Criticism of big data has focused on showing that more is not necessarily better, in the sense that data may lose their value when taken out of context and aggregated together. The next step is to incorporate an awareness of pitfalls for aggregation into the design of data infrastructure and institutions. A common strategy minimizes aggregation errors by increasing the precision of our conventions for identifying and classifying data. As a counterpoint, we argue that there are pragmatic trade-offs between precision and ambiguity that are key to designing effective solutions for generating big data about biodiversity. We focus on the importance of theory-dependence as a source of ambiguity in taxonomic nomenclature and hence a persistent challenge for implementing a single, long-term solution to storing and accessing meaningful sets of biological specimens. We argue that ambiguity does have a positive role to play in scientific progress as a tool for efficiently symbolizing multiple aspects of taxa and mediating between conflicting hypotheses about their nature. Pursuing a deeper understanding of the trade-offs and synthesis of precision and ambiguity as virtues of scientific language and communication systems then offers a productive next step for realizing sound, big biodiversity data services.
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We use “data aggregation” to refer to merging multiple sets of data of the same kind (e.g., multiple collections of specimens or multiple runs of the same experiment) as distinct from “data integration,” which refers to combining multiple kinds of data to solve an inference problem (Berman 2013). The limits of this distinction, where aggregation and integration become hard to tell apart, are an important topic outside the scope of this article.
A taxonomic concept is a description of what a taxonomic name refers to as stated by a particular author in a particular publication. A taxonomic concept can be defined in terms of rules for appropriate use (an intensional definition), by a set of organisms included under the concept (an extensional definition), or by a mixture of these two approaches.
For more on the concept of trajectory as a tool for comparative research in the social sciences, see Strauss (1993).
Note that these hypotheses are about the nature of individual species as entities, not the nature of biological species in general, which has been another source of ongoing debate among biologists and philosophers.
More generally, we also include vouchered occurrence records, such as image-vouchered observations or tissue samples not linked to physical specimen depositions, under our use of the term “specimen data.”
While some of these treatments may provide nomenclatural synonymy information intended to resolve such conflicts, this information can nonetheless still be incomplete, incorrect, or out of date.
Our argument in this article does not presuppose that named taxa are monophyletic groups, but we will set aside the debate over how to define biological species as a further complication that only magnifies the difficulty of meaningful classification of specimen data.
Interestingly, the type method is not mandatory above the family level where the codes of nomenclature have no regulatory power (cf. Franz and Thau 2010).
Figure 2 illustrates the relationship between these two taxon concepts.
These statements are hypothetical examples and should not be taken as necessarily true.
Although see Figures 3C and 3D in Remsen (2016) for a visual analog to sentence (2).
Note that Berendsohn’s extended syntax maintains coherence with existing practices in taxonomy by adding onto the binomial system rather than replacing it wholesale.
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The authors are grateful to Hong Cui, Bertram Ludäscher, and Jonathan Rees for helpful feedback on this subject. Support of the authors’ research through the National Science Foundation is kindly acknowledged (NMF: DEB–1155984, DBI–1342595; BS: SES–1153114).
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Sterner, B., Franz, N.M. Taxonomy for Humans or Computers? Cognitive Pragmatics for Big Data. Biol Theory 12, 99–111 (2017). https://doi.org/10.1007/s13752-017-0259-5
- Big data
- Cognitive pragmatics
- Concept taxonomy
- Data aggregation
- Knowledge representation and reasoning