New method for taxonomic descriptions with coded notation, producing dynamic and interchangeable output.

Area of Science:

• Taxonomy
• Bioinformatics
• Computational Biology

Background:

• Traditional species descriptions rely on subjective, text-based narratives.
• This limits data integration with computational tools and interdisciplinary research.
• Enhancing species description efficiency is crucial for biodiversity cataloging.

Purpose of the Study:

• To propose a novel coded matrix notation for taxonomic species descriptions.
• To replace traditional descriptive texts with a structured, objective format.
• To improve the rate and accessibility of species descriptions for diverse scientific applications.

Main Methods:

• Developed a coded matrix system based on a detailed character list.
• Applied the method to describe five new species of *Pararrhopalites* (Collembola Symphypleona).
• Ensured the method is dynamic, allowing for future amendments and data additions.

Main Results:

• Successfully described five new species using the coded matrix notation.
• Demonstrated the potential for coded descriptions to be dynamic and expandable.
• Generated coded outputs that are readily usable in other scientific disciplines.

Conclusions:

• Coded taxonomic descriptions represent a significant advancement over traditional text-based methods.
• The proposed notation can enhance global species description rates and data accessibility.
• Facilitates non-expert access to taxonomic data for phylogenetic, ecological, and metadata analyses, supporting semi-automated taxon recognition and future AI-driven tools.