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700 results for “Dynamical model”
Impact of infectious diseases on wild bovidae populations in Thailand: Insights from population modelling and disease dynamics
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Data from: A dynamical model of growth and maturation in Drosophila
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'Biosim' for cellular automata modelling of leukaemic stem cell dynamics in acute myeloid leukaemia: insights into predictive outcomes and targeted therapies
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The evolution, complexity and diversity of models of long-term forest dynamics
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Statistical analysis code for output from a model used to simulate foot-and-mouth disease dynamics in the United Kingdom
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Data from: An open spatial capture–recapture model for estimating density, movement, and population dynamics from line-transect surveys
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An integrated population model reveals source-sink dynamics for competitively subordinate African wild dogs linked to anthropogenic prey depletion
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Occurrences and R code for: Dynamic distribution modeling of the Swamp Tigertail dragonfly Synthemis eustalacta (Odonata: Anisoptera: Synthemistidae) over a 20-year bushfire regime
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Data from: Evaluating the effects of wolf culling on livestock predation when considering wolf population dynamics in an individual-based model
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Data from: Evaluating the importance of individual heterogeneity in reproduction to Weddell seal population dynamics using integral projection models
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Data from: Phylogenetic biogeography inference using dynamic paleogeography models and explicit geographic ranges
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McMurdo Dry Valleys Nitrogen and Phosphorus Dynamics in Green Creek, January 1995. Field and Modeling data
McMurdo Dry Valleys, Antarctica, contain numerous glacial meltwater streams that drain into lakes on the valley floors. Many of the streams have abundant perennial mats of filamentous cyanobacteria. The algal mats grow during streamflow in the austral summer and are in a dormant freeze-dried state during the rest of the year. NO3 and soluble reactive P (SRP) concentrations were lower in streams with abundant algal mats than in streams with sparse algal mats. Here we describe and distribute the data associated with this study: An experimental injection of LiCl, NaNO3, and K3PO4 was conducted in Green Creek, which has abundant algal mats. Substantial hyporheic exchange occurred. A model in which PO4 uptake occurred only in the main channel and NO3 uptake occurred in the main channel and in the hyporheic zone. Green Creek (Gooseff, 2015) aerial flowing into Fryxell
Data supplement for "Gradient dynamics model for drops spreading on polymer brushes"
<p>This dataset contains the data and source files for the diagrams of the following publication:</p> <p><em>Thiele, U. & Hartmann, S.<br> Gradient dynamics model for drops spreading on polymer brushes<br> arXiv preprint arXiv:1910.10582, 2019 </em></p> <p>We provide the data and sources necessary to generate the figures 3 & 4 of the manuscript.</p> <p>For more information, please see the included README.md</p>
Model data repository of "How sediment thickness influences subduction dynamics and seismicity"
<p>This repository provides the code and data to run the Seismo-Thermo-Mechanical model with a sediment thickness T<sub>sed</sub> of 4 km on a cluster using executables.</p>
VTFT_Demography: global ageclass simulation data from the LPJ-wsl v2.0 Dynamic Global Vegetation Model
<p>Forest ecosystem processes follow classic responses with age, peaking production around canopy closure and declining thereafter. Although age dynamics might be more dominant in certain regions over others, demographic effects on net primary production (NPP) and heterotrophic respiration (Rh) are bound to exist. Yet, explicit representation of ecosystem demography is notably absent in most global ecosystem models. This is concerning because the global community relies on these models to regularly update our collective understanding of the global carbon cycle. This paper aims to fill this gap in understanding by presenting the technical developments of a computationally-efficient approach for representing age-class dynamics within a global ecosystem model, the LPJ-wsl v2.0 Dynamic Global Vegetation Model. The modeled age-classes are initially created by fire feedbacks, wood harvesting, and abandonment of managed land, otherwise aging naturally until a stand-clearing disturbance is simulated or prescribed. In this paper, we show that the age-module can capture classic demographic patterns in stem density and tree height compared to inventory data, and that patterns of ecosystem function follow classic responses with age. We also present a few scientific applications of the model to assess the modeled age-class distribution over time and to determine the demographic effect on ecosystem fluxes relative to climate. Simulations show that, between 1860 and 2016, zonal age distribution on Earth was driven predominately by fire, causing a ~45-year difference in ages between boreal (50N-90N) and tropical (23S-23N) latitudes. Land use change and land management was responsible for an additional decrease in zonal age by -6 years in boreal and by -21 years in temperate (23N-50N) and tropical latitudes, with the anthropogenic effect on zonal age distribution increasing over time. A statistical model helped reduced LPJ-wsl complexity by predicting per-grid-cell annual NPP and Rh fluxes by three terms: precipitation, temperature and age-class; at global scales, R<sup>2</sup> was between 0.95 and 0.98. As determined by the statistical model, the demographic effect on ecosystem function was often less than 0.10 kg C m<sup>-2</sup> yr<sup>-1</sup> but as high as 0.60 kg C m<sup>-2</sup> yr<sup>-1</sup> where the effect was greatest. In eastern forests of North America, the demographic effect was of similar magnitude, or greater than, the effects of climate; demographic effects were similarly important in large regions of every vegetated continent. Spatial datasets are provided for global ecosystem ages and the estimated coefficients for effects of precipitation, temperature and demography on ecosystem function. The discussion focuses on our finding of an increasing role of demography in the global carbon cycle, the effect of demography on relaxation times (resilience) following a disturbance event and its implications at global scales, and a finding of a 40-Pg C increase in turnover from age dynamics at global scales. Whereas time is the only mechanism that increases ecosystem age, any additional disturbance not explicitly modeled will decrease age. This LPJ-based age-module therefore simulates the upper limit of age-class distributions on Earth and represents another step forward towards understanding the role of demography in global ecosystems.</p>
Eco‐evolutionary dynamics driven by fishing: from single species models to dynamic evolution within complex food webs
<p>Evidence of contemporary evolution across ecological time scales stimulated research on the eco-evolutionary dynamics of natural populations. Aquatic systems provide a good setting to study eco-evolutionary dynamics owing to a wealth of long-term monitoring data and the detected trends in fish life-history traits across intensively harvested marine and freshwater systems. In the present study, we focus on modelling approaches to simulate eco-evolutionary dynamics of fishes and their ecosystems. Firstly, we review the development of modelling from single-species to multispecies approaches. Secondly, we advance the current state-of-the-art methodology by implementing evolution of life-history traits of a top predator into the context of complex food web dynamics as described by the allometric trophic network (ATN) framework. The functioning of our newly developed eco-evolutionary ATNE framework is illustrated using a well-studied lake food web. Our simulations show how both natural selection arising from feeding interactions and size-selective fishing cause evolutionary changes in the top predator and how those feed back to its prey species and further cascade down to lower trophic levels. Finally, we discuss future directions, particularly the need to integrate genomic discoveries into eco-evolutionary projections.</p>
Improving the representation of high-latitude vegetation distribution in dynamic global vegetation models
<p></p><p>Vegetation is an important component in global ecosystems, affecting the physical, hydrological and biogeochemical properties of the land surface. Accordingly, the way vegetation is parameterised strongly influences predictions of future climate by Earth system models. To capture future spatial and temporal changes in vegetation cover and its feedbacks to the climate system, dynamic global vegetation models (DGVM) are included as important components of land surface models. Variation in the predicted vegetation cover from DGVMs therefore has large impacts on modelled radiative and non-radiative properties, especially over high-latitude regions. DGVMs are mostly evaluated by remotely sensed products, but rarely by other vegetation products or by in-situ field observations. In this study, we evaluate the performance of three methods for spatial representation of vegetation cover with respect to prediction of plant functional type (PFT) profiles – one based upon distribution models (DM), one that uses a remote sensing (RS) dataset and a DGVM (CLM4.5BGCDV). PFT profiles obtained from an independently collected vegetation data set from Norway were used for the evaluation. We found that RS-based PFT profiles matched the reference dataset best, closely followed by DM, whereas predictions from DGVM often deviated strongly from the reference. DGVM predictions overestimated the area covered by boreal needleleaf evergreen trees and bare ground at the expense of boreal broadleaf deciduous trees and shrubs. Based on environmental predictors identified by DM as important, we suggest implementation of three novel PFT-specific thresholds for establishment in the DGVM. We performed a series of sensitivity experiments to demonstrate that these thresholds improve the performance of the DGVM. The results highlight the potential of using PFT-specific thresholds obtained by DM in development and benchmarking of DGVMs for broader regions. Also, we emphasize the potential of establishing DM as a reliable method for providing PFT distributions for evaluation of DGVMs alongside RS. </p><p></p>
Real-time benchmark dynamics of the Ohmic Spin-Boson Model computed with Time-Dependent Variational Matrix Product States. (TDVMPS) coupling strength and temperature parameter space
<p>Data describing the complete propagators (maps) for the evolution of the Ohmic Spin-Boson Model are made available, here. Using a time-dependent variotnal matrix product states (TDVMPS) respresentation of the complete spin-environment wave function, non -perturbative results are presented over a wide range of coupling strengths, temperatures and initial conditions. The results in this repository are associated with the article: </p> <p>https://www.preprints.org/manuscript/202012.0016/v1 </p> <p>A mathematica notebook that allows the data to be visualised and manipulated is also provided. </p>
Data for manuscript: An ecogeomorphic framework coupling sediment modeling with invasive riparian vegetation dynamics
<p>Datasets (aside from the publicly available GIS datasets) used in the analyses presented in the manuscript "An ecogeomorphic framework coupling sediment modeling with invasive riparian vegetation dynamics." </p>
Exact Spin-Boson-Model Tunneling Dynamics with Time Dependent Variation Matrix Product States (TDVMPS). Barrier height and temperature parameter space
<p>Spin-Boson tunnelling data acquired using the T-TEDOA method for Time-Dependent-Variational-Matrix-Product-States (TDVMPS) accompanying the paper <a href="https://doi.org/10.3389/fchem.2020.600731">https://doi.org/10.3389/fchem.2020.600731</a>.</p> <p> </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.