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281 results for “source code”
Code for "An increasing Arctic-boreal CO2 sink offset by wildfires and source regions"
<p>This repository includes the codes used to produce the results in Virkkala et al. (2024) in review. "An increasing Arctic-boreal CO2 sink offset by wildfires and source regions" (preprint: https://doi.org/10.1101/2024.02.09.579581). In short, there are R codes used to train random forest models, estimate importance scores and partial dependences for the variables, assess model predictive performance and uncertainties, predict (i.e. upscale) with the models and summarize model outputs using R version 4.2. Note that some of the datasets and results (e.g. monthly geospatial predictors or upscaling outputs) were not included in the repository due to their large file size (hundreds of GBs). Therefore, some of the paths to the files included in the codes are not working. These additional files are available from the corresponding author upon request. </p> <p><strong>Folder structure:</strong></p> <p><em>flux_upscaling_data</em>: includes the model training data, i.e. in-situ data as well as environmental data extracted from geospatial datasets</p> <p><em>abcflux_modeling: </em>includes the R analysis scripts in the codes-folder</p> <p><em>abcflux_modeling_bigfiles: </em>includes the key results and figures produced by the scripts</p> <p><strong>More details from avirkkala@woodwellclimate.org and from manuscript:</strong></p> <div>Anna-Maria Virkkala, Brendan M. Rogers, Jennifer D. Watts, Kyle A. Arndt, Stefano Potter, Isabel Wargowsky, Edward A. G. Schuur, Craig See, Marguerite Mauritz, Julia Boike, Syndonia M. Bret-Harte, Eleanor J. Burke, Arden Burrell, Namyi Chae, Abhishek Chatterjee, Frederic Chevallier, Torben R. Christensen, Roisin Commane, Han Dolman, Bo Elberling, Craig A. Emmerton, Eugenie S. Euskirchen, Liang Feng, Mathias Goeckede, Achim Grelle, Manuel Helbig, David Holl, Järvi Järveoja, Hideki Kobayashi, Lars Kutzbach, Junjie Liu, Ingrid Liujkx, Efrén López-Blanco, Kyle Lunneberg, Ivan Mammarella, Maija E. Marushchak, Mikhail Mastepanov, Yojiro Matsuura, Trofim Maximov, Lutz Merbold, Gesa Meyer, Mats B. Nilsson, Yosuke Niwa, Walter Oechel, Sang-Jong Park, Frans-Jan W. Parmentier, Matthias Peichl, Wouter Peters, Roman Petrov, William Quinton, Christian Rödenbeck, Torsten Sachs, Christopher Schulze, Oliver Sonnentag, Vincent St.Louis, Eeva-Stiina Tuittila, Masahito Ueyama, Andrej Varlagin, Donatella Zona, and Susan M. Natali. An increasing Arctic-boreal CO<sub>2</sub> sink offset by wildfires and source regions.</div> <div>bioRxiv 2024.02.09.579581; doi: https://doi.org/10.1101/2024.02.09.579581</div>
Source code of tables for the publication "K- and L-shell theoretical fluorescence yields for the Fe isonuclear sequence"
<p>In this work, we present K- and L- shell fluorescence yield values of the full isonuclear sequence of Fe ions, using a state-of-the-art multiconfiguration Dirac-Fock approach. These results may be of importance for spectral fitting and plasma modeling, both in laboratory and astrophysical studies, where Fe is an important benchmark element. The K-shell fluorescence yields were found to be very similar up to the removal of 14 electrons.</p>
Automated Model Discovery for Tensional Homeostasis: Constitutive Machine Learning in Growth and Remodeling (Source code and data)
<p>This dataset contains</p> <ul> <li>the source code</li> <li>the data and examples</li> <li>the material subroutine with examples of uniaxial strain and stress</li> </ul> <p>of the inelastic Constitutive Artificial Neural Network (iCANN) enhanced by the concept of homeostatic surfaces to discover tensional homeostasis.</p> <p>The corresponding publication is:</p> <p>Holthusen, H., Brepols, T., Linka, K., & Kuhl, E..<em> </em></p> <p><em>Automated Model Discovery for Tensional Homeostasis: Constitutive Machine Learning in Growth and Remodeling.</em></p> <p> </p> <p><strong>Standalone_Materialroutine</strong></p> <ul> <li>00_Materialroutine: Contains the material subroutine implemented in FORTRAN</li> <li>01_uniaxial_strain: Example of the material subroutine in a uniaxial strain driven manner</li> <li>02_uniaxial_stress: Example of the material subroutine in a uniaxial stress driven manner</li> </ul> <p> </p> <p><strong>TensorFlow</strong></p> <ul> <li> <p>iCANN:</p> <ul> <li> <p>01_Biax/biax_l1: Keras/TensorFlow implementation of the iCANN. Example of the cross specimen with L1 (Lasso) regularization</p> </li> <li> <p>01_Biax/biax_l2: Keras/TensorFlow implementation of the iCANN. Example of the cross specimen with L2 (ridge) regularization</p> </li> <li> <p>02_Uniax/uniax_l1: Keras/TensorFlow implementation of the iCANN. Example of the stripe specimen with L1 (Lasso) regularization</p> </li> <li>02_Uniax/uniax_l1: Keras/TensorFlow implementation of the iCANN. Example of the stripe specimen with L2 (ridge) regularization</li> </ul> </li> <li> <p>iCANN_ABS_activation: Same four examples as above, however, with the absolute value activation function</p> </li> <li> <p>installed_packages: File containing a list of installed Python modules used to implement the iCANN</p> </li> </ul> <p>The TensorFlow implementations in all 01_Biax/ and 02_Uniax/ sub-directories are the same.</p> <p>The implementation in iCANN_ABS_activation is different with respect to the activation functions of the pseudo potential.</p> <p> </p> <p>The experimental data for the cross and stripe specimen are taken from the literature:</p> <p>Eichinger, J. F., Paukner, D., Szafron, J. M., Aydin, R. C., Humphrey, J. D., & Cyron, C. J. (2020).</p> <p>Computer-controlled biaxial bioreactor for investigating cell-mediated homeostasis in tissue equivalents. <em>Journal of biomechanical engineering</em>, <em>142</em>(7), 071011.</p> <p><a href="https://doi.org/10.1115/1.4046201">https://doi.org/10.1115/1.4046201</a></p>
Improving Build Outcome Prediction usingTextual Analysis of Source Code
<p>The uploaded data sets contain code deltas and their corresponding feature vectors. Those were derived from 13 open-source Java-based projects. </p>
Codes and source data files for: Proximity labeling identifies LOTUS domain proteins that promote the formation of perinuclear germ granules in C. elegans
<p>The germ line produces gametes that transmit genetic and epigenetic information to the next generation. Maintenance of germ cells and development of gametes require germ granules—well-conserved membraneless and RNA-rich organelles. The composition of germ granules is elusive owing to their dynamic nature and their exclusive expression in the germ line. Using <i>C. elegans</i> germ granule, called P granule, as a model system, we employed a proximity-based labeling method in combination with mass spectrometry to comprehensively define its protein components. This set of experiments identified over 200 proteins, many of which contain intrinsically disordered regions. An RNAi-based screen identified factors that are essential for P granule assembly, notably EGGD-1 and EGGD-2, two putative LOTUS-domain proteins. Loss of <i>eggd-1</i> and <i>eggd-2</i> results in separation of P granules from the nuclear envelope, germline atrophy and reduced fertility. We show that intrinsically disordered regions of EGGD-1 are required to anchor EGGD-1 to the nuclear periphery while its LOTUS domains are required to promote perinuclear localization of P granules. Together, our work expands the repertoire of P granule constituents and provides new insights into the role of LOTUS-domain proteins in germ granule organization.</p>
To Automatically Map Source Code Entities to Architectural Modules with Naive Bayes: Replication Package
<p>This is the replication package for the JSS article To Automatically Map Source Code Entities to Architectural Modules with Naive Bayes. It provides the source data files and the r-script to produce analysis and images.</p>
Dynamic antagonism between key repressive pathways maintains the placental epigenome (source data and custom code)
<p>DNA and Histone-3 Lysine 27 methylation typically function as repressive modifications and operate within distinct genomic compartments. In mammals, the majority of the genome is kept in a DNA methylated state, whereas the Polycomb Repressive Complexes regulate the CpG-rich promoters of developmental genes. In contrast to this general framework, the extraembryonic lineages display noncanonical, globally intermediate DNA methylation levels that includes disruption of local Polycomb domains. To better understand this unusual landscape’s molecular properties, we genetically and chemically perturbed major epigenetic pathways in mouse Trophoblast Stem Cells (TSCs). We find that the extraembryonic epigenome reflects ongoing and dynamic de novo methyltransferase recruitment, which is continuously antagonized by Polycomb to maintain intermediate, locally disordered methylation. Despite its disorganized appearance, our data point to a highly controlled equilibrium between counteracting repressors within extraembryonic cells, one that can seemingly persist indefinitely without bistable features typically seen for embryonic forms of epigenetic regulation.</p> <p> </p>
DATASET - On the Investigation of Empirical Contradictions - Aggregated Results of Local Studies on Readability and Comprehensibility of Source Code
<p>Study package containing raw and analyzed data from the work entitled "On the Investigation of Empirical Contradictions - Aggregated Results of Local Studies on Readability and Comprehensibility of Source Code".</p> <p>The package comprises: 1) a summary of the information extracted from all papers mentioned in the Background; 2) the source code snippets used in the three studies; 3) the consent and characterization forms distributed to the participants; 4) the raw data, the aggregated data and other material generated with the collected data.</p>
Using the Uniqueness of Global Identifiers to Determine the Provenance of Python Software Source Code
<p>A replication package for the paper "Using the Uniqueness of Global Identifiers to Determine the Provenance of Python Software Source Code", Journal of Empirical Software Engineering.</p>
Data sources and code for: "Species-specific acclimation capacity of key traits explains global vertical distributions of seagrass species"
<p>Minguito-Frutos_etal_2023_Data1.xlsx contains the data for analyzing the relationship between plant size and seagrass growth reproductive strategy and the species-specific vertical distribution of seagrasses. </p> <p>Minguito-Frutos_etal_2023_Data2.xlsx contains the data for the meta-analityc approach studying the relationship between the vertical distribution of seagrass species and the plasticity of their traits (physiological, morphological, structural and growth). </p> <p>Scripts_Minguito_Frutos_etal_2023_GEB_Ref.GEB-2022-0592.R contains the R reproducible code to run all the analyses carried out in this study. </p>
Over and Under Sampled Data-sets of Code Issues in Java Open-Source Projects
<p>The dataset comprises code changes made to 15 Java Open-Source projects, classified with sentiment values (0 for negative and 1 for positive) based on developer reviews during various revision submissions. The dataset is available in 8 versions, each containing a sampled dataset using an over or under-sampling technique.</p>
ODNA Data and Analysis Source Code
<p>This upload contains the analysis source code and processed data for developing the software ODNA, including the machine learning pipeline. ODNA is software for identifying organellar DNA sequences from genome assemblies using genome annotation derived from the Modular Open-Source Genome Annotator (MOSGA).</p>
QAnubis - open source coding tool
<p>QAnubis - open source coding tool</p> <p>The main activity of qualitative research is data analysis, which involves a process of collecting and analyzing narrative data, including texts, photos, audiovisual elements, and various digital file formats. Different approaches and methodologies are available for qualitative data investigation, with a greater emphasis on coding, where artifacts and their contents are categorized under a finite set of categories. Similar to other fields, efforts are made to develop and enhance computerized tools known as Computer-Assisted Qualitative Data Analysis Software (CAQDAS) that aim to increase the efficiency and effectiveness of the analysis process by providing means to assist in data identification, organization, interpretation, exploration, and integration.</p> <p>To contribute to the field of qualitative analysis, this work presents an open-source web tool called QAnubis for performing coding on PDF files while preserving their original content and formatting.</p>
Replication Package for the paper "AI-based Fault-proneness Metrics for Source Code Changes"
<p>This is the replication package for the paper "<em>AI-based Fault-proneness Metrics for Source Code Changes</em>", submitted at the <em>IWSM-Mensura '23 </em>conference.</p> <p>The archive is a <em>Docker </em>image file with a fully setup and working environment to re-execute the experiments involved in the manuscript. We pre-loaded all libraries and codeBERT models to ease the replication process and avoid compatibility issues, as the environment cannot be easily managed using <em>Dockerfile</em>s.</p> <p>To run the image, a <em>Docker</em> installation is needed. Once downloaded, from the command line type:</p> <pre><code>docker load -i </path/to/downloaded/ai-proneness-replication.tar></code></pre> <p>After the loading process, you can run the container by typing:</p> <pre><code>docker run -it mensura/ai-proneness-replication:1.0</code></pre> <p>All the source code and the dataset to re-execute the experiment is located into the <em>/Replication</em> folder. The folder contains the results of our experimentation in CSV and MS Excel format, along with the following subdirectories:</p> <ul> <li><em>dataset</em>: a replication of the used dataset. The file <em>dataset.csv</em> gives information on all the entries, while the <em>code </em>folder contains a subdirectory for each sample, named by its id. In the folder, the file <em>old.txt </em>and<em> </em><em>new.txt </em>refers to the older and newer version of the method, respectively; <em>gitdiff.txt </em>stores the raw <em>git-diff</em> command output, while <em>diff.html</em> stores a more human-readable version of the differences.</li> <li><em>ai-fault-proneness-tk-replication</em>: the Java code used to apply Tree Kernel techniques on the dataset (we used JDK-11, embedded within the container). To build and execute the package, refer to the file <em>README.md</em> in the folder. For convenience, we also provided an executable JAR file <em>ai-fault-proneness-tk-replication-1.0-jar-with-dependencies.jar </em>that can be run directly and saves the output in a CSV file in the <em>results</em> folder of the replication package.</li> <li><em>code-embeddings-and-analysis</em>: python scripts to execute the <em>codeBERT</em>-based approaches and to extract the <em>diff</em> statistics. To execute all the steps, a convenience shell script <em>execute.sh</em> has been pre-loaded and can be executed to automatize all the process.</li> </ul>
Code and source data for the paper: "Heat over heritability: increasing body size in response to global warming is not stabilized by genetic effects in Bechstein's bats"
<p>The first two script include code for the model building testing different fixed effect strucutes.</p> <p>The next two script includes all code for the descriptive analysis, all figures, as well as the animal models that compare heritabilty between the different environments (Q1-Q4) as well as between the birth environments differentials of mothers and daughters.</p> <p>Data contain the pedigree ('pedigree_Model_cod.csv'), further information ('pedigree_information_cod.csv) and weather data ('Weather_summer.csv')</p>
Source code and data for aerosol emission and indirect feedback paper
<p>A physics suite under development at NOAA’s Global System Laboratory (GSL) includes the aerosol-aware double moment Thompson-Eidhammer microphysics scheme (TH-E MP). This microphysics scheme uses two aerosol variables (water friendly (WFA) and ice friendly (IFA) aerosol number concentrations) to include interaction with some of the physical processes. In the original implementation, WFA and IFA depend on emissions derived from climatologies. In our approach, using the Common Community Physics Package (CCPP), we embedded sea-salt, dust, and biomass burning emission modules as well as anthropogenic aerosol emissions into the Unified Forecast System (UFS) to provide realistic aerosol emissions for these two variables. This represents a very simple approach with no additional tracer variables and therefore very limited additional computing cost. We then evaluate a comparison of simulations using the original TH-E MP approach, which derives the two aerosol variables using empirical emission formulas from climatologies (CTL) and simulations that use the online emissions (EXP). Aerosol Optical Depth (AOD) is derived from the 2 variables and appears quite realistic in the runs with online emissions when compared to analyzed fields. We find less resolved precipitation over Europe and North America from the EXP run, which represents an improvement compared to observations. Also interesting are moderately increased aerosol concentrations over Southern Ocean from the EXP run invigorating the development of cloud water and enhances the resolved precipitation in those areas. This study shows that a more realistic representation of aerosol emission may be useful when using double moment microphysics schemes.</p>
Source code and data files for the fetal kick simulator developed in the Biomechatronics Lab at Imperial College London, UK
<p>This repository holds the code for Fetal Kick Simulator</p> <p>Copyright (c) 2020, Imperial College London All rights reserved.</p> <p>Authors: Abhishek Kumar Ghosh, Ravi Vaidyanathan, Niamh C Nowlan. Imperial College London.</p> <p>This program is a free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.</p> <p>This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details.</p> <p>Acknowledgements: If this software is helpful, then Please consider acknowledging or referencing the authors.</p> <p>Publication: Link for the publication related to this repository is <a href="https://www.mdpi.com/1424-8220/20/21/6020">https://www.mdpi.com/1424-8220/20/21/6020</a></p>
Modeling Attack Resistant Strong PUF Exploiting Stagewise Obfuscated Interconnections With Improved Reliability [DATASET and Source Code]
<p>Thanks for your interest in our work!</p> <p>In order to facilitate your assessment and replication, we provides the dataset and source codes (verilog/python model/matlab) of our work (OIPUF) here. </p> <p>By the way, our latest work (SOI PUF and cSOI PUF) published in IEEE TIFS (2024) is based on OIPUF. </p> <blockquote> <p>If you have any questions, please feel free to contact with us: <a href="mailto:chongyaoxu@126.com">chongyaoxu@126.com</a> / <a href="mailto:mklaw@um.edu.mo">mklaw@um.edu.mo</a></p> <p>Full text about OIPUF can be downloaded from <a href="https://ieeexplore.ieee.org/document/10103139">https://ieeexplore.ieee.org/document/10103139</a></p> <p>Full text about SOI PUF and cSOI PUF can be downloaded from <a href="https://ieeexplore.ieee.org/document/10458688">https://ieeexplore.ieee.org/document/10458688</a></p> <p>Source code and FPGA project of SOI PUF and cSOI PUF can be download from <a href="https://github.com/yg99992/SOI_PUF">https://github.com/yg99992/SOI_PUF</a>. </p> </blockquote> <p> </p> <p>Matlab code</p> <p><code>matlab/Generate_OI_block.m</code><br>This is a matlab manuscript used for generating the verilog code of random OI block.</p> <p><code>matlab/OIPUF_64x4_placement.m</code><br>This is a matlab function used for generating XDC file for constraining the placement of (64,4)-OI block</p> <p><code>matlab/OIPUF_64x8_placement.m</code><br>This is a matlab function used for generating XDC file for constraining the placement of (64,8)-OI block</p> <p><code>matlab/OIPUF_placement_example.m</code><br>An example manuscript used for demonstrating the usage of OIPUF_64x4_placement.m and OIPUF_64x8_placement.m</p> <p> </p> <p>Python code</p> <p><code>python/puf_models.py</code><br>The python models of XOR PUFs and OIPUFs, which can be used to generate CRPs.</p> <p>for example:</p> <pre><code>from puf_models import oi_puf # generate a (64,4)-OIPUF and further use the generated OIPUF to generate 1M CRPs crps, puf_instance = oi_puf.gen_CRPs_PUF(64, 4, 1_000_000) </code></pre> <p> </p> <p><code>python/attack_pypuf.py</code><br>A manuscript used to conduct to ANN attack on XOR PUF and OIPUF ('pypuf' package should be installed correctly).</p> <p> </p> <p>Verilog code</p> <p><code>verilog/OIPUF_64_4/</code><br>All the verilog files of (64, 4)-OIPUF</p> <p><code>verilog/OIPUF_64_8/</code><br>All the verilog files of (64, 8)-OIPUF</p> <p> </p> <p>CRP datasets extracted from FPGA</p> <p>It consists of 13 CRP files (All the CRPs are extracted from FPGA):</p> <p><code>FPGA_CRPs/FPGA3_CHAL_100M.csv</code><br>The 100 million 64-bit challenges</p> <p><code>FPGA_CRPs/FPGA3_k4_PUF0.csv</code><br>The 100 million 1-bit responses extracted from (64,4)-OIPUF0</p> <p><code>FPGA_CRPs/FPGA3_k4_PUF1.csv</code><br>The 100 million 1-bit responses extracted from (64,4)-OIPUF1</p> <p><code>FPGA_CRPs/FPGA3_k4_PUF2.csv</code><br>The 100 million 1-bit responses extracted from (64,4)-OIPUF2</p> <p><code>FPGA_CRPs/FPGA3_k4_PUF3.csv</code><br>The 100 million 1-bit responses extracted from (64,4)-OIPUF3</p> <p><code>FPGA_CRPs/FPGA3_k4_PUF4.csv</code><br>The 100 million 1-bit responses extracted from (64,4)-OIPUF4</p> <p><code>FPGA_CRPs/FPGA3_k4_PUF5.csv</code><br>The 100 million 1-bit responses extracted from (64,4)-OIPUF5</p> <p><code>FPGA_CRPs/FPGA3_k8_PUF0.csv</code><br>The 100 million 1-bit responses extracted from (64,8)-OIPUF0</p> <p><code>FPGA_CRPs/FPGA3_k8_PUF1.csv</code><br>The 100 million 1-bit responses extracted from (64,8)-OIPUF1</p> <p><code>FPGA_CRPs/FPGA3_k8_PUF2.csv</code><br>The 100 million 1-bit responses extracted from (64,8)-OIPUF2</p> <p><code>FPGA_CRPs/FPGA3_k8_PUF3.csv</code><br>The 100 million 1-bit responses extracted from (64,8)-OIPUF3</p> <p><code>FPGA_CRPs/FPGA3_k8_PUF4.csv</code><br>The 100 million 1-bit responses extracted from (64,8)-OIPUF4</p> <p><code>FPGA_CRPs/FPGA3_k8_PUF5.csv</code><br>The 100 million 1-bit responses extracted from (64,8)-OIPUF5</p>
Datasets and code for "Multi-site transfer function approach for real-time modeling of the ground electric field induced by laterally-nonuniform ionospheric source" by Kruglyakov et al. (2023)
<ol> <li>Archive calculate_weights_for_rt.tgz contains the code for calculation of weights used for computation of electric fields based on multi-site transfer function approach following Kruglyakov et al. (2023). The code is written in Fortran 2003 and the only external dependency is LAPACK/BLAS -compatible library, for example OpenBLAS from https://www.openblas.net. See READ.ME for details.</li> <li>Files GICs*.dat contain observed and modelled geomagnetically induced currents (GICs) at Mäntsälä compressor station in southern Finland (60.6 N, 25.2 E) (https://space.fmi.fi/gic/) for three events (in 2000, 2001, and 2003).</li> <li>Files E_x*. E_y* contain corresponding components of measured (detrended and downsampled from 1s to 10s) and modeled electric fields at sites M02 and M05 from 05:15 to 06:15 UT, 11 Sep 2005.</li> <li>Files MS_TF*.dat contain multi-site transfer functions for different sets of IMAGE magnetometers (based on the data availability during the simulated events) in the frequency domain and the corresponding weights for calculation of electric field in the time domain.</li> <li>File E_to_GICs_W.dat contains coefficients for computation of GICs at Mäntsälä station from electric fields at 18 sites used in the simulation. See Equation (15) of Kruglyakov et al. (2023) for details.</li> </ol> <p> </p> <p> </p>
Slantwise Convection in the West Greenland Current (Source Code)
<p>The source code of two idealized experiments described in 'Slantwise Convection in the West Greenland Current'.</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.