Quality Assurance Framework for the Design, Collection and Curation of Standard Data Products From a Distributed...

Area of Science:

• Ecology
• Data Science
• Environmental Science

Background:

• Ecological data collection is increasingly collaborative, requiring robust data management.
• Coordinated data generation is crucial for creating reusable datasets (Standard Data Products).
• Existing methods often lack standardization for distributed ecological projects.

Purpose of the Study:

• To describe and apply a Quality Assurance framework for reproducible Standard Data Products in distributed ecology.
• To identify critical elements for high-value ecological datasets.
• To demonstrate the framework's utility using the Plantpopnet project.

Main Methods:

• Developed a six-element Quality Assurance framework (QA1-6).
• Applied the framework to the Plantpopnet project, a distributed system for population macroecology.
• Mapped project activities to framework elements, including objectives, governance, protocols, participant engagement, data management, and output production.

Main Results:

• The Quality Assurance framework was successfully applied to the Plantpopnet project.
• Standardized methods and data management tools facilitated high-quality data product creation.
• The framework supports the design, collection, maintenance, and sustainability of ecological data.

Conclusions:

• Explicit use of Quality Assurance and data management tools is vital for reproducible ecological Standard Data Products.
• The framework supports a sustainable distributed funding model for ecological research.
• This approach enhances the value and longevity of ecological data for diverse applications.