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Characterization of the material behavior and identification of effective elastic moduli based on molecular dynamics simulations of coarse-grained silica: dataset

Abstract: (from [1]) <blockquote> The addition of fillers can significantly improve the mechanical behavior of polymers. The responsible mechanisms at the molecular level can be well assessed by particle-based simulation techniques, such as molecular dynamics. However, the high computational cost of these simulations prevents the study of macroscopic samples. Continuum-based approaches, particularly micromechanics, offer a more efficient alternative but require precise constitutive models for all constituents, which are usually unavailable at these small length scales. In this contribution, we derive a molecular-dynamics-informed constitutive law by employing a characterization strategy introduced in a previous publication. We choose silicon dioxide (silica) as an exemplary filler material used in polymer composites and perform uniaxial and shear deformation tests with molecular dynamics. The material exhibits elastoplastic behavior with a pronounced anisotropy. Based on the pseudo-experimental data, we calibrate an anisotropic elastic constitutive law and reproduce the material response for small strains accurately.   The study validates the characterization strategy that facilitates the calibration of constitutive laws from molecular dynamics simulations. Furthermore, the obtained material model for coarse-grained silica forms the basis for future continuum-based investigations of polymer nanocomposites. In general, the presented transition from a fine-scale particle model to a coarse and  computationally efficient continuum description adds to the body of knowledge of molecular science as well as the engineering community.   </blockquote> Contact: Maximilian Ries Institute of Applied Mechanics Friedrich-Alexander-Universität Erlangen-Nürnberg Egerlandstr. 5 91058 Erlangen Software: All simulations were performed with LAMMPS [3], version: 29 Oct 2020 / 20201029 Compiled with Compiler: GNU C++ 4.8.5 20150623 (Red Hat 4.8.5-39) with OpenMP not enabled C++ standard: C++11 Active compile time flags: -DLAMMPS_GZIP -DLAMMPS_SMALLBIG Installed packages: CLASS2, KSPACE, MANYBODY, MC, MOLECULE, MPIIO, OPT, VORONOI, USER-INTEL, USER-MISC, USER-MOLFILE, USER-NETCD License: Creative Commons Attribution 4.0 International   Context: Data set supplementing  journal paper: [1] Ries, M.; Bauer, C.; Weber, F.; Steinmann, P. & Pfaller, S., "Characterization of the material behavior and identification of effective elastic moduli based on molecular dynamics simulations of coarse-grained silica", Mathematics and Mechanics of Solids, 2022, 108128652211080. This dataset contains the results presented in [1] and the necessary data to obtain those. Content: The files to reproduce our simulations and their results are structured as follows: <ul> <li>01_potentials tabulated potentials calibrated via iterative Boltzmann inversion in [2] kindly provided by the Müller-Plathe group at Technische Universität Darmstadt <ul> <li>Angle_table angular interactions</li> <li>Bond_table bond interactions</li> <li>Nonbond_table pair interactions</li> </ul> </li> <li>02_sample Lammps data file (molecular style) of the investigated silica sample</li> <li>03_simulations The condensed simulation directories with the naming convention given below are organized in the following subfolders: <ul> <li>01_time-proportional time-proportional simulation data</li> <li>02_time-periodic time-periodic simulation data</li> </ul> </li> </ul> Each simulation directory contains: <ul> <li>lammps input file (*.in) of the specific simulation</li> <li>input.prm: input parameters of the specific simulation (read by the input file)</li> <li>meta.info: meta data of the specific simulation run</li> <li>LAMMPS_out: simulation results (lammps thermo_out) in tabulated form, an overview of columns is given below <ul> <li>thermo_out.Dat: raw output</li> <li>thermo_out_SG.Dat: smoothed output (Savitzky-Golay filter)</li> <li>thermo_out_STD.Dat: standard deviation of raw output</li> </ul> </li> </ul> Naming convention: Silica-[deformation]-[direction]_[deformation function]-[deformation magnitude]_[deformation rate] ●    [deformation]: uniaxial tension (UT), simple shear (SS) ●    [direction]: deformation carried out in X/Y/Z (UT) or XY/XZ/YZ (SS) ●    [deformation function]: time-proportional (strain), time-periodic (strain_ampl) ●    [deformation magnitude]: maximum strain (time-proportional), strain amplitude (time-periodic); unitless ●    [deformation rate]: rate-[strain rate] (only time-proportional): 0.001/ns-0.1/ns Output quantities (columns of *.Dat files): ●    Step: time step ●    Time: time in fs ●    TotEng: total energy in kcal/mol ●    PotEng: potential energy in kcal/mol ●    KinEng: kinetic energy in kcal/mol ●    E_pair: pair energy in kcal/mol ●    E_bond: bond energy in kcal/mol ●    E_angle: angle energy in kcal/mol ●    E_dihed: dihedral energy in kcal/mol ●    Temp: temperature in K ●    Press: hydrostatic pressure in atm ●    Pxx: xx component of pressure tensor in atm ●    Pyy: yy component of pressure tensor in atm ●    Pzz: zz component of pressure tensor in atm ●    Pxy: xy component of pressure tensor in atm ●    Pxz: xz component of pressure tensor in atm ●    Pyz: yz component of pressure tensor in atm ●    Volume: volume of simulation box in (Angstroms)^3 ●    Lx: box length in x direction in Angstroms ●    Ly: box length in y direction in Angstroms ●    Lz: box length in z direction in Angstroms ●    Density: density in g/(cm^3) ●    c_RG: radius of gyration in Angstroms ●    c_RG[1]: squared radius of gyration tensor (xx component) in (Angstroms)^2 ●    c_RG[2]: squared radius of gyration tensor (yy component) in (Angstroms)^2 ●    c_RG[3]: squared radius of gyration tensor (zz component) in (Angstroms)^2 ●    c_RG[4]: squared radius of gyration tensor (xy component) in (Angstroms)^2 ●    c_RG[5]: squared radius of gyration tensor (xz component) in (Angstroms)^2 ●    c_RG[6]: squared radius of gyration tensor (yz component) in (Angstroms)^2 ●    c_bondave[1]: bond energy averaged over all atoms in kcal/mol ●    c_bondave[2]: bond distance averaged over all atoms in  Angstroms ●    c_bondave[3]: squared bond distance averaged over all atoms in (Angstroms)^2 ●    c_angleave[1]: angle energy averaged over all atoms in kcal/mol ●    c_angleave[2]: angle averaged over all atoms degree ●    c_angleave[3]: cosine of angle (unitless) ●    c_angleave[4]: squared cosine of angle (unitless) ●    c_MSD[1]: mean squared displacement x-direction in (Angstroms)^2 ●    c_MSD[2]: mean squared displacement y-direction in (Angstroms)^2 ●    c_MSD[3]: mean squared displacement z-direction in (Angstroms)^2 ●    c_MSD[4]: total mean squared displacement in (Angstroms)^2 ●    c_COM[1]: x coordinate of center of mass in Angstroms ●    c_COM[2]: y coordinate of center of mass in Angstroms ●    c_COM[3]: z coordinate of center of mass in Angstroms ●    v_strain_xx: xx component of engineering strain tensor (unitless)   ●    v_strain_yy: yy component of engineering strain tensor (unitless)    ●    v_strain_zz: zz component of engineering strain tensor (unitless)    ●    v_vMisesequivstress: von Mises equivalent stress in MPa ●    v_Cauchy_xx: xx component of stress tensor in MPa   ●    v_Cauchy_yy: yy component of stress tensor in MPa ●    v_Cauchy_zz: zz component of stress tensor in MPa ●    v_Cauchy_xy: xy component of stress tensor in MPa ●    v_Cauchy_xz: xz component of stress tensor in MPa ●    v_Cauchy_yz: yz component of stress tensor in MPa ●    v_strain_xy: xy component of engineering strain tensor (unitless)   ●    v_strain_xz: xz component of engineering strain tensor (unitless)   ●    v_strain_yz: yz component of engineering strain tensor (unitless)   References: [1] Ries, M.; Bauer, C.; Weber, F.; Steinmann, P. & Pfaller, S., "Characterization of the material behavior and identification of effective elastic moduli based on molecular dynamics simulations of coarse-grained silica", Mathematics and Mechanics of Solids, 2022, 108128652211080. [2] Ghanbari, A.; Ndoro, T. V. M.; Leroy, F.; Rahimi, M.; Böhm, M. C. & Müller-Plathe, F., “Interphase Structure in Silica-Polystyrene Nanocomposites: A Coarse-Grained Molecular Dynamics Study”, Macromolecules, 2012, 45, 572-584. [3] Plimpton, S., “Fast parallel algorithms for short-range molecular dynamics,” Journal of computational physics, 1995, 117, 1-19.

Outputs of molecular dynamics simulations of two NS1 ZIKV variants in the membrane presence

Files corresponding to outputs obtained through Molecular Dynamics (MD) simulations of two Non-structural (NS) proteins 1 of the Zika virus from Uganda (ZIKV-UG) and Brazil (ZIKV-BR). Simulations were performed using GROMACS 5.1.5 or later versions.  Systems were built based on atomistic models (https://zenodo.org/record/5608521#.YvDNZTlBzJw) and converted to a coarse-grained representation employing MARTINI 2.2p ElNeDyn. It was assumed to be NS1 systems in apo and holo forms (i.e., in the absence and presence of a lipid bilayer, respectively). The membrane model tries to reproduce a lipid concentration of an endoplasmic reticulum lipid bilayer. Holo and apo systems were simulated until they reached 20 and 10 µs, respectively. Trajectories do not include water molecules. For the specific case of holo systems, frames were skipped every 5 frames, which means that processed trajectories are equivalent to simulations when it is recorded every 1000 ps. More details can be found at  <a href="https://doi.org/10.1021/acs.jcim.2c01461">https://doi.org/10.1021/acs.jcim.2c01461</a> Note: Some topology and index files important for MD analysis are also present.

1H Hyperpolarization of Solutions by Overhauser Dynamic Nuclear Polarization with 13C-1H Polarization Transfer

The dataset here contains the raw data used for the publication 10.1021/acs.jpclett.2c01956 There are two folders and one readme file. NMR data in JCAMP or Topspin are in these two folders and are organised according to the figure or table mentioned in the publication. For details, please refer to the readme file.

opencc-zeroAug 2022View details →

dryad40/100

Data to reproduce: Cultural diffusion dynamics depend on behavioral production rules

This repository contains data to reproduce the manuscript titled "Cultural diffusion dynamics depend on behavioural production rules". These RDAs contain simulation data, as well as inference data from NBDA and EWA analyses. They are intended to be used with the analysis code found at <a href="http://www.github.com/michaelchimento/acquisition_production_abm" rel="noopener">www.github.com/michaelchimento/acquisition_production_abm</a>.

Molecular Dynamics of Jelly Candies by Means of Nuclear Magnetic Resonance Relaxometry

1H spin-lattice Nuclear Magnetic Resonance relaxation studies have been performed for different kinds of Haribo jelly and Vidal jelly in a very broad frequency range from about 10 kHz to 10 MHz to obtain insight into the dynamic and structural properties of jelly candies on the molecular level. This extensive data set has been thoroughly analyzed revealing three dynamic processes, referred to as slow, intermediate and fast dynamics occurring on the timescale of 10−6 s, 10−7 s and 10−8 s, respectively. The parameters have been compared for different kinds of jelly for the purpose of revealing their characteristic dynamic and structural properties as well as to enquire into how increasing temperature affects these properties. It has been shown that dynamic processes in different kinds of Haribo jelly are similar (this can be treated as a sign of their quality and authenticity) and that the fraction of confined water molecules is reduced with increasing temperature. Two groups of Vidal jelly have been identified. For the first one, the parameters (dipolar relaxation constants and correlation times) match those for Haribo jelly. For the second group including cherry jelly, considerable differences in the parameters characterizing their dynamic properties have been revealed.

Haematopoietic multi-potent progenitor cells dynamics

Data accompanying the paper: "Heterogeneous run-and-tumble motion accounts for transient non-Gaussian super-diffusion in haematopoietic multi-potent progenitor cells" by Benjamin Partridge,Sara Gonzalez Anton, Reema Khorshed, George Adams, Constandina Pospori, Cristina Lo Celso, and Chiu Fan Lee

opencc-zeroAug 2022View details →

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

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. 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. 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. 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. 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.

Additional Sponge Rheology Data for "Rheology of Marine Sponge Tissue Reveals Anisotropic Mechanics and Tuned Dynamics"

These plots further support the general conclusions of the manuscript "Rheology of Marine Sponge Tissue Reveals Anisotropic Mechanics and Tuned Dynamics". These data were collected on a Kinexus rheometer and the sponges were from a distinct shipment of sponges than those presented in the main manuscript.

Investigation on Dynamic Characteristics of Lightning Return Stroke Channel

The dynamic characteristics in lightning discharge plasma channel are of great significance in studying the micro-physical mechanism of the discharge process. This paper established a simplified radiation-hydrodynamic model (RHM) of the return stroke channel radial expansion based on the fluid dynamics theory, and for the first time, considered the affect of the light radiation loss in the discharge process. The temporal evolutions of the characteristic parameters, such as temperature, pressure and expansion rate for return stroke channel were analyzed. The results indicated that the current peak value and the risetime are important factors in determining the  channel dynamics characteristics. At the initial stage of return stroke, the light radiation loss has a distinct influence on channel temperature, which leads to a transitory drop in temperature forming a bimodal waveform structure. Following the peak current, the channel reaches peak pressure, which leads to a subsequent  secondary temperature peak and accelerates the expansion of the channel. Peak current and the risetime are the indicator parameters of strong discharge and the main factors of lightning disaster. This work provides reference data for further research on the radial energy transport of lightning return stroke channel and the formation mechanism of shock waves.The dynamic characteristics in lightning discharge plasma channel are of great significance in studying the micro-physical mechanism of the discharge process. This paper established a simplified radiation-hydrodynamic model (RHM) of the return stroke channel radial expansion based on the fluid dynamics theory, and for the first time, considered the affect of the light radiation loss in the discharge process. The temporal evolutions of the characteristic parameters, such as temperature, pressure and expansion rate for return stroke channel were analyzed. The results indicated that the current peak value and the risetime are important factors in determining the  channel dynamics characteristics. At the initial stage of return stroke, the light radiation loss has a distinct influence on channel temperature, which leads to a transitory drop in temperature forming a bimodal waveform structure. Following the peak current, the channel reaches peak pressure, which leads to a subsequent  secondary temperature peak and accelerates the expansion of the channel. Peak current and the risetime are the indicator parameters of strong discharge and the main factors of lightning disaster. This work provides reference data for further research on the radial energy transport of lightning return stroke channel and the formation mechanism of shock waves.

Datasets for 'Label-free imaging of 3D pluripotent stem cell differentiation dynamics on chip'

Here we publish the datasets associated with our publication ‘Label-free imaging of 3D pluripotent stem cell differentiation dynamics on chip’. 3D cultures of human induced pluripotent stem cells (hiPSCs) were imaged while undergoing definitive endoderm (DE) differentiation. Data were acquired either on the live 3D cultures at various time points during the 3 days DE differentiation, or after fixation and immunostaining, with the 3D cultures being fixed at regular 24 h intervals during differentiation, as to form a timeline. Images were recorded with a standard confocal microscope using a xy-resolution of 0.25 μm/px and a z-resolution of 1 μm/plane.

opencc-zeroSep 2022View details →

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Stability of patch-turnover relationships under equilibrium and nonequilibrium metapopulation dynamics driven by biogeography

Two controversial tenets of metapopulation biology are whether patch quality and the surrounding matrix are more important to turnover (colonization and extinction) than biogeography (patch area and isolation) and whether factors governing turnover during equilibrium also dominate nonequilibrium dynamics. We tested both tenets using 18 years of surveys for two secretive wetland birds, black and Virginia rails, during (1) a period of equilibrium with stable occupancy and (2) after drought and arrival of West Nile Virus (WNV), which resulted in WNV infections in rails, increased extinction and decreased colonization probabilities modified by WNV, nonequilibrium dynamics for both species, and occupancy decline for black rails. Area (primarily) and isolation (secondarily) drove turnover during both stable and unstable metapopulation dynamics, greatly exceeding the effects of patch quality and matrix conditions. Moreover, slopes between turnover and patch characteristics changed little between equilibrium and nonequilibrium, confirming the overriding influences of biogeographic factors on turnover.

A dynamical view of protein-protein complexes: studies by molecular dynamics simulations

All-atom MD simulations generated for the manuscript "A dynamical view of protein-protein complexes: studies by molecular dynamics simulations". Eight binary protein-protein complexes from the Docking benchmark and the Affinity benchmark are studied in this work: 2OOB (an ubiquitin/ubiquitin ligase complex), 1AY7 (a ribonuclease Sa/barstar complex), 1BRS (a barnase/barstar complex), 3SGB (a proteinase B/inhibitor), 1EMV (a colicin/immunity protein complex), 1PVH  (complex between Interleukine 6 receptor and leukemia inhibitory factor), 1GCQ (Vav/GRB2 SH3 domains complex) and 1AK4 (cyclophilin/HIV capsid complex) Each folder for a binary complex is organised as followed: - in A and B there are the dry MD simulations for the unbound proteins - in complex there are two folders (without_water and water) where the dry simulation and the simulation with water molecules are provided

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

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 the ArchaeoSciences Division of the Research Institute of the University of Bucharest (ICUB)  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 "Vasile Pârvan" Institute of Archaeology (Romania), under the "Sultana School of Archaeology" initiative. 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.

opencc-zeroSep 2022View details →

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

Ebb and Flow gameplay data and trained model parameters for: Jaffe, P.I., Poldrack, R.A., Schafer, R.J. & Bissett, P.G. Modelling human behaviour in cognitive tasks with latent dynamical systems. Nat Hum Behav (2023). https://doi.org/10.1038/s41562-022-01510-8  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 (140 participants; 245 models). Within each model directory, “data_pre_split.pickle” 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 “model_params.pth”. Some intermediate analysis products are contained in the subfolder “model_analysis”. Metadata for all models can be found in “model_metadata.csv”. The metadata field “switch_cost_type” identifies models that were trained on data with (sc+) or without (sc-) a switch cost (note that models marked “NA”, 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. <h2>Fast command line download instructions (macOS/linux) </h2> For help downloading on Windows, see <a href="https://github.com/dvolgyes/zenodo_get">https://github.com/dvolgyes/zenodo_get</a>. 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.  2) Install parallel if it's not already installed: <pre><code>sudo apt-get install parallel</code></pre> 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.: <pre><code>cat files_test.txt | parallel -j8 wget {}</code></pre> 4) Unzip the files and cleanup: <pre><code>unzip "*.zip" rm *.zip files.txt</code></pre> <h2>List of files</h2> https://zenodo.org/records/7102065/files/ages80to89_u4120_exgauss.zip https://zenodo.org/records/7102065/files/optimal_square9.zip https://zenodo.org/records/7102065/files/optimal_square8.zip https://zenodo.org/records/7102065/files/optimal_square7.zip https://zenodo.org/records/7102065/files/optimal_square6.zip https://zenodo.org/records/7102065/files/optimal_square5.zip https://zenodo.org/records/7102065/files/optimal_square4.zip https://zenodo.org/records/7102065/files/optimal_square3.zip https://zenodo.org/records/7102065/files/optimal_square2.zip https://zenodo.org/records/7102065/files/optimal_square1.zip https://zenodo.org/records/7102065/files/optimal_square10.zip https://zenodo.org/records/7102065/files/model_metadata.csv https://zenodo.org/records/7102065/files/ages80to89_u5484_expt1.zip https://zenodo.org/records/7102065/files/ages80to89_u5484_early.zip https://zenodo.org/records/7102065/files/ages80to89_u5441_expt1.zip https://zenodo.org/records/7102065/files/ages80to89_u5441_early.zip https://zenodo.org/records/7102065/files/ages80to89_u5150_expt1.zip https://zenodo.org/records/7102065/files/ages80to89_u5121_expt2.zip https://zenodo.org/records/7102065/files/ages80to89_u5121_expt1.zip https://zenodo.org/records/7102065/files/ages80to89_u5121_exgauss.zip https://zenodo.org/records/7102065/files/ages80to89_u5121_early.zip https://zenodo.org/records/7102065/files/ages80to89_u4928_expt2.zip https://zenodo.org/records/7102065/files/ages80to89_u4928_expt1.zip https://zenodo.org/records/7102065/files/ages80to89_u4760_expt2.zip https://zenodo.org/records/7102065/files/ages80to89_u4760_expt1.zip https://zenodo.org/records/7102065/files/ages80to89_u4760_exgauss.zip 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opencc-by-4.0Jul 2022View details →

Travel time by rail as a determinant of the system of cross-border linkages of Polish metropolises – dynamic approach 1970–2020

The dataset is an outcome of the research project "Travel time by rail as a determinant of the system of cross-border linkages of Polish metropolises – dynamic approach 1970–2020" supported by the National Science Centre (Poland) under the "Miniatura 4" funding scheme (project no. 2020/04/X/HS4/00525). The exploratory aim of the project is to assess a dynamics of the system of cross-border linkages of Polish metropolises over the past five decades, by investigating the duration of international rail connections. Covering such a long period was possible thanks to utilising the archival train timetables, copies of which can be found, among others, in the holdings of the Polish National Library in Warsaw. Furthermore, the methodological aim of the project intends to test a universal measure of functional linkages – the relative travel deceleration index. The linkages between two sets of cities were studied: eleven Polish cities, considered in the literature as existing or potential metropolises (Warszawa, Katowice, Kraków, Gdańsk, Łódź, Poznań, Wrocław, Szczecin, Bydgoszcz, Lublin, Białystok), and twelve foreign cities – capitals of neighbouring countries (Berlin, Praha, Bratislava, Kyiv, Minsk, Vilnius) and the cities with more than 400,000 inhabitants, located within a buffer of 250 km from the Polish border (Leipzig, Dresden, Wien, Budapest, Lviv, Kaliningrad). Eleven time sections are taken into account, from 1970 to 2021. Spatial analyses using GIS software were the starting point. The characteristics of the ideal linkage arrangement were identified. The actual situation occurring in selected years, as described in the timetables, was then compared with it. The proposed deceleration index, measured in %, makes it possible to assess a degree of variation of the shortest travel time observed between a given pair of cities in relation to the time that would occur in a situation with a straight railway line, constant speed, no stops and barriers such as national borders. The factors contributing to this deceleration (the so-called components of deceleration) were also analysed: a layout of rail network, a course of train route, a condition of infrastructure, an organisation of services (waiting time for a change of train) and operations associated with crossing of border affecting its permeability (in the case of Polish eastern border additional time-consuming gauge change of carriages). The dataset consists of: <ol> <li>Methodological framework for the research explaining the way in which the components of travel deceleration are defined [.pdf file; name: 1_schemat_metodyki_badan; description in Polish].</li> <li>Geospatial vector data in shapefile format [.zip file; name: 2_dane_GIS_shapefile] featuring: <ul> <li>buffer of 250 km around the state borders of Poland [name: bufor_250km; type: polygon];</li> <li>spatial distribution of the sets of 11 cities in Poland [name: miasta_polskie; type: point] and 12 cities abroad [name: miasta_zagranica; type: point];</li> <li>ideal arrangement of the 132 studied cross-border linkages – the shortest paths connecting pairs of the cities and representing a physical distance measured along the orthodrome [name: powiazania_miast_stan_idealny; type: line];</li> <li>optimal arrangement of the 132 studied cross-border linkages – the shortest paths connecting pairs of the cities and representing a distance measured along the railway network in two variants, before and after the opening of the CMK and LHS lines in Poland [names: najkrotsze_trasy_1970-1975_komponent_1.shp and najkrotsze_trasy_1980-2020_komponent_1.shp; type: polyline]; </li> </ul> </li> <li>Records of the train timetable analyses including the relative travel deceleration index and the structure of the components of deceleration [.xlsx file; name: 3_struktura_komponentow_spowolnienia; description in Polish];</li> <li>Supplementary table 1 – cross-border distances (in km) according to the train timetable [.xlsx file; name: 4_tebala_pomocnicza_odleglosci_transgraniczne; description in Polish];</li> <li>Supplementary table 2 – domestic distances (in km) according to the train timetable [.xlsx file; name: 5_tebala_pomocnicza_odleglosci_w_polsce; description in Polish];</li> <li>Supplementary table 3 – estimation of time losses in the total travel time associated with accelerating and braking of train for the theoretical variant without intermediate stops (simplified calculation based on kinematics principles) [.xlsx file; name: 6_tebala_pomocnicza_straty_czasu_rozruch_hamowanie; description in Polish].</li> </ol>

Applying a generic and fast coarse-grained molecular dynamics model to extensively study the mechanical behavior of polymer nanocomposites: supplementary information and dataset

Abstract: (from [1]) <blockquote> 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’ 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.  </blockquote>   Contact: Maximilian Ries Institute of Applied Mechanics Friedrich-Alexander-Universität Erlangen-Nürnberg Egerlandstr. 5 91058 Erlangen Software: All MD simulations were performed with LAMMPS [2], version: 29 Oct 2020 / 20201029 Compiled with Compiler: GNU C++ 4.8.5 20150623 (Red Hat 4.8.5-39) with OpenMP not enabled C++ standard: C++11 Active compile time flags: -DLAMMPS_GZIP -DLAMMPS_SMALLBIG Installed packages: CLASS2, KSPACE, MANYBODY, MC, MOLECULE, MPIIO, OPT, VORONOI, USER-INTEL, USER-MISC, USER-MOLFILE, USER-NETCD Polymer and polymer composite samples generated with self-avoiding random-walk algorithm [3] Post-processing Matlab R2019b Evaluation of polymer entanglements with Z1-Algorithm [4]   License: Creative Commons Attribution 4.0 International   Context: Data set supplementing  journal paper: [1] M. Ries, J. Seibert, P. Steinmann, S. Pfaller. “Applying a generic and fast coarse-grained molecular dynamics model to extensively study the mechanical behavior of polymer nanocomposites”, Express Polymer Letters, 2022, 16. This dataset contains the results presented in [1] and the necessary data to obtain those as well as supplementary information. Content: supplementary material: supplementary_information.pdf data:     folder names vary depending on the context, explained in the following:   01_matrix <ul> <li> 01_equilibration sample equilibration to different temperatures nomenclature: equil_<chains>-<chain_atoms>-box_<initial_box_length>-min_<SARW_distance>-angle_<SARW_angle>-T_<final_temperature>[-<batch_ID>] <ul> <li> chains: 200 </li> <li> chain_atoms: 200 </li> <li> initial_box_length: 100 </li> <li> SARW_distance: 0.9 </li> <li> SARW_angle: 50 </li> <li> final_temperature: 0.1-1.0 </li> <li> batch_ID: 2-5  </li> </ul> </li> <li> 02_temperature_dependence uniaxial tension simulations to identify temperature dependence nomenclature: 01_UT_<chains>-<chain_atoms>-box_<initial_box_length>-min_<SARW_distance>-angle_<SARW_angle>-T_<final_temperature> <ul> <li> chains: 200 </li> <li> chain_atoms: 200 </li> <li> initial_box_length: 100 </li> <li> SARW_distance: 0.9 </li> <li> SARW_angle: 50 </li> <li> final_temperature: 0.1-1.0 </li> </ul> </li> <li> 03_directional_dependence uniaxial tension simulations to prove isotropy in Y and Z direction; X direction in 04_rate_dependence nomenclature: 03_UT_<chains>-<chain_atoms>-box_<initial_box_length>-min_<SARW_distance>-angle_<SARW_angle>-T_<final_temperature>-rate_<strain_rate>-<batchID> <ul> <li> chains: 200 </li> <li> chain_atoms: 200 </li> <li> initial_box_length: 100 </li> <li> SARW_distance: 0.9 </li> <li> SARW_angle: 50 </li> <li> final_temperature: 0.3 </li> <li> strain_rate: 5E-5 </li> <li> batchID: 1-5 </li> </ul> </li> <li> 04_rate_dependence uniaxial tension simulations to identify strain rate dependence nomenclature: 03_UT_<chains>-<chain_atoms>-box_<initial_box_length>-min_<SARW_distance>-angle_<SARW_angle>-T_<final_temperature>-rate_<strain_rate>[-<batchID>] <ul> <li> chains: 200 </li> <li> chain_atoms: 200 </li> <li> initial_box_length: 100 </li> <li> SARW_distance: 0.9 </li> <li> SARW_angle: 50 </li> <li> final_temperature: 0.3 </li> <li> strain_rate: 5E-4, 5E-5, 5E-6 </li> <li> batchID: 1-5 </li> </ul> </li> <li> 05_cyclic_loading sinusoidal uniaxial deformation nomenclature: 05_UT_<chains>-<chain_atoms>-box_<initial_box_length>-min_<SARW_distance>-angle_<SARW_angle>-T_<final_temperature>-rate_<strain_rate>-sin_<strain_amplitude> <ul> <li> chains: 200 </li> <li> chain_atoms: 200 </li> <li> initial_box_length: 100 </li> <li> SARW_distance: 0.9 </li> <li> SARW_angle: 50 </li> <li> final_temperature: 0.3 </li> <li> strain_rate: 5E-4 </li> <li> strain_amplitude: 0.01, 0.05, 0.15, 0.2 </li> </ul> </li> <li> 06_relaxation relaxation subsequent to time-proportional deformation nomenclature: 07_UT_<chains>-<chain_atoms>-box_<initial_box_length>-min_<SARW_distance>-angle_<SARW_angle>-T_<final_temperature>-rate_<strain_rate>-sin_<strain_amplitude>_relax <ul> <li> chains: 200 </li> <li> chain_atoms: 200 </li> <li> initial_box_length: 100 </li> <li> SARW_distance: 0.9 </li> <li> SARW_angle: 50 </li> <li> final_temperature: 0.3 </li> <li> strain_rate: 5E-4 </li> <li> strain_amplitude: 0.01, 0.05, 0.15, 0.2 </li> </ul> </li> <li> 07_simple_shear time-proportional simple shear deformation with different strain rates nomenclature: SS_P2VPSi-rate_<strain_rate>-<batchID> <ul> <li> strain_rate: 5E-4, 5E-5, 5E-6 </li> <li> batchID: 1-5 </li> </ul> </li> <li> 08_large_deformation uniaxial deformation up to 100% strain nomenclature: 02_UT_<chains>-<chain_atoms>-box_<initial_box_length>-min_<SARW_distance>-angle_<SARW_angle>-T_<final_temperatur>-strain_<max_strain> <ul> <li> chains: 200 </li> <li> chain_atoms: 200 </li> <li> initial_box_length: 100 </li> <li> SARW_distance: 0.9 </li> <li> SARW_angle: 50 </li> <li> final_temperature: 0.3 </li> <li> max_strain: 1 </li> </ul> </li> </ul> 02_filler <ul> <li> 01_Silica_equilibration sample equilibration </li> <li> 02_time_proportional time-proportional uniaxial and simple shear tests nomenclature: Silica_BV-<loadcase>_<direction>-strain_<max_strain>-rate_<strain_rate> <ul> <li> loadcase: uniaxial tension (UT), simple shear (SS) </li> <li> max_strain: 0.1 </li> <li> direction: X, Y, Z (UT); XY, XZ, YZ (SS) </li> <li> strain_rate: 5E-4, 5E-5, 5E-6 </li> </ul> </li> <li> 03_time_periodic time-periodic uniaxial and simple shear tests nomenclature: Silica_BV-<loadcase>_<direction>_sin-ampl_<strain_amplitude>-rate_<max_strain_rate> <ul> <li> loadcase: uniaxial tension (UT), simple shear (SS) </li> <li> direction: X, Y, Z (UT); XY, XZ, YZ (SS) </li> <li> strain_amplitude: 0.025 </li> </ul> </li> </ul> 03_composite <ul> <li> 01_equilibration sample equilibration nomenclature: equil_P2VPSi-rNP_<filler_radius>-nNP_<filler_number>-<batchID> <ul> <li> filler_radius: 2.5-10.0 </li> <li> filler_number: 1-160 (depending on filler_radius) </li> <li> batchID: 1-5 </li> </ul> </li> <li> 02_uniaxial-tension uniaxial tension simulations nomenclature: UT_P2VPSi-rNP_<filler_radius>-nNP_<filler_number>-<batchID> <ul> <li> filler_radius: 2.5-10.0 </li> <li> filler_number: 1-160 (depending on filler_radius) </li> <li> batchID: 1-5 </li> </ul> </li> <li> 03_filler-maxtrix-adhesion equilibration and uniaxial deformation of samples with mid and weak filler-matrix adhesion (for strong adhesion see 01_equilibration and 02_uniaxial-tension nomenclature: see above </li> <li> 04_IP_equilibration equilibration of samples to evaluate the microstructure for neat polymer and composites with filler radius 2.5-7.5 nomenclature: P2VPSi-<chains>x<chain_atoms>_rNP_<filler_radius>-nNP_<filler_number>_pos_<filler_pos>-<batchID> <ul> <li> chains: 200 </li> <li> chain_atoms: 200 </li> <li> filler_radius: 0 (neat), 2.5, 5.0, 7.5 </li> <li> filler_number: 0 (neat), 1 </li> <li> batchID: 1-20 </li> </ul> </li> </ul>       Each simulation directory contains: <ul> <li> lammps input file (*.in) of the specific simulation </li> <li> data file (*.data) containing the initial sample configuration </li> <li> input.prm: input parameters of the specific simulation (read by the input file) </li> <li> meta.info: meta data of the specific simulation run </li> <li> LAMMPS_out: simulation results (lammps thermo_out) in tabulated form, an overview of columns is given below <ul> <li> thermo_out.Dat: raw output  </li> <li> thermo_out_SG.Dat: smoothed output (Savitzky-Golay filter) </li> <li> thermo_out_STD.Dat: standard deviation of raw output </li> </ul> </li> </ul>   Output quantities (columns of *.Dat files): 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]. <ul> <li> Step: time step  </li> <li> Time: time  </li> <li> TotEng: total energy  </li> <li> PotEng: potential energy </li> <li> KinEng: kinetic energy  </li> <li> E_pair: pair energy  </li> <li> E_bond: bond energy  </li> <li> E_angle: angle energy  </li> <li> E_dihed: dihedral energy  </li> <li> Temp: temperature </li> <li> Press: hydrostatic pressure </li> <li> Pxx: xx component of pressure tensor  </li> <li> Pyy: yy component of pressure tensor  </li> <li> Pzz: zz component of pressure tensor  </li> <li> Pxy: xy component of pressure tensor </li> <li> Pxz: xz component of pressure tensor </li> <li> Pyz: yz component of pressure tensor </li> <li> Volume: volume of simulation box  </li> <li> Lx: box length in x direction   </li> <li> Ly: box length in y direction   </li> <li> Lz: box length in z direction   </li> <li> Density: density   </li> <li> c_RG: radius of gyration scalar  </li> <li> c_RG[1]: squared radius of gyration tensor (xx component)   </li> <li> c_RG[2]: squared radius of gyration tensor (yy component)   </li> <li> c_RG[3]: squared radius of gyration tensor (zz component)   </li> <li> c_RG[4]: squared radius of gyration tensor (xy component)   </li> <li> c_RG[5]: squared radius of gyration tensor (xz component)   </li> <li> c_RG[6]: squared radius of gyration tensor (yz component)   </li> <li> c_bondave[1]: bond energy averaged over all atoms   </li> <li> c_bondave[2]: bond distance averaged over all atoms   </li> <li> c_bondave[3]: squared bond distance averaged over all atoms   </li> <li> c_angleave[1]: angle energy averaged over all atoms   </li> <li> c_angleave[2]: angle averaged over all atoms degree </li> <li> c_angleave[3]: cosine of angle  </li> <li> c_angleave[4]: squared cosine of angle  </li> <li> c_MSD[1]: mean squared displacement x-direction   </li> <li> c_MSD[2]: mean squared displacement y-direction   </li> <li> c_MSD[3]: mean squared displacement z-direction   </li> <li> c_MSD[4]: total mean squared displacement   </li> <li> c_COM[1]: x coordinate of center of mass   </li> <li> c_COM[2]: y coordinate of center of mass   </li> <li> c_COM[3]: z coordinate of center of mass   </li> <li> v_strain_xx: xx component of engineering strain tensor    </li> <li> v_strain_yy: yy component of engineering strain tensor     </li> <li> v_strain_zz: zz component of engineering strain tensor     </li> <li> v_vMisesequivstress: von Mises equivalent stress  </li> <li> v_Cauchy_xx: xx component of stress tensor   </li> <li> v_Cauchy_yy: yy component of stress tensor </li> <li> v_Cauchy_zz: zz component of stress tensor </li> <li> v_Cauchy_xy: xy component of stress tensor  </li> <li> v_Cauchy_xz: xz component of stress tensor  </li> <li> v_Cauchy_yz: yz component of stress tensor  </li> <li> v_strain_xy: xy component of engineering strain tensor    </li> <li> v_strain_xz: xz component of engineering strain tensor    </li> <li> v_strain_yz: yz component of engineering strain tensor    </li> </ul>   References: [1] M. Ries, J. Seibert, P. Steinmann, S. Pfaller. “Applying a generic and fast coarse-grained molecular dynamics model to extensively study the mechanical behavior of polymer nanocomposites”, Express Polymer Letters, 2022, 16. [2] S. Plimpton, “Fast parallel algorithms for short-range molecular dynamics,” Journal of computational physics, 1995, 117, 1-19. [3] A. P. Thompson et al., “LAMMPS - a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales,” Computer Physics Communications, vol. 271, p. 108171, 2022. [4] M. Ries, V. Dötschel, J. Seibert, S. Pfaller. “A self-avoiding random walk algorithm (SARW) for generic thermoplastic polymers and nanocomposites”, Zenodo, 2022. <a href="https://doi.org/10.5281/zenodo.6245699">https://doi.org/10.5281/zenodo.6245699</a>

X-ray diffraction images recorded for Aumonier et al., (2022) Slow protein dynamics probed by time-resolved oscillation crystallography at room temperature, IUCrJ

The present repository contains diffraction images corresponding to 27 distinct datasets collected at room temperature on the ESRF beamline ID30A-3 using an Eiger X 4M detector. Datasets have been uploaded with their original names to maintain the metadata integrity. The two following tables match the original names with those attributed in the supplementary table S1 of  Aumonier et al., IUCrJ (2022) (https://doi.org/10.1107/S2052252522009150). <table> <tbody> <tr> <td> Data set name on Zenodo </td> <td> X06_01 </td> <td> X12_05 </td> <td> X07_02_ </td> <td> X06_08 </td> <td> X14_06 </td> <td> X13_03 </td> <td> X08_06 </td> <td> X11_05 </td> <td> X13_05 </td> <td> X06_02 </td> <td> X11_01 </td> <td> X08_01 </td> <td> X14_01 </td> <td> X13_01 </td> <td> X06_03 </td> </tr> <tr> <td> Data set in Aumonier et al. 2022 </td> <td> Dark </td> <td> PS2 </td> <td> PS2 </td> <td> PS3 </td> <td> PS4 </td> <td> PS5 </td> <td> PS6 </td> <td> PS7 </td> <td> R2” </td> <td> R3” </td> <td> R7” </td> <td> R10” </td> <td> R13” </td> <td> R21” </td> <td> R35” </td> </tr> </tbody> </table>   <table> <tbody> <tr> <td> Data set on Zenodo </td> <td> X08_02 </td> <td> X11_02 </td> <td> X12_02 </td> <td> X14_02 </td> <td> X13_04 </td> <td> X13_02 </td> <td> X12_06 </td> <td> X06_09 </td> <td> X09_04 </td> <td> X12_04 </td> <td> X06_07 </td> <td> X13_07 </td> </tr> <tr> <td> Data set in Aumonier et al. 2022 </td> <td> R51” </td> <td> R62” </td> <td> R62” </td> <td> R67” </td> <td> R72” </td> <td> R80” </td> <td> R90” </td> <td> R130” </td> <td> R166” </td> <td> R258” </td> <td> R630” </td> <td> R1620” </td> </tr> </tbody> </table> One dataset consists of a master file, four data files and two metadata files.

r-process abundances in neutron star merger dynamical ejecta given different fission yields

This data release contains nucleosynthesis predictions following Vassh et al. (2020) for the r-process abundances of binary neutron star merger ejecta based on the simulation trajectories of Radice et al. (2018), which were mapped to parametrized trajectories based on their neutron-richness (Ye), entropy (s), and expansion timescale (tau). The neutron star merger scenarios considered here cover a wide range of neutron star masses. Calculations were performed with the PRISM code (Mumpower et al. 2018) which accounts for nuclear reheating. Results are reported for two fission yield sets, the Finite Range Liquid Drop Model (FRLDM, Mumpower et al. 2020) and 50/50 symmetric splits. All calculations assumed FRDM12 (Möller et al. 2016) for the nuclear mass model and apply the SFHO equation of state when obtaining the initial composition from nuclear statistical equilibrium (NSE). When using these nucleosynthesis yields, please cite this Zenodo data release (Vassh 2022). Refer to Vassh et al. (2020) for further details on the nuclear physics inputs, to Mumpower et al. (2018) for further details on FRLDM, and to Radice et al. (2018) for further details on the merger ejecta trajectories.

opencc-by-4.0Oct 2022View details →

Dataset: Global range dynamics of the Bearded Vulture (Gypaetus barbatus) from the Last Glacial Maxima to climate change scenarios

This dataset consists of Bearded Vulture Gypaetus barbatus occurrence points which were used to develop a distribution model to study its suitable habitat of this species. Using these data, we modelled the current distribution of Bearded Vulture throughout its entire range and projected the Last Glacial Maxima (LGM), Mid-Holocene (MH) and future distribution under 2070s climate change scenarios. We compiled these data from the entire distribution range in Asia, Europe and Africa using different sources: freely accessible online resources including, eBird and GBIF repositories, published reports and grey literature and occurrence data collected by the authors in the field, mostly in Nepal.