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575 results for “Modeling Codes”
Characterizing Code Clones from Large Language Models Dataset and Scripts
<p>characterizing_code_clones_data.zip: <br><br>This dataset contains a collection of code snippets generated by Large Language Models (LLMs) such as GPT-3.5 and GPT-4 in response to specific programming prompts derived from LeetCode. Each sub-directory within the dataset corresponds to a particular LLM version and contains code snippets, preprocessed data, and SLACC input files. </p><p>characterizing_code_clones_project.zip: </p><p>This zipped directory encompasses the core scripts and results used in the "Characterizing Code Clones of LLMs" research. It features the Python script <strong>collect_samples.py</strong> for collecting LLM-generated code snippets, as well as a suite of scripts in the <strong>slacc_scripts</strong> sub-directory for processing and analyzing the data using SLACC. The directory also includes the results of the LeetCode test suites, providing insights into the correctness and efficiency of the code generated by GPT-3.5 and GPT-4. </p>
Source code and model outputs for 'Increasing Aerosol Direct Effect Despite Declining Global Emissions'
<p>Source code, model outputs and python scripts used for the publication of Hermant, A., Huusko, L., & Mauritsen, T: Increasing Aerosol Direct Effect Despite Declining Global Emissions</p>
ROM data and code for Dakar Niño variability under global warming investigated by a high-resolution regionally coupled model
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Model codes, data, and plot scripts for the paper "A numerical modeling study on the Earth's surface brightening effect of cirrus thinning"
<p>The model codes, data, and plot scripts used in the paper "A numerical modeling study on the Earth's surface brightening effect of cirrus thinning".</p><ul><li>Mods: the modified CAM model code used in these three experiments.</li><li>Results: post-processing NCL scripts and the results used for making plots.</li><li>figs: the NCL scripts and figures used in the paper.</li><li>Parcel: parcel model that represents the ice nucleation process.</li></ul><p> </p>
Data and code for manuscript ``Insights on the vulnerability of Antarctic glaciers from the ISMIP6 ice sheet model ensemble and associated uncertainty''
<p>Supporting data and code for manuscript:</p><p>Seroussi, H., Verjans, V., Nowicki, S., Payne, A. J., Goelzer, H., Lipscomb, W. H., Abe-Ouchi, A., Agosta, C., Albrecht, T., Asay-Davis, X., Barthel, A., Calov, R., Cullather, R., Dumas, C., Galton-Fenzi, B. K., Gladstone, R., Golledge, N. R., Gregory, J. M., Greve, R., Hattermann, T., Hoffman, M. J., Humbert, A., Huybrechts, P., Jourdain, N. C., Kleiner, T., Larour, E., Leguy, G. R., Lowry, D. P., Little, C. M., Morlighem, M., Pattyn, F., Pelle, T., Price, S. F., Quiquet, A., Reese, R., Schlegel, N.-J., Shepherd, A., Simon, E., Smith, R. S., Straneo, F., Sun, S., Trusel, L. D., Van Breedam, J., Van Katwyk, P., van de Wal, R. S. W., Winkelmann, R., Zhao, C., Zhang, T., and Zwinger, T.: Insights into the vulnerability of Antarctic glaciers from the ISMIP6 ice sheet model ensemble and associated uncertainty, The Cryosphere, 17, 5197–5217, https://doi.org/10.5194/tc-17-5197-2023, 2023.</p><p> </p><p>It contains the code to prepare the datasets, to create the figures and the data for the analysis, and the scalar values computed for the 198 Antarctic glaciers stored by ice flow models.</p><p>The files Glacier_XX contain the data to emulate the results for individual glaciers.</p><p>The files Antarctica and AntarcticaWithCtrl contain the data to emulate the results for the Antarctic runs without and with the ctrl_proj experiment.</p><p>The files GROUP_ICEFLOW contain the ice flow model data for all the experiments recomputed for the 198 glaciers in Antarctica.</p>
Supplementary data and code for "A network toxicology approach for mechanistic modelling of nanomaterial hazard and adverse outcomes"
<p>This repository contains the relevant data and code supporting the study "A network toxicology approach for mechanistic modelling of nanomaterial hazard and adverse outcomes". Files are password protected during the revision process. A completely public version of the repository will be availble after the revision process is completed. </p> <p>In detail, the uploaded archive folder contains the following data sources:</p> <ul> <li>the relevant code and supporting data (code_to_upload and supporting_data);</li> <li>supplementary materials of the paper, including: <ul> <li>individual enrichment results of the 93 exposures to the 31 ENMs (enrichments_results);</li> <li>comparison between the mechanism of action retrieved from differentially expressed genes and network modelling (network_comparison_results);</li> <li>overrepresented network edges in categories of networks (overrepresented_structures)</li> </ul> </li> </ul>
Data and code of Covid-19 SIRDS model with fuzzy transitions between epidemic periods
<p>Repository for code and data of project that implement SIRDS model with fuzzy transitions between epidemic periods.</p>
Evaluating the Robustness of Deep-learning Algorithm-selection Models by Evolving Adversarial Instances - Code and Data
<p>This repository contains the code and data for reproducibility of the paper 'Evaluating the Robustness of Deep-learning Algorithm-selection Models by Evolving Adversarial Instances'. </p> <p>The following files are included:</p> <ul> <li>Data.zip : contains the original instances in the datasets;</li> <li>Models.zip : trained Deep Neural Networks models used in the paper;</li> <li>New_instances.zip : generated instances using the approach;</li> <li>Parsed_data.zip : results and statistics of the experiments;</li> <li>script_adversarial_v3.py : Python script used to generate the results</li> </ul>
The evaluation data and source codes of a new conceptual coupled Earth system model and the MOC box model.
<p>The dataset contains the results of a conceptual Atmosphere-Ocean-Ice-Land coupled Earth system model and a MOC box model and the evaluation data of their.</p>
Model Output and Validation Data for Online Determination of GNSS Differential Code Biases using Rao-Blackwellized Particle Filtering
<p>This dataset contains the outputs of 10 test runs of the A-CHAIM data assimilation model used to evaluate the performance of the bias estimation procedure used by the model. The 5-minute output files for each test run are included in a separate folder.</p> <p>The IGS DCBs in SINEX format and the various global ionospheric maps used in the study are included.</p> <p>This dataset also includes all of the processed GNSS data used in the study, which were used to generate the DCBs used in each test run.<br> <br> The in-situ electron density measurements from DMSP as well as the ionosonde data from GIRO which were used to measure the performance are also included.</p>
Code and Data for Atmospheric River Induced Precipitation in California as Simulated by the Regionally Refined Simple Convective Resolving E3SM Atmosphere Model Version 0
<p>Includes the code used for all simulations and grid configurations for the paper entitled "Atmospheric River Induced Precipitation in California as Simulated by the Regionally Refined Simple Convective Resolving E3SM Atmosphere Model Version 0" submitted to Geoscientific Model Development. Also included are the model output files for all cases and grid configurations used to generate the analysis and figures in the paper. </p>
Data and code for Causality analysis and prediction of riverine algal blooms by combining empirical dynamic modeling and machine learning techniques
<p>Hydrological data (including daily water levels, flow velocities, and streamflow discharges) from two hydrological stations, the Hankou Station in the Yangtze River (YR) and the Hanchuan Station in the Han River (HR), were obtained from Hubei Province Hydrology and Water Resources Center.</p> <p>Water quality data (i.e., total nitrogen (TOTN), total phosphorus (TOTP), and water temperature in the Han River) and algae densities at three sections (Baihezui, Qinduankou and Zongguan) were acquired from the Yangtze River Basin Ecological and Environmental Supervision Authority. </p> <p><span>The R script(s) for machine learning models can also be found at <a href="../api/records/10901736/draft/files/Code%20for%20machine%20learning%20classification%20model.R/content" target="_blank" rel="noopener noreferrer">Code for machine learning classification model.R</a>.</span></p> <p> </p>
Dataset and code for "Evident decrease in future European soil moisture in the Kiel Climate Model grand ensemble"
<p>Here, you only have the processed data. Most calculations have been made with CDO (version 2.0.6 or 1.9.9). The history of commands made can be seen in the file with a simple ncdump -h file. If you need to see more data, please contact the corresponding author.</p>
Data and Code for Modeling Paradox Valley Unit
<p>Data and Code for Modeling Paradox Valley Unit. Includes Abaqus files, post-processing files, ML codes, and scripts to generate figures.</p>
Data and Codes of A Deep Learning-Based Consistency Test for Earth System Models on Heterogeneous Many-Core Systems
<p>These are the supporting information to verify the results in the paper, including input data, model outputs, the postprocessing scripts and the source codes.</p>
Raw datasets for mechnaical results and code generation for modelling the strength characteristics of TBC incorporating VPA and CP
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Code for simple BTSP models
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Original features and code of clinical prediction model
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Leveraging Search-Based and Pre-Trained Code Language Models for Automated Program Repair
<p>This page serves as supplementary material for the article: <strong>Leveraging Search-Based and Pre-Trained Code Language Models for Automated Program Repair</strong>. Here, we provide the ARJACLM code utilized in the study, enabling other researchers to replicate the experiments and further develop the tool. </p>
Model code and output for "Transformation Processes in the Oder Lagoon as seen from a Model Perspective"
<p>This dataset contains the numerical model output files that were used in the analysis, the numerical model input files that were used to produce the model simulation, and the specific source code for the model components presented in "Transformation Processes in the Oder Lagoon as seen from a Model Perspective", submitted to Biogeoscience.</p> <p>data1.tar is the direct diagnostic model output. data2.tar are condensed data which are used for the analysis.</p> <p>forcing.tar contains data to run the model. Most files should be placed inside a directory named INPUT/ that resides in the working directory where the model is being run. The following files should be at the top level in the working directory: data_table, diag_table, field_table, input.nml.</p> <p>For large files, a subset in time (1995) is provided for the first year of the simulation. This dataset does not include the coastdat atmospheric data, which can be downloaded directly from https://www.wdc-climate.de/ui/entry?acronym=coastDat-2_COSMO-CLM.</p> <p>src.tar contains the model source code.</p>
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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.