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100 results for “Reproduction package”
Replication package of "Search-based Crash Reproduction using Behavioral Model Seeding"
<p>Search-based crash reproduction approaches assist developers during debugging by generating a test case which reproduces a crash given its stack trace. One of the fundamental steps of this approach is creating objects needed to trigger the crash. One way to overcome this limitation is seeding: using information about the application during the search process. With seeding, the existing usages of classes can be used in the<br> search process to produce realistic sequences of method calls which create the required objects. In this study, we introduce behavioral model seeding: a new seeding method which learns class usages from both<br> the system under test and existing test cases. Learned usages are then synthesized in a behavioral model (state machine). Then, this model serves to guide the evolutionary process. To assess behavioral model-seeding, we evaluate it against test-seeding (the state-of-the-art technique for seeding realistic objects) and no-seeding (without seeding any class usage). For this evaluation, we use a benchmark of 122 hard-to-reproduce crashes stemming from six open-source projects. Our results indicate that behavioral model-seeding outperforms both test seeding and no-seeding by a minimum of 6% without any notable negative impact on efficiency.</p>
Reproduction package for the paper "Bottling the Champagne: Dynamics and Radiation Trapping of Wind-Driven Bubbles around Massive Stars"
<p>Research Data Management package for "Bottling the Champagne: Dynamics and Radiation Trapping of Wind-Driven Bubbles around Massive Stars"</p> <p>Authors: Sam Geen & Alex de Koter</p> <p>Status: Accepted by MNRAS<br> This package aims to provide a full data reproduction pipeline. Please see Readme.md for more information.</p>
Replication package of "Good Things Come In Threes: Improving Search-based Crash Reproduction With Helper Objectives"
<p>The replication package for the study about using new helper objectives (MOHO) for crash reproduction. This study has been accepted at ASE 2020.</p> <p> </p> <p>Abstract:</p> <p>Evolutionary intelligence approaches have been successfully applied to assist developers during debugging by generating a test case reproducing reported crashes. These approaches use a single fitness function called <em>Crash Distance</em> to guide the search process toward reproducing a target crash. Despite the reported achievements, these approaches do not always successfully reproduce some crashes due to a lack of test diversity (premature convergence). In this study, we introduce a new approach, called <em>MO-HO</em>, that addresses this issue via multi-objectivization. In particular, we introduce two new Helper-Objectives for crash reproduction, namely <em>test length</em> (to minimize) and <em>method sequence diversity</em> (to maximize), in addition to <em>Crash Distance</em>.</p> <p>We assessed <em>MO-HO</em> using five multi-objective evolutionary algorithms (NSGA-II, SPEA2, PESA-II, MOEA/D, FEMO) on 124 hard-to-reproduce crashes stemming from open-source projects. Our results indicate that SPEA2 is the best-performing multi-objective algorithm for <em>MO-HO</em>.</p> <p>We evaluated this best-performing algorithm for <em>MO-HO</em> against the state-of-the-art: single-objective approach (Single-Objective Search) and decomposition-based multi-objectivization approach (<em>De-MO</em>). Our results show that <em>MO-HO</em> reproduces five crashes that cannot be reproduced by the current state-of-the-art. Besides, <em>MO-HO</em> improves the effectiveness (+10% and +8% in reproduction ratio) and the efficiency in 34.6% and 36% of crashes (i.e., significantly lower running time) compared to Single-Objective Search and <em>De-MO</em>, respectively. For some crashes, the improvements are very large, being up to +93.3% for reproduction ratio and -92% for the required running time. </p>
Reproduction package for paper "How far are we from reproducible research on code smell detection? A systematic literature review"
<p>Checklist and data extracted from publications analyzed for "How far are we from reproducible research on code smell detection? A systematic literature review" paper, together with processing scripts and calculations of Cohen's Kappa.</p> <p>Paper that describes details of the data is available here: https://doi.org/10.1016/j.infsof.2021.106783</p>
Reproduction package for the publication 'Galaxy cluster photons alter the ionisation state of the nearby warm-hot intergalactic medium'
<p>The following files can be used to reproduce the figures and data from the paper <strong>Galaxy cluster photons alter the ionisation state of the nearby warm-hot intergalactic medium</strong><strong> </strong>by L. Štofanová, A. Simionescu, N. A. Wijers, J. Schaye, and J. Kaastra to be accepted in Monthly Notices of the Royal Astronomical Society (MNRAS).</p>
Reproduction package for the paper "Exploring the directly imaged HD 1160 system through spectroscopic characterization and high-cadence variability monitoring"
<p>This is a basic reproduction package for the paper <a href="https://doi.org/10.1093/mnras/stae1315">"Exploring the directly imaged HD 1160 system through spectroscopic characterization and high-cadence variability monitoring" by Sutlieff et al. (2024)</a>. It aims to provide the most important data products to check and reproduce the main results of the paper.</p>
Reproduction package for: 'A NICER View of the Nearest and Brightest Millisecond Pulsar: PSR J0437–4715'
<div>Posterior sample files for the headline result associated with the publication "A NICER View of the Nearest and Brightest Millisecond Pulsar: PSR J0437–4715" by Choudhury et al. (2024; <a href="https://doi.org/10.3847/2041-8213/ad5a6f" target="_blank" rel="noopener"><em>ApJL</em> <strong>971</strong> L20</a>; <a href="https://arxiv.org/abs/2407.06789" target="_blank" rel="noopener">arXiv:2407.06789</a>).</div> <div> </div> <div>Also included are: the data products; the numeric model files including the telescope calibration products; model modules in the Python language using the X-PSI framework; and Jupyter analysis notebooks.</div> <div> </div> <div> <div> <p>Please refer to the README for detailed information.</p> </div> </div>
Reproduction package for the paper "Two waves of massive stars running away from the young cluster R136"
<h2>Reproduction package for the paper "Two waves of massive stars running away from the young cluster R136".</h2> <ul> <li>This reproduction package aims for open science, with the internal API designation of 'Gold'</li> <li>Authors: M. Stoop, A. de Koter, L. Kaper, S. Brands, S. Portegies Zwart, H. Sana, F. Stoppa, M. Gieles, L. Mahy, T. Shenar, D. Guo, G. Nelemans, S. Rieder</li> <li>Paper DOI: https://doi.org/10.1038/s41586-024-08013-8</li> <li>Zenodo DOI: http://doi.org/10.5281/zenodo.10058762</li> <li>Published in Nature (date of publication: 2024/10/09)</li> </ul> <h2>Hardware</h2> <ul> <li>Tested on a MacBook Pro (13-inch, 2020, Four Thunderbolt 3 ports)</li> <li>Processor: 2 GHz Quad-Core Intel Core i5</li> <li>Memory: 32 GB 3733 MHz LPDDR4X</li> <li>Graphics: Intel Iris Plus Graphics 1536 MB</li> </ul> <h2>Required non-standard hardware</h2> <ul> <li>None</li> </ul> <h2>Software dependencies</h2> <ul> <li>Jupyterlab (4.0.8)</li> <li>Notebook (7.0.6)</li> <li>Programming languages used: Python (3.11.7)</li> <li>Python packages used: numpy (1.25.2), pandas (2.1.4), matplotlib (3.8.0), os (comes with Python) scipy (1.11.4), gaiadr3-zeropoint (0.0.4) https://gitlab.com/icc-ub/public/gaiadr3_zeropoint), astroquery (0.4.6), pymc (5.6.1), corner (2.2.2), arviz (0.16.0), pytensor (2.12.3), lmfit (1.2.2), powerlaw (1.5), rpy2 (3.5.16), seaborn (0.12.2), consistencytest (0.0.2)</li> </ul> <h2>Instructions</h2> <ul> <li>The Anaconda conda environment is given should this be needed</li> <li>All Jupyter Notebooks are ready-made to produce the raw data, intermediate and end data products</li> <li>Gaia raw data is downloaded in the Jupyter Notebook "R136_runaway_candidates.ipynb"</li> <li>Data from the literature is given in the subdirectory /tables/ or /input_files/</li> <li>Input images and files are given in the subdirectory /input_files/</li> <li>Intermediate and end data products are given in /output_files/</li> <li>Figures in the paper are produced in the Jupyter Notebooks in the subdirectory /figures/ and stored in the subdirectory /figures/figures_paper/</li> </ul> <h2>Expected Output</h2> <ul> <li>Jupyter Notebooks can be executed by "Run" -> "Run All Cells"</li> <li>The Jupyter Notebook show the expected output in their respective cell</li> <li>Expected runtime are given at the top of each Jupyter Notebook</li> <li>The Jupyter Notebook which takes the longest "R136_runaway_search.ipynb" takes 7-8 hours for the entire dataset</li> <li>A small dataset has been given in this Jupyter Notebook as a proof-of-concept</li> </ul> <h2>Instructions for use</h2> <ul> <li>Jupyter Notebooks can be executed by "Run" -> "Run All Cells"</li> </ul> <h2>Figures</h2> <ul> <li>Figures can be reproduced from the /figures/ folder.</li> <li>All material and data used are available either in the Raw Data or in the Intermediate Data</li> <li>The figures shown in the paper will be saved in ./figures/figures_paper/ folder.</li> </ul> <pre> </pre>
Reproduction package for: 'Exploring Waveform Variations among Neutron Star Ray-tracing Codes for Complex Emission Geometries'
<p>Data files, python scripts and notebooks to reproduce the code output comparisons performed in "Exploring Waveform Variations among Neutron Star Ray-tracing Codes for Complex Emission Geometries" by Choudhury et al. (2024; <a href="https://doi.org/10.3847/1538-4357/ad7255" target="_blank" rel="noopener"><em>ApJ</em> <strong>975</strong> 202</a>, <a href="https://doi.org/10.48550/arXiv.2406.07285" target="_blank" rel="noopener">arXiv.2406.07285</a>).</p> <p>Please refer to the README for detailed information.</p> <p>N.B. The neutral hydrogen column density (${\rm N}_{\rm H}$) value is mentioned in the paper to be $0.2 \times 10^{20} {\rm cm}^{-2}$, whereas all the analyses in the paper, as reflected in this Zenodo package, actually uses ${\rm N}_{\rm H} = 2 \times 10^{20} {\rm cm}^{-2}$.</p>
Reproduction package for the paper "High-contrast observations of brown dwarf companion HR 2562 B with the vector Apodizing Phase Plate coronagraph"
<p>This is a basic reproduction package for the paper <a href="https://doi.org/10.1093/mnras/stab1893">"High-contrast observations of brown dwarf companion HR 2562 B with the vector Apodizing Phase Plate coronagraph" by Sutlieff et al. (2021)</a>. It aims to provide the most important data products to check and reproduce the main results of the paper.</p>
Reproduction package for the publication 'New radiative loss curve from updates to collisional excitation in the low-density, optically thin plasmas in SPEX'
<p>The following files can be used to reproduce the Figures and data from the paper <strong>New radiative loss curve from updates to collisional excitation in the low-density, optically thin plasmas in SPEX </strong>by L. Štofanová, J. Kaastra, M. Mehdipour, and J. de Plaa accepted to be publish in Section 12. Atomic, molecular, and nuclear data of Astronomy and Astrophysics (acceptance date - 27/06/2021).</p> <p> </p> <p>Note: version 2 is the most updated version (change in Fig.7).</p>
Reproduction package for the paper "Measuring the variability of directly imaged exoplanets using vector Apodizing Phase Plates combined with ground-based differential spectrophotometry"
<p>This is a basic reproduction package for the paper <a href="https://doi.org/10.1093/mnras/stad249">"Measuring the variability of directly imaged exoplanets using vector Apodizing Phase Plates combined with ground-based differential spectrophotometry" by Sutlieff et al. (2023)</a>. It aims to provide the most important data products to check and reproduce the main results of the paper.</p>
Reproduction package for the publication 'Prospects for detecting the circum- and intergalactic medium in X-ray absorption using the extended intracluster medium as a backlight'
<p>The uploaded files can be used to reproduce the dataset and figures in the paper<strong> Prospects for detecting the circum- and intergalactic medium in X-ray absorption using the extended intracluster medium as a backlight</strong> by Lýdia Štofanová, Aurora Simionescu, Nastasha A. Wijers, Joop Schaye, Jelle Kaastra, Yannick M. Bahé, and Andrés Arámburo-García.</p><p>NOTE: Files will be published with a new version. </p>
Reproduction package for the paper "Near-ultraviolet detections of four dwarf nova candidates in the globular cluster 47 Tucanae"
<p>This is a basic reproduction package for the paper "Near-ultraviolet detections of four dwarf nova candidates in the globular cluster 47 Tucanae" by <a href="https://www.aanda.org/articles/aa/abs/2020/02/aa37043-19/aa37043-19.html">Modiano et al. (2020)</a>. It aims to provide the most important data products to check and reproduce the main results of the paper.</p>
Reproduction package for the paper "The effects of surface fossil magnetic fields on massive star evolution - II. Implementation of magnetic braking in MESA and implications for the evolution of surface rotation in OB stars "
<p>This is a reproduction package for the paper "The effects of surface fossil magnetic fields on massive star evolution - II. Implementation of magnetic braking in MESA and implications for the evolution of surface rotation in OB stars" by Keszthelyi et al. (2020), https://doi.org/10.1093/mnras/staa237</p>
Replication package of A benchmark-based evaluation of search-based crash reproduction
<p>Release of the reproduction package of Soltani, M., Derakhshanfar, P., Devroey, X. and van Deursen, A. (2020). A benchmark-based evaluation of search-based crash reproduction. In Empirical Software Engineering. 25, 1 (Jan. 2020), pp. 96–138.</p>
Reproduction package for the paper "The variable radio counterpart of Swift J1858.6-0814"
<p>This is a basic reproduction package for the paper "The variable radio counterpart of Swift J1858.6-0814" by J. van den Eijnden et al. (2020). It aims to provide the data products underlying the figures in the paper, report where the analyzed observations can be accessed, and list the software used to perform the analysis. </p> <p>An open access version of the paper can be found at <a href="https://arxiv.org/abs/2006.06425">https://arxiv.org/abs/2006.06425</a>. </p>
Reproduction packages for the paper "Spectral and Imaging properties of Sgr A∗ from High-Resolution 3 DGRMHD Simulations with Radiative Cooling"
<p>This is a basic reproduction package for the paper"Spectral and Imaging properties of Sgr A∗ from High-Resolution 3D GRMHD Simulations with Radiative Cooling" by Yoon et al. (2020). It aims to provide the most important data products to check and reproduce the main results of the paper.</p>
Reproduction package for the paper "Correlating spectral and timing properties in the evolving jet of themicro blazar MAXI J1836-194"
<p>Basic reproduction package for the paper in the title; it contains all the necessary information and scripts required to replicate the results and plots, minus the proprietary code used (which can be found on github on request).</p>
Reproduction package for the paper "Mapping the spectral index of Cas A: evidence for flattening from radio to infrared"
<p>This is a basic reproduction package for the paper "Mapping the spectral index of Cas A: evidence for flattening from radio to infrared" by V. Domček et al. (2021). It provides raw, intermediate and final data sets, including figures and scripts to allow the reproduction of the work performed in this paper. It also lists software used and data archives containing the public observational data. </p> <p>An open access version of the paper can be found at https://arxiv.org/abs/2005.12677</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.