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453 results for “Dynamic dataset”
Data from dissertation: 'Landscape and Aboveground Biomass Dynamics of Brazilian Savanna using airborne LIDAR and MapBiomas datasets : case study of Rio Vermelho Watershed, Brazil'
<p>This dissertation was submitted to University of Manchester as part of MSc GIS program</p> <p>This repository contains:</p> <p>1) Contains the R language code used in the dissertation (CHM_&_LiDAR_metric.R; Landscape_metric.R; Generalized_Linear_Model.R; Random_Forest_Model.R).</p> <p><br> 2) Canopy Height Model (CHM) and 56 LiDAR metric raster files with a resolution of 1m (CHM_&_LiDAR_metric_2014.zip; CHM_&_LiDAR_metric_2018.zip), the original LiDAR data come from Brazil project supported by the Brazilian Agricultural Research Corporation (EMBRAPA), the US Forest Service, USAID, and the US Department of State.</p> <p><br> 3) AGB raster files with a resolution of 10m (AGB_2014.tif; AGB_2018.tif; AGB_dynamic.tif), field plots used for AGB estimation come from Sabrinado Couto de Miranda from University of Goiás State (UEG), Brazil, and her team.</p> <p><br> 4) Landscape metric interpolation raster file (SHDI.tif; SHEI.tif; AREA_CV.tif; CIRCLE_MN.tif; SHAPE_MN.tif) with a resolution of 10m, land cover map Map come from Biomas team for landscape metric calculation.</p>
Dataset for Journal of Applied Physics 130, 124502 (2021) - Force microscopy cantilevers locally heated in a fluid: temperature fields and effects on the dynamics
<p><strong>"Fig7.fig"</strong>: Matlab figures including all the measured and treated data used to plot figure 7 of the article.</p> <p><strong>"Fig7_data.mat "</strong>: Matlab files containing the data to plot figure 7 of the article.</p> <p><strong>"Fig7_plot.m "</strong>: Matlab scripts to plot the data of the Matlab files "Fig7_data.mat"</p> <p><strong>"Fig10.fig"</strong>: Matlab figures including all the measured and treated data used to plot figure 10 of the article.</p> <p><strong>"Fig13.fig"</strong>: Matlab figures including all the measured and treated data used to plot figure 13 (in the appendix) of the article.</p> <p> </p>
Dataset for the paper Exploring the Use of Static and Dynamic Analysis to Improve the Performance of the Mining Sandbox Approach for Android Malware Identification
<p><strong>Short Description: </strong>This is the dataset for the paper "Exploring the Use of Static and Dynamic Analysis to Improve the Performance of the Mining Sandbox Approach for Android Malware Identification", accepted for publication in the Journal of Systems and Software. </p> <p><strong>Link to this repository: </strong><a href="https://github.com/droidxp/paper-replication-package">https://github.com/droidxp/paper-replication-package</a></p> <p><strong>Authors of the Paper</strong></p> <ul> <li>Francisco Handrick da Costa</li> <li>Ismael Medeiros</li> <li>Thales Menezes</li> <li>João Victor da Silva</li> <li>Ingrid Lorraine da Silva</li> <li>Rodrigo Bonifácio</li> <li>Krishna Narasimhanb</li> <li>Márcio Ribeiro</li> </ul> <p><strong>Abstract</strong></p> <p>The popularization of the Android platform and the growing number of Android applications (apps) that manage sensitive data turned the Android ecosystem into an attractive target for malicious software. For this reason, researchers and practitioners have investigated new approaches to address Android's security issues, including techniques that leverage dynamic analysis to mine Android sandboxes. The mining sandbox approach consists in running dynamic analysis tools on a benign version of an Android app. This exploratory phase records all calls to sensitive APIs. Later, we can use this information to (a) prevent calls to other sensitive APIs (those not recorded in the exploratory phase) or (b) run the dynamic analysis tools again in a different version of the app. During this second execution of the fuzzing tools, a warning of possible malicious behavior is raised whenever the new version of the app calls a sensitive API not recorded in the exploratory phase.</p> <p>The use of a mining sandbox approach is an effective technique for Android malware analysis, as previous research works revealed. Particularly, existing reports present an accuracy of almost 70% in the identification of malicious behavior using dynamic analysis tools to mine android sandboxes. However, although the use of dynamic analysis for mining Android sandboxes has been investigated before, little is known about the potential benefits of combining static analysis with a mining sandbox approach for identifying malicious behavior. Accordingly, in this paper we present the results of two studies that investigate the impact of using static analysis to complement the performance of existing dynamic analysis tools tailored for mining Android sandboxes, in the task of identifying malicious behavior.</p> <p>In the first study we conduct a non-exact replication of a previous study (hereafter BLL-Study) that compares the performance of test case generation tools for mining Android sandboxes. Differently from the original work, here we isolate the effect of an independent static analysis component (DroidFax) they used to instrument the Android apps in their experiments. This decision was motivated by the fact that DroidFax could have influenced the efficacy of the dynamic analyses tools positively---through the execution of specific static analysis algorithms DroidFax also implements. In our second study, we carried out a new experiment to investigate the efficacy of taint analysis algorithms to complement the mining sandbox approach previously used to identify malicious behavior. To this end, we executed the FlowDroid tool to mine the source-sink flows from benign/malign pairs of Android apps used in previous research work.</p> <p>Our study brings several findings. For instance, the first study reveals that DroidFax alone (static analysis) can detect 43.75% of the malwares in the BLL-Study dataset, contributing substantially in the performance of the dynamic analysis tools in the BLL-Study. The results of the second study show that taint analysis is also practical to complement the mining sandboxes approach, with a performance similar to that reached by dynamic analysis tools.</p> <p> </p>
Modeling cyanobacteria life cycle dynamics and historical nitrogen fixation in the Baltic Proper - Datasets
<p>Observational and model datasets used in: Modeling cyanobacteria life cycle dynamics and historical nitrogen fixation in the Baltic Proper.</p>
Dataset (VII) related to publication: Decisive Role of Water and Protein Dynamics in Residence Time of p38a MAP Kinase Inhibitors
<p>MD simulation data of compound <strong>1</strong> in MSM <strong>2-<em>S</em><sub>3</sub></strong> conformations of the related to the publication Pantsar et al.: <em>Decisive Role of Water and Protein Dynamics in Residence Time of p38a MAP Kinase Inhibitors.</em></p> <p>Individual .zip files contain raw-desmond trajectories (-out.cms files and trj-files).</p> <p>All datasets related to this publication:</p> <p><a href="https://doi.org/10.5281/zenodo.4568113">https://doi.org/10.5281/zenodo.4568113</a>(compound <strong>1</strong>; dataset: I)</p> <p><a href="https://doi.org/10.5281/zenodo.4572444">https://doi.org/10.5281/zenodo.4572444</a> (compound <strong>1</strong>; dataset: II)</p> <p><a href="https://doi.org/10.5281/zenodo.4561797">https://doi.org/10.5281/zenodo.4561797</a>(compound <strong>2</strong>; dataset: III)</p> <p><a href="https://doi.org/10.5281/zenodo.4563896">https://doi.org/10.5281/zenodo.4563896</a> (compound <strong>2</strong>; dataset: IV)</p> <p><a href="https://doi.org/10.5281/zenodo.5563359">https://doi.org/10.5281/zenodo.5563359</a> (<strong>SB203580</strong>; dataset: V)</p> <p><a href="https://doi.org/10.5281/zenodo.5563655">https://doi.org/10.5281/zenodo.5563655</a> (<strong>SB203580</strong>; dataset: VI)</p> <p><a href="https://doi.org/10.5281/zenodo.5564118%20">https://doi.org/10.5281/zenodo.5564118 </a>(compound <strong>1</strong> simulated in compound <strong>2</strong> metastable state <strong>2-<em>S</em><sub>3</sub></strong>; dataset: VII)</p> <p><a href="https://doi.org/10.5281/zenodo.5564208%20">https://doi.org/10.5281/zenodo.5564208 </a>(compound <strong>1</strong> simulated in compound <strong>2</strong> metastable state <strong>2-<em>S</em><sub>3</sub></strong>; dataset: VIII)</p> <p><a href="https://doi.org/10.5281/zenodo.5564586">https://doi.org/10.5281/zenodo.5564586</a> (well-tempered metadynamics simulations of compounds <strong>1</strong> and <strong>2</strong>; dataset: IX)</p> <p><a href="https://doi.org/10.5281/zenodo.5570882">https://doi.org/10.5281/zenodo.5570882</a> (well-tempered metadynamics simulations of compounds <strong>1</strong> and <strong>2</strong>; dataset: X)</p> <p><a href="https://doi.org/10.5281/zenodo.5571352">https://doi.org/10.5281/zenodo.5571352</a> (well-tempered metadynamics simulations of compounds <strong>1</strong> and <strong>2</strong>; dataset: XI)</p> <p>The datasets include original Desmond raw-trajectories (datasets I–VIII), PDB-coordinates for the energy minimized metastable state derived structures (datasets II, IV and VI) and raw-trajectories of the well-tempered metadynamics simulations (dataset IX–XI).</p>
Dataset (VIII) related to publication: Decisive Role of Water and Protein Dynamics in Residence Time of p38a MAP Kinase Inhibitors
<p>MD simulation data of compound <strong>1</strong> in MSM <strong>2-<em>S</em><sub>3</sub></strong> conformations of the related to the publication Pantsar et al.: <em>Decisive Role of Water and Protein Dynamics in Residence Time of p38a MAP Kinase Inhibitors.</em></p> <p>Individual .zip files contain raw-desmond trajectories (-out.cms files and trj-files).</p> <p>All datasets related to this publication:</p> <p><a href="https://doi.org/10.5281/zenodo.4568113">https://doi.org/10.5281/zenodo.4568113</a>(compound <strong>1</strong>; dataset: I)</p> <p><a href="https://doi.org/10.5281/zenodo.4572444">https://doi.org/10.5281/zenodo.4572444</a> (compound <strong>1</strong>; dataset: II)</p> <p><a href="https://doi.org/10.5281/zenodo.4561797">https://doi.org/10.5281/zenodo.4561797</a>(compound <strong>2</strong>; dataset: III)</p> <p><a href="https://doi.org/10.5281/zenodo.4563896">https://doi.org/10.5281/zenodo.4563896</a> (compound <strong>2</strong>; dataset: IV)</p> <p><a href="https://doi.org/10.5281/zenodo.5563359">https://doi.org/10.5281/zenodo.5563359</a> (<strong>SB203580</strong>; dataset: V)</p> <p><a href="https://doi.org/10.5281/zenodo.5563655">https://doi.org/10.5281/zenodo.5563655</a> (<strong>SB203580</strong>; dataset: VI)</p> <p><a href="https://doi.org/10.5281/zenodo.5564118%20">https://doi.org/10.5281/zenodo.5564118 </a>(compound <strong>1</strong> simulated in compound <strong>2</strong> metastable state <strong>2-<em>S</em><sub>3</sub></strong>; dataset: VII)</p> <p><a href="https://doi.org/10.5281/zenodo.5564208%20">https://doi.org/10.5281/zenodo.5564208 </a>(compound <strong>1</strong> simulated in compound <strong>2</strong> metastable state <strong>2-<em>S</em><sub>3</sub></strong>; dataset: VIII)</p> <p><a href="https://doi.org/10.5281/zenodo.5564586">https://doi.org/10.5281/zenodo.5564586</a> (well-tempered metadynamics simulations of compounds <strong>1</strong> and <strong>2</strong>; dataset: IX)</p> <p><a href="https://doi.org/10.5281/zenodo.5570882">https://doi.org/10.5281/zenodo.5570882</a> (well-tempered metadynamics simulations of compounds <strong>1</strong> and <strong>2</strong>; dataset: X)</p> <p><a href="https://doi.org/10.5281/zenodo.5571352">https://doi.org/10.5281/zenodo.5571352</a> (well-tempered metadynamics simulations of compounds <strong>1</strong> and <strong>2</strong>; dataset: XI)</p> <p>The datasets include original Desmond raw-trajectories (datasets I–VIII), PDB-coordinates for the energy minimized metastable state derived structures (datasets II, IV and VI) and raw-trajectories of the well-tempered metadynamics simulations (dataset IX–XI).</p>
Dataset (V) related to publication: Decisive Role of Water and Protein Dynamics in Residence Time of p38a MAP Kinase Inhibitors
<p>MD simulation data of <strong>SB203580</strong> related to the publication Pantsar et al.: <em>Decisive Role of Water and Protein Dynamics in Residence Time of p38a MAP Kinase Inhibitors.</em></p> <p>Individual .zip files contain raw-desmond trajectories (-out.cms files and trj-files).</p> <p>All datasets related to this publication:</p> <p><a href="https://doi.org/10.5281/zenodo.4568113">https://doi.org/10.5281/zenodo.4568113</a>(compound <strong>1</strong>; dataset: I)</p> <p><a href="https://doi.org/10.5281/zenodo.4572444">https://doi.org/10.5281/zenodo.4572444</a> (compound <strong>1</strong>; dataset: II)</p> <p><a href="https://doi.org/10.5281/zenodo.4561797">https://doi.org/10.5281/zenodo.4561797</a>(compound <strong>2</strong>; dataset: III)</p> <p><a href="https://doi.org/10.5281/zenodo.4563896">https://doi.org/10.5281/zenodo.4563896</a> (compound <strong>2</strong>; dataset: IV)</p> <p><a href="https://doi.org/10.5281/zenodo.5563359">https://doi.org/10.5281/zenodo.5563359</a> (<strong>SB203580</strong>; dataset: V)</p> <p><a href="https://doi.org/10.5281/zenodo.5563655">https://doi.org/10.5281/zenodo.5563655</a> (<strong>SB203580</strong>; dataset: VI)</p> <p><a href="https://doi.org/10.5281/zenodo.5564118%20">https://doi.org/10.5281/zenodo.5564118 </a>(compound <strong>1</strong> simulated in compound <strong>2</strong> metastable state <strong>2-<em>S</em><sub>3</sub></strong>; dataset: VII)</p> <p><a href="https://doi.org/10.5281/zenodo.5564208%20">https://doi.org/10.5281/zenodo.5564208 </a>(compound <strong>1</strong> simulated in compound <strong>2</strong> metastable state <strong>2-<em>S</em><sub>3</sub></strong>; dataset: VIII)</p> <p><a href="https://doi.org/10.5281/zenodo.5564586">https://doi.org/10.5281/zenodo.5564586</a> (well-tempered metadynamics simulations of compounds <strong>1</strong> and <strong>2</strong>; dataset: IX)</p> <p><a href="https://doi.org/10.5281/zenodo.5570882">https://doi.org/10.5281/zenodo.5570882</a> (well-tempered metadynamics simulations of compounds <strong>1</strong> and <strong>2</strong>; dataset: X)</p> <p><a href="https://doi.org/10.5281/zenodo.5571352">https://doi.org/10.5281/zenodo.5571352</a> (well-tempered metadynamics simulations of compounds <strong>1</strong> and <strong>2</strong>; dataset: XI)</p> <p>The datasets include original Desmond raw-trajectories (datasets I–VIII), PDB-coordinates for the energy minimized metastable state derived structures (datasets II, IV and VI) and raw-trajectories of the well-tempered metadynamics simulations (dataset IX–XI).</p>
Coupled stochastic modelling of hierarchical channel network dynamics and metapopulation persistency - Dataset
<p>Dynamic changes in the active portion of stream networks represent a phenomenon common to diverse climates and geologic settings. However, the ecological implications of river network expansions/retractions remain poorly understood owing to operational difficulties in mechanistically describing these processes at the relevant spatio-temporal scales. Here we present a novel Bayesian framework for the simulation of event-based channel network dynamics capitalizing on the concept of "hierarchical structuring of temporary streams" - a general principle to identify the activation/deactivation order of network nodes. The framework incorporates a dynamic version of a stochastic occupancy metapopulation model, and is used to analyze the impact of pulsing river networks on species persistence in different scenarios. Climate strongly controls temporal variations of the active length, influencing the preferential configuration of the active channels and the speed of network retraction during drying. We also identify a climate-dependent detrimental effect of network dynamics on species spread and persistence. This effect is enhanced by dry climates, where flashy expansions and retractions of the flowing channels induce metapopulation extinction. Survival probabilities are particularly reduced in settings where the spatial heterogeneity of network connectivity is pronounced. The proposed framework provides novel insight on the multi-faced ecological legacies of channel network dynamics.</p>
Dataset for: Dynamics of a pair of magnetic dipoles with nonreciprocal interactions due to a moving conductor
<p>Includes data and code for the corresponding article. The code is written in Python with a corresponding list of packages included in the archive. The README.txt file includes more detail on the different folders and files.</p>
Dataset for "Does fluid structure encode predictions of glassy dynamics?"
<p>This folder contains data in support of "Does fluid structure encode predictions of glassy dynamics?", T. M. Obadiya and D. M. Sussman, arXiv preprint arXiv:2211.00604, (2022). It has three subfolders (described below); all data has been stored in Python's pickle format using protocol version 4, with the pickled files structured as dictionaries.</p> <p>## "Trajectories/" folder</p> <p>Each file in this folder is the raw saved output of a molecular dynamics simulation of an 80:20 Kob-Andersen mixture. To generate the trajectories of particles in this mixture, we first equilibrated a system with random initial conditions for 5000 tau at a temperature of T=0.45. We used the final configuration of this as an initial seed for our other simulations: a snapshot was loaded as the initial configuration for our other simulations, each of which was allowed to equilibrate for 1000 tau at its target temperature. The simulations were done in an NVT ensemble with the coupling constant of the thermostat set to 10 tau. The simulations were evolved using a timestep of dt = 0.001 tau, with frames saved every 1 tau.</p> <p>Each file name contains the temperature at which the simulation was run, and all simulations are of a set of N=4096 particles. The pickle dictionary for each file contains three elements.<br> "Box_size" contains an array of three elements describing the cubic box simulation box's size along the x, y, and z axes.<br> "Particle types" has a list of 4096 integers with 0 corresponding to a particle of type A and 1 corresponding to a particle of type B.<br> "Positions" contains the particle positions for every frame in the saved trajectory.</p> <p>## "Training_data_V1/" folder</p> <p>This folder contains the training data for five training temperatures used in the above paper. The paper considered two different training sets (defined by using either phop or cumulative squared displacement as the dynamical label), and the training sets corresponding to this choice of dynamical label are in the corresponding subfolders.</p> <p>Within each subfolder, the file name indicates the temperature at which the data were obtained. The pickle dictionary for each file contains two elements.<br> "X_train_rawdata" for every particle in the training set, this contains a list of 100 local structural features (corresponding to the AA and AB local radial distribution functions described in the paper above -- all particles in the training set are particles of type A); the "rawdata" part of the name indicates that these features have not been standardized.<br> "Y_train" is the corresponding dynamical label, with 1 indicating a large value of dynamical label and a -1 indicating a small value of the dynamical label.</p> <p>## "Training_data_V2/" folder</p> <p>This folder contains similar training data for the same five training temperatures, but does not make any assumptions about how to define the local structural features. The pickle dictionaries have the same names as in the Training_data_V1, and the "Y_train" element has the same structure.</p> <p>Here, though, "X_train_rawdata" for each element contains a list of 4-vectors that correspond to the positions of all particles within 5 sigma of the particle whose dynamical label is being considered. The first 3 elements of each 4-vector give the relative separation between the target particle and its neighboring particle, and the 4th element is the particle type of the neighboring particle (0 for particles of type A, and 1 for particles of type B).<br> </p>
Learning Dissipative Dynamics in Chaotic Systems (Datasets)
<p>We present the datasets for NeurIPS 2022 paper <a href="https://arxiv.org/abs/2106.06898">"Learning Dissipative Dynamics in Chaotic Systems."</a> In this work, we propose a machine learning framework, which we call the Markov Neural Operator (MNO), to learn the underlying solution operator for dissipative chaotic systems, showing that the resulting learned operator accurately captures short-time trajectories and long-time statistical behavior.</p> <p>In our work, we present results in the finite-dimensional toy system Lorenz-63. We showcase results on the 1D Kuramoto–Sivashinsky (KS) and on the 2D Navier-Stokes (Kolmogorov flows) PDEs. We present the datasets for Lorenz-63, KS, and Navier-Stokes (Reynolds numbers 40, 500, and 5000).</p> <p>The data is stored as .npy and .mat files:</p> <ul> <li><strong>L63.mat: </strong>Lorenz-63 data (one long trajectory of 10000 seconds)</li> <li><strong>KS.mat:</strong> 1D Kuramoto–Sivashinsky data (1200 trajectories, 500 time-steps each)</li> <li><strong>2D_NS_Re40.npy: </strong>2D Navier-Stokes data (200 trajectories, 500 time-steps each) at 64 x 64 spatial resolution.</li> <li><strong>2D_NS_Re500.npy: </strong>2D Navier-Stokes data (1000 trajectories, 500 time-steps each) at 64 x 64 spatial resolution with Reynolds number 500.</li> <li><strong>2D_NS_Re5000.npy: </strong>2D Navier-Stokes data (100 trajectories, 500 time-steps each) at 128 x 128 spatial resolution with Reynolds number 5000.</li> </ul>
On equilibrating non-periodic molecular dynamics samples for coupled particle-continuum simulations of amorphous polymers: dataset
<p><strong>Abstract:</strong><br> (from [1])<br> In the context of fracture simulations of polymers, the molecular mechanisms in the vicinity of the<br> crack tip are of particular interest. Nevertheless, to keep the computational cost to a minimum, a<br> coarser resolution must be used in the remaining regions of the numerical sample. For the specific<br> case of amorphous polymers, the Capriccio method bridges the gap between the length and time scales<br> involved at the different levels of resolution by concurrently coupling molecular dynamics (MD) with<br> the finite element method (FEM). Within the scope of the Capriccio approach, the coupling to the<br> molecular MD region introduces non-periodic, so-called stochastic boundary conditions (SBC). In<br> similarity to typical simulations under periodic boundary conditions (PBC), the SBC MD simulations<br> must reach an equilibrium state before mechanical loads are exerted on the coupled systems. In this<br> contribution, we hence extensively study the equilibration properties of non-periodic MD samples<br> using the Capriccio method. We demonstrate that the relaxation behavior of an MD-FE coupled<br> MD domain utilizing non-periodic boundary conditions is rather insensitive to the specific coupling<br> parameters of the method chosen to implement the boundary conditions. The behavior of an exemplary<br> system equilibrated with the parameter set considered as optimal is further studied under uniaxial<br> tension and we observe some peculiarities in view of creep and relaxation phenomena. This raises<br> important questions to be addressed in the further development of the Capriccio method.<br> <br> <strong>Contact:</strong><br> Felix Weber<br> Institute of Applied Mechanics<br> Friedrich-Alexander-Universität Erlangen-Nürnberg<br> Egerlandstr. 5<br> 91058 Erlangen<br> Germany<br> <br> <strong>Context:</strong><br> This dataset contains the results presented in [1] and related data.</p> <p><strong>Content:</strong><br> Throughout this data set, Lammps [2] real units are used. The following folders contain the results obtained under periodic boundary conditions:<br> - biax_PBC: biaxial loading <br> - equil_PBC: equilibration<br> - ut_PBC: uniaxial tension<br> Each simulation directory contains:<br> - input.prm: input parameters of the specific simulation (read by the input file)<br> - job.out: simulation log file<br> - meta.info: meta data of the specific simulation run<br> - Lammps input file (*.in) of the specific simulation<br> - Lammps data file (*.data, molecular style) of the investigated sample<br> - LAMMPS_out: resulting Lammps data and restart (*.rst) files and simulation results (Lammps thermo_out) in <br> tabulated form, an overview of the columns is given in the respective folders</p> <p>The following folders contain the results obtained under stochastic boundary conditions using the Capriccio method [3]:<br> - best: equilibration with the parameter set considered to be most suitable for the MD-FE coupled equilibration<br> - biax: biaxial loading<br> - bridging: equilibration with different adaptivity levels of the bridging domain<br> - descr_obs: equilibration with a Lagrangian frame for the description of the observation region<br> - dpd: equilibration with different thicknesses of the dissipative particle dynamics (DPD) region<br> - friccoeff: equilibration with different friction coefficients applied in the dissipative particle dynamics region<br> - fur: equilibration with different numbers of fur beads <br> - gausspoints: equilibration with different numbers of quadrature points per direction <br> - min: equilibration applying an initial, static energy minimization<br> - nodes: equilibration with a higher number of finite element nodes<br> - shifted: equilibration with particle systems obtained at different positions and points in time within the periodic master system<br> - smdcub: equilibration with different remaining stiffness ratios for the cubic modified weighting factor<br> - smdlin: equilibration with different remaining stiffness ratios for the linear modified weighting factor<br> - timestepsize: equilibration with different molecular dynamics time step sizes<br> - ut: uniaxial tension<br> - ut_friccoeff: uniaxial tension with different friction coefficients applied in the dissipative particle dynamics region<br> - weighting: equilibration with different energy weighting functions<br> - youngsmod: equilibration with different Young's moduli<br> Each simulation directory contains:<br> - input_files: Abaqus [4] input file (*.inp) and Lammps data file (*.data, molecular style) of the investigated sample<br> - MD_data: Results evaluated in the molecular dynamics region. MD_data contains the following subfolders: <br> anchorforces (dumped force components on the anchor points (AP) in kcal/mol, files anchorforce_[load step]_[MD-FE iteration].AF), <br> data (resulting Lammps data files *.[load step].[MD-FE iteration].data), Density (dumped mass density in the observation region in kg/m^3), <br> Energy (dumped total (kinetic + potential), angle, bond, and pair energies in kcal/(mol*Angstrom)), Strain (integral strains in the<br> observation region in x-, y-, and z-direction calculated by means of the Matlab [5] script calc_OBSstrain.m),<br> Stress (stresses in the observation region in MPa), and Temperature (temperature in the observation region in K)<br> - job.out: simulation log file<br> - meta.info: meta data of the specific Lammps simulation<br> Information on the subfolders is given in the respective folders.</p> <p><strong>References:</strong><br> [1] F. Weber, M. Ries, C. Bauer, C. R. Wick, S. Pfaller, "On equilibrating non-periodic molecular dynamics samples for coupled<br> particle-continuum simulations of amorphous polymers", Forces in Mechanics, 2023, 10, 100159.<br> [2] A. P. Thompson, H. M. Aktulga, R. Berger, D. S. Bolintineanu, W. M. Brown, P. S. Crozier, P. J. in 't Veld, A. Kohlmeyer, <br> S. G. Moore, T. D. Nguyen, R. Shan, M. J. Stevens, J. Tranchida, C. Trott, S. J. Plimpton, "LAMMPS - a flexible simulation tool <br> for particle-based materials modeling at the atomic, meso, and continuum scales", Computer Physics Communications, 2022, 271, 108171.<br> [3] S. Pfaller, M. Rahimi, G. Possart, P. Steinmann, F. Müller-Plathe, M. C. Böhm, "An Arlequin-based method to couple molecular dynamics <br> and finite element simulations of amorphous polymers and nanocomposites", Computer Methods in Applied Mechanics and Engineering, <br> 2013, 260, 109-129. <br> [4] Dassault Systèmes, "Abaqus documentation", URL: https://abaqus-docs.mit.edu/2017/English/SIMACAEEXCRefMap/simaexc-c-docproc.htm.<br> [5] The MathWorks, Inc, "MATLAB. The Language of Technical Computing", URL: https://de.mathworks.com/help/matlab/.</p> <p><strong>Funding:</strong><br> This research was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) -<br> 377472739/GRK 2423/1-2019. The authors are very grateful for this support. Sebastian Pfaller is furthermore<br> funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - 396414850 (Individual<br> Research Grant ’Identifikation von Interphaseneigenschaften in Nanokompositen’).</p>
Datasets of the article "Sub-millisecond Photoinduced Dynamics of Free and EL222-bound FMN by Stimulated Raman and Visible Absorption Spectroscopies"
<p>Femtosecond-stimulated Raman spectra (FSRS) and visible transient absorption (visTA) spectra of free FMN (flavin mononucleotide), FMN bound to EL222-WT, and FMN bound to EL222-C78A. The dataset includes also the lifetime distributions, decay-associated difference spectra (DADS), and evolution-associated difference spectra (EADS). Please check the README file for more information.</p> <p>If you use any of these data, please cite: </p> <p>Liu, Y.; Chaudhari, A.S.; Chatterjee, A.; Andrikopoulos, P.C.; Picchiotti, A.; Rebarz, M.; Kloz, M.; Lorenz-Fonfria, V.A.; Schneider, B.; Fuertes, G. Sub-Millisecond Photoinduced Dynamics of Free and EL222-Bound FMN by Stimulated Raman and Visible Absorption Spectroscopies. <em>Biomolecules</em> 2023, <em>13</em>, 161. https://doi.org/10.3390/biom13010161</p>
Dataset of "Stabilized explicit 𝑢−𝑝𝑤 solution in soil dynamic problems near the undrained-incompressible limit"
<p>Dataset of "Stabilized explicit 𝑢−𝑝𝑤 solution in soil dynamic problems near the undrained-incompressible limit"</p>
Dataset for 'Effects of bubble plumes on lake dynamics, near-bottom turbulence, and transfer of dissolved oxygen at the sediment-water interface'
<p>The dataset associated with the article "Effects of bubble plumes on lake dynamics, near-bottom turbulence, and transfer of dissolved oxygen at the sediment-water interface". </p>
Examining the Influence of Focal Point Location on Heat Transfer Dynamics in Phase Change Material: A Dataset Publication
<p>Dataset including experimental data from small-scale solar facade module that consists of several components: Fresnel lens; cone-shaped air gap; aerogel (semi-transparent) as insulation layer; PCM container. Variations of the experimental settings in test setups are different cone diameter (2, 3 and 4 cm) and insulation layer thickness is (3,5 and 7cm). Nine different compositions were tested in laboratory to evaluate the ompact of focal point location. The focal point has been described with three different values that reflect its location on the inner and outer surfaces and in the middle of the PCM container. The corresponding figures can be found in the paper The Effect of Fresnel Lens Focal Point Location on Heat Transfer in Phase Change Material (PCM) Enhanced Dynamic Solar Facade.</p> <p>This study has been supported by Fundamental and Applied Research project ‘Smart building EnVElope with solaR Energy STorage (EVEREST)’, project No. lzp-2019/1-0363, funded by the Latvian Council of Science.</p> <p> </p>
Datasets of the article "Femtosecond-to-nanosecond dynamics of flavin mononucleotide monitored by stimulated Raman spectroscopy and simulations"
<p>Experimental time-resolved femtosecond-stimulated Raman spectra (FSRS) and visible transient absorption (visTA) spectra of flavin mononucleotide (FMN) in water, MES pH=6, and TRIS pH=8 buffers (DATA files). The dataset includes also the amplitudes and evolution-associated difference spectra (EADS) arising from global kinetic analysis using a sequential model. </p> <p>In the DATA files, the first column contains the Stokes shift (in cm<sup>-1</sup>) or wavelength (in nm), and the first row contains the time delays (in seconds).</p> <p>In the AMPLITUDES files, the first column contains the time delays (in seconds), and the first row contains the lifetimes (in seconds).</p> <p>In the EADS files, the first column contains the wavenumbers (in cm<sup>-1</sup>) or wavelength (in nm), and the first row contains the lifetimes (in seconds). Note that the FSRS spectra have been normalized to 1.</p> <p>If you use these datasets please cite:</p> <p>Andrikopoulos, P. C.; Liu, Y.; Picchiotti, A.; Lenngren, N.; Kloz, M.; Chaudhari, A. S.; Precek, M.; Rebarz, M.; Andreasson, J.; Hajdu, J.; Schneider, B.; Fuertes, G.* Femtosecond-to-Nanosecond Dynamics of Flavin Mononucleotide Monitored by Stimulated Raman Spectroscopy and Simulations. <em>Phys. Chem. Chem. Phys. PCCP</em> <strong>2020</strong>, <em>22</em>, 6538–6552.</p>
Supplementary Tables and Datasets for publication: Spatial and finely tuned temporal metagenomics of river compartments reveals viral community dynamics in an urban stream
<p>This is a data dump of the tables, genomes, and .faa files that were too large to submit as part of the publication titled: Spatial and finely tuned temporal metagenomics of river compartments reveals viral community dynamics in an urban stream</p> <p> </p> <p>Files here include:</p> <p>-Fasta file containing 1230 vMAGs.</p> <p>-Zip file containing individual fasta files for 125 MAGs</p> <p>-Annotations output for DRAM and DRAM-v for all MAGs and vMAGs</p> <p>-.faa proteins file for the full Freshwater / Wastewater / TARA Oceans dataset that was used for vContact2 biogeography analyses</p>
Dataset from publication: Long-term changes in bloom dynamics of Southern and Central Baltic cold-water phytoplankton
<p>This data set contains the output of the numerical ocean model GETM used in the publication "Long-term changes in bloom dynamics of Southern and Central Baltic cold-water phytoplankton"</p>
Dataset for manuscript "Host-related and environmental factors influence long-term ectoparasite infestation dynamics of mouse lemurs in northwestern Madagascar" to be published in the American Journal of Primatology
<p>This Excel-file contains three datasets, corresponding to the initial raw dataset resulting from all ectoparasite inspections (n = 2,241), the merged dataset used for host-related and temporal modeling (n = 1940), and the even more condensed dataset with one datapoint per individual used for climatic modeling (n = 583), respectively.</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.