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33 results for “non-equilibrium”
Data for "Electroferrofluids with Non-Equilibrium Voltage-Controlled Magnetism, Diffuse Interfaces, and Patterns"
<p>This dataset contains the raw data used for the publication "Electroferrofluids with Non-Equilibrium Voltage-Controlled Magnetism, Diffuse Interfaces, and Patterns".</p>
Data for "Diversity of Non-Equilibrium Patterns and Emergence of Activity in Confined Electrohydrodynamically Driven Liquids"
<p>Raw data (microscopy videos and image sequences) and scripts used for the analysis for the publication "Diversity of Non-Equilibrium Patterns and Emergence of Activity in Confined Electrohydrodynamically Driven Liquids", Science Advances 7 (38), eabh1642</p>
Synthetic Colors for Brown Dwarfs using ATMO Non-Equilibrium Non-Adiabatic Atmospheres
<p>The Tables in the spreadsheets give magnitudes in the Vega system, calculated from synthetic spectra generated by ATMO non-equilibrium non-adiabatic atmospheres (Tremblin et al. 2015, Phillips et al. 2020, Leggett et al. 2021). Each file has three tabs corresponding to three metallicities: [m/H] = 0, -0.5 and -1.0. The file 2023_ATMO_MKO_WISE_Spitzer_phot gives MKO Y, J, H, K, Ks, and L'; WISE W1, W2, W3, and W4; and Spitzer [3.6] and [4.5] (columns 5 - 16 in the Tables). The other three files give colors for JWST NIRCam, NIRISS and MIRI filters, as indicated by the file name.</p> <p>The photometry is given for an observer at the Earth, for a brown dwarf at 10 pc with a radius of 0.1 Rsun. Column 3 in the Tables give the radius determined by evolutionary models for different metallicities by Marley et al. 2021 (and https://zenodo.org/record/5063476), for the specified temperature and gravity (columns 1 and 2). Column 4 gives the correction to the magnitudes for the correct (theoretical) radius. NOTE THAT THE CORRECTION MUST BE ADDED TO THE MAGNITUDES GIVEN IN COLUMNS 5 - 16 TO OBTAIN THE ABSOLUTE MAGNITUDE. For an analysis of the model color trends, using a comparison to observations, see Meisner et al. 2023.</p> <p>The photometry covers the following atmospheric parameters: effective temperatures between 1200 and 250 K (step size 100 K between 1200 and 400 K, with the step size decreasing for lower effective temperatures); log(g) with three values 4.0, 4.5 and 5.0; effective adiabatic index of 1.25; metallicity with three values -1.0, -0.5, and 0. A grid of synthetic spectra was computed at medium resolution (R ~3000) for wavelengths of 0.2 to 30 microns. All the models, for a wider range of parameters, are available at https://opendata.erc-atmo.eu. </p> <p>The models include rainout of condensates which depletes refractory species, but they do not include clouds. Tremblin et al. 2016 shows that diabatic convective processes (Tremblin et al. 2019) can reduce the temperature gradient in the atmosphere and reproduce the spectral reddening previously explained by clouds. Adjustments to the atmospheric temperature gradient have also been shown to be necessary to reproduce the energy distributions of the coldest brown dwarfs (Leggett et al. 2021). The grids used here modify the temperature gradient by adopting an effective adiabatic index. The levels modified are in between 0.15 and 15 bars at log g = 4.5 and are scaled by ×10<sup>(log(g)−4.5)</sup> at other surface gravities. Out-of-equilibrium chemistry is used with Kzz = 10<sup>5 </sup>cm<sup>2</sup>/s at log(g) = 5.0 and is scaled by ×10<sup>(2(5−log(g)))</sup> at other surface gravities. The mixing length is assumed to be 2 scale heights at 1.5 bars and higher pressures at log(g) = 4.5 and is scaled down by the ratio between the local pressure and the pressure at 1.5 bars for lower pressures. The 1.5 bars limit is scaled by ×10<sup>(log(g)−5) </sup>at other surface gravities. The chemistry includes 277 species and out-of-equilibrium chemistry has been performed using the model of Tsai et al. 2017. Opacity sources include H<sub>2</sub>-H<sub>2</sub>, H<sub>2</sub>-He, H<sub>2</sub>O, CO<sub>2</sub>, CO, CH<sub>4</sub>, NH<sub>3</sub>, Na, K, Li, Rb, Cs, TiO, VO, FeH, PH<sub>3</sub>, H<sub>2</sub>S, HCN, C<sub>2</sub>H<sub>2</sub>, SO<sub>2</sub>, Fe, H<sup>-</sup>, and the Rayleigh scattering opacities for H<sub>2</sub>, He, CO, N<sub>2</sub>, CH<sub>4</sub>, NH<sub>3</sub>, H<sub>2</sub>O, CO<sub>2</sub>, H<sub>2</sub>S, SO<sub>2</sub>.</p> <p> </p> <p>REFERENCES</p> <p>Leggett et al 2021 ApJ 918, 11</p> <p>Marley et al. 2021 ApJ 920, 85 (and https://zenodo.org/record/5063476)</p> <p>Meisner et al. 2023, ApJ in press </p> <p>Phillips et al. 2020 A & Ap 637, 38</p> <p>Tremblin et al. 2015 ApJ 804, L17</p> <p>Tremblin et al. 2016 ApJ 817, L19</p> <p>Tremblin et al. 2019 ApJ 876, 144</p>
Performance of wave function and Green's function methods for non-equilibrium many-body dynamics
<p>In this repository we have compiled 1-RDMs on a time grid obtained from various methods, namely, time-dependent full configuration interaction (TD-FCI), time-dependent coupled cluster (TD-CC), time-dpendent Hartree-Fock (TD-HF), Kadanoff-Baym Equations, and generalized Kadanoff-Baym approximation (GKBA). We have evaluated the 1-RDMs from Hubbard model in presence of an external drive. We have included a PySCF script to generate the integrals with a specific choice for various parameters. One can reproduce the HF results from that script. The Python script to evaluate various observables that we have analyzed in our article, namely, time-dependent dipole moment, Von-Neumann entropy are also added. </p>
Data for Non-Equilibrium Sensing of Volatile Compounds Using Active and Passive Analyte Delivery
<blockquote> <p>Version 2: Added missing files to <code>sniffing_data.zip</code></p> </blockquote> <p>See GitHub repository for data processing functions and examples: <a href="https://github.com/soerenbrandt/sniffing-sensor">https://github.com/soerenbrandt/sniffing-sensor</a></p> <p><strong>Abstract</strong>:<br>Sensor technologies have allowed us to outperform the human senses of sight, hearing, and touch; however, the development of artificial noses is significantly behind their biological counterparts. This is largely due to the complexity of natural olfaction, as it incorporates complex fluid dynamics within the nasal anatomy together with the response patterns of hundreds to thousands of unique molecular-scale receptors for odor interpretation. We designed a sensing approach to identify volatiles that exploits time-dependent information from a single sensor (here, the reflectance spectra from a mesoporous one-dimensional photonic crystal) by augmenting and accentuating differences in the non-equilibrium mass-transport dynamics of vapors stemming from their distinct physicochemical properties, thus obviating the need for a large sensor array. By training a machine learning algorithm on the sensor output, we clearly identify polar and nonpolar volatile organic compounds, determine the mixing ratios of binary mixtures, and accurately predict the boiling point, flash point, vapor pressure, and viscosity of several volatile liquids within those used for training as well as compounds unknown to the model. We further implement a bioinspired active sniffing approach, in which the fluid dynamics and patterns of analyte delivery are controlled, enabling an additional modality of differentiation and reducing the duration of data collection and analysis to seconds. These results outline a strategy to build accurate and rapid artificial noses for volatile liquids that can provide useful information on chemicals such as their composition and properties, and can be applied in a variety of fields, including disease diagnosis, hazardous waste management, and healthy building monitoring.</p>
Non-equilibrium fractionation during ice cloud formation in iCAM5: evaluating the common parameterization of supersaturation as a linear function of temperature
<p>This archive includes data and python scripts to create the figures in</p> <p>Duetsch, M., Blossey, P. N., Steig, E. J., and Nusbaumer, J. M. (2019). Non-equilibrium fractionation during ice cloud formation in iCAM5: evaluating the common parameterization of supersaturation as a linear function of temperature. Submitted to J. Adv. Model. Earth Sy.</p> <p><strong>Model</strong></p> <p>Model output data are saved in model.tar.gz</p> <p>For all simulations, monthly averages of the following variables are saved in h0 files (used for Figures 3, 6, 8, 9):<br> T: temperature (Si_real simulations only)<br> PS: surface pressure (Si_real simulations only)<br> U: zonal wind (Si_real simulations only)<br> V: meridional wind (Si_real simulations only)<br> LANDFRAC: fraction of surface area covered by land<br> PRECT_H2O: total precipitation rate for H2O<br> PRECT_HDO: total precipitation rate for HDO<br> PRECT_H218O: total precipitation rate for H218O<br> H2OV: H2O mixing ratio for vapor<br> HDOV: HDO mixing ratio for vapor<br> H218OV: H218O mixing ratio for vapor<br> H2OI: H2O mixing ratio for cloud ice<br> HDOI: HDO mixing ratio for cloud ice<br> H218OI: H218O mixing ratio for cloud ice<br> H2OL: H2O mixing ratio for cloud liquid<br> HDOL: HDO mixing ratio for cloud liquid<br> H218OL: H218O mixing ratio for cloud liquid</p> <p>For the control and Si_real microphysical sensitivity simulations, 6-hourly averages of the following variables are saved in h1 files (used for Figures 4, 5, 7):<br> PRECL_H2O: Large-scale precipitation rate for H2O (Si_real simulations only)<br> PRECL_HDO: Large-scale precipitation rate for HDO<br> PRECL_H218O: Large-scale precipitation rate for H218O<br> PRECL_SAT: Large-scale precipitation rate for Si tracer (Si_real simulations only)<br> PRECL_TMP: Large-scale precipitation rate for T tracer (Si_real simulations only)<br> PRECL_RVD: Large-scale precipitation rate for R^D tracer<br> PRECL_RVO: Large-scale precipitation rate for R^18O tracer</p> <p>To limit the size of the data set, only the first 5 years of the simulations are saved.</p> <p><strong>Measurements</strong></p> <p>Ice core measurements for present-day climate and last glacial maximum are saved in icecores_PD.txt and icecores_LGM.txt, respectively.</p> <p>LGM values are averaged from 19000 BCE to 16000 BCE, PD values are averaged from 1000 BCE to 2000 CE.</p> <p>Antarctic surface snow measurements from Masson-Delmotte et al. (2008) are available at https://doi.org/10.1594/PANGAEA.681697</p> <p><strong>Python scripts</strong></p> <p>Scripts run with python 3.6</p> <p>Required packages:<br> - numpy<br> - matplotlib<br> - netCDF4<br> - datetime<br> - Basemap<br> - copy<br> - intergrid<br> - cmocean</p>
Precisely controlled colloids: A playground for path-wise non-equilibrium physics
<p>Particle Trajectories from Non Equilibrium Steady State (Driven) and Equilibrium state.</p> <p>Number of trajectories can be found in the Book1.xlx </p> <p>File format description on the individual *.dat files is explained in the instructions.pdf</p> <p> </p>
Datasets for "Cross-helicity effect on alpha-type dynamo in non-equilibrium turbulence"
<p>This directory contains an index.html file with links to the run directories for Runs A-E and idl plotting routines with secondary data for the other figures for the paper "Cross-helicity effect on alpha-type dynamo in non-equilibrium turbulence" by Mizerski, Yokoi, & Brandenburg.</p>
Shear viscosity coefficient of acqueous glycerol from non-equilibrium Molecular Dynamics simulations
<p>This dataset contains the results of non-equilibrium atomistic Molecular Dynamics simulations of water-glycerol liquid mixtures, at various relative concentrations. The goal of the simulations is to quantify the shear viscosity coefficient of said mixtures using the periodic perturbation technique [1]. </p> <p>The pattern "Glycerol***" refers to the mass fraction of glycerol ("000": pure water, "100": pure glycerol). Each folder contains three sets of simulations, with different perturbation force parameters ("Em*"), where configuration files necessary to reproduce molecular simulations simulations are provided. Maps of density and velocity field are in "Em*"->"Flow".</p> <p>A small self-contained Python script to fit the velocity fields to a periodic cosine perturbation is provided (fit-periodic.py). Alternatively, viscosity can be obtained from energy outputs by running:</p> <pre><code>gmx energy -f ener.edr</code></pre> <p>and selecting "1/Viscosity". Simulations are performed with Gromacs. We refer to the code documentation for further information (<a href="https://manual.gromacs.org/">https://manual.gromacs.org/</a>).</p> <p>References:</p> <p>[1] B. Hess, Determining the shear viscosity of model liquids from molecular dynamics simulations, J. Chem. Phys. 116, 209–217 (2002) <a href="https://doi.org/10.1063/1.1421362">https://doi.org/10.1063/1.1421362</a></p>
Model simulation data used in "How non-equilibrium aerosol chemistry impacts particle acidity: the GMXe AERosol CHEMistry (GMXe-AERCHEM, v1.0) sub-submodel of MESSy"
<p>This dataset contains the output of the ECHAM/MESSy Atmospheric Chemistry (EMAC) model simulation analyzed in the work "How non-equilibrium aerosol chemistry impacts particle acidity: the GMXe AERosol CHEMistry (GMXe-AERCHEM, v1.0) sub-submodel of MESSy" submitted to Geoscientific Model Development.</p>
Raw experimental data and matlab codes for the paper "State-dependent driving : A route to non-equilibrium stationary states"
<p>Raw experimental data and matlab codes used for numerical simulation in the paper - "State-dependent driving : A route to non-equilibrium stationary states". The data and codes to generate figures 2, 3 and 5 are available in the folders named "Fig 2", "Fig 3" and "Fig 5" respectively.</p>
Relative binding free energy between chemically distant compounds using a bidirectional non-equilibrium approach
<p>Data from "Relative binding free energy between chemically distant compounds using a bidirectional nonequilibrium approach"</p> <p>Submitted to J Chem Theory Comput, February 2022.</p> <p>This archive contains the following directories:</p> <p><br> traj -> This directory contains trajectory files for SAMPL9:</p> <p> g??_w.pdb.gz file contains ~ 160 snapshots from the 48 ns HREM sampling<br> of the target bound state of G1-G13 including water solvent.</p> <p> g??_1.pdb.gz file contains ~ 3500 snapshots (no solvent included) of the<br> target bound state of G1-G13</p> <p><br> work -> This trajectory contains the work data (in kJ/mol) obtained in<br> the NE trajectories for SAMPL9:</p> <p> gxx-gyy_b_TIME.wrk is the work sample obtained for the xx->yy transmutation<br> in the bound state with a duration time of TIME.</p> <p> gxx-gyy_u_TIME.wrk is the work sample obtained for the xx->yy transmutation<br> in the unbound state with a duration time of TIME.</p> <p>pdb -> This directory contains the pdb initial structures of the G1-G18 guests and the WP6 host</p> <p>ff -> This directory contains the force field specification for SAMPL9:</p> <p> the PrimaDORAC generated (http://www1.chim.unifi.it/orac/primadorac/)<br> tpg and prm files for the guests and the host in orac format. (see<br> http://ftp.chim.unifi.it/orac/MAN/orac-manual.html)</p> <p>bin -> This directory contains the script files to compute all bidirectional and unidirectional RBFE estimates as reported in Table 2 of the paper.<br> In order to compute an RBFE using the work data in the work dir, do the following:<br> 1) cd into the bin directory<br> 2) issue the command<br> source source_this_file.bash<br> (N.B.: gfortran must be installed)<br> 3) cd ../<br> 4) from the main dir, issue the command:<br> RBFE.bash -b 720 -u 360 B A<br> where B and A are the ghost and physical compound, respectively<br> Results for DG(A->B) are printed to the standard output</p> <p> Example: <br> RBFE.bash -b 720 -u 360 g01 g03 > g03g01<br> In the g03g01 file, estimates and properties of the work data are<br> printed in the format<br> "g05->g02 DG_bar= -5.13 0.37 DG_ff_G= -4.2 0.8 DG_ff_J= -3.0 0.4 sig_AB_u= 0.90 sig_AB_b= 2.16 ADT_AB_u= 0.26 ADT_AB_b= 0.23 DG_rr_G= 130.5 33.5 DG_rr_J= -7.2 0.8 sig_BA_u= 1.10 sig_BA_b= 13.61 ADT_BA_u= 0.48 ADT_BA_b= 3.40 DG_fr_G= -4.27 1.04 DG_fr_J= -3.23 0.58 bias_fr= 0.2 DG_rf_G= 127.88 30.30 DG_rf_J= -7.16 0.66 bias_rf= 0.0 DG_BAR= -5.26 0.0 tb= 720 tu= 360"</p> <p> To compute all DDG estimates of Table 2 launch the script<br> "do_all.bash" from the main dir </p> <p>shift-pot -> This directory contains two gnuplot scripts showing the evolution of the<br> Beutler LJ and elec soft-core potentials (soft.gplt) and of the shifted LJ and<br> elec soft-core potentials used in this work (shift.gplt)<br> </p>
Thermal conductivity of hydrous wadsleyite determined by non-equilibrium molecular dynamics based on machine learning
<p>This repository contains data used in "Thermal conductivity of hydrous wadsleyite determined by non-equilibrium molecular dynamics based on machine learning" submitted by Dong Wang, Zhongqing Wu and Xin Deng.</p> <p>Figure S4 : "MLP test-Energy" in <strong><a href="https://zenodo.org/api/files/353c5b70-3e53-4192-92af-7bf7f12328fd/Data%20for%20Figures.xlsx?versionId=96ac296c-13f9-4871-976a-d7ad087e0b62">Data for Figures.xlsx</a></strong>、<strong><a href="https://zenodo.org/api/files/353c5b70-3e53-4192-92af-7bf7f12328fd/MLP%20test-force.txt?versionId=acfeccd1-ecbf-42c8-a6a3-0d6daf72b078">MLP test-force.txt</a></strong></p> <p>Figure 1 : "MLP test-NEMD" in <strong><a href="https://zenodo.org/api/files/353c5b70-3e53-4192-92af-7bf7f12328fd/Data%20for%20Figures.xlsx?versionId=96ac296c-13f9-4871-976a-d7ad087e0b62">Data for Figures.xlsx</a></strong></p> <p>Figure 3 : "Modeing" in <strong><a href="https://zenodo.org/api/files/353c5b70-3e53-4192-92af-7bf7f12328fd/Data%20for%20Figures.xlsx?versionId=96ac296c-13f9-4871-976a-d7ad087e0b62">Data for Figures.xlsx</a></strong></p>
Depressurization of CO2 in a pipe: Effect of initial state on non-equilibrium two-phase flow – dataset
<p>This dataset contains data from depressurization of pure CO<sub>2</sub> in a tube from a dense-liquid state. The data are described in the accompanying paper (DOI: <a href="https://doi.org/10.1016/j.ijmultiphaseflow.2023.104624">10.1016/j.ijmultiphaseflow.2023.104624</a>).</p> <p>Test number; fluid; pressure (MPa); temperature (deg C):<br> 19; CO2; 12.47; 10.2<br> 22; CO2; 12.48; 14.9<br> 23; CO2; 12.19; 31.5<br> 24; CO2; 11.56; 35.8<br> </p> <p> </p>
A non-equilibrium species distribution model reveals unprecedented depth of time lag responses to past environmental change trajectories
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Data from: Equilibrium and non-equilibrium phases in the radiation of Hakea and the drivers of diversity in Mediterranean-Type Ecosystems
Mediterranean-Type ecosystems (MTEs) contain exceptional plant diversity. Explanations for this diversity are usually classed as either "equilibrium", with elevated MTE diversity resulting from greater ecological carrying capacities, or "non-equilibrium", with MTEs having a greater accumulation of diversity over time. These models have typically been considered as mutually exclusive. Here we present a trait-based explanatory framework that incorporates both equilibrium and non-equilibrium dynamics. Using a large continental Australian plant radiation (Hakea) as a case study, we identify traits associated with niche partitioning in co-occurring species (α traits) and with environmental filtering (β traits), and reconstruct the mode and relative timing of diversification of these traits. Our results point to a radiation with an early, non-equilibrium phase marked by divergence of β traits as Hakea diversified exponentially and expanded from the southwest Australian MTE into biomes across the Australian continent. This was followed from 7Mya by an equilibrium phase, marked by diversification of α traits and a slowdown in lineage diversification as the MTE niches became saturated. These results suggest that processes consistent with both equilibrium and non-equilibrium models have been important during different stages of the radiation of Hakea, and together they provide a richer explanation of present-day diversity patterns.
Data from: A model for non-equilibrium metapopulation dynamics utilizing data on species occupancy, patch ages and landscape history
1. The distribution pattern of many species reflects the past rather than the current structure of landscapes. Consequently, species are most often not in equilibrium with the current landscape structure. Yet this is a well-known fact, there is no appropriate approach to estimate the colonization rate of non-equilibrium species based on only data on the species occurrence pattern in the landscape. 2. We present an approach to estimate the colonization rate of non-equilibrium metapopulations. The approach requires only data on species presence/absence among its patches (occurrence pattern), data on patch ages and data on the historic distribution of the patches in the landscape. By estimating the past occurrence patterns and colonization events leading to the current pattern of occupied and non-occupied patches, we estimate the colonization rate, including the dispersal kernel. We also show how to estimate effects of local patch conditions and how to include an independent estimate of the local extinction rate based on other data. We use nine epiphytic lichen species confined to beech trees to illustrate the method. 3. Five species had restricted dispersal range, between 200 and 4700 m, and their colonization rate decreased with increasing fragmentation. Species colonization rates were related to niche width. Among the demographic parameters, the force of colonization was more important than the dispersal range in explaining the colonization rates. Local patch conditions did not explain the colonization probability of any species. In metapopulation projections that did not account for restricted dispersal range, higher future metapopulation sizes were projected. 4. Synthesis. The presented approach uses data on only species occurrence, patch age and landscape history to estimate the species colonization rate and dispersal kernel. It can also utilize independent data on local extinction rate. Rather than identifying factors explaining the occurrence pattern, the model estimates the rate of change in the occurrence pattern. This dynamic modelling allows testing general and applied questions on the dynamics or viability of metapopulations of sessile species. The approach is applicable for species whose distribution pattern reflects the past rather than the current landscape structure, e.g. certain epiphytes and ground-floor plants.
Data from: A statistical mechanics framework for constructing non-equilibrium thermodynamic models
<p><span>Far-from-equilibrium phenomena are critical to all natural and engi</span><span>neered systems, and essential to biological processes responsible </span><span>for life. For over a century and a half, since Carnot, Clausius, Maxwell, </span><span>Boltzmann, and Gibbs, among many others, laid the foundation for </span><span>our understanding of equilibrium processes, scientists and engineers </span><span>have dreamed of an analogous treatment of non-equilibrium systems. </span><span>But despite tremendous efforts, a universal theory of non-equilibrium </span><span>behavior akin to equilibrium statistical mechanics and thermodynam</span><span>ics has evaded description. Several methodologies have proved their </span><span>ability to accurately describe complex non-equilibrium systems at </span><span>the macroscopic scale, but their accuracy and predictive capacity is </span><span>predicated on either phenomenological kinetic equations fit to mi</span><span>croscopic data, or on running concurrent simulations at the particle </span><span>level. Instead, we provide a framework for deriving stand-alone macro</span><span>scopic thermodynamics models directly from microscopic physics </span><span>without fitting in overdamped Langevin systems.</span> <span>The only neces</span><span>sary ingredient is a functional form for a parameterized, approximate </span><span>density of states, in analogy to the assumption of a uniform density </span><span>of states in the equilibrium microcanonical ensemble. We highlight </span><span>this framework's effectiveness by deriving analytical approximations </span><span>for evolving mechanical and thermodynamic quantities in a model of </span><span>coiled-coil proteins and double stranded DNA, thus producing, to the </span><span>authors' knowledge, the first derivation of the governing equations for </span><span>a phase propagating system under general loading conditions without </span><span>appeal to phenomenology. The generality of our treatment allows </span><span>for application to any system described by Langevin dynamics with </span><span>arbitrary interaction energies and external driving, including colloidal </span><span>macromolecules, hydrogels, and biopolymers.</span></p>
Data from: Non-equilibrium conditions explain spatial variability in genetic structuring of little penguin (Eudyptula minor)
Factors responsible for spatial structuring of population genetic variation are varied, and in many instances there may be no obvious explanations for genetic structuring observed, or those invoked may reflect spurious correlations. A study of little penguins (Eudyptula minor) in southeast Australia documented low spatial structuring of genetic variation with the exception of colonies at the western limit of sampling, and this distinction was attributed to an intervening oceanographic feature (Bonney Upwelling), differences in breeding phenology, or sea level change. Here, we conducted sampling across the entire Australian range, employing additional markers (12 microsatellites and mitochondrial DNA, 697 individuals, 17 colonies). The zone of elevated genetic structuring previously observed actually represents the eastern half of a genetic cline, within which structuring exists over much shorter spatial scales than elsewhere. Colonies separated by as little as 27 km in the zone are genetically distinguishable, while outside the zone, homogeneity cannot be rejected at scales of up to 1400 km. Given a lack of additional physical or environmental barriers to gene flow, the zone of elevated genetic structuring may reflect secondary contact of lineages (with or without selection against interbreeding), or recent colonization and expansion from this region. This study highlights the importance of sampling scale to reveal the cause of genetic structuring.
Using Molecular Dynamics Simulations to Interrogate T Cell Receptor Non-Equilibrium Kinetics
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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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.