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Detailed Implementation of a Reproducible Machine Learning-Enabled Workflow Cover

Detailed Implementation of a Reproducible Machine Learning-Enabled Workflow

Open Access
|Apr 2024

Figures & Tables

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Figure 1

Flowchart of overall study design to identify biodata resources from the scientific literature. The fine-tuning procedure is not shown. Reproduced unmodified from (Imker et al. 2023) under Creative Commons Attribution License.

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Figure 2

Graphical overview of the objectives of the study and the tools and methods used to address them regarding reproducibility, code quality, and data standards. The execution of these objectives was assessed by external review and validation.

Table 1

Glossary of Software.

NAMEDESCRIPTIONREFERENCE
condaPackage and environment management system(Conda 2017)
flake8Python linter (static code checking)(Ziade and Cordasco 2011)
MakeBuild automation tool, used here for creating shell command aliases(GNU Make 1988)
MermaidDiagram generator for Markdown(Sveidqvist 2014)
mypyStatic type checker for Python(Lehtosalo 2012)
pipPackage manager for Python(pypi n.d.)
pylintPython linter (static code checking)(Thénault 2001)
pytestPython testing framework(Krekel 2004)
pytest-flake8Pytest plugin to run flake8(Lockhert 2015)
pytest-mypyPytest plugin to run mypy(Bader 2016)
pytest-pylintPytest plugin to run pylint(Gee 2015)
renvDependency manager for R(Ushey 2022)
snakefmtCode formatter for Snakemake(Hall and Letcher 2020)
SnakemakeGeneral-purpose workflow manager(Köster and Rahmann 2012)
stylerCode formatter for R(Müller et al 2021)
yapfCode formatter for Python(Google Inc.)
Language: English
Submitted on: Dec 9, 2023
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Accepted on: Apr 8, 2024
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Published on: Apr 29, 2024
Published by: Ubiquity Press
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year

© 2024 Kenneth E. Schackart III, Heidi J. Imker, Charles E. Cook, published by Ubiquity Press
This work is licensed under the Creative Commons Attribution 4.0 License.