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.
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Our notion of a big data trajectory is distinct from Sabina Leonelli’s concept of data journeys, although they are connected in important ways. Briefly, Leonelli uses data journeys to evoke how data travel across time and place from their original situations of production to new situations of use. In contrast, the idea of a big data trajectory is meant to describe the progress a scientific community makes as a function of increasing the amount of data available for a problem.
We use terminology common to OWL here since it is the easiest to grasp intuitively, but researchers in the first-order logic and description logic communities use different terms for operationally equivalent ideas.
Smith and Ceusters give different general characterizations of universals and particulars in different places, though see Merrill (2010a). For example, in Smith (2003), universals are multiply located entities that exist in particulars, while particulars are entities with only one location in space at a time. Things that can have predicates thus include universals as well as particulars. Smith also adds a further logical primitive, the instantiation relation, and stipulates that only particulars can instantiate universals (Smith 2003).
Systematized Nomenclature of Medicine.
Note that we are not mentioning a design like this to endorse it. As Minelli (2017, this issue) has adroitly pointed out, there are major worries over the quality and durability of these and other name-based aggregators that include ‘grey’ non-Linnaean names. In the next section we will return to taxonomic names and consider different design solutions.
This empiricist concern about the theory-dependence of anatomical data has parallels to the earlier disputes between pheneticists, cladists, and evolutionary systematists about the best methodology for inferring classifications (Hull 1988; Sterner and Lidgard 2018), and would be a fruitful point of contact between philosophical analyses of homology and scientific practice.
If the circumscription includes two or more type specimens, the name associated with the most senior type specimen is the valid/correct name for the species and the other names become (junior) synonyms.
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Our thanks to the editors for proposing and organizing the special issue on Taxonomy as an Information Science. Their comments and the referees’ responses both helped substantially improve the final text. This work was also supported by the McDonnell Foundation via the Marine Biological Laboratory and ASU’s Special Initiative Fund for Biodiversity Data Science.
Funding was provided by Division of Social and Economic Sciences (Grant No. STS 1827993), Netherlands Organisation for Scientific Research (Grant No. 275-20-060) and National Science Foundation (Grant Nos. SES-1827993, DEB-1754731).
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Sterner, B., Witteveen, J. & Franz, N. Coordinating dissent as an alternative to consensus classification: insights from systematics for bio-ontologies. HPLS 42, 8 (2020). https://doi.org/10.1007/s40656-020-0300-z
- Big data
- Consensus principle
- Ontology alignment
- Biodiversity informatics