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57 results for “mass distribution”
X-rays across the galaxy population: The distribution of AGN accretion rates as a function of stellar mass and redshift
<p>We provide measurements of the probability distribution function of specific black hole accretion rates within a sample of galaxies of a given stellar mass and redshift, <span class="math-tex">\(p(\log \lambda_{sBHAR} | M_*,z)\)</span>. Measurements are provided for all galaxies, star-forming galaxies and quiescent galaxies. We also provide estimates of the AGN duty cycle, <span class="math-tex">\(f(\lambda_{sBHAR} >0.01)\)</span> i.e. the fraction of galaxies with an AGN above a given limit in specific accretion rate, based on the probability distribution functions. Full details are provided in Aird et al. (2018, MNRAS, 474, 1225); please cite this publication if you use these measurements. </p>
Inferring cosmology from gravitational waves using non-parametric detector-frame mass distribution: Data Release
<p>Dataset release accompanying Inferring cosmology from gravitational waves using non-parametric detector-frame mass distribution.</p>
Figure 1 in Osteoderm distribution has low impact on the centre of mass of stegosaurs
Figure 1. The CAD models of Kentrosaurus (top) and Stegosaurus (bottom). Kentrosaurus: main part depicts osteoderms (red) in distribution as reconstructed by Janensch (1925) ("starting configuration" model); scale bar = 1 m. Smaller versions below the tail depict other arrangements with more anterior COM: "hip spike to shoulder" (gold), "all trunk to neck" (green), "all to neck" (light blue), and with more posterior COM: "all tail to tip" (lilac). Colours correspond to dots marking COM positions of all model variants; black dot corresponds to animal without osteoderms. Stegosaurus: red osteoderms are known from fossil "Sarah"; pink plate is reconstructed. Dots marking COM positions: black – without osteoderms, red – with osteoderms, dark blue – with osteoderms and additional shoulder spike. Enlarged inset scale bars for both models = 10 cm.
Characterization of heteroatom distributions in the polar fraction of North Sea oils using high-resolution mass spectrometry
<p>Supplementary data for <a href="https://doi.org/10.1016/j.petrol.2019.106563">10.1016/j.petrol.2019.106563</a></p> <p>Mass spectra were measured on a Q Exactive HF at 240k@200 m/z resolution in nanospray-ESI direct infusion using a Advion TriVersa NanoMate source. Broadband mass spectra were generated from SIM-segments using dimspy (https://github.com/computational-metabolomics/dimspy). Peaks were annotated using Formularity v.1.0.0 (10.1021/acs.analchem.7b03318) after internal calibration using a homologous CHN series (identified from preliminary KMD/KM plots). All plots were generated using python 3.6.6 and the plotly graphing library (https://plot.ly/python/).</p> <p> </p>
Figure. Distribution of body mass of 22 edible dormouse juveniles at the last weighing before hibernation. in Changes in body mass of postweaning juveniles of the edible dormouse, Glis glis (L.), in captivity
Figure. Distribution of body mass of 22 edible dormouse juveniles at the last weighing before hibernation.
COSIPY distributed simulations of Mera Glacier mass and energy balance (20161101-20201101)
<p>The four netCDF files contain outputs from COSIPY model (Sauter et al., 2020) for Mera Glacier for the period 20161101 to 20201101. The model is run on a 0.003°*0.003° grid, and forced with meteological variables collected locally and distributed with constant gradients. The "constants.py" is the python file that contains the specific model settings.</p>
Strength-mass scaling law governs mass distribution inside honey bee swarms
<p>To survive during colony reproduction, bees create dense clusters of thousands of suspended individuals. How does this swarm, which is orders of magnitude larger than the size of an individual, maintain mechanical stability? We hypothesize that the internal structure in the bulk of the swarm, about which there is little prior information, plays a key role in mechanical stability. Here, we provide the first-ever 3D reconstructions of the positions of the bees in the bulk of the swarm using x-ray computed tomography. We find that the mass of bees in a layer decreases with distance from the attachment surface. By quantifying the distribution of bees within swarms varying in size (made up of 4000–10,000 bees), we find that the same power law governs the smallest and largest swarms, with the weight supported by each layer scaling with the mass of each layer to the ≈1.5 power. This arrangement ensures that each layer exerts the same fraction of its total strength, and on average a bee supports a lower weight than its maximum grip strength. This illustrates the extension of the scaling law relating weight to strength of single organisms to the weight distribution within a superorganism made up of thousands of individuals.</p>
Data from: Heterogeneous distribution of kinesin-streptavidin complexes revealed by mass photometry
<p>Kinesin-streptavidin complexes are widely used in microtubule-based active-matter studies. The stoichiometry of the complexes is empirically tuned but experimentally challenging to determine. Here, mass photometry measurements reveal heterogenous distributions of kinesin-streptavidin complexes. Our binding model indicates that heterogeneity arises from both the kinesin-streptavidin mixing ratio and the kinesin-biotinylation efficiency.</p>
Evolution models of helium white dwarf-main-sequence star merger remnants: the mass distribution of single low-mass white dwarfs
<p>Inlists and data for "<a href="https://ui.adsabs.harvard.edu/#abs/2018MNRAS.474..427Z/abstract">Evolution models of helium white dwarf-main-sequence star merger remnants: the mass distribution of single low-mass white dwarfs</a>"</p>
A set of synthetic spectral energy distributions for "diskless", intermediate-mass young stars
<p>These models were published in conjunction with <em>The Duration of Star Formation in Galactic Giant Molecular Clouds. I. The Great Nebula in Carina, </em>by <a href="https://arxiv.org/abs/1906.01730">Povich et al. (2019)</a>. They will also be used in subsequent papers in that series.</p> <p>The format of these models conforms with the standards of <a href="https://doi.org/10.1051/0004-6361/201425486">Robitaille (2017)</a>, so they are compatible with the <a href="http://sedfitter.readthedocs.io/en/stable/installation.html">python implementation</a> of the <a href="https://doi.org/10.1086/512039">Robitaille et al. (2007)</a> SED fitting tool. We have pre-convolved these models with a number of useful filters, including Johnson/Bessel <em>UB</em><em>VRI, </em>UKIRT <em>ZYJHK, </em>VISTA <em>ZYJHK<sub>S</sub></em>, 2MASS <em>JHK<sub>S</sub></em>, <em>Spitzer/</em>IRAC and MIPS. </p> <p>A software pipeline implementing these models to constrain the age and mass distributions of young stellar populations is also <a href="https://doi.org/10.5281/zenodo.3234101">publicly available</a>.</p> <p><strong>Limitations of these models</strong></p> <p><em>These models produce the best results for stars more massive than the Sun and older than about 0.5 Myr. </em>They employ the pre-main-sequence evolutionary tracks of <a href="https://arxiv.org/abs/astro-ph/0003477">Siess et al. (2000)</a> and <a href="https://ui.adsabs.harvard.edu/abs/1996A&A...307..829B/abstract">Bernasconi & Maeder (1996)</a>. Numerous modern tracks offer significant improvement in the treatment of subsolar-mass stars. In addition, the Kurucz stellar atmospheres used in these synthetic SEDs work best for T<sub>eff </sub>> 4,000 K; for cooler temperatures other models, for example the PHOENIX photospheres, may be more appropriate.</p> <p>Newer evolutionary tracks covering the intermediate-mass range are now available, for example the Geneva pre-MS tracks of <a href="https://doi.org/10.1051/0004-6361/201935051">Haemmerlé et al. (2019)</a>. The principal innovation of these modern models is the treatment of accretion and location of the intermediate-mass stellar birthline. The coolest, most luminous models in this set are likely <em>unphysical</em>, representing fully-convective stars of >2 solar masses and <0.5 Myr isochronal age.</p>
Dataset related to article "Quantitative determination of niraparib and olaparib tumor distribution by mass spectrometry imaging"
<p><em>The .zip file contains raw data related to the article "Quantitative determination of niraparib and olaparib tumor distribution by mass spectrometry imaging", available from <a href="https://www.ijbs.com/v16p1363.htm">https://www.ijbs.com/v16p1363.htm</a>.</em></p> <ul> <li><em>The folder "<strong>fig 1 2 3 NIRA</strong>" contains raw data related to the experiments with niraparib presented in figures 1, 2 and 3 </em></li> <li><em>The folder "<strong>fig 1 2 3 OLA</strong>" contains raw data related to the experiments with olaparib presented in figures 1, 2 and 3 </em></li> <li><em>The folders "<strong>fig 4</strong>" and "<strong>fig 6</strong>" contain raw data related to figures 4 and 6, respectively.</em></li> </ul> <p><strong>For any additional information on how to read and reuse the dataset please contact Dr. Ubezio at paolo.ubezio@marionegri.it.</strong></p> <p> </p> <p><strong>ABSTRACT OF THE MANUSCRIPT:</strong></p> <p><strong>Rationale</strong>: Optimal intratumor distribution of an anticancer drug is fundamental to reach an active concentration in neoplastic cells, ensuring the therapeutic effect. Determination of drug concentration in tumor homogenates by LC-MS/MS gives important information about this issue but the spatial information gets lost. Targeted mass spectrometry imaging (MSI) has great potential to visualize drug distribution in the different areas of tumor sections, with good spatial resolution and superior specificity. MSI is rapidly evolving as a quantitative technique to measure the absolute drug concentration in each single pixel.</p> <p><strong>Methods</strong>: Different inorganic nanoparticles were tested as matrices to visualize the PARP inhibitors (PARPi) niraparib and olaparib. Normalization by deuterated internal standard and a custom preprocessing pipeline were applied to achieve a reliable single pixel quantification of the two drugs in human ovarian tumors from treated mice.</p> <p><strong>Results</strong>: A quantitative method to visualize niraparib and olaparib in tumor tissue of treated mice was set up and validated regarding precision, accuracy, linearity, repeatability and limit of detection. The different tumor penetration of the two drugs was visualized by MSI and confirmed by LC-MS/MS, indicating the homogeneous distribution and higher tumor exposure reached by niraparib compared to olaparib. On the other hand, niraparib distribution was heterogeneous in an ovarian tumor model overexpressing the multidrug resistance protein P-gp, a possible cause of resistance to PARPi.</p> <p><strong>Conclusions</strong>: The current work highlights for the first time quantitative distribution of PAPRi in tumor tissue. The different tumor distribution of niraparib and olaparib could have important clinical implications. These data confirm the validity of MSI for spatial quantitative measurement of drug distribution providing fundamental information for pharmacokinetic studies, drug discovery and the study of resistance mechanisms.</p>
Data from: Heterogeneous distribution of kinesin-streptavidin complexes revealed by mass photometry
Open the record for dataset details and reuse information.
Strength-mass scaling law governs mass distribution inside honey bee swarms
Open the record for dataset details and reuse information.
Fast and sensitive flow-injection mass spectrometry metabolomics by analyzing sample specific ion distributions
<p>Data generated and analyzed in "Fast and sensitive flow-injection mass spectrometry metabolomics by analyzing sample specific ion distributions"</p> <p>Boris Sarvin<sup>‡1</sup>, Shoval Lagziel<sup>‡2</sup>, Nikita Sarvin<sup>1</sup>, Dzmitry Mukha<sup>1</sup>, Praveen Kumar<sup>1</sup>, Elina Aizenshtein<sup>3</sup>, Tomer Shlomi *<sup>123</sup></p> <p><sup>1</sup> Faculty of Biology, Technion – Israel Institute of Technology, 32000 Haifa, Israel.</p> <p><sup>2</sup> Faculty of Computer Science, Technion – Israel Institute of Technology, 32000 Haifa, Israel.</p> <p><sup>3</sup> Lokey Center for Life Science and Engineering, Technion – Israel Institute of Technology, 32000 Haifa, Israel.</p> <p><sup>‡</sup> BS and SL contributed equally to this work.</p>
The extraterrestrial dust flux: size distribution and mass contribution estimates inferred from the Transantarctic Mountain (TAM) micrometeorite collection
<p>This study explores the long-duration (0.8-2.3Ma), time-averaged micrometeorite flux (mass and size distribution) reaching Earth, as recorded by the Transantarctic Mountain (TAM) micrometeorite collection. We investigate a single sediment trap (TAM65), performing an exhaustive recovery and characterization effort and identifying 1643 micrometeorites (between 100-2000μm). Approximately 7% of particles are unmelted or scoriaceous, of which 75% are fine-grained. Among cosmic spherules, 95.6% are silicate-dominated S-types, and further subdivided into porphyritic (16.9%), barred olivine (19.9%), cryptocrystalline (51.6%) and vitreous (7.5%). Our (rank)-size distribution is fit against a power law with a slope of -3.9 (R<sup>2</sup>=0.98) over the size range 200-700μm. However, the distribution is also bimodal, with peaks centered at ~145μm and ~250μm. Remarkably similar peak positions are observed in the Larkman Nunatak data. These observations suggest that the micrometeorite flux is composed of multiple dust sources with distinct size distributions. In terms of mass, the TAM65 trap contains 1.77g of extraterrestrial dust in 15kg of sediment (<5mm). Upscaling to a global annual estimate gives 1,555 (±753) t/yr – consistent with previous micrometeorite abundance estimates and almost identical to the previous South Pole Water Well flux estimate (~1,600 t/yr) and potentially suggesting minimal variation in the background cosmic dust flux over the Quaternary. The greatest uncertainty in our mass flux calculation is the accumulation window. A minimum age (0.8Ma) is robustly inferred from the presence of Australasian microtektites, while the upper age (~2.3Ma) is loosely constrained based on <sup>10</sup>Be exposure dating of glacial surfaces at Roberts Butte (6km from our sample site).</p>
Data from: Taxonomic composition and body-mass distribution in the terminal Pleistocene mammalian fauna from the Marmes site, southeastern Washington state, U.S.A.
Mean adult body mass of mammal taxa is a fundamental ecological variable. Variability in the distributions of body masses of a mammal fauna suggest variability in habitat structure. Mammal remains from the Marmes archaeological site in southeastern Washington State date between 13,200 and 10,400 b.p., during the Pleistocene–Holocene transition (PHT). Known environmental history prompts the expectations that the Marmes PHT mammal remains should represent greater species richness and a larger array of body-mass sizes than modern faunas in the Marmes locale and in open shrub-steppe habitats, and lower species richness and a smaller array of body-mass sizes than modern faunas in closed forest habitats; species richness and the array of body-mass sizes should be similar to that for a mixed habitat of cool shrub-steppe with scattered conifers. The Marmes PHT cenogram meets these expectations. Body-mass clumps displayed by the Marmes PHT mammal fauna fall between those of closed forests and open shrub-steppe habitats in terms of clump richness and breadth, and in terms of gap width. Marmes PHT body-mass clumps are very similar to those for the mixed habitat. Cenograms and body-mass clumps confirm conclusions drawn 40 years ago that the Marmes PHT habitat was much like that of today but cooler and with more plant biomass and greater structural diversity than today.
Data from: Distributions of mammals in Southeast Asia: the role of the legacy of climate and species body mass
Aim: Current species distributions are shaped by present and past biotic and abiotic factors. Here we assessed whether abiotic factors (habitat availability) in combination with past connectivity and a biotic factor (body mass) can explain the unique distribution pattern of Southeast Asian mammals, which are separated by the enigmatic biogeographic transition zone, the Isthmus of Kra (IoK), for which no strong geophysical barrier exists. Location: Southeast Asia Taxon: Mammals Methods: We projected habitat suitability for 125 mammal species using climate data for the present period and for two historic periods: mid-Holocene (6 kya) and last glacial maximum (LGM 21 kya). Next, we employed a phylogenetic linear model to assess how present species distributions were affected by the suitability of areas in these different periods, habitat connectivity during LGM and species body mass. Results: Our results show that cooler climate during LGM provided suitable habitat south of IoK for species presently distributed north of IoK (in mainland Indochina). However, the potentially suitable habitat for these Indochinese species did not stretch very far southwards onto the exposed Sunda Shelf. Instead, we found that the emerged landmasses connecting Borneo and Sumatra provided suitable habitat for forest dependent Sundaic species. We show that for species whose current distribution ranges are mainly located in Indochina, the area of the distribution range that is located south of IoK is explained by the suitability of habitat in the past and present in combination with the species body mass. Main conclusions: We demonstrate that a strong geophysical barrier may not be necessary for maintaining a biogeographic transition zone for mammals, but that instead a combination of abiotic and biotic factors may suffice.
Bivalve body size distribution through the Late Triassic mass extinction event
<p><span>The synergic relationship between physiology, ecology and evolutionary process makes the body size distribution (BSD) an essential component of the community ecology. Body size is highly susceptible to environmental change, and extreme upheavals, such as during a mass extinction event, could exert drastic changes on a taxon's BSD. It has been hypothesized that the Late Triassic mass extinction event (LTE) was triggered by intense global warming, linked to massive volcanic activity associated with the Central Atlantic Magmatic Province. We test the effects of the LTE on the BSD of fossil bivalve assemblages from three study sites spanning the Triassic/Jurassic boundary in the UK.</span> <span>Our results show that the effects of the LTE were rapid and synchronous across sites, and the BSDs of the bivalves record drastic changes associated with species turnover. No phylogenetic signal of size selectivity was recorded, although semi-infaunal species were apparently most susceptible to change. Each size class had the same likelihood of extinction during the LTE, which resulted in a platykurtic BSD with negative skew. The immediate post-extinction assemblage exhibits a leptokurtic BSD although with negatively skewed, where surviving species and newly appearing small-sized colonizers exhibit body sizes near the modal size. Recovery was relatively rapid (~100kyr), and larger bivalves began to appear during the Pre-Planorbis Zone, despite recurrent dysoxic/anoxic conditions. This study demonstrates how a mass extinction acts across the size spectrum in bivalves and shows how BSDs emerge from evolutionary and ecological processes.</span></p>
The data for Reconstruction of Cosmic Black Hole Growth and Mass Distribution from Quasar Luminosity Functions at z>4: Implications for Faint and Low-mass Populations in JWST
<p>The data for Figure 1 in the AAS article: Reconstruction of Cosmic Black Hole Growth and Mass Distribution from Quasar Luminosity Functions at z>4: Implications for Faint and Low-mass Populations in JWST</p> <p>in each file, the column heads are x: M1450; y_f0: total Phi(Mpc^-3 mag^-1) for f_seed=1.0 y1_f0: unobscured Phi for f_seed=1.0 y_f1: total Phi for f_seed=0.1 y1_f1: unobscured Phi for f_seed=0.1</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
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.