Guidance framework to apply best practices in ecological data analysis: lessons learned from building Galaxy-Ecology

Area of Science:

• Ecology
• Computational Biology
• Data Science

Background:

• Existing frameworks like Open Science and FAIR principles need practical application for ecological research.
• Improving transparency, reproducibility, and confidence in ecological data analysis remains a challenge.
• A gap exists in operationalizing best practices for ecological analytical procedures.

Purpose of the Study:

• To propose a practical and operational framework for best practices in ecological research data analysis.
• To introduce the concept of 'atomization' for generalizing analytical steps.
• To enhance the accessibility and reusability of analytical workflows in ecology.

Main Methods:

• Development of a framework based on the concept of 'atomization' of analytical steps.
• Generalization of individual analytical steps ('atoms') for broader application.
• Implementation and demonstration through the Galaxy-Ecology web platform.

Main Results:

• The proposed framework facilitates the creation of production-level analytical pipelines from individual research projects.
• Atomization enables analytical steps to be generalized and reused across multiple analyses.
• The Galaxy-Ecology platform showcases the practical application of atomized and generalized workflows.

Conclusions:

• Atomization provides a practical approach to achieve higher levels of reproducibility in ecological sciences.
• Increased accessibility and reusability of generalized analytical workflows boost scientific confidence.
• The framework supports the transition from single analyses to robust, production-level analytical pipelines.