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575 results for “Modeling Codes”
Model code, data, and plot scripts for the paper "Impacts of Ice-Particle Size Distribution Shape Parameter on Climate Simulations with the Community Atmosphere Model Version 6 (CAM6)".
<p>The code, scripts, and data used in the paper "Impacts of Ice-Particle Size Distribution Shape Parameter on Climate Simulations with the Community Atmosphere Model Version 6 (CAM6)".</p> <ul> <li>All Figures&Table and their corresponding NCL scripts are under the directory of Figs&Table. </li> <li>The modified model code, corresponding original model code, and model run scripts are under the directory of Mods_Scripts.</li> <li>The postprocessing NCL scripts, which select useful variables from simulation results, are under the directory of PostProcessing.</li> <li>The zonal mean data from model results used for making figures and corresponding data processing scripts are under the directory of Model_Results.</li> <li>The FORTRAN code used for offline tests is under the directory of Offline_Code.</li> <li>The code, data, and NCL scripts used for the figures and table in the Appendix are under the directory of Appendix.</li> </ul>
Dataset of AI-generated code created by various versions of GPT model
<p>This is the dataset used for the paper "<span>Human vs AI: Investigation of Security Risks in AI-generated </span><span>Code via Comparison with Human-written Code".</span></p>
Paper samples for the SLR "A systematic literature review on the impact of AI models on the security of code generation"
<p>Here we provide the whole list of papers that were queried for the SLR "A systematic literature review on the impact of AI models on the security of code generation" by Negri-Ribalta et al. The dataset provides all the information of all the papers gathered, their database of origin, and if it was accepted/rejected/duplicated. </p> <p>The file is in xls format .</p>
3D Models and Code for the Analysis of Perforated stones from Nahal Ein Gev II
<p>3D models (.wrl / .vrml) of perforated stones from the late Natufian site Nahal Ein GEv in the Southern Levant.</p> <p>MATLAB script for exctracting the perforated part as 3D model</p> <p>This dataset accompanies the submitted manuscript "12,000-year-old Spindle Whorls and the Innovation of Wheeled Rotational Technologies" by Talia Yashuv and Leore Grosman</p> <p> </p> <p> </p>
Detailed dataset and code generation for Artificial intelligence-based modelling of compressive strength of slurry infiltrated fiber concrete
Open the record for dataset details and reuse information.
Dataset and codes for "The Warmth of Sarudango: Modelling the Huddling Behaviour of Japanese Macaques (Macaca fuscata)"
<p>Dataset and codes for "<strong><span>The Warmth of Sarudango: Modelling the Huddling Behaviour of Japanese Macaques (<em>Macaca fuscata</em>)"</span></strong></p> <p><strong><span><span>Huddling behaviour is observed across various mammalian and avian species. Huddling, a behaviour wherein animals maintain close physical contact with conspecifics for warmth and social bonding, is widely documented among species in cold environments as a crucial thermoregulatory mechanism. Interestingly, on Shodoshima, Japanese macaques form exceptionally large huddling clusters, often exceeding 50 individuals, a significant deviation from the smaller groups observed in other populations (Arashyama, Katsuyama, Taksakiyama) and climates. This study aims to uncover the mechanisms behind the formation and size of these huddling clusters, proposing that such behaviours can be explained by simple probabilistic rules influenced by environmental conditions, the current cluster size, and individual decisions. Employing a computational model developed in Netlogo, we seek to demonstrate how emergent properties like the formation and dissolution of clusters arise from collective individual actions. We investigate whether the observed differences in huddling behaviour, particularly the larger cluster sizes on Shodoshima compared to those in colder habitats, reflect variations in social tolerance and cohesion. The model incorporates factors such as environmental temperature, cluster size, and individual decision-making, offering insights into the adaptability of social behaviours under environmental pressures. The findings suggest that temperature plays a crucial role in influencing huddling behaviour, with larger clusters forming in colder climates as individuals seek warmth. However, the study also highlights the importance of joining and leaving a cluster in terms of probability in the dynamics of huddling behaviour. We discussed the large clusters on Shodoshima as a result from a combination of environmental factors and a unique social tolerance and cohesion among the macaques. This study contributes to our understanding of complex social phenomena through the lens of self-organisation, illustrating how simple local interactions can give rise to intricate social structures and behaviours.</span></span></strong></p>
Dataset related to: An Empirical Study on Low Code Programming using Traditional vs Large Language Model Support
<p>This repository contains data and prompts related to our research. The files included are:</p> <p><strong>prompt.py</strong>: This script contains the original prompts used in our study.</p> <p><strong>LLM_lowcode.mx20</strong>: This file includes posts and annotation data related to LLM-based low-code platforms.</p> <p><strong>Traditional_lowcode.mx20</strong>: This file includes posts and annotation data related to traditional low-code platforms.</p> <p>The .mx20 files can be opened using the MAXQDA software, which can be downloaded from the official website. MAXQDA offers a 14-day free trial.</p>
Analyzing the Dependability of Large Language Models for Code Clone Generation.
<div> <p>data.zip: <br><br>This dataset includes a collection of ten LeetCode programming problems used in the study "Analyzing the Dependability of Large Language Models for Code Clone Generation". At the top level, you will find a CSV file containing all the initial LeetCode data. Each subdirectory at this level represents a specific LeetCode problem. Within these subdirectories, you will find the original solutions, their behaviors, the input corpus, as well as folders dedicated to various temperatures, models, and code cloning tasks. Additionally, within the "repeated" folder, you will find the original LLM-generated snippets, the preprocessed snippets with the snippet behavior, and the results.</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. The common folder was used to run the whole pipeline, the various parts of the pipeline are each in a folder as well as the various data analysis scripts! </p> <p> </p> </div>
Analyzing the Dependability of Large Language Models for Code Clone Generation
<p>data.zip: <br><br>This dataset includes a collection of ten LeetCode programming problems used in the study "Analyzing the Dependability of Large Language Models for Code Clone Generation". At the top level, you will find a CSV file containing all the initial LeetCode data. Each subdirectory at this level represents a specific LeetCode problem. Within these subdirectories, you will find the original solutions, their behaviors, the input corpus, as well as folders dedicated to various temperatures, models, and code cloning tasks. Additionally, within the "repeated" folder, you will find the original LLM-generated snippets, the preprocessed snippets with the snippet behavior, and the results.</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. The common folder was used to run the whole pipeline, the various parts of the pipeline are each in a folder as well as the various data analysis scripts! </p>
Supplemental materials for AaceGEN: Attention Guided Adversarial Code Example Generation for Deep Code Models
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Codes: An early warning indicator trained on stochastic disease-spreading models with different noises
<p>This dataset contains the training data (Version V1) and all the codes (Version V2) of the paper entitled "An early warning indicator trained on stochastic disease-spreading models with different noises." </p> <p>Time series and corresponding residuals from white noise (equation 2.5), environmental noise (equation 2.8), and demographic noise (equation 2.9) are stored in the training_data_WhiteN, training_data_EnvN, and training_data_DemN folders, respectively. All residuals of the time series are contained in the training_resids folder, which also includes labels and groups of the training data. For details on the data generation process, please refer to section 3.1 in the paper. </p>
Data, scripts and code for 'Space-time species distribution modeling with opportunistic presence-only data: a case study of passerines in a protected area'
<p>Three Zenodo repositories are linked to the preprint <em>Space-time Species Distribution Modeling for Opportunistic Presence-Only Data: A Case Study of Passerines in a Protected Area </em>(Lasgorceux et al., unpublished, <a href="https://hal.science/hal-04616332">https://hal.science/hal-04616332</a>):</p> <ul> <li>Data, scripts, and code (Lasgorceux et al., Zenodo, <a href="https://doi.org/10.5281/zenodo.12545052">https://doi.org/10.5281/zenodo.12545052</a>)</li> <li>Outputs from fitted models across the cross-validation scenarios (Lasgorceux et al., Zenodo, <a href="https://doi.org/10.5281/zenodo.12544212">https://doi.org/10.5281/zenodo.12544212</a>)</li> <li>Supplementary information at (Lasgorceux et al., Zenodo, <a href="https://doi.org/10.5281/zenodo.12541412">https://doi.org/10.5281/zenodo.12541412</a>)</li> </ul> <p>This repository contains data, scripts, and code. It is organized into two main directories: <em>Materials and Methods,</em> and <em>Results</em>.</p> <h2>Materials and Methods</h2> <p>The raw data can be accessed in the <em>Materials_and_Methods/Data directory</em>. The processed data used for modeling is available in <em>Materials_and_Methods/Data_for_modeling/Data_for_modeling.RData</em>. All scripts for data processing are located in <em>Materials_and_Methods/Processing_scripts</em>. The <em>Plots_and_Figures </em>directory<em> </em>includes illustrations of the data used for modeling, such as PCA correlation plots presented in Supplementary information. The main script for running the model for each species is <em>Main_script.R</em>.</p> <h2>Results</h2> <p>The <em>Results</em> directory contains three subdirectories and the script <em>Models_Outputs.R</em>. The <em>Results/Fitted_models</em> directory includes all .RData files with fitted models for each species. The script <em>Models_Outputs.R </em>generates all outputs (Figures, Tables, Numbers) included in the paper and additional results in the Supplementary Information, except for Figure 1 and AUC values. For these, refer to Lasgorceux et al., Zenodo, <a href="https://doi.org/10.5281/zenodo.12544212">https://doi.org/10.5281/zenodo.12544212</a>. The plots are stored in the <em>Results/Plots </em>folder, while <em>Results/RData</em> contains intermediate .RData files created by <em>Models_Outputs.R</em> to manage computational costs.</p>
On Inter-dataset Code Duplication and Data Leakage in Large Language Models
<p>This dataset encompasses the sparse graph referenced in the publication titled "On Inter-dataset Code Duplication and Data Leakage in Large Language Models."</p> <p>This resource is a snapshot of the original <a href="https://github.com/Antolin1/code-inter-dataset-duplication">repository</a>, and the graph is preserved in the <em>interduplication.db</em> database. The schema of this database is easily understandable and is available in the original repository. Each code snippet is identified by a unique identifier (id_within_dataset) that corresponds to its identification within the dataset from which it was extracted. The complete datasets are stored in .jsonl files within their respective folders (e.g., python-150/data.jsonl, codetrans/data.jsonl, etc.).</p> <p> </p> <p> </p> <p> </p>
Model data and figure code for results and figures in the manuscript submitted to Geophysical Research Letters "Hysteresis of the Antarctic ice sheet with a coupled ice sheet climate model"
<p>This folder contains the model data and figure code for results and figures in the manuscript submitted to Geophysical Research Letters "Hysteresis of the Antarctic ice sheet with a coupled ice sheet climate model"</p> <p>The code for plotting the figures is the notebook Plot_figures.ipynb</p> <p>Fig1/simulation_output/ : Model output necessary for plotting the first figure </p> <p>The last timestep of each simulation is provided. There is one file for 1D variables (ice volume, ice volume above flotation), and one file for 2D variables (ice sheet thickness for instance).</p> <ul> <li><span>melt_insoPI_output/ : melt branch, pre-industrial insolation. Results for different CO2 levels</span></li> <li><span>growth_insoPI_output/ : growth branch, pre-industrial insolation. Results for different CO2 levels</span></li> <li><span>melt_insoMAX_output/ : melt branch, maximum insolation. Results for different CO2 levels</span></li> <li><span>growth_insoMIN_output/ : growth branch, minimum insolation. Results for different CO2 levels</span></li> </ul> <p><span>compute_SLR_equivalent.py : code to compute the ice sheet volume in SLRe based on model output</span></p> <p><span>Fig1/SLR_files/ : contains the equilibrium ice sheet volume of the different simulations according to the CO2 level</span></p> <p> </p> <p>Fig2/simulation_output/ : Model output necessary for plotting the second figure </p> <p>The last timestep of each simulation is provided. </p> <ul> <li><span>melt_insoPI_enhancedmelt_albfb/ : melt branch, pre-industrial insolation, enhanced melt and albedo feedback. Results for different CO2 levels</span></li> <li><span>growth_insoPI_enhancedmelt_albfb/ : growth branch, pre-industrial insolation, enhanced melt and albedo feedback. Results for different CO2 levels</span></li> <li><span>melt_insoPI_enhancedmelt_fixedalb/ : melt branch, pre-industrial insolation, enhanced melt, no albedo feedback. Results for different CO2 levels</span></li> <li><span>growth_insoPI_enhancedmelt_fixedalb/: growth branch, pre-industrial insolation, enhanced melt, no albedo feedback. Results for different CO2 levels</span></li> </ul> <p><span>compute_SLR_equivalent.py : code to compute the ice sheet volume in SLRe based on model output</span></p> <p><span>Fig2/SLR_files/ : contains the equilibrium ice sheet volume of the different simulations according to the CO2 level</span></p> <p> </p> <p><span>Fig3/simulation_output/ : Model output necessary for plotting the third figure </span></p> <ul> <li><span>1xCO2_nocoupling/ : simulation with pre-industrial CO2 levels and insolation and no coupling to the ice sheet model</span></li> <li><span>8xCO2_nocoupling/ : simulation with 8xpiCO2 (pre-industrial CO2) levels, pre-industrial insolation and no coupling to the ice sheet model</span></li> <li><span>8xCO2_transient_albfb/ : quasi transient simulation, 8xpiCO2 levels, pre-industrial insolation, coupling with the ice sheet model </span></li> <li><span>8xCO2_transient_fixedalb/ : quasi transient simulation, 8xpiCO2 levels, pre-industrial insolation, coupling with the ice sheet model excluding the albedo-melt feedback</span></li> </ul> <p> </p>
Scherrenberg et al. (2024) supplement (Climate of the past): Ice-sheet model code, and output of Northern Hemisphere ice-sheet evolution of the past 800 kyr
<p>Supplement to Scherrenberg et al. (2024), article in Climate of the Past.</p> <p>This data-set contains ice-sheet model (IMAU-ICE) code (see IMAU_ICE_Code.zip; see https://github.com/IMAU-paleo/IMAU-ICE/tree/main for the most recent version of the model), the model output and configuration files (see Data_output.zip), and scripts to create figures (see Scripts_and_Figures.zip).</p> <p>Please note that additional input fields are required to run IMAU-ICE and to produce the figures. See Scherrenberg et al., (2024) for more information or contact the corresponding author.</p> <p>Citation: M.D.W. Scherrenberg, C.J. Berends, R.S.W. van de Wal: Late Pleistocene glacial terminations accelerated by proglacial lakes, climate of the past, special issue "icy landscapes of the past", 2024</p>
Matlab code for the 'Force–displacement response of in-plane loaded unreinforced brick masonry walls: the Critical Diagonal Crack model'
<p>The repository contains the the Matlab code and the test data (.csv files) used for the analyses and results presented in the research article:</p> <p>Force–displacement response of in-plane loaded unreinforced brick masonry walls: the Critical Diagonal Crack model, <em>Bulletin of Earthquake Engineering</em>,<em> </em>2017, <a href="https://link.springer.com/article/10.1007%2Fs10518-016-0049-7">Link</a></p>
Morphodynamic modeling of sediment pulse dynamics: Raw code and output
<p>Fortran source code and raw output files (.csv) for a 1D morphodynamic model simulating sediment pulse evolution.</p> <p>This material is provided as is, with absolutely no warranty expressed or implied. Any use is at your own risk.</p>
Matlab code for 'Analytical model for the out-of-plane response of vertically spanning unreinforced masonry walls'
<p>This repository contains the Matlab scripts needed to reproduce the results shown in the article:</p> <blockquote> <p>Godio M, Beyer K. Analytical model for the out-of-plane response of vertically-spanning unreinforced masonry walls. Earthquake Engng Struct Dyn. 2017;46:2757-2776. <a href="https://onlinelibrary.wiley.com/doi/full/10.1002/eqe.2929">DOI:10.1002/eqe.2929</a></p> </blockquote> <p>Version history:<br> V1: Matlab code needed to reproduce the plots of Figure 4 of the article is added<br> V2: content PDF-file is added</p>
Supplementary dataset and source code for the paper "Using Weaker Consistency Models with Monitoring and Recovery for Improving Performance of Key-Value Stores"
<p>Supplementary dataset and source code for the paper "Using Weaker Consistency Models with Monitoring and Recovery for Improving Performance of Key-Value Stores" submitted to Journal of the Brazilian Computer Society, LADC special issue.</p>
Supporting Model Code and Output for https://doi.org/10.5194/gmd-2019-78, 2019
<p>Model code and timeseries output for Travis et al. 2019 (GMD) https://doi.org/10.5194/gmd-2019-78, 2019.</p> <p> </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.