Find research datasets worth reusing

opencc-by-4.0Mar 2022View details →

Constraining Andean Propagation of Exhumation at the Limit of the Eastern Cordillera, NW Argentina, using Low-Temperature Thermochronology in a Structural Context - Supporting Information

Supporting information accompanying the publication "Constraining Andean Propagation at the Limit of the Eastern Cordillera, NW Argentina, using Low-Temperature Thermochronology in a Structural Context" published in Tectonics. The dataset contains apatite and zircon (U-Th-Sm)/He and apatite fission track data from the Tilcara Range and San Lucas block, Jujuy, Argentina, as well as additional QTQt thermal models that are discussed in the paper. Table S1 contains full single-grain results from apatite fission track, apatite (AHe) (U-Th-Sm)/He and zircon (ZHe) (U-Th-Sm)/He analyses. Outliers are marked in grey and are not included in the weighted mean age. Figure S1 supports (U-Th-Sm)/He data graphically. Apatite fission track (AFT) data is supported by radial plots in Figure S2. Figure S3 shows QTQt thermal models using either AHe, AFT or ZHe single-grain ages. All of the models results are explained in the main text.

Supporting information for the paper: The temporal relationship between Terrestrial Gamma-ray flashes and associated optical pulses from lightning

Supporting information for the paper: The temporal relationship between Terrestrial Gamma-ray flashes and associated optical pulses from lightning, consisting of 2 data files and 221 presentations of TGF-Optical emission events observed by ASIM between end of March 2019 and November 2020. See 0_READ_ME for information about the individual files and variables.

Supporting Information for "From Hofmeister to hydrotrope: Effect of anion hydrocarbon chain length on a polymer brush"

This deposition contains the data and analysis (Jupyter notebooks) detailed in “From Hofmeister to hydrotrope: Effect of anion hydrocarbon chain length on a polymer brush”. All Jupyter notebooks have also been converted into PDF files for ease of viewing. All data and code (notebooks) required to reproduce the analysis can be found within the “supporting_data_analysis.zip” archive. This archive contains four sub-directories: <ul> <li>Computional_data <ul> <li>Optimised geometry files (.xyz) for all short chain fatty acid (SCFA) anion-solvent and anion-NIPAM fragment structures.</li> <li>Summary of analysed data (“SAPT_computational_data.xlsx”) describing the different interaction energies between a SCFA anion-NIPAM fragment and anion-solvent: electrostatic, exchange, induction, dispersion and total interaction energy contributions.</li> </ul> </li> <li>Ellipsometry <ul> <li>Data directory containing all raw ellipsometry data.</li> <li>“refellips_Dry.ipynb” and “refellips_Spectroscopic_SL.ipynb” notebooks to reproduce the analysis of a dry (solid-air) and hydrated (solid-liquid) polymer brush, respectively. </li> <li>A spatial map of the polymer brush used for spectroscopic ellipsometry data analysis: “PNIPAM_brush_spatial_map.png”.</li> <li>“Ellipsometry_sigmoid_fitting.ipynb” notebook and “Water_data.csv” file for the demonstration of the extraction of a thermotransition temperature from an ellipsometry dataset. Relevant plotting tools can be found in the <a href="https://github.com/refnx/refellips">refellips repo</a>.</li> </ul> </li> <li>Neutron_reflectometry <ul> <li>Data directory containing all relevant reduced reflectivity profiles from the Platypus reflectometry at ANSTO.</li> <li>“refnx_dry.ipynb”, “refnx_D2O.ipynb” and “refnx_SCFA_electrolytes.ipynb notebooks required to reproduce the analysis pertaining to a dry, polymer brush and a brush exposed to pure D2O and various SCFA electrolytes, respectively.</li> <li>Additional code required to model the hydrated polymer brush and various plotting tools can be in the <a href="https://github.com/igresh/refnxtoolbox">refnxtoolbox repo</a>.</li> </ul> </li> <li>QCMD_data <ul> <li>Data directory containing all processed QCM-D data (.csv)</li> </ul> </li> </ul>

High level information - Neurophysiological data

The dataset includes the the neurometric data derived from the neurophysiological data that was collected during the experimental phases of the WorkingAge project. In particular, Electroculographic (EOG), Photoplethysmographic (PPG) and Electro Dermal Activity (EDA) features are included in the Excel files. Furthermore, the high level information about the mental states, such as the mental workload, the stress and the emotional state, related to the workers involved in the experimental protocols is included.

opencc-by-3.0-usAug 2022View details →

Tagging and tracking information for radiotagged Chinook Salmon in the Copper River, Alaska 2021

The first worksheet (2021 Raw Data) consists of each radiotagged fish and its relevant information including date of capture, the frequency and code of the transmitter, length (MEF) and age. Subsequent columns are Julian dates when they passed fixed tracking stations. The final 4 columns are fate columns. The last column is a general description of the general fate of each fish.   The final worksheet (2021 summary) summarizes fates of all fish by tagging date. This is the primary input file for the Program R which has a code written to do the data analyses for this study.

opencc-by-4.0May 2022View details →

Data to accompany the publication "Combined biophysical and genetic modelling approaches reveal complementary information about population connectivity of New Zealand green-lipped mussels"

Data to accompany the publication "Combined biophysical and genetic modelling approaches reveal complementary information about population connectivity of New Zealand green-lipped mussels".  migrationmatrix14.txt contains the particle tracking matrix, with the total number of particles that migrated from row i to column j (out of a total of 2217864 particles released per population). mussel_microsat_Genepop.txt contains the microsatellite data for each population in Genepop format.

Environmental morphing enables informed dispersal of the dandelion diaspore

This repository contains source data for the following preprint:  Seale M, Zhdanov O, Soons MB, Cummins C, Kroll E, Blatt MR, Zare-Behtash H, Busse A, Mastropaolo E, Bullock JM, Viola IM, Nakayama N (2022) Environmental morphing enables informed dispersal of the dandelion diaspore, bioRxiv, https://doi.org/10.1101/542696

Pre- and post-intervention responses to a knowledge, attitudes, and practices survey for the study, "Disseminating vaccination information in baby soap products increases knowledge and vaccine uptake in central Uganda: A non-randomized controlled trial"

This dataset contains responses to the pre- and post-intervention knowledge, attitudes, and practices surveys utilized for the study, "Disseminating vaccination information in baby soap products increases knowledge and vaccine uptake in central Uganda: A non-randomized controlled trial."

Supplementary Information of "Introgression between highly divergent sea squirt genomes: an adaptive breakthrough?"

Supplementary Figures Figure S1 Population genetic statistics calculated in non-overlapping 10 Kb windows along the 14 chromosomes in the sea squirt genome. Figure S2 C. robusta introgression into C. intestinalis shown across the 14 chromosomes. Figure S3 Population genetic statistics of the C. robusta introgressed coding sequences. Figure S4 ABBA-BABA introgression patterns using C. edwardsi as an outgroup. Figure S5 Inference of the divergence history between C. robusta and C. intestinalis with moments. Figure S6 Selection tests. Figure S7 C. robusta ancestry along chromosome 5 in C. intestinalis individuals. Figure S8 Neighbor-joining trees of 50 Kb windows framing the “missing data region” (grey band) at the center of the chromosome 5 hotspot. Figure S9 Copy number variation at candidate SNPs in the introgression hotspot on chromosome 5 (700 Kb - 1.5 Mb). Figure S10 Structural analysis of the “missing data region” on chromosome 5 (from 1,009,000 to 1,055,000 bp).   Supplementary Tables Table S1 Sample information. Table S2 Correlation between chromosomes of the individual C. robusta ancestry fraction. Table S3 Demographic results with moments – excluding chromosome 5. Table S4 Demographic results with moments – including chromosome 5. Table S5 Description of the Supplementary Data.   Supplementary Scripts Bioinformatic pipeline used for genotyping and haplotyping. Script #1: prepare the reference genome for BWA and GATK. reference_bwa_GATK_CF.sh Script #2: mapping the reads to the reference with BWA. mapping_bwa-mem_CF.sh Script #3: indel realignment with GATK. indel_realignment_CF.sh Script #4: individual variant calling in gVCF format with GATK. snpindel_callingGVCF_raw_CF.sh Script #5: joint genotyping with GATK. joint_genotyping_raw_CF.sh Script #6: genotype refinement with GATK. genotype_refinement_raw_CF.sh Script #7: SNPs and indels recalibration with GATK. snpindel_recalibration_CF.sh Script #8: genotype refinement after recalibration with GATK. genotype_refinement_recal_CF.sh Script #9: genotype correction. phase_by_transmission_correctCalling_CF@2020.sh Script #10: phasing with GATK and BEAGLE. phase_by_transmission_clean_CF@2020.sh Pipeline used for the demographic inferences with moments. Script #11: define the demographic models. moments_models_2pop_bb_parallel_folded_2periods.py Script #12: run the demographic inferences. moments_inference_dualanneal_bb_parallel_folded_2periods_bounds.py

opencc-zeroAug 2022View details →

dryad40/100

Genomics‐informed delineation of conservation units in a desert amphibian

Delineating conservation units (CUs, e.g., evolutionarily significant units, ESUs, and management units, MUs) is critical to the recovery of declining species because CUs inform both listing status and management actions. Genomic data have strengths and limitations in informing CU delineation and related management questions in natural systems. We illustrate the value of using genomic data in combination with landscape, dispersal, and occupancy data, to inform CU delineation in Nevada populations of the Great Basin Distinct Population Segment of the Columbia spotted frog (Rana luteiventris). R. luteiventris occupies naturally fragmented aquatic habitats in this xeric region, but beaver removal, climate change, and other factors have put many of these populations at high risk of extirpation without management intervention. We addressed three objectives: (1) assessing support for ESUs within Nevada; (2) evaluating and revising, if warranted, the current delineation of MUs; and (3) evaluating genetic diversity, effective population size, adaptive differentiation, and functional connectivity to inform ongoing management actions. We found little support for ESUs within Nevada but did identify potential revisions to MUs based on unique landscape drivers of connectivity that distinguish these desert populations from those in the northern portion of the species range. Effective sizes were uniformly small, with low genetic diversity and weak signatures of adaptive differentiation. Our findings suggest that management actions, including translocations and genetic rescue, might be warranted. Our study illustrates how a carefully planned genetic study, designed to address priority management goals that include CU delineation, can provide multiple insights to inform conservation action.

dryad40/100

Extracting abundance information from DNA-based data

The accurate extraction of species-abundance information from DNA-based data (metabarcoding, metagenomics) could contribute usefully to the reconstruction of diets and quantitative foodwebs, the inference of species interactions, the modelling of population dynamics and species distributions, the biomonitoring of environmental state and change, and the inference of false positives and negatives. However, capture bias, capture noise, species pipeline biases, and pipeline noise all combine to inject error into DNA-based datasets. This review focuses on methods for correcting the latter two error sources, as the first two are addressed extensively in the ecological survey literature. To extract abundance information from DNA-based data, it is useful to distinguish two concepts. (1) Across-species quantification describes relative species abundances within a single sample. (2) In contrast, within-species quantification describes how the abundance of each individual species varies across samples, where the samples could be a time series, an environmental gradient, or different experimental treatments. In the first part of this paper, we review methods to remove species pipeline biases and pipeline noise. In the second part, we provide a detailed protocol and demonstrate experimentally how to use a 'DNA spike-in' (an internal standard) to remove pipeline noise and recover within-species abundance information.

opencc-zeroSep 2022View details →

Data archive for the peer-reviewed journal article "Information content and aerosol property retrieval potential for different types of in situ polar nephelometer data"

Data archive accompanying the peer-reviewed journal article "Information content and aerosol property retrieval potential for different types of in situ polar nephelometer data". This article was accepted for publication in the journal Atmospheric Measurement Techniques in 2022. The original contributions presented in the study are included in the article and its supplementary information. The GRASP-OPEN model was used to perform forward calculations: this model is publicly available on the official GRASP website (https://www.grasp-open.com/; last access: 14 September, 2022). The specific GRASP-OPEN model outputs that were used for the study are contained in this data archive.

Supplementary information for yqiC and global transcriptome in Salmonella

Supplementary information (Additional files 1-22, including 3 files, Table S1-S9, Fig. S1-S10) in the article entitled "Effects of colonization-associated gene yqiC on global transcriptome, cellular respiration, and oxidative stress in Salmonella Typhimurium"

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>

Soil information on a regional scale: Two machine learning based approaches for predicting saturated hydraulic conductivity

Version 1.0 - This version is the final revised one. This is the dataset accompanying the paper: Zeitfogel et al., Soil information on a regional scale: Two machine learning based approaches for predicting saturated hydraulic conductivity, published at Geoderma, 2023 (https://doi.org/10.1016/j.geoderma.2023.116418). Soil property and Ksat maps for Austria. The digital soil maps were generated based on a  Machine Learning and PTF-based approach (indirect approach) and a pure Machine Learning based approach (direct approach). By downloading the datasets, you agree that we nor the provider of the used source datasets cannot be liable for the data provided. This study was funded by the Austrian Federal Ministry of Agriculture, Regions and Tourism (Project InfCapAT), the Austrian Academy of Science (Project RechAUT) and the Austrian Science Fund project P 31213.

opencc-by-4.0Jul 2022View details →

Supplementary information for the paper about foreign authors in russian journals (2000-2021, ca 600 journals)

This is a supplementary dataset for the article about foreign authors in Russian Scopus-indexed journals (in Russian). It contains publication and citation data for countries, journals, country clusters, subject areas and topics Based on the set of ca.90000 papers with strictly foreign affiliations (without Russian affiliations) in ca 600 journals for 2000-2021

opencc-by-4.0Nov 2017View details →

Numerosity estimation benefits from transsaccadic information integration

Dataset from the following publication: Hübner, C., & Schütz, A. C. (2017). Numerosity estimation benefits from transsaccadic information integration. Journal of Vision, 17(13):12, 1–16, doi:10.1167/17.13.12.

opencc-by-4.0Oct 2010View details →

Figure 3. Combination of Neural and Symbolic Information Processing Strategies

The second model developed is a model for human-like machine perception based on research findings in neuroscience and neuro-psychology. The principal idea of the model is to use so-called neuro-symbols as basic processing units. This concept is inspired by the fact that the brain is made up of neurons but we think in term of symbols. In analogy to the brain, starting from sensor values, the sensory information is combined and condensed in a modular hierarchical manner to more and more complex neuro-symbolic information until this results in a complete, unitary, multimodal perception of the environment (see figure 3).

opencc-by-4.0Nov 2011View details →

Figure 3. Activated change-Definition of Information

A typical example of this kind is an electric light activated by an electrical button or lever when someone (something) switches it on and the electrical bulb lights up. The sequence of actions includes following four activities: • external force presses the button; • the button closes the electrical circuit thereby unlocking the energy source; • electricity flows to the bulb; • bulb lights up. The general graphical schema of this change is as follows (Figure 3).