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When developing expected outcomes for a patient care plan, the nurse should adhere to the following recommendations:
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R is a powerful software environment for statistical computing and graphics. Originating as an implementation of the S language, developed at Bell Laboratories, R has evolved into a robust, open-source statistical software favored by statisticians and data scientists worldwide. Its comprehensive suite includes data manipulation, calculation, and graphical display capabilities, making it versatile for data analysis and visualization. Its programming language is at the core of R's...
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Related Experiment Video

Updated: Oct 13, 2025

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Ten simple rules on writing clean and reliable open-source scientific software.

Haley Hunter-Zinck1,2,3,4, Alexandre Fioravante de Siqueira1, Váleri N Vásquez1,5

  • 1Berkeley Institute for Data Science, University of California, Berkeley, Berkeley, California, United States of America.

Plos Computational Biology
|November 11, 2021
PubMed
Summary

This study introduces 10 rules for clean code and testing to improve open-source scientific software. These practices enhance code readability, maintainability, and reproducibility for researchers.

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Area of Science:

  • Computer Science
  • Scientific Computing
  • Software Engineering

Background:

  • Open-source software is critical for scientific research.
  • There's a lack of formal training in software development and maintainability for scientists.
  • This variability impacts the quality and reliability of scientific software.

Discussion:

  • Proposes 10 rules focusing on clean code and testing practices.
  • Clean code enhances readability, reduces cognitive load, and standardizes software organization.
  • Testing, including unit tests, verifies code functionality, identifies errors, and aids in debugging complex codebases.

Key Insights:

  • Clean code practices make software more amenable to effective testing.
  • Unit tests ensure modularity, isolate behavior, and provide usage examples.
  • Implementing these practices improves software correctness, quality, usability, and maintainability.
  • Foundational tools like clean code and testing are essential for reproducible scientific results.

Outlook:

  • Suggests practical tips for adopting clean code and testing.
  • Recommends specific tools for Python, R, and Julia.
  • Aims to improve the overall quality and reliability of open-source scientific software.