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281 results for “source code”
An Empirical Validation of Cognitive Complexity as a Measure of Source Code Understandability - Data, Code and Documentation
<p>Release version of the data, code and documentation used in and generated by our data analysis and literature search to ensure reproducibility, repeatability, and transparency, to be published alongside our paper "An Empirical Validation of Cognitive Complexity as a Measure of Source Code Understandability".</p>
Source code for models of floral initiation in pea and gene expression data extracted from published sources
<p>The dataset contains the source code for computational models of a gene network controlling transition to flowering in pea (<em>Pisum sativum</em>). The models were based on ordinary differential equations (ODE) or neural networks. It also includes data on the expression dynamics of genes involved in the network, which was used for model fitting. The expression data was extracted from the following papers: </p> <p>Hecht, V., Laurie, R. E., Schoor, K. Vander, Ridge, S., Knowles, C. L., Liew, L. C., Sussmilch, F. C., et al. (2011). The Pea GIGAS Gene Is a FLOWERING LOCUS T Homolog Necessary for Graft-Transmissible Specification of Flowering but Not for Responsiveness to Photoperiod. 23, 147–161. doi:10.1105/tpc.110.081042</p> <p>Sussmilch, F. C., Berbel, A., Hecht, V., Schoor, K. Vander, Ferrándiz, C., Madueño, F., et al. (2015). Pea VEGETATIVE2 Is an FD Homolog That Is Essential for Flowering and Compound In fl orescence Development. 27, 1046–1060. doi:10.1105/tpc.115.136150</p> <p>The source code of the DEEP software used for parameter optimization in the model fitting can be found in the Gitlab repository (https://gitlab.com/mackoel/deepmethod/-/tree/master).</p> <p>The files are the supplement to the following manuscript, submitted to Frontiers in Genetics:</p> <p>"Dynamical Modeling of the Core Gene Network Controlling Transition to Flowering in <em>Pisum sativum</em>" by Polina Pavlinova, Maria G. Samsonova, and Vitaly V. Gursky.</p> <p>All possible questions can be sent to: Polina Pavlinova (polina.pavlina1004@gmail.com), Vitaly Gursky (gursky@math.ioffe.ru).</p>
Vulnerable source code dataset
<p>Vulnerable source code dataset. Includes support dataset containing commit messages of security patches. </p>
Source code and datasets used to link new waves of plague outbreaks in medieval Europe to climate fluctuations affecting the reservoirs of the disease in Asia.
<p>The zipfile contains the project directory which includes the source code and datasets used in the paper on <strong>Climate-driven introduction of the Black Death and successive plague reintroductions into Europe</strong>, as published in <em>Proceedings of the National Academy of Sciences</em> (PNAS). Access the paper at http://www.doi.org/pnas.1412887112</p> <p>If you are not familiar with Clojure, Leiningen, and its project directory format, see http://clojure.org/getting_started for one of the IDE's to run the clojure code in, and use http://leiningen.org/ as the project / dependency manager.</p>
CALLISTO-SPK: A Stochastic Point Kinetics Code for Performing Low Source Nuclear Power Plant Start-up and Power Ascension Calculations Data Repository
<p>This dataset provides data to accompany the submission named "CALLISTO-SPK: A Stochastic Point Kinetics Code for Performing Low Source Nuclear Power Plant Start-up and Power Ascension Calculations" which has been submitted to Annals of Nuclear Energy. Details of the file included may be found in the readme file.</p>
Dataset and source code for ICSME2017 paper "Supervised vs Unsupervised Models: A Holistic Look at Effort-Aware Just-in-Time Defect Prediction"
<p>Dataset and source code for ICSME2017 paper “Supervised vs Unsupervised Models: A Holistic Look at Effort-Aware Just-in-Time Defect Prediction”</p> <p>There are four different models in the paper (i.e., EALR, LT, CBS and OneWay). Each model was implemented in a single Java file in the model package. To reproduce the experiment results of each model in the paper, just run the main method in the corresponding Java file. </p> <p> </p>
Scenario data, model source code and plotting routine for manuscript: Separating CO2 emission from removal targets comes with limited cost impacts
<p>This data archive contains REMIND model setup, results data and data analysis files for manuscript:<br><strong>Separating CO2 emission reduction from removal targets comes with limited cost impact.<br><br>plotting</strong>(directory) contains results data, manuscript specific data analysis and plotting routine scripts used to generate the figures of the manuscript.<br><strong>remind</strong>(directory) contains REMIND model source code and scenario set-up. Detailed scenario configurations are set in remind/config/scenario_config_SepMark.csv.<br><strong>remind2</strong>(directory) contains the slightly modified R-library package used for post-processing of REMIND output.<br><br>AMENDMENT<br><strong>Plots_SeparateMarkets_afterReviewProcess.Rmd</strong> After the review process, the new plotting script was added including the additional figures in the Supplementary Material. This file should replace the previous R-markdown file SepMark_essential/plotting/Plots_SeparateMarkets.Rmd.</p>
AnalyzAIRR: A user-friendly guided workflow for AIRR data analysis: example data and analysis source-code
<p>This repository contains:</p> <ul> <li>Annotated TCR-seq data files named <em>tripod-XX-XXXX</em></li> <li>The metadata corresponding to the annotated files</li> <li>The RepSeqExperiment object, which integrates the annotated files and the metadata and was used in the analysis pipeline</li> <li>The analysis script to generate the plots of the different figures</li> </ul>
Sample Stripped Pre-supernova Progenitors for open-source code CHIPS (Complete History for Interaction-Powered Supernovae)
<p>Inlists, mainly based on the test suite "example_make_pre_ccsn" in r12778, with slight amendments for removal of hydrogen (and helium, for Ic progenitors) envelope at core hydrogen (helium) exhausion.</p><p>For details: https://ui.adsabs.harvard.edu/abs/2023arXiv230810785T/abstract</p>
Theory and implementation of inelastic Constitutive Artificial Neural Networks: Source code and data
<p>This dataset contains the source code of the inelastic Constitutive Artificial Neural Network (iCANN) as well as the data for the examples from the publication:</p> <p>Holthusen, H., Lamm, L., Brepols, T., Reese, S., & E. Kuhl.<em> Theory and implementation of inelastic Constitutive Artificial Neural Networks.</em></p> <p>arXiv: <a href="https://doi.org/10.48550/arXiv.2311.06380">https://doi.org/10.48550/arXiv.2311.06380</a></p> <p>Computer Methods in Applied Mechanics and Engineering: <a href="https://doi.org/10.1016/j.cma.2024.117063">https://doi.org/10.1016/j.cma.2024.117063</a></p> <p> </p> <p><strong>01_Example01: </strong> Artificially generated data</p> <p>This example investigates whether the iCANN is able to discover a model for the data generated by a continuum mechanical model.</p> <p> </p> <p><strong>02_Example02:</strong> Discovering a model for the polymer VHB 4910 subjected to cyclic loading</p> <p>Here, we investigate the ability of iCANN to discover and learn a model for the material response of VHB 4910 polymer subjected to cyclic loading at different stretch rates.</p> <p>The experimental data are taken from the literature:</p> <p>Hossain, M., Vu, D. K., & Steinmann, P. (2012). Experimental study and numerical modelling of VHB 4910 polymer. <em>Computational Materials Science</em>, <em>59</em>, 65-74.</p> <p><a href="https://doi.org/10.1016/j.commatsci.2012.02.027">https://doi.org/10.1016/j.commatsci.2012.02.027</a></p> <p> </p> <p><strong>03_Example03: </strong>Discovering a model for passive skeletal muscle subjected to relaxation</p> <p>In this example, we investigate whether the iCANN is able to discover a model for the material behavior of passive skeletal muscles. A total of five independent experiments are carried out in which the maximum applied compression stretch and the stretch rate are varied. In addition, the learning performance of the iCANN is investigated. Training is first carried out in each of the five experiments and then in each of four of the five experiments.</p> <p>The experimental data are taken from the literature:</p> <p>Van Loocke, M., Lyons, C. G., & Simms, C. K. (2008). Viscoelastic properties of passive skeletal muscle in compression: stress-relaxation behaviour and constitutive modelling. <em>Journal of biomechanics</em>, <em>41</em>(7), 1555-1566.</p> <p><a href="https://doi.org/10.1016/j.jbiomech.2008.02.007">https://doi.org/10.1016/j.jbiomech.2008.02.007</a></p> <p> </p> <p><strong>python_requirements.txt: </strong>File containing a list of installed Python modules used to implement the iCANN</p>
Source code and data for manuscript "Large-scale deep tissue voltage imaging with targeted illumination confocal microscopy"
<p>Source code and data for manuscript "Large-scale deep tissue voltage imaging with targeted illumination confocal microscopy", <em>Nat Methods</em> (2024), https://doi.org/10.1038/s41592-024-02275-w.</p>
Extraembryonic gut endoderm cells undergo programmed cell death during development (source data and custom code)
<p>Despite a distinct developmental origin, extraembryonic cells in mice contribute to gut endoderm and converge to transcriptionally resemble their embryonic counterparts. Notably, extraembryonic progenitors share a non-canonical epigenome, raising several pertinent questions, including whether this landscape is reset to match the embryonic regulation and if these cells persist into later development. Here, we developed a two-color lineage tracing strategy to track and isolate extraembryonic cells over time. We find that extraembryonic gut cells display substantial memory of their developmental origin including retention of their original DNA methylation landscape and resulting transcriptional signatures. Furthermore, we show that extraembryonic gut cells undergo programmed cell death and neighboring embryonic cells clear their remnants via non-professional phagocytosis. By midgestation, we no longer detect extraembryonic cells in the wild type gut while they persist and differentiate further in p53 mutant embryos. Our study provides key insights into the molecular and developmental fate of extraembryonic cells inside the embryo.</p>
An enhanced single Gaussian point continuum finite element formulation using automatic differentiation: Source code and data
<p>This dataset contains the source code and the data with an example of uniaxial strain of an enhanced single Gaussian point continuum finite elemnet formulation using automatic differentiation.</p> <p> </p> <p>This contribution presents a low-order 3D finite element formulation with hourglass stabilization using automatic differentiation. Here, the former Q1STc element formulation is enhanced by an approximation-free computation of the inverse of the Jacobian. The improved version is termed "Q1STc+."</p> <p> </p> <p>The corresponding publication is:</p> <p><br>Pacolli, N., Awad, A., Kehls, J., Sauren, B., Klinkel, S., Reese, S., Holthusen, H.<br><em>An enhanced single Gaussian point continuum finite elemnet formulation using automatic differentiation.</em></p> <p>Standalone_Elementroutine: <em>Q1STc+_Codes</em> contains:</p> <ul> <li><strong>main.f90</strong>: Standalone routine for local uniaxial strain test</li> <li><strong>Makefile</strong>: Makefile to create executable "Q1STc+"</li> <li><strong>elem40.f90</strong>: Element routine "Q1STc+" with elem_sub.f90 as the subroutine written in AceGen</li> <li><strong>mat52.f90</strong>: Elasto-plastic material routine with all subroutines written in AceGen</li> <li><strong>elem_mat_select.f90</strong>: The selected material routine (Here: mat52)</li> <li><strong>elem_subs.f90</strong>: Subroutines for elem40.f90</li> <li><strong>mat_subs.f90</strong>: Subroutines for mat52.f90</li> </ul>
Dataset and source code for "Explanation and optimizing multi-model blending algorithm using random variables theory"
<p>this dataset contain: </p> <ol> <li>2m temperature de-biased model forecast data on station location, ECMWF, NCEP, JP and CMA</li> <li>2m temperature observaton data, obs_t2m</li> <li>24H QPF model forecast data on station location, ECMWF, NCEP, CMA-GFS, in raw_data_r24.zip</li> <li>24H precipitation data, in raw_data_r24.zip</li> <li>source code (in python)</li> </ol> <p> </p> <p>how to use it: </p> <p>1. prepare data and python environment<br> 1.1 if you want to run [Station_FCST_MMWB.py] or [Station_FCST_MMWB_r24.py] , please download the station forecast and observation data<br> 1.2 neet meteva package to read/write micaps-3 format data: https://github.com/nmcdev/meteva<br> 1.3 need cartopy to draw picture FigS01. </p> <p>2. try the 2m temperature blending methods <optional><br> 2.1 unzip the [CMA.zip, ECMWF.zip, jp.zip, NCEP.zip, obs_t2m.zip] file into ./raw_data/<br> 2.2 run the Station_FCST_MMWB.py in python environment </p> <p>3. try the 24h QPF multi blending methods <optional><br> 3.1 unzip the [raw_data_r24.zip] file into ./raw_data_r24/<br> 3.2 run the Station_FCST_MMWB_r24.py in python environment</p> <p>4. draw figures<br> 4.1 run Fig01.py in python environment <br> 4.2 run Fig02.py in python environment <br> 4.3 run Fig03.py in python environment <br> 4.4 run FigA01.py in python environment <br> 4.5 run FigS01.py in python environment </p>
Data and Source codes for: Real-time Radial Tagging for Quantification of Left Ventricular Torsion
<p> </p> <p>Magnetic Resonance Imaging measurement raw data, simulation, and reconstruction codes used in our paper about ‘Real-time Radial Tagging for Quantification of Left Ventricular Torsion' (DOI:10.1002/mrm.29169).</p> <p> </p> <p> </p> <p> </p>
EZ publication: source code, profiling, analysis and simulation data
<p>Data of the PIConGPU simulations as used in the publication: EZ: An Efficient, Charge Conserving Current Deposition Algorithm for Electromagnetic Particle-In-Cell Simulations</p> <p>Data overview:</p> <ul> <li>picongpu_source.zip: <ul> <li>source code forked from the PIConGPU mainline version 0.7.0-dev</li> <li>used input set `share/picongpu/examples/PaperThermal`</li> </ul> </li> <li>runs_charge_conservation.zip: <ul> <li>output including hdf5 dumps to validate charge conservation property for the PaperThermal setup (warm plasma)</li> </ul> </li> <li>runs_performance.zip: <ul> <li>simulation timings output for Spock CPU, Spock GPU and Summit GPU runs</li> </ul> </li> <li>runs_profiling.zip: <ul> <li>profile data for Spock GPU and Summit GPU runs</li> </ul> </li> <li>runs_singleParticleTest.zip: <ul> <li>output including hdf5 dumps to validate charge conservation property for the single particle test</li> </ul> </li> <li>analysis_scripts.zip: <ul> <li>jupyter notebooks for setup and analysis of PaperThermal setup</li> <li>python script to plot charge conservation from hdf5 simulation output over time</li> <li>bash script for statistical analysis of performance runs</li> </ul> </li> </ul>
Source code for dynamic models and simulations of mate sampling behavior
<p>Theory predicts that the strength of sexual selection (i.e., how well a trait predicts mating or fertilization success) should increase with population density, yet empirical support remains mixed. We explore how this discrepancy might reflect a disconnect between current theory and our understanding of the strategies individuals use to choose mates. We demonstrate that the density-dependence of sexual selection predicted by previous theory arises from the assumption that individuals automatically sample more potential mates at higher densities. We provide an updated theoretical framework for the density-dependence of sexual selection by (1) developing models that clarify the mechanisms through which density-dependent mate sampling strategies might be favored by selection and (2) using simulations to determine how sexual selection changes with population density when individuals use those strategies. We find that sexual selection may increase strongly with density if sampling strategies change adaptively in response to density-dependent sampling costs, whereas within-individual plasticity in sampling over time (e.g., due to adaptation to increasing sampling costs as the breeding season progresses) produces weaker density-dependent sexual selection. Our findings suggest that density-dependence of sexual selection depends on the ecological context in which mate sampling has evolved.</p>
Code and source data for the paper: Global warming leads to larger bats with a faster life history pace in the long-lived Bechstein's bat (Myotis bechsteinii)
<p>Contains two R scripts necessary to perfom the analysis for the paper "Global warming leads to a faster life history pace in the long-lived Bechstein’s bat (Myotis bechsteinii)"</p> <ul> <li>1st Script (" Script_analysis paper_bodysize_AFR_fecundity_LRS_GAMs_revised": Descriptive statistics, calculation of all GAMs and code for figure 1, 2 and 3</li> <li>2nd Script (" Script_size specific generation times"): Calculation of reproductive and mortality rates, calculation of generation time and population growth rates (lambda) as well as code for figure 4 and 5</li> </ul> <p>And also .csv files with the data points of all figures.</p>
Data and source code from: Contingency and selection in mitochondrial genome dynamics
<p>Eukaryotic cells contain numerous copies of mitochondrial DNA (mtDNA), allowing for the coexistence of mutant and wild-type mtDNA in individual cells. The fate of mutant mtDNA depends on their relative replicative fitness within cells and the resulting cellular fitness within populations of cells. Yet the dynamics of the generation of mutant mtDNA and features that inform their fitness remain unaddressed. Here we utilize long read single-molecule sequencing to track mtDNA mutational trajectories in Saccharomyces cerevisiae. We show a previously unseen pattern that constrains subsequent excision events in mtDNA fragmentation. We also provide evidence for the generation of rare and contentious non-periodic mtDNA structures that lead to persistent diversity within individual cells. Finally, we show that measurements of relative fitness of mtDNA fit a phenomenological model that highlights important biophysical parameters governing mtDNA fitness. Altogether, our study provides techniques and insights into the dynamics of large structural changes in genomes that may be applicable in more complex organisms.</p>
Third-order momentum advection on the quasi-hexagonal C-grid on the sphere: Data and source code
<p>This upload contains data and source code accompanying the paper submitted to JAMES (Journal of advances in modeling Earth systems) under the title 'Third-order momentum advection on the quasi-hexagonal C-grid on the sphere'</p> <p>See README files for further details.</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.