Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
50
datasets available to search
ShareScore release 0.9.0
Dataset results
50 results for “mass dynamics”
Inlists for "An Alternative Channel to Black Hole Low-Mass X-ray Binaries: Dynamical Friction of Dark Matter? "
<p>In this work, we employ MESA code to diagnose whether the dynamical friction between dark matter and the companion stars can drive BH binaries to evolve toward the observed BH LMXBs and alleviate the effective temperature problem. Assuming that there exists a density spike of dark matter around BH, the dynamical friction can produce an efficient angular momentum loss, driving BH binaries with an intermediate-mass companion star to evolve into BH LMXBs for a spike index higher than $\gamma = 1.58$.</p> <p>MESA version: r12115; SDK compiler: mesasdk-x86_64-linux-20190830</p> <p> </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>
Datasets for the submitted manuscript entitled as "Thermodynamic and Dynamic Changes in Japan Sea Polar Air Mass Convergence Zone and its Associated Heavy Snowfall Under Warmer Climate"
<p>These are datasets for the submitted manuscript entitled as "Thermodynamic and Dynamic Changes in Japan Sea Polar Air Mass Convergence Zone and its Associated Heavy Snowfall Under Warmer Climate". The thirteen cases of WRF domain 2 simulation in historical and PGW experiments were uploaded, respectively. </p>
Precise dynamical masses of new directly imaged companions from combining relative astrometry, radial velocities, and HIPPARCOS-Gaia eDR3 accelerations
<p>The VLT/SPHERE reduced images using the Geneva reduction pipeline, GRAPHIC, used to obtain the astrometry and photometry as published in Rickman et al. 2022. Each .fits file has been cosmetically corrected (i.e. bad pixels), background-subtracted, and flat-fielded. These files correspond to the 'flux frames' of the imaging observing sequence that was used to calculate the astrometry and photometry for each companion. The raw data are also available on the ESO archive with the relevant program numbers as listed in Rickman et al. 2022.</p>
Laser Ablation Inductively Coupled Plasma Mass Spectrometric Quantification of Isotope Trace Elements in Human Carcinoma Tissue - Stochastic Dynamics and Theoretical Analysis (SUPPORTING INFORMATION)
<p>Supporting information file of publication [https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4334866]. </p>
Data from: Mass turnover and recovery dynamics of a diverse Australian continental radiation
Open the record for dataset details and reuse information.
Coping with seasonality: dynamics of adult body mass and survival in an alpine hibernator
Open the record for dataset details and reuse information.
Data from: Microbe biogeography tracks water masses in a dynamic oceanic frontal system
Open the record for dataset details and reuse information.
Data from: Integrating viscoelastic mass spring dampers into position-based dynamics to simulate soft tissue deformation in real time
We propose a novel method to simulate soft tissue deformation for virtual surgery applications. The method considers the mechanical properties of soft tissue, such as its viscoelasticity, nonlinearity, and incompressibility; its speed, stability, and accuracy also meet the requirements for a surgery simulator. Modifying the traditional equation for mass spring dampers introduces nonlinearity and viscoelasticity into the calculation of elastic force. Then, the elastic force is used in the constraint projection step for naturally reducing constraint potential. The node position is enforced by the combined spring force and constraint conservative force through Newton's second law. We conduct a comparison study of conventional mass spring dampers and position based dynamics (PBD) for our new integrating method. Our approach enables stable, fast, and large step simulation by freely controlling visual effects based on nonlinearity, viscoelasticity and incompressibility. We implement a laparoscopic cholecystectomy simulator to demonstrate the practicality of our method, in which liver and gallbladder deformation can be simulated in real time. Our method is an appropriate choice for the development of real-time virtual surgery applications.
Lean mass dynamics in hibernating bats and implications for energy and water budgets
<p>Hibernation requires balancing energy and water demands over several months with no food intake for many species. Many studies have considered the importance of fat for hibernation energy budgets because it is energy dense and can be stored in large quantities. However, protein catabolism in hibernation has received less attention and whole animal changes in lean mass have not previously been considered. We used quantitative magnetic resonance body composition analysis to measure fat and lean mass in two systems of hibernating bats, emphasizing the importance of lean mass for energy and water budgets. For cave myotis ( Myotis velifer ), lean mass represented 38 and 25% (male and female respectively) of pre-hibernation mass gain. In Townsend's big-eared bats ( Corynorhinus townsendii ), lean mass accounted for 18 – 35% of mass change during hibernation, but lean only contributed 3 – 7% of the energy budget. Water is produced from the catabolism of both fat and lean, but net water production is much less than gross water production when accounting for the water required to excrete urea. Although most mammals can't rely on protein catabolism for metabolic water production due to the water cost of excreting urea, we propose a variation on the protein-for-water strategy whereby hibernators could temporally compartmentalize the benefits of protein catabolism to periods of torpor, and the water cost to periodic arousals when free drinking water is typically available. Combined, our analyses demonstrate that lean mass is dynamic in hibernation, with important functional consequences for both energy and water budgets. </p>
Fig. 5 in Diversity dynamics of Early-Middle Jurassic brachiopods of Caucasus, and the Pliensbachian-Toarcian mass extinction
Fig. 5. Brachiopods diversity changes in Lias of Bakony Mts (after Vörös 1993, 1995) and Swiss Alps and Jura Mts. (after Sulser 1999).
Fig. 1 in Diversity dynamics of Early-Middle Jurassic brachiopods of Caucasus, and the Pliensbachian-Toarcian mass extinction
Fig. 1. Geographic location of studied area (A) and paleogeographic situation of areas discussed in the text (B; studied area indicated by 1). Paleogeographic base map is modified from Owen 1983 and Dommergues et al. 2001.
Fig. 2 in Diversity dynamics of Early-Middle Jurassic brachiopods of Caucasus, and the Pliensbachian-Toarcian mass extinction
Fig. 2. Liassic composite lithological section of NW Caucasus and comparison of regional and global (simplified from Hallam and Wignall 1999) sea−level changes.
Data from: Integrating viscoelastic mass spring dampers into position-based dynamics to simulate soft tissue deformation in real time
Open the record for dataset details and reuse information.
Data from: Radiolarian biodiversity dynamics through the Triassic and Jurassic: implications for proximate causes of the end-Triassic mass extinction
Open the record for dataset details and reuse information.
Lean mass dynamics in hibernating bats and implications for energy and water budgets
Open the record for dataset details and reuse information.
Quantitative mass spectrometry-based proteomics reveals the dynamic protein landscape during initiation of human Th17 cell polarization
GEO Series GSE118974. Homo sapiens. 10 samples. Type: Expression profiling by high throughput sequencing.
Data from: Effects of mass extinction and recovery dynamics on long-term evolutionary trends: a morphological study of Strophomenida (Brachiopoda) across the Late Ordovician mass extinction
Mass extinctions affect the history of life by decimating existing diversity and ecological structure and creating new evolutionary and ecological pathways. Both the loss of diversity during these events and the rebound in diversity following extinction had a profound effect on Phanerozoic evolutionary trends. Phylogenetic trees can be used to robustly assess the evolutionary implications of extinction and origination. We examine both extinction and origination during the Late Ordovician mass extinction. This mass extinction was the second largest in terms of taxonomic loss but did not appear to radically alter Paleozoic marine assemblages. We focus on the brachiopod order Strophomenida, whose evolutionary relationships have been recently revised, to explore the disconnect between the processes that drive taxonomic loss and those that restructure ecological communities. A possible explanation for this disconnect is if extinction and origination were random with respect to morphology. We define morphospace using principal coordinate analysis (PCO) of character data from 61 Ordovician-Devonian taxa and their 45 ancestral nodes, defined by a most parsimonius reconstruction in Mesquite. A bootstrap of the centroid of PCO values indicates that genera were randomly removed from morphospace by the Late Ordovician mass extinction, and new Silurian genera were clustered within a smaller previously unoccupied region of morphospace. Diversification remained morphologically constrained throughout the Silurian and into the Devonian. This suggests that the recovery from the Late Ordovician mass extinction resulted in a long-term shift in strophomenide evolution. More broadly, recovery intervals may hold clues to understanding the evolutionary impact of mass extinctions.
Interest of Evaluating the Movement of the Center of Mass in a Dynamic Avoidance Task as the Endpoint of the Dynamic Equilibrium of Hemiplegic Patients
ClinicalTrials.gov study NCT02350556. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Data from: Effects of mass extinction and recovery dynamics on long-term evolutionary trends: a morphological study of Strophomenida (Brachiopoda) across the Late Ordovician mass extinction
Open the record for dataset details and reuse information.
ScienceDex guides
Understand access before you commit
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.