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Fig. 18 in Seasonal dynamics of small-scale fisheries in the Adriatic Sea
Fig. 18: Total value and selling price per unit weight of trammel net landings.
Fig. 16 in Seasonal dynamics of small-scale fisheries in the Adriatic Sea
Fig. 16: Size-frequency distribution of trap landings. ML = mantle length.
Seasonal precipitation distribution determines ecosystem CO₂ and H₂O exchange by regulating spring soil water-salt dynamics in a brackish wetland
<p>The intensification of the global hydrological cycle is anticipated to increase the variability of precipitation patterns. Brackish wetlands respond to changes in precipitation patterns by regulating the absorption and release of CO<sub>2</sub> and H<sub>2</sub>O to maintain the stability of ecosystem functions. However, there is limited understanding of how the inter-seasonal precipitation distribution affects ecosystem CO<sub>2</sub> and H<sub>2</sub>O exchange compared to annual precipitation totals. Here, we conducted four consecutive years of field experiments in a brackish wetland, manipulating the proportion of precipitation across different seasons while maintaining a constant annual precipitation total. We utilized five inter-seasonal precipitation distribution proportions (+73%, +56%, control (CK), -56%, and -73%) to examine the effects of seasonal precipitation distribution (SPD) on ecosystem CO<sub>2</sub> and H<sub>2</sub>O exchange. Our findings revealed that the ecosystem CO<sub>2</sub> and H<sub>2</sub>O fluxes showed a trend of decreasing with the decrease of spring precipitation distribution. Among them, the annual net ecosystem CO<sub>2 </sub>exchange (NEE), evapotranspiration (ET), carbon use efficiency (CUE), and water use efficiency (WUE) were shown to be more sensitive to decrease in spring precipitation distribution and increase in summer and autumn precipitation distribution. This negative asymmetric response pattern suggests that annual ecosystem CO<sub>2</sub> and H<sub>2</sub>O exchange is primarily governed by seasonal precipitation variability, with spring soil water-salt dynamics identified as the key driver. Therefore, this association can be explained by the fact that drought of the early growth stage exacerbates soil salinization and inhibits vegetation colonization and growth, thereby greatly impairing the annual CO<sub>2</sub>-H<sub>2</sub>O exchange capacity of brackish wetlands. Our results emphasized that the spring's extreme precipitation-induced soil water-salt conditions will greatly influence CO<sub>2</sub> and H<sub>2</sub>O exchange in brackish wetlands in the future. These findings are crucial for improving predictions of the carbon sequestration and water-holding capacity of brackish wetlands.</p>
Quantifying Salt Crystallization Impact on Evaporation Dynamics from Porous Surfaces
<p>The file contains cumulative evaporative mass losses during evaporation from sand columns saturated with 10%, 15%, and 20% NaCl solutions (mass basis).</p>
Population dynamics and socio-ecological trajectories explain the emergence of farming in Late Neolithic South-eastern Norway
<p>The repository contains radiocarbon data and code for the paper "Population dynamics and socio-ecological trajectories explain the emergence of farming in Late Neolithic South-eastern Norway", published in the Journal of Neolithic Archaeology.</p> <p><a href="https://www.jna.uni-kiel.de/index.php/jna/article/view/1562">Population Dynamics and Socio-ecological Trajectories Explain the Emergence of Farming in Late Neolithic Southeast Norway | Journal of Neolithic Archaeology</a>.</p> <p><strong>Abstract</strong></p> <p>This paper examines the emergence of farming in Late Neolithic Southeast Norway. A summed probability distribution of radiocarbon dates is used to infer population dynamics and together with a chronological model using dated samples from post-built houses, cultivation layers and cereal grains, it is argued that the introduction of farming was swift and caused by incoming farmers. It is further explored how a low population during the Middle Neolithic period, cause by shifting environmental conditions, was central to the rapid population shift and economic change seen in the Late Neolithic, from ca. 2200 cal BC. </p>
Roles of Upwelling on the Dynamics and Freshwater Transport of a River Plume over the Inner Shelf_original
Open the record for dataset details and reuse information.
Evaluating the impact of filler size and filler content on the stiffness, strength, and toughness of polymer nanocomposites using coarse-grained molecular dynamics: dataset
<div><strong>Abstract:</strong></div> <div>(from [1])</div> <div>Their great versatility makes polymer nanocomposites an important class of engineering materials. In order to gain detailed insights into the nanoscale mechanisms underlying their macroscopic mechanical properties, molecular dynamics (MD) simulations are a valuable tool to complement experimental studies. In this work, we modify the analytical potential functions of an efficient bead-spring model representing a generic polymer nanocomposite to account for the breaking of covalent bonds. We perform uniaxial tensile simulations of double-notched specimens and validate the model using experimental trends for overall stiffness, strength, and toughness. First, we study the effects of sample size, notch geometry, strain rate, temperature, and molar mass for the pure thermoplastic matrix material. Second, we analyze the influence of filler size and filler content on the mechanical behavior of the polymer nanocomposite. With this study, we show that in both the development of new materials and the optimization of established materials, it is possible to gain important preliminary insights into the effects of pertinent material characteristics with a simple MD setup, which can then be further refined by increasing the complexity of the material description and the boundary conditions. </div> <div> </div> <div> </div> <div><strong>Contact:</strong></div> <div>Felix Weber</div> <div>Institute of Applied Mechanics</div> <div>Friedrich-Alexander-Universität Erlangen-Nürnberg</div> <div>Egerlandstr. 5</div> <div>91058 Erlangen</div> <div>Germany</div> <div> </div> <div> </div> <div><strong>Software:</strong></div> <div>All simulations were performed with LAMMPS [2,3] (version 23 June 2022, patch_23Jun2022_update3) </div> <div> </div> <div>Compiler: GNU C++ 11.2.0 with OpenMP not enabled</div> <div>C++ standard: C++11</div> <div> </div> <div>Active compile time flags:</div> <div>-DLAMMPS_GZIP</div> <div>-DLAMMPS_SMALLBIG</div> <div> </div> <div>Installed packages:</div> <div>BPM CLASS2 DPD-BASIC EXTRA-DUMP EXTRA-FIX EXTRA-MOLECULE INTEL KSPACE MANYBODY </div> <div>MC MISC MOLECULE MOLFILE MPIIO NETCDF OPT </div> <div> </div> <div>Moreover, we employ a self-avoiding random walker [4,5] implemented in MATLAB [6] for the initial positioning of the polymer chains and nanoparticles.</div> <div> </div> <div> </div> <div><strong>License:</strong></div> <div>Creative Commons Attribution 4.0 International</div> <div> </div> <div> </div> <div><strong>Context:</strong></div> <div>This dataset contains the results presented in [1] and the necessary data to obtain those.</div> <div> </div> <div> </div> <div><strong>Content:</strong></div> <div>Throughout this data set, LAMMPS lj units are used. The files to reproduce our simulations and their results are structured as follows:</div> <div>- 01_neat: Neat polymer systems</div> <div> - 01_EQU: Equilibration simulations</div> <div> - 02_UT: Uniaxial tensile simulations, including the notch insertion (token "initcrack")</div> <div> - 1.1: Simulations for different sample sizes/numbers of chains (token "chains") at constant molar mass/number of beads per chain</div> <div> - 1.3: Simulations for different widths of the Dirichlet boundary (token "diri")</div> <div> - 2.1: Simulations for different critical bond lengths (token "bondcrit")</div> <div> - 2.2: Simulations for different bond breaking probabilities (token "bondcprob")</div> <div> - 3.1: Simulations for different crack widths (token "crackwidth")</div> <div> - 3.2: Simulations for different crack lengths (token "crackdepth")</div> <div> - 4: Simulations for different strain rates (token "strainrate")</div> <div> - 5: Simulations for different temperatures (token "tem")</div> <div> - 6: Simulations for different molar masses/numbers of beads per chain (token "chain-len")</div> <div>- 02_PNC: Polymer nanocomposite (PNC) systems </div> <div> - 01_EQU: Equilibration simulations</div> <div> - 02_UT: Uniaxial tensile simulations for different filler radii (token "rF") and filler contents/numbers (token "nF"), including the notch insertion (token "initcrack")</div> <div>- parameter_study: Postprocessing of the MD results </div> <div> - parameter_study.xlsx: Overview of the simulations with their respective parameters and statistical analysis of stiffness, strength, and toughness from filtered stress-strain curves (Savitzky-Golay filter applying a linear polynomial and frame length 21)</div> <div> - .csv files of the single sheets of parameter_study.xlsx:</div> <div> - samples.csv: Individual specimens</div> <div> - averages.csv: Statistical analysis of the different samples corresponding to one batch</div> <div> </div> <div>Each simulation directory contains:</div> <div>- LAMMPS input script (*.in) of the simulation</div> <div>- input.prm: Input parameters of the simulation (read by the input script)</div> <div>- LAMMPS data file (*.data, molecular style) of the investigated sample</div> <div>- LAMMPS_out: Resulting LAMMPS data files, log files and simulation results in tabulated form</div> <div> - additional files for the tensile tests: </div> <div> - brokenbonds.dat: Fix print output for fix brokenbondsprint (step time brokenbondsPerStep brokenbondsSum)</div> <div> - stressstrain.dat: Time-averaged data for fix dumpOpt (step v_strain_xx v_OBSstrain_xx v_Piola_xx) with the local strain at the crack tip v_OBSstrain_xx</div> <div> - thermo_out.Dat: Thermodynamic output in condensed tabulated form</div> <div> - thermo_out_SG.Dat: Thermodynamic output in condensed tabulated form, filtered by a Savitzky-Golay filter (linear polynomial, frame length 21)</div> <div> - thermo_out_STD.Dat: Standard deviation between the filtered and unfiltered data</div> <div>- job.out: Simulation log file</div> <div>- meta.info: Meta data of the simulation run</div> <div> </div> <div>Naming convention:</div> <div>- 01_neat: GTPm-[number of chains]_chains-[number of beads per chain]_chain_len-[temperature]_tem-[parameter value]_[parameter]-[sample]</div> <div> - [parameter]: Parameter studied, i.e. diri/bondcrit/bondcprob/crackwidth/crackdepth/strainrate/tem (see above)</div> <div> - [parameter value]: Value of the parameter studied</div> <div> - [sample]: Sample ID</div> <div>- 02_PNC: GTPm_rF-[filler radius]_nF-[number of fillers]_[sample]</div> <div> - [sample]: Sample ID</div> <div> </div> <div>Output quantities (columns of *.Dat files):</div> <div>- Step: time step</div> <div>- Time: time</div> <div>- TotEng: total energy</div> <div>- PotEng: potential energy</div> <div>- KinEng: kinetic energy</div> <div>- E_pair: pair energy</div> <div>- E_bond: bond energy</div> <div>- E_angle: angle energy</div> <div>- E_dihed: dihedral energy</div> <div>- Temp: temperature</div> <div>- Press: hydrostatic pressure</div> <div>- Pxx: xx component of pressure tensor</div> <div>- Pyy: yy component of pressure tensor</div> <div>- Pzz: zz component of pressure tensor</div> <div>- Pxy: xy component of pressure tensor</div> <div>- Pxz: xz component of pressure tensor</div> <div>- Pyz: yz component of pressure tensor</div> <div>- Volume: volume of simulation box</div> <div>- Lx: box length in x direction</div> <div>- Ly: box length in y direction</div> <div>- Lz: box length in z direction</div> <div>- Density: mass density</div> <div>- c_RG: radius of gyration</div> <div>- c_RG[1]: squared radius of gyration tensor (xx component)</div> <div>- c_RG[2]: squared radius of gyration tensor (yy component)</div> <div>- c_RG[3]: squared radius of gyration tensor (zz component)</div> <div>- c_RG[4]: squared radius of gyration tensor (xy component)</div> <div>- c_RG[5]: squared radius of gyration tensor (xz component)</div> <div>- c_RG[6]: squared radius of gyration tensor (yz component)</div> <div>- c_bondave[1]: bond energy averaged over all atoms</div> <div>- c_bondave[2]: bond distance averaged over all atoms</div> <div>- c_bondave[3]: squared bond distance averaged over all atoms</div> <div>- c_angleave[1]: angle energy averaged over all atoms</div> <div>- c_angleave[2]: angle averaged over all atoms degree</div> <div>- c_angleave[3]: cosine of angle</div> <div>- c_angleave[4]: squared cosine of angle</div> <div>- c_MSD[1]: mean squared displacement x-direction</div> <div>- c_MSD[2]: mean squared displacement y-direction</div> <div>- c_MSD[3]: mean squared displacement z-direction</div> <div>- c_MSD[4]: total mean squared displacement</div> <div>- c_COM[1]: x coordinate of center of mass</div> <div>- c_COM[2]: y coordinate of center of mass</div> <div>- c_COM[3]: z coordinate of center of mass</div> <div>- v_strain_xx: xx component of engineering strain tensor </div> <div>- v_strain_yy: yy component of engineering strain tensor </div> <div>- v_strain_zz: zz component of engineering strain tensor </div> <div>- v_vMisesequivstress: von Mises equivalent stress</div> <div>- v_Piola_xx: xx component of the virial stress tensor normalized by the initial volume</div> <div>- v_Piola_yy: yy component of the virial stress tensor normalized by the initial volume</div> <div>- v_Piola_zz: zz component of the virial stress tensor normalized by the initial volume</div> <div>- v_Piola_xy: xy component of the virial stress tensor normalized by the initial volume</div> <div>- v_Piola_xz: xz component of the virial stress tensor normalized by the initial volume</div> <div>- v_Piola_yz: yz component of the virial stress tensor normalized by the initial volume</div> <div>- v_strain_xy: xy component of engineering strain tensor </div> <div>- v_strain_xz: xz component of engineering strain tensor </div> <div>- v_strain_yz: yz component of engineering strain tensor </div> <div> </div> <div> </div> <div><strong>References:</strong></div> <div>[1] F. Weber, V. Dötschel, P. Steinmann, S. Pfaller, M. Ries, "Evaluating the impact of filler size and filler content on the stiffness, strength, and toughness of polymer nanocomposites using coarse-grained molecular dynamics", Engineering Fracture Mechanics, vol. 307, p. 110270, 2024.</div> <div>[2] S. Plimpton, "Fast parallel algorithms for short-range molecular dynamics", Journal of computational physics, vol. 117, no. 1, pp. 1-19, 1995.</div> <div>[3] A. P. Thompson, H. M. Aktulga, R. Berger, D. S. Bolintineanu, W. M. Brown, P. S. Crozier, P. J. in 't Veld, A. Kohlmeyer, S. G. Moore, T. D. Nguyen, R. Shan, M. J. Stevens, J. Tranchida, C. Trott, S. J. Plimpton, "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.</div> <div>[4] V. Dötschel, S. Pfaller, and M. Ries, "Studying the mechanical behavior of a generic thermoplastic by means of a fast coarse-grained molecular dynamics model", Polymers and Polymer Composites, vol. 31, pp. 1–11, 2023.</div> <div>[5] M. Ries, V. Dötschel, J. Seibert, and S. Pfaller, A self-avoiding random walk algorithm (SARW) for generic thermoplastic polymers and nanocomposites, Zenodo, 2022, https://doi.org/10.5281/zenodo.6245699.</div> <div>[6] The MathWorks, Inc., "Matlab. the language of technical computing", https://de.mathworks.com/help/matlab/.</div> <div> </div> <div> </div> <div><strong>Funding:</strong></div> <div>The authors gratefully acknowledge funding by various sources:</div> <div>The overall research was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - 377472739/GRK 2423/2-2023. Sebastian Pfaller is furthermore funded by the DFG projects 396414850 (Individual Research Grant 'Identifikation von Interphaseneigenschaften in Nanokompositen') and 505866713 together with the Agence nationale de la recherché (ANR, French Research Agency) – ANR-22-CE92-0049 (Individuel Research Grant 'BIO ART'). In addition, scientific support and HPC resources have been provided by the Erlangen National High Performance Computing Center (NHR@FAU) of the Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) under the NHR project b136dc. NHR funding is provided by federal and Bavarian state authorities. NHR@FAU hardware is partially funded by the DFG project 440719683.</div>
Seasonal footprints on ecological time series and jumps in dynamic states of protein configurations from a nonlinear forecasting method characterization (DataSet)
<p>Data from the article: <br>"Seasonal footprints on ecological time series and jumps in dynamic states of protein configurations from a nonlinear forecasting method characterization", L. Reyes, K. Campos, G. D. Avendaño, L. González-Paz, A. Vivas, Y. J. Alvarado, and S. Flores.</p> <p>Data to be used with some implementation of the forecasting method of reference:<br>Sugihara G. and May R. M., Nonlinear forecasting as a way of distinguishing chaos from measurement error in time series, <em>Nature</em> <strong>344</strong>, 734–741 (1990).</p>
Fiber-optic seismic sensing of vadose zone soil moisture dynamics data sets
<p>CC_daily.h5: Daily cross-correlation functions for common-offset DAS channels.</p> <p>RCC_dv_v_full.csv: Summary of all the measured dv/v from the ballistic surface waves in daily cross-correlation functions.</p>
Identification and Exploration of Immunity-Related Genes and Natural Products for Alzheimer's Disease Based on Bioinformatics, Molecular Docking and Molecular Dynamics
<p>Supplementary material to the article: Identification and Exploration of Immunity-Related Genes and Natural Products for Alzheimer’s Disease Based on Bioinformatics, Molecular Docking and Molecular Dynamics,These data are available to researchers.</p>
3D dynamic synchrotron images of sandstone rock and trained weights of the P3T-Net
<p>These are the dataset and trained models to demo the ability of P3T-Net, including:</p> <ol> <li>A trained model for the semantic indication module, which uses a U-ResNet architecture: Semantic_Indication_Modules.pt</li> <li>A trained model for the domain transfer module, which uses a CycleGAN-type architecture: Domain_transfer.pt</li> <li>A trained model for misalignment fixing, which uses a GAN-based architecture: Misalignment_Fixing_Module.pt</li> <li>A noisy synchrotron scan of a sandstone image under a core flooding condition.</li> </ol> <p>These are the pretrained weight that can be directly applied to transfer the dynamic synchrotron sandstone image to a clean version. The source code of P3T-Net can be found: https://github.com/KunningTang1/P3T-Net-for-3D-large-image-transfer.git</p>
A Black-Box Physics-Informed Estimator based on Gaussian Process Regression for Robot Inverse Dynamics Identification
<p><strong>Introduction</strong></p> <p>The Robot Inverse Dynamics Dataset is a collection of trajectories and joint torque measurements of two robotic manipulators, a 7 DoF Franka Emika Panda, and a 6 DOF MELFA RV4FL. Additionally, the dataset contains the inverse dynamical models and other useful quantities learned to reproduce the results reported on our reference paper "A Black-Box Physics-Informed Estimator based on Gaussian Process Regression for Robot Inverse Dynamics Identification". The proposed model relies on a novel multidimensional kernel, called Lagrangian Inspired Polynomial (LIP) kernel.</p> <p><strong>At a Glance</strong></p> <ul> <li>The size of the unzipped dataset is ~700MB.</li> <li>The dataset contains</li> <ul> <li>collections of joint trajectories and joint torque measurements of two robot manipulators: a 7 DoF Franka Emika Panda, and a 6 DOF MELFA RV4FL.</li> <li>models of the inverse dynamics of the two manipulators learned on the datasets</li> </ul> <li>The main directories are</li> <ul> <li>Simulated_PANDA/ contains the trajectories, models and results obtained on different configurations of a Franka Emika PANDA robot, simulated in sympybotics.</li> <li>Robots/ contains the data, models and results obtained on two real robots, a Franka Emika PANDA and a Mitsubishi Electric MELFA RV4FRL</li> </ul> <li>See the README.md file for a detailed description of the directories.</li> </ul> <p><strong>Other Resources</strong></p> <p>Python code to train the models and reproduce the results in the paper are available <a href="https://github.com/merlresearch/LIP4RobotInverseDynamics">here</a>.</p> <p><strong>Citation</strong></p> <p>If you use the Robot Inverse Dynamics dataset in your research, please cite our contribution:</p> <pre><code>@InProceedings{ title={A Black-Box Physics-Informed Estimator based on Gaussian Process Regression for Robot Inverse Dynamics Identification}, author={Giacomuzzo, G., Dalla Libera, A., Romeres, D.,}, booktitle={IEEE Transaction on Robotics}, year={2024} } </code></pre> <p><strong>License</strong></p> <p>The Robot Inverse Dynamics dataset is released under <a href="https://creativecommons.org/licenses/by-sa/4.0/">CC-BY-SA-4.0 license</a>.</p> <p>All data:</p> <pre><code>Created by Mitsubishi Electric Research Laboratories (MERL), 2024 SPDX-License-Identifier: CC-BY-SA-4.0</code></pre>
Initial model and Molecular Dynamics trajectory of WGR domain of PARP2 with ZN ions
<div>Files:</div> <div>1. Initial model of PARP2 WGR domain (based on PDB entry: 6F5B) with two sites potentially capable of binding zinc ions: one formed by residues E97, C98, H160 and another - by residues H106, C109, E138</div> <div>2. PARP2 WGR domain with Zn ions bound after energy minimization.</div> <div>3. 400 ns MD trajectory of PARP2 WGR domain with Zn ions.</div>
Fig.1 in Dynamics And Factors Influencing Zooplankton In The Lakes Svente, Riča, Dridzis And Geraņimovas-Ilzas (Eastern Latvia)
Fig.1. Location of the studied lakes.
Fig. 2 in Formation Dynamics Of Herpetocomplexes On Sections Of Secondary Succession In Terrestrial Ecosystems Of Belarus
Fig. 2. Dynamics of number the herpetoсomplexes on the plots of secondary succession.
Heatwave ontogenetic timing impacts on potato crop-pest dynamics
<p>Heat waves – brief periods of unusually high temperatures – are damaging to agroecosystems and are increasing in frequency and intensity due to climate change. Despite growing appreciation for the threat that heat waves pose pose to agricultural sustainability, we have a poor understanding of what determines their impact on agroecological interactions in the field. Here we report a field experiment that examined how heat waves and their timing interact with crop pest resistance to influence the interactions between potato (<em>Solanum tuberosum</em>) and its most damaging pest, the Colorado potato beetle (CPB; <em>Leptinotarsa decemlineata</em>). We used open-top chambers and ceramic heaters to generate heat wave conditions in field plots with pest-resistant (Atlantic) and -susceptible (King Harry) potato varieties at four CPB developmental stages (neonate, late-larval, pupa, and adult stages). We then assessed CPB performance at larval and adult stages, leaf herbivory, and tuber yield.</p>
Fig. 1 in Number And Dynamics Of Zooplankton Taxa In The Daugava River And Pļaviņas Reservoir
Fig. 1. Localities of sampling sities in 2014 and 2015.
Fig.6 in The Experimental Data On Sun-Basking Activity Of European Pond Turtle Emys Orbicularis In Natural Climate In Latvia: Dynamics And Correlation With The Meteorological Factors
Fig.6. Daily dynamics of sun-basking activity of Emys orbicularis.
Fig.1 in The Experimental Data On Sun-Basking Activity Of European Pond Turtle Emys Orbicularis In Natural Climate In Latvia: Dynamics And Correlation With The Meteorological Factors
Fig.1. The schema of the experimental out-door terrarium.
FIGURE A6-1 in Morphospace dynamics and intraspecies variety of Sorex araneus and S. tundrensis according to recent and fossil data
FIGURE A6-1. Consensus tree based on the seven concatenated genes data set.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
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.