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1,271 results for “Data Flow”
Data underpinning "Unraveling long-time quantum dynamics using flow equations"
<p>The study of many-body quantum dynamics in strongly-correlated systems is extremely challenging. To date few numerical methods exist which are capable of simulating the non-equilibrium dynamics of two-dimensional quantum systems, in part reflecting complexity theoretic obstructions. In this work, we present a new technique able to overcome this obstacle, by combining continuous unitary flow techniques with the newly developed method of scrambling transforms. We overcome the prejudice that approximately diagonalizing the Hamiltonian cannot lead to reliable predictions for relatively long times. To the contrary, we show that the method works well in both localized and delocalized phases, and makes reliable predictions for a number of quantities including infinite-temperature autocorrelation functions. We complement our findings with rigorous incremental bounds on the truncation error. This approach shows that in practice, the exploration of intermediate-scale time evolution may be more feasible than is commonly assumed, challenging near-term quantum simulators.</p>
Data from: Contrasting sap flow characteristics between pioneer and late-successional tree species in secondary tropical montane forests of Eastern Himalaya, India
Abstract The interactive role of life-history traits and environmental forcing on plant-water relations is crucial for understanding species response to climate change but remains poorly understood in secondary tropical montane forests (TMFs). Comparing contrasting life-history traits (pioneer vs late-successional species) in a biodiverse Eastern Himalayan secondary TMF, we investigated sap flow responses in co-occurring pioneer species, Symplocos racemosa (n=5) and Eurya acuminata (n=5), and late-successional species, Castanopsis hystrix (n=3), using modified Granier's Thermal Dissipation probes. The fast-growing pioneers S. racemosa and E. acuminata) had 2.1- and 1.6-times higher sap flux density than the late-successional C. hystrix, respectively, and exhibited characteristics of long-lived pioneer species. Significant radial and azimuthal variability in sap flow (V) between species was observed and attributed to life history traits and the canopy's access to sunlight. Nocturnal V (1800-0500 hr) was 13.8 % of daily V and is attributed to stem recharge for evening V (1800-2300 hr) and to endogenous stomatal controls for pre-dawn V (0000-0500 hr). Both the shallow-rooted pioneer species exhibited midday depression in V attributed to photosensitivity and diel moisture stress response. In contrast, deep-rooted C. hystrix transpired unaffected across the dry season likely accessing groundwater. Thus, the secondary broadleaved TMFs, with the dominance of shallow-rooted pioneers, are more prone to the negative impacts of drier and warmer winters than primary forests, which are dominated by deep-rooted species. The study provides an empirical understanding of life-history traits and microclimate modulating plant-water use in widely distributed secondary TMFs in Eastern Himalaya and highlights their vulnerability against warmer winters and reduced snowfall due to climate change.
Data and Code for: Reproductive strategies and their consequences for divergence, gene flow, and genetic diversity in three taxa of Clarkia
<p><span>Differences in reproductive strategies can have important implications for macro- and micro-evolutionary processes. We used a comparative approach through a population genetics lens to evaluate how three distinct reproductive strategies shape patterns of divergence among as well as gene flow and genetic diversity within three closely related taxa in the genus <em>Clarkia</em>. One taxon is a predominantly autonomous self-fertilizer and the other two taxa are predominantly outcrossing but vary in the primary pollinator they attract. In genotyping populations using genotyping-by-sequencing and comparing loci shared across taxa, our results suggest that differences in reproductive strategies in part promote evolutionary divergence among these closely related taxa. Contrary to expectations, we found that the selfing taxon had the highest levels of heterozygosity but a low rate of polymorphism. The high levels of fixed heterozygosity for a subset of loci suggests this pattern is driven by the presence of structural rearrangements in chromosomes common in other <em>Clarkia </em>taxa. In evaluating patterns within taxa, we found a complex interplay between reproductive strategy and geographic distribution. Differences in the mobility of primary pollinators did not translate to a difference in rates of genetic diversity and gene flow within taxa – a pattern likely due to one taxon having a patchier distribution and a less temporally and spatially reliable pollinator. Taken together, this work advances our understanding of the factors that shape gene flow and the distribution of genetic diversity within and among closely related taxa.</span></p>
Data from: Demographically explicit scans for barriers to gene flow using gIMble
<p>Identifying regions of the genome that act as barriers to gene flow between recently diverged taxa has remained challenging given the many evolutionary forces that generate variation in genetic diversity and divergence along the genome, and the stochastic nature of this variation. Here we implement a composite likelihood approach for the quantification of barriers to gene flow. This analytic framework captures background selection and selection against locally maladaptive alleles (i.e. genomic barriers) in a model of isolation with migration (IM) as heterogeneity in effective population size (Ne) and effective migration rate (me), respectively. Variation in both effective demographic parameters is estimated in sliding windows via pre-computed likelihood grids. We have implemented genomewide IM blockwise likelihood estimation (gIMble) as a modular tool, which includes modules for pre-processing/filtering of genomic data and performing parametric bootstraps using coalescent simulations. To demonstrate the new approach, we analyse data from a well-studied sister species pair of tropical butterflies with a known history of post-divergence gene flow: Heliconius melpomene and H. cydno. Our analysis uncovers both large effect barrier loci (including well-known wing-pattern genes) and a genome-wide signal of polygenic barrier architecture.</p>
Data for the paper: Drag, lift and torque correlations for axi-symmetric rod-like non-spherical particles in locally linear shear flows
<p>These are the data files for the paper:</p> <p>Drag, lift and torque correlations for axi-symmetric rod-like non-spherical particles in locally linear shear flows</p> <p>authored by: Victor Chéron, Fabien Evrard, and Berend van Wachem</p> <p>#Files<br> Temporally averaged drag, lift and torque coefficients are written in .txt files stored in the folder ResultsCoefficients.<br> Python scripts used to derive the correlations are stored in the folder PythonScripts.<br> Results of an example simulation are provided in the folder SimulationResults.<br> A Python script with the final correlations of the manuscript is also included.</p> <p>#ResultsCoefficients<br> The .txt files are split per coefficient, aspect ratio and shear rate, which can be identified by the name of the .txt file<br> The results obtained for the torque coefficient of the particle of aspect ratio 2.5 for a uniform flow configuration are given in the file:<br> ### Uniform-Torque-Angles-Size2-5.txt<br> The results obtained for the lift coefficient of the particle of aspect ratio 10 for a shear rate 0.2 configuration are given in the file:<br> ### Shear02-Lift-Angles-Size10.txt<br> In these files, the results are ordered per orientation angle and particle Reynolds number.</p> <p>#PythonScripts<br> The python scripts for processing the data are split among three main functions in two files:<br> - Getter.py (reads the .txt files storing the coefficients - separate functions for the drag, lift and torque coefficients - as well as fill up the missing points for spherical particles using the Kurose and Komori correlations)<br> - generalmain (calls the Getter.py function). The Getter.py is called from the generalmain.py file. (run python3<br> generalmain.py). This will return a 1D column vector ordering the variables used to derive the correlations:<br> - Coefficients<br> - Reynolds number<br> - Orientation Angle<br> - Shear rate<br> - Aspect ratio<br> - Additional coefficients.</p> <p>The Python script ManuscriptCorrelations.py has the correlations as derived in the manuscript. This routine can be used to query the correlations, for plotting them or using them in a simulation.</p> <p><br> #SimulationResults<br> Simulation results of one case are provided:<br> - Aspect ratio 2.5, particle Reynolds number 200, orientation angle 90, Shear rate 0.2<br> The fields and particles information are stored in hdf5 file format.<br> A .xmf wrapper file is provided to read the simulation results in paraview.<br> Data up to 40 seconds of real time are provided due to storage limits.</p> <p><br> This research was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - Project-ID 448292913.</p>
A Data-facilitated Numerical Method for Richards Equation to Model Water Flow Dynamics in Soil Dataset
<p>This dataset contains the reference solutions used for training the two neural networks in 1-, 2- and 3-D cases for the article:"A Data-facilitated Numerical Method for Richards Equation to Model Water Flow Dynamics in Soil" by Zeyuan Song and Zheyu Jiang, submitted to the journal Water Resources Research. </p> <p>This dataset which describes the relationship between the pressure head and number of particles used to train two MLPs in D-GRW based solvers consists of three files, i.e., 1-, 2- and 3-D case study. There are two parts, original reference solutions and reference solutions, corresponding to the original solutions generated by coarse mesh solvers and solutions after data augmentation process, respectively.The dataset is generated by GRW based solvers and simulation results (e.g., Celia's finite difference method). Original reference solutions admit GRW proportionality assumption. We initialize the number of particles by multiplying the initial condition and 1E10. </p>
Data and code from Pierson et al. "Revisiting a cryptic species complex: interspecific gene flow among woodland salamanders in the Blue Ridge Mountains of northern Georgia"
<p>ABSTRACT</p> <p>The complex topography of mountainous landscapes can create biogeographic barriers and promote allopatric speciation—even among morphologically cryptic species. However, these biogeographic barriers are rarely permanent, and secondary contact between previously independently evolving populations may result in hybridization and introgression. Here, we use genome-wide SNP data to reexamine a classic example of cryptic species in the Appalachian Mountains: the slimy salamander (<em>Plethodon glutinosus</em>) species complex. This group of morphologically similar terrestrial salamanders includes 14 species with parapatric distributions across the eastern United States. In this study, we focused on the Chattahoochee Slimy Salamander in northern Georgia and used a series of complementary phylogenomic and population genomic analyses to evaluate spatial genetic structure within this species and demonstrate admixture with at least four other species of parapatric woodland salamanders. Our results highlight the utility of genomic data in clarifying species boundaries and underscore the difficulty of species delimitation in organisms with complex evolutionary histories.</p>
research data for the article Elenius et al, 'Where can rewetting of forested peatland reduce extreme flows?'
<p>This dataset includes data of changes in hydrology when rewetting of drained forested peatland is performed. Details are explained in the companion article, Elenius et al, 'Where can rewetting of forested peatland reduce extreme flows?'</p> <p>Data worksheets and columns are described on the readme worksheet in the file.</p>
Data from: Gene flow in the anemone Anthopleura elegantissima limits signatures of local adaptation across an extensive geographic range
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Flow virometry for water-quality assessment: Protocol optimization for a model virus and automation of data analysis
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Data for: Axisymmetric Displacement Flows in Flow-driven Fractures
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Data from: Genetic diversity and gene flow decline with elevation in montane mayflies
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Data from: Forest connectivity boosts pollen flow among populations of the oil-producing Nierembergia linariifolia
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Data for: Plio-Pleistocene climatic fluctuations and divergence with gene flow drive continent-wide diversification in an African bird
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Data from: Widespread selection and gene flow shape the genomic landscape during a radiation of monkeyflowers
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Data and code from: Imaging flow cytometry enables label-free cell sorting of morphological variants from populations of the unculturable bacterium <em>Pasteuria ramosa</em>
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Data from: Anthropogenic land‐use change intensifies the effect of low flows on stream fishes
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Data from: Contrasting sap flow characteristics between pioneer and late-successional tree species in secondary tropical montane forests of Eastern Himalaya, India
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Data and scripts from: Balanced polymorphism fuels rapid selection in an invasive crab despite high gene flow and low genetic diversity
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Data from: Living in flowing water increases resistance to ultraviolet B radiation
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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)
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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.