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This study introduces eight coding principles for plant ecophysiologists to write reproducible and resilient code. Adopting these standards enhances data analysis and simulation in plant ecophysiology research.

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Area of Science:

  • Plant ecophysiology
  • Computational biology
  • Scientific programming

Background:

  • Plant ecophysiology relies on physical and chemical theories.
  • Connecting theory with empirical data can be challenging.
  • Coding facilitates reproducible modeling and data analysis in plant ecophysiology.

Purpose of the Study:

  • To establish unified coding principles for plant ecophysiologists.
  • To improve the ease of learning, using, and modifying code.
  • To advance scientific discovery through reproducible simulations and data analysis.

Main Methods:

  • Identified eight principles for writing resilient code.
  • Illustrated principles using the R package {photosynthesis}.
  • Focused on standardized nomenclature, consistency, modularity, scalability, documentation, tutorials, and testing.

Main Results:

  • Eight principles proposed to enhance code quality and reproducibility.
  • {photosynthesis} R package demonstrates practical application of these principles.
  • Principles aim to make code easier to learn, use, and maintain for ecophysiologists.

Conclusions:

  • Adoption of coding standards can significantly improve plant ecophysiology research.
  • Easier-to-use and reproducible code accelerates scientific discovery.
  • The proposed principles and tools support advanced simulation and data analysis.