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226 results for “growth model”
Exploring controls on coastal dune growth through a simplified model [Dataset]
<p>This dataset contains the Duna model output data, included in the article “Exploring controls on coastal dune growth through a simplified model” published in the <em>Journal of Geophysical Research - Earth Surface</em>.</p> <p>The data is provided as “Fig*.mat” files, processed in MatLab (R2023a), and organised following the structure of the figures presented in the manuscript (e.g., Fig1.mat corresponds to data shown in Fig.1). </p> <p>'Dataset.docx' provides information on the individual files contained in the dataset.</p>
Phase field modelling combined with data-driven approach to unravel the orientation influenced growth of interfacial Cu6Sn5 intermetallics under electric current stressing
<p><strong>Description:</strong></p> <p>The datasets are constituted by two folders, namely, (A) data_features_and_metric.zip and (B) grain_area_prediction.zip. </p> <p><strong>(A) data_features_and_metric.zip:</strong></p> <p>The following are the contents of this folder</p> <p>(i) <em>grainTheta.csv file</em> : The "grainTheta.csv" file consists the datasets generated from multiple phase field simulations. Name of the columns in the csv file are:</p> <p> <strong>gnid </strong>= grain id number "n", <strong>ntheta = </strong>orientation angle of n<sup>th</sup> grain (<sup>o</sup>); <strong>nltheta </strong>= orientation angle of grain to the left of n<sup>th</sup> grain (<sup>o</sup>); <strong>nrtheta </strong>= orientation angle of grain to the right of n<sup>th</sup> grain (<sup>o</sup>); <strong>j </strong>= current density (A/m<sup>2</sup>);<strong> t =</strong> time (s); <strong>area</strong> = area of n<sup>th</sup> grain (m<sup>2</sup>);<strong> tl</strong> = horizontal length of the top edge of grain "n" (m) ; <strong>bl </strong>= horizontal length of the bottom edge of grain "n" (m) </p> <p>The features gnid, ntheta, nltheta and nrtheta for a given observation are determined during the design of initial conditions of the corresponding phase field simulation. The value of "j" for the observation is determined via the boundary condition in the same numerical simulation. The result from the finite element method based phase field simulation has provided the numerical quantities for t, area, tl and bl attributes. The multiple observations in the data file have been obtained from multiple phase field simulations. </p> <p>(ii) <em>imc_theta.ipynb, imc_theta.py and imc_theta.html files</em>: These files contain the code to build the Pearson's Correlation Coefficient (PCC) heatmap analysis of the data contained in grainTheta.csv file. </p> <p>(iii) <em>comparison_mse.csv</em>: This data file includes the information about mean square error for training data (tmse) and mean square error for validation data (vmse) at Epoch = 199 resulting from 10 different artificial neural network (ANN) models distinguished by 10 different values of learning rates (lr) . Thus, the name of the columns in this csv file are <strong>modelno</strong>, <strong>lr</strong>, <strong>tmse</strong> and <strong>vmse</strong>. </p> <p>(iv) <em>mse_comparison.gnu</em>: This file consists the codes required to output a png image from the data provided in comparison_mse.csv<em>. </em></p> <p>(v) train_loss.csv and val_loss.csv: These files consist of the data of tmse and vmse at all points of Epochs for the ANN model with lr = 2.5E-4 . Thus, the first column in train_loss.csv file corresponds to tmse whereas the second column is Epochs number. Similarly, vmse and Epochs represent the two columns in val_loss.csv file. </p> <p> </p> <p>(vi) <em>mse_lr2p5e-4.gnu</em> : This file consists the codes required to output a png image from the data provided in train_loss.csv and val_loss.csv<em>. </em></p> <p><strong>(B) grain_area_prediction.zip:</strong></p> <p>Inside this folder, there is a folder named "prediction_of_grain_area" consisting of the following files:</p> <p><em>initial_area.csv file</em>: This file consists the value of the initial grain area of grain 4. It is a constant at all orientation angle.</p> <p><em>predicted_result_00_5e4.csv</em>: This file consists of the prediction result of grain 4 area (at different orientation angles and t = 1250 s) for grain 3 and grain 5 at orientation angles of 0<sup>o</sup> and 0<sup>o </sup>respectively, and for applied current density of 5.0E+4 J/m<sup>2</sup> .</p> <p><em>predicted_result_00_5e5.csv</em>: This file consists of the prediction result of grain 4 area (at different orientation angles and t = 1250 s) for grain 3 and grain 5 at orientation angles of 0<sup>o</sup> and 0<sup>o </sup>respectively, and for applied current density of 5.0E+5 J/m<sup>2</sup> .</p> <p><em>predicted_result_9090_5e4.csv</em>: This file consists of the prediction result of grain 4 area (at different orientation angles and t = 1250 s) for grain 3 and grain 5 at orientation angles of 90<sup>o</sup> and 90<sup>o </sup>respectively, and for applied current density of 5.0E+4 J/m<sup>2</sup> .</p> <p><em>predicted_result_9090_5e5.csv</em>: This file consists of the prediction result of grain 4 area (at different orientation angles and t = 1250 s) for grain 3 and grain 5 at orientation angles of 90<sup>o</sup> and 90<sup>o </sup>respectively, and for applied current density of 5.0E+5 J/m<sup>2</sup> .</p> <p><em>area_00_adj.gnu</em> : This gnu file contains the code to produce the png image from the data contained in <em>predicted_result_00_5e4.csv </em>and<em> predicted_result_00_5e5.csv </em>. The information about the initial area of grain 4 is obtained from <em>initial_area.csv</em> file by the code.</p> <p><em>area_9090_adj.gnu</em> : This gnu file contains the code to produce the png image from the data contained in <em>predicted_result_9090_5e4.csv </em>and<em> predicted_result_9090_5e5.csv </em>. The information about the initial area of grain 4 is obtained from <em>initial_area.csv</em> file by the code.</p> <p> </p>
Data and model code from: Tracing growth patterns in cod (Gadus morhua L.) using bioenergetic modelling
<p><span>Understanding individual growth in commercially exploited fish populations is key to successful stock assessment and informed ecosystem-based fisheries management. Traditionally, growth rates in marine fish are estimated using otolith age-reading in combination with age-length relationships from field samples, or tag-recapture field experiments. However, for some species, otolith-based approaches have been proven unreliable, and tag-recapture experiments suffer from high working effort and costs as well as low recapture rates. An important alternative approach for estimating fish growth is represented by bioenergetic modelling, which, in addition to pure growth estimation, can provide valuable insights into the processes leading to temporal growth changes resulting from environmental and related behavioural changes. We here developed an individual-based bioenergetic model for Western Baltic cod (<em>Gadus</em> <em>morhua</em>), traditionally a commercially important fish species that however collapsed recently and likely suffers from climate change effects. Western Baltic cod is an ideal case study for bioenergetic modelling because of recently gained in-situ process knowledge on spatial distribution and feeding behaviour based on highly resolved data on stomachs and fish distribution. Additionally, physiological processes such as gastric evacuation, consumption, net-conversion efficiency, and metabolic rates have been well studied for cod in laboratory experiments. Our model reliably reproduced seasonal growth patterns observed in the field. </span><span>Importantly, our bioenergetic modelling approach implementing depth-use patterns and food intake allowed us to explain the potentially detrimental effect summer heat periods have on growth of Western Baltic cod that likely will increasingly occur in the future. Hence our model simulations highlighted a potential mechanism of how warming due to climate change affects the growth of a key species that may apply for similar environments elsewhere. </span></p> <p><span>Here we provide access to the individual-based bioenergetic growth model which is set up to model the growth of cod in ages 2 to 4 (<em>Gadus</em> <em>morhua</em> L.) in the Belt Sea (western part of the Western Baltic Sea) on a daily basis within one year. The model incorporates contemporary in-situ process knowledge on food intake and seasonal- and temperate-related spatial distribution of cod and allows us to identify seasonal growth patterns. The model is written in the statistical and programming environment R.<br></span></p>
Plant root growth against a mechanical obstacle: The early growth response of a maize root facing an axial resistance is consistent with the Lockhart model
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Modelling trait heterogeneity and inferring causal links in the macroevolution of growth habit in eudicot angiosperms
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Unoccupied aerial system enabled functional modeling of maize (Zea mays L.) height reveals dynamic expression of loci associated to temporal growth
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Saguaro recruitment data obtained by inverse-growth modelling
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Data and model code from: Tracing growth patterns in cod (Gadus morhua L.) using bioenergetic modelling
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Data from: Improving structured population models with more realistic representations of non-normal growth
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Supplemental information and Data for: Colloidal physics modeling reveals how per-ribosome productivity increases with growth rate in E. coli
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Extreme model exploration of a multi-scale simulation of tumor growth
<p>The dataset comprises the output of several simulations of a model of tumor growth with different parameter values. The model is a multi-scale agent-based model of a tumor spheroid that is treated with periodic pulses of the cytokine tumor necrosis factor (TNF). The multi-scale model simulates processes including i) the diffusion, uptake, and secretion of molecular entities such as oxygen, or TNF; ii) the mechanical interaction between cells; and iii) cellular processes including cell life cycle, cell death models, signal transduction.</p> <p>The multi-scale model was implemented and simulated using the PhysiBoSS framework (Letort et al. 2019). The dataset corresponds to different simulations trajectories obtained for alternative parameter values. The parameter explored are: i) the decay rate of the TNF after it binds the cell; ii) the TNF binding rate; and iii) the TNF secretion rate by NFkB activated cells.</p> <p>The dataset includes 48 different combinations of parameters. Each simulation is stored in a folder instance_[0-9]+ which includes the PhysiBoSS standard output files (<a href="https://github.com/gletort/PhysiBoSS/wiki">https://github.com/gletort/PhysiBoSS/wiki</a>). The root folder also includes other settings, logs, and outputs as well as the binary used to run the simulation.</p>
Modeling of Emittance Growth Due to Coulomb Collisions in Plasma-based Accelerators
<p>This dataset contains some supplementary material for the paper titled "Modeling of Emittance Growth Due to Coulomb Collisions in Plasma-based Accelerators" published in the journal of Physics of Plasmas. There are two folders in the compressed file. One includes two input files that are used for the open-source particle-in-cell code WarpX(https://github.com/ECP-WarpX/WarpX/). The other includes the data file, figure files, and the plotting scripts.</p>
ShellTrace v2.0 - Bivalve growth and trace element model
<p>This file contains the R-script used to model growth and trace element uptake in bivalve shells, as described in the equally named Geoscientific Model Development publication (de Winter et al., 2017), including a copy of the manuscript preprint.</p>
Data from: Variation in growth drives the duration of parental care: a test of Ydenberg's model
The duration of parental care in animals varies widely, from none to lifelong. Such variation is typically thought to represent a trade-off between growth and safety. Seabirds show wide variation in the age at which offspring leave the nest, making them ideal to test the idea that a trade-off between high energy gain at sea and high safety at the nest drives variation in departure age (Ydenberg's model). To directly test the model assumptions, we attached time-depth recorders to murre parents (fathers [which do all parental care at sea] and mothers; of each). Except for the initial mortality experienced by chicks departing from the colony, the mortality rate at sea was similar to the mortality rate at the colony. However, energy gained by the chick per day was ∼2.1 times as high at sea compared with at the colony because the father spent more time foraging, since he no longer needed to spend time commuting to and from the colony. Compared with the mother, the father spent ∼2.6 times as much time diving per day and dived in lower-quality foraging patches. We provide a simple model for optimal departure date based on only (1) the difference in growth rate at sea relative to the colony and (2) the assumption that transition mortality from one life-history stage to the other is size dependent. Apparently, large variation in the duration of parental care can arise simply as a result of variation in energy gain without any trade-off with safety.
Data from: Wear, tear and systematic repair: testing models of growth dynamics in conodonts with high-resolution imaging
Conodont elements are the earliest mineralised vertebrate dental tools and the only ones capable of extensive repair. Two models of conodont growth, as well as the presence of a larval stage, have been hypothesised. We analysed normally and pathologically developed elements to test these hypotheses and identified three ontogenetic stages characterised by different anisometric growth and morphology. The distinction of these stages is independently corroborated by differences in tissue strontium content. The onset of the last stage is marked by the appearance of wear resulting from mechanical food digestion. At least five episodes of damage and repair could be identified in the normally developed specimen. In the pathological element, function was compromised by development of abnormal denticles. This development can be reconstructed as addition of new growth centres out of the main growth axis during an episode of renewed growth. Our findings support the model of periodic retraction of elements and addition of new growth centres. Changes in strontium content coincident with distinct morphology and lack of wear in the early life stage indicate that conodonts might have assumed their mature feeding habit of predators or scavengers after an initial larval stage characterised by a different feeding mode.
Data for model analysis in "Beyond the growth rate of cosmic structure: Testing modified gravity models with an extra degree of freedom", arXiv:1502.03710
<p>SQLite databases containing theoretical predictions for the model comparison in arXiv:1502:03710.</p>
Supplementary data and microkinetic model for 'Key Role of CO Coverage for Chain Growth in Co-Based Fischer-Tropsch Synthesis'
<p>This repository contains:</p> <ol> <li>The DFT data (energies, frequencies and structures) of all important intermediates of the microkinetic model constructed for the publication ‘Key Role of CO Coverage for Chain Growth in Co-Based Fischer-Tropsch Synthesis’.</li> <li>Input files for the high CO coverage microkinetic model in Chemkin.</li> <li>(update 2025-06-19) Input files for the high CO coverage microkinetic model in Chemkin with CO2 activation (sim2.zip).</li> </ol> <p>DOI: <u>10.1021/acscatal.5c03024</u> and <u>10.1021/acscatal.3c04844</u></p>
A flashing flow model for the rapid depressurization of CO2 in a pipe accounting for bubble nucleation and growth – dataset
<p>This dataset contains data from the depressurization of pure CO2 in a tube from a cold, dense-liquid state. The data is described in the accompanying paper (DOI: <a href="https://doi.org/10.1016/j.ijmultiphaseflow.2023.104666">10.1016/j.ijmultiphaseflow.2023.104666</a>).</p><p>Test number; fluid; pressure (MPa); temperature (deg C):<br>25; CO2; 12.3; 4.6</p>
The dataset for Evaluation of plume-induced continental crust growth rate in early Earth: Insight from integrated petrological-thermo-mechanical modeling
<p>All data of model results for the paper <em>Evaluation of plume-induced continental crust growth rate in early Earth: Insight from integrated petrological-thermo-mechanical modeling</em>.</p> <p>Contents of this dataset includes:</p> <p>A list for models and data of model results are provided in the zip file. </p> <p>The files suffixed *.grd are grid datas that can be draw by GMT.</p> <p>The files suffixed *.info are datas of the evolution of volume of the continental crust and TTGs in the model.</p> <p>The files suffixed *.prn are datas of P-T condtions of continental crust generation by partial melting.</p> <p>The files suffixed *.py are python codes for visualization of the volume of continental crust and P-T datas.</p>
Modelling the growth of biofilms on soft substrates
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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.
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DANDI Archive for NWB datasets
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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.