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575 results for “Modeling Codes”
Robust Active Measuring under Model Uncertainty - Code
<p>Repository containing code, as well as gathered data, as used for the paper</p> <blockquote> <p>Merlijn Krale, Thiago D. Simao, Jana Tumova, Nils Jansen<br>Robust Active Measuring under Model Uncertainty<br>In AAAI, 2024.</p> </blockquote> <p>For instructions, see the readme.md file in the repository.</p> <p>All code can also be found on GitHub, at <a href="https://github.com/LAVA-LAB/RATM">https://github.com/LAVA-LAB/RATM</a>.</p>
The code and data for paper "Reassessing Java Code Readability Models with a Human-Centered Approach"
<p>The code and data for paper "Reassessing Java Code Readability Models with a Human-Centered Approach".<br> Please refer to README.md for detailed instructions.</p>
Model hydrodynamic outputs and example codes for running different Lagrangian particle tracking packages
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Data for ICSE 2024 paper "Learning in the Wild: Towards Leveraging Unlabeled Data for Effectively Tuning Pre-trained Code Models".
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The Matlab application code of these models used in this study.
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Pruned DNN model data for pruning example code
<p>Pruned DNN model datasets for example codes of neural network pruning.</p> <p>Example pruning codes are published in "https://github.com/FujitsuLaboratories/CAC/tree/main/cac/pruning".</p>
Source code and data from informed dispersal model on range expansion
<p>In those files, you can find the source code to run the model and the R codes to process the data and create the figures</p>
CodeQual: A dataset for fine-tuning Large Language Models for code quality assessment task
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Regularized Benders Decomposition for High Performance Capacity Expansion Models: Data and Code
<p>This dataset contains all code, input and results data relevant to the working paper <a href="https://arxiv.org/abs/2403.02559">‘Regularized Benders Decomposition for High Performance Capacity Expansion Models’</a>, in IEEE Transactions on Power Systems DOI 10.1109/TPWRS.2025.3526413.</p> <p>The dataset is comprised of three folders:<br><br><code><em>GenX-Benders.zip</em></code> Contains the source code used in this study, which extends the open-source electricity capacity expansion model GenX (<a href="https://github.com/GenXProject/GenX.jl/releases/tag/v0.3.5">@v0.3.5</a>), implementing different regularized Benders decomposition algorithms as solving routines.</p> <p><em><code>Test_Systems.zip</code> </em>Includes the input files for generating the GenX model for the five considered test systems. Note that GenX-Brazil has been derived from the <a href="https://gitlab.com/dlr-ve/esy/open-brazil-energy-data">Open Brazil Energy Data</a> as detailed in the manuscript.<br><br><em><code>Results.zip</code> </em>Includes all result files for the computaitonal experiments reported in the manuscript.</p>
Dataset and Model Code for "Fortitudinous fisher: the role of individuality and context dependency in informing habitat selection in risky environments."
<p>Fine-scale GPS location data for 43 fisher in Rhode Island formatted for habitat analysis using step selection function (200avail_7030_95x) as R file. R-script containing model code.</p>
Supplementary Data: Code, Input Data and Model data: PyPSA-Eur: An Open Optimisation Model of the European Transmission System
<p>Supplementary Data (preliminary version)</p> <p>PyPSA-Eur: An Open Optimisation Model of the European Transmission System</p> <p>Authors: J. Hörsch, F. Hofmann, D. Schlachtberger, T. Brown</p> <p>and</p> <p>The role of spatial scale in joint optimisations of generation and transmission for European highly renewable scenarios</p> <p>Authors: J. Hörsch, T. Brown</p> <p>The files in this record contain the scripts to build a <a href="http://pypsa.org/">PyPSA</a> model of the European Electricity System including renewable feed-in from wind, solar and hydro installations derived from reanalysis weather data satellite irradiation. The model PyPSA-Eur is described in the above publication.</p> <p><strong>Scripts</strong></p> <p>To use the scripts, you need the following free software Python libraries:</p> <ul> <li><a href="https://github.com/PyPSA/PyPSA">PyPSA</a> for the modelling framework</li> <li><a href="https://github.com/FRESNA/vresutils">vresutils</a> for various helper functions to build the model instance</li> <li><a href="https://github.com/FRESNA/atlite">atlite</a> to process weather data into power system data</li> <li><a href="https://snakemake.readthedocs.io/en/latest/">snakemake</a> to organise the execution of the software</li> </ul> <p>and other standard libraries from the <a href="https://pypi.python.org/pypi">Python Package Index</a> (PyPI), such as pandas, pyomo, countrycode, etc.</p> <p>snakemake requires that all code runs with Python version 3. The code setup is known to work with the following versions: PyPSA 0.12.0, pandas 0.21.1, numpy 0.14.0, scipy 0.19.1, pyomo 5.2. You may need to downgrade your libraries to these versions for the scripts to work.</p> <p>The Python scripts in this repository (in the directory scripts/) are released under the <a href="https://www.gnu.org/licenses/gpl-3.0.en.html">GNU General Public Licence Version 3.0</a> (GPL 3.0).</p> <p>The scripts build_*.py process all raw input data into a form where it can be used in the model.</p> <p>base_network.py creates the initial PyPSA network topology.</p> <p>add_electricity.py adds generators and storage units to the models, it generates the detailed resolved model described in the PyPSA-Eur paper.</p> <p>simplify_network.py removes stub ac-buses from network topology and simplifies long dc lines.</p> <p>cluster_network.py creates clustered representations of the electricity network for a given number of buses following the topology described in the "spatial scale" paper.</p> <p>prepare_network.py adds parameters like the CO2 limit and the transmission expansion volume relevant for the optimization to the model.</p> <p>All scripts are managed with the <a href="http://snakemake.readthedocs.io/en/latest/">snakemake</a> workflow management tool.</p> <p>To run the scripts, adjust the parameters in config.yaml and cluster.yaml to your local configuration. Then simply execute</p> <pre><code>snakemake</code></pre> <p>for the rule you want to run.</p> <p><strong>Data</strong></p> <p>The input data include:</p> <ul> <li>Electricity sector data</li> <li>Topology derived from the analysis of an extract of the <a href="https://www.entsoe.eu/data/map/">ENTSO-E online map</a> using <a href="https://github.com/bdw/GridKit">GridKit</a> .</li> <li>A cost database with literature sources.</li> </ul> <p> </p>
Code and data for: A computational model for driver's cognitive state, visual perception and intermittent attention in a distracted car following task
<p>A source code and data dump for analyses of the article "A computational model for driver’s cognitive state, visual perception and intermittent attention in a distracted car following task"</p> <p>Code is under GNU AGPL-v3. Data under CC-BY-4.0</p> <p>Versioned code is available at https://gitlab.com/mulsimco/follow17 and https://gitlab.com/mulsimco/cfmodels</p> <p>See README.md in follow17 for usage.</p>
MODFLOW model source code, files, and output
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Simulation data and code for "Modeling radiation belt dynamics using a positivity-preserving finite volume method on general meshes"
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Model Code and Various Datasets for Evaluation
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Datasets accompanying Deep generative AI models analyzing circulating orphan non-coding RNAs enable accurate detection of early-stage non-small cell lung cancer
<p>These datasets accompany the manuscript "Deep generative AI models analyzing circulating orphan non-coding RNAs enable accurate detection of early-stage non-small cell lung cancer".</p> <p>The datasets include:</p> <ul> <li> <p><code>metadata.tsv.gz</code> Phenotype information of the samples</p> </li> <li> <p><code>mirna_counts.tsv.gz</code> MicroRNA count matrix (for data normalization)</p> </li> <li> <p><code>oncrna_counts.tsv.gz</code> oncRNA count matrix</p> </li> <li> <p><code>orion/</code> Ensemble of Orion models</p> </li> </ul>
GENiPPI: data, code, models and notebooks
<p>Interface-aware molecular generative framework for protein-protein interaction modulators</p>
Processed Data, Code, & Supplementary Material for de la Torre Cerro et al. (2024) Modelling asynchrony in phenology using a dynamic representation of meteorological variables
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Advancing Automated Code Review Comment Generation Using Large Language Models
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R code to perform distribution models of Plain Tyrannulet
<p>Archiving to Zenodo for publication</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.