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575 results for “Modeling Codes”
Protein secondary-structure description with a coarse-grained model: code and datasets in ActivePapers format
<p>This file contains the supplementary material for the publication</p> <p><em>Protein secondary-structure description with a coarse-grained model</em><br /> by Gerald R. Kneller and K. Hinsen<br /> http://dx.doi.org/10.1107/S1399004715007191<br /> Acta Cryst. (2015). D<strong>71</strong>, 1411-1422</p> <p><strong>Datasets in this file</strong></p> <p>1) ScrewFit and ScrewFrame parameters for ideal secondary-structure elements</p> <p> Scripts:<br /> /code/import_ideal_structures<br /> /code/analyze_ideal_structures</p> <p>1.1) The PDB files generated with Chimera</p> <p> /data/ideal_structures/3-10.pdb<br /> /data/ideal_structures/alpha.pdb<br /> /data/ideal_structures/beta-antiparallel.pdb<br /> /data/ideal_structures/beta-parallel.pdb<br /> /data/ideal_structures/pi.pdb</p> <p>1.2) The corresponding MOSAIC datasets</p> <p> /data/ideal_structures/3-10<br /> /data/ideal_structures/alpha<br /> /data/ideal_structures/beta-antiparallel<br /> /data/ideal_structures/beta-parallel<br /> /data/ideal_structures/pi</p> <p>1.3) The ScrewFit parameters</p> <p> /data/ideal_structures/screwfit/3-10<br /> /data/ideal_structures/screwfit/alpha<br /> /data/ideal_structures/screwfit/beta-antiparallel<br /> /data/ideal_structures/screwfit/beta-parallel<br /> /data/ideal_structures/screwfit/pi</p> <p>1.4) The ScrewFrame parameters</p> <p> /data/ideal_structures/screwframe/3-10<br /> /data/ideal_structures/screwframe/alpha<br /> /data/ideal_structures/screwframe/beta-antiparallel<br /> /data/ideal_structures/screwframe/beta-parallel<br /> /data/ideal_structures/screwframe/pi</p> <p><br /> 2) Statistics for ScrewFit and ScrewFrame parameters computed<br /> for the ASTRAL SCOPe subset with less than 40% sequence identity.</p> <p> Scripts:<br /> /code/astral_analysis<br /> /code/fit_rho_distributions<br /> /code/plot_histograms</p> <p>2.1) The ASTRAL database (link to published ActivePaper)</p> <p> /data/astral_2.04</p> <p>2.2) The histograms for the ScrewFit and ScrewFrame parameters<br /> for the all-alpha and all-beta subsets</p> <p> /data/histograms/astral_alpha/screwfit<br /> /data/histograms/astral_alpha/screwframe</p> <p> /data/histograms/astral_beta/screwfit<br /> /data/histograms/astral_beta/screwframe</p> <p>2.3) The Gaussians fitted to the peaks in the distributions for rho</p> <p> /data/fitted_rho_distributions/screwfit<br /> /data/fitted_rho_distributions/screwframe</p> <p>2.4) Plots</p> <p> /documentation/delta.pdf<br /> /documentation/delta_q.pdf<br /> /documentation/delta_r.pdf<br /> /documentation/p.pdf<br /> /documentation/rho-detail.pdf<br /> /documentation/rho.pdf<br /> /documentation/sigma.pdf<br /> /documentation/tau.pdf</p> <p><br /> 3) Comparison of secondary-structure identification between ScrewFrame<br /> and DSSP.</p> <p> Script:<br /> /code/compare_secondary_structure_assignments<br /> /code/plot_histograms</p> <p>3.1) The histograms of the lengths of secondary-structure elements</p> <p> /data/histograms/secondary_structure/length-alpha-dssp<br /> /data/histograms/secondary_structure/length-alpha-screwframe<br /> /data/histograms/secondary_structure/length-beta-dssp<br /> /data/histograms/secondary_structure/length-beta-screwframe</p> <p>3.2) The 2D histograms of the number of residues inside identified<br /> secondary-structure elements</p> <p> /data/histograms/secondary_structure/n-alpha<br /> /data/histograms/secondary_structure/n-beta</p> <p>3.3) The distribution of rho inside alpha helices</p> <p> /data/histograms/secondary_structure/rho-alpha-dssp</p> <p>3.3) Plots</p> <p> /documentation/lengths-alpha.pdf<br /> /documentation/lengths-beta.pdf<br /> /documentation/n-alpha.pdf<br /> /documentation/n-beta.pdf<br /> /documentation/rho-alpha-dssp.pdf</p> <p><br /> 4) Illustration for myoglobin and VADC-1</p> <p> Scripts:<br /> /code/import_myoglobin_vdac<br /> /code/analyze_myoglobin<br /> /code/analyze_vdac<br /> /code/perturbation_analysis</p> <p>4.1) Imported structures in MOSAIC format:<br /> PDB code 1A6G for myoglobin<br /> PDB code 2K4T for VDAC-1</p> <p> /data/myoglobin<br /> /data/VDAC-1</p> <p>4.2) Plots showing rho and delta</p> <p> /documentation/rho-myoglobin.pdf<br /> /documentation/delta-myoglobin.pdf</p> <p>4.3) Tube models for visualization with Chimera</p> <p> /documentation/myoglobin-tube.bld<br /> /documentation/VDAC-1-tube.bld</p> <p>4.4) Sensitivity to perturbations in the coordinates</p> <p> /documentation/rho-perturbed-myoglobin.pdf<br /> /documentation/delta-perturbed-VDAC-1.pdf<br /> /documentation/rho-perturbed-myoglobin.pdf<br /> /documentation/delta-perturbed-VDAC-1.pdf<br /> /documentation/myoglobin-perturbation.pdf<br /> /documentation/VDAC-1-perturbation.pdf</p> <p>5) Analysis of CA-only structures in the PDB</p> <p> Scripts:<br /> /code/ca_analysis<br /> /code/import_calpha_structures<br /> /code/plot_histograms</p> <p>5.1) Imported CA-only structures in MOSAIC format</p> <p> /data/pdb_ca_only_structures</p> <p>5.2) Histograms for ScrewFrame parameters</p> <p> /data/histograms/ca_only_structures</p> <p>5.3) Plots</p> <p> /documentation/delta_ca.pdf<br /> /documentation/delta_q_ca.pdf<br /> /documentation/delta_r_ca.pdf<br /> /documentation/p_ca.pdf<br /> /documentation/rho_ca.pdf<br /> /documentation/sigma_ca.pdf<br /> /documentation/tau_ca.pdf</p> <p> </p>
Data and code for "A Signal-Detection Approach to Modeling Forgiveness Decisions"
<p>Contains the data and code that supplements the journal article, "A Signal-Detection Approach to Modeling Forgiveness Decisions".</p> <p>Article doi: 10.1016/j.evolhumbehav.2016.06.004</p>
Individual-based simulation model of annual movement paths for the Darwin's frog (R code and data)
<p>Desprition of the R code</p> <p>I constructed an individual-based simulation model that describes the movement path of an individual<em> Rhinoderma darwinii</em> through 3-month displacement steps. This model was primarily developed to evaluate the age-specific movement behaviour of Darwin's frogs, however, I also used it to provide better estimates (i.e. alleviating for movement censoring) of age-specific annual displacements in the species. I developed several variations of this model through a combination of different random walk sub-models for juveniles and adults: uncorrelated non-stationary random walks (NRW), correlated non-stationary random walks (CRW), and stationary random walks (SRW). The NRW and CRW were modelled as a first-order Markovian process where the location of an individual <em>i</em> in time<em> t</em> depends on its spatial location in <em>t </em>- 1. The NRW is unbiased, i.e., there is no preferred direction in each movement step. In contrast, the CRW includes persistence in the directionality of movement, so there is a correlation between successive step orientations. Finally, the SRW assumes that individuals have an activity centre to which all their spatial locations are related.</p> <p>Related data are provided (y.txt, x.txt and age.txt)</p>
Code and supplementary plots for "Flexible distributed lag models for count data using mgcv"
<p>R code and supplementary plots accompanying the paper: "Flexible distributed lag models for count data using mgcv".</p>
Model output and figure code for "Tradeoffs Between Vertical and Lateral Resilience in a Salt Marsh Restoration Model"
<p>Simulation output data from COLT_Restorations model stored on the Community Surface Dynamics Modeling System public model repository at <a href="https://csdms.colorado.edu/wiki/Model:COLT_Restorations">https://csdms.colorado.edu/wiki/Model:COLT_Restorations</a> and on a GitHub public repository at<a href="https://github.com/mbbarksdale/CoLT_Restorations"> https://github.com/mbbarksdale/CoLT_Restorations</a>.</p> <p>These data were processed and graphed with model code that can also be found in a GitHub public repository at <a href="https://github.com/mbbarksdale/COLTRestorationsPaper_dataANDfigures">https://github.com/mbbarksdale/COLTRestorationsPaper_dataANDfigures</a>. </p>
Data and code for the paper "Precision Groundwater Modeling: when cokriging meets evolutionary and iterative algorithms"
<ul> <li>exemplary dataset for 2019 yearly water table measurements in Northeaster Italy</li> <li>MATLAB code for the pre-processing GA-driven and the post-processing iterative validation part</li> </ul>
Data and code for "Assessment of the accuracy in UV index modelling using the UVIOS2 system during the UVC-III campaign"
<p>Datasets nd codes that have been used for the paper with tittle "Assessment of the accuracy in UV index modelling using the UVIOS2 system during the UVC-III campaign"</p>
Data and code for: Acute heat priming promotes short-term climate resilience of early life stages in a model sea anemone
<p>Across diverse taxa, sublethal exposure to abiotic stressors early in life can lead to benefits such as increased stress tolerance upon repeat exposure. This phenomenon, known as hormetic priming, is largely unexplored in early life stages of marine invertebrates, which are increasingly threatened by anthropogenic climate change. To investigate this phenomenon, larvae of the sea anemone and model marine invertebrate <em>Nematostella vectensis</em> were exposed to control (18°C) or elevated (24°C, 30°C, 35°C, or 39°C) temperatures for 1 hour at 3 days post-fertilization (DPF), followed by return to control temperatures (18°C). The animals were then assessed for growth, development, metabolic rates, and heat tolerance at 4, 7, and 11 DPF. Priming at intermediately elevated temperatures (24°C, 30°C, or 35°C) augmented growth and development compared to controls or priming at 39°C. Indeed, priming at 39°C hampered developmental progression, with around 40% of larvae still in the planula stage at 11 DPF, in contrast to 0% for all other groups. Total protein content, a proxy for biomass, and respiration rates were not significantly affected by priming, suggesting metabolic resilience. Heat tolerance was quantified with acute heat stress exposures, and was significantly higher for animals primed at intermediate temperatures (24°C, 30°C, or 35°C) compared to controls or those primed at 39°C at all time points. To investigate a possible molecular mechanism for observed changes in heat tolerance, the expression of heat shock protein 70 (HSP70) was quantified at 11 DPF. Expression of HSP70 significantly increased with increasing priming temperature, with the presence of a doublet band for larvae primed at 39°C, suggesting persistent negative effects of priming on protein homeostasis. Interestingly, primed larvae in a second cohort cultured to 6 weeks post-fertilization continued to display hormetic growth responses, whereas benefits for heat tolerance were lost; in contrast, negative effects of short-term exposure to extreme heat stress (39°C) persisted. These results demonstrate that some dose-dependent effects of priming waned over time while others persisted, resulting in heterogeneity in organismal performance across ontogeny following priming. Overall, these findings suggest that heat priming may augment the climate resilience of marine invertebrate early life stages via the modulation of key developmental and physiological phenotypes, while also affirming the need to limit further anthropogenic ocean warming.</p>
Code and data for publication "Assessing carbon cycle projections from complex and simple models under SSP scenarios" published in "Climatic Change"
<p>Data and scripts for the article "Assessing carbon cycle projections from complex and simple models under SSP scenarios" by I. Melnikova, P. Ciais, O. Boucher and K. Tanaka was accepted for publication in Climatic Change (https://doi.org/10.1007/s10584-023-03639-5)</p><p> </p><p>We use bash, CDO, and python.</p><p>SSP2.xlsx contains preprocessed annual estimates of climate and carbon cycle variables from ESMs and SCMs used in the paper.</p><p>Two bash scripts contain preprocessing cdo commands for ESM output.s SCMs were preprocessed directly in python.</p><p>Jupyter notebook (python) contains preprocessing of data and plotting of all figures of the manuscript. The folder "additional" contains some more Excel files needed to run Jupyter-Notebook. Please adapt the folder names.</p><p>If you have any questions, please contact the corresponding author Irina MELNIKOVA at melnikova . irina@nies.go.jp</p><p> </p>
Paper data and code of manuscript: Intraspecific variation on heat tolerance in a model ectotherm: effects of body mass, cell size, oxygen and sex
<p>When using the data or code from this manuscript, please cite it as:</p><p><strong>Leiva FP</strong>, Santos M, Rezende E, & Verberk WCEP. 2021. Paper data and code of manuscript: Intraspecific variation on heat tolerance in a model ectotherm: effects of body mass, cell size, oxygen and sex. Zenodo. <a href="https://doi.org/10.5281/zenodo.5120028">https://doi.org/10.5281/zenodo.5120028</a>.</p>
Code and data for Bayesian joint species distribution model selection for community-level prediction
<p>Code and data for reproducing the analysis in the manuscript "Bayesian joint species distribution model selection for community-level prediction." Provided data include percent cover observations for 39 modeled vascular plant species within boreal forest understory communities and environmental model covariates. R code is provided to generate model inputs, apply alternative models, generate out-of-sample predictions, and calculate associated community and species log scores and alternative model evaluation metrics. Further, R source code is provided to implement the multinomial joint species distribution model defined in the manuscript. Details on the data, its processing, and the alternative model definitions and structure can be found in the main text of the manuscript. Provided data are currently being used in ongoing analyses and coordination with authors may be warranted to avoid duplicate publication. Potential users are encouraged to consider collaboration with authors when useful and appropriate. Misinterpretation of data may occur if used outside the context of the original analysis. All data are made available in their current state. While significant efforts have been made to ensure data accuracy, complete accuracy cannot be guaranteed. Data may be updated periodically. It is the responsibility of the data user to check for updated versions of the data.</p>
Supplementary data and code to article "Single-Well Microseismic Focal Mechanism Inversions Using Different Source Models: A Case Study in the Ordos Basin, China"
<p>Supplementary data and code to article "Single-Well Microseismic Focal Mechanism Inversions Using Different Source Models: A Case Study in the Ordos Basin, China".</p><p>Transformations among the parameters of the moment tensor model refer to the code package from Tape and Tape (https://github.com/carltape/mtbeach/; https://github.com/carltape/surfacevel2strain; Tape and Tape, 2009, 2012, 2013, 2015).</p><p>Tape, C., P. Muse, M. Simons, D. Dong, and F. Webb (2009). Multiscale estimation of GPS velocity fields, Geophys. J. Int. 179, no.2, 945-971, doi: 10.1111/j.1365-246X.2009.04337.x.</p><p>Tape, W., and C. Tape (2012). A geometric setting for moment tensors, Geophys. J. Int. 190, no. 1, 476–498, doi: 10.1111/j.1365-246X.2012.05491.x.</p><p>Tape, W., and C. Tape (2013). The classical model for moment tensors, Geophys. J. Int. 195, no. 3, 1701–1720, doi: 10.1093/gji/ggt302.</p><p>Tape, W., and C. Tape (2015). A uniform parametrization of moment tensors, Geophys. J. Int. 202, no. 3, 2074–2081, doi: 10.1093/gji/ggv262.</p>
Matlab code to calibrate a structured-PDE model to data from in vitro experiments
<p>Matlab code for the calibration of a PDE model of evolutionary dynamics of a well-mixed population of aggressive breast cancer cells from in vitro data on MCF7-sh-WISP2 cell line and bootstrapping for uncertainty quantification. For more details, see the associated publication: "Evolutionary dynamics of glucose-deprived cancer cells: insights from experimentally-informed mathematical modelling", by L. Almeida, J. Denis, N. Ferrand, T. Lorenzi, A. Prunet, M. Sabbah, C. Villa (corresponding author, author of code), 2023. In press in the journal of the Royal Society Interface.</p>
Lost in Translation: A Study of Bugs Introduced by Large Language Models while Translating Code
<p>Artifact repository for the paper <a href="http://arxiv.org/abs/2308.03109" rel="nofollow"><em>Lost in Translation: A Study of Bugs Introduced by Large Language Models while Translating Code</em></a>, accepted at <em>ICSE 2024</em>, Lisbon, Portugal. Authors are <a href="https://rangeetpan.github.io/" rel="nofollow">Rangeet Pan</a>* <a href="https://alirezai.cs.illinois.edu/" rel="nofollow">Ali Reza Ibrahimzada</a>*, <a href="http://rkrsn.us/" rel="nofollow">Rahul Krishna</a>, Divya Sankar, Lambert Pougeum Wassi, Michele Merler, Boris Sobolev, Raju Pavuluri, Saurabh Sinha, and <a href="https://reyhaneh.cs.illinois.edu/index.htm" rel="nofollow">Reyhaneh Jabbarvand</a>.</p> <h3>Install</h3> <p>This repository contains the source code for reproducing the results in our paper. Please start by cloning this repository:</p> <div> <pre><code>git clone https://github.com/Intelligent-CAT-Lab/PLTranslationEmpirical </code></pre> </div> <p>We recommend using a virtual environment for running the scripts. Please download <code>conda 23.11.0</code> from this <a href="https://docs.conda.io/projects/miniconda/en/latest/miniconda-other-installer-links.html" rel="nofollow">link</a>. You can create a virtual environment using the following command:</p> <div> <pre><code>conda create -n plempirical python=3.10.13 </code></pre> </div> <p>After creating the virtual environment, you can activate it using the following command:</p> <div> <pre><code>conda activate plempirical </code></pre> </div> <p>You can run the following command to make sure that you are using the correct version of Python:</p> <div> <pre><code>python3 --version && pip3 --version </code></pre> </div> <h3>Dependencies</h3> <p>To install all software dependencies, please execute the following command:</p> <div> <pre><code>pip3 install -r requirements.txt </code></pre> </div> <p>As for hardware dependencies, we used 16 NVIDIA A100 GPUs with 80GBs of memory for inferencing models. The models can be inferenced on any combination of GPUs as long as the reader can properly distribute the model weights across the GPUs. We did not perform weight distribution since we had enough memory (80 GB) per GPU.</p> <p>Moreover, for compiling and testing the generated translations, we used Python 3.10, g++ 11, GCC Clang 14.0, Java 11, Go 1.20, Rust 1.73, and .Net 7.0.14 for Python, C++, C, Java, Go, Rust, and C#, respectively. Overall, we recommend using a machine with Linux OS and at least 32GB of RAM for running the scripts.</p> <p>For running scripts of alternative approaches, you need to make sure you have installed <a href="https://github.com/immunant/c2rust">C2Rust</a>, <a href="https://github.com/gotranspile/cxgo">CxGO</a>, and <a href="https://github.com/paulirwin/JavaToCSharp">Java2C#</a> on your machine. Please refer to their repositories for installation instructions. For Java2C#, you need to create a <code>.csproj</code> file like below:</p> <div> <pre><code><Project Sdk="Microsoft.NET.Sdk"> <PropertyGroup> <OutputType>Exe</OutputType> <TargetFramework>net7.0</TargetFramework> <ImplicitUsings>enable</ImplicitUsings> <Nullable>enable</Nullable> </PropertyGroup> </Project> </code></pre> </div> <h3>Dataset</h3> <p>We uploaded the dataset we used in our empirical study to <a href="../doi/10.5281/zenodo.8190051" rel="nofollow">Zenodo</a>. The dataset is organized as follows:</p> <ol> <li><a href="https://github.com/IBM/Project_CodeNet">CodeNet</a></li> <li><a href="https://github.com/wasiahmad/AVATAR">AVATAR</a></li> <li><a href="https://github.com/evalplus/evalplus">Evalplus</a></li> <li><a href="https://github.com/apache/commons-cli">Apache Commons-CLI</a></li> <li><a href="https://github.com/pallets/click">Click</a></li> </ol> <p>Please download and unzip the <code>dataset.zip</code> file from Zenodo. After unzipping, you should see the following directory structure:</p> <div> <pre><code>PLTranslationEmpirical ├── dataset ├── codenet ├── avatar ├── evalplus ├── real-life-cli ├── ... </code></pre> </div> <p>The structure of each dataset is as follows:</p> <p>1. CodeNet & Avatar: Each directory in these datasets correspond to a source language where each include two directories <code>Code</code> and <code>TestCases</code> for code snippets and test cases, respectively. Each code snippet has an <code>id</code> in the filename, where the <code>id</code> is used as a prefix for test I/O files.</p> <p>2. Evalplus: The source language code snippets follow a similar structure as CodeNet and Avatar. However, as a one time effort, we manually created the test cases in the target Java language inside a maven project, <code>evalplus_java</code>. To evaluate the translations from an LLM, we recommend moving the generated Java code snippets to the <code>src/main/java</code> directory of the maven project and then running the command <code>mvn clean test surefire-report:report -Dmaven.test.failure.ignore=true</code> to compile, test, and generate reports for the translations.</p> <p>3. Real-life Projects: The <code>real-life-cli</code> directory represents two real-life CLI projects from Java and Python. These datasets only contain code snippets as files and no test cases. As mentioned in the paper, the authors manually evaluated the translations for these datasets.</p> <h3>Scripts</h3> <p>We provide bash scripts for reproducing our results in this work. First, we discuss the translation script. For doing translation with a model and dataset, first you need to create a <code>.env</code> file in the repository and add the following:</p> <div> <pre><code>OPENAI_API_KEY=<your openai api key> LLAMA2_AUTH_TOKEN=<your llama2 auth token from huggingface> STARCODER_AUTH_TOKEN=<your starcoder auth token from huggingface> </code></pre> </div> <p>1. Translation with GPT-4: You can run the following command to translate all <code>Python -> Java</code> code snippets in <code>codenet</code> dataset with the <code>GPT-4</code> while top-k sampling is <code>k=50</code>, top-p sampling is <code>p=0.95</code>, and <code>temperature=0.7</code>:</p> <div> <pre><code>bash scripts/translate.sh GPT-4 codenet Python Java 50 0.95 0.7 0 </code></pre> </div> <p>2. Translation with CodeGeeX: Prior to running the script, you need to clone the CodeGeeX repository from <a href="https://github.com/THUDM/CodeGeeX">here</a> and use the instructions from their artifacts to download their model weights. After cloning it inside <code>PLTranslationEmpirical</code> and downloading the model weights, your directory structure should be like the following:</p> <div> <pre><code>PLTranslationEmpirical ├── dataset ├── codenet ├── avatar ├── evalplus ├── real-life-cli ├── CodeGeeX ├── codegeex ├── codegeex_13b.pt # this file is the model weight ├── ... ├── ... </code></pre> </div> <p>You can run the following command to translate all <code>Python -> Java</code> code snippets in <code>codenet</code> dataset with the <code>CodeGeeX</code> while top-k sampling is <code>k=50</code>, top-p sampling is <code>p=0.95</code>, and <code>temperature=0.2</code> on GPU <code>gpu_id=0</code>:</p> <div> <pre><code>bash scripts/translate.sh CodeGeeX codenet Python Java 50 0.95 0.2 0 </code></pre> </div> <p>3. For all other models (StarCoder, CodeGen, LLaMa, TB-Airoboros, TB-Vicuna), you can execute the following command to translate all <code>Python -> Java</code> code snippets in <code>codenet</code> dataset with the <code>StarCoder|CodeGen|LLaMa|TB-Airoboros|TB-Vicuna</code> while top-k sampling is <code>k=50</code>, top-p sampling is <code>p=0.95</code>, and <code>temperature=0.2</code> on GPU <code>gpu_id=0</code>:</p> <div> <pre><code>bash scripts/translate.sh StarCoder codenet Python Java 50 0.95 0.2 0 </code></pre> </div> <p>4. For translating and testing pairs with traditional techniques (i.e., C2Rust, CxGO, Java2C#), you can run the following commands:</p> <div> <pre><code>bash scripts/translate_transpiler.sh codenet C Rust c2rust fix_report bash scripts/translate_transpiler.sh codenet C Go cxgo fix_reports bash scripts/translate_transpiler.sh codenet Java C# java2c# fix_reports bash scripts/translate_transpiler.sh avatar Java C# java2c# fix_reports </code></pre> </div> <p>5. For compile and testing of CodeNet, AVATAR, and Evalplus (Python to Java) translations from GPT-4, and generating fix reports, you can run the following commands:</p> <div> <pre><code>bash scripts/test_avatar.sh Python Java GPT-4 fix_reports 1 bash scripts/test_codenet.sh Python Java GPT-4 fix_reports 1 bash scripts/test_evalplus.sh Python Java GPT-4 fix_reports 1 </code></pre> </div> <p>6. For repairing unsuccessful translations of Java -> Python in CodeNet dataset with GPT-4, you can run the following commands:</p> <div> <pre><code>bash scripts/repair.sh GPT-4 codenet Python Java 50 0.95 0.7 0 1 compile bash scripts/repair.sh GPT-4 codenet Python Java 50 0.95 0.7 0 1 runtime bash scripts/repair.sh GPT-4 codenet Python Java 50 0.95 0.7 0 1 incorrect </code></pre> </div> <p>7. For cleaning translations of open-source LLMs (i.e., StarCoder) in codenet, you can run the following command:</p> <div> <pre><code>bash scripts/clean_generations.sh StarCoder codenet </code></pre> </div> <p>Please note that for the above commands, you can change the dataset and model name to execute the same thing for other datasets and models. Moreover, you can refer to <a href="https://github.com/Intelligent-CAT-Lab/PLTranslationEmpirical/blob/main/prompts/README.md"><code>/prompts</code></a> for different vanilla and repair prompts used in our study.</p> <h3>Artifacts</h3> <p>Please download the <code>artifacts.zip</code> file from our <a href="../doi/10.5281/zenodo.8190051" rel="nofollow">Zenodo</a> repository. We have organized the artifacts as follows:</p> <ol> <li>RQ1 - Translations: This directory contains the translations from all LLMs and for all datasets. We have added an excel file to show a detailed breakdown of the translation results.</li> <li>RQ2 - Manual Labeling: This directory contains an excel file which includes the manual labeling results for all translation bugs.</li> <li>RQ3 - Alternative Approaches: This directory contains the translations from all alternative approaches (i.e., C2Rust, CxGO, Java2C#). We have added an excel file to show a detailed breakdown of the translation results.</li> <li>RQ4 - Mitigating Translation Bugs: This directory contains the fix results of GPT-4, StarCoder, CodeGen, and Llama 2. We have added an excel file to show a detailed breakdown of the fix results.</li> </ol> <h3>Contact</h3> <p>We look forward to hearing your feedback. Please contact <a href="mailto:rangeet.pan@ibm.com">Rangeet Pan</a> or <a href="mailto:alirezai@illinois.edu">Ali Reza Ibrahimzada</a> for any questions or comments 🙏.</p>
Train and Evaluation Code, Road Classification Models and Test set of the paper "Impact of Image Resolution and Image Overlap on the Prediction Performance of Convolutional Neural Networks Trained for Road Classification"
<p>This repository contains the Python scripts built for training and evaluation of the implementation, together with the test data and the resulting road classification models corresponding to the paper "Impact of Image Resolution and Image Overlap on the Prediction Performance of Convolutional Neural Networks Trained for Road Classification". The scripts make use of the Tensorflow with Keras framework and the additional required dependencies.</p> <p>The training and validation set is based on the binary SROADEX dataset (https://zenodo.org/records/6482346) that was re-split into tiles that feature the image resolutions (256 x 256, 512 x 512, and 1024 x 1024 pixels) and image overlaps (0% and 12.5%) considered in this study. The data have been generated using scripts developed in Python using Open Source libraries (GDAL/OGR and MapScript) for rasterization of vector cartography that represents the axes of the different types of roads (urban, interurban and rural). This binary road data contains information from 16 full orthoimages (28.5 km * 18.5 km) with spatial resolution of 0.5 m/pixel from the insular and peninsular Spanish territory. Due to the size on disk of approximately 546 gigabytes, this training and validation data is only available upon request from the corresponding author. The test set has been generated from a novel area of 28.5 km * 18.5 km and features binary road labels. The test sets are provided in the repository for each resolution (with no overlap), so that additional DL models can be evaluated on the same data and compared with the results achieved in this study.</p> <p>The structure of the information shared in this repository is as follows:<br>The scripts have been grouped by tile resolution (256, 512 and 1024). First, the test set and the evaluation script can be found. For each tile resolution, there are two subfolders (corresponding to the "no overlap" and "12.5% overlap"). In each case, the Python scripts for training the models in the three repetitions are shared, and the trained models (H5 format) are shared in compressed form. Finally, for each resolution we also share the testing dataset which consists of two folders.</p> <p>The material is distributed under a CC-BY 4.0 license.</p>
Specification-Driven Code Translation By Large Language Models: How Far Are We?
<p>The artifacts and dataset for "Specification-Driven Code Translation By Large Language Models: How Far Are We?"</p>
Code and Data to "Quantile regression for temporal streamflow modeling"
<p>This is the accompanying code to "Quantile regression for temporal streamflow modeling", which is part of the manuscript "The Role of Process Heterogeneity in Statistical Modeling", which was submitted to the Austrian Journal of Statistics. </p> <p> </p> <p>The data used in this publication is fully accessible through the <a href="https://doi.org/10.5194/essd-13-4529-2021" target="_blank" rel="noopener">LamaH-CE</a> dataset. The two scripts "functions_create_data.R" and "create_data.R" will create the final dataset used for modelling. </p> <p>"functions_modelling.R" provide the functions for tuning the XGBoost model and computing the SHAP values. An example script is also attached (calc_predictions_shap.R). "analyzing_results.R" and "error_metrics.R" will produce the final output used in the manuscript. Finally, two plots produced in the script are added as pdf. </p> <p>All data analysis was performed in R, and we want to acknowledge the following packages: <a href="https://dplyr.tidyverse.org/">dplyr</a>, <a href="https://tidyr.tidyverse.org/">tidyr</a>, <a href="https://www.jstatsoft.org/v40/i03/">lubridate</a>, <a href="https://purrr.tidyverse.org/">purrr</a>, <a href="https://doi.org/10.18637/jss.v033.i01">glmnet</a>, <a href="https://cran.r-project.org/web/packages/xgboost/index.html">xgboost</a>, <a href="https://CRAN.R-project.org/package=shapr">shapr</a>, <a href="https://CRAN.R-project.org/package=Metrics">Metrics</a>, <a href="https://CRAN.R-project.org/package=gridExtra" target="_blank" rel="noopener">gridExtra</a>, <a href="https://doi.org/10.18637/jss.v014.i06">zoo</a> and <a href="https://CRAN.R-project.org/package=wesanderson">wesanderson</a>. </p> <p> </p>
Data and codes related to the article: Renard et al. A Hidden Climate Indices Modeling Framework for Multi-Variable Space-Time Data. Water Resources Research.
<p>This package contains data and codes related to the article:</p> <p>B. Renard, M. Thyer, D. McInerney, D. Kavetski, M. Leonard and S. Westra. A Hidden Climate Indices Modeling Framework for Multi-Variable Space-Time Data. <em>Water Resources Research</em>.</p> <p><strong>R scripts</strong></p> <p>The main computations of the paper have been performed using a computing code named <a href="https://github.com/STooDs-tools">STooDs</a>, which is called using the bash script launchpad.sh.</p> <p>The R scripts in this package only perform pre-processing (create configuration files) and post-processing (analyze results) steps.</p> <ul> <li>Funk.R: a set of functions called by other scripts.</li> <li>1_defineModel.R: define the model to be inferred and create STooDs configuration files in <em>dataset_XXX/runs.</em></li> <li>2_analyzeResults.R: analyze the outputs of STooDs runs.</li> <li>3_crossValidation.R: analyze the outputs of cross-validation experiments in <em>dataset_XV</em> and <em>dataset_XV_1971-1990</em>.</li> </ul> <p><strong>Data</strong></p> <p>Data for the 3 cases (full dataset and 2 cross-validation experiments) are located in folders <em>dataset_XXX/data</em>.</p> <ul> <li>dat.txt: raw dataset in text format.</li> <li>dataset.RData: dataset in RData format.</li> <li>DMI.txt, NINO.txt, SAM.txt: 3 standard climate indices.</li> <li>spaceP.txt, spaceQ.txt, spaceT.txt: properties of Precipitation (P), Streamflow (Q) and Temperature (T) stations.</li> <li>[only for cross-validation experiments] validation.RData: left-out data used for validation.</li> </ul> <p> </p> <p> </p>
Associated code and data for "Multi-level computational modeling of anti-cancer dendritic cell vaccination utilized to select molecular targets for therapy optimization (doi: 10.3389/fcell.2021.74635)"
<p>This deposit contains the data, code, and analysis to reproduce the results in the manuscript - Lai X, Keller C, Santos-Rosales G, Schaft N, Dörrie J, Vera J. Multi-level computational modeling of anti-cancer dendritic cell vaccination utilized to select molecular targets for therapy optimization. Frontiers in Cell and Developmental Biolology. 2022; 9:746359; <a href="https://www.researchgate.net/publication/358461035_Multi-Level_Computational_Modeling_of_Anti-Cancer_Dendritic_Cell_Vaccination_Utilized_to_Select_Molecular_Targets_for_Therapy_Optimization">doi:10.3389/fcell.2021.746359</a>.</p> <p>If you have used the code for your research, please cite the original publication. Thank you very much.</p> <p> </p>
Model output data and figures' code for Fujimori & Wu et al., Land-based climate change mitigation measures can affect agricultural markets and food security
<p>Model output data and figures' code for "Fujimori & Wu et al., Land-based climate change mitigation measures can affect agricultural markets and food security" in Nature Food (DOI: 10.1038/s43016-022-00464-4)</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.