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575 results for “Modeling Codes”
"A theoretical model of Surtseyan bomb fragmentation" code and data
<p>This is Matlab code and permeability and porosity data to accompany the manuscript "A theoretical model of Surtseyan bomb fragmentation", which is accepted for publication in the Proceedings of the Royal Society London, Series A, and for which a preprint will be deposited in arXiv.</p>
Data and Codes for Evaluating and Improving Scale-Awareness of a Convective Parameterization Closure Using Cloud-Resolving Model Simulations of Convection
<p>Provide necessary fields averaged over different subdomain sizes from 64 km to 4 km (see 64 to 4 .7z files) processed from the output of CRM simulation of MC3E case (for TWP-ICE case, please get the processed data and associated codes from http://doi.org/10.5281/zenodo.4542461). Also, associated codes for calculation of important fields (like dCAPEls, dCAPEe, Msa and so on) are also provided in code.7z. Please see all "note.txt" files in code.7z to know how to use these codes.</p>
Source code and data for Ou et al. 2021 (US state-level capacity expansion pathways with improved modeling of the power sector dynamics within a multisector model)
<p>For details, please check the "readme" file.</p>
Codes and datasets associated with the paper "Simulating an extreme over-the-horizon optical propagation event over Lake Michigan using a coupled mesoscale modeling and ray tracing framework"
<p>Here, you will find some of the codes, images, and datasets utilized in the article: </p> <p>Basu (2017). "Simulating an extreme over-the-horizon optical propagation event over Lake Michigan using a coupled mesoscale modeling and ray tracing framework", Optical Engineering, 56(7), 071505 (https://doi.org/10.1117/1.OE.56.7.071505)</p> <p>WRF codes: namelist.wps, namelist.input, myoutfields.txt</p> <p>NCL codes: d02_terrain.ncl, wrf_SurfaceASTD_d02.ncl</p> <p>RADAR loop: KGRR.gif</p> <p>MATLAB codes: Plot_Buoy.m</p> <p>Note: buoy datasets are available publicly from https://www.ndbc.noaa.gov/ </p>
Model codes and data for ``A control volume finite element model for predicting the morphology of cohesive-frictional debris flow deposits"
<p>The code and the dataset can be read/run by using Matlab. The description is as follows:</p> <p>1. Dataset (field_data) includes transect data of three field debris flow deposits from Coussot et al. (1996). The data can be read and calibrated with the analytical solution by the code field_calibration.m.</p> <p>2. Dataset (data_T01-T04, T11-T15_DT) includes experimental fan topography data, calibrated parameters, and simulation outputs. </p> <p>3. Two calibration codes are used for the model parameter calibrations of the two sets of experiments.</p> <p>4. Function aggradation_DT.m is the CVFEM model for simulating fan morphology. Use CVFEM_exp_simulation.m code to run simulations for the experiments.</p>
Model code and outputs of "Modeling the Dispersal of the San Francisco Bay Plume over the Northern and Central California Shelf"
<p>- "model_code.rar" contains the model source code.</p> <p>- "model_grid.nc" contains the grid file.</p> <p>- "model_outputs.nc" contains the model outputs.</p>
Model code and output data for Tang et al. 2022 (doi:10.3389/fenvs.2022.1013875)
<p>flex_extract.tar: The code used to prepare inputdata for flexpart and flexdust.</p> <p>FLEXDUST_default_20210301-20210331.nc: The output of default FLEXDUST.</p> <p>FLEXDUST_updated_20210311-20210320.nc: The output of updated FLEXDUST.</p> <p>flexpart_1h_default.nc: The output of forward FLEXPART simulation using default FLEXDUST.</p> <p>flexpart_1h_erode_topoTc_true_moreveg0.1_76FVThresh_noblock_KOK_AN05.nc: The output of forward FLEXPART simulation using updated FLEXDUST.</p>
Efficient Probabilistic Prediction and Uncertainty Quantification of Tropical Cyclone-driven Storm Tides and Inundation: Model Data and Analysis Code
<p>This repository contains model data and analysis codes related to the manuscript entitled "Efficient Probabilistic Prediction and Uncertainty Quantification of Tropical Cyclone-driven Storm Tides and Inundation", as follows:</p> <ol> <li>Model data are maximum water surface elevations of ensemble 48-hr forecast ADCIRC model simulations for three historical US landfalling hurricanes: 2017 Irma, 2018 Florence, and 2020 Laura. These are located in the "NameYYYY_Results.tar" archive files as "maxele.63.nc" files. Also included in the tar files are the hurricane forecast track files in Automated Tropical Cyclone Forecasting (ATCF) system format (*.22) and the error variable parameters (*.json) for each forecast. </li> <li>Model data of best-track runs for the 2017 Irma, 2018 Florence, and 2020 Laura hurricanes, and astronomical tide-only runs for the corresponding time periods are located in the "NameYYYY_besttrack+tides.tar" archive files. Both the maximum water surface elevations "maxele.63.nc" and the time series of water surface elevations "fort.63.nc" are included. </li> <li>ADCIRC input mesh (*.14) and mesh property files (*.13) are included in "ADCIRC_mesh_files.zip".</li> <li>Joint Karhunen-Loeve Polynomial Chaos (KL-PC) analysis python scripts with and without considering inundation are located in "klpc_analysis_scripts.zip". Requires <a href="https://github.com/noaa-ocs-modeling/EnsemblePerturbation">EnsemblePerturbation</a> python toolbox. </li> <li>Python scripts for analyzing and plotting the KL-PC results (Figures 6-14 and Table 1 in the manuscript) are located in "results_plotting_scripts.zip". Requires <a href="https://github.com/noaa-ocs-modeling/EnsemblePerturbation">EnsemblePerturbation</a> python toolbox. </li> </ol>
Data and Code for Modeling Lake Cahuilla
<p>Data and Code for Modeling Lake Cahuilla</p>
The data and code for "Gap-Filling of Turbulent Heat Fluxes over Rice–Wheat-Rotation Croplands Using the Random Forest Model""
<p>This file contains the dataset and code for the paper "Gap-Filling of Turbulent Heat Fluxes over Rice–Wheat-Rotation Croplands Using the Random Forest Model".</p>
Modeling hydraulic heads with impulse response functions in different environmental settings (dataset and code supplement)
<p>These folders contain the input data, the scripts, and the output to reproduce the results and figures of Jemeljanova et al. "Modeling hydraulic heads with impulse response functions in different environmental settings".<br> </p> <p>This research is funded by the Latvian Council of Science, project “Spatial and temporal prediction of groundwater drought with mixed models for multilayer sedimentary basin under climate change”, project No. lzp-2019/1-0165 and PostDoc research project agreement No. 1.1.1.2/VIAA/3/19/524.</p>
Highlighting the potential of multilevel statistical models for analysis of individual agroforestry systems (companion working R code)
<p>A companion working R code and crop yield dataset that illustrate key concepts presented in a scientific publication titled 'Highlighting the potential of multilevel statistical models for analysis of individual agroforestry systems' published in Agroforestry Systems (July 2023). For the shape file of tree strips, please refer to the publication's supplementary material (adjust the name accordingly). </p>
Data and code for, "Large language models design sequence-defined macromolecules via evolutionary optimization"
<div> <pre># Codes and data for "Large language models design sequence-defined macromolecules via evolutionary optimization"<br><br>Note this repository contains codes and data files for the manuscript. This is a snapshot of the repository, frozen at the time of submission.<br><br># Codes<br><br>## LLM codes<br>- `run_claude.py` - the routine for performing LLM-based rollouts; intended for command line execution using argparse<br>- `message_utils.py` - utilities for constructing and parsing messages for LLM I/O<br>- `model_utils.py` - lightweight utilities for retrieving formatted predictions from the RNN ensemble<br>- `target_defs.py` - defines the sequence, locations, and natural language descriptions of the target structures<br>- `ask_about_oracle.ipynb` - asks the LLM to speculate about the nature of the optimization task<br><br>## other algorithms<br>- `active_learning.ipynb` - use EI acquisition with RF surrogate to label new sequences; includes an unused tokenization scheme<br>- `evolutionary_algorithm.ipynb` - use DEAP library to perform evolutionary optimization<br>- `random_sampling.ipynb` - sample sequences randomly from all possible sequences<br><br>## postprocessing<br>- `process_aggregated_logs.py` - reads data from the raw log files and prepares them for visualization<br>- `process_sample_rollouts.py` - reads data from the raw log files and prepares individual rollouts<br><br>## visualization<br>- `figure1b.ipynb` - renders panel b of Fig. 1<br>- `figure1efg.ipynb` - renders the last row of Fig. 1 (panels e-g)<br>- `figure2.ipynb` - renders all of Fig. 2<br>- `figure_si.ipynb` - renders Figs. S1 and S2<br>- `figure_md_validation.ipynb` - renders Fig. S3<br><br># Data files<br><br>- `prompts/`<br> - `prompt-scientific-v4.4.yml` - the full text of the scientific prompt, to be read by `run_claude.py`<br> - `prompt-oracle-v4.4.yml` - the full text of the oracle prompt, to be read by `run_claude.py`<br>- `models/` - the TorchScript RNN models used to make predictions<br>- `data/`<br> - `embeddings` - calculated embeddings for a collection of sequences from our prior work<br> - `llm-logs` - the raw logs obtained from the Claude 3.5 Sonnet LLM (other algorithms made to look like the LLM logs after the fact)<br> - `llm-logs-opus` - the raw logs obtained from the Claude 3.0 Opus LLM (used in the first draft of the article, replaced by Claude 3.5 Sonnet) <br> - `all-rollouts-kltd.csv` - postprocessed logs for all the rollouts using the "top $k < d^*$" metric<br> - `all-rollouts-topkd.csv` - postprocessed logs for all the rollouts using the "mean $d$ for top $k$" metric<br> - `sample-rollout-membranes-x-3.csv` - postprocessed logs for a single rollout replica, `x` = each algorithm type<br> - `snapshots` - png snapshots of MD simulation results at different locations in the manifold</pre> </div>
Data and code for simulation study and case study in "A Bayesian Dirichlet process community occupancy model to estimate community structure and species similarity"
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Constitutive model of erythrocyte membranes with distributions of spectrin orientations and lengths - pipette aspiration data and MatLab Code
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Supplementary code for: Polygenic local adaptation in metapopulations: a stochastic eco-evolutionary model
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The performance of permutations and exponential random graph models when analysing animal networks (R code and data)
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MATLAB code related to the thermal model of hyperthermia treatment
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Data and code from: Growth strategies of a model picoplankter depend on social milieu and pCO2
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A model of the optimal selection of crypto assets - simulation code
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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)
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.