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18,658 results for “impacts”
Supporting GIS file for: Tectonic landform and lithologic age impact uncertainties in fault displacement hazard models
<p>This project aims to understand how the error in mapped fault location and the residual between the modeled and observed coseismic displacements vary with tectonic landform and the surficial lithologic age. We focus on four historical earthquakes: the M6.9 Borah Peak, 2014 M6.0 Napa, 2016 M7.0 Kumamoto, and 2016 M7.8 Kaikoura earthquakes.</p> <p>The GIS shape file contains information about the tectonic landform, the surficial landscape age, the observed and modelled coseismic displacement, fault location error, and the confidence ranking of the mapped fault trace. Each entry corresponds to a location where a displacement measurement was made following the earthquake of focus. Additional detail is given in the readme.</p> <p>The entries in the GIS file are collected from the following references:</p> <p>Chiou, B., Chen, R., Thomas, K., Milliner, C. W. D., Dawson, T., & Petersen, M. D. (2022). Surface Fault Displacement Models for Strike-Slip Faults. <em>Natural Hazards Risk and Resiliency Research Center B. John Garrick Institute for the Risk Sciences University of California, Los Angeles</em>, <em>Report GIRS‐2022‐07</em>, 186. https://doi.org/10.34948/N3RG6X</p> <p>Crone, A. J., Machette, M. N., Bonilla, M., Lienkaemper, J. J., Pierce, K., Scott, W., & Bucknam, R. (1987). Surface faulting accompanying the Borah Peak earthquake and segmentation of the lost river fault, central Idaho. <em>Bulletin of the Seismological Society of America</em>, <em>77</em>.</p> <p>Graymer, R. W., Brabb, E., Jones, D. L., Barnes, J., Nicholson, R. S., & Stamski, R. E. (2007). <em>Geologic Map and Map Database of Eastern Sonoma and Western Napa Counties, California</em> (No. U.S. Geological Survey Scientific Investigations Map 2956). Retrieved from https://doi.org/10.3133/sim2956</p> <p>Heron, D. W. (2018). Geological Map of New Zealand 1:250 000. GNS Science Geological Map 1 (2nd ed.) Lower Hutt, New Zealand. GNS New Zealand. Retrieved from https://www.gns.cri.nz/data-and-resources/geological-map-of-new-zealand/</p> <p>Hoshizumi, H., Ozaki, M., Miyazaki, K., Matsuura, H., Toshimitsu, S., Uto, K., et al. (2004). Geological Map of Japan 1:200,000: Kumamoto. Geological Survey of Japan. Retrieved from https://www.gsj.jp/Map/EN/geology2-6.html#Kumamoto</p> <p>Janecke, S. U., & Wilson, E. (1992). Geologic map of the Borah Peak, Burnt Creek, Elkhorn Creek, and Leatherman Peak 7.5’ quadrangles, Custer County, Idaho, Scale 1:24,000. Idaho Geological Survey Technical Report 92-5. Retrieved from https://www.idahogeology.org/product/T-92-5</p> <p>Kuehn, Nicolas, Kottke, A., Madugo, C., Sarmiento, A., & Bozorgnia, Y. (2022). Report GIRS 2022-06: UCLA–PG&E Fault Displacement Model. https://doi.org/10.34948/N3X59H</p> <p>Lewis, R. S., Link, P., Stanford, L. R., & Long, S. P. (2012). <em>Geologic Map of Idaho</em>. Moscow, Boise, Pocatello: Idaho Geologic Survey. Retrieved from https://www.idahogeology.org/maps-pubs-data/state-geologic-map</p> <p>Ponti, D. J., Blair, J. L., & Rosa, C. M. (2019). Digital Datasets Documenting Fault Rupture and Ground Deformation Features Produced by the Mw 6.0 South Napa Earthquake of August 24, 2014 [Data set]. U.S. Geological Survey. https://doi.org/10.5066/F7P26W84</p> <p>Sarmiento, A., Madugo, D., Bozorgnia, Y., Shen, A., Mazzoni, S., Lavrentiadis, G., et al. (2021). Fault Displacement Hazard Initiative Database. <em>Report No. GIRS-2021-08, Revision 3.3 Dated 29 May 2024. Los Angeles, CA: The B. John Garrick Institute for the Risk Sciences at UCLA Engineering</em>. https://doi.org/10.34948/N36P48</p> <p>Scott, C., Adam, R., Arrowsmith, R., Madugo, C., Powell, J., Ford, J., et al. (2023). Evaluating how well active fault mapping predicts earthquake surface-rupture locations. <em>Geosphere</em>, <em>19</em>(4), 1128–1156. https://doi.org/10.1130/GES02611.1</p> <p>Scott, C. P., Arrowsmith, J. R., Nissen, E., Lajoie, L., Maruyama, T., & Chiba, T. (2018). The <em>M</em> 7 2016 Kumamoto, Japan, Earthquake: 3-D Deformation Along the Fault and Within the Damage Zone Constrained From Differential Lidar Topography. <em>Journal of Geophysical Research: Solid Earth</em>, <em>123</em>, 6138–6155. https://doi.org/10.1029/2018JB015581</p> <p>Vincent, K. R. (1995). Implications for models of fault behavior from earthquake surface displacement along adjacent segments of the Lost River fault, Idaho<em>:</em> University of Arizona.</p> <p>Wagner, D., & Gutierrez, C. (2017). <em>Preliminary Geologic Map of the Napa and Bodega Bay 30’ x 60’ Quadrangles, California</em>. California Department of Conservation. Retrieved from https://ngmdb.usgs.gov/Prodesc/proddesc_105819.htm</p> <p>Zinke, R., Hollingsworth, J., Dolan, J. F., & Van Dissen, R. (2019). Three‐Dimensional Surface Deformation in the 2016 M <sub>W</sub> 7.8 Kaikōura, New Zealand, Earthquake From Optical Image Correlation: Implications for Strain Localization and Long‐Term Evolution of the Pacific‐Australian Plate Boundary. <em>Geochemistry, Geophysics, Geosystems</em>, <em>20</em>(3), 1609–1628. https://doi.org/10.1029/2018GC007951</p>
Adopting Micro-Frontends: Impacts and Challenges – A Systematic Mapping Study
<p><strong>Context: </strong>Incorporating new technologies into monolithic frontend projects has been challenging for software development teams, due to issues like code redundancy, inconsistency, and scalability. To address these challenges, the micro-frontend architecture emerged, offering a more modular and independent solution.</p> <p><strong>Goal:</strong>This study aims to investigate the impacts and challenges of adopting the micro-frontend architecture. <br><strong>Method:</strong> We conducted a Systematic Mapping Study (SMS) to identify the architectural patterns and approaches used in micro-frontends. Additionally, we carried out a survey to validate the strategies and challenges of adopting this architecture.</p> <p><strong>Results:</strong> The findings highlighted the relevance of the micro-frontend architecture in modernizing the frontend layer. However, its adoption is closely tied to the complexity and size of the project and team. Therefore, implementing the micro-frontend architecture with a well-defined set of methodological and technological decisions increases the likelihood of successful adoption.</p> <p><em>Keywords:</em> <em>Micro-frontend, Systematic Mapping Study, Challenges, Survey</em></p> <p> </p> <p> </p>
Raw data - manuscript - Impact of methyl jasmonate on leaf consumption and development of Tuta absoluta (Lepidoptera: Gelechiidae) on tomato plants
<p>Raw data - manuscript - Impact of methyl jasmonate on leaf consumption and development of Tuta absoluta (Lepidoptera: Gelechiidae) on tomato plants</p>
Data - Mobile phones and their impact on the socioeconomic development of rural women in Peru
<p>Project database Mobile phones and their impact on the socioeconomic development of rural women in Peru</p>
Effects of European emissions trading on the transformation of primary steelmaking: Assessment of economic and climate impacts in a case study from Germany
<p>Supplementary Information 1: This supporting information provides information on the modeling of material and energy flows for conventional and low-carbon steelmaking in an integrated steel mill. To this end, the production activities (unit processes) are provided. Also, information on the assessment of economic and climate impacts is included.</p> <p>Supplementary Information 2: This supporting information provides information on the main mechanisms of the European emissions trading system. Thereby, a focus is laid on regulations for the free allocation of EU allowances toward companies from the sectors with a high risk of carbon leakage. Additionally, the assessment and optimization models for designing favorable transformation pathways of integrated steel mills are provided. Also, the main assumptions and complementary results are provided.</p>
Impact of Rhizosphere Quantitative Microbiome and Soil Properties on Alkaloid Dynamics in Lycoris aurea
Open the record for dataset details and reuse information.
How did COVID-19 Impact Software Design Activities in Global Software Engineering — Systematic Review
<p>This dataset is part of research publication <a href="https://doi.org/10.1142/S0218194024500098" target="_blank" rel="noopener">How did COVID-19 Impact Software Design Activities in Global Software Engineering — Systematic Review</a> published in <em>International Journal of Software Engineering and Knowledge Engineering</em>. This study systematically analyzes the evolution of research emphasis in the field of Global Software Engineering (GSE), particularly focusing on the software design phase during the COVID-19 pandemic.</p> <h3>Key contribution</h3> <ol> <li>Systematic analysis of GSE research <ul> <li>Phase 1 - Mapped GSE research over the two decades leading to the pandemic (2000–2020)</li> <li>Phase 2 - Used the forward snowballing approach to examine literature on the software design phase published between 2020 and 2022.</li> </ul> </li> <li>Analyzed trends in GSE research through 592 research studies across both phases.</li> </ol> <h3>Dataset content</h3> <p>The dataset consis of following files:</p> <ol> <li>Readme file - Overview and instructions for the dataset.</li> <li>Search string and snowballing - Contains the search strings used in the first phase and details of the forward snowballing approach used in the second phase.</li> <li>Data extraction template and Inclusion/exclusion criteria - Includes the template for organizing key features of papers relevant to the research questions and lists the inclusion and exclusion criteria applied.</li> <li>Screening process - Describes the three-level screening process applied in both phases (title, abstract, full text). A separate tab analyzes primary studies used in the second phase.</li> <li>RQs analysis - Provides detailed analysis of research questions (RQs) from the final set of 592 papers.</li> </ol>
Cultivating Flavor: Unveiling the Impact of Lactobacillus Delbrueckii Subsp. Bulgaricus and Bean Sprouts (Phaseolus Vulgarison) on Fermentation Arabica and Robusta Coffee
<p>This material has presented on 2nd International Conference on Advance Research in Agriculture and Food 2023 in October 25, 2023.</p>
The Impact of Feed-Solvent Ratio on Contella Asiatica Extraction Using Microwave-Assisted Extraction (MAE)
<div>This material has presented on 2nd International Conference on Advance Research in Agriculture and Food 2023 in October 25, 2023.</div>
Data set for paper 'Impact of post-ion implantation annealing on Se-hyperdoped Ge'
<p><strong>Description of the files</strong></p> <p>Files are named by the same figure order appearing in the paper and supplementary material. File types contain:<br>- *.csv: Comma separated values. <br>- *.fibps: Raw data obtained with the 3D optical profilometer, which can be opened and analyzed by ProfilmOnline at https://www.profilmonline.com/. Figures shown in the paper were croped from the top-left corner with areas of about 60x60 µm2 from the raw data. Alternatively all 3D profilometer data shown in the paper can be obtained and analyzed in https://www.profilmonline.com/shared-folder?token=S32NP82HtLC3<br>- *.tif: Image generated from scanning electron microscope. </p> <p> </p> <p><strong>Original paper:</strong> https://doi.org/10.1063/5.0213637</p> <p><strong>Supplementary material:</strong> https://doi.org/10.60893/figshare.apl.c.7322108</p> <p> </p> <p><strong>When using the dataset, please cite the original paper: Xiaolong Liu, Patrick McKearney, Sören Schäfer, Behrad Radfar, Yonder Berencén, Ulrich Kentsch, Ville Vähänissi, Shengqiang Zhou, Stefan Kontermann, Hele Savin; Impact of post-ion implantation annealing on Se-hyperdoped Ge. Appl. Phys. Lett. 22 July 2024; 125 (4): 042102. https://doi.org/10.1063/5.0213637.</strong></p> <p> </p>
Evolution of digital consumer behavior: A bibliometric analysis of the impact of social networks on purchase determinations
<p><span>In the current context of a globalized and globalized and highly digitized marketplace, the study of customer behavior has become fundamental to behavior has become fundamental to understanding the complex interactions between social, cultural and commercial between social, cultural and commercial factors that influence purchasing decisions. purchasing decisions. This research aims to analyze the the impact of social media and purchase determinations on customer behavior through a bibliometric behavior through a bibliometric study. A qualitative, descriptive, non-experimental descriptive approach with a non-experimental and longitudinal design, analyzing 1,213 documents 1,213 documents extracted from Scopus, using VOSviewer and Bibliometrix for scientific mapping and bibliometric scientific mapping and bibliometric analysis. The results reveal a rapidly growing field (13.33% per Year field (13.33% per year) with an average of 16.63 citations per document and a 28.77% co-citation rate and 28.77% of international co-authorships. Emerging trends were identified the influence of social networks on purchase intent, the impact of influencers and the importance of social commerce. Semantic análisis semantic analysis highlighted the centrality of terms such as "social media", "consumer behavior" and "purchase intention", "consumer behavior" and "purchase intention". It is concluded that the field is dynamic, interdisciplinary and globally diverse, with a growing integration of technology and consumer behavior. integration of technology and consumer behavior. The implications suggest the need for more sophisticated digital marketing strategies that consider the influence of that consider the influence of social networks, the credibility of influencers and the influencer credibility and trust, and the need for more sophisticated digital marketing strategies that consider the influence of social networks, influencer credibility and trust.</span></p>
Global evolution of educational marketing in the digital age: A bibliometric analysis of its impact on student satisfaction
<p><span>Educational marketing has emerged as a crucial strategy to improve student satisfaction in an increasingly competitive and globalized academic environment. This bibliometric research aimed to analyze the evolution and trends in the scientific literature on educational marketing as a strategy for student satisfaction. A qualitative and descriptive approach with a non-experimental and longitudinal design was used. The study analyzed 201 documents extracted from Scopus, using bibliometric techniques implemented through VOSviewer and Bibliometrix. The results reveal an annual growth of 10.51% in scientific production, with significant contributions from the United States, United Kingdom, Australia and India, as well as a growing participation of Latin American countries. A core of specialized journals leading publication in this field was identified. Keyword co-occurrence analysis highlighted central themes such as higher education, e-learning and student satisfaction. The research shows an expanding interdisciplinary field, with approaches that address internationalization, the use of digital technologies and the personalization of educational services. It is concluded that educational marketing is an effective strategic tool to improve student satisfaction, retention and academic performance, highlighting the need to adapt these strategies to the rapid technological and social changes in the educational sector.</span></p>
The dataset for the paper "Ambient noise tomography reveals asymmetric impact damage zone beneath Lonar crater, India: Implications for oblique impact cratering in heterogeneous basalt with planetary applications"
<p>These datasets are used to produce the results and generate figures and tables in the manuscript titled as "Ambient Noise Tomography Reveals Asymmetric Impact Damage Zone Beneath Lonar Crater, India: Implications for Oblique Impact Cratering in Heterogeneous Basalt with Planetary Applications" submitted to JGR-Planets.</p>
The impact of cognitive remediation therapy on lower eye movement and word fluency function: A prospect for improvement of eye movement and cognitive function in schizophrenia
Open the record for dataset details and reuse information.
Impact of atmospheric water-soluble iron on α-pinene-derived SOA formation and transformation in the presence of aqueous droplets
<p>The following data sets belong to the article "Impact of atmospheric water-soluble iron on α-pinene-derived SOA formation and transformation in the presence of aqueous droplets" The impact of water-soluble atmospheric iron on formation, growth and aging of secondary organic aerosol (SOA) is a controversial subject in the literature. Iron chemistry drives Fenton-like reactions in the aqueous phase which is dependent on pH. Flow reactor experiments in the dark and under humid conditions were conducted to investigate systematically the influence of ferrous iron in the aqueous phase on α-pinene SOA by online physical analysis and offline high-resolution mass spectrometry. In total 31 flow reactor experiments were conducted in four sets of experiments. All SMPS data and mass spectra in negative mode are uploaded sorted by set of experiments and figures in the paper. You can find the workflow of SMPS data analysis as well as the retention times of the target analysis in the paper and SI.</p>
Exploring doxorubicin transport in 2D and 3D models of MDA-MB-231 sublines: impact of hypoxia and cellular heterogeneity on doxorubicin accumulation in cells
<p><span>This data set includes raw data supporting the paper "</span><strong><span>Exploring doxorubicin transport in 2D and 3D models of MDA-MB-231 sublines: impact of hypoxia and cellular heterogeneity on doxorubicin accumulation in cells</span></strong><span>".</span></p> <p><strong><span>The files include the following data</span></strong><span>:</span></p> <p><span>1. MTT assay results used for calculation of <strong>cell viability</strong> / <em><strong><span>EC</span></strong></em></span><strong><sub><span>50</span></sub></strong><strong><span> values</span></strong><span> for doxorubicin in MDA-MB-231 cell line and its sublines;</span></p> <p><span>2. The raw data used for calculating <strong><span>doxorubicin </span></strong></span><strong><span>transport</span></strong><strong><span> </span></strong><span>in <strong><span>2D</span></strong> cells under <strong>normoxia</strong>;</span></p> <p><span>3. The raw data used for calculating <strong><span>doxorubicin </span></strong></span><strong><span>transport</span></strong><strong><span> </span></strong><span>in <strong><span>2D</span></strong> cells under <strong>hypoxia</strong>;</span></p> <p><span>4. The raw data used for calculating <strong><span>doxorubicin </span></strong></span><strong><span>transport</span></strong><strong><span> </span></strong><span>in cancer spheroids (<strong><span>3D</span></strong> cultures).</span></p>
Midpoint Characterization Factors to assess impacts of turbined water use from hydropower production
<p>This repository contains "Supporting Information 2 (SI2)" and a shapefile with the CFs at basin level for the publication titled "Midpoint Characterization Factors to assess impacts of turbine water use from hydropower production", published in "The International Journal of Life Cycle Assessment" .<br><br></p> <p>Content:</p> <p>SI2 is an excel file with the following sheets: </p> <ul> <li>Midpoints_CFs_Basin <ul> <li>Midpoint CF values [HDOR·y/m3] for each basin, including TWU, RV and Q input parameters and sensitivity results</li> </ul> </li> <li>TE <ul> <li>TE values used for TWU calculations</li> </ul> </li> <li>Midpoints_CFs_Country <ul> <li>Midpoint CF values [HDOR·y/m3] aggregated to country level (Weighted average based on TWU). Country delination obtained from: The Word Bank. (2021). World International Borders - Very High Definition. https://datacatalog.worldbank.org/search/dataset/0038272</li> </ul> </li> </ul> <div> <ul> <li>Case_Study <ul> <li>Underlying data for the case study performed in Sections 2.6 and 3.3 of the main publication</li> </ul> </li> </ul> </div> <p>CF_Basin_shapefile.zip contains:</p> <ul> <li>Shapefile with CF values at basin level. <br> <ul> <li>Basins obtained from from Lin et al. (2021), https://doi.org/10.1038/s41597-021-00819-9 </li> <li>basid matches with "Midpoints_CFs_Basin"</li> </ul> </li> </ul> <p> </p> <p> </p> <p> </p> <p> </p>
Burial impact on the Tatra Mts from a rock magnetic and magnetic fabric perspective: anisotropy of magnetic susceptibility data
<p>The dataset contains anisotropy of magnetic susceptibility (in-phase, out-of-phase and frequency dependent) analyses of Cretaceous and Paleogene rocks of the Tatra Mts and their surrounding area.</p>
Understanding the impact of sustainable finance on bank profitability
<p><span>This research examines the impact of green credit, competition, and risk taking on bank profitability as a form of sustainable finance practices that can increase economic growth. This research applies balanced panel data <span>regression analysis</span> using commercial banks data from 2005 to 2021<span>.</span><span> </span><span>The results show that increasing green credit allocation </span>and the level of competition will increase bank profitability in Indonesia. The<span> </span>increased<span> </span>bank<span> </span>profitability<span> </span>resulting<span> </span>from<span> </span>green<span> </span>credit<span> </span>allocation<span> </span>and bank competition can boost economic growth.<span> </span>Furthermore,<span> </span>high<span> </span>risk-taking<span> </span>behavior<span> </span>will<span> </span>reduce<span> </span>bank<span> </span>profitability.<span> </span>This finding<span> </span>is<span> </span>in<span> </span>line with sustainable development goals (SDGs) eight and nine. The results<span> </span>emphasize<span> </span>the<span> </span>importance<span> </span>of<span> </span>strategy<span> </span>in<span> </span>green<span> </span>credit allocation, competitive banking strategies, and risk management in bank’s decision making to realize sustainable finance.</span></p>
Impact of the unimodal molar mass distribution on the mechanical behavior of polymer nanocomposites below the glass transition temperature: A generic, coarse-grained molecular dynamics study - dataset
<p>Abstract:<br>from [1]</p> <p>Polymer nanocomposites (PNCs) have shown great potential to meet the ever-growing requirements of modern engineering applications. Nowadays, molecular dynamics (MD) simulations are increasingly employed to complement experimental work and thereby gain a deeper understanding of the complex structure–property relations of PNCs. However, with respect to the thermoplastic’s mechanical behavior, the role of its average molar mass is rarely addressed, and many MD studies only consider uniform (monodispersed) polymers. Therefore, this contribution investigates the impact that and the dispersity Đ have on the stiffness and strength of PNCs through coarse-grained MD. To this end, we employed a Kremer–Grest bead–spring model and observed the expected increase in the mechanical performance of the neat polymer for larger . Our results indicated that the unimodal molar mass distribution does not impact the mechanical behavior in the investigated dispersity range Đ. For the PNC, we obtained the same -dependence and Đ-independence of the mechanical properties over a wide range of filler sizes and contents. This contribution proves that even simple MD models can reproduce the experimentally well researched effect of the molar mass. Hence, this work is an important step in understanding the complex structure–property relations of PNCs, which is essential to unlock their full potential.</p> <p>Contact:</p> <p>Maximilian Ries<br>Institute of Applied Mechanics<br>Friedrich-Alexander-Universität Erlangen-Nürnberg<br>Egerlandstr. 5<br>91058 Erlangen</p> <p>Software:</p> <p>All MD simulations were performed with LAMMPS [2,3], version: 23 Oct 2022 / 20220623</p> <p>Compiled with<br>Compiler: GNU C++ 11.2.0 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:<br>CLASS2 DPD-BASIC EXTRA-DUMP INTEL KSPACE MANYBODY MC MISC MOLECULE MOLFILE MPIIO NETCDF OPT PERI</p> <p>Polymer and polymer composite samples generated with self-avoiding random-walk algorithm [4]</p> <p>Post-processing Matlab R2019b</p> <p>License:</p> <p>Creative Commons Attribution 4.0 International</p> <p>Context:</p> <p>Data set supplementing journal paper:</p> <p>[1] M. Ries, L. Laubert, P. Steinmann, & S. Pfaller, “Impact of the unimodal molar mass distribution on the mechanical behavior of polymer nanocomposites below the glass transition temperature: A generic, coarse-grained molecular dynamics study,” European Journal of Mechanics - A/Solids, vol. 107, p. 105 379, 2024.</p> <p>Content:</p> <p>structure of data set:</p> <p> -01_neat <br> containing the neat polymer simulations<br> -01_uniform<br> containing samples with uniform chain lengths<br> -02_distributed<br> containing samples with distributed chain lengths<br> -100-dist<br> samples with mean molar mass 100<br> -200-dist<br> samples with mean molar mass 200<br> -02_PNC<br> containing the polymer nanocomposite simulations<br> -01_uniform<br> containing samples with uniform chain lengths<br> -T_0.2<br> simulations at temperature 0.2<br> -T_0.3<br> simulations at temperature 0.3<br> -T_0.4<br> simulations at temperature 0.4<br> -02_distributed<br> containing samples with distributed chain lengths<br> -T_0.2<br> simulations at temperature 0.2<br> -T_0.3<br> simulations at temperature 0.3<br> -T_0.4<br> simulations at temperature 0.4<br> </p> <p>naming convention for simulation folders</p> <p> - neat polymer simulations<br> example: GTP_UT_num_chains-80_num_beads_per_chain-500-8<br> * num_chains: number of polymer chains<br> * num_beads_per_chain: molar mass (chain length)<br> * distribution: standard deviation of gauss distribution govering dispersity<br> * "trailing number": batch number of sample<br> <br> - polymer nanocomposite simulations<br> example: GTP_rF-5_nF-10_chainlen-5_7-T_0.2<br> * rF: nanofiller radius<br> * nF: number of nanofillers<br> * chainlen: molar mass (chain length)</p> <p> </p> <p>Each simulation directory contains:</p> <p> lammps input file (*.in) of the specific simulation</p> <p> data file (*.data) containing the initial sample configuration</p> <p> input.prm: input parameters of the specific simulation (read by the input file)</p> <p> meta.info: meta data of the specific simulation run</p> <p> LAMMPS_out:<br> simulation results (lammps thermo_out) in tabulated form, an overview of columns is given below</p> <p> thermo_out.Dat: raw output </p> <p> thermo_out_SG.Dat: smoothed output (Savitzky-Golay filter)</p> <p> thermo_out_STD.Dat: standard deviation of raw output</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> <p> Step: time step </p> <p> Time: time </p> <p> TotEng: total energy </p> <p> PotEng: potential energy</p> <p> KinEng: kinetic energy </p> <p> E_pair: pair energy </p> <p> E_bond: bond energy </p> <p> E_angle: angle energy </p> <p> E_dihed: dihedral energy </p> <p> Temp: temperature</p> <p> Press: hydrostatic pressure</p> <p> Pxx: xx component of pressure tensor </p> <p> Pyy: yy component of pressure tensor </p> <p> Pzz: zz component of pressure tensor </p> <p> Pxy: xy component of pressure tensor</p> <p> Pxz: xz component of pressure tensor</p> <p> Pyz: yz component of pressure tensor</p> <p> Volume: volume of simulation box </p> <p> Lx: box length in x direction </p> <p> Ly: box length in y direction </p> <p> Lz: box length in z direction </p> <p> Density: density </p> <p> c_RG: radius of gyration scalar </p> <p> c_RG[1]: squared radius of gyration tensor (xx component) </p> <p> c_RG[2]: squared radius of gyration tensor (yy component) </p> <p> c_RG[3]: squared radius of gyration tensor (zz component) </p> <p> c_RG[4]: squared radius of gyration tensor (xy component) </p> <p> c_RG[5]: squared radius of gyration tensor (xz component) </p> <p> c_RG[6]: squared radius of gyration tensor (yz component) </p> <p> c_bondave[1]: bond energy averaged over all atoms </p> <p> c_bondave[2]: bond distance averaged over all atoms </p> <p> c_bondave[3]: squared bond distance averaged over all atoms </p> <p> c_angleave[1]: angle energy averaged over all atoms </p> <p> c_angleave[2]: angle averaged over all atoms degree</p> <p> c_angleave[3]: cosine of angle </p> <p> c_angleave[4]: squared cosine of angle </p> <p> c_MSD[1]: mean squared displacement x-direction </p> <p> c_MSD[2]: mean squared displacement y-direction </p> <p> c_MSD[3]: mean squared displacement z-direction </p> <p> c_MSD[4]: total mean squared displacement </p> <p> c_COM[1]: x coordinate of center of mass </p> <p> c_COM[2]: y coordinate of center of mass </p> <p> c_COM[3]: z coordinate of center of mass </p> <p> v_strain_xx: xx component of engineering strain tensor </p> <p> v_strain_yy: yy component of engineering strain tensor </p> <p> v_strain_zz: zz component of engineering strain tensor </p> <p> v_vMisesequivstress: von Mises equivalent stress </p> <p> v_Cauchy_xx: xx component of stress tensor </p> <p> v_Cauchy_yy: yy component of stress tensor</p> <p> v_Cauchy_zz: zz component of stress tensor</p> <p> v_Cauchy_xy: xy component of stress tensor </p> <p> v_Cauchy_xz: xz component of stress tensor </p> <p> v_Cauchy_yz: yz component of stress tensor </p> <p> v_strain_xy: xy component of engineering strain tensor </p> <p> v_strain_xz: xz component of engineering strain tensor </p> <p> v_strain_yz: yz component of engineering strain tensor </p> <p>References:</p> <p>[1] M. Ries, L. Laubert, P. Steinmann, & S. Pfaller, “Impact of the unimodal molar mass distribution on the mechanical behavior of polymer nanocomposites below the glass transition temperature: A generic, coarse-grained molecular dynamics study,” European Journal of Mechanics - A/Solids, vol. 107, p. 105 379, 2024.</p> <p>[2] S. Plimpton, “Fast parallel algorithms for short-range molecular dynamics,” Journal of computational physics, 1995, 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,” Computer Physics Communications, vol. 271, p. 108171, 2022.</p> <p>[4] J. Roksvaag, M.Ries . “A fast self-avoiding random walk algorithm (SARW) for generic thermoplastic polymers and nanocomposites”, manuscript in preparation</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
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