Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
11,506
datasets available to search
ShareScore release 0.9.0
Dataset results
11,506 results for “Dynamics”
Code for Manuscript - Near-term lake water temperature forecasts can be used to anticipate the ecological dynamics of freshwater species -
<p>Code for Manuscript - Near-term lake water temperature forecasts can be used to anticipate the ecological dynamics of freshwater species -</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>
Dataset: electrophysiological dynamics during internal–external attention switching
<p>data_raw.zip contains raw iEEG data, the structure of which is described in the .docx file.</p> <p>code_figures.zip contains numerical source data and code for each figure in the manuscript.</p> <p>code_SEEG_dataAnalysis.zip contains additional functions and code to reproduce the figures, as well as code library used to generate the results in the manuscript.</p> <p>Hammer2024_attentionSwitching.pdf is the published paper in Communications Biology.</p>
Illustrations for 'Disturbances in the evergreen boreal forest and their impact on 21st century vegetation and climate dynamics - A stochastic modeling approach' (Doctoral thesis)
<p>This repository contains all the original illustrations I created for my doctoral thesis at the Technical University of Munich. This work is published under a Creative Commons CC-BY-SA license, which means that you are free to use and adapt this work under the same license for commercial and non-commercial applications as long as you credit the original work. To credit, please cite this repository as well as my doctoral thesis.</p> <p> </p> <p> </p>
Live Magnetic Observation of Parahydrogen Hyperpolarization Dynamics
<p>The entry contains numerical data files, processing code (Wolfram Mathematica) and simulation code (Wolfram Mathematica running the 'SpinDynamica' packages) to generate Figures 1 to 6 of the manuscript titled above, arXiv preprint: <a href="https://arxiv.org/abs/2402.10766">https://arxiv.org/abs/2402.10766</a>. A PDF export of the Mathematica code is also provided.</p> <p><strong>Authors: </strong><a href="https://arxiv.org/search/physics?searchtype=author&query=Eills,+J">James Eills</a>, <a href="https://arxiv.org/search/physics?searchtype=author&query=Mitchell,+M+W">Morgan W. Mitchell</a>, <a href="https://arxiv.org/search/physics?searchtype=author&query=Rius,+I+M">Irene Marco Rius</a>, <a href="https://arxiv.org/search/physics?searchtype=author&query=Tayler,+M+C+D">Michael C. D. Tayler</a></p> <p><strong>Abstract:</strong> Hyperpolarized nuclear spins in molecules exhibit high magnetization that is unachievable by classical polarization techniques, making them widely used as sensors in physics, chemistry, and medicine. The state of a hyperpolarized material, however, is typically only studied indirectly and with partial destruction of magnetization, due to the nature of conventional detection by resonant-pickup nuclear magnetic resonance spectroscopy or imaging. Here we establish atomic magnetometers with sub-pT sensitivity as an use an alternative modality to detect <em>in real time</em> the complex dynamics of hyperpolarized materials without disturbing or interrupting the magnetogenesis process. As an example of dynamics that are impossible to detect in real time by conventional means, we examine parahydrogen-induced 1H and 13C magnetization during adiabatic eigenbasis transformations at μT-field avoided crossings. Continuous but nondestructive magnetometry reveals previously unseen spin dynamics, fidelity limits, and magnetization back-action effects. As a second example, we apply magnetometry to observe the chemical-exchange-driven 13C hyperpolarization of [1-13C]-pyruvate — the most important spin tracer for clinical metabolic imaging. The approach can be readily combined with other high-sensitivity magnetometers and is applicable to a broader range of general observation scenarios involving production, transport and systems interaction of hyperpolarized compounds.</p>
Molecular dynamics of free HMO1 model and HMO1 bound to the DNA
<div>Files:</div> <div>1. Initial models of free HMO1 and HMO1 bound to DNA.</div> <div>2. 1 µs MD trajectories of free HMO1 and HMO1 bound to DNA.</div> <div>3. MD protocol for GROMACS</div>
Impact of water models on structure and dynamics of enzyme tunnels
<ul> <li>1-initial_topologies_coordinates.tar.gz <ul> <li>primary input coordinates and parameter-topology files of all initial systems (LinBwt, LinB32, and Linb86 variants of haloalkane dehalogenase) in OPC and TIP3P water models</li> <li>prepared with the tleap module of AMBER18 package</li> <li>parm7 and crd formatted</li> </ul> </li> <li>2-cap_domain_gate_distances.tar.gz <ul> <li>datasets with minimum distance calculation between Asp146 and Leu176</li> <li>calculated by CPPTRAJ module of AMBER 18 for each performed simulation</li> <li>plain text formatted</li> </ul> </li> <li>3-protein_trajectories-linbwt.tar.gz <ul> <li>three replicated 400 ns (20,000 frames, i.e., every second frame) dry production phase trajectories of LinBwt in OPC and Tip3P water models with corresponding parameter-topology files</li> <li>produced by pmemd.cuda module of AMBER 18</li> <li>netcdf and parm7 formatted</li> </ul> </li> <li>3-protein_trajectories-linb32-closed.tar.gz <ul> <li>two replicated 400 ns (20,000 frames, i.e., every second frame) dry production phase trajectories of closed state LinB32 in OPC and Tip3P water models with corresponding parameter-topology files</li> <li>produced by pmemd.cuda module of AMBER 18</li> <li>netcdf and parm7 formatted</li> </ul> </li> <li>3-protein_trajectories-linb32-open.tar.gz <ul> <li>two replicated 400 ns (20,000 frames, i.e., every second frame) dry production phase trajectories of open state LinB32 in OPC and Tip3P water models with corresponding parameter-topology files</li> <li>produced by pmemd.cuda module of AMBER 18</li> <li>netcdf and parm7 formatted</li> </ul> </li> <li>3-protein_trajectories-linb86-closed.tar.gz <ul> <li> two replicated 400 ns (20,000 frames, i.e., every second frame) dry production phase trajectories of closed state LinB86 in OPC and Tip3P water models with corresponding parameter-topology files</li> <li>produced by pmemd.cuda module of AMBER 18</li> <li>netcdf and parm7 formatted</li> </ul> </li> <li>3-protein_trajectories-linb86-open.tar.gz <ul> <li>two replicated 400 ns (20,000 frames, i.e., every second frame) dry production phase trajectories of open state LinB86 in OPC and Tip3P water models with corresponding parameter-topology files</li> <li>produced by pmemd.cuda module of AMBER 18</li> <li>netcdf and parm7 formatted</li> </ul> </li> <li>4-basic_analyses.tar.gz <ul> <li>datasets on RMSF, RMSD, RoG, and RDF from the CPPTRAJ module of AMBER 18</li> <li>for selected replicas 3x LinBwt, 2x LinB32-closed, 2x LinB32-open, 2x LinB86-closed and 2x LinB86-open</li> <li>plain text and PDB formatted</li> </ul> </li> <li>5-caver_analyses.tar.gz <ul> <li>results of tunnel analyses for two replicas of open & closed state each for LinB32 & LinB86 in OPC and TIP3P, and three replicas of LinBWT in OPC and TIP3P</li> <li>generated by CAVER 3.0 using "Divide-and-conquer approach" (MethodsX, 10, 2023, 101968)</li> <li>comprising csv and pdb formatted: tunnel_profiles.csv and bottlenecks.csv, stripped_system.10001.pdb, v_origins.pdb</li> <li>For this and following analyses, the names of the trajectories were modified as follows: <ul> <li>linbwt_opc1_2 = md1_opc_linbwt; linbwt_opc2_2 = md2_opc_linbwt; linbwt_opc3_2 = md3_opc_linbwt;</li> <li>linbwt_tip3p1_2 = md1_tip3p_linbwt; linbwt_tip3p2_2 = md2_tip3p_linbwt; linbwt_tip3p3_2 = md3_tip3p_linbwt;</li> <li>linb32-closed_opc1_2 = md1_closed_opc_linb32; linb32-closed_opc2_2 = md2_closed_opc_linb32;</li> <li>linb32-open_opc1_2 = md1_open_opc_linb32; linb32-open_opc2_2 = md2_open_opc_linb32;</li> <li>linb32-closed_tip3p1_2 = md1_closed_tip3p_linb32; linb32-closed_tip3p2_2 = md2_closed_tip3p_linb32;</li> <li>linb32-open_tip3p1_2 = md1_open_tip3p_linb32; linb32-open_tip3p2_2 = md2_open_tip3p_linb32;</li> <li>linb86-closed_opc1_2 = md1_closed_opc_linb86; linb86-closed_opc2_2 = md2_closed_opc_linb86;</li> <li>linb86-open_opc1_2 = md1_open_opc_linb86; linb86-open_opc2_2 = md2_open_opc_linb86;</li> <li>linb86-closed_tip3p1_2 = md1_closed_tip3p_linb86; linb86-closed_tip3p2_2 = md2_closed_tip3p_linb86;</li> <li>linb86-open_tip3p1_2 = md1_open_tip3p_linb86; linb86-open_tip3p2_2 = md2_open_tip3p_linb86.</li> </ul> </li> </ul> </li> <li>6-transport_tools_analyses.tar.gz <ul> <li>results of comparative analyses for all simulations generated in 5-caver_analyses.tar.gz</li> <li>generated by TransportTools 0.9.3</li> <li>comprising csv, pdb, plain text and py formatted: configuration file (config_TT.ini), tunnel_profiles (data folder) for all filtered tunnels and bottlenecks (data folder) for all filtered tunnels, statistics (statistics folder) and visualization (visualization folder)<br> </li> </ul> </li> </ul> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p>
Decoding the Epigenetics and Chromatin Loop Dynamics of Androgen Receptor-Mediated Transcription
<p>This repository stores the datasets for the "Decoding the Dynamic Regulation and Chromatin Architecture of Androgen Receptor-mediated Gene Expression" paper.</p>
Dataset for the paper "Network-Based Differential Abundance Analysis: Bridging Community Interactions and Host-Microbiome Dynamics."
<p>The files with extension rds are files that contain simulated data and the tsv files contain original data along with their meta data.</p>
Fisher zeroes and dynamical quantum phase transitions for two- and three-dimensional models
<p>Data for the publication <em>Fisher zeroes and dynamical quantum phase transitions for two- and three-dimensional models</em>. Included are data for return rates and Fisher zeroes. The names of the data files contain all metadata necessary for specifying the type of data, with a list of the paramters used. For more details on the models and paramters see the article.</p> <ul> <li>Firstly the three models are considered labelled by <em>Kit</em> for the 2D spinless px+ipy topological superconductor, <em>Sq</em> for the 2D spinfull topological superconductor, and <em>3D</em> for the 3D topological insulator.</li> <li>After <em>_N_</em> the system size is written, referring the number of sites along one direction (for finite size calcualtions only).</li> <li><em>RR</em> refers to the return rate. For <em>Kit</em> the columns are $t$, $l(t), $\dot{l}(t)$, $\ddot{l}(t)$. If only two columns exit then there is just $t$, $\dot{l}(t)$. For <em>Sq</em> the columns are $t$, $l(t), $\dot{l}(t)$, $|\lambda_0(t)|$. For <em>3D</em> the columns are $t$, $\dot{l}(t)$, $\ddot{l}(t)$.</li> <li><em>Density</em> refers to the density of Fisher zeroes along the real time axis, with the columns being simply time and density.</li> <li><em>Fisher</em> to Fisher zeroes in the complex plane with <em>kx</em>, etc meaning it is resolved along this momentum direction. The columns are the real and imaginary parts of the complex time argument.</li> <li><em>Critical_k</em> are the critical momenta.</li> <li><em>Critical_k_En</em> lists the energy for each critical momenta in the matching <em>Critical_k </em>file.</li> <li>For <em>Kit</em> the list of quench parameters are 100 times {$\mu_0$, $\Delta_0$, $\mu_1$, $\Delta_1$}.</li> <li>For <em>Sq</em> the list of quench parameters are 100 times {$\nu_0$, \nu_1$, $\mu_0$, $\Delta_0$, $\alpha_0$, $B_0$, $\mu_1$, $\Delta_1$, $\alpha_1$, $B_1$}.</li> <li>For <em>3D</em> the list of quench parameters are 100 times {$v_0$,$w_0$,$v_1$,$w_1$}.</li> <li>For <em>Fisher</em> data <em>branch</em> labels naturally the branch p.</li> <li><em>Zoom</em>, etc, label different zooms in differnet regions, as made explicit by the times inside the files.</li> </ul> <p>This work was supported by the National Science Centre (NCN, Poland) under the grant 2019/35/B/ST3/03625, by the German Research Council (DFG) via the Re- search Unit FOR 2316 and by the National Science and Engineering Resource Council (NSERC) of Canada via the Discovery Grant program.</p>
Data for "Observation of microscopic confinement dynamics by a tunable topological θ-angle"
<p>This dataset is for the research article "Observation of microscopic confinement dynamics by a tunable topological θ-angle". A preprint version is available on arXiv with the identifier <a title="Observation of microscopic confinement dynamics by a tunable topological &theta;-angle" href="https://arxiv.org/abs/2306.11794" target="_blank" rel="noopener">arXiv:2306.11794</a>. </p> <p>Note that Figure 1 in the main text and Extended Data Figures S2 and S3 in the Methods section show the mapping relationship and initial state preparation, respectively, and do not contain original data. These details are stated in the corresponding files.</p>
Phosphorylation regulated conformational diversity and topological dynamics of an intrinsically disordered nuclear receptor
<p>Molecular dynamics simulations of AF1c region of human glucocorticoid receptor and its phosphovariants as described in the below paper: </p> <p><strong>Phosphorylation regulated conformational diversity and topological dynamics of an intrinsically disordered nuclear receptor</strong></p> <p>Vasily Akulov, Alba Jiménez Panizo, Eva Estébanez-Perpiñá, John van Noort, Alireza Mashaghi</p> <p> </p> <p>The data related to this project has been deposited in two repositories. This repository contains the first part of the data; the second part can be found at the DOI: 10.5281/zenodo.13822438</p>
Coopetition as an ecological dynamic: interactions between dense seasonal macroalgal mats and oysters on temperate shellfish reefs
<p>We document the proliferation of dense algal mats on intertidal oyster reefs in the Southeastern USA. We also use data from a small field experiment to understand the effects that oysters have on macroalgae. The data and code included here can be used to replicate all analyses and figures in the manuscript.</p>
Dataset to: Controlling 4f antiferromagnetic dynamics via itinerant electronic susceptibility
<p>This is a dataset for the article titled 'Controlling 4f antiferromagnetic dynamics via itinerant electronic susceptibility' which is accepted to Physical Review Research in 2024.</p> <ul> <li>GdT2Si2_eDOS.tar.gz (and GdT2Si2_eDOS.zip) file includes electronic density of states of the studied materials that can be extracted in Linux (in Windows) environment.</li> <li>Other .nxs files include the data file presented in the corresponding figure of the linked Physical Review Research article.</li> </ul>
Climate change impacts pair-bond dynamics in a long-lived monogamous species
<p>Manuscript title: Climate change impacts pair-bond dynamics in a long-lived monogamous species</p> <p><br>Author(s):<br>Ruijiao Sun<br>Woods Hole Oceanographic Institution<br>Massachusetts Institute of Technology<br>University of California, Santa Barbara, USA<br>ruijiaos@ucsb.edu<br>ruijiaos@icloud.com</p> <p>Christophe Barbraud<br>Centre d’Etudes Biologiques de Chizé, <br>CNRS-La Rochelle University UMR7372, <br>79360 Villiers en Bois, France<br>christophe.barbraud@cebc.cnrs.fr </p> <p>Stéphanie Jenouvrier<br>Biology Department, <br>Woods Hole Oceanographic Institution, <br>Woods Hole, MA 02543, USA<br>sjenouvrier@whoi.edu</p> <p>Karine Delord<br>Centre d’Etudes Biologiques de Chizé, <br>CNRS-La Rochelle University UMR7372, <br>79360 Villiers en Bois, France</p> <p>Kristen Krumhardt<br>Climate and Global Dynamics, <br>NSF National Center for Atmospheric Research (NCAR), <br>Boulder, Colorado, USA</p> <p>Rémi Fay<br>Laboratoire de Biom´etrie et Biologie Évolutive, <br>UMR 5558, Université Claude Bernard Lyon 1, <br>Villeurbanne, France</p> <p>Francesco Ventura<br>Biology Department, <br>Woods Hole Oceanographic Institution, <br>Woods Hole, MA 02543, USA<br>sjenouvrier@whoi.edu</p> <p>Bilgecan Şen<br>Center for Environmental Science,<br>University of Maryland,<br>Cambridge, MD 21613,USA</p> <p> </p> <p>Abstract:<br>Climate change can influence populations of monogamous species by affecting pair-bond dynamics. This study examined the impact of climate on widowhood and divorce, and the subsequent effects on individual vital rates and life-history outcomes over 54 years in a snow petrel (Pagodroma nivea) population. We found that environmental conditions can affect pair-bond dynamics both directly and indirectly. Divorce was adaptive, occurring more frequently after breeding failure and leading to improved breeding success. Divorce probabilities also increased under severe climatic conditions, regardless of prior breeding success, supporting the "Habitat-mediated” mechanisms. Generally, pair-bond disruptions reduced subsequent vital rates and lifetime outcomes. Climate forecasts from an Atmosphere-Ocean General Circulation Model projected increased male widowhood rates due to decreased sea ice negatively affecting female survival, despite considerable uncertainty. These findings highlight the importance of environmentally induced changes in demographic and pair-bond disruption rates as crucial factors shaping demographic responses to climate change. </p> <p>Funding information: <br>This work was supported by the National Science Foundation (OPP 1840058) to SJ and RS. Field data has been collected since 1963 on Ile des Pétrels as part of ORNITHOECO Project 109 "Seabirds and marine mammals as sentinels of global changes in the Southern Ocean”, funded by the French Polar Institute Paul-Emile Victor (IPEV; PI C. Barbraud). Institut Polaire Françis, Paul-Emile Victor (IPEV), Terres Australes et Antarctiques Françaises, and Zone Atelier Antarctique et Terres Australes (LTSER France, CNRS-INEE) provided logistical and financial support. This study is part of the long-term Studies in Ecology and Evolution (SEE-Life) program of the CNRS and a contribution to Project SENSEI (Sentinels of the sea ice) funded by Fondation BNP Paribas.</p> <p>Data location:<br>Ile des Pétrels, Pointe Géologie Archipelago (66°40'S, 140°01'E), Terre Adélie, Antarctica</p> <p>File folder list:<br>Forecast<br>JAGS_models<br>LHO</p> <p>Software:<br>Bayesian multi-state capture-mark-recapture analyses were conducted using JAGS through R. Life history analyses and pair-bond disruption forecasts were performed using MATLAB. All figures were produced in MATLAB.</p> <p>Description:<br>These files contain all the data, code, and model outputs needed to reproduce the results and figures in our study. <br>The data comes from a long-term monitoring project of snow petrels breeds at Ile des Pétrels, Pointe Géologie Archipelago (66°40'S, 140°01'E), Terre Adélie, Antarctica. Since 1963, an annual long-term monitoring study has been conducted. Adults and chicks were leg-banded with stainless-steel bands, with adult sex determined by vocalization and relative size. Nest surveys during incubation and fledging periods determined breeding success and pair identities.</p> <p>There are three main subfolders included: <br>(1) JAGS_models: JAGS code and R code to perform our Multi-state capture-mark-recapture models and corresponding model output; <br>(2) LHO: Code for life-history outcome analysis; <br>(3) Forecast: Climate projection data and code for pair-bond disruption forecast.<br>Each subfolder includes a README.txt file that describes its contents.</p> <p> </p>
Legion: A Platform for Gaussian Wavepacket Nonadiabatic Dynamics
<p>These datasets contain the initial conditions and trajectories generated by the newly developed Legion using <em>Ab Initio</em> Multiple Spawning (AIMS) for both fulvene with different initial condistions and DMABN. We performed the dynamics varying multiple parameters and introduced new approximations that avoid the need to compute the nonadiabatic coupling (NACV).</p> <p>The initial condition for fulvene were obtained from (doi.org/10.1039/D0CP01353F and doi.org/10.1021/acs.jctc.3c01159) and for DMABN were obtained from (doi.org/10.1039/D0CP01353F). For fulvene the dynamics used CASSCF in the <strong>Columbus software (version 7.2)</strong> and <strong>OpenMolcas (version 24.06)</strong>. For CASPT2 it used only OpenMolcas. The DMABN dynamics were performed with TDDFT using <strong>ORCA (version 5.0.4)</strong> and <strong>Gaussian (version 16)</strong>.</p>
Data related to: Fast low-temperature irradiation creep driven by athermal defect dynamics
<h2>Data set related to article "Fast low-temperature irradiation creep driven by athermal defect dynamics"</h2> <h2>Simulation data</h2> <h3>Molecular dynamics data</h3> <p>The molecular dynamics data is the output of simulations of single-crystal tungsten with periodic boundary conditions evolving under irradiation up to high dose (0.5 dpa). The simulations are performed under a constant externally applied stress, uniaxial to z-direction, and zero stress conditions otherwise. Simulations were performed for stresses of -1.0 GPa, -0.5 GPa, 0 GPa, 0.5 GPa, 1.0 GPa, 1.5 GPa, and 2.0 GPa. Each stress condition was simulated five times independently. Simulations were performed in <a href="https://www.lammps.org/">LAMMPS</a> (see below for the simulation script). Only every 100th frame in LAMMPS Dump format is uploaded here. A frame corresponds to a dose increment of approximately 0.0002 dpa, i.e. frame 1000 corresponds to a dose of approximately 0.2 dpa. The files are zipped. A finer resolution can be supplied upon request (up to every 5th frame).</p> <p>The zip archive naming convention is as follows:</p> <blockquote> <p>For example, archive "srim_neg1p0.zip" contains data of the 5 simulations for -1.0 GPa (='negative 1 point 0'), where snapshot "srim_neg1p0_2/srim_neg1p0_2.1300.dump" refers to independent simulation ID 2, frame 1300, i.e. at a dose of 0.26 dpa. </p> <p>"srim_pos0p5.zip" contains the 5 simulations for 0.5 GPa (='positive 0 point 5'), and so on.</p> </blockquote> <p>Archive "logfiles.zip" contains the simulation output information, containing stresses and box dimensions for every cascade iteration of each simulation. These can be used to generate the box eigenstrains. The columns are defined as follows:</p> <blockquote> <p>iteration number, dose (dpa), total potential energy (eV), pressure xx (bar), pressure yy (bar), pressure zz (bar), pressure xy (bar), pressure xz (bar), pressure yz (bar), simulation box width x (Å), simulation box width y (Å), simulation box width z (Å)</p> </blockquote> <p>The files contain a few lines labelled with "# restart", which marks points at which the simulation was terminated and then continued.</p> <p>Archive "md_eigenstrains.zip" contains the eigenstrain tensor components parallel and perpendicular to the uniaxial stress direction obtained directly from the MD simulations. The format is as follows:</p> <blockquote> <p>For example, files "eigenpara_pos0p5_0.dat" and "eigenperp_pos0p5_0" contain the MD eigenstrains parallel and perpendicular to the uniaxial loading direction of simulation "eigenpara_pos0p5_0", respectively. The first column is the NRT dose (dpa), and the second column is the eigenstrain value at this dose. The perpendicular eigenstrain is the average of the eigenstrain components xx and yy.</p> </blockquote> <h3>Molecular dynamics script</h3> <p>The LAMMPS script for performing high-dose collision cascade simulations is available at:</p> <p> <a href="https://github.com/mb4512/ezcascades/">https://github.com/mb4512/ezcascades</a></p> <p>The simulation input files required to replicate the molecular dynamics simulations of this work are supplied here. Archive "md_input_files.zip" contains simulation input files, specifying box dimensions, stress constraints, the interatomic potential, paths to simulation and scratch directories, and so on. For example, "srim_neg1p0_0.json" is the input file for the "srim_neg1p0_0" simulation.</p> <p>The interatomic potential "W_MNB_JPCM17.eam.fs" used here is the embedded atom method potential for tungsten developed by Mason et al: <a href="https://doi.org/10.1088/1361-648X/aa9776">10.1088/1361-648X/aa9776</a>, available at the <a href="https://www.ctcms.nist.gov/potentials/">NIST Interatomic Potentials Repository</a>.</p> <h3>Surrogate model data</h3> <p>Archive "eigenstrain_models.zip" contains the MLE model parameters for the eigenstrain surrogate models. The format is as follows:</p> <blockquote> <p>For example, files "eigenpara_neg0p5.log" and "eigenperp_neg0p5.log" contain the cubic spline knot points for the MLE model of eigenstrains parallel and perpendicular to the uniaxial loading direction at -0.5 GPa, respectively. The file contains columns labelled as x, y, and sigma, which correspond to dose (dpa), eigenstrain mean, and eigenstrain standard deviation, respectively. The spline boundary conditions are f(x = 0) = 0, f''(x = 0) = 0, and f'(x_end) = 0, where x_end is the final dose value in the list. The model is extrapolated for higher doses: f(x > x_end) = f(x_end).</p> <p>The log files also contain the covariance matrix of the eigenstrain mean values, from which model uncertainties can be derived.</p> </blockquote> <p>Archive "doseprofile.zip" contains the irradiation dose profile as generated using <a href="https://www.srim.org/">SRIM</a> data following the procedure described in the Supplemental Information. The content is as follows:</p> <blockquote> <p>Files "vacgrid_x_8micro_HR.dat", "vacgrid_y_8micro_HR.dat" contain the x and y coordinates of the 2 dimensional grid, respectively. File "vacgrid_z_8micro_HR.dat" contains a list of dose values (in arbitrary units). The values are ordered consistently, that is, the n-th values of each file give the matching tuple (x, y, dose(x,y)).</p> </blockquote> <p> </p>
Data analysis & code: Quantifying the impact of climate change and forest management on Swedish forest ecosystems using the dynamic vegetation model LPJ-GUESS
<p><span>This file contains code to optimize the allometric parameters, to plot the figures, and details of the underlying data analysis in "Quantifying the impact of climate change and forest management on Swedish forest ecosystems using the dynamic vegetation model LPJ-GUESS" (Bergkvist et al.). <br></span></p>
Climate, inter-serotype competition and arboviral interactions shape Dengue dynamics in Thailand
Open the record for dataset details and reuse information.
A fMRI dataset in response to large number of short natural dynamic facial expression videos
<pre>#A fMRI dataset in response to large number of short natural dynamic facial expression videos<br>Natural facial expressions dataset (NFED),a dataset of functional magnetic resonance imaging (fMRI) responses to 1,320 short (3s) facial expression video clips.NFED offers researchers fMRI data that enables them to investigate the neural mechanisms involved in processing emotional information communicated by facial expression videos in real-world environments.<br>The dataset contains raw data, pre-processed volume data,pre-processed surface data and suface-based analyzed data.<br>To get more details, please refer to the paper at {website} and the dataset at https://openneuro.org/datasets/ds005047<br><br>## Preprocess procedure<br>The MRI data were preprocessed by using Kay et al, combining code written in MATLAB and certain tools from FreeSurfer, SPM,and FSL(http://github.com/kendrickkay). We used FreeSurfer software (http://surfer.nmr.mgh.harvard.edu) to construct the pial and white surfaces of participants from the T1 volume. Additionally, we established an intermediate gray matter surface between the pial and the white surfaces for all participants.<br>**code: https://openneuro.org/datasets/ds005047/derivatives/validation/code/volume_pre-process/**<br>Detailed usage notes are available in codes, please read carefully and modify variables to satisfy your customed environment.<br>## GLM of main experiment<br>We utilized a single-trial General Linear Model (GLMsingle) (https://github.com/cvnlab/GLMsingle) approach, an advanced denoising approach in MATLAB R2019a, to model the pre-processed fMRI data from main experiment. For single trials, the method of GLM was developed to offer estimations of BOLD response magnitudes ('betas'). GLMsingle requires only fMRI time series data and a design matrix as inputs, integrating three techniques to enhance the accuracy of experimental GLM beta estimates. Firstly, for each voxel, a custom HRF is identified from a library of candidate functions. Secondly, cross-validation is utilized to derive a set of noise regressors from voxels unrelated to the experimental paradigm. Thirdly, to improve the stability of beta estimates for closely spaced trials, ridge regression is employed on a voxel-wise basis to regularize the betas. In this study, three betas were calculated by analyzing the BOLD response corresponding to individual video onset ranging from 1 to 3 seconds with 1-second intervals. We produced individual GLMsingle models for each session (consisted of 4 training runs and 2 test runs). In general, for each video within the training set, 2 (repetitions) x 3 (seconds) betas were acquired. Similarly, for each video within the testing set, 10 (repetitions) x 3 (seconds) betas were acquired. The utilization of repetitions enabled us to acquire video-evoked responses with a high signal-to-noise ratio (SNR).<br><br>The regressors in the GLMsingle toolbox mainly include the following categories:<br>1.Experimental Design Matrix: This is constructed based on the experimental design, including experimental conditions, task events, etc., and is used to estimate the BOLD (Blood - Oxygen - Level - Dependent) signal response.<br>2.Data-driven nuisance regressors: The data-driven nuisance regressors used in the GLMdenoise technique. These are identified by analyzing the data itself and are used to remove noise and improve the accuracy of beta estimation.<br>3.Physiological noise regressors: May include indicators of physiological signals such as heart rate and respiration, and are used to correct the influence of physiological noise on the BOLD signal.<br>4.Movement parameter regressors: Usually include head movement parameters, such as translation and rotation, and are used to correct signal changes caused by head movement.<br>5.Polynomial regressors: Polynomial terms used to o characterize the baseline signal level.<br><br><br>**code: https://openneuro.org/datasets/ds005047/derivatives/validation/code/GLMsingle-main-experiment/matlab/NFED_GLMsingle.m**<br><br>#### retinotopic mapping<br><br>The fMRI data from the the population receptive field experiment were analyzed by a pRF model implemented in the analyzePRF toolbox (http://cvnlab.net/analyzePRF/) to characterize individual retinotopic representation. Make sure to download required software mentioned in the code.<br><br>**code: https://openneuro.org/datasets/ds005047/derivatives/validation/code/Functional-localizer-experiment-analysis/s4a_analysis_prf.m**<br><br>#### fLoc experiment<br><br>We used GLMdenoise,a data-driven denoising method,to analyze the pre-processed fMRI data from the fLoc experiment.We used a "condition-split" strategy to code the 10 stimulus categories, splitting the trials related to each category into individual conditions in each run. Six response estimates (beta values) for each category were produced by using six condition-splits.To quantify selectivity for various categories and domains,we computed t-values using the GLM beta values after fitting the GLM.The regions of interest with category selectivity for each participant were defined by using the resulting maps.<br>**code: https://openneuro.org/datasets/ds005047/derivatives/validation/code/Functional-localizer-experiment-analysis/s4a_analysis_floc.m**<br><br><br>## Validation<br>### Basic quality control<br>The fundamental quality control suggests that the data displays good quality. To evaluate the quality of the structural data obtained from NFED, we employed four crucial metrics:Coefficient of Joint Variation(CJV), Contrast-to-Noise Ratio (CNR), Signal-to-Noise Ratio in Grey Matter (SNR_GM), and Signal-to-Noise Ratio in White Matter (SNR_WM). Specifically, the CJV is between white matter(WM) and grey matter(GM).The CNR assesses the relationship between the contrast of GM and WM with the noise present in the image. The SNR assesses the connection between the mean signal measurements and the noise present in the image. The SNR assessment is conducted individually for GM and WM.For quality control of the NFED’s functional scans, we assessed the amount of head motion for each participant and the temporal signal-to-noise ratio (tSNR) of the time-series data, separately.<br>**code: https://openneuro.org/datasets/ds005047/derivatives/validation/code/validation/T1_Image-quality-metrics/T1data/noiseeval/cal_SNRindex.m**<br>**code: https://openneuro.org/datasets/ds005047/derivatives/validation/code/validation/FD/FD.py**<br>**code: https://openneuro.org/datasets/ds005047/derivatives/validation/code/validation/tSNR/tSNR.py**<br>### The visual cortex exhibits reliable BOLD responses to natural facial expression videos stimuli in main experiment.<br>In the main experiment, there were 5 participants who accomplished 10 sessions, each consisting of of 4 training runs and 2 test runs, making a total of 60 runs(40 training and 20 test runs) in all. For each run, 30 (videos)*2 (repetitions) x 3 (seconds) betas were acquired. Z-score normalization was performed on the raw betas at each voxel for each run.The betas were then averaged across stimulus repetitions to generate a vector of betas. In general, 44 runs (40 training runs and 4 test runs)*90 betas were acquired. Hence, the test-retest reliability of responses to these videos in main experiment were evaluated through computing the Pearson correlation between the 90 betas obtained from the even runs and odd runs on each vertex.<br>**code: https://openneuro.org/datasets/ds005047/derivatives/validation/code/validation/reliability_face/reliability_face.py**<br>### noise celling<br>The code are available at https://openneuro.org/datasets/ds005047/validation/code/noise_celling/sub-xx" store the intermediate files required for running the program<br>**code: https://openneuro.org/datasets/ds005047/derivatives/validation/code/noise_celling/Noise_Ceiling.py**<br><br>### Correspondence between human brain and DCNN<br>The code are available at https://openneuro.org/datasets/ds005047. We combined the data from main experiment and functional localizer experiments to build an encoding model to replicate the hierarchical correspondences of representation between the brain and the DCNN. The encoding models were built to map artificial representations from each layer of the pre-trained VideoMAEv2 to neural representations from each area of the human visual cortex as defined in the multimodal parcellation atlas.<br>**code: https://openneuro.org/datasets/ds005047/derivatives/validation/code/dnnbrain/**<br><br>### Semantic metadata of action and expression labels reveal that NFED can encode temporal and spatial stimuli features in the brain<br>The code are available at https://openneuro.org/datasets/ds005047/validation/code/semantic_metadata/xx_xx_semantic_metadata" store the intermediate files required for running the program.<br>**code:https://openneuro.org/datasets/ds005047/derivatives/validation/code/semantic_metadata/**<br><br>## results<br> The results can be viewed at "https://openneuro.org/datasets/ds005047/derivatives/validation/results/brain_map_individual".<br><br>## Whole-brain mapping<br>The whole-brain data mapped to the cerebral cortex as obtained from the technical validation.<br>**code: https://openneuro.org/datasets/ds005047/derivatives/validation/results/show_results_allbrain/Showresults.m**<br><br>## Mannually prepared environment<br>We provide the *requirements.txt* to install python packages used in these codes. However, some packages like *GLM* and *pre-processing* require external dependecies and we have provided the packages in the corresponding file.<br><br>## stimuli<br>The video stimuli used in the NFED experiment are saved in the "stimuli" folders.<br><br></pre>
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.