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114 results for “grain size”
Sediment grain size in seagrass restoration plots in the Virginia coastal bays, 2010-2016
This dataset contains particle size distributions for sediment samples collected from Z. marina restoration plots in Hog Island Bay and South Bay, VA. Samples were collected every three years starting in 2010 from 64 sites in Hog Island Bay (58 restored seagrass sites, 6 bare sites), and 12 sites in South Bay (6 restored seagrass sites, 6 bare sites). Bare sites in South Bay were no longer sampled after 2013, due to colonization of the sites by seagrass. Restored sites were seeded between 2001-2008; plot-level particle size distributions were combined based on the age of the plots during each sampling year.
Sediment Grain Size Transects on the coast of Virginia, August 2012
Sediment grain size distributions were determined for five sediment sampling transects in four of the coastal bays in Northampton, Co, VA.
Data from: Effects of grain size and niche breadth on species distribution modeling
Scale is a vital component to consider in ecological research, and spatial resolution or grain size is one of its key facets. Species distribution models (SDMs) are prime examples of ecological research in which grain size is an important component. Despite this, SDMs rarely explicitly examine the effects of varying the grain size of the predictors for species with different niche breadths. To investigate the effect of grain size and niche breadth on SDMs, we simulated four virtual species with different grain sizes/niche breadths using three environmental predictors (elevation, aspect, and percent forest) across two real landscapes of differing heterogeneity in predictor values. We aggregated these predictors to seven different grain sizes and modeled the distribution of each of our simulated species using MaxEnt and GLM techniques at each grain size. We examined model accuracy using the AUC statistic, Pearson's correlations of predicted suitability with the true suitability, and the binary area of presence determined from suitability above the maximum True Skill Statistic (TSS) threshold. Habitat specialists were more accurately modeled than generalist species, and the models constructed at the grain size from which a species was derived generally performed the best. The accuracy of models in the homogenous landscape deteriorated with increasing grain size to a greater degree than models in the heterogenous landscape. Variable effects on the model varied with grain size, with elevation increasing in importance as grain size increased while aspect lost importance. The area of predicted presence was drastically affected by grain size, with larger grain sizes over predicting this value by up to a factor of 14. Our results have implications for species distribution modeling and conservation planning, and we suggest more studies include analysis of grain size as part of their protocol.
Grain size and clay mineralogy data of the Esplugafreda sequence (Spain) with stable carbon, oxygen and clumped isotope data of soil carbonates
<p>This datasets contains 1) grain size distribution data of mudstone paleosols of the Esplugafreda sequence (Esplugafreda and Claret Formations) measured by laser diffraction, and 2) clay mineralogy measured by powder X-ray diffraction, as well as 3) stable carbon, oxygen and clumped (D47) isotope compositions of soil carbonates. The Esplugafreda sequence is found in the Tremp-Graus Basin in the southern forefront of the Pyrenees and consists of continental sediments formed in a coastal alluvial setting during the late Paleocene and early Eocene.</p> <p>In addition, a proxy dataset of late Paleocene and PETM continental temperatures of the northern hemisphere is also included.</p>
Table S1 The grain size, TOC and elemental composition of sediment samples in the northern South China Sea
<p><span>Grain size measurements of the sediments were carried out on a Mastersizer 3000 laser particle size analyzer</span><span>. Prior to analysis organic matter was removed by leaching the sediment with 15% H<sub>2</sub>O<sub>2</sub>. Total organic carbon (TOC) of the sediments was measured in </span><span>Thermo EA-IsoLink elemental analyzer</span><span>. Prior to analysis carbon bound to carbonate minerals was removed by leaching the sediment with 10% HCl. Total element concentrations (Al, Ca, Ti, Fe, Mn) of the bulk sediments were measured after acid digestion (HF, HNO<sub>3</sub> and HCl) by </span><span>Thermo iCAP 7400 ICP-OES</span><span>. Solid phase iron speciation data were measured following sequential Fe extraction (Fe<sub>carb</sub>, Fe<sub>ox</sub>, Fe<sub>mag</sub>, Fe<sub>py</sub>) by T</span><span>hermo iCAP 7400 ICP-OES</span><span>.</span></p>
Data for manuscript: An extrapolation algorithm for estimating river bed grain size distributions across basins
<p>Pebble counts collected and used for the analysis presented in the manuscript: An extrapolation agorithm for estimating river bed grain size distributions across drainage basins.</p>
Grain size distribution of Amazon river sediment samples collected over the period 2005-2008; and ADCP water velocity profiles collected on the major tributaries of the Amazon in Bolivia and Peru, 2007-2008
<p>This dataset contains two items:</p> <p>- The grain size distribution of river sediment samples collected along the Amazon River and its tributaries during four sampling campaigns performed in June 2005 (lower Amazon, Brazil), March 2006 (lower Amazon, Brazil), May 2007 (Upper Madeira, Bolivia), and April 2008 (Upper Solimões-Amazonas, Peru). [spreadsheet "Grain_size_distribution_dataset_Amazon_2005-2008_Bouchez_data.xlsx"].</p> <p>- River water velocity profiles derived from Acoustic Doppler Current Profiler (ADCP) measurements performed on the major tributaries of the Amazon in May 2007 (Upper Madeira, Bolivia) and April 2008 (Upper Solimões-Amazonas, Peru) [folder "ADCP_dataset_Amazon_2007-2008_Bouchez_data"].</p> <p>The dataset description and the relevant references are provided in the text files "Grain_size_distribution_dataset_Amazon_2005-2008_Bouchez_description.docx" and "ADCP_dataset_Amazon_2007-2008_Bouchez_description.docx" .</p> <p>These data were acquired thanks to the support of the French National Service for Observation "HYBAM" ("Hydrogeochemistry of the Amazon Basin"), part of the CNRS National Infrastructure "OZCAR" ("Critical Zone Observatories: Applications and Research").</p>
Grain Size Distribution of slickenlines from Plan de los Platanos and Big Piute Faults
<p>Grain Size Distribution (GSD) of Plan de Platanos (<strong>PP</strong>) and Big Piute (<strong>BP</strong>) Fault Slickenlines</p> <p>Grains were manually segmented from images of increasing magnifications obtained through optical and SEM microscopy. The size of the grains was then estimated from the grains' area by calculating the diameter (<strong>d<sub>equ</sub></strong>) of a circle of equivalent area.</p> <p><strong>Instruments:</strong></p> <ul> <li>ZEISS AX10 Petrographic microscope</li> <li>ZEISS Merlin HR-SEM (BSE)</li> </ul> <p><strong>PP</strong>---> sample <strong>PP_AU01a</strong>_--> Orientation <strong>XZ</strong>---->N=8617----->andesite</p> <p><strong>BP</strong>---> sample <strong>BP_07b</strong>--> Orientation <strong>YZ</strong>---->N=11425------>quartzite</p> <p><strong>Unit of length (1st colum): </strong>micro meters <strong>(µm</strong>)</p> <p>We use Tikoff et al (2019) naming convention for thin section orientation.</p> <p> </p> <p> </p> <p> </p> <p>Tikoff, B., Chatzaras, V., Newman, J., & Roberts, N. M. (2019). Big data in microstructure analysis: Building a universal orientation system for thin sections. <em>Journal of Structural Geology</em>, <em>125</em>, 226–234. https://doi.org/10.1016/j.jsg.2018.09.019</p>
Code and experimental images - Stable and unstable capillary fingering in porous media with a gradient in grain size
<p>Matlab script. Invasion Percolation code in graded matrices and gravity.<br> Pictures: experimental invasion of air at constant flow rate in a 3D-printed graded porous medium wet by glycerol/water.<br> <br> Originally designed for the following article: https://arxiv.org/abs/2203.15524.<br> Accepted in Communications Physics (2022).<br> <br> Can easily be tweaked for other problems</p>
ODP Site 1249, ODP Site 1252, and IODP Site U1325: X-ray fluoresence core scanning, laser diffraction grain size, CNS elemental/isotopic, environmental magnetism, and age model data
<p>We present data used as part of an integrative early diagenesis study (submitted September 2022 to Marine Geology) focused on identifying zones of magnetite dissolution and pyrite precipitation in which magnetic susceptibility records are altered in gas-hydrate bearing sediments on the Cascadia Margin using archived cores from the Ocean Drilling Program (ODP) and Integrated Ocean Drilling Program (IODP). We analyzed the upper 85 to 100 m below seafloor (mbsf) from ODP Sites 1249 and 1252, and IODP Site U1325. ODP 1249 is at the summit of Hydrate Ridge in an area of active methane seepage and massive gas hydrate accumulations and ODP 1252 is in a nearby slope basin with little occurrence of hydrate. IODP Site U1325 is on the northern Cascadia Margin in a slope basin, with turbidite-hosted gas hydrate. We also include XRF data from the upper sections of ODP Site 1251, IODP U1327, and U1328.</p> <p>We measured X-ray fluorescence using an Avaatech core scanner at the IODP Gulf Coast Repository at Texas A&M University. We measured total carbon, total organic carbon (TOC), total nitrogen, and total sulfur using a Perkin Elmer 2400 Series CHNS/O Analyzer at the university of New Hampshire (ODP Site 1249 and 1252 only). A subset was analyzed for δ<sup>13</sup>C-TOC using a Costech ECS 4010 elemental analyzer interfaced with a Thermo Finnegan Delta Plus XP continuous flow isotope ratio mass spectrometer at Washington State University. Grain size was measured with a Malvern Mastersizer 2000 laser diffraction particle size analyzer and Hydro 2000G dispersal unit at the University of New Hampshire. Mass frequency-dependent magnetic susceptibility was measured using a Bartington MS2 Magnetic Susceptibility Meter and Bartington MS2B dual frequency sensor (Site U1325 only). Isothermal remanent magnetization and thermal demagnetization curves were measured using a HSM2 SQUID-based Spinner Magnetometer with an ASC Scientific IM-10-30 Impulse Magnetizer and ASC Scientific TD-48SC magnetically-shielded oven (Site U1325 only). Radiocarbon was measured on mixed planktic foraminifers at the Radiocarbon was measured at National Ocean Sciences Accelerator Mass Spectrometry (NOSAMS) facility at Woods Hole Oceanographic Institution (ODP Site 1252 and IODP Site U1325).. Radiocarbon ages were calibrated to calendar ages using CALIB 8.2 and the Marine20 calibration curve. For ODP Site 1252 we used a reservoir correction of 230 ± 50 years (Yaquina Bay, Oregon, USA) and for IODP Site U1325 we used a reservoir correction of 202 ± 50 years (Amphitrite Point, British Columbia, Canada). δ<sup>18</sup>O was measured on benthic foraminifer <em>Uvigerina peregrina</em> tests using a Finnegan MAT 252 isotope ratio mass spectrometer with Kiel III device at the Oregon State University Stable Isotope Laboratory (ODP 1252) and a Finnegan MAT 253 isotope ratio mass spectrometer with Kiel IV device at the University of Michigan Stable Isotope Laboratory (ODP Site 1249 and IODP Site U1325).</p>
Supporting data tables and Python scripts for the paper: "Multi Grain-Size Total Sediment Load Model Based on the Disequilibrium Length"
<p>This repository contains all the data tables and Python scripts necessary to generate the results presented in Le Minor et al. (2022): "Multi Grain-Size Total Sediment Load Model Based on the Disequilibrium Length".</p>
Dataset on the effects of mineral grain size and seawater salinity on Mg(OH)2 dissolution and CaCO3 precipitation kinetics.
<p>Dataset from the manuscript "Effects of grain size and seawater salinity on magnesium hydroxide dissolution and secondary calcium carbonate precipitation kinetics: implications for ocean alkalinity enhancement" from Moras et al., 2024 (https://doi.org/10.5194/egusphere-2024-645). The dataset compiles all data used in the manuscript. The manuscript covers Mg(OH)2 dissoluton and CaCO3 precipitation kinetics for Ocean Alkalinity Enhancement. These kinetics are reported under different conditions, such as varying grain size and seawater salinity.</p>
Supplementary Dataset for "Grain Size Measurements of the Eolian Stimson Formation, Gale Crater, Mars and Implications for Sand Provenance and Paleoatmospheric Conditions"
<p>Grain size measurements and results in support of "Grain Size Measurements of the Eolian Stimson Formation, Gale Crater, Mars and Implications for Sand Provenance and Paleoatmospheric Conditions".</p> <p>The subfolder "MAHLI images" consists of images taken by the Mars Science Laboratory <em>Curiosity</em> Mars Hand Lens Imager; these images are accesible via the MSL Analyst's Notebook at an.rsl.wustl.edu.</p> <p>The subfolder "ImageJ ROIs" contains .zip files that can be opened with the ImageJ software, available at imagej.nih.gov/ij.</p> <p>The subfolder "GRADISTAT results" contains PDFs with grain size statistics that were created from the data in "Grain size measurements" using the GRADISTAT software, available at https://doi.org/10.1002/esp.261. </p>
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>
Sedimentation, sediment grain size, vegetation composition and vegetation characteristics on a salt marsh in nature reserve the Slufter (Wadden island Texel, the Netherlands)
<p>Data on sedimentation, sediment grain size, vegetation composition and vegetation characteristics of a field experiment on a salt marsh in nature reserve the Slufter (Wadden island Texel, the Netherlands).</p> <p>The data is described in the following publication:<br> Baaij, B.M., Kooijman, J., Limpens, J., Marijnissen, R.J.C., van Loon-Steensma, J.M. Monitoring Impact of Salt-Marsh Vegetation Characteristics on Sedimentation: an Outlook for Nature-Based Flood Protection. Wetlands 41, 76 (2021). https://doi.org/10.1007/s13157-021-01467-w</p> <p>A short description of the aim, study design, measurements and data files is given in README.txt.</p>
Data from: Effects of grain size and niche breadth on species distribution modeling
Open the record for dataset details and reuse information.
Gini index mean score grain size estimates for Murray formation rocks in the Vera Rubin ridge (Gale crater, Mars) from ChemCam LIBS data (sols 1808-2298)
<p>Data in this repository are included as main research results in the Journal of Geophysical Research: Planets manuscripts entitled:</p> <p><em>"</em>Extensive diagenesis revealed by fine-scale features at Vera Rubin ridge, Gale crater, Mars<em>" </em></p> <p>"A lacustrine paleoenvironment recorded at Vera Rubin ridge, Gale crater: Overview of the sedimentology and stratigraphy observed by the Mars Science Laboratory Curiosity rover".</p> <p> </p> <p><strong>Table captions:</strong></p> <p><em>Table 1. </em>Murray formation targets from the Vera Rubin ridge used in the Gini mean index score (GIMS) analysis (sols 1808-2298) with summary information and G<sub>MEAN </sub>values with associated standard deviation errors. The grain size regimes (GSRs) were defined during the calibration procedure in Rivera-Hernández et al. (2020) and are defined as: mud (G<sub>MEAN</sub>=0.00-0.07; GSR1) and coarse silt to very fine sand (G<sub>MEAN</sub>=0.07-0.10; GSR2). Rocks with G<sub>MEAN</sub>=0.07 are exactly at GSR1 and GSR2 boundary and are reported as GSR1/GSR2. Next to the target names, the symbol * denotes that the ChemCam target was imaged by the Mars Hand Lens Imager (MAHLI), the symbol ** denotes that the dust removal tool was used before the MAHLI image was taken, and ~ signifies that a location close to the ChemCam target was imaged by the MAHLI. </p> <p><em>Table 2</em>. The mean, median, minimum and maximum G<sub>MEAN</sub> and the minimum and maximum grain size regime (GSR) for each Murray formation locality in the Vera Rubin ridge.</p> <p> </p> <p>References:</p> <p>Rivera-Hernández, F., Sumner, D. Y., Mangold N., Wiens, R.W., Edgett, K., Fedo, C., Schieber, J., Banham, S.G., Newsom, H., Gupta, S., Heydari, E., Stack, K.M., Nachon, M., Stein, N., & Maurice, S. (2020) Grain Size Variations in the Murray Formation: Stratigraphic Evidence for Changing Depositional Environments in Gale Crater, Mars. <em>Journal of Geophysical Research:</em><em> Planets.</em> doi:10.1029/2019JE006230</p>
Grain-size control on detrital zircon cycloprovenance in the late Paleozoic Paradox and Eagle basins, USA
<p>Detrital zircon U-Pb and grain size data for JGR: Solid Earth: "Grain size control on detrital zircon cycloprovenance in the late Paleozoic Paradox and Eagle basins, USA" by Ryan J. Leary, M. Elliot Smith, and Paul Umhoefer. </p>
Data from: The variation of grain size distribution in rock granular material in seepage process considering the mechanical-hydrological-chemical coupling effect: An experimental research
<p>As a common solid waste in geotechnical engineering, rock granular material should be properly treated and recycled. Rock granular material often coexists with water when it is used as the filling material in geotechnical engineering. Water flowing in rock granular materials is a complex progress with the mechanical-hydrological-chemical (MHC) coupling effect, i. e. the water scours in the gaps and spaces in the rock granular material structure, produces chemical reactions with rock grains, rock grains squeeze each other under the water pressure and compression leading re-breakage and producing secondary rock grains, the fine rock grains are migrated with water and rushed out. In this process, rock grain size distribution (GSD) changes, it affects the physical and mechanical characteristics of the rock granular materials, and even influences the seepage stability of the rock granular materials. To study the variation of GSD in the rock granular material considering the MHC coupling effect after the seepage process, seepage experiments of rock grain samples are carried out and analyzed in this paper. The result is expected to have a positive impact on further studies of the properties of the rock granular material.</p>
Field Observations (2018; 2021), Grain-Size Measurements, and Componentry of the Cleetwood Eruption of Mount Mazama
<p>This dataset accompanies the paper "Using Eruption Source Parameters and High-Resolution Grain-Size Distributions of the 7.7 ka Cleetwood Eruption of Mount Mazama to Reveal Primary and Secondary Eruptive Processes".</p>
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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.