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3,264 results for “FAST”
AI-Based Tracking of Fast-Moving Alpine Landforms Using High Frequency Monoscopic Time-Lapse Imagery
<p><span>This repository contains data and scripts used in the study titled 'AI-Based Tracking of Fast-Moving Alpine Landforms Using High Frequency Monoscopic Time-Lapse Imagery' published as a <a href="https://egusphere.copernicus.org/preprints/2024/egusphere-2024-2570/" target="_blank" rel="noopener">preprint </a>in Earth Surface Dynamcis (EGU) . Please check the README.docx for </span><span>folder structure with descriptions of each folder and file.</span></p>
Dataset of the publication: Design and processing as ultrathin films of a sublimable Iron(II) spin crossover material exhibiting efficient and fast light-induced spin transition
<p>Dataset of the publication: Design and processing as ultrathin films of a sublimable Iron(II) spin crossover material exhibiting efficient and fast light-induced spin transition</p> <p>DOI: 10.1021/acs.chemmater.3c01704</p> <p>M. Gavara-Edo, F. J. Valverde-Muñoz, M. C. Muñoz, S. Elidrissi, F. Marques-Moros, J. Herrero-Martín, K. Znovjyak, M. Seredyuk, J. A. Real, E. Coronado <br><br>Chem. Mater., 35, 22, 9591-9602 (2023)</p>
A phenopushing platform to identify compounds that alleviate acute hypoxic stress by fast-tracking cellular adaptation
<p><span>Severe acute hypoxic stress is a major contributor to the pathology of human diseases, including ischemic disorders. Current treatments focus on managing consequences of hypoxia, with few addressing cellular adaptation to low-oxygen environments. Here, we investigate whether accelerating hypoxia adaptation could provide a strategy to alleviate acute hypoxic stress. We develop a high-content phenotypic screening platform to identify compounds that fast-track adaptation to hypoxic stress. </span><span>Our platform</span><span> captures a high-dimensional phenotypic hypoxia response trajectory consisting of normoxic, acutely stressed, and chronically adapted cell states. Leveraging this trajectory, we identify compounds that phenotypically shift cells from the acutely stressed towards the adapted state, revealing mTOR/PI3K or BET inhibition as strategies to induce this phenotypic shift. Importantly, </span><span>our</span><span> compound hits promote the survival of liver cells exposed to ischemia-like stress, and rescue cardiomyocytes from hypoxic stress. Our “phenopushing” platform offers a general, target-agnostic approach to identify compounds and targets that accelerate cellular adaptation, applicable across various stress conditions.</span></p>
Pauli Decomposition via the Fast Walsh-Hadamard Transform v3
<p>Scripts and data used to produce results for the paper "Pauli Decomposition via the Fast Walsh-Hadamard Transform" (https://arxiv.org/abs/2408.06206; Georges et al 2025 <em>New J. Phys.</em> <a href="https://doi.org/10.1088/1367-2630/adb44d" target="_blank" rel="noopener">https://doi.org/10.1088/1367-2630/adb44d</a>) . </p> <p>In the previous version, there was a bug in generating kinetic energy matrix where one of the FFTs should have been transformed and not only conjugated. This did not practically change the timings but the input matrix was incorrect. Now it's fixed in "kinetic_energy_matrix.zip". All other files are the same as in the second version.</p>
Example data for microclimf: Fast above, below or within canopy gridded microclimate modelling with R
<p>runmicrobig.zip - example data required to run function runmicro_big in package vignette</p> <p>Package available: https://github.com/ilyamaclean/microclimf</p>
Reproduction package for the paper "Constraining a neutron star merger origin for localized fast radio bursts"
<p>This is a reproduction package for the paper <a href="https://academic.oup.com/mnras/article/497/3/3131/5875920">"Constraining a neutron star merger origin for localized fast radio bursts"</a> by Gourdji et al. (2020) and published in MNRAS. This package provides a Jupyter notebook and the necessary information to reproduce the figures and main results of this paper.</p>
Fast and accurate spectral estimation axial super resolution optical coherence tomography
<p>This depository contains the data and code underlying the results of the publication '<em>Fast and accurate spectral estimation axial super resolution optical coherence tomography</em>' in Optics Express (doi.org/<em>10.1364</em>/<em>OE</em>.<em>439761). </em>The reader is free to use the scripts and data in this depository, as long as the manuscript is correctly cited in their work. For further questions, please contact the corresponding author.</p> <p><strong>Description of the code and datasets</strong></p> <p>Table 1 describes the Matlab scripts and functions in this depository that were used in the publication. For reproducing the figures of the publication, refer to the scripts <em>SE_OCT_figure(..).m</em>. For understanding the method and applying it on other datasets from the reader, <em>Bscan_reconstruction.m </em>and <em>Cscan_reconstruction.m</em> are the most convenient scripts to start with. For simulating OCT data as presented in the publication, <em>OCT_simulations.m</em> could be applied. Details on the variables and parameters, such as number of iterations, grid interpolation factor and number of data chunks are commented on in the code itself and should be understandable with the publication as reference. </p> <p>Table 2 describes the datasets that have been used for the publication and are free for the readers to be used with their methods. Table 3 then gives a brief explanation of the variables that are contained in the dataset <em>.mat</em> files.</p> <table> <caption>Table 1. The Matlab scripts in this depository with brief description.</caption> <thead> <tr> <th scope="col">script name</th> <th scope="col">description</th> </tr> </thead> <tbody> <tr> <td>Bscan_reconstruction.m</td> <td>This script loads a B-scan .mat file and applies the four processing methods FBW-DFT, PBW-DFT, AR, RFIAA on the data. </td> </tr> <tr> <td>Cscan_resonstruction.m</td> <td>This script loads a 3Ddata .mat file and applies the four processing methods FBW-DFT, PBW-DFT, AR, RFIAA on the data. </td> </tr> <tr> <td>oct_ar.m</td> <td>This function applies the auto-regressive spectral estimation on the input data.</td> </tr> <tr> <td>oct_iaa.m</td> <td>This function applies RFIAA or FIAA on the input data.</td> </tr> <tr> <td>fiaa_oct.m</td> <td>This function is called within oct_iaa.m for applying FIAA (without the recursive scheme) and within rfiaa_oct.m for the first line. This function applies FIAA on a single A-scan.</td> </tr> <tr> <td>rfiaa_oct.m</td> <td>This function is called within oct_iaa.m for applying RFIAA (with the recursive scheme) on a chunk of data. It initializes the first line of the chunk with fiaa_oct.m, and then it applies rec_fiaa_oct.m with the initialization taken from the previous scanline.</td> </tr> <tr> <td>rec_fiaa_oct.m</td> <td>This function applies RFIAA on a single A-scan, taking the initialization from the previous scanline as extra input parameter. </td> </tr> <tr> <td>RayleighThreshold.m</td> <td>This function automatically determines the lower limit of the dynamic range for plotting an OCT image. It fits a Rayleigh distribution on the input data (preferably noise, but also a full image could be used) and returns a threshold in dB. </td> </tr> <tr> <td>morgenstemning.m</td> <td>This function defines the colormap as used in the publication.</td> </tr> <tr> <td>Bscan_reconstruction_function.m</td> <td>This function takes the interference OCT signal, reference spectra and reconstruction parameters as input and returns the reconstructed images according to the four methods in the publication. This function is used in the scripts for reproducing the figures in the publication. It follows the same structure as the script <em>Bscan_reconstruction.m.</em></td> </tr> <tr> <td>SE_OCT_figure3.m</td> <td>This script does the processing for and plots figure 3 in the manuscript. For this script, the .zip file <em>wedge_simulation_data </em>needs to be unpacked and placed as folder in the folder where this script is executed.</td> </tr> <tr> <td>SE_OCT_figure4.m</td> <td>This script reproduces figure 4 in the publication.</td> </tr> <tr> <td>SE_OCT_figure5.m</td> <td>This script reproduces figure 5 in the publication.</td> </tr> <tr> <td>SE_OCT_figure6.m</td> <td> <p>This script reproduces figure 6 in the publication</p> </td> </tr> <tr> <td>OCT_simulations.m</td> <td>This script reproduces the OCT simulations as described in the publication. As the noise is random, any new realization might slightly differ from the data in the publication.</td> </tr> </tbody> </table> <p> </p> <table> <caption>Table 2. The OCT datasets contained in this depository with a brief description. Table 3 describes the variables that are contained in each of these datasets.</caption> <thead> <tr> <th scope="col">dataset name</th> <th scope="col">description</th> </tr> </thead> <tbody> <tr> <td>wedge_Bscan_data.mat</td> <td>Experimental data from the wedge phantom as visualized in figure 3 of the publication. No spectrum averaging is applied.</td> </tr> <tr> <td>wedge_simulation_data.zip</td> <td>This zipped folder contains 16 simulation datasets with different noise levels, which form the basis of Figure 3 (f) in the publication.</td> </tr> <tr> <td>interfaces_simulation_Bscan_data.mat</td> <td>This file contains the simulation data for 8 interfaces with decreasing intensity and forms the basis of Figure 4 in the publication.</td> </tr> <tr> <td>layered_phantom_Bscan_data.mat</td> <td>This file contains the experimental data from the layered phantom, as used in Figure 4 (c-d) in the publication. No spectrum averaging is applied.</td> </tr> <tr> <td>onion_Bscan_data.mat</td> <td>This file contains the experimental data from the onion sample as used in Figure 5 in the publication. No spectrum averaging is applied.</td> </tr> <tr> <td>skin_Bscan_data.mat</td> <td>This file contains the experimental data from the skin sample as used in Figure 5 in the publication. No spectrum averaging is applied.</td> </tr> <tr> <td>intralipid_Bscan_data.mat</td> <td>This file contains the experimental data from the intralipid sample as used in Figure 6 in the publication. No spectrum averaging is applied.</td> </tr> <tr> <td>speckle_simulation_Bscan_data.mat</td> <td>This file contains simulation data for 3 speckle regions as used in Figure 6 in the publication.</td> </tr> <tr> <td>reference_spectrum.mat</td> <td>This file just contains a spectrum from the used experimental setup which is used as input for the simulations.</td> </tr> <tr> <td>onion_3Ddata.mat</td> <td>This file contains 3D data of the onion sample, which is used for visualization 1. The OCT spectra are obtained from averaging 8 spectra from the experimental setup.</td> </tr> <tr> <td>skin_3Ddata.mat</td> <td>This file contains 3D data of the skin sample, which is used for visualization 2. The OCT spectra are obtained from averaging 8 spectra from the experimental setup.</td> </tr> </tbody> </table> <table> <caption>Table 3. This table contains the variables in the .mat files and their description.</caption> <thead> <tr> <th scope="col">variable name</th> <th scope="col">description</th> </tr> </thead> <tbody> <tr> <td><em>iRawdata</em></td> <td>OCT interference spectra interpolated to a linear grid in k-domain, before subtracting the reference spectrum</td> </tr> <tr> <td><em>sk</em></td> <td>the reference spectrum, interpolated to a linear grid in k-domain</td> </tr> <tr> <td><em>phasep</em></td> <td>4 polynomial coeficients, which can be used in 'polyval' to correct for dispersion</td> </tr> <tr> <td><em>sizeX</em></td> <td>the lateral size of the scan in mm</td> </tr> <tr> <td><em>sizeY</em></td> <td>(only for 3D datasets) the lateral size in the direction perpendicular to x in mm</td> </tr> <tr> <td><em>sizeZ</em></td> <td>the axial field of view (one-sided) before range reduction in mm </td> </tr> <tr> <td><em>ROIp</em></td> <td>the best axial region of interest for this dataset to apply RFIAA on a reduced reconstruction range (in pixels of the DFT reconstruction without zero-padding)</td> </tr> </tbody> </table> <p> </p>
DALES LES data for GABLS1 (only ninth hour), ASTEX, and slow and fast strato-cumulus cases
<p>These files contain DALES LES data for:</p> <p>GABLS1 (only ninth hour) in GABLS1_LES.dat (ascii file, explanation in header)</p> <p>ASTEX profiles in dales_profiles.nc (netcdf and information in header)</p> <p>slow and fast strato-cumulus cases in vddussen_profiles* (netcdf and information in header)</p>
Particle-based Fast Jet Simulation at the LHC with Variational Autoencoders: generator-level and reconstruction-level jets dataset
<p>Jets at generator and reconstruction level saved in .npy format.</p> <p>Each jet is represented as an array of jet constituents characterized by their particle momentum in Cartesian coordinates, i.e., (px, py, pz). For both generator-level and reconstruction-level jets, jet constituents are ordered by decreasing pT.</p> <p>The shape of the datasets is [N, 50, 3], where N is the total number of jets, 50 is the number of particles per jet, and 3 is the number of particle features (in order): [px, py, pz].<br> About 1.7M jets split into training, validation and testing sets at 60%, 20% and 20% respectively.</p>
Nitrogen resorption dynamics and slow-fast strategies in 137 Arabidopsis thaliana genotypes
<p>Leaf nitrogen resorption efficiency (R_efficiency, %) and the relative maximum resorption rate of nitrogen (R_rate_max_perN, mg d<sup>-1</sup> mg<sup>-1</sup>) were estimated together with the flowering time (FT, days after germination) and specific leaf area (SLA, m<sup>2</sup> kg<sup>-1</sup>) in 137 Arabidopsis thaliana accessions selected from the 1001 genomes project accession list. The dataset contains genotype means for every measured accession.</p>
Fast Calorimeter Simulation Challenge 2022 - Dataset 2
<p>This is dataset 2 of the “Fast Calorimeter Simulation Challenge 2022”. It consists of two files with 100k GEANT4-simulated showers each of electrons with energies sampled from a log-uniform distribution ranging from 1 GeV to 1 TeV. The detector has a concentric cylinder geometry with 45 layers, where each layer consists of active (silicon) and passive (tungesten) material. Each layer has 144 readout cells, 9 in radial and 16 in angular direction, yielding a total of 9x16x45 = 6480 voxels.</p> <p>dataset_2_1.hdf5 should be used for training, dataset_2_2.hdf5 can be used as reference in the evaluation.</p> <p>More details, in particular helper scripts to parse the data and calculate and visualize basic high-level physics features, are available at <a href="https://calochallenge.github.io/homepage/">https://calochallenge.github.io/homepage/</a></p>
Fast Calorimeter Simulation Challenge 2022 - Dataset 3
<p>This is dataset 3 of the “Fast Calorimeter Simulation Challenge 2022”. It consists of four files with 50k GEANT4-simulated showers each of electrons with energies sampled from a log-uniform distribution ranging from 1 GeV to 1 TeV. The detector geometry is similar to dataset 2, but has a much higher granularity. Each of the 45 layer has now 18 radial and 50 angular bins, totalling 18x50x45=40500 voxels. This dataset was produced using the <a href="https://gitlab.cern.ch/geant4/geant4/-/tree/master/examples/extended/parameterisations/Par04">Par04 Geant4 example</a>.</p> <p>dataset_3_1.hdf5 and dataset_3_2.hdf5 should be used for training, dataset_3_3.hdf5 and dataset_3_4.hdf5 can be used as reference in the evaluation.</p> <p>More details, in particular helper scripts to parse the data and calculate and visualize basic high-level physics features, are available at <a href="https://calochallenge.github.io/homepage/">https://calochallenge.github.io/homepage/</a></p>
Data from 'Fast-growing species shape the evolution of reef corals'
<p>Datasets and scripts generated and/or analysed in the paper 'Fast-growing species shape the evolution of reef corals', published in Nature Communications. More details can be found in the README file.</p>
Prosociality as response to slow- and fast-onset climate hazards
<p>This repository contains the information videos used in the treatments, as well as the data and code that replicates tables and figures for the following paper:<br> <strong>Title:</strong> Prosociality as response to slow- and fast-onset climate hazards<br> <strong>Authors:</strong> Ivo Steimanis<sup>1</sup> & Björn Vollan<sup>1,*</sup><br> <strong>Affiliations:</strong> <sup>1</sup> Department of Economics, Philipps University Marburg, 35032 Marburg, Germany<br> <strong>*Correspondence to:</strong> Björn Vollan <a href="mailto:bjoern.vollan@wiwi.uni-marburg.de">bjoern.vollan@wiwi.uni-marburg.de</a><br> <strong>ORCID:</strong> Steimanis: 0000-0002-8550-4675; Vollan: 0000-0002-5592-4185<br> <strong>Classification:</strong> Social Sciences, Economic Sciences<br> <strong>Keywords:</strong> climate hazards, prosociality, in-group favoritism, antisociality</p> <p> </p>
Fig. 2 in Fast Recent Expansion Of The Spanish Slug (Gastropoda, Stylommatophora, Arionidae) Across Ukraine
Fig. 2. Distribution of the Spanish slug in Ukraine. Black circles — anatomically confirmed locations; empty circles — by external view only.
Code-to-code SIMMER/FRENETIC comparison for the neutronic simulation of lead-cooled fast reactors (dataset)
<p>Dataset in support of publication "Code-to-code SIMMER/FRENETIC comparison for the neutronic simulation of lead-cooled fast reactors". README.txt has been written in compliance with the Dublin Core Standard for metadata, https://dublincore.org/.</p>
Fast Surface Warming of Global Cities
<p>Using satellite land surface temperatures (2002 to 2021), this study investigates the surface warming trends over urban core, rural background and transitional land based on the time series decomposition algorithm. The individual contributions from background climate change, urbanization, and landscape greening to overall urban surface warming trends are then quantified using the statistical attribution approach.</p>
BioMates - Bio-oil from ablative fast pyrolysis: Identifiers for WP3- samples and blends
<p>Ablative Fast Pyrolysis (AFP) is the first step in the BioMates-concept to convert herbaceous biomass into co-feed with reliable properties for conventional refineries (www.biomates.eu). The document provides the coding behind the identifiers used for samples and sample blends produced by RISE via AFP within the H2020-project BioMates.</p>
Incidence and predictor of diabetic foot ulcer and its association with change in fasting blood sugar among diabetes mellitus patients at referral hospitals in Northwest Ethiopia, 2021
<p>Abstract</p> <p> </p> <p><strong>Background</strong></p> <p>Diabetes mellitus is one of the global public health problems and fasting blood sugar is an important indicator of diabetes management. Uncontrolled diabetes can lead to diabetic foot ulcers, which is a common and disabling complication. The association between fasting blood glucose level and the incidence of diabetic foot ulcers is rarely considered, and knowing its predictors is good for clinical decision-making. Therefore, the aim of this study was to determine the incidence and predictors of diabetic foot ulcers and its association with changes in fasting blood sugar among diabetes mellitus patients at referral hospitals in Northwest Ethiopia.</p> <p><strong>Methods</strong></p> <p>A multicenter retrospective follow-up study was conducted at a referral hospital in Northwest Ethiopia. A total of 539 newly diagnosed DM patients who had follow-up from 2010 to 2020 were selected using a computer-generated simple random sampling technique. Data was entered using Epi-Data 4.6 and analyzed in R software version 4.1. A Cox proportional hazard with a linear mixed effect model was jointly modeled and 95% Cl was used to select significant variables. AIC and BIC were used for model comparison.</p> <p><strong>Result</strong></p> <p>A total of 539 diabetes patients were followed for a total of 28727.53 person-month observations. Overall, 65 (12.1%) patients developed diabetic foot ulcers with incidence rate of 2.26/1000-person month observation with a 95% CI of [1.77, 2.88]. Being rural (AHR= 2.30, 95%CI: [1.23, 4.29]), being a DM patient with Diabetic Neuropathy (AHR= 2.61, 95%CI: [1.12, 6.06]), and having peripheral arterial disease(PAD) (AHR= 2.96, 95%CI: [1.37, 6.40]) were significant predictors of DFU. The time-dependent lagged value of fasting blood sugar change was significantly associated to the incident of DFU (α = 1.85, AHR=6.35, 95%CI [2.40, 16.79]).</p> <p><strong>Conclusion and recommendation</strong></p> <p>In this study, the incidence of DFU was higher than in previous studies and was influenced by multiple factors like rural residence, having neuropathy, and PAD were significant predictors of the incidence of DFU. In addition, longitudinal changes in fasting blood sugar were associated with an increased risk of DFU. Health professionals and DM patients should give greater attention to the identified risk factors for DFU were recommended.</p>
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’ 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. </p> </blockquote> <p> </p> <p><strong>Contact:</strong></p> <p>Maximilian Ries<br> Institute of Applied Mechanics<br> Friedrich-Alexander-Universität Erlangen-Nürnberg<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> </p> <p><strong>License:</strong></p> <p>Creative Commons Attribution 4.0 International</p> <p> </p> <p><strong>Context:</strong></p> <p>Data set supplementing journal paper:</p> <p>[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, <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> folder names vary depending on the context, explained in the following:</p> <p> </p> <p>01_matrix</p> <ul> <li> <p>01_equilibration<br> sample equilibration to different temperatures<br> nomenclature: equil_<chains>-<chain_atoms>-box_<initial_box_length>-min_<SARW_distance>-angle_<SARW_angle>-T_<final_temperature>[-<batch_ID>]</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 </p> </li> </ul> </li> <li> <p>02_temperature_dependence<br> uniaxial tension simulations to identify temperature dependence<br> nomenclature: 01_UT_<chains>-<chain_atoms>-box_<initial_box_length>-min_<SARW_distance>-angle_<SARW_angle>-T_<final_temperature></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_<chains>-<chain_atoms>-box_<initial_box_length>-min_<SARW_distance>-angle_<SARW_angle>-T_<final_temperature>-rate_<strain_rate>-<batchID></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_<chains>-<chain_atoms>-box_<initial_box_length>-min_<SARW_distance>-angle_<SARW_angle>-T_<final_temperature>-rate_<strain_rate>[-<batchID>]</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_<chains>-<chain_atoms>-box_<initial_box_length>-min_<SARW_distance>-angle_<SARW_angle>-T_<final_temperature>-rate_<strain_rate>-sin_<strain_amplitude></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_<chains>-<chain_atoms>-box_<initial_box_length>-min_<SARW_distance>-angle_<SARW_angle>-T_<final_temperature>-rate_<strain_rate>-sin_<strain_amplitude>_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_<strain_rate>-<batchID></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: 02_UT_<chains>-<chain_atoms>-box_<initial_box_length>-min_<SARW_distance>-angle_<SARW_angle>-T_<final_temperatur>-strain_<max_strain></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-<loadcase>_<direction>-strain_<max_strain>-rate_<strain_rate></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-<loadcase>_<direction>_sin-ampl_<strain_amplitude>-rate_<max_strain_rate></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_<filler_radius>-nNP_<filler_number>-<batchID></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_<filler_radius>-nNP_<filler_number>-<batchID></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-<chains>x<chain_atoms>_rNP_<filler_radius>-nNP_<filler_number>_pos_<filler_pos>-<batchID></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> </p> <p> </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 </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> </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 </p> </li> <li> <p>Time: time </p> </li> <li> <p>TotEng: total energy </p> </li> <li> <p>PotEng: potential energy</p> </li> <li> <p>KinEng: kinetic energy </p> </li> <li> <p>E_pair: pair energy </p> </li> <li> <p>E_bond: bond energy </p> </li> <li> <p>E_angle: angle energy </p> </li> <li> <p>E_dihed: dihedral energy </p> </li> <li> <p>Temp: temperature</p> </li> <li> <p>Press: hydrostatic pressure</p> </li> <li> <p>Pxx: xx component of pressure tensor </p> </li> <li> <p>Pyy: yy component of pressure tensor </p> </li> <li> <p>Pzz: zz component of pressure tensor </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 </p> </li> <li> <p>Lx: box length in x direction </p> </li> <li> <p>Ly: box length in y direction </p> </li> <li> <p>Lz: box length in z direction </p> </li> <li> <p>Density: density </p> </li> <li> <p>c_RG: radius of gyration scalar </p> </li> <li> <p>c_RG[1]: squared radius of gyration tensor (xx component) </p> </li> <li> <p>c_RG[2]: squared radius of gyration tensor (yy component) </p> </li> <li> <p>c_RG[3]: squared radius of gyration tensor (zz component) </p> </li> <li> <p>c_RG[4]: squared radius of gyration tensor (xy component) </p> </li> <li> <p>c_RG[5]: squared radius of gyration tensor (xz component) </p> </li> <li> <p>c_RG[6]: squared radius of gyration tensor (yz component) </p> </li> <li> <p>c_bondave[1]: bond energy averaged over all atoms </p> </li> <li> <p>c_bondave[2]: bond distance averaged over all atoms </p> </li> <li> <p>c_bondave[3]: squared bond distance averaged over all atoms </p> </li> <li> <p>c_angleave[1]: angle energy averaged over all atoms </p> </li> <li> <p>c_angleave[2]: angle averaged over all atoms degree</p> </li> <li> <p>c_angleave[3]: cosine of angle </p> </li> <li> <p>c_angleave[4]: squared cosine of angle </p> </li> <li> <p>c_MSD[1]: mean squared displacement x-direction </p> </li> <li> <p>c_MSD[2]: mean squared displacement y-direction </p> </li> <li> <p>c_MSD[3]: mean squared displacement z-direction </p> </li> <li> <p>c_MSD[4]: total mean squared displacement </p> </li> <li> <p>c_COM[1]: x coordinate of center of mass </p> </li> <li> <p>c_COM[2]: y coordinate of center of mass </p> </li> <li> <p>c_COM[3]: z coordinate of center of mass </p> </li> <li> <p>v_strain_xx: xx component of engineering strain tensor </p> </li> <li> <p>v_strain_yy: yy component of engineering strain tensor </p> </li> <li> <p>v_strain_zz: zz component of engineering strain tensor </p> </li> <li> <p>v_vMisesequivstress: von Mises equivalent stress </p> </li> <li> <p>v_Cauchy_xx: xx component of stress tensor </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 </p> </li> <li> <p>v_Cauchy_xz: xz component of stress tensor </p> </li> <li> <p>v_Cauchy_yz: yz component of stress tensor </p> </li> <li> <p>v_strain_xy: xy component of engineering strain tensor </p> </li> <li> <p>v_strain_xz: xz component of engineering strain tensor </p> </li> <li> <p>v_strain_yz: yz component of engineering strain tensor </p> </li> </ul> <p><br> </p> <p><strong>References</strong>:</p> <p>[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”, <em>Express Polymer Letters</em>, <strong>2022</strong>, 16.</p> <p>[2] S. Plimpton, “Fast parallel algorithms for short-range molecular dynamics,” <em>Journal of computational physics</em>, <strong>1995</strong>, 117, 1-19.</p> <p>[3] A. P. Thompson et al., “LAMMPS - a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales,” <em>Computer Physics Communications</em>, vol. 271, p. 108171, <strong>2022</strong>.</p> <p>[4] M. Ries, V. Dötschel, J. Seibert, S. Pfaller. “A self-avoiding random walk algorithm (SARW) for generic thermoplastic polymers and nanocomposites”, <em>Zenodo</em>, 2022. <a href="https://doi.org/10.5281/zenodo.6245699">https://doi.org/10.5281/zenodo.6245699</a></p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.