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11,506 results for “Dynamics”
The Kling-Gupta efficiency (KGE) and latent variable data to analyze the dynamic changes and driving mechanisms of predictability
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Intermolecular interactions probed by rotational dynamics in gas-phase clusters
<p>all the raw data for the main figures of our literature "Intermolecular interactions probed by rotational dynamics in gas-phase clusters"</p>
Replication package for: Efficient and Convergent Sequential Pseudo-Likelihood Estimation of Dynamic Discrete Games
<p>The package contains all the code necessary to reproduce the figures and tables in Dearing and Blevins (forthcoming). "Efficient and Convergent Sequential Pseudo-Likelihood Estimation of Dynamic Discrete Games." Review of Economic Studies. Details are also given about accessing the raw data for the application.</p>
Breast cancer dataset used in: Biologically informed NeuralODEs for genome-wide regulatory dynamics
<p> The original data set comes from a cross-sectional breast cancer study (GEO accession GSE7390) consisting of microarray expression values for 22000 genes from 198 breast cancer patients, that we sorted along a pseudotime axis. We noted that the same data set was also used in the PROB paper (Sun, X., et al 2021, Inferring latent temporal progression and regulatory networks from cross-sectional transcriptomic data of cancer samples). <br><br>PROB is a GRN inference method that infers a random-walk-based pseudotime to sort cross-sectional samples and reconstruct the GRN. For consistency and convenience in pseudotime inference, we obtained the same version of this data that was already preprocessed and sorted by PROB. This was shared with us by the authors of PROB. We have uploaded the shared files here, as well as the versions obtained after pre-processing to apply PHOENIX. </p>
Data for article "Non-Stokesian dynamics of magnetic helical nanoswimmers under confinement
<h4><strong>Data for article "Non-Stokesian dynamics of magnetic helical nanoswimmers under confinement", A. Fazeli, V. Thakore, T. Ala-Nissila, M. Karttunen</strong></h4> <p><span>● </span>Published version: https://doi.org/10.1093/pnasnexus/pgae182</p> <p><span>● </span>Preprint version: https://doi.org/10.48550/arXiv.2311.00839</p> <p> </p> <p>The article studied the effect of the degree of confinement on the propulsion of electromagnetically propelled helical nanoswimmers. The helices were subjected to different degrees of confinement between two infinite parallel plates and driven using a rotating magnetic field at different frequencies. The degree of confinement (Gamma) is defined as the ratio of a helix’s outer diameter over the distance between the two boundary plates.</p> <p>At the highest level in the directory hierarchy, there are two folders, i.e., “Simulation_Production_Files”, and “Simulations_by_Nanoswimmers”. The first one consists of the source code of the Lattice-Boltzmann Method package implemented in LAMMPS used for running the simulations. It also includes a Matlab script used for calculating the required simulation variables to put in the LAMMPS input script for a given simulation.</p> <p>The other folder, i.e., “Simulations_by_Nanoswimmers”, has subfolders first broken down by the swimmer’s name (i.e., H0-8), then by Gamma, and finally by field driving frequency. Each simulation folder, i.e., at “Simulations_by_Nanoswimmers/H0*/Gamma_0.*/Field_Frequency_*/”, consists of seven types of file as described in the table below.</p> <table> <tbody> <tr> <td> <p><strong>File</strong></p> </td> <td> <p><strong>Description</strong></p> </td> </tr> <tr> <td> <p>script.in</p> </td> <td> <p>LAMMPS input script. LAMMPS executes calculations by reading commands from this file.</p> </td> </tr> <tr> <td> <p>H*.data</p> </td> <td> <p>Swimmer’s geometry data file. This lists the xyz coordinates of the constituent atoms of the helix’s surface area. </p> </td> </tr> <tr> <td> <p>bodies-*.lammpstrj</p> </td> <td> <p>A simulation output file. This lists the xyz coordinates of all helix’s atoms per timestep.</p> </td> </tr> <tr> <td> <p>H1cm-*.lammpstrj</p> </td> <td> <p>A simulation output file. This lists the xyz coordinates and linear velocities of the helix’s center of mass per timestep.</p> </td> </tr> <tr> <td> <p>H1omega-*.lammpstrj</p> </td> <td> <p>A simulation output file. This lists the xyz coordinates and linear velocities of an outer atom (i.e., an atom with a distance of minor radius plus major radius from the helix’s centerline). The tangential velocities of this atom are used to calculate the helix’s angular velocity. </p> </td> </tr> <tr> <td> <p>H1plane-*.lammpstrj</p> </td> <td> <p>A simulation output file. This lists the xyz coordinates of the three atoms used to construct a hypothetical plane fixed with respect to the helix body. This plane is used to keep track of orientation when calculating the helix’s angular velocity.</p> </td> </tr> <tr> <td> <p>torques-*.txt</p> </td> <td> <p>A simulation output file. These list the viscous drag torque experienced by the helix around the x-, y-, and z- axes.</p> </td> </tr> </tbody> </table> <p> </p> <p>The asterisks refer to the Shell (bash) wildcard character.</p> <p> </p>
Molecular dynamics simulations of a4b2 nAChR receptor with epibatidine
<p>Molecular dynamics simulation trajectories of a4b2 nAChR receptor widetype and mutations with epibatidine</p>
Assessing Mammalian population dynamics in Mizoram
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Chemically reactive thin films: dynamics and stability -- Data
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Experimental data: "Dynamical clustering and wetting phenomena in inertial active matter".
<p>In each zip folder, we report experimental data for shaker frequency f=90 Hz, 120 Hz, 150 Hz at different packing fraction, corresponding to different number of particles N=30, 60, 90, 120, 150, 180, 210, 240</p>
Computational data for "Measurement of Coherent Vibrational Dynamics with X-ray Transient Absorption Spectroscopy Simultaneously at the Carbon K- and Chlorine L$_{2,3}$- Edges"
<p>Contains:<br><br>1. Code for and results from, time-dependent Schroedinger equation simulations for the molecular normal modes under strong electric fields inducing impulsive stimulated Raman processes.</p> <p>2. Molecular orbitals obtained from ROKS calculations for the C 1s and Cl 2p excited states (with spin-free one-electron X2C). </p>
Quantum Hardware-Enabled Molecular Dynamics via Transfer Learning
<p>This zip file contains datasets for training Behler-Parrinello neural networks using the n2p2 software. Both DFT and VQE training sets are given for both the water monomer and dimer dataset.</p>
Input files for molecular dynamics simulations of holo CaM tagged with Alexa Fluor 488 and Texas Red dyes using Amber20
<p>Here we share the input files for molecular dynamics simulations of holo CaM tagged with Alexa Fluor 488 and Texas Red dyes using Amber20, as well as the TrESP charges used in TrESP-MMPol electronic coupling calculations of FRET properties. Links to the TrADA tool used to derive TrESP charges and the Trespcoup software used to compute electronic couplings for FRET are indicated below:</p> <div> <div> <div> <p>Cupellini, L., Jurinovich, S., & Mennucci, B. (2024). TraDA - Transition Density Analyzer. Zenodo. https://doi.org/10.5281/zenodo.10966411</p> <p>Cupellini, L., Lipparini, F., & Cignoni, E. (2024). trespcoup - Software to compute TrEsp couplings. Zenodo. https://doi.org/10.5281/zenodo.10966391</p> </div> </div> </div>
MD simulation trajectories for paper "Distinct Structures and Dynamics of Chromatosomes with Different Human Linker Histone Isoforms"
<div> <div>The dataset includes the MD simulation trajectories for the paper "Distinct Structures and Dynamics of Chromatosomes with Different Human Linker Histone Isoforms". </div> <div>Citation: Zhou BR, Feng H, Kale S, Fox T, Khant H, de Val N, Ghirlando R, Panchenko AR, Bai Y. Distinct Structures and Dynamics of Chromatosomes with Different Human Linker Histone Isoforms. Mol Cell. 2021 Jan 7;81(1):166-182.e6. PMID: 33238161; PMCID: PMC7796963.</div> </div>
Successful conservation translocation: Population dynamics of tiger recovery in Panna Tiger Reserve, Central India
<p>Tiger (<em>Panthera tigris</em>) is an indicator species of ecological health and conservation efforts. Due to excessive poaching, the tiger was locally extinct in Panna Tiger Reserve, central India. Subsequent successful reintroduction efforts have brought the species back from the verge of extinction and have demonstrated the success of conservation translocations in response to such critical situations.</p> <p>To understand the demographic characteristics of the tigers reintroduced to Panna Tiger Reserve, we used an ensemble approach of different sampling techniques and direct observations from a long-term data-set spanning more than 10 years. We evaluated different demographic indicators (population status, growth rate, mean litter size, inter-birth interval, and survival probability).</p> <p>Since reintroduction in 2009, 18 females have recruited 120 cubs from 45 litters. This led to 59 individuals in 2021 with a growth rate of ~26%. The mean litter size was 2.66 (SE 0.1), and the inter-birth interval was 19.16 months (SE 0.5). The high survival rate of the reintroduced population (0.82±0.2) helped to achieve the success of reintroduction. We observed non-constant mortality trajectories for both sexes (higher survival probabilities for females) with a moderately higher risk of death in younger (<1 year) and older (>10 years) individuals.</p> <p>Our results showed the effectiveness of translocation and conservation efforts. The recovered population can be used as a founder for augmentation in other recovering tiger populations. A long-term tiger-centric management plan should be implemented in the area adjacent to Panna Tiger Reserve to conserve and secure the habitat of the entire landscape for the long-term survival of the reintroduced population in a metapopulation framework.</p>
MDD-Molecular Dynamics Dataset: Collection of protein-ligand complex simulations
<p>Dataset is part of the paper: https://chemrxiv.org/engage/chemrxiv/article-details/664c73f6418a5379b0de8152.</p> <p>This dataset consists of molecular dynamics (MD) simulations of 862 unique protein-ligand complexes, covering a wide range of protein families and diverse chemical classes of ligands. It is derived from publicly available repositories and represents the largest single source of MD simulations to date.</p> <p>All protein-ligand complexes included in the dataset were prepared following a standardized protocol. Missing atoms in the protein structures were added using the PDBFixer tool. The protein targets were parameterized using the AMBER99SB-ILDN force field, while ligands were parameterized with the ANTECHAMBER module within the ACPYPE tool. Ligand partial charges were determined to match the quantum-mechanically generated electrostatic potential via the Restrained Electrostatic Potential (RESP) method, and the remaining parameters were set using the GAFF2 force field. The molecular dynamics simulations were performed using GROMACS. The simulations were configured in a cubic simulation box with periodic boundary conditions and employed a TIP3P water model within an electrostatically neutral environment. The simulation protocol included an initial minimization cycle, followed by temperature equilibration in the NVT ensemble and pressure equilibration in the NPT ensemble. Production simulations were conducted over a period of 200 ns, with a timestep of 100 ps.</p> <p>Constructing a large, representative set of MD simulations poses challenges due to the high computational costs and complexities associated with preparing molecular systems. Moreover, given the limited number of suitable training examples (complexes) and the large volume of MD data from each simulation, careful filtering and feature selection are crucial. This dataset is valuable for exploring how molecular dynamics simulation data can be integrated with protein-ligand binding affinity prediction tasks, an essential component of in silico drug discovery pipelines. MD simulations, in particular, offer a dynamic view by illustrating the temporal interactions within protein-ligand complexes, potentially providing additional insights for affinity and specificity estimates.</p>
Supplementary Videos for "Monitoring electrochemical dynamics through single-molecule imaging of hBN surface emitters in organic solvents"
<p><strong>Supplementary Video 1: Out-of-plane emitter control. </strong>The video included is a wide-field view of an hBN flake immersed in acetonitrile while an electrochemical potential of the ITO working electrode is cycled in the three-electrode out-of-plane configuration between +1.5 V and 0 V vs Ag/AgCl. The change in potential induces a change in density of emitters. This data corresponds to a flake used in spectral analysis (data presented in <strong>Fig. 3c–e</strong>). Continuous ~1.6 kW cm<sup>–2</sup> illumination with a 561 nm laser is used. The original images were acquired at a rate of 50.091 ms per frame but here we combined frames to present a lighter video with a lower sampling rate (1 s). The total experiment took 750 seconds, but we present the image stack at a rate of 10 frames per second to make the video only 75 seconds. The scale bar is 5 µm.</p> <p><strong><span>Supplementary Video 2: In-plane emitter control.</span></strong> Here we present a wide-field video of the hBN flake shown in <strong>Figure 4b-d</strong> in between two titanium electrodes. Continuous ~1 kW cm<sup>–2</sup> illumination is used with a 561 nm laser while the polarization of the electrodes is cycled in the two-electrode in-plane configuration, inducing a change in density of emitters correlated with potential. The high-density region follows the negatively charged electrode. The original images were acquired with a 50.091 ms rate, but here we combined frames to present a lighter video with a lower sampling rate (2.5 s). The total experiment took 540 seconds, but we display 5 of the stacked images per second, making make the video around 40 seconds. The scale bar is 5 µm.</p>
Sociotechnical Dynamics in Open Source Smart Contract Repositories: An Exploratory Data Analysis of Curated High Market Value Projects
<p>This is the replication package for the paper “Sociotechnical Dynamics in Open Source Smart Contract Repositories: An Exploratory Data Analysis of Curated High Market Value Projects”.</p> <p>In project_curation_selection, there is the curation process of the 100 selected projects including the identification of GitHub repositories and classification of evolution scenarios. </p> <p>In distribution_commits_issues_contributors_market_value_before_after_deploy, data collection from GitHub projects includes the distribution of total commits, contributors, and issues before and after deployment of each investigated project. </p> <p>In analysis_commit_messages, there is qualitative analysis of commit message content from all investigated projects. </p> <p>In the analysis_contributors section, the data focuses on analyzing the profiles of each GitHub contributor involved in the investigated projects.</p> <p>In analysis_market_value_by_project, data refers to the market value and volume of each investigated project. </p> <p>In codes, there are scripts used to obtain the analyzed data.</p> <p> </p>
Supplementary tables for manuscript Dynamics of alternative polyadenylation in single root cells of Arabidopsis thaliana
<p>Supplementary tables for manuscript Dynamics of alternative polyadenylation in single root cells of Arabidopsis thaliana</p>
Water slowing down drives the occurrence of the low temperature dynamical transition in microgels
<p>Data presented in the article entitled <strong>Water slowing down drives the occurrence of the low temperature dynamical transition in microgels</strong>.</p>
Dynamics of terminal fraying-peeling and hydrogen bonds dictate the sequential vs co-operative melting pathways of nanoscale DNA and PNA triplexes
<div>##################################################################################################</div> <div> </div> <div>The simulation dataset used for the analysis reported in the manuscript titled -</div> <div> </div> <div> "Dynamics of terminal fraying-peeling and hydrogen bonds dictate the sequential vs co-operative melting pathways of nanoscale DNA and PNA triplexes."</div> <div> by Sandip Mandal, Krishna N. Ganesh, and Prabal K. Maiti*</div> <div> </div> <div> </div> <div>*E-mail: maiti@iisc.ac.in</div> <div>Center for Condensed Matter Theory, Department of Physics,</div> <div>Indian Institute of Science, Bangalore 560012, India</div> <div> </div> <div>##################################################################################################</div> <div> </div> <div>Packages required:</div> <div> Visual Molecular Dynamics (VMD)</div> <div> xmgrace</div> <div> numpy</div> <div> matplotlib</div> <div> scipy</div> <div> tleap/xleap</div> <div> AMBER</div> <div> CPPTRAJ</div> <div> MMGBSA</div> <div> Gaussian09 for partial charge calculation of the PNA protonated cytosine residues</div> <div> </div> <div>##################################################################################################</div> <div> </div> <div> </div> <div>Contents:</div> <div> </div> <div>The First main directory contains three folders and a README file--</div> <div>(1)PNA_DNA_PNA_Triplex</div> <div> (2)DNA_DNA_DNA_Triplex</div> <div>(3)Sequence_Dependence</div> <div>(4)README file</div> <div> </div> <div> </div> <div>##################################################################################################</div> <div> </div> <div> </div> <div>1. PNA_DNA_PNA_Triplex directory contains sub-directory for Protonated PNA-DNA-PNA triplexes simulation data, such as ---</div> <div>(a) Setup_files</div> <div>(b) Results</div> <div> </div> <div> </div> <div>(a)Setup_files:</div> <div>It contains input coordinates (PDP_18bp_PP.pdb, Protonated_PNA_DNA_PNA.inpcrd ..), topology files, and files required to build to complete </div> <div>system for simulation including library files for non-standard PNA residues and tleap script for the system setup.</div> <div> </div> <div>(b)Results: --folder contains simulation data and analysis scripts for Protonated PNA-DNA-PNA triplexes -----</div> <div> </div> <div>(a)Simulation Trajectories ( It contains simualtion trajectories for three independent trial runs named run1, run2, and run3)</div> <div>(b)RMSD</div> <div>(c)Hbond (hydrogen bonding)</div> <div>(d)Stiffness </div> <div>(e)Free Energy Landscape (FEL)</div> <div>(f)PCA (Principal Component Analysis)</div> <div>(g)Simulation Movies for the Protonated PNA-DNA-PNA triplex</div> <div> </div> <div> </div> <div>##################################################################################################</div> <div> </div> <div> </div> <div>2. DNA_DNA_DNA_Triplex directory contains sub-directory for Protonated DNA-DNA-DNA triplexes simulation data, such as ---</div> <div> (a) Setup_files</div> <div> (b) Results</div> <div> </div> <div> </div> <div>(a)Setup_files :</div> <div>It contains input coordinates (DDD_18bp_Protonated_NAB.pdb, 18bp_Protonated_NAB.inpcrd ...) and files required to build to complete</div> <div>system for simulation, including amber input files in the "Sander-Input-files" subdirectory, a script to run the simulation in a GPU clusters, and a leap script for the system setup.</div> <div> </div> <div>(b)Results --folder contains simulation data and analysis scripts for-----</div> <div> </div> <div> (a)Simulation Trajectories ( It contains simulation trajectory for three independent trial runs named run1, run2, and run3)</div> <div> (b)RMSD</div> <div> (c)Hydrogen bonding</div> <div> (d)Stiffness</div> <div> (e)Free Energy Landscape</div> <div> (f)PCA</div> <div> (g)Simulation Movies for the Protonated DNA-DNA-DNA triplex</div> <div> </div> <div> </div> <div>##################################################################################################</div> <div> </div> <div>3. Sequence_Dependence directory contains sub-directory for triplexes with all TAT base triples simualtion data, such as ---</div> <div> (A) DNA_DNA_DNA_with_all_TAT_sequence</div> <div> (B) PNA_DNA_PNA_with_all_TAT_sequence</div> <div> </div> <div>(A)Sub-directory contains simulation data for DNA-DNA-DNA triplexes with 18 TAT base triples (no protonated cytosine residues) ---</div> <div>(a)Set_up_files</div> <div>(b)Trajectory</div> <div>(c)RMSD</div> <div>(d)Hbond</div> <div> </div> <div> </div> <div>(B)Sub-directory contains simulation data for PNA-DNA-PNA triplexes with 18 TAT base triples (no protonated cytosine residues) ---</div> <div> (a)Set_up_files</div> <div> (b)Trajectory</div> <div> (c)RMSD</div> <div> (d)Hbond</div> <div> </div> <div>##################################################################################################</div> <div> </div> <div> </div> <div>The trajectories are sampled at 1 ns intervals (since the full trajectory dumped at 10 ps interval, will take huge dataspace) in each trajectory file:</div> <div>(a)The first frame corresponds to the structure immediately after 5 ns NPT equilibriation.</div> <div>(b)The next 200 frames are from the production run, at 1 to 200 ns.</div> <div> </div> <div> </div> <div>References:</div> <div>The DNA-DNA-DNA and PNA-DNA-PNA models were constructed using NAB code [1].</div> <div>System preparation with water and ions are done with the help of tleap/xleap [2].</div> <div>All simulation runs are conducted in AMBER2020 [3].</div> <div>For visualization we have used VMD [4].</div> <div> </div> <div>[1] T. J. Macke and D. A. Case, Modeling unusual nucleic acid structures, 1998</div> <div>[2] D. R. Roe and T. E. Cheatham III, Journal of chemical theory and computation, 2013, 9, 3084–3095</div> <div>[3]D. A. Case, H. M. Aktulga, K. Belfon, I. Ben-Shalom, S. R.Brozell, D. S. Cerutti, T. E. Cheatham III, V. W. D. Cruzeiro,T. A. Darden, R. E. Duke et al., Amber 2021, University of California, San Francisco, 2021</div> <div>[4] Humphrey W, Dalke A, Schulten K. VMD: visual molecular dynamics. J. Mol. Graph. 14(1), 33-38 (1996).</div>
ScienceDex guides
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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.