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ZENODO
Dataset . 2025
License: CC BY
Data sources: ZENODO
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2025
License: CC BY
Data sources: ZENODO
ZENODO
Dataset . 2025
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2025
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2025
License: CC BY
Data sources: Datacite
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Metadata for Multi-dimensional Models of Mixed H/He Flames in X-ray Bursts

Authors: Johnson, Eric T.; Zingale, Michael;

Metadata for Multi-dimensional Models of Mixed H/He Flames in X-ray Bursts

Abstract

Metadata, input files, and scripts used for the paper “Multi-dimensional Models of Mixed H/He Flames in X-ray Bursts”. Each of the run_* directories corresponds to a Castro run using CNO_He_burn. They contain: inputs.*: the inputs file used for the run job_info: information about the run, including the total GPU time used, all the compilation options, git hashes of Castro, AMReX, and Microphysics, and the parsed input file settings. Extracted from the last full plotfile for each run. density_profile.dat: generated by weighted_profile.py front_tracking*.dat: generated by the front_tracker.py script any Slurm submission scripts used Python scripts: flame_speed.py: reads flame front tracking data generated by the front_tracker.py, performs fits, makes a plot (figure 3), and writes LaTeX tables containing the fit parameters and velocities at different times (tables 2 and 3) make_plots.py: used to generate figures 2, 4, and 7 weighted_profile.py: reads all the plotfiles for a run and generates the weighted profile data for figure 2 interactive.py: meant to be loaded into an interactive IPython session as ipython -i interactive.py; imports util.py Helper libraries: plot_utils.py: contains common code used in make_plots.py util.py: contains functions for interactively inspecting the reactions occurring in a plotfile. Notably, network_plot() was used to generate figures 5 and 6.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average