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575 results for “Modeling Codes”
Inferring the evolutionary model of community-structuring traits with convolutional kitchen sinks: Code and data
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Redwood Creek Chinook salmon life cycle model code and data
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SBC LTER: Regional Oceanic Modeling System (ROMS) Setup Files, Code, and Lagrangian Model Setup Files
This data contains all the necessary code, grid, forcing, initial, and boundary condition files for running the UCLA version of the Regional Oceanic Modeling System (ROMS) for the Santa Barbara Channel nested solution set that is analyzed in Dauhajre and McWilliams (2019): Nearshore Lagrangian Connectivity: Resolution Sensitivity and Submesoscale Influence. Along with this abstract and separate methods, a brief readme file (README_ROMS) contains other relevant details for running the simulations. The ROMS files in this data correspond to a one-way grid-nesting with the following horizontal resolutions: dx=1km, 300m, 100m, 36m that correspond to R1km, R300m, R100m, and R36m in the publication. In the data directories here, these grids have the following prefixes for all relevant files (grids, forcing, initial condition, and boundary conditions): “usw1” (dx=1km), “usw2” (dx=300m), “usw3” (dx=100m), and “usw4sbc” (dx=36m). The ROMS code is in fortran and requires compilation, with compilation instructions in the directory /src_ROMS_UCLA_2018 in the file “compile.sh”. All grid, forcing, initial, and boundary condition files are in netcdf form. The grid files are in the directory “grids”. Note that the R1km grid has 2 files: usw1_grd.nc and usw1_grd_samp.nc. The latter is a smaller version of the grid that focuses on the Santa Barbara Channel that can be used for the offline Lagrangian model for faster computation (which requires an analogous sub-sampling of the ROMS output). For running ROMS, usw1_grd.nc needs to be used as it corresponds to the entire domain. The atmospheric forcing, derived from a Weather Research and Forecasting model at dx=6km resolution is given in the directory “forcing_files”; all atmospheric forcing files are interpolated to the relevant ROMS grid and formatted to be used as inputs in ROMS. Each grid contains a file corresponding to precipitation (e.g., usw1_prec.nc), radiation (e.g., usw1_rad.nc), atmospheric temperature and specific humidity (
The MATLAB code for "A kinematic model for understanding rain formation efficiency of a convective cell"
<p>Please check the code for the 1D model and the plotting commands. Please start from main.m </p>
Spectral data and R modeling code from: Polarized light sensitivity in Pieris rapae is dependent on both color and intensity
<p>This dataset provides supplementary spectral data and the R code underlying the spectral sensitivy moding of female <em>Pieris rapae</em> photoreceptors used in the manuscript "Polarized light sensitivity in <em>Pieris rapae</em> is dependent on both color and intensity".</p>
Data and code for training and evaluating machine learning models for thunderstorm prediction from reanalysis data
<p>FIXED Data and Python code for training and evaluating machine learning models for predicting thunderstorms, associated with the paper:</p> <p>"Evaluation of machine learning classifiers for predicting deep convection"</p> <p>by Peter Ukkonen and Antti Mäkelä (to appear in JAMES)</p> <p>The data (preprocessed inputs and outputs) is stored as netCDF files and .mat files which can be loaded with Python. </p>
Source code for models of floral initiation in pea and gene expression data extracted from published sources
<p>The dataset contains the source code for computational models of a gene network controlling transition to flowering in pea (<em>Pisum sativum</em>). The models were based on ordinary differential equations (ODE) or neural networks. It also includes data on the expression dynamics of genes involved in the network, which was used for model fitting. The expression data was extracted from the following papers: </p> <p>Hecht, V., Laurie, R. E., Schoor, K. Vander, Ridge, S., Knowles, C. L., Liew, L. C., Sussmilch, F. C., et al. (2011). The Pea GIGAS Gene Is a FLOWERING LOCUS T Homolog Necessary for Graft-Transmissible Specification of Flowering but Not for Responsiveness to Photoperiod. 23, 147–161. doi:10.1105/tpc.110.081042</p> <p>Sussmilch, F. C., Berbel, A., Hecht, V., Schoor, K. Vander, Ferrándiz, C., Madueño, F., et al. (2015). Pea VEGETATIVE2 Is an FD Homolog That Is Essential for Flowering and Compound In fl orescence Development. 27, 1046–1060. doi:10.1105/tpc.115.136150</p> <p>The source code of the DEEP software used for parameter optimization in the model fitting can be found in the Gitlab repository (https://gitlab.com/mackoel/deepmethod/-/tree/master).</p> <p>The files are the supplement to the following manuscript, submitted to Frontiers in Genetics:</p> <p>"Dynamical Modeling of the Core Gene Network Controlling Transition to Flowering in <em>Pisum sativum</em>" by Polina Pavlinova, Maria G. Samsonova, and Vitaly V. Gursky.</p> <p>All possible questions can be sent to: Polina Pavlinova (polina.pavlina1004@gmail.com), Vitaly Gursky (gursky@math.ioffe.ru).</p>
Data and code for paper "A gray-box model for a probabilistic estimate of regional ground magnetic perturbations: Enhancing the NOAA operational Geospace model with machine learning"
<p>Simulation results from the NOAA/SWPC Geospace model used in the paper Camporeale et al. (2020) "A gray-box model for a probabilistic estimate of regional ground magnetic perturbations: Enhancing the NOAA operational Geospace model with machine learning" published in J. Geophys. Res. (2020)</p> <p>MATLAB code is provided to train process the data and train the machine learning model and plot results.</p> <p>Manuscript available on <a href="https://arxiv.org/abs/1912.01038">https://arxiv.org/abs/1912.01038</a></p>
Code-Level Model Checking in the Software Development Workflow -- Replication Package
<p>This experience report describes a style of applying symbolic model checking developed over the course of four years at Amazon Web Services (AWS). Lessons learned are drawn from proving properties of numerous C-based systems, e.g., custom hypervisors, encryption code, boot loaders, and an IoT operating system. Using our methodology, we find that we can prove the correctness of industrial low-level C-based systems with reasonable effort and predictability. Furthermore, AWS developers are increasingly writing their own formal specifications. All proofs discussed in this paper are publicly available on GitHub. All proofs and specifications described in the paper are available, under the Apache 2.0 license, on the GitHub repository located at <a href="https://github.com/awslabs/aws-c-common/">https://github.com/awslabs/aws-c-common/</a> This is the master repository for AWS C Common library, and is in active use by the AWS C Common development team. The description of the contents of this repository are based off commit <code>b0ea9f35df8934f9e03fc3bab3919d55efd69b88</code>, although they are not expected to change significantly in the future.</p>
Data and Code for: Comparative species abundance modeling of Capitellidae (Annelida) in Tampa Bay, Florida, USA
<p>This publication contains the R-Scripts, RData, data frames, and other supplementary files necessary to reproduce the analyses and figures of Hilliard J, Karlen D, Dix T, Markham S, Schulze A (2020) Comparative species abundance modeling of Capitellidae (Annelida) in Tampa Bay, Florida, USA. Mar Ecol Prog Ser 653:105-119. <a href="https://doi.org/10.3354/meps13484">https://doi.org/10.3354/meps13484</a></p> <p>All files are compressed into a single ZIP folder. The main directory contains a README file that explains the contents of the subdirectories.</p>
All simulation results, figures and code regarding the manuscript: Calibrating models of cancer invasion: parameter estimation using Approximate Bayesian Computation and gradient matching
<p>We present two different methods to estimate parameters within a partial differential equation (PDE) model of cancer invasion. The model describes the spatio-temporal evolution of three variables -- tumour cell density, extracellular matrix density and matrix degrading enzyme concentration -- in a one-dimensional tissue domain. The first method is a likelihood-free approach associated with Approximate Bayesian Computation (ABC); the second is a two-stage gradient matching method based on smoothing the data with a Generalized Additive Model (GAM) and matching gradients from the GAM to those from the model. Both methods performed well on simulated data. To increase realism, additionally we tested the gradient matching scheme with simulated measurement error and found that the ability to estimate some model parameters deteriorated rapidly as measurement error increased.</p>
Discrete Element Model for subglacial till: code and data
<p>This is a permanent archive of the code and data for the Discrete Element Model, Sphere, developed by Anders Damsgaard, and used for simulations of deforming subglacial till. File is compressed in the tar.bz2 format.</p> <p>The Sphere code and documentation can be found at <a href="https://src.adamsgaard.dk/sphere/">https://src.adamsgaard.dk/sphere/</a> .</p> <p>Run sphere/python/MyVisualization.py to generate the figures in sphere/Images/.</p>
Data and R code from: Modelling the evolution of cognitive styles
Background <p>Individuals consistently differ in behaviour, exhibiting so-called personalities. In many species, individuals differ also in their cognitive abilities. When personalities and cognitive abilities occur in distinct combinations, they can be described as 'cognitive styles'. Both empirical and theoretical investigations produced contradicting or mixed results regarding the complex interplay between cognitive styles and environmental conditions.</p> Results <p>Here we use individual-based simulations to show that, under just slightly different environmental conditions, different cognitive styles exist and under a variety of conditions, can also co-exist. Co-existences are based on individual specialization on different resources, or, more generally speaking, on individuals adopting different niches or microhabitats.</p> Conclusions <p>The results presented here suggest that in many species, individuals of the same population may adopt different cognitive styles. Thereby the present study may help to explain the variety of styles described in previous studies and why different, sometimes contradicting, results have been found under similar conditions.</p>
Adjudicating between face-coding models with individual-face fMRI responses: Data and analysis software
<p>Computational model fits to human neuroimaging data. Please see included readme.txt file.</p>
Aerodynamics code used in Wind Energy Science paper "Comparison of a coupled near- and far-wake model with a free-wake vortex code"
<p>This research code has been developed from the start of my PhD as a first step before the HAWC2 implementation of the near wake model.</p> <p>It can be used to make aerodynamic computations of a stiff wind turbine rotor, and it includes</p> <ul> <li>A BEM and far wake model implementation based on the one in HAWC2</li> <li>An attached flow unsteady airfoil aerodynamics model including the modifications described in the WES article</li> <li>Most importantly a near wake model implementation including all major modifications except the recent stand still extension presented at TORQUE 2016</li> </ul> <p>All the data files need to be in a subfolder 'NREL_5MW' located in the same folder as the compiled source code.</p> <p>With the present (hardcoded) settings, the program will simulate the NREL 5 MW reference turbine for 650 seconds, with blade vibrations according to different prescribed mode shapes after steady state is reached. The aerodynamics model is a coupled near and far wake model. The integrated aerodynamic work during 1 period of the different prescribed vibrations will be output in the file 'aerowork.out' .</p> <p>The NREL 5 MW turbine is described in:</p> <p>Jonkman, J., Butterfield, S., Musial,W., and Scott, G.: Definition of a 5-MW Reference Wind Turbine for Offshore System Development, National Renewable Energy Laboratory, 2009.</p>
Tailoring Code Smells to Model-View-Controller Architectures: Online appendix
<p>Appendix for our paper "Tailoring Code Smells to Model-View-Controller Architectures" (EMSE journal)</p>
Dataset and source code for ICSME2017 paper "Supervised vs Unsupervised Models: A Holistic Look at Effort-Aware Just-in-Time Defect Prediction"
<p>Dataset and source code for ICSME2017 paper “Supervised vs Unsupervised Models: A Holistic Look at Effort-Aware Just-in-Time Defect Prediction”</p> <p>There are four different models in the paper (i.e., EALR, LT, CBS and OneWay). Each model was implemented in a single Java file in the model package. To reproduce the experiment results of each model in the paper, just run the main method in the corresponding Java file. </p> <p> </p>
Scenario data, model source code and plotting routine for manuscript: Separating CO2 emission from removal targets comes with limited cost impacts
<p>This data archive contains REMIND model setup, results data and data analysis files for manuscript:<br><strong>Separating CO2 emission reduction from removal targets comes with limited cost impact.<br><br>plotting</strong>(directory) contains results data, manuscript specific data analysis and plotting routine scripts used to generate the figures of the manuscript.<br><strong>remind</strong>(directory) contains REMIND model source code and scenario set-up. Detailed scenario configurations are set in remind/config/scenario_config_SepMark.csv.<br><strong>remind2</strong>(directory) contains the slightly modified R-library package used for post-processing of REMIND output.<br><br>AMENDMENT<br><strong>Plots_SeparateMarkets_afterReviewProcess.Rmd</strong> After the review process, the new plotting script was added including the additional figures in the Supplementary Material. This file should replace the previous R-markdown file SepMark_essential/plotting/Plots_SeparateMarkets.Rmd.</p>
Matlab codes implementing the XDROM+ data-driven ENSO forecast model and some analysis of it
<p>This is the BEST forecast model of large scale features of ENSO as of today, beating (Zhao et al. Nature 2024).</p> <p>This archive is supplementary to a comment article concerning (Zhao et al. Nature 2024) intended as a "Matters Arising" piece to be submitted to Nature (https://www.researchsquare.com/article/rs-5336072/v1). Given that i criticise also the handling editor and 3 reviewers of (Zhao et al. Nature 2024) calling their incompetence out, do not be surprised if you have to look for the paper in some other journal instead. Oh well, integrity is above all else, no?! On that note, may I interest you in a bit of sci-fi? https://www.linkedin.com/pulse/crime-punishment-bit-differently-tamas-bodai-g4cvf/?trackingId=WdQkSNjgSuyxlyWVLRonrw%3D%3D</p>
Data, code and supplementary plots for "A hierarchical spline model for correcting and hindcasting temperature data"
<p>Data, code and supplementary plots for the paper "A hierarchical spline model for correcting and hindcasting temperature data". Please see README.txt for detailed description of the files and relevant instructions.</p>
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.