Decentralized but Globally Coordinated Biodiversity Data

Area of Science:

• Biodiversity informatics
• Data science
• Ecology

Background:

• Centralized biodiversity data aggregation faces systemic quality issues, failing to meet societal needs.
• Current models struggle with data quality, limiting scientific progress and impact.

Purpose of the Study:

• To propose a novel, decentralized approach for biodiversity data aggregation.
• To leverage the principles of the World Wide Web for improved data quality and engagement.

Main Methods:

• Implementing a decentralized coordination model for data across scientific languages and communities.
• Empowering localized communities to control their data infrastructures and versioning.
• Developing automatable data sharing agreements for cross-community propagation and translation.

Main Results:

• Fostering emergence of self-identifying communities of practice (regional, taxonomic, institutional).
• Creating loosely connected networks of mid-level data aggregators.
• Addressing integration challenges, particularly conflicting classifications and phylogenies.

Conclusions:

• Decentralization offers long-term benefits: sustained expert engagement, higher-quality data, and greater societal impact.
• Global coordination should prioritize shared languages for data signal translation over data homogenization.
• This model enables flexible, scalable biodiversity data management and utilization.