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  1. The galaxy of the non-Linnaean nomenclature.Alessandro Minelli - 2019 - History and Philosophy of the Life Sciences 41 (3):31.
    Contrary to the traditional claim that needs for unambiguous communication about animal and plant species are best served by a single set of names ruled by international Codes, I suggest that a more diversified system is required, especially to cope with problems emerging from aggregation of biodiversity data in large databases. Departures from Linnaean nomenclature are sometimes intentional, but there are also other, less obvious but widespread forms of not Code-compliant grey nomenclature. A first problem is due to the circumstance (...)
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  • Coordination Instead of Consensus Classification: Insights From Systematics for Bio-Ontologies.Beckett Sterner, Joeri Witteveen & Nico Franz - forthcoming - History and Philosophy of the Life Sciences.
    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, (...)
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  • Coordinating Dissent as an Alternative to Consensus Classification: Insights From Systematics for Bio-Ontologies.Beckett Sterner, Joeri Witteveen & Nico Franz - 2020 - History and Philosophy of the Life Sciences 42 (1):1-25.
    The collection and classification of data into meaningful categories is a key step in the process of knowledge making. In the life sciences, the design of data discovery and integration tools has relied on the premise that a formal classificatory system for expressing a body of data should be grounded in consensus definitions for classifications. On this approach, exemplified by the realist program of the Open Biomedical Ontologies Foundry, progress is maximized by grounding the representation and aggregation of data on (...)
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