Coordinating dissent as an alternative to consensus classification: insights from systematics for bio-ontologies

Area of Science:

• Life Sciences
• Bioinformatics
• Systematic Biology

Background:

• Data classification is crucial for knowledge discovery in life sciences.
• Current tools often rely on formal classificatory systems based on consensus definitions, like the Open Biomedical Ontologies Foundry.
• This approach grounds data representation and integration in established scientific knowledge.

Purpose of the Study:

• To propose an alternative approach to data classification and dissemination in the life sciences.
• To highlight the utility of historical practices in systematic biology for data management.
• To demonstrate how coordinative consensus can facilitate data discovery and integration.

Main Methods:

• Examining historical data classification and dissemination practices in systematic biology.
• Analyzing the principles of coordinative consensus versus definitional consensus.
• Evaluating the effectiveness of taxonomic entity referencing systems in data discovery.

Main Results:

• Systematic biology employs a robust system for referring to taxonomic entities.
• This system enables high-quality data discovery and integration.
• It achieves these goals without necessitating consensus on scientific reality or "settled" knowledge.

Conclusions:

• A coordinative consensus model, derived from systematic biology, offers a viable alternative for life sciences data classification.
• This approach can improve data discovery and integration tools.
• It bypasses the need for agreement on definitive classifications, fostering more flexible data management.