <strong>PARACAT_WP4_20230706_02_HYSCORE </strong>and <strong>PARACAT_WP4_20230706_03_ENDOR </strong>folders include X-band HYSCORE and ENDOR data; original data are in DTA/DSC and txt formats.</li> <li>Files in <strong>PARACAT_WP4_20230706_ 04_MATLAB</strong> and<strong> PARACAT_WP4_20230706_ 05_Modelling</strong> folders include matlab and computer simulations/analyses of the EPR measurements; data are in m and txt formats.</li> <li>File <strong>PARACAT_WP4_20230706_ 06_Origin</strong> include origin plotted data</li> </ul> </li> </ul> <p> </p> <ul> <li><strong>Information on</strong>: <ul> <li>specialized abbreviations: <strong>MFU– </strong>MFU-4l:NO<sub>2</sub> MOF material</li> <li>NiNO – NO adsorbed MFU-4l:NO<sub>2</sub> MOF</li> <li>@10K – measured at 10 K</li> <li>definitions of variables: <strong>Magnetic field, Temperature.</strong></li> <li>units of measurement: <strong>Gauss (G), K, degree (°), milliTesla (mT)</strong>.</li> </ul> </li> </ul>
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>
Extended ensemble molecular dynamics study of ammonia–cellulose I complex crystal models: free-energy landscape and atomistic pictures of ammonia diffusion in the crystalline phase.
<p>The data deposited here accompany the manuscript "Extended ensemble molecular dynamics study of ammonia–cellulose I complex crystal models: free-energy landscape and atomistic pictures of ammonia diffusion in the crystalline phase" and include the molecular dynamics trajectories and the AMBER topology (parm) files. Detailed file contents are summarized in the README file.</p>
Supporting Information for the Journal Article "Self-Parametrizing System-Focused Atomistic Models"
<p>This dataset contains the supporting information published together with the article "Self-Parametrizing System-Focused Atomistic Models" (<a href="https://doi.org/10.1021/acs.jctc.9b00855"><em>J. Chem. Theory Comput.</em>, <strong>2020</strong>, <em>16</em>, 1646</a>).</p>
Atomistic, Macromolecular Model of the Populus Secondary Cell Wall Informed by Solid-State NMR
<p>Solid-state NMR dataset used to inform molecular model of the Populus secondary cell wall.</p> <p>Contains:</p> <p>- 2D through-bond and through-space ssNMR data on 13C-enriched Populus wood. Data used to inform spectral deconvolution of selective 1D spin-diffusion data</p> <p>- 2D gelHSQC NMR data for understanding of lignin composition</p> <p>- MultiCP-1D-DARR datasets on 5 replicates. Includes raw NMR data and ascii files of processed spectra. For each replicate, three experiments are conducted: non-selective 1D MultiCP-DARR, 22ppm selective MultiCP-DARR, and 150ppm selective MultiCP-DARR. 13C-13C spin-diffusion mixing times ranged from 0.001 to 5000 ms.</p> <p>- Excel files containing tabulated deconvoluted signal areas, and averages for key signal groups.</p> <p>- Excel files containing tabulated magnetization recovery values for each signal and signal groups for all replicates. Averaged values for X2C, X2L, L2C and L2X at the longest mixing times (see manuscript) are used as key metrics for evaluating molecular models.</p> <p>- Excel files containing tabulated T1-adjusted spin-diffusion rate constants for each replicate, and key averages.</p>
An atomistic characterisation of HDL subpopulation models.
<p><span>MD simulation files realted to the original research article entitled "An atomistic characterisation of high-density lipoprotein subpopulation models and the conserved ‘LN’ region of ApoA-I" by Chris J. Malajczuk and Ricardo L. Mancera. </span></p> <p><span>Files include HDL model snapshots (final CG model structures, backmapped structures, starting structures, and final structures), backmapping schemes, parameter sets, and a selection of trajectories. </span></p>
Atomistic modelling of lysophospholipids from the Campylobacter jejuni lipidome
<p>Input files for and trajectory files from the modelling and simulation of lysophospholipids from the C. jejuni lipidome. </p>
Validating x-ray line-profile defect analysis using atomistic models of deformed material
<p>Data and analysis (scripts and Jupyter notebooks) associated with publications:</p> <p>Validating x-ray line-profile defect analysis using atomistic models of deformed material (submitted to Physical Review Materials)</p> <p>Validation of x-ray line-profile analysis of extended defects in deformed crystals (submitted to Physical Review Letters)</p> <p>Preprints of these papers are included here</p>
Molecular dynamics simulation primer for: Introduction to atomistic modeling and simulation of biomolecular systems
Open the record for dataset details and reuse information.
Research data for "Device-scale atomistic modelling of phase-change memory materials"
<p>This is a dataset related to the publication "Device-scale atomistic modelling of phase-change memory materials".</p> <p>Two folders have been provided for (1) the production data shown in this work, and (2) GAP models trained in this work:</p> <p>(1) The production data have been categorised according to the main text figures:</p> <ul> <li>"reference_database": Reference databases (i.e., training structures) of three GAP models discussed in this work. The structure data are provided in (extended) XYZ format, as labelled using either the PBEsol or the PBE functional. <ul> <li>"main_GST-GAP-22_PBEsol": the main GST-GAP-22 database, which was fitted using a two-step iterative training protocol. The resulting GAP model was used to obtain the results shown in the main text. The reference database is visualised in Fig. 1 of the main text. </li> <li>"refitted_GST-GAP-22_PBE": this dataset contains the same structures as the original GST-GAP-22 training data, with all structures having been relabelled using the PBE functional.</li> <li>"extended_GST-GAP-22_for_efield_PBEsol": an extension of the GST-GAP-22 database to a new, task-specific application, i.e., electromigration under an external electric field (cf. Extended Data Fig. 4).</li> </ul> </li> </ul> <ul> <li>"crystallization_simulations": three trajectories for the crystallization simulations shown in Fig. 2, of which all were obtained from GAP-MD. <ul> <li>"fig2b_growth_GAP-MD": Growth of Ge<sub>1</sub>Sb<sub>2</sub>Te<sub>4</sub> (1008 atoms).</li> <li>"fig2c_cumulative_set_cycles_GAP-MD": Cumulative set process of Ge<sub>1</sub>Sb<sub>2</sub>Te<sub>4</sub> (1008 atoms).</li> <li>"fig2d_crystallization_12096at_GAP-MD": Crystallization of Ge<sub>1</sub>Sb<sub>2</sub>Te<sub>4</sub> (12096 atoms), in which three crystalline seeds were used.</li> </ul> </li> </ul> <ul> <li>"RESET_mushroom_model": two non-isothermal simulations shown in Fig. 3, obtained from GAP-MD. <ul> <li>"Fig3b_small_pulse": The 70 ps NVE equilibrium process after a small heating pulse was imposed in the focal area, giving an excess kinetic energy of 1,650 eV for the atoms in the focal area.</li> <li>"Fig3d_large_pulse": The 70 ps NVE equilibrium process after a large heating pulse was imposed in the focal area, giving an excess kinetic energy of 3,900 eV for the atoms in the focal area.</li> </ul> </li> </ul> <ul> <li>"RESET_device_scale_simulations": the GAP-MD simulations of the melting and heat dissipation process of a device-scale structural model shown in Fig. 4. <ul> <li>"Fig4b_device_scale<strong>_</strong>heating_10ps": The melting process of the device-scale model over 10 ps.</li> <li>"Fig4c_device_scale<strong>_</strong>cooling_40ps": The heat dissipation process of the device-scale model over another 40 ps.</li> </ul> </li> </ul> <p>(2) The GAP models trained in this work:</p> <ul> <li>"main_GAP_potential": the main GAP model used for the production data of this work, which is fitted based on PBEsol data. The XML identifier of this GAP model is GAP_2022_4_7_480_18_6_12_970.</li> </ul> <ul> <li>"other_GAP_potentials": two derivatives of the original GAP model. <ul> <li>"refitted_GST-GAP-22_PBE": using the same reference structures as the original GAP model but re-labelled using the PBE functional. The XML identifier of this GAP model is GAP_2022_5_7_480_0_58_2_26.</li> <li>"extended_GST-GAP-22_for_efield_PBEsol": An extension of the original GAP model to a new, task-specific application, i.e., electromigration under an external electric field. The XML identifier of this GAP model is GAP_2023_3_19_480_18_14_10_174.</li> </ul> </li> </ul> <p> </p>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.