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 (...) settled knowledge. We argue that historical practices in systematic biology provide an important and overlooked alternative approach to classifying and disseminating data, based on a principle of coordinative rather than definitional 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. (shrink)

Mental health research faces a suite of unresolved challenges that have contributed to a stagnation of research efforts and treatment innovation. One such challenge is how to reliably and validly account for the subjective side of patient symptomatology, that is, the patient’s inner experiences or patient phenomenology. Providing a structured, standardised semantics for patient phenomenology would enable future research in novel directions. In this contribution, we aim at initiating a standardized approach to patient phenomenology by sketching a tentative formalisation within (...) the framework of an applied ontology, in the broader context of existing open-source Open Biomedical Ontologies resources such as the Mental Functioning Ontology. We further discuss a number of prevailing challenges and observations bearing on this task. (shrink)

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. (shrink)