Sterner, Beckett and Witteveen, Joeri and Franz, Nico (2019) Alternatives to Realist Consensus in Bio-Ontologies: Taxonomic Classification as a Basis for Data Discovery and Integration. [Preprint]

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Abstract

Big data is opening new angles on old questions about scientific progress. Is scientific knowledge cumulative? If yes, how does it make progress? In the life sciences, what we call the Consensus Principle has dominated the design of data discovery and integration tools: the design of a formal classificatory system for expressing a body of data should be grounded in consensus. Based on current approaches in biomedicine and systematic biology, we formulate and compare three types of the Consensus Principle: realist, contextual-best, and coordinative. Contrasted with the realist program of the Open Biomedical Ontologies Foundry, we argue that historical practices in systematic biology provide an important and overlooked alternative based on coordinative consensus. Systematists have developed a robust system for referring to taxonomic entities that can deliver high quality data discovery and integration without invoking consensus about reality or “settled” science.

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Item Type:	Preprint
Creators:	Creators Email ORCID Sterner, Beckett bsterne1@asu.edu 0000-0001-5219-7616 Witteveen, Joeri j.wit@me.com 0000-0003-1291-9156 Franz, Nico nmfranz@asu.edu 0000-0001-7089-7018
Keywords:	social epistemology, data-centrism, ontology alignment
Subjects:	General Issues > Data Specific Sciences > Biology Specific Sciences > Biology > Systematics Specific Sciences > Computer Science General Issues > Realism/Anti-realism General Issues > Social Epistemology of Science General Issues > Theory Change
Depositing User:	Dr. Beckett Sterner
Date Deposited:	20 Jun 2019 18:16
Last Modified:	20 Jun 2019 18:16
Item ID:	16139
Subjects:	General Issues > Data Specific Sciences > Biology Specific Sciences > Biology > Systematics Specific Sciences > Computer Science General Issues > Realism/Anti-realism General Issues > Social Epistemology of Science General Issues > Theory Change
Date:	2019
URI:	https://philsci-archive.pitt.edu/id/eprint/16139

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