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700 results for “Dynamical model”

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dryad40/100

Modelling seasonal dynamics of secondary growth in R

<p>The monitoring of seasonal radial growth of woody plants addresses the ultimate question of when, how, and why trees grow. Assessing the growth dynamics is important to quantify the effect of environmental drivers and understand how woody species will deal with the ongoing climatic changes. One of the crucial steps in the analyses of seasonal radial growth is to model the dynamics of xylem and phloem formation based on increment measurements on samples taken at relatively short intervals during the growing season. The most common approach is the use of the Gompertz equation, while other approaches, such as general additive models (GAMs) and generalised linear models (GLMs), have also been tested in recent years. For the first time, we explored artificial neural networks with Bayesian regularisation algorithm (BRNNs) and show that this method is easy to use, resistant to overfitting, tends to yield s-shaped curves and is therefore suitable for deriving temporal dynamics of secondary tree growth. We propose two data processing algorithms that allow more flexible fits. The main result of our work is the XPSgrowth() function implemented in the radial Tree Growth (rTG) R package, that can be used to evaluate and compare three modelling approaches: BRNN, GAM and the Gompertz function. The newly developed function, tested on intra-seasonal xylem and phloem formation data, has potential applications in many ecological and environmental disciplines where growth is expressed as a function of time. Different approaches were evaluated in terms of prediction error, while fitted curves were visually compared to derive their main characteristics. Our results suggest that there is no single best fitting method, therefore we recommend testing different fitting methods and selection of the optimal one.</p>

opencc-zeroJun 2022View details →
zenodo40/100

Engineering Dust Coma Model (EDCM) for ESA's Comet Interceptor mission to a dynamically new comet

<p>This data-set contains all results from the Engineering Dust Coma Model (EDCM) for ESA&#39;s Comet Interceptor (CI) mission to a dynamically new comet.</p> <p>A full description of the model behind the data can be found in the peer-reviewed paper <strong>Marschall, Zakharov et al. (2022), <a href="https://doi.org/10.1051/0004-6361/202243648">https://doi.org/10.1051/0004-6361/202243648</a>.</strong> Please cite this data-set and the paper when using the data.</p> <p>Contemporary numerical models of dusty-gas coma are used to obtain spatial distribution of dust for a given set of parameters. By varying parameters within a range of possible values we obtain an ensemble of possible dust distributions. Then, this ensemble is statistically evaluated in order to define the most probable cases and hence reduce the dispersion. This ensemble can be used to estimate not only the likely dust abundance along e.g. a fly-by trajectory of a spacecraft but also quantify the associated uncertainty.</p> <p>The dust environment assessment for the case when the target comet is not known beforehand (or when its parameters are known with large uncertainty) is critical for spacecraft safety and planning. The EDCM provides an assessment of dust environment for the CI mission.</p>

opencc-by-4.0Jul 2022View details →
zenodo40/100

Data for: "Dynamic species distribution modeling reveals the pivotal role of human-mediated long-distance dispersal in plant invasion"

<p>All the data needed to reproduce the results and Figures of our article:</p> <p>Botella, C., Bonnet, P., Hui, C., Joly, A., &amp; Richardson, D. M. (2022). Dynamic Species Distribution Modeling Reveals the Pivotal Role of Human-Mediated Long-Distance Dispersal in Plant Invasion. <em>Biology</em>, <em>11</em>(9), 1293. <a href="https://doi.org/10.3390/biology11091293">https://doi.org/10.3390/biology11091293</a></p> <p>Please, find the R scripts and guidelines to reproduce our results on the article&#39;s Github repository :</p> <p><a href="https://github.com/ChrisBotella/plectranthus_barbatus/tree/main">https://github.com/ChrisBotella/plectranthus_barbatus/tree/main</a></p>

opencc-by-4.0Mar 2022View details →
dryad40/100

The evolution, complexity and diversity of models of long-term forest dynamics

<p><span>1.  To assess the impacts of climate change on vegetation from stand to global scales, models of forest dynamics that include tree demography are needed. Such models are now available for 50 years, but the currently existing diversity of model formulations and its evolution over time are poorly documented. This hampers systematic assessments of structural uncertainties in model-based studies.</span></p> <p><span>2.  We conducted a meta-analysis of 28 models, focusing on models that were used in the past five years for climate change studies. We defined 52 model attributes in five groups (basic assumptions, growth, regeneration, mortality and soil moisture) and characterized each model according to these attributes. Analyses of model complexity and diversity included hierarchical cluster analysis and redundancy analysis.</span></p> <p><span>3.  Model complexity evolved considerably over the past 50 years. Increases in complexity were largest for growth processes, while complexity of modelled establishment processes increased only moderately. Model diversity was lowest at the global scale, and highest at the landscape scale. We identified five distinct clusters of models, ranging from very simple models to models where specific attribute groups are rendered in a complex manner and models that feature high complexity across all attributes.</span></p> <p><span>4.  Most models in use today are not balanced in the level of complexity with which they represent different processes. This is the result of different model purposes, but also reflects legacies in model code, modelers' preferences, and the 'prevailing spirit of the epoch'. The lack of firm theories, laws and 'first principles' in ecology provides high degrees of freedom in model development, but also results in high responsibilities for model developers and the need for rigorous model evaluation.</span></p> <p><span>5.  Synthesis. The currently available model diversity is beneficial: convergence in simulations of structurally different models indicates robust projections, while convergence of similar models may convey a false sense of certainty. The existing model diversity – with the exception of global models – can be exploited for improved projections based on multiple models. We strongly recommend balanced further developments of forest models that should particularly focus on establishment and mortality processes, in order to provide robust information for decisions in ecosystem management and policymaking.</span></p>

opencc-zeroAug 2022View details →
zenodo40/100

3D models: the dynamics of the prehistoric communities located in the Mostiștea Valley and Danube Plain (between Oltenița and Călărași)

<p>This dataset is part of a larger project on the dynamics of the prehistoric communities located in the Mostiștea Valley and Danube Plain (between Oltenița and Călărași), supervised by&nbsp;the ArchaeoSciences Division of the Research Institute of the University of Bucharest (ICUB)&nbsp; and Kiel University (Germany), in partnership with HOGENT, University of Applied Sciences and Arts (Belgium), Museum of Bucharest, Museum of the Lower Danube Călărași, Museum of Gumelnița Civilization Oltenița, and &quot;Vasile P&acirc;rvan&quot; Institute of Archaeology (Romania), under the &quot;Sultana School of Archaeology&quot; initiative.</p> <p>Spatial data play a crucial role in archaeological research, and orthophotos, digital elevation models, and 3D models are frequently used for the mapping, documentation, and monitoring of archaeological sites. Thanks to the availability of compact and low-cost uncrewed airborne vehicles, the use of UAV-based photogrammetry is well matured in this field over the last two decades. More recently, compact airborne systems are also available that allow the recording of thermal data, multispectral data, and airborne laser scanning. For this project, various platforms and sensors are applied at the Chalcolithic archaeological sites in the Mostiștea Basin and Danube Valley (Southern Romania). By analyzing the performance of the systems and the resulting data, insight is given into the selection of the appropriate system for the right application. This analysis requires thorough knowledge of data acquisition and data processing as well. As both laser scanning and photogrammetry typically result in very large amounts of data, a special focus is also required on the storage and publication of the data. Hence, the objective of this project is to provide a full overview of various aspects of 3D data acquisition for UAV-based mapping. Based on the conclusions drawn in our related publications, it is stated that photogrammetry and laser scanning can result in data with similar geometrical properties when acquisition parameters are appropriately set. On the one hand, however, the used ALS-based system outperforms the photogrammetric platforms in terms of operational time and the area covered. On the other hand, conventional photogrammetry provides flexibility that might be required for very low-altitude flights, or emergency mapping. Furthermore, as the used ALS sensor only provides a geometrical representation of the topography, photogrammetric sensors are still required to obtain true color- or false color composites of the surface. Lastly, the variety of data, like pre- and post-rendered raster data, 3D models, and point clouds, requires the implementation of multiple methods for the online publication of data. Various client-side and server-side solutions are presented to make the data available for other researchers.</p>

opencc-by-4.0Sep 2022View details →
zenodo40/100

Data and models for "Modelling human behaviour in cognitive tasks with latent dynamical systems"

<p>Ebb and Flow gameplay data and trained model parameters for:</p> <p>Jaffe, P.I., Poldrack, R.A., Schafer, R.J. &amp; Bissett, P.G.<em> </em>Modelling human behaviour in cognitive tasks with latent dynamical systems.&nbsp;<em>Nat Hum Behav</em>&nbsp;(2023). https://doi.org/10.1038/s41562-022-01510-8&nbsp;</p> <p>Ebb and Flow is a task-switching game offered as a part of the Lumosity cognitive training platform (Lumos Labs, Inc.). The data and model parameters are organized by participant/model in individual archived directories&nbsp;(140 participants; 245&nbsp;models). Within each model directory, &ldquo;data_pre_split.pickle&rdquo; contains the raw Ebb and Flow data. The processed model inputs for the training, validation, and holdout/test splits are contained in the files "train_model_inputs.pt", "val_model_inputs.pt", and "test_model_inputs.pt", respectively. Other metadata associated with each split is contained in "train_other_data.pkl", "val_other_data.pkl", and "test_other_data.pkl". The parameters from the trained model are stored in &ldquo;model_params.pth&rdquo;. Some intermediate analysis products are contained in the subfolder &ldquo;model_analysis&rdquo;.</p> <p>Metadata for all models can be found in &ldquo;model_metadata.csv&rdquo;. The metadata field &ldquo;switch_cost_type&rdquo; identifies models that were trained on data with (sc+) or without (sc-) a switch cost (note that models marked &ldquo;NA&rdquo;, except for the optimal models, were also trained on data with a switch cost but were not included in the paired comparison of the sc+ and sc- models; see manuscript for details). The "exgauss" field identifies models that were trained with an exGaussian response template (coded as "exgauss+"); models identified as "exgauss-" were trained with a Gaussian kernel and were used in paired comparisons with the exgauss+ models. The "early" field identifies models that were trained with early-stage practice data if set to TRUE. The "optimal" field identifies models that were trained to perform the task optimally if set to TRUE. The other metadata fields are self-explanatory.</p> <h2><strong>Fast command line download instructions (macOS/linux)&nbsp;</strong></h2> <p>For help downloading on Windows, see <a href="https://github.com/dvolgyes/zenodo_get">https://github.com/dvolgyes/zenodo_get</a>.<strong><br></strong></p> <p>1) Copy and save the complete list of files below to a text file, e.g. "files.txt". Save it to the same directory you would like to save the data to.&nbsp;</p> <p>2) Install parallel if it's not already installed:</p> <pre><code>sudo apt-get install parallel</code></pre> <p>3) Run the following from the directory with files.txt (all data will be saved here). The flag -jN will create N parallel wget instances to download the files, e.g.:</p> <pre><code>cat files_test.txt | parallel -j8 wget {}</code></pre> <p>4) Unzip the files and cleanup:</p> <pre><code>unzip "*.zip" rm *.zip files.txt</code></pre> <h2><strong>List of files</strong></h2> 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opencc-zeroSep 2022View details →
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Applying a generic and fast coarse-grained molecular dynamics model to extensively study the mechanical behavior of polymer nanocomposites: supplementary information and dataset

<p><strong>Abstract:</strong><br> (from [1])</p> <blockquote> <p>The addition of nano-sized filler particles enhances the mechanical performance of polymers. The resulting properties of the polymer nanocomposite depend on a complex interplay of influence factors such as material pairing, filler size, and content as well as filler-matrix adhesion. As a complement to experimental studies, numerical methods, such as molecular dynamics (MD), facilitate an isolated examination of the individual factors in order to understand their interaction better. However, particle-based simulations are, in general, computationally very expensive, rendering a thorough investigation of nanocomposites&rsquo; mechanical behavior both expensive and time-consuming. Therefore, this paper presents a fast coarse-grained MD model for a generic nanoparticle-reinforced thermoplastic. First, we examine the matrix and filler phase individually, which exhibit isotropic elasto-viscoplastic and anisotropic elastic behavior, respectively. Based on this, we demonstrate that the effect of filler size, filler content, and filler-matrix adhesion on the stiffness and strength of the nanocomposite corresponds very well with experimental findings in the literature. Consequently, the presented computationally efficient MD model enables the analysis of a generic polymer nanocomposite. In addition to the obtained insights into the mechanical behavior, the material characterization provides the basis for a future continuum mechanical description, which bridges the gap to the engineering scale.&nbsp;</p> </blockquote> <p>&nbsp;</p> <p><strong>Contact:</strong></p> <p>Maximilian Ries<br> Institute of Applied Mechanics<br> Friedrich-Alexander-Universit&auml;t Erlangen-N&uuml;rnberg<br> Egerlandstr. 5<br> 91058 Erlangen</p> <p><strong>Software:</strong></p> <p>All MD simulations were performed with LAMMPS [2], version: 29 Oct 2020 / 20201029</p> <p>Compiled with<br> Compiler: GNU C++ 4.8.5 20150623 (Red Hat 4.8.5-39) with OpenMP not enabled<br> C++ standard: C++11</p> <p>Active compile time flags:<br> -DLAMMPS_GZIP<br> -DLAMMPS_SMALLBIG</p> <p>Installed packages<strong>:</strong><br> CLASS2, KSPACE, MANYBODY, MC, MOLECULE, MPIIO, OPT, VORONOI, USER-INTEL, USER-MISC, USER-MOLFILE, USER-NETCD</p> <p>Polymer and polymer composite samples generated with self-avoiding random-walk algorithm [3]</p> <p>Post-processing Matlab R2019b</p> <p>Evaluation of polymer entanglements with Z1-Algorithm [4]</p> <p>&nbsp;</p> <p><strong>License:</strong></p> <p>Creative Commons Attribution 4.0 International</p> <p>&nbsp;</p> <p><strong>Context:</strong></p> <p>Data set supplementing&nbsp; journal paper:</p> <p>[1] M. Ries, J. Seibert, P. Steinmann, S. Pfaller. &ldquo;Applying a generic and fast coarse-grained molecular dynamics model to extensively study the mechanical behavior of polymer nanocomposites&rdquo;, Express Polymer Letters, <strong>2022</strong>, 16.</p> <p>This dataset contains the results presented in [1] and the necessary data to obtain those as well as supplementary information.</p> <p><strong>Content:</strong></p> <p>supplementary material:</p> <p>supplementary_information.pdf</p> <p>data:<br> &nbsp;&nbsp;&nbsp; folder names vary depending on the context, explained in the following:</p> <p>&nbsp;</p> <p>01_matrix</p> <ul> <li> <p>01_equilibration<br> sample equilibration to different temperatures<br> nomenclature: equil_&lt;chains&gt;-&lt;chain_atoms&gt;-box_&lt;initial_box_length&gt;-min_&lt;SARW_distance&gt;-angle_&lt;SARW_angle&gt;-T_&lt;final_temperature&gt;[-&lt;batch_ID&gt;]</p> <ul> <li> <p>chains: 200</p> </li> <li> <p>chain_atoms: 200</p> </li> <li> <p>initial_box_length: 100</p> </li> <li> <p>SARW_distance: 0.9</p> </li> <li> <p>SARW_angle: 50</p> </li> <li> <p>final_temperature: 0.1-1.0</p> </li> <li> <p>batch_ID: 2-5&nbsp;</p> </li> </ul> </li> <li> <p>02_temperature_dependence<br> uniaxial tension simulations to identify temperature dependence<br> nomenclature: 01_UT_&lt;chains&gt;-&lt;chain_atoms&gt;-box_&lt;initial_box_length&gt;-min_&lt;SARW_distance&gt;-angle_&lt;SARW_angle&gt;-T_&lt;final_temperature&gt;</p> <ul> <li> <p>chains: 200</p> </li> <li> <p>chain_atoms: 200</p> </li> <li> <p>initial_box_length: 100</p> </li> <li> <p>SARW_distance: 0.9</p> </li> <li> <p>SARW_angle: 50</p> </li> <li> <p>final_temperature: 0.1-1.0</p> </li> </ul> </li> <li> <p>03_directional_dependence<br> uniaxial tension simulations to prove isotropy in Y and Z direction; X direction in 04_rate_dependence<br> nomenclature: 03_UT_&lt;chains&gt;-&lt;chain_atoms&gt;-box_&lt;initial_box_length&gt;-min_&lt;SARW_distance&gt;-angle_&lt;SARW_angle&gt;-T_&lt;final_temperature&gt;-rate_&lt;strain_rate&gt;-&lt;batchID&gt;</p> <ul> <li> <p>chains: 200</p> </li> <li> <p>chain_atoms: 200</p> </li> <li> <p>initial_box_length: 100</p> </li> <li> <p>SARW_distance: 0.9</p> </li> <li> <p>SARW_angle: 50</p> </li> <li> <p>final_temperature: 0.3</p> </li> <li> <p>strain_rate: 5E-5</p> </li> <li> <p>batchID: 1-5</p> </li> </ul> </li> <li> <p>04_rate_dependence<br> uniaxial tension simulations to identify strain rate dependence<br> nomenclature: 03_UT_&lt;chains&gt;-&lt;chain_atoms&gt;-box_&lt;initial_box_length&gt;-min_&lt;SARW_distance&gt;-angle_&lt;SARW_angle&gt;-T_&lt;final_temperature&gt;-rate_&lt;strain_rate&gt;[-&lt;batchID&gt;]</p> <ul> <li> <p>chains: 200</p> </li> <li> <p>chain_atoms: 200</p> </li> <li> <p>initial_box_length: 100</p> </li> <li> <p>SARW_distance: 0.9</p> </li> <li> <p>SARW_angle: 50</p> </li> <li> <p>final_temperature: 0.3</p> </li> <li> <p>strain_rate: 5E-4, 5E-5, 5E-6</p> </li> <li> <p>batchID: 1-5</p> </li> </ul> </li> <li> <p>05_cyclic_loading<br> sinusoidal uniaxial deformation<br> nomenclature: 05_UT_&lt;chains&gt;-&lt;chain_atoms&gt;-box_&lt;initial_box_length&gt;-min_&lt;SARW_distance&gt;-angle_&lt;SARW_angle&gt;-T_&lt;final_temperature&gt;-rate_&lt;strain_rate&gt;-sin_&lt;strain_amplitude&gt;</p> <ul> <li> <p>chains: 200</p> </li> <li> <p>chain_atoms: 200</p> </li> <li> <p>initial_box_length: 100</p> </li> <li> <p>SARW_distance: 0.9</p> </li> <li> <p>SARW_angle: 50</p> </li> <li> <p>final_temperature: 0.3</p> </li> <li> <p>strain_rate: 5E-4</p> </li> <li> <p>strain_amplitude: 0.01, 0.05, 0.15, 0.2</p> </li> </ul> </li> <li> <p>06_relaxation<br> relaxation subsequent to time-proportional deformation<br> nomenclature: 07_UT_&lt;chains&gt;-&lt;chain_atoms&gt;-box_&lt;initial_box_length&gt;-min_&lt;SARW_distance&gt;-angle_&lt;SARW_angle&gt;-T_&lt;final_temperature&gt;-rate_&lt;strain_rate&gt;-sin_&lt;strain_amplitude&gt;_relax</p> <ul> <li> <p>chains: 200</p> </li> <li> <p>chain_atoms: 200</p> </li> <li> <p>initial_box_length: 100</p> </li> <li> <p>SARW_distance: 0.9</p> </li> <li> <p>SARW_angle: 50</p> </li> <li> <p>final_temperature: 0.3</p> </li> <li> <p>strain_rate: 5E-4</p> </li> <li> <p>strain_amplitude: 0.01, 0.05, 0.15, 0.2</p> </li> </ul> </li> <li> <p>07_simple_shear<br> time-proportional simple shear deformation with different strain rates<br> nomenclature: SS_P2VPSi-rate_&lt;strain_rate&gt;-&lt;batchID&gt;</p> <ul> <li> <p>strain_rate: 5E-4, 5E-5, 5E-6</p> </li> <li> <p>batchID: 1-5</p> </li> </ul> </li> <li> <p>08_large_deformation<br> uniaxial deformation up to 100% strain<br> nomenclature:&nbsp;02_UT_&lt;chains&gt;-&lt;chain_atoms&gt;-box_&lt;initial_box_length&gt;-min_&lt;SARW_distance&gt;-angle_&lt;SARW_angle&gt;-T_&lt;final_temperatur&gt;-strain_&lt;max_strain&gt;</p> <ul> <li> <p>chains: 200</p> </li> <li> <p>chain_atoms: 200</p> </li> <li> <p>initial_box_length: 100</p> </li> <li> <p>SARW_distance: 0.9</p> </li> <li> <p>SARW_angle: 50</p> </li> <li> <p>final_temperature: 0.3</p> </li> <li> <p>max_strain: 1</p> </li> </ul> </li> </ul> <p>02_filler</p> <ul> <li> <p>01_Silica_equilibration<br> sample equilibration</p> </li> <li> <p>02_time_proportional<br> time-proportional uniaxial and simple shear tests<br> nomenclature: Silica_BV-&lt;loadcase&gt;_&lt;direction&gt;-strain_&lt;max_strain&gt;-rate_&lt;strain_rate&gt;</p> <ul> <li> <p>loadcase: uniaxial tension (UT), simple shear (SS)</p> </li> <li> <p>max_strain: 0.1</p> </li> <li> <p>direction: X, Y, Z (UT); XY, XZ, YZ (SS)</p> </li> <li> <p>strain_rate: 5E-4, 5E-5, 5E-6</p> </li> </ul> </li> <li> <p>03_time_periodic<br> time-periodic uniaxial and simple shear tests<br> nomenclature: Silica_BV-&lt;loadcase&gt;_&lt;direction&gt;_sin-ampl_&lt;strain_amplitude&gt;-rate_&lt;max_strain_rate&gt;</p> <ul> <li> <p>loadcase: uniaxial tension (UT), simple shear (SS)</p> </li> <li> <p>direction: X, Y, Z (UT); XY, XZ, YZ (SS)</p> </li> <li> <p>strain_amplitude: 0.025</p> </li> </ul> </li> </ul> <p>03_composite</p> <ul> <li> <p>01_equilibration<br> sample equilibration<br> nomenclature: equil_P2VPSi-rNP_&lt;filler_radius&gt;-nNP_&lt;filler_number&gt;-&lt;batchID&gt;</p> <ul> <li> <p>filler_radius: 2.5-10.0</p> </li> <li> <p>filler_number: 1-160 (depending on filler_radius)</p> </li> <li> <p>batchID: 1-5</p> </li> </ul> </li> <li> <p>02_uniaxial-tension<br> uniaxial tension simulations<br> nomenclature: UT_P2VPSi-rNP_&lt;filler_radius&gt;-nNP_&lt;filler_number&gt;-&lt;batchID&gt;</p> <ul> <li> <p>filler_radius: 2.5-10.0</p> </li> <li> <p>filler_number: 1-160 (depending on filler_radius)</p> </li> <li> <p>batchID: 1-5</p> </li> </ul> </li> <li> <p>03_filler-maxtrix-adhesion<br> equilibration and uniaxial deformation of samples with mid and weak filler-matrix adhesion (for strong adhesion see 01_equilibration and 02_uniaxial-tension<br> nomenclature: see above</p> </li> <li> <p>04_IP_equilibration<br> equilibration of samples to evaluate the microstructure for neat polymer and composites with filler radius 2.5-7.5<br> nomenclature: P2VPSi-&lt;chains&gt;x&lt;chain_atoms&gt;_rNP_&lt;filler_radius&gt;-nNP_&lt;filler_number&gt;_pos_&lt;filler_pos&gt;-&lt;batchID&gt;</p> <ul> <li> <p>chains: 200</p> </li> <li> <p>chain_atoms: 200</p> </li> <li> <p>filler_radius: 0 (neat), 2.5, 5.0, 7.5</p> </li> <li> <p>filler_number: 0 (neat), 1</p> </li> <li> <p>batchID: 1-20</p> </li> </ul> </li> </ul> <p>&nbsp;&nbsp;&nbsp;</p> <p>&nbsp;</p> <p>Each simulation directory contains:</p> <ul> <li> <p>lammps input file (*.in) of the specific simulation</p> </li> <li> <p>data file (*.data) containing the initial sample configuration</p> </li> <li> <p>input.prm: input parameters of the specific simulation (read by the input file)</p> </li> <li> <p>meta.info: meta data of the specific simulation run</p> </li> <li> <p>LAMMPS_out:<br> simulation results (lammps thermo_out) in tabulated form, an overview of columns is given below</p> <ul> <li> <p>thermo_out.Dat: raw output&nbsp;</p> </li> <li> <p>thermo_out_SG.Dat: smoothed output (Savitzky-Golay filter)</p> </li> <li> <p>thermo_out_STD.Dat: standard deviation of raw output</p> </li> </ul> </li> </ul> <p>&nbsp;</p> <p>Output quantities (columns of *.Dat files):<br> Please note that the normalized Lennard-Jones unit set is used, so all quantities are normalized to fundamental mass, length, energy, time and the Boltzmann constant. Thus all entries are unitless [1].</p> <ul> <li> <p>Step: time step&nbsp;</p> </li> <li> <p>Time: time&nbsp;</p> </li> <li> <p>TotEng: total energy&nbsp;</p> </li> <li> <p>PotEng: potential energy</p> </li> <li> <p>KinEng: kinetic energy&nbsp;</p> </li> <li> <p>E_pair: pair energy&nbsp;</p> </li> <li> <p>E_bond: bond energy&nbsp;</p> </li> <li> <p>E_angle: angle energy&nbsp;</p> </li> <li> <p>E_dihed: dihedral energy&nbsp;</p> </li> <li> <p>Temp: temperature</p> </li> <li> <p>Press: hydrostatic pressure</p> </li> <li> <p>Pxx: xx component of pressure tensor&nbsp;</p> </li> <li> <p>Pyy: yy component of pressure tensor&nbsp;</p> </li> <li> <p>Pzz: zz component of pressure tensor&nbsp;</p> </li> <li> <p>Pxy: xy component of pressure tensor</p> </li> <li> <p>Pxz: xz component of pressure tensor</p> </li> <li> <p>Pyz: yz component of pressure tensor</p> </li> <li> <p>Volume: volume of simulation box&nbsp;</p> </li> <li> <p>Lx: box length in x direction&nbsp;&nbsp;</p> </li> <li> <p>Ly: box length in y direction&nbsp;&nbsp;</p> </li> <li> <p>Lz: box length in z direction&nbsp;&nbsp;</p> </li> <li> <p>Density: density&nbsp;&nbsp;</p> </li> <li> <p>c_RG: radius of gyration scalar&nbsp;</p> </li> <li> <p>c_RG[1]: squared radius of gyration tensor (xx component)&nbsp;&nbsp;</p> </li> <li> <p>c_RG[2]: squared radius of gyration tensor (yy component)&nbsp;&nbsp;</p> </li> <li> <p>c_RG[3]: squared radius of gyration tensor (zz component)&nbsp;&nbsp;</p> </li> <li> <p>c_RG[4]: squared radius of gyration tensor (xy component)&nbsp;&nbsp;</p> </li> <li> <p>c_RG[5]: squared radius of gyration tensor (xz component)&nbsp;&nbsp;</p> </li> <li> <p>c_RG[6]: squared radius of gyration tensor (yz component)&nbsp;&nbsp;</p> </li> <li> <p>c_bondave[1]: bond energy averaged over all atoms&nbsp;&nbsp;</p> </li> <li> <p>c_bondave[2]: bond distance averaged over all atoms&nbsp;&nbsp;</p> </li> <li> <p>c_bondave[3]: squared bond distance averaged over all atoms&nbsp;&nbsp;</p> </li> <li> <p>c_angleave[1]: angle energy averaged over all atoms&nbsp;&nbsp;</p> </li> <li> <p>c_angleave[2]: angle averaged over all atoms degree</p> </li> <li> <p>c_angleave[3]: cosine of angle&nbsp;</p> </li> <li> <p>c_angleave[4]: squared cosine of angle&nbsp;</p> </li> <li> <p>c_MSD[1]: mean squared displacement x-direction&nbsp;&nbsp;</p> </li> <li> <p>c_MSD[2]: mean squared displacement y-direction&nbsp;&nbsp;</p> </li> <li> <p>c_MSD[3]: mean squared displacement z-direction&nbsp;&nbsp;</p> </li> <li> <p>c_MSD[4]: total mean squared displacement&nbsp;&nbsp;</p> </li> <li> <p>c_COM[1]: x coordinate of center of mass&nbsp;&nbsp;</p> </li> <li> <p>c_COM[2]: y coordinate of center of mass&nbsp;&nbsp;</p> </li> <li> <p>c_COM[3]: z coordinate of center of mass&nbsp;&nbsp;</p> </li> <li> <p>v_strain_xx: xx component of engineering strain tensor&nbsp;&nbsp;&nbsp;</p> </li> <li> <p>v_strain_yy: yy component of engineering strain tensor&nbsp;&nbsp;&nbsp;&nbsp;</p> </li> <li> <p>v_strain_zz: zz component of engineering strain tensor&nbsp;&nbsp;&nbsp;&nbsp;</p> </li> <li> <p>v_vMisesequivstress: von Mises equivalent stress&nbsp;</p> </li> <li> <p>v_Cauchy_xx: xx component of stress tensor&nbsp;&nbsp;</p> </li> <li> <p>v_Cauchy_yy: yy component of stress tensor</p> </li> <li> <p>v_Cauchy_zz: zz component of stress tensor</p> </li> <li> <p>v_Cauchy_xy: xy component of stress tensor&nbsp;</p> </li> <li> <p>v_Cauchy_xz: xz component of stress tensor&nbsp;</p> </li> <li> <p>v_Cauchy_yz: yz component of stress tensor&nbsp;</p> </li> <li> <p>v_strain_xy: xy component of engineering strain tensor&nbsp;&nbsp;&nbsp;</p> </li> <li> <p>v_strain_xz: xz component of engineering strain tensor&nbsp;&nbsp;&nbsp;</p> </li> <li> <p>v_strain_yz: yz component of engineering strain tensor&nbsp;&nbsp;&nbsp;</p> </li> </ul> <p><br> &nbsp;</p> <p><strong>References</strong>:</p> <p>[1] M. Ries, J. Seibert, P. Steinmann, S. Pfaller. &ldquo;Applying a generic and fast coarse-grained molecular dynamics model to extensively study the mechanical behavior of polymer nanocomposites&rdquo;, <em>Express Polymer Letters</em>, <strong>2022</strong>, 16.</p> <p>[2] S. Plimpton, &ldquo;Fast parallel algorithms for short-range molecular dynamics,&rdquo; <em>Journal of computational physics</em>, <strong>1995</strong>, 117, 1-19.</p> <p>[3] A. P. Thompson et al., &ldquo;LAMMPS - a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales,&rdquo; <em>Computer Physics Communications</em>, vol. 271, p. 108171, <strong>2022</strong>.</p> <p>[4] M. Ries, V. D&ouml;tschel, J. Seibert, S. Pfaller. &ldquo;A self-avoiding random walk algorithm (SARW) for generic thermoplastic polymers and nanocomposites&rdquo;, <em>Zenodo</em>, 2022. <a href="https://doi.org/10.5281/zenodo.6245699">https://doi.org/10.5281/zenodo.6245699</a></p>

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"Chirality and accurate structure models by exploiting dynamical effects in continuous-rotation 3D ED data". Raw data and JANA refinement files.

<p><strong>Chirality and accurate structure models by exploiting dynamical effects in continuous-rotation 3D ED data</strong><br> 3D ED data sets of 5 compounds and JANA refinement files of 12 compounds</p> <p><strong>Relevant tools</strong><strong>:</strong></p> <ul> <li>PETS2: data reduction and analysis of electron diffraction patterns <ul> <li>Download program and access step-by-step tutorials at <a href="http://pets.fzu.cz/">http://pets.fzu.cz/</a></li> <li>Palatinus, L. <em>et al.</em> Specifics of the data processing of precession electron diffraction tomography data and their implementation in the program PETS2.0. <em>Acta Cryst. B</em><strong>75</strong>, 512&ndash;522 (2019). <a href="https://doi.org/10.1107/S2052520619007534">DOI: 10.1107/S2052520619007534</a></li> </ul> </li> <li>JANA2006: crystal structure model refinement program <ul> <li>Download program from <a href="http://jana.fzu.cz/">http://jana.fzu.cz/</a> and access step-by-step tutorials at <a href="http://pets.fzu.cz/">http://pets.fzu.cz/</a></li> <li>Results here were obtained with JANA2006. We recommend using JANA2020.</li> <li>Petricek, V., Dusek, M. &amp; Palatinus, L. Crystallographic Computing System JANA2006: General features. <em>Z. Kristallogr.</em> <strong>229</strong>, 345&ndash;352 (2014). <a href="https://doi.org/10.1515/zkri-2014-1737">DOI: 10.1515/zkri-2014-1737</a></li> </ul> </li> <li>DYNGO: Bloch wave program, calculates dynamical diffraction intensities and derivatives <ul> <li>Program automatically included in JANA2006/JANA2020</li> <li>Palatinus, L., Petř&iacute;ček, V. &amp; Corr&ecirc;a, C. A. Structure refinement using precession electron diffraction tomography and dynamical diffraction: theory and implementation. <em>Acta Cryst. A</em><strong>71</strong>, 235&ndash;244 (2015). <a href="https://doi.org/10.1107/S2053273315001266">DOI: 10.1107/S2053273315001266</a></li> </ul> </li> </ul> <p><strong>3D ED data sets:</strong></p> <p>STW_HPM-1 (RT) was measured on a JEOL JEM-2100-LaB6 and diffraction patterns were recorded with an ASI Timepix detector. Another sample of STW_HPM-1 was measured at a temperature of 100 K after cryotransfer with a Titan Krios (CETA-D detector). The other data sets were measured on an FEI Tecnai G2 20 (Olympus SIS Veleta, CCD). Each data set contains the raw diffraction patterns (*.tif) and the basic input files needed to reproduce the data reduction with PETS2 as used in the associated publication (*.pts2, *.celllist, *.cenloc). Step-by-step tutorials are provided for quartz and glycine (and selected steps for abiraterone acetate) at <a href="http://pets.fzu.cz/">http://pets.fzu.cz/</a>.</p> <ul> <li>&alpha;-quartz, stepwise continuous-rotation and precession-assisted (2 data sets from the same crystal)</li> <li>natrolite, stepwise continuous-rotation and precession-assisted (2 data sets from the same crystal)</li> <li>cobalt aluminophosphate (CAP), static ED patterns recorded in 0.1&deg; steps (3 data sets from 2 crystals)</li> <li>abiraterone acetate, stepwise continous-rotation (5 data sets from 5 crystals)</li> <li>STW_HPM-1, continuous-rotation (1 data set, room temperature)</li> <li>STW_HPM-1, continuous-rotation (1 data set, <em>T</em> = 100 K, cryotransfer)</li> </ul> <p><strong>JANA refinement and CIF files:</strong></p> <p>CIF (Crystallographic Information Framework) files include two data items. The first is related to the dynamical and the second to the kinematical refinement. Relevant parameters and statistics specific for dynamical refinement are found in the field _refine_special_details.</p> <p>JANA files are provided for the dynamical and kinematical refinement at the stage after the final refinement cycle together with the original input files generated by PETS2. For quartz and natrolite, relevant files for the refinements against precession-assisted 3D ED data are included. For abiraterone acetate and limaspermidine, relevant files for the absolute structure determination are included.</p> <ul> <li>&alpha;-quartz</li> <li>albite</li> <li>mordenite</li> <li>natrolite</li> <li>STW_HPM-1</li> <li>cobalt aluminophosphate (CAP)</li> <li>CAU-36</li> <li>&alpha;-glycine</li> <li>carbamazepine</li> <li>(+)-limaspermidine</li> <li>abiraterone acetate</li> <li>MBBF4</li> </ul> <p>For the kinematical refinements based on more than one data set, the self-written tool &quot;CompInt&quot; (unpublished) was used. The tool can be found in the file &quot;tool_scalehkl_compint.zip&quot;. Input (*.hkl, *.compint) and output files (*.scalehkl) are provided in the respective folder with the JANA files.</p> <p>Raw data sources of other data sets relevant for the associated publication are given in the SI of the associated publication.</p>

opencc-by-4.0Oct 2021View details →
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data sets of "Dynamic Linear Modeling estimates of long-term ozone trends from homogenized Dobson Umkehr profiles at Arosa, Switzerland"

<p>data sets from&nbsp;&quot;Dynamic Linear Modeling estimates of long-term ozone trends from homogenized Dobson Umkehr profiles at Arosa, Switzerland&quot;</p> <p>Monthly means&nbsp;ozone profiles data sets of MCH homogenized Dobson D051 and of Brewer B040 used in the article entitled: &quot;Dynamic Linear Modeling estimates of long-term ozone trends from homogenized Dobson Umkehr profiles at Arosa, Switzerland&quot;&nbsp;by Eliane&nbsp;Maillard Barras, Alexander Haefele, Ren&eacute; St&uuml;bi, Achille Jouberton, Herbert Schill, Irina Petropavlovskikh, Koji Miyagawa, Martin Stanek, and Lucien Froidevaux.</p> <p><a href="https://doi.org/10.5194/acp-2022-344">https://doi.org/10.5194/acp-2022-344</a></p>

opencc-by-4.0Oct 2022View details →
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Occurrences and R code for: Dynamic distribution modeling of the Swamp Tigertail dragonfly Synthemis eustalacta (Odonata: Anisoptera: Synthemistidae) over a 20-year bushfire regime

<p>Intensity and severity of bushfires in Australia have increased over the past few decades due to climate change, threatening habitat loss for numerous species. Although the impact of bushfires on vertebrates is well-documented, the corresponding effects on insect taxa are rarely examined, although they are responsible for key ecosystem functions and services. Understanding the effects of bushfire seasons on insect distributions could elucidate long-term impacts and patterns of ecosystem recovery. Here, we investigated the effects of recent bushfires, land-cover change, and climatic variables on the distribution of a common and endemic dragonfly, the swamp tigertail (<em>Synthemis</em> <em>eustalacta</em> (Burmeister, 1839)), which inhabits forests that have recently undergone severe burning. We used a temporally dynamic species distribution modeling approach that incorporated 20 years of community-science data on dragonfly occurrence and predictors based on fire, land cover, and climate to make yearly predictions of suitability. We also compared this to an approach that combines multiple temporally static models that use annual data. We found that for both approaches, fire-specific variables had negligible importance for the models, while percent of tree and non-vegetative cover were the most important. We also found that the dynamic model outperformed the static ones when evaluated with cross-validation. Model predictions indicated temporal variation in area and spatial arrangement of suitable habitat but no patterns of habitat expansion, contraction, or shifting. These results highlight not only the efficacy of dynamic modeling to capture spatiotemporal variables, such as vegetation cover for an endemic insect species, but also provide a novel approach to mapping species distributions with sparse locality records.</p>

opencc-zeroOct 2022View details →
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Figure 7: Cultural equipment dynamics modeling-MODELING SELF-ORGANIZING SYSTEMS WITH SOCIAL INSECTS ALGORITHMS

<p>The multi-template modelling can be used to model cultural equipment<br> dynamics as described in figure 7. On this figure, we associate a queen to each<br> cultural center (cinema, theatre, ...). Each queen will emit many pheromon<br> templates, each template is associated to a specific criterium (according to age,<br> sex, ...). Initially, we put the material in the residential place. Each material<br> has some characteristics, corresponding to the people living in this residential<br> area. The simulation shows the self-organization processus as the result of the<br> set of the attractive effect of all the centers and all the templates.</p>

opencc-by-4.0Jun 2010View details →
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Supplementary data: A machine learning approach for dynamical modelling of Al distributions in zeolites via 23Na/27Al solid-state NMR

<p><strong>Content:</strong></p> <p>This dataset provides supplementary data to "A machine learning approach for dynamical modelling of Al distributions in zeolites via 23Na/27Al solid-state NMR". It contains trained Neural Network Potentials (NNP), energy and force data used for accuracy evaluation of the NNPs. Energy and forces are stored as ASE trajectory files (traj), readable by the&nbsp;<a href="https://wiki.fysik.dtu.dk/ase/index.html">Atomic Simulation Environment </a>(ASE). In addition, this repository contains the generated training database with DFT (SCAN+D3(BJ)) energies and forces as SchNetPack1.0 database (SiAlOHNa.db) file readable by ASE and&nbsp;<a href="https://github.com/atomistic-machine-learning/schnetpack/tree/schnetpack1.0">SchNetPack version 1.0</a>.&nbsp; Also, the structure files used to calculate NMR properties are involved.</p> <ul> <li>"nnps.zip" - (pytorch) NNP model files (compatible with <a href="https://github.com/atomistic-machine-learning/schnetpack/tree/schnetpack1.0">SchNetPack version 1.0</a>)</li> <li>"SiAlOHNa.db" - DFT (SCAN+D3(BJ)) training database as SchNetPack1.0 database file readable by ASE and <a href="https://github.com/atomistic-machine-learning/schnetpack/tree/schnetpack1.0">SchNetPack version 1.0</a></li> <li>"error_stats.zip" - traj files storing energies/forces at the DFT (SCAN+D3(BJ)) and NNP level for all test simulations to calcuate energy/force errors</li> <li>"Structures_CHA17.zip" - the structures files of CHA(17).&nbsp;</li> </ul>

opencc-by-4.0Apr 2024View details →
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Impact of infectious diseases on wild bovidae populations in Thailand: Insights from population modelling and disease dynamics

<p>The wildlife and livestock interface is vital for wildlife conservation and habitat management. Infectious diseases maintained by domestic species may impact threatened species such as Asian bovids, as they share natural resources and habitats. To predict the population impact of infectious diseases with different traits, we used stochastic mathematical models to simulate the population dynamics over 100 years for 100 times a model gaur (<em>Bos gaurus</em>) population with and without disease. We simulated repeated introductions from a reservoir, such as domestic cattle. We selected six bovine infectious diseases; anthrax, bovine tuberculosis, hemorrhagic septicaemia, lumpy skin disease, foot and mouth disease and brucellosis, all of which have caused outbreaks in wildlife populations. From a starting population of 300, the disease-free population increased by an average of 228% over 100 years. Brucellosis with frequency-dependent transmission showed the highest average population declines (-97%), with population extinction occurring 16% of the time. Foot and mouth disease with frequency-dependent transmission showed the lowest impact, with an average population increase of 200%. Overall, acute infections with very high or low fatality had the lowest impact, whereas chronic infections produced the greatest population decline. These results may help disease management and surveillance strategies support wildlife conservation.</p>

opencc-zeroJun 2024View details →
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FIGURE 12 in Computational fluid dynamics modeling of fossil ammonoid shells

FIGURE 12. Simulated Nautilus data plotted alongside live Nautilus behavior data from Niel and Askew (2018).

opencc-by-4.0Dec 2020View details →
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FIGURE 8 in Computational fluid dynamics modeling of fossil ammonoid shells

FIGURE 8. Plot of the coefficient of drag versus Reynolds number for each of the 10 morphotypes in this study. Drag coefficient and Reynolds number were calculated following the equations of Jacobs (1992). Only shells that had a uniform diameter of approx. 5 cm from aperture to venter are shown.

opencc-by-4.0Dec 2020View details →
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FIGURE 7 in Computational fluid dynamics modeling of fossil ammonoid shells

FIGURE 7. Plot of drag force versus velocity for each of the 10 different morphotypes used in this study. Only shells that had a uniform diameter of approx. 5 cm from aperture to venter are shown.

opencc-by-4.0Dec 2020View details →
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FIGURE 5 in Computational fluid dynamics modeling of fossil ammonoid shells

FIGURE 5. Coefficient of drag results from Scheme 1 (green) and Scheme 3 (blue) plotted against Re compared against the data from Jacobs (1992; black). Comparisons shown are for Sphenodiscus (left) and Oppelia (right).

opencc-by-4.0Dec 2020View details →
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FIGURE 3 in Computational fluid dynamics modeling of fossil ammonoid shells

FIGURE 3. An illustration of the computational domain of the simulation. The model target (an ammonoid in this case) is shown as a circle. Each arrow indicates a distance from the shell to a target face of the computational domain. These arrows represent the straight-line distance between the nearest edge of the shell (not the shell's midpoint) and the corresponding wall as per the methods of Shiino, Kuwazuru, and Yoshikawa (2009). Dimensions in the figured example correspond to those of Scheme 3 (see Table 1)

opencc-by-4.0Dec 2020View details →
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FIGURE 4 in Computational fluid dynamics modeling of fossil ammonoid shells

FIGURE 4. Hemisphere simulation data plotted as velocity versus % difference from the literature baseline (Blevins 1984). Velocities shown are within a range in which the drag coefficient of a hemisphere is relatively stable around a value of 1.17 (Blevins, 1984). The drag values used to derive this plot are given in Appendix 3.

opencc-by-4.0Dec 2020View details →
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FIGURE 1 in Computational fluid dynamics modeling of fossil ammonoid shells

FIGURE 1. An outline of the workflow from model creation to completed simulation. Boxes are colored based on the general process they are included in: Case generation (blue), Mesh generation (purple), and numerical set-up (green). Two tracks are shown for case generation: one in which a model is created in blender from measurement data (below the dotted line) and the other where the model is created using a Structure from Motion technique such as laser scanning or photogrammetry (above the dotted line). Software used in each process is noted in "()" outside its respective step.

opencc-by-4.0Dec 2020View details →

ScienceDex guides

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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

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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.

allen-brain-atlas
neuroscienceopenDocumentation, web resources, and API references are available online.
Last verified 2026-04-30Open record

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.

abode-home-cage
behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.
Last verified 2026-04-30Open record

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.

dandi-nwb
electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.
Last verified 2026-04-30Open record

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.

ibl
behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.
Last verified 2026-04-29Open record

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.

openneuro
neuroscienceopenPublished datasets are available on demand over the internet.
Last verified 2026-04-29Open record